From 8f1f53abda72b46eefe122f51a00012fe83620fc Mon Sep 17 00:00:00 2001 From: AndreasEppler Date: Wed, 6 Nov 2024 10:26:40 +0100 Subject: [PATCH 01/22] Typo and documentation corrections. --- .../notebooks/01_minimal_manual_example.ipynb | 69 +++++++++++++------ 1 file changed, 47 insertions(+), 22 deletions(-) diff --git a/examples/notebooks/01_minimal_manual_example.ipynb b/examples/notebooks/01_minimal_manual_example.ipynb index 52e6ab71c..bb82982dd 100644 --- a/examples/notebooks/01_minimal_manual_example.ipynb +++ b/examples/notebooks/01_minimal_manual_example.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "# 1. Minimal manual tutorial\n", - "In this notebook, we will walk through a minimal example of how to use the ASSUME framework. We will first initialize the world instance, next we will create a single market and its operator, afterwards we wll add a generation and a demand agents, and finally start the simulation." + "In this notebook, we will walk through a minimal example of how to use the ASSUME framework. We will first initialize the world instance, next we will create a single market and its operator, afterwards we will add a generation and a demand agent, and finally start the simulation." ] }, { @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "vscode": { "languageId": "shellscript" @@ -34,7 +34,7 @@ }, "outputs": [], "source": [ - "!pip install assume-framework" + "#!pip install assume-framework" ] }, { @@ -46,9 +46,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.world:connected to db\n", + "INFO:assume.world:Learning Strategies are not available. Check that you have torch installed.\n" + ] + } + ], "source": [ "import logging\n", "import os\n", @@ -98,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -121,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -163,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -201,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -227,20 +236,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This code segment sets up a demand unit managed by the \"my_demand\" unit operator, equipped with a naive demand forecast, and establishes its operational parameters within the electricity market simulation framework.\n", + "This code segment sets up a demand unit managed by the \"demand_operator\" unit operator, equipped with a naive demand forecast, and establishes its operational parameters within the electricity market simulation framework.\n", "\n", "In this code:\n", - "- `world.add_unit_operator(\"demand_operator\")` adds a unit operator with the identifier \"my_demand\" to the simulation world. A unit operator manages a group of similar units within the simulation.\n", + "- `world.add_unit_operator(\"demand_operator\")` adds a unit operator with the identifier \"demand_operator\" to the simulation world. A unit operator manages a group of similar units within the simulation.\n", "\n", "- `demand_forecast = NaiveForecast(index, demand=100)` creates a naive demand forecast object named `demand_forecast`. This forecast is initialized with an index and a constant demand value of 100.\n", "\n", "- `world.add_unit(...)` adds a demand unit to the simulation world with the following specifications:\n", "\n", - " - `id=\"demand_unit\"` assigns the identifier \"demand1\" to the demand unit.\n", + " - `id=\"demand_unit\"` assigns the identifier \"demand_unit\" to the demand unit.\n", "\n", " - `unit_type=\"demand\"` specifies that this unit is of type \"demand\", indicating that it represents a consumer of electricity.\n", "\n", - " - `unit_operator_id=\"demand_operator\"` associates the unit with the unit operator identified as \"my_demand\".\n", + " - `unit_operator_id=\"demand_operator\"` associates the unit with the unit operator identified as \"demand_operator\".\n", "\n", " - `unit_params` provides various parameters for the demand unit, including minimum and maximum power, bidding strategies, and technology type.\n", "\n", @@ -249,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -306,9 +315,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "world_script_simulation 2023-12-05 00:00:00: : 5356801.0it [00:05, 899395.33it/s] \n" + ] + } + ], "source": [ "world.run()" ] @@ -319,7 +336,7 @@ "source": [ "## Conclusion\n", "\n", - "In this notebook, we have demonstrated the basic steps involved in setting up and running a simulation using the ASSUME framework for simulating electricity markets. This example is intended to provide a detailed overview of internal workings of the framework and its components. This approach can be used for small simulations with a few agents and markets. In the next notebook we will explore how this process is automated for large scale simulation using input files." + "In this notebook, we have demonstrated the basic steps involved in setting up and running a simulation using the ASSUME framework for simulating electricity markets. This example is intended to provide a detailed overview of internal workings of the framework and its components. This approach can be used for small simulations with a few agents and markets. In the next notebook we will explore how this process is automated for large scale simulations using input files." ] }, { @@ -331,21 +348,22 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "INFO:assume.world:connected to db\n" + "INFO:assume.world:connected to db\n", + "INFO:assume.world:Learning Strategies are not available. Check that you have torch installed.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "world_script_simulation 2023-03-31 00:00:00: : 7689601.0it [00:02, 2611673.02it/s] \n" + "world_script_simulation 2023-03-31 00:00:00: : 7689601.0it [00:07, 992635.69it/s] \n" ] } ], @@ -436,6 +454,13 @@ "\n", "world.run()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -445,7 +470,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "assume-framework", + "display_name": "assume", "language": "python", "name": "python3" }, @@ -459,7 +484,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.12.7" }, "nbsphinx": { "execute": "never" From c066b57f98e43770a086e8b67599e7284f309d47 Mon Sep 17 00:00:00 2001 From: AndreasEppler Date: Mon, 11 Nov 2024 13:27:55 +0100 Subject: [PATCH 02/22] Fixed typos and smaller errors. --- docs/source/learning.rst | 24 +-- docs/source/learning_algorithm.rst | 33 ++-- .../notebooks/01_minimal_manual_example.ipynb | 14 +- .../notebooks/02_automated_run_example.ipynb | 159 +++++++++++++----- .../notebooks/03_custom_unit_example.ipynb | 80 ++++++--- ...forcement_learning_algorithm_example.ipynb | 63 ++----- 6 files changed, 221 insertions(+), 152 deletions(-) diff --git a/docs/source/learning.rst b/docs/source/learning.rst index 442c7b407..0fd5a8914 100644 --- a/docs/source/learning.rst +++ b/docs/source/learning.rst @@ -9,7 +9,7 @@ Reinforcement Learning Overview One unique characteristic of ASSUME is the usage of Reinforcement Learning (RL) for the bidding of the agents. To enable this the architecture of the simulation is designed in a way to accommodate the learning process. In this part of the documentation, we give a short introduction to reinforcement learning in general and then pinpoint you to the -relevant parts of the code. the descriptions are mostly based on the following paper +relevant parts of the code. The descriptions are mostly based on the following paper Harder, Nick & Qussous, Ramiz & Weidlich, Anke. (2023). Fit for purpose: Modeling wholesale electricity markets realistically with multi-agent deep reinforcement learning. Energy and AI. 14. 100295. `10.1016/j.egyai.2023.100295 `. If you want a hands-on introduction check out the prepared tutorial in Colab: https://colab.research.google.com/github/assume-framework/assume @@ -18,7 +18,7 @@ If you want a hands-on introduction check out the prepared tutorial in Colab: ht The Basics of Reinforcement Learning ===================================== -In general RL and deep reinforcement learning (DRL), in particular, open new prospects for agent-based electricity market modeling. +In general, RL and deep reinforcement learning (DRL) in particular, open new prospects for agent-based electricity market modeling. Such algorithms offer the potential for agents to learn bidding strategies in the interplay between market participants. In contrast to traditional rule-based approaches, DRL allows for a faster adaptation of the bidding strategies to a changing market environment, which is impossible with fixed strategies that a market modeller explicitly formulates. Hence, DRL algorithms offer the @@ -105,8 +105,8 @@ Similar to TD3, the smaller value of the two critics and target action noise :ma y_i,k = r_i,k + γ * min_j=1,2 Q_i,θ′_j(S′_k, a_1,k, ..., a_N,k, π′(o_i,k)) -where r_i,k is the reward obtained by agent i at time step k, γ is the discount factor, S′_k is the next state of the -environment, and π′(o_i,k) is the target policy of agent i. +where :math:`r_i,k` is the reward obtained by agent :math:`i` at time step :math:`k`, :math:`\gamma` is the discount factor, :math:`S'_k` is the next state of the +environment, and :math:`\pi'(o_i,k)` is the target policy of agent :math:`i`. The critics are trained using the mean squared Bellman error (MSBE) loss: @@ -120,8 +120,8 @@ The actor policy of each agent is updated using the deterministic policy gradien ∇_a Q_i,θ_j(S_k, a_1,k, ..., a_N,k, π(o_i,k))|a_i,k=π(o_i,k) * ∇_θ π(o_i,k) -The actor is updated similarly using only one critic network Q_{θ1}. These changes to the original DDPG algorithm allow increased stability and convergence of the TD3 algorithm. This is especially relevant when approaching a multi-agent RL setup, as discussed in the foregoing section. -Please note that the actor and critics are updated by sampling expereience from the buffer where all intercations of the agents are stroed, namley the observations, actions and rewards. There are more complex buffers possible, like those that use importance sampling, but the default buffer is a simple replay buffer. You can find a documentation of the latter in :doc:`buffers` +The actor is updated similarly using only one critic network :math:`Q_{θ1}`. These changes to the original DDPG algorithm allow increased stability and convergence of the TD3 algorithm. This is especially relevant when approaching a multi-agent RL setup, as discussed in the foregoing section. +Please note that the actor and critics are updated by sampling experience from the buffer where all intercations of the agents are stored, namely the observations, actions and rewards. There are more complex buffers possible, like those that use importance sampling, but the default buffer is a simple replay buffer. You can find a documentation of the latter in :doc:`buffers` The Learning Implementation in ASSUME @@ -136,15 +136,15 @@ The Actor We will explain the way learning works in ASSUME starting from the interface to the simulation, namely the bidding strategy of the power plants. The bidding strategy, per definition in ASSUME, defines the way we formulate bids based on the technical restrictions of the unit. In a learning setting, this is done by the actor network. Which maps the observation to an action. The observation thereby is managed and collected by the units operator as -summarized in the following picture. As you can see in the current working version the observation space contains of a residula load forecast for the next 24 h and aprice forecast for 24 h as well as the -the current capacity of the powerplant and its marginal costs. +summarized in the following picture. As you can see in the current working version, the observation space contains a residual load forecast for the next 24 hours and a price +forecast for 24 hours, as well as the current capacity of the power plant and its marginal costs. .. image:: img/ActorTask.jpg :align: center :width: 500px -The action space is a continuous space, which means that the actor can choose any price between 0 and the maximum bid price defined in the code. It gives two prices for two different party of its capacity. -One, namley :math:`p_inlfex` for the minimum capacity of the power plant and one for the rest ( :math:`p_flex`). The action space is defined in the config file and can be adjusted to your needs. +The action space is a continuous space, which means that the actor can choose any price between 0 and the maximum bid price defined in the code. It gives two prices for two different parts of its capacity. +One, namley :math:`p_{inflex}` for the minimum capacity of the power plant and one for the rest ( :math:`p_{flex}`). The action space is defined in the config file and can be adjusted to your needs. After the bids are formulated in the bidding strategy they are sent to the market via the units operator. .. image:: img/ActorOutput.jpg @@ -171,9 +171,9 @@ You can read more about the different algorithms and the learning role in :doc:` The Learning Results in ASSUME ===================================== -Similarly, to the other results, the learning progress is tracked in the database, either with postgresql or timescale. The latter, enables the usage of the +Similarly to the other results, the learning progress is tracked in the database, either with postgresql or timescale. The latter enables the usage of the predefined dashboards to track the leanring process in the "Assume:Training Process" dashboard. The following pictures show the learning process of a simple reinforcement learning setting. -A more detailed description is given in the dashboard itsel. +A more detailed description is given in the dashboard itself. .. image:: img/Grafana_Learning_1.jpeg :align: center diff --git a/docs/source/learning_algorithm.rst b/docs/source/learning_algorithm.rst index c38faed33..61a0e4714 100644 --- a/docs/source/learning_algorithm.rst +++ b/docs/source/learning_algorithm.rst @@ -6,22 +6,23 @@ Reinforcement Learning Algorithms ################################## -In the chapter :doc:`learning` we got an general overview about how RL is implementes for a multi-agent setting in Assume. In the case one wants to apply these RL algorithms -to a new problem, one does not necessarly need to understand how the RL algorithms are are working in detail. The only thing needed is the adaptation of the bidding startegies, -which is covered in the tutorial. Yet, for the interested reader we will give a short overview about the RL algorithms used in Assume. We start with the learning role which is the core of the leanring implementation. - +In the chapter :doc:`learning` we got a general overview of how RL is implemented for a multi-agent setting in Assume. +If you want to apply these RL algorithms to a new problem, you do not necessarily need to understand how the RL algorithms work in detail. +All that is needed is to adapt the bidding strategies, which is covered in the tutorial. +However, for the interested reader, we will give a brief overview of the RL algorithms used in Assume. +We start with the learning role, which is the core of the learning implementation. The Learning Role ================= -The learning role orchestrates the learning process. It initializes the training process and manages the experiences gained in a buffer. -Furthermore, it schedules the policy updates and, hence, brings the critic and the actor together during the learning process. -Particularly this means, that at the beginning of the simulation, we schedule recurrent policy updates, where the output of the critic is used as a loss -of the actor, which then updates its weights using backward propagation. +The learning role orchestrates the learning process. It initializes the training process and manages the experience gained in a buffer. +It also schedules policy updates, thus bringing critic and actor together during the learning process. +Specifically, this means that at the beginning of the simulation we schedule recurrent policy updates, where the output of the critic +is used as a loss for the actor, which then updates its weights using backward propagation. With the learning role, we can also choose which RL algorithm should be used. The algorithm and the buffer have base classes and can be customized if needed. But without touching the code there are easy adjustments to the algorithms that can and eventually need to be done in the config file. -The following table shows the options that can be adjusted and gives a short explanation. For more advanced users is the functionality of the algorithm also documented below. +The following table shows the options that can be adjusted and gives a short explanation. For more advanced users, the functionality of the algorithm is also documented below. @@ -43,7 +44,7 @@ The following table shows the options that can be adjusted and gives a short exp batch_size The batch size of experience considered from the buffer for an update. gamma The discount factor, with which future expected rewards are considered in the decision-making. device The device to use. - noise_sigma The standard deviation of the distribution used to draw the noise, which is added to the actions and forces exploration. noise_scale + noise_sigma The standard deviation of the distribution used to draw the noise, which is added to the actions and forces exploration. noise_dt Determines how quickly the noise weakens over time. noise_scale The scale of the noise, which is multiplied by the noise drawn from the distribution. early_stopping_steps The number of steps considered for early stopping. If the moving average reward does not improve over this number of steps, the learning is stopped. @@ -58,7 +59,7 @@ TD3 (Twin Delayed DDPG) ----------------------- TD3 is a direct successor of DDPG and improves it using three major tricks: clipped double Q-Learning, delayed policy update and target policy smoothing. -We recommend reading OpenAI Spinning guide or the original paper to undertsand the algorithm in detail. +We recommend reading the OpenAI Spinning guide or the original paper to understand the algorithm in detail. Original paper: https://arxiv.org/pdf/1802.09477.pdf @@ -66,7 +67,7 @@ OpenAI Spinning Guide for TD3: https://spinningup.openai.com/en/latest/algorithm Original Implementation: https://github.com/sfujim/TD3 -In general the TD3 works in the following way. It maintains a pair of critics and a single actor. For each step so after every time interval in our simulation, we update both critics towards the minimum +In general, the TD3 works in the following way. It maintains a pair of critics and a single actor. For each step (after every time interval in our simulation), we update both critics towards the minimum target value of actions selected by the current target policy: @@ -77,7 +78,7 @@ target value of actions selected by the current target policy: Every :math:`d` iterations, which is implemented with the train_freq, the policy is updated with respect to :math:`Q_{\theta_1}` following the deterministic policy gradient algorithm (Silver et al., 2014). -TD3 is summarized in the following picture from the others of the original paper (Fujimoto, Hoof and Meger, 2018). +TD3 is summarized in the following picture from the authors of the original paper (Fujimoto, Hoof and Meger, 2018). .. image:: img/TD3_algorithm.jpeg @@ -88,10 +89,10 @@ TD3 is summarized in the following picture from the others of the original paper The steps in the algorithm are translated to implementations in ASSUME in the following way. The initialization of the actors and critics is done by the :func:`assume.reinforcement_learning.algorithms.matd3.TD3.initialize_policy` function, which is called in the learning role. The replay buffer needs to be stable across different episodes, which corresponds to runs of the entire simulation, hence it needs to be detached from the -entities of the simualtion that are killed after each episode, like the elarning role. Therefore, it is initialized independently and given to the learning role +entities of the simualtion that are killed after each episode, like the learning role. Therefore, it is initialized independently and given to the learning role at the beginning of each episode. For more information regarding the buffer see :doc:`buffers`. -The core of the algorithm is embodied by the :func:`assume.reinforcement_learning.algorithms.matd3.TD3.update_policy` in the learning algorithms. Here the critic and the actor are updated according to the algorithm. +The core of the algorithm is embodied by the :func:`assume.reinforcement_learning.algorithms.matd3.TD3.update_policy` in the learning algorithms. Here, the critic and the actor are updated according to the algorithm. The network architecture for the actor in the RL algorithm can be customized by specifying the network architecture used. In stablebaselines3 they are also referred to as "policies". The architecture is defined as a list of names that represent the layers of the neural network. @@ -99,6 +100,6 @@ For example, to implement a multi-layer perceptron (MLP) architecture for the ac This will create a neural network with multiple fully connected layers. Other available options for the "policy" include Long-Short-Term Memory (LSTMs). The architecture for the observation handling is implemented from [2]. -Note that the specific implementation of each network architecture is defined in the corresponding classes in the codebase. You can refer to the implementation of each architecture for more details on how they are implemented. +Note, that the specific implementation of each network architecture is defined in the corresponding classes in the codebase. You can refer to the implementation of each architecture for more details on how they are implemented. [2] Y. Ye, D. Qiu, J. Li and G. Strbac, "Multi-Period and Multi-Spatial Equilibrium Analysis in Imperfect Electricity Markets: A Novel Multi-Agent Deep Reinforcement Learning Approach," in IEEE Access, vol. 7, pp. 130515-130529, 2019, doi: 10.1109/ACCESS.2019.2940005. diff --git a/examples/notebooks/01_minimal_manual_example.ipynb b/examples/notebooks/01_minimal_manual_example.ipynb index bb82982dd..18b04affe 100644 --- a/examples/notebooks/01_minimal_manual_example.ipynb +++ b/examples/notebooks/01_minimal_manual_example.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "vscode": { "languageId": "shellscript" @@ -34,7 +34,7 @@ }, "outputs": [], "source": [ - "#!pip install assume-framework" + "!pip install assume-framework" ] }, { @@ -53,8 +53,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "INFO:assume.world:connected to db\n", - "INFO:assume.world:Learning Strategies are not available. Check that you have torch installed.\n" + "INFO:assume.world:connected to db\n" ] } ], @@ -322,7 +321,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "world_script_simulation 2023-12-05 00:00:00: : 5356801.0it [00:05, 899395.33it/s] \n" + "world_script_simulation 2023-12-05 00:00:00: : 5356801.0it [00:06, 860204.20it/s] \n" ] } ], @@ -355,15 +354,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "INFO:assume.world:connected to db\n", - "INFO:assume.world:Learning Strategies are not available. Check that you have torch installed.\n" + "INFO:assume.world:connected to db\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "world_script_simulation 2023-03-31 00:00:00: : 7689601.0it [00:07, 992635.69it/s] \n" + "world_script_simulation 2023-03-31 00:00:00: : 7689601.0it [00:09, 796703.78it/s] \n" ] } ], diff --git a/examples/notebooks/02_automated_run_example.ipynb b/examples/notebooks/02_automated_run_example.ipynb index 0b3e6b1d4..1ce700854 100644 --- a/examples/notebooks/02_automated_run_example.ipynb +++ b/examples/notebooks/02_automated_run_example.ipynb @@ -14,17 +14,17 @@ "\n", "## Tutorial outline:\n", "\n", - "- Introduction\n", - "- Setting up the environment\n", - "- Creating input files\n", - " - Power plant units\n", - " - Fuel prices\n", - " - Demand units\n", - " - Demand time series\n", - "- Creating a configuration file\n", - "- Running the simulation\n", - "- Adjusting market configuration\n", - "- Conclusion" + "- Introduction \n", + "- [Setting up the environment](#setting-up-the-environment)\n", + "- [Creating input files](#creating-input-files)\n", + " - [Power plant units](#power-plant-units)\n", + " - [Fuel prices](#fuel-prices)\n", + " - [Demand units](#demand-units)\n", + " - [Demand time series](#demand-time-series)\n", + "- [Creating a configuration file](#creating-a-configuration-file)\n", + "- [Running the simulation](#running-the-simulation)\n", + "- [Adjusting market configuration](#adjusting-market-configuration)\n", + "- [Conclusion](#conclusion)" ] }, { @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -114,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -177,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -203,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -222,6 +222,25 @@ "demand_units_df.to_csv(f\"{input_path}/demand_units.csv\", index=False)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's what each attribute in our dataset represents:\n", + "\n", + "- `name`: This is the identifier for the demand unit. In our case, we have a single demand unit named `demand_EOM`, which could represent the total electricity demand of an entire market or a specific region within the market.\n", + "\n", + "- `technology`: Indicates the type of demand. Here, `inflex_demand` is used to denote inelastic demand, meaning that the demand does not change in response to price fluctuations within the short term. This is a typical assumption for electricity markets within a short time horizon.\n", + "\n", + "- `bidding_EOM`: Specifies the bidding strategy for the demand unit. Even though demand is typically price-inelastic in the short term, it still needs to be represented in the market. The `naive` strategy here bids the demand value into the market at a price of 3000 EUR/MWh.\n", + "\n", + "- `max_power`: The maximum power that the demand unit can request. In this example, we've set it to 1,000,000 MW, which is a placeholder. This value can be used for more sophisticated bidding strategies.\n", + "\n", + "- `min_power`: The minimum power level that the demand unit can request. In this case it also serves as a placeholder for more sophisticated bidding strategies.\n", + "\n", + "- `unit_operator`: The entity responsible for the demand unit. In this example, `eom_de` could represent an electricity market operator in Germany." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -230,14 +249,14 @@ "\n", "Lastly, we'll create a time series for the demand. \n", "\n", - "You might notice, that the column name we use if demand_EOM, which is similar to the name of our demand unit. The framework is designed in such way, that multiple demand units can be defined in the same file. The column name is used to match the demand time series with the correct demand unit. Afterwards, each demand unit following a naive bidding strategy will bid the respecrive demand value into the market.\n", + "You might notice, that the column name we use is demand_EOM, which is similar to the name of our demand unit. The framework is designed in such way, that multiple demand units can be defined in the same file. The column name is used to match the demand time series with the correct demand unit. Afterwards, each demand unit following a naive bidding strategy will bid the respective demand value into the market.\n", "\n", "Also, the length of the demand time series must be at least as long as the simulation time horizon. If the time series is longer than the simulation time horizon, the framework will automatically truncate it to the correct length. If the resolution of the time series is higher than the simulation time step, the framework will automatically resample the time series to match the simulation time step. If it is shorter, an error will be raised." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -252,37 +271,18 @@ "demand_profile.to_csv(f\"{input_path}/demand_df.csv\", index=False)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here's what each attribute in our dataset represents:\n", - "\n", - "- `name`: This is the identifier for the demand unit. In our case, we have a single demand unit named `demand_EOM`, which could represent the total electricity demand of an entire market or a specific region within the market.\n", - "\n", - "- `technology`: Indicates the type of demand. Here, `inflex_demand` is used to denote inelastic demand, meaning that the demand does not change in response to price fluctuations within the short term. This is a typical assumption for electricity markets within a short time horizon.\n", - "\n", - "- `bidding_EOM`: Specifies the bidding strategy for the demand unit. Even though demand is typically price-inelastic in the short term, it still needs to be represented in the market. The `naive` strategy here bids the demand value into the market at price of 3000 EUR/MWh.\n", - "\n", - "- `max_power`: The maximum power that the demand unit can request. In this example, we've set it to 1,000,000 MW, which is a placeholder. This value can be used for more sophisticated bidding strategies.\n", - "\n", - "- `min_power`: The minimum power level that the demand unit can request. In this case also serves as a placeholder for more sophisticated bidding strategies.\n", - "\n", - "- `unit_operator`: The entity responsible for the demand unit. In this example, `eom_de` could represent an electricity market operator in Germany." - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating a Configuration File\n", "\n", - "With our input files ready, we'll now create a configuration file that ASSUME will use to load the simulation. The confi file allows easy customization of the simulation parameters, such as the simulation time horizon, the time step, and the market configuration. The configuration file is written in YAML format, which is a human-readable markup language that is commonly used for configuration files." + "With our input files ready, we'll now create a configuration file that ASSUME will use to load the simulation. The config file allows easy customization of the simulation parameters, such as the simulation time horizon, the time step, and the market configuration. The configuration file is written in YAML format, which is a human-readable markup language that is commonly used for configuration files." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -367,9 +367,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.world:connected to db\n", + "INFO:assume.scenario.loader_csv:Starting Scenario example_01/hourly_market from inputs\n", + "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", + "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", + "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", + "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", + "INFO:assume.scenario.loader_csv:save_frequency_hours is disabled due to CSV export being enabled. Data will be stored in the CSV files at the end of the simulation.\n", + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01_hourly_market 2021-03-07 00:00:00: : 518401it [00:04, 124410.59it/s] \n" + ] + } + ], "source": [ "# define the database uri. In this case we are using a local sqlite database\n", "db_uri = \"sqlite:///local_db/assume_db.db\"\n", @@ -402,7 +433,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -447,9 +478,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.world:connected to db\n", + "INFO:assume.scenario.loader_csv:Starting Scenario example_01/daily_market from inputs\n", + "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", + "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", + "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", + "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", + "INFO:assume.scenario.loader_csv:save_frequency_hours is disabled due to CSV export being enabled. Data will be stored in the CSV files at the end of the simulation.\n", + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01_daily_market 2021-03-07 00:00:00: : 518401it [00:00, 659017.51it/s] \n" + ] + } + ], "source": [ "data_format = \"local_db\" # \"local_db\" or \"timescale\"\n", "\n", @@ -490,8 +552,13 @@ "\n", "Congratulations! You've learned how to automate the setup and execution of simulations in ASSUME using configuration files and input files. This approach is particularly useful for handling large and complex simulations. \n", "\n", - "You are welcome to experiment with different configurations and variying input data. For example, you can try changing the bidding strategy for the power plant units to a more sophisticated strategy, such as a `flexable_eom`" + "You are welcome to experiment with different configurations and varying input data. For example, you can try changing the bidding strategy for the power plant units to a more sophisticated strategy, such as a `flexable_eom`" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] } ], "metadata": { @@ -501,7 +568,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "assume-framework", + "display_name": "assume", "language": "python", "name": "python3" }, @@ -515,7 +582,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.7" }, "nbsphinx": { "execute": "never" diff --git a/examples/notebooks/03_custom_unit_example.ipynb b/examples/notebooks/03_custom_unit_example.ipynb index b9c84174e..ad64b0322 100644 --- a/examples/notebooks/03_custom_unit_example.ipynb +++ b/examples/notebooks/03_custom_unit_example.ipynb @@ -10,11 +10,11 @@ "\n", "**We will cover the following topics:**\n", "\n", - "1. Essential concepts and terminology in electricity market modeling\n", - "2. Setting up the ASSUME framework\n", - "3. Developing a new Demand Side Unit\n", - "4. Formulating a rule-based bidding strategy\n", - "5. Integrating the new unit and strategy into the ASSUME simulation" + "1. [Essential concepts and terminology in electricity market modeling](#1-introduction-to-unit-agents-and-bidding-strategy)\n", + "2. [Setting up the ASSUME framework](#2-setting-up-assume)\n", + "3. [Developing a new Demand Side Unit](#3-developing-a-new-demand-side-unit)\n", + "4. [Formulating a rule-based bidding strategy](#4-rule-based-bidding-strategy)\n", + "5. [Integrating the new unit and strategy into the ASSUME simulation](#5-integrating-the-new-unit-and-strategy-into-assume)" ] }, { @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "vscode": { "languageId": "shellscript" @@ -69,7 +69,7 @@ } ], "source": [ - "# this cell is used to display the image in the notebook when using collab\n", + "# this cell is used to display the image in the notebook when using colab\n", "# or running the notebook locally\n", "\n", "from IPython.display import Image, display\n", @@ -170,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -208,12 +208,12 @@ "- **Bidding Strategies**: The strategies used by the unit for bidding in the electricity market.\n", "- **Max Power and Min Power**: The maximum and minimum electrical power that the unit can handle.\n", "- **Max Hydrogen and Min Hydrogen**: The maximum and minimum hydrogen production levels.\n", - "- **Fixed Cost**: The fixed operational cost for the unit." + "- **Additional Cost**: The fixed operational cost for the unit." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -338,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -396,7 +396,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -444,7 +444,7 @@ "source": [ "The `NaiveStrategyElectrolyser` class inherits from the `BaseStrategy` class and implements the `calculate_bids` method, which is responsible for formulating the market bids:\n", "\n", - "`calculate_bids` method takes several arguments, including the unit to be dispatched (`unit`), the market configuration (`market_config`), and a list of products (`product_tuples`). It returns an `Orderbook` containing the bids.\n", + "The `calculate_bids` method takes several arguments, including the unit to be dispatched (`unit`), the market configuration (`market_config`), and a list of products (`product_tuples`). It returns an `Orderbook` containing the bids.\n", "\n", "In this case, we use **Marginal Revenue** to determine the price at which the unit should make its bid. The equation used in the code is as follows:\n", "\n", @@ -461,7 +461,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -544,9 +544,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "electrolyser_demo 2019-01-30 00:00:00: : 2505601it [00:12, 203669.90it/s] \n" + ] + } + ], "source": [ "# import packages\n", "import logging\n", @@ -699,9 +707,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.world:connected to db\n", + "INFO:assume.scenario.loader_csv:Starting Scenario example_01e/base from ../inputs\n", + "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Downsampling demand_df successful.\n", + "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", + "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", + "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", + "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", + "INFO:assume.scenario.loader_csv:save_frequency_hours is disabled due to CSV export being enabled. Data will be stored in the CSV files at the end of the simulation.\n", + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", + "INFO:assume.scenario.loader_csv:Adding electrolyser units\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01e_base 2019-01-30 00:00:00: : 2505601it [00:11, 220050.96it/s] \n" + ] + } + ], "source": [ "# import the main World class and the load_scenario_folder functions from assume\n", "# import the function to load custom units\n", @@ -775,7 +815,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "assume", "language": "python", "name": "python3" }, @@ -789,7 +829,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.7" }, "nbsphinx": { "execute": "never" diff --git a/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb b/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb index 6a910a7f8..5de6769df 100644 --- a/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb +++ b/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb @@ -9,7 +9,7 @@ "source": [ "# 4.1 RL Algorithm tutorial\n", "\n", - "This tutorial will introduce users into the MATD3 implementation in ASSUME and hence how we use reinforcement learning (RL). The main objective of this tutorial is to ensure participants grasp the steps required to equip ASSUME with a RL algorithm. It ,therefore, start one level deeper, than the RL_application example and the knowledge from this tutorial is not required, if the already perconfigured algorithm in Assume should be used. The algorithm explained here is usable as a plug and play solution in the framework. The following coding snippets will highlight the key in the algorithm class and will explain the interactions with the learning role and other classes along the way. \n", + "This tutorial will introduce users into the MATD3 implementation in ASSUME and hence how we use reinforcement learning (RL). The main objective of this tutorial is to ensure participants grasp the steps required to equip ASSUME with a RL algorithm. It therefore starts one level deeper than the RL_application example and the knowledge from this tutorial is not required if the already per-configured algorithm in Assume is to be used. The algorithm explained here is usable as a plug and play solution in the framework. The following coding snippets will highlight the key in the algorithm class and will explain the interactions with the learning role and other classes along the way. \n", "\n", "The outline of this tutorial is as follows. We will start with an introduction to the changed simulation flow when we use reinforcement learning (1. From one simulation year to learning episodes). If you need a refresher on RL in general, please visit our readthedocs (https://assume.readthedocs.io/en/latest/). Afterwards, we dive into the tasks and reason behind a learning role (2. What role has a learning role) and then dive into the characteristics of the algorithm (3. The MATD3).\n", "\n", @@ -112,11 +112,7 @@ "cell_type": "code", "execution_count": null, "id": "7d9899ff", - "metadata": { - "vscode": { - "languageId": "python" - } - }, + "metadata": {}, "outputs": [], "source": [ "import importlib.util\n", @@ -159,10 +155,7 @@ "execution_count": null, "id": "ade14744", "metadata": { - "id": "xUsbeZdPJ_2Q", - "vscode": { - "languageId": "python" - } + "id": "xUsbeZdPJ_2Q" }, "outputs": [], "source": [ @@ -219,10 +212,7 @@ "execution_count": null, "id": "94517a3e", "metadata": { - "id": "UXYSesx4Ifp5", - "vscode": { - "languageId": "python" - } + "id": "UXYSesx4Ifp5" }, "outputs": [], "source": [ @@ -424,11 +414,7 @@ "cell_type": "code", "execution_count": null, "id": "daed035c", - "metadata": { - "vscode": { - "languageId": "python" - } - }, + "metadata": {}, "outputs": [], "source": [ "class Learning(Learning):\n", @@ -521,10 +507,7 @@ "execution_count": null, "id": "632844c2", "metadata": { - "id": "0ww-L9fABnw3", - "vscode": { - "languageId": "python" - } + "id": "0ww-L9fABnw3" }, "outputs": [], "source": [ @@ -582,11 +565,7 @@ "cell_type": "code", "execution_count": null, "id": "c715f90e", - "metadata": { - "vscode": { - "languageId": "python" - } - }, + "metadata": {}, "outputs": [], "source": [ "class TD3(TD3):\n", @@ -654,11 +633,7 @@ "cell_type": "code", "execution_count": null, "id": "753dbab1", - "metadata": { - "vscode": { - "languageId": "python" - } - }, + "metadata": {}, "outputs": [], "source": [ "class TD3(TD3):\n", @@ -853,10 +828,7 @@ "execution_count": null, "id": "6bb09b5b", "metadata": { - "id": "moZ_UD7FfkOh", - "vscode": { - "languageId": "python" - } + "id": "moZ_UD7FfkOh" }, "outputs": [], "source": [ @@ -885,10 +857,7 @@ "execution_count": null, "id": "5cff2f6a", "metadata": { - "id": "iPz8v4N5hpfr", - "vscode": { - "languageId": "python" - } + "id": "iPz8v4N5hpfr" }, "outputs": [], "source": [ @@ -926,10 +895,7 @@ }, "id": "ZlWxXxZr54WV", "lines_to_next_cell": 0, - "outputId": "e30f4279-7a4e-4efc-9cfb-61416e4fe2f1", - "vscode": { - "languageId": "python" - } + "outputId": "e30f4279-7a4e-4efc-9cfb-61416e4fe2f1" }, "outputs": [], "source": [ @@ -986,7 +952,7 @@ " study_case=study_case,\n", " )\n", "\n", - " # after the learning is done we make a normal run of the simulation, which equasl a test run\n", + " # after the learning is done we make a normal run of the simulation, which equals a test run\n", " world.run()" ] }, @@ -995,10 +961,7 @@ "execution_count": null, "id": "df2ba59b", "metadata": { - "lines_to_next_cell": 2, - "vscode": { - "languageId": "python" - } + "lines_to_next_cell": 2 }, "outputs": [], "source": [] From fa7c54136e72d8dd12c1dae3cae2bce3e3bc64e8 Mon Sep 17 00:00:00 2001 From: AndreasEppler Date: Tue, 19 Nov 2024 16:41:14 +0100 Subject: [PATCH 03/22] Added if-query for colab-specific shell commands, typo corrections. --- .../notebooks/01_minimal_manual_example.ipynb | 20 +- .../notebooks/02_automated_run_example.ipynb | 16 +- .../notebooks/03_custom_unit_example.ipynb | 38 +- .../04_reinforcement_learning_example.ipynb | 111 +- examples/notebooks/05_market_comparison.ipynb | 327 +- .../06_advanced_orders_example.ipynb | 50 +- .../notebooks/08_market_zone_coupling.ipynb | 14406 +++++++--------- .../notebooks/09_example_Sim_and_xRL.ipynb | 62 +- examples/notebooks/11_redispatch.ipynb | 36 +- 9 files changed, 6342 insertions(+), 8724 deletions(-) diff --git a/examples/notebooks/01_minimal_manual_example.ipynb b/examples/notebooks/01_minimal_manual_example.ipynb index 18b04affe..fb3585ed0 100644 --- a/examples/notebooks/01_minimal_manual_example.ipynb +++ b/examples/notebooks/01_minimal_manual_example.ipynb @@ -26,15 +26,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "shellscript" - } - }, + "execution_count": 1, + "metadata": {}, "outputs": [], "source": [ - "!pip install assume-framework" + "import importlib.util\n", + "\n", + "# Check whether notebook is run in google colab\n", + "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", + "\n", + "if IN_COLAB:\n", + " !pip install assume-framework" ] }, { @@ -354,7 +356,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "INFO:assume.world:connected to db\n" + "INFO:assume.world:connected to db\n", + "INFO:assume.world:activating container\n", + "INFO:assume.world:all agents up - starting simulation\n" ] }, { diff --git a/examples/notebooks/02_automated_run_example.ipynb b/examples/notebooks/02_automated_run_example.ipynb index 1ce700854..cecfcea7c 100644 --- a/examples/notebooks/02_automated_run_example.ipynb +++ b/examples/notebooks/02_automated_run_example.ipynb @@ -45,15 +45,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "shellscript" - } - }, + "execution_count": 1, + "metadata": {}, "outputs": [], "source": [ - "!pip install assume-framework" + "import importlib.util\n", + "\n", + "# Check whether notebook is run in google colab\n", + "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", + "\n", + "if IN_COLAB:\n", + " !pip install assume-framework" ] }, { diff --git a/examples/notebooks/03_custom_unit_example.ipynb b/examples/notebooks/03_custom_unit_example.ipynb index b57518e1a..c5d543aab 100644 --- a/examples/notebooks/03_custom_unit_example.ipynb +++ b/examples/notebooks/03_custom_unit_example.ipynb @@ -59,7 +59,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "" ] @@ -129,28 +129,26 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "shellscript" - } - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ - "!pip install assume-framework" + "import importlib.util\n", + "\n", + "# Check if 'google.colab' is available\n", + "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", + "if IN_COLAB:\n", + " !pip install assume-framework" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "shellscript" - } - }, + "execution_count": 3, + "metadata": {}, "outputs": [], "source": [ - "!git clone --depth=1 https://github.com/assume-framework/assume.git assume-repo" + "if IN_COLAB:\n", + " !git clone --depth=1 https://github.com/assume-framework/assume.git assume-repo" ] }, { @@ -175,11 +173,6 @@ "metadata": {}, "outputs": [], "source": [ - "import importlib.util\n", - "\n", - "# Check if 'google.colab' is available\n", - "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", - "\n", "colab_inputs_path = \"assume-repo/examples/inputs\"\n", "local_inputs_path = \"../inputs\"\n", "\n", @@ -812,6 +805,11 @@ "source": [ "This concludes our tutorial. By following these steps, you have successfully created a Demand Side Unit with a Rule-Based Bidding Strategy and integrated it into the ASSUME framework." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] } ], "metadata": { diff --git a/examples/notebooks/04_reinforcement_learning_example.ipynb b/examples/notebooks/04_reinforcement_learning_example.ipynb index 65bfdb57e..89680eacb 100644 --- a/examples/notebooks/04_reinforcement_learning_example.ipynb +++ b/examples/notebooks/04_reinforcement_learning_example.ipynb @@ -12,7 +12,7 @@ "This tutorial will introduce users into ASSUME and its ways of using reinforcement learning (RL). The main objective of this tutorial is to ensure participants grasp the steps required to equip a new unit with RL strategies or modify the action dimensions.\n", "Our emphasis lies in the bidding strategy, with less focus on the algorithm and role. The latter are usable as a plug-and-play solution in the framework. The following coding tasks will highlight the key aspects to be adjusted, as already outlined in the learning_strategies.py file.\n", "\n", - "The outline of this tutorial is as follows. We will start with a basic summary of the implementation of reinforcement learning (RL) in ASSUME and its architecture (1. ASSUME & Learning Basics) . If you need a refresher on RL in general, please visit our readthedocs (https://ASSUME.readthedocs.io/en/latest/). Afterwards, we install ASSUME in this Google Colab (2. Get ASSUME running) and then we dive into the learning_strategies.py file and explain how we need to adjust conventional bidding strategies. to incorporate RL (3. Make ASSUME learn).\n", + "The outline of this tutorial is as follows. We will start with a basic summary of the implementation of reinforcement learning (RL) in ASSUME and its architecture (1. ASSUME & Learning Basics) . If you need a refresher on RL in general, please visit our readthedocs (https://ASSUME.readthedocs.io/en/latest/). Afterwards, we install ASSUME in this Google Colab (2. Get ASSUME running) and then we dive into the learning_strategies.py file and explain how we need to adjust conventional bidding strategies to incorporate RL (3. Make ASSUME learn).\n", "\n", "**As a whole, this tutorial covers the following coding tasks:**\n", "\n", @@ -36,11 +36,29 @@ "\n", "ASSUME in general is intended for researchers, planners, utilities and everyone searching to understand market dynamics of energy markets. It provides an easy-to-use tool-box as a free software that can be tailored to the specific use case of the user.\n", "\n", - "In the following figure the architecture of the framework is depicted. It can be roughly divided into two parts. On the left side of the world class the markets are located and on the right side the market participants, which are here named units. Both world are connected via the orders that market participants place on the markets. The learning capability is sketched out with the yellow classes on the right side, namely the units side.\n", + "In the following figure the architecture of the framework is depicted. It can be roughly divided into two parts. On the left side of the world class the markets are located and on the right side the market participants, which are here named units. Both worlds are connected via the orders that market participants place on the markets. The learning capability is sketched out with the yellow classes on the right side, namely the units side." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d3327313", + "metadata": {}, + "outputs": [], + "source": [ + "# this cell is used to display the image in the notebook when using colab\n", + "# or running the notebook locally\n", "\n", + "import os\n", + "from IPython.display import SVG, display\n", "\n", + "image_path = \"assume-repo/docs/source/img/architecture.svg\"\n", + "alt_image_path = \"../../docs/source/img/architecture.svg\"\n", "\n", - "![architecture.svg]" + "if os.path.exists(image_path):\n", + " display(SVG(image_path))\n", + "elif os.path.exists(alt_image_path):\n", + " display(SVG(alt_image_path))" ] }, { @@ -151,7 +169,13 @@ }, "outputs": [], "source": [ - "!pip install 'assume-framework[learning]'" + "import importlib.util\n", + "\n", + "# Check if 'google.colab' is available\n", + "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", + "\n", + "if IN_COLAB:\n", + " !pip install 'assume-framework[learning]'" ] }, { @@ -161,7 +185,7 @@ "id": "IIw_QIE3pY34" }, "source": [ - "And easy like this we have ASSUME installed. Now we can let it run. Please note though that we cannot use the functionalities tied to docker and, hence, cannot access the predefined dashboards in colab. For this please install docker and ASSUME on your personal machine.\n", + "And easy as this we have ASSUME installed. Now we can let it run. Please note though that we cannot use the functionalities tied to docker and, hence, cannot access the predefined dashboards in colab. For this please install docker and ASSUME on your personal machine.\n", "\n", "Further we would like to access the predefined scenarios in ASSUME which are stored on the git repository. Hence, we clone the repository." ] @@ -187,7 +211,8 @@ }, "outputs": [], "source": [ - "!git clone --depth=1 https://github.com/assume-framework/assume.git assume-repo" + "if IN_COLAB:\n", + " !git clone --depth=1 https://github.com/assume-framework/assume.git assume-repo" ] }, { @@ -215,7 +240,8 @@ }, "outputs": [], "source": [ - "!cd assume-repo && assume -s example_01b -db \"sqlite:///./examples/local_db/assume_db_example_01b.db\"" + "if IN_COLAB:\n", + " !cd assume-repo && assume -s example_01b -db \"sqlite:///./examples/local_db/assume_db_example_01b.db\"" ] }, { @@ -230,16 +256,11 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "7f3dd8f1", "metadata": {}, "outputs": [], "source": [ - "import importlib.util\n", - "\n", - "# Check if 'google.colab' is available\n", - "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", - "\n", "colab_inputs_path = \"assume-repo/examples/inputs\"\n", "local_inputs_path = \"../inputs\"\n", "\n", @@ -255,7 +276,7 @@ "source": [ "## 3. Make your agents learn\n", "\n", - "Now it is time to get your hands dirty and actually dive into coding in ASSUME. The main objective of this session is to ensure participants grasp the steps required to equip a new unit with RL strategies or modify the action dimensions. Our emphasis lies in the bidding strategy, with less focus on the algorithm and role. Coding tasks will highlight the key aspects to be a djusted, as already outlined in the learning_strategies.py file. Subsequent\n", + "Now it is time to get your hands dirty and actually dive into coding in ASSUME. The main objective of this session is to ensure participants grasp the steps required to equip a new unit with RL strategies or modify the action dimensions. Our emphasis lies in the bidding strategy, with less focus on the algorithm and role. Coding tasks will highlight the key aspects to be adjusted, as already outlined in the learning_strategies.py file. Subsequent\n", "sections will present the tasks and provide the correct answers for the coding exercises.\n", "\n", "We start by initializing the class of our Learning Strategy. This is very closely related to the general structure of a bidding strategy.\n", @@ -266,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "04d17e03", "metadata": { "id": "xUsbeZdPJ_2Q" @@ -292,7 +313,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "383bbbfd", "metadata": { "id": "UXYSesx4Ifp5" @@ -372,21 +393,21 @@ "source": [ "### 3.1 The \"Step Function\"\n", "\n", - "The key function in an RL problem is the step that is taken in the so called environment. It consist the following parts:\n", + "The key function in an RL problem is the step that is taken in the so called environment. It consist of the following parts:\n", "\n", "1. Get an observation\n", "2. Choose an action\n", "3. Get a reward\n", "4. Update your policy\n", "\n", - "In ASSUME we do not have such a straight forward step function. The steps 1 & 2 are combined in the calculate_bids() function which is called as soon as an offer on the market is placed. The step 3, however, can only happen after we get the market feedback from the simulation run and, hence, is in the calculate_reward() function. Step 4 is solely handeled by the learning_role as it schedules the policy update manages the buffer and what not. Hence, it is actually not included in this notebook, since we only focus on transforming the bidding strategy into a learning one.\n", + "In ASSUME we do not have such a straightforward step function. The steps 1 & 2 are combined in the calculate_bids() function which is called as soon as an offer on the market is placed. The step 3, however, can only happen after we get the market feedback from the simulation run and, hence, is in the calculate_reward() function. Step 4 is solely handeled by the learning_role as it schedules the policy update, manages the buffer and what not. Hence, it is actually not included in this notebook, since we only focus on transforming the bidding strategy into a learning one.\n", "\n", "**Step 1-3 will be implemented in the following sections 3.2 to 3.4. If there is a coding task for you it will be marked accordingly.**" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "12e33c9e", "metadata": { "id": "iApbQsg5x_u2" @@ -442,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "a2907489", "metadata": { "id": "_4cJ8Y8uvMgV" @@ -480,9 +501,9 @@ "\n", "The decision about the observations received by each agent plays a crucial role when designing a multi-agent RL setup. The following describes the task of learning agents representing profit-maximizing electricity market participants who either sell a generating unit's output or optimize a storage unit's operation. They are represented through their plants' techno-economic parameters, such as minimal operational capacity $P^{min}$, start-up $c^{su}$, and shut-down $c^{sd}$ costs. This information is all know by the unit itself and, hence, also accessible in the bidding strategy.\n", "\n", - "During the training phase, the centralized critic receives observations from all agents, resulting in an input size that grows linearly with the number of agents. This can lead to unstable training behavior of the critic networks, which limits the maximal number of agents in the simulation. This effect is known as the dimensionality curse, which likely contributed to the small number of learning agents in existing approaches. To address the dimensionality curse, we use a single observation that is the same for all agents and added a small size of unique observations for each agent to improve their performance. This modification allows the use of only one observation in the centralized critic, decoupled from the number of learning agents, significantly reducing the observation size and enabling simultaneous training of hundreds of learning agents with stable training behavior. The only limiting factor is the available working memory.\n", + "During the training phase, the centralized critic receives observations from all agents, resulting in an input size that grows linearly with the number of agents. This can lead to unstable training behavior of the critic networks, which limits the maximal number of agents in the simulation. This effect is known as the dimensionality curse, which likely contributed to the small number of learning agents in existing approaches. To address the dimensionality curse, we use a single observation that is the same for all agents and add a small size of unique observations for each agent to improve their performance. This modification allows the use of only one observation in the centralized critic, decoupled from the number of learning agents, significantly reducing the observation size and enabling simultaneous training of hundreds of learning agents with stable training behavior. The only limiting factor is the available working memory.\n", "\n", - "At time-step $t$, agent $i$ receives the observation $o_{i,t}$ consisting of vectors $[L_{\\mathrm{h},t}, L_{\\mathrm{f},t}, M_{\\mathrm{h},t}, M_{\\mathrm{f},t}, mc_{i,t}]$. Here $L_{\\mathrm{h},t}, L_{\\mathrm{f},t}$ and $M_{\\mathrm{h},t}, M_{\\mathrm{f},t}$ are the past and the forecast residual loads and market prices, respectively. These information stems from the world, where a overall forecasting role generates them. The price forecast is calculated ahead of the simulation run using a simple merit order model based on the residual load forecast and the marginal cost of power plants. This part of the observation is the same for all agents. In addition, each agent receives its current marginal cost $mc_{i,t}$. Information about the marginal cost is shared with a centralized critic during the training phase. Still, it is not shared with other agents during the execution phase. All the inputs are normalized to improve the performance of the training process.\n" + "At time-step $t$, agent $i$ receives the observation $o_{i,t}$ consisting of vectors $[L_{\\mathrm{h},t}, L_{\\mathrm{f},t}, M_{\\mathrm{h},t}, M_{\\mathrm{f},t}, mc_{i,t}]$. Here $L_{\\mathrm{h},t}, L_{\\mathrm{f},t}$ and $M_{\\mathrm{h},t}, M_{\\mathrm{f},t}$ are the past and the forecast residual loads and market prices, respectively. This information stems from the world, where an overall forecasting role generates them. The price forecast is calculated ahead of the simulation run using a simple merit order model based on the residual load forecast and the marginal cost of power plants. This part of the observation is the same for all agents. In addition, each agent receives its current marginal cost $mc_{i,t}$. Information about the marginal cost is shared with a centralized critic during the training phase. Still, it is not shared with other agents during the execution phase. All the inputs are normalized to improve the performance of the training process.\n" ] }, { @@ -506,7 +527,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "7c7be3eb", "metadata": { "id": "0ww-L9fABnw3" @@ -533,7 +554,7 @@ "\n", " end_excl = end - unit.index.freq\n", "\n", - " # get the forecast length depending on the tme unit considered in the modelled unit\n", + " # get the forecast length depending on the time unit considered in the modelled unit\n", " forecast_len = pd.Timedelta((self.foresight - 1) * unit.index.freq)\n", "\n", " # =============================================================================\n", @@ -552,7 +573,7 @@ " # marginal cost\n", " scaling_factor_marginal_cost = self.max_bid_price\n", "\n", - " # checks if we are at end of simulation horizon, since we need to change the forecast then\n", + " # checks if we are at the end of the simulation horizon, since we need to change the forecast then\n", " # for residual load and price forecast and scale them\n", " if (\n", " end_excl + forecast_len\n", @@ -605,7 +626,7 @@ " current_volume = unit.get_output_before(start)\n", " current_costs = unit.calc_marginal_cost_with_partial_eff(current_volume, start)\n", "\n", - " # scale unit outpus\n", + " # scale unit outputs\n", " scaled_total_capacity = current_volume / scaling_factor_total_capacity\n", " scaled_marginal_cost = current_costs / scaling_factor_marginal_cost\n", "\n", @@ -618,7 +639,7 @@ " ]\n", " )\n", "\n", - " # transfer arry to GPU for NN processing\n", + " # transfer array to GPU for NN processing\n", " observation = (\n", " th.tensor(observation, dtype=self.float_type)\n", " .to(self.device, non_blocking=True)\n", @@ -643,7 +664,7 @@ "\n", "Additionally, scaling promotes uniformity in the learning process. Many optimization algorithms, like gradient descent, adjust model parameters based on the magnitude of gradients. When features have vastly different scales, some may dominate the learning process, while others receive less attention. This imbalance can hinder convergence and result in a suboptimal model. Scaling addresses this issue, allowing the algorithm to treat all features equally and progress more efficiently towards an optimal solution. This not only expedites the learning process but also enhances the model's ability to generalize to new, unseen data. In essence, scaling observations is a fundamental practice that enhances the performance and robustness of machine learning models across a wide array of applications.\n", "\n", - "According to this the scaling should ensure a similar range for all input parameters. You can achieve that by choosing the following scaling factors. If you add new observations, choose your scaling factors wisely." + "According to this, the scaling should ensure a similar range for all input parameters. You can achieve that by choosing the following scaling factors. If you add new observations, choose your scaling factors wisely." ] }, { @@ -712,7 +733,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "9a2f135a", "metadata": { "id": "8ehlm5Z9CbRw" @@ -731,10 +752,10 @@ " Get actions\n", " \"\"\"\n", "\n", - " # distinction whetere we are in learning mode or not to handle exploration realised with noise\n", + " # distinction whether we are in learning mode or not to handle exploration realised with noise\n", " if self.learning_mode:\n", - " # if we are in learning mode the first x episodes we want to explore the entire action space\n", - " # to get a good initial experience, in the area around the costs of the agent\n", + " # if we are in learning mode, the first x episodes we want to explore the entire action space\n", + " # to get a good initial experience in the area around the costs of the agent\n", " if self.collect_initial_experience_mode:\n", " # define current action as solely noise\n", " noise = (\n", @@ -785,7 +806,7 @@ "\n", "So how do we define the base bid?\n", "\n", - "Assuming the described auction is a efficient market with full information and competition, we know that bidding the marginal costs of the power plant is the economically best bid. With the RL strategy we can recreate the abuse of market power and incomplete information, which enables us to model different market settings. Yet, starting of with the theoretically styleized optimal solution guides our RL agents properly. As the marginal costs of the power plant are part of the oberservations we can define the base bid in the following way. " + "Assuming the described auction is an efficient market with full information and competition, we know that bidding the marginal costs of the power plant is the economically best bid. With the RL strategy we can recreate the abuse of market power and incomplete information, which enables us to model different market settings. Yet, starting off with the theoretically styleized optimal solution guides our RL agents properly. As the marginal costs of the power plant are part of the oberservations we can define the base bid in the following way. " ] }, { @@ -818,7 +839,7 @@ "#### **Task 2.2**\n", "**Goal: Define the actual bids with the outputs of the actors**\n", "\n", - "Similarly to every other output of a neuronal network, the actions are in the range of 0-1. These values need to be translated into the actual bids $a_{i,t} = [ep^\\mathrm{inflex}_{i,t}, ep^\\mathrm{flex}_{i,t}] \\in [ep^{min},ep^{max}]$. This can be done in a way that further helps the RL agent to learn, if we put some thought into.\n", + "Similarly to every other output of a neuronal network, the actions are in the range of 0-1. These values need to be translated into the actual bids $a_{i,t} = [ep^\\mathrm{inflex}_{i,t}, ep^\\mathrm{flex}_{i,t}] \\in [ep^{min},ep^{max}].$ This can be done in a way that further helps the RL agent to learn, if we put some thought into.\n", "\n", "For this we go back into the calculate_bids() function and instead of just defining bids=actions, which was just a place holder, we actually make them into bids. Think about a smart way to transform them and fill the gaps in the following code. Remember:\n", "\n", @@ -828,7 +849,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "337833a5", "metadata": { "id": "Y81HzlkjNHJ0" @@ -1017,12 +1038,12 @@ "#### **Task 3**\n", "**Goal**: Define the reward guiding the learning process of the agent.\n", "\n", - "As the reward plays such a crucial role in the learning think of ways how to integrate further signals exceeding the monetary profit. One example could be integrating a regret term, namely the opportunity costs. Your task is to define the rewrad using the opportunity costs and to scale it." + "As the reward plays such a crucial role in the learning think of ways how to integrate further signals exceeding the monetary profit. One example could be integrating a regret term, namely the opportunity costs. Your task is to define the reward using the opportunity costs and to scale it." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "43e813e2", "metadata": { "id": "U9HX41mODuBU" @@ -1064,7 +1085,7 @@ " end = order[\"end_time\"]\n", " end_excl = end - unit.index.freq\n", "\n", - " # depending on way the unit calaculates marginal costs we take costs\n", + " # depending on whether the unit calaculates marginal costs we take costs\n", " if unit.marginal_cost is not None:\n", " marginal_cost = (\n", " unit.marginal_cost[start]\n", @@ -1196,7 +1217,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "6aa54f30", "metadata": { "id": "ZwVtpK3B5gR6" @@ -1253,7 +1274,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "23b299f8", "metadata": { "id": "moZ_UD7FfkOh" @@ -1289,7 +1310,7 @@ "source": [ "In order to let the simulation run with the integrated learning we need to touch up the main file that runs it in the following way.\n", "\n", - "In the following cell, we let the example run in case 1 of [1], where we have one big reinforcement learning power plan exists that technically can exert my power.\n", + "In the following cell, we let the example run in case 1 of [1], where one big reinforcement learning power plant exists that technically can exert max power.\n", "\n", "[1] Harder, N.; Qussous, R.; Weidlich, A. Fit for purpose: Modeling wholesale electricity markets realistically with multi-agent deep reinforcement learning. *Energy and AI* **2023**. 14. 100295. https://doi.org/10.1016/j.egyai.2023.100295.\n", "\n" @@ -1324,8 +1345,8 @@ " world = World(database_uri=db_uri, export_csv_path=csv_path)\n", "\n", " # we import our defined bidding strategey class including the learning into the world bidding strategies\n", - " # in the example files we provided the name of the learning bidding strategeis in the input csv is \"pp_learning\"\n", - " # hence we define this strategey to be one of the learning class\n", + " # in the example files we provided the name of the learning bidding strategies in the input csv \"pp_learning\"\n", + " # hence we define this strategey to be the one of the learning class\n", " world.bidding_strategies[\"pp_learning\"] = RLStrategy\n", "\n", " # then we load the scenario specified above from the respective input files\n", @@ -1642,7 +1663,7 @@ ], "metadata": { "kernelspec": { - "display_name": "assume-framework", + "display_name": "assume", "language": "python", "name": "python3" }, @@ -1656,7 +1677,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.7" } }, "nbformat": 4, diff --git a/examples/notebooks/05_market_comparison.ipynb b/examples/notebooks/05_market_comparison.ipynb index f451942b0..facadc21e 100644 --- a/examples/notebooks/05_market_comparison.ipynb +++ b/examples/notebooks/05_market_comparison.ipynb @@ -64,20 +64,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "m0DaRwFA7VgW", - "outputId": "5655adad-5b7a-4fe3-9067-6b502a06136b", - "vscode": { - "languageId": "shellscript" - } + "outputId": "5655adad-5b7a-4fe3-9067-6b502a06136b" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting matplotlib\n", + " Using cached matplotlib-3.9.2-cp312-cp312-win_amd64.whl.metadata (11 kB)\n", + "Collecting contourpy>=1.0.1 (from matplotlib)\n", + " Using cached contourpy-1.3.0-cp312-cp312-win_amd64.whl.metadata (5.4 kB)\n", + "Collecting cycler>=0.10 (from matplotlib)\n", + " Using cached cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)\n", + "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from matplotlib) (4.54.1)\n", + "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from matplotlib) (1.4.7)\n", + "Requirement already satisfied: numpy>=1.23 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from matplotlib) (2.1.3)\n", + "Requirement already satisfied: packaging>=20.0 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from matplotlib) (24.1)\n", + "Requirement already satisfied: pillow>=8 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from matplotlib) (11.0.0)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from matplotlib) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from matplotlib) (2.9.0)\n", + "Requirement already satisfied: six>=1.5 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", + "Using cached matplotlib-3.9.2-cp312-cp312-win_amd64.whl (7.8 MB)\n", + "Using cached contourpy-1.3.0-cp312-cp312-win_amd64.whl (218 kB)\n", + "Using cached cycler-0.12.1-py3-none-any.whl (8.3 kB)\n", + "Installing collected packages: cycler, contourpy, matplotlib\n", + "Successfully installed contourpy-1.3.0 cycler-0.12.1 matplotlib-3.9.2\n" + ] + } + ], "source": [ - "!pip install assume-framework\n", + "import importlib.util\n", + "\n", + "# Check if 'google.colab' is available\n", + "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", + "\n", + "if IN_COLAB:\n", + " !pip install assume-framework\n", "!pip install matplotlib" ] }, @@ -90,15 +119,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "shellscript" - } - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ - "!git clone --depth=1 https://github.com/assume-framework/assume.git assume-repo" + "if IN_COLAB:\n", + " !git clone --depth=1 https://github.com/assume-framework/assume.git assume-repo" ] }, { @@ -112,15 +138,10 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "import importlib.util\n", - "\n", - "# Check if 'google.colab' is available\n", - "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", - "\n", "colab_inputs_path = \"assume-repo/examples/inputs\"\n", "local_inputs_path = \"../inputs\"\n", "\n", @@ -140,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -167,9 +188,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['eom_case', 'ltm_case']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import yaml\n", "\n", @@ -197,7 +229,7 @@ "\n", "This is a behavior which works similarly well for both markets, though the results can not be taken to reality for various reasons.\n", "\n", - "But lets look at the scenarios themselves:" + "But let's look at the scenarios themselves:" ] }, { @@ -211,9 +243,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'start_date': '2019-01-01 00:00',\n", + " 'end_date': '2019-01-21 00:00',\n", + " 'time_step': '1h',\n", + " 'markets_config': {'EOM': {'operator': 'EOM_operator',\n", + " 'product_type': 'energy',\n", + " 'products': [{'duration': '1h', 'count': 24, 'first_delivery': '1h'}],\n", + " 'opening_frequency': '1d',\n", + " 'opening_duration': '1h',\n", + " 'volume_unit': 'MWh',\n", + " 'maximum_volume': 1000000,\n", + " 'price_unit': 'EUR/MWh',\n", + " 'market_mechanism': 'pay_as_clear'}}}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# let us take a look at the configuration we are about to run:\n", "config[\"eom_case\"]" @@ -229,9 +283,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[{'duration': '1h', 'count': 24, 'first_delivery': '1h'}]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "config[\"eom_case\"][\"markets_config\"][\"EOM\"][\"products\"]" ] @@ -247,14 +312,48 @@ "For more information on the market configuration and an example gantt chart, look here:\n", "https://assume.readthedocs.io/en/latest/market_config.html\n", "\n", - "So lets run this:" + "So let's run this:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.world:connected to db\n", + "INFO:assume.scenario.loader_csv:Starting Scenario example_01f/eom_case from ../inputs\n", + "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Downsampling demand_df successful.\n", + "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Downsampling availability_df successful.\n", + "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", + "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Downsampling fuel_prices_df successful.\n", + "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", + "INFO:assume.world:activating container\n", + "INFO:assume.world:all agents up - starting simulation\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01f_eom_case 2019-01-21 00:00:00: : 1728001.0it [04:09, 6937.39it/s] \n" + ] + } + ], "source": [ "world = World(database_uri=DB_URI)\n", "load_scenario_folder(\n", @@ -276,14 +375,19 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "vscode": { - "languageId": "shellscript" + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Das System kann den angegebenen Pfad nicht finden.\n" + ] } - }, - "outputs": [], + ], "source": [ - "!cd assume && assume -s example_01f -c eom_case -db \"sqlite:///./examples/local_db/assume_db_example_01f.db\"" + "if IN_COLAB:\n", + " !cd assume-repo && assume -s example_01f -c eom_case -db \"sqlite:///./examples/local_db/assume_db_example_01f.db\"" ] }, { @@ -297,9 +401,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'start_date': '2019-01-01 00:00',\n", + " 'end_date': '2019-01-21 00:00',\n", + " 'time_step': '1h',\n", + " 'markets_config': {'EOM': {'operator': 'EOM_operator',\n", + " 'product_type': 'energy',\n", + " 'start_date': '2019-01-01 00:00',\n", + " 'products': [{'duration': '1h', 'count': 24, 'first_delivery': '1h'}],\n", + " 'opening_frequency': '1d',\n", + " 'opening_duration': '1h',\n", + " 'volume_unit': 'MWh',\n", + " 'maximum_volume': 1000000,\n", + " 'price_unit': 'EUR/MWh',\n", + " 'market_mechanism': 'pay_as_clear'},\n", + " 'LTM_OTC': {'operator': 'LTM_operator',\n", + " 'product_type': 'energy',\n", + " 'start_date': '2019-01-01 00:00',\n", + " 'products': [{'duration': '7d', 'count': 1, 'first_delivery': '2h'}],\n", + " 'opening_frequency': '7d',\n", + " 'opening_duration': '1h',\n", + " 'volume_unit': 'MW',\n", + " 'maximum_volume': 1000000,\n", + " 'price_unit': 'EUR/MW',\n", + " 'market_mechanism': 'pay_as_bid'}}}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "config[\"ltm_case\"]" ] @@ -318,9 +455,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[{'duration': '7d', 'count': 1, 'first_delivery': '2h'}]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "config[\"ltm_case\"][\"markets_config\"][\"LTM_OTC\"][\"products\"]" ] @@ -337,9 +485,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.world:connected to db\n", + "INFO:assume.scenario.loader_csv:Starting Scenario example_01f/ltm_case from ../inputs\n", + "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Downsampling demand_df successful.\n", + "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Downsampling availability_df successful.\n", + "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", + "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Downsampling fuel_prices_df successful.\n", + "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", + "INFO:assume.world:activating container\n", + "INFO:assume.world:all agents up - starting simulation\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01f_ltm_case 2019-01-21 00:00:00: : 1728001.0it [03:37, 7957.47it/s] \n" + ] + } + ], "source": [ "world = World(database_uri=DB_URI)\n", "load_scenario_folder(\n", @@ -361,14 +543,19 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "vscode": { - "languageId": "shellscript" + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Das System kann den angegebenen Pfad nicht finden.\n" + ] } - }, - "outputs": [], + ], "source": [ - "!cd assume && assume -s example_01f -c ltm_case -db \"sqlite:///./examples/local_db/assume_db_example_01f.db\"" + "if IN_COLAB:\n", + " !cd assume-repo && assume -s example_01f -c ltm_case -db \"sqlite:///./examples/local_db/assume_db_example_01f.db\"" ] }, { @@ -384,9 +571,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import os\n", "\n", @@ -459,7 +657,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -467,7 +665,26 @@ "id": "qoWI_agIJOE4", "outputId": "9b40e670-bfef-4560-d6e8-61a1b29d1975" }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\AEppl\\AppData\\Local\\Temp\\ipykernel_27556\\1389063350.py:30: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", + " ddf = ddf.T.fillna(method=\"ffill\")\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# second plot\n", "sql = \"\"\"\n", @@ -535,7 +752,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "assume-testing-minimal", + "display_name": "assume", "language": "python", "name": "python3" }, @@ -549,7 +766,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.7" }, "nbsphinx": { "execute": "never" diff --git a/examples/notebooks/06_advanced_orders_example.ipynb b/examples/notebooks/06_advanced_orders_example.ipynb index 535869dbb..25d8d87c2 100644 --- a/examples/notebooks/06_advanced_orders_example.ipynb +++ b/examples/notebooks/06_advanced_orders_example.ipynb @@ -12,9 +12,9 @@ "\n", "In this example the advanced strategies using regular block orders and linked orders are presented and their impact on the market outcome both on system and unit level compared to strategies using only single hourly orders.\n", "\n", - "With the integration of block orders, minimum acceptance ratios are added to the orders as additional field. To account for those in the clearing, a new market clearing algorithm becomes necessary.\n", + "With the integration of block orders, minimum acceptance ratios are added to the orders as an additional field. To account for those in the clearing, a new market clearing algorithm becomes necessary.\n", "\n", - "In this tutorial, we will show, how to create and integrate this advanced market clearing and adjust bidding strategies to allow the use of regular block orders and linked orders.\n", + "In this tutorial, we will show how to create and integrate this advanced market clearing and adjust bidding strategies to allow for the use of regular block orders and linked orders.\n", "Finally, we will create a small comparison study of the results using matplotlib.\n", "\n", "**As a whole, this tutorial covers the following**\n", @@ -88,7 +88,7 @@ "\n", "According to flexABLE, the inflexible and flexible power of a unit is bid separately, compare [Qussous et al. 2022](https://doi.org/10.3390/en15020494).\n", "\n", - "The inflexible power $P^{\\mathrm{inflex}}_{t}$ at time $t$ is the minimum volume that can by dispatched. \n", + "The inflexible power $P^{\\mathrm{inflex}}_{t}$ at time $t$ is the minimum volume that can be dispatched. \n", "It is defined by the current operation status of the unit, ramp-down limitations and the must-run time.\n", "The inflexible bid price depends on the marginal cost $C^{\\mathrm{marginal}}_t$ at time $t$ and the power dispatch of the previous time step $P^{\\mathrm{dispatch}}_{t-1}$ and adds a markup, if the unit has to be started newly and a reduction, to prevent a shut-down, including the start-up costs $C^{\\mathrm{su}}_t$.\n", "Here, the average time of continuous operation is given by $T^{\\mathrm{op, avg}}$ and the average time of continuous shut down is given by $T^{\\mathrm{down, avg}}$:\n", @@ -131,14 +131,16 @@ "base_uri": "https://localhost:8080/" }, "id": "m0DaRwFA7VgW", - "outputId": "5655adad-5b7a-4fe3-9067-6b502a06136b", - "vscode": { - "languageId": "shellscript" - } + "outputId": "5655adad-5b7a-4fe3-9067-6b502a06136b" }, "outputs": [], "source": [ - "!pip install 'assume-framework'" + "import importlib.util\n", + "\n", + "# Check if 'google.colab' is available\n", + "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", + "if IN_COLAB:\n", + " !pip install 'assume-framework'" ] }, { @@ -151,14 +153,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "vscode": { - "languageId": "shellscript" - } - }, + "metadata": {}, "outputs": [], "source": [ - "!git clone --depth=1 https://github.com/assume-framework/assume.git assume-repo" + "if IN_COLAB:\n", + " !git clone --depth=1 https://github.com/assume-framework/assume.git assume-repo" ] }, { @@ -176,11 +175,6 @@ "metadata": {}, "outputs": [], "source": [ - "import importlib.util\n", - "\n", - "# Check if 'google.colab' is available\n", - "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", - "\n", "colab_inputs_path = \"assume-repo/examples/inputs\"\n", "local_inputs_path = \"../inputs\"\n", "\n", @@ -234,7 +228,7 @@ "* \"bid_type\" defines the order structure and can be \"SB\" for single hourly orders (Simple Bid), \"BB\" for block orders (Block Bid) or \"LB\" for linked orders (Linked Bid).\n", "* \"min_acceptance_ratio\" defines how much a bid can be curtailed before it is rejected. If it is set to 1, the bid is either accepted or rejected with it's full volume.\n", "* \"parent_bid_id\" is needed to include linked bids. Here the id of the parent order is defined, where the child order is linked to.\n", - "The market clearing algorithm then ensures, that the minimum acceptance ratio of the child order is less or equal to the one of its parent order." + "The market clearing algorithm then ensures that the minimum acceptance ratio of the child order is less or equal to the one of its parent order." ] }, { @@ -538,7 +532,7 @@ " orderbook.sort(key=itemgetter(\"start_time\", \"end_time\", \"only_hours\"))\n", "\n", " # create a list of all orders linked as child to a bid\n", - " # this helps to late check the surplus for linked bids\n", + " # this helps to later check the surplus for linked bids\n", " child_orders = []\n", " for order in orderbook:\n", " order[\"accepted_price\"] = {}\n", @@ -643,7 +637,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "So lets add the advanced clearing algorithm to the possible clearing mechanisms in world and load the example." + "So let's add the advanced clearing algorithm to the possible clearing mechanisms in world and load the example." ] }, { @@ -674,7 +668,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In the market config we have 24 single market product which have a duration of one hour.\n", + "In the market config we have 24 single market products which have a duration of one hour.\n", "And every time the market opens, the next 24 hours can be traded (see count).\n", "The first delivery of the market is 24 hours after the opening of the market (to have some spare time before delivery).\n", "\n", @@ -967,9 +961,9 @@ "source": [ "With this the strategy is ready to test.\n", "As before, we add the new class to our world and load the scenario.\n", - "Additionally, we now have to change the set bidding strategy for one example unit. Here we choose the combined cycle gas turbine and set its strategy to our modified class 'blockStrategy'.\n", + "Additionally, we now have to change the set bidding strategy for one example unit. Here, we choose the combined cycle gas turbine and set its strategy to our modified class 'blockStrategy'.\n", "\n", - "Don't forget to add also the defined advanced clearing mechanism to the newly generated world." + "Don't forget to also add the defined advanced clearing mechanism to the newly generated world." ] }, { @@ -1001,7 +995,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now lets run this example" + "Now let's run this example" ] }, { @@ -1479,7 +1473,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "assume-framework", + "display_name": "assume", "language": "python", "name": "python3" }, @@ -1493,7 +1487,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.12.7" }, "nbsphinx": { "execute": "never" diff --git a/examples/notebooks/08_market_zone_coupling.ipynb b/examples/notebooks/08_market_zone_coupling.ipynb index ef23084c4..28dca59a4 100644 --- a/examples/notebooks/08_market_zone_coupling.ipynb +++ b/examples/notebooks/08_market_zone_coupling.ipynb @@ -1,8676 +1,6080 @@ { - "cells": [ + "cells": [ + { + "cell_type": "markdown", + "id": "ff81547a", + "metadata": { + "id": "ff81547a" + }, + "source": [ + "# 8. Market Zone Coupling in the ASSUME Framework\n", + "\n", + "Welcome to the **Market Zone Coupling** tutorial for the ASSUME framework. In this workshop, we will guide you through understanding how market zone coupling is implemented within the ASSUME simulation environment. By the end of this tutorial, you will gain insights into the internal mechanisms of the framework, including how different market zones interact, how constraints are managed, how bids are assigned, and how market prices are extracted.\n", + "\n", + "**We will cover the following topics:**\n", + "\n", + "1. [**Introduction to Market Zone Coupling**](#1-introduction-to-market-zone-coupling)\n", + "2. [**Setting Up the ASSUME Framework for Market Zone Coupling**](#2-setting-up-the-assume-framework-for-market-zone-coupling)\n", + "3. [**Understanding the Market Clearing Optimization**](#3-understanding-the-market-clearing-optimization)\n", + "4. [**Creating Exemplary Input Files for Market Zone Coupling**](#4-creating-exemplary-input-files-for-market-zone-coupling)\n", + " - 4.1. [Defining Buses and Zones](#41-defining-buses-and-zones)\n", + " - 4.2. [Configuring Transmission Lines](#42-configuring-transmission-lines)\n", + " - 4.3. [Setting Up Power Plant and Demand Units](#43-setting-up-power-plant-and-demand-units)\n", + " - 4.4. [Preparing Demand Data](#44-preparing-demand-data)\n", + "5. [**Understanding the Market Clearing with Zone Coupling**](#5-reproducing-the-market-clearing-process)\n", + " - 5.1. [Calculating the Incidence Matrix](#51-calculating-the-incidence-matrix)\n", + " - 5.2. [Implementing the Simplified Market Clearing Function](#52-creating-and-mapping-market-orders)\n", + " - 5.3. [Running the Market Clearing Simulation](#53-running-the-market-clearing-simulation)\n", + " - 5.4. [Extracting and Interpreting the Results](#54-extracting-and-interpreting-the-results)\n", + " - 5.5. [Comparing Simulations](#55-comparing-simulations)\n", + "6. [**Execution with ASSUME**](#6-execution-with-assume)\n", + "7. [**Analyzing the Results**](#7-analyzing-the-results)\n", + "\n", + "Let's get started!" + ] + }, + { + "cell_type": "markdown", + "id": "76281e67", + "metadata": { + "id": "76281e67" + }, + "source": [ + "## 1. Introduction to Market Zone Coupling\n", + "\n", + "**Market Zone Coupling** is a mechanism that enables different geographical zones within an electricity market to interact and trade energy seamlessly. In the ASSUME framework, implementing market zone coupling is straightforward: by properly defining the input data and configuration files, the framework automatically manages the interactions between zones, including transmission constraints and cross-zone trading.\n", + "\n", + "This tutorial aims to provide a deeper understanding of how market zone coupling operates within ASSUME. While the framework handles much of the complexity internally, we will explore the underlying processes, such as the calculation of transmission capacities and the market clearing optimization. This detailed walkthrough is designed to enhance your comprehension of the framework's capabilities and the dynamics of multi-zone electricity markets.\n", + "\n", + "Throughout this tutorial, you will:\n", + "\n", + "- **Define Multiple Market Zones:** Segment the market into distinct zones based on geographical or operational criteria.\n", + "- **Configure Transmission Lines:** Establish connections that allow energy flow between different market zones.\n", + "- **Understand the Market Clearing Process:** Examine how the market clearing algorithm accounts for interactions and constraints across zones.\n", + "\n", + "By the end of this workshop, you will not only know how to set up market zone coupling in ASSUME but also gain insights into the internal mechanisms that drive market interactions and price formations across different zones." + ] + }, + { + "cell_type": "markdown", + "id": "42ff364e", + "metadata": { + "id": "42ff364e" + }, + "source": [ + "## 2. Setting Up the ASSUME Framework for Market Zone Coupling\n", + "\n", + "Before diving into market zone coupling, ensure that you have the ASSUME framework installed and set up correctly. If you haven't done so already, follow the steps below to install the ASSUME core package and clone the repository containing predefined scenarios.\n", + "\n", + "**Note:** If you already have the ASSUME framework installed and the repository cloned, you can skip executing the following code cells." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0dd1c254", + "metadata": { + "id": "0dd1c254", + "vscode": { + "languageId": "shellscript" + } + }, + "outputs": [ { - "cell_type": "markdown", - "id": "ff81547a", - "metadata": { - "id": "ff81547a" - }, - "source": [ - "# 8. Market Zone Coupling in the ASSUME Framework\n", - "\n", - "Welcome to the **Market Zone Coupling** tutorial for the ASSUME framework. In this workshop, we will guide you through understanding how market zone coupling is implemented within the ASSUME simulation environment. By the end of this tutorial, you will gain insights into the internal mechanisms of the framework, including how different market zones interact, how constraints are managed, how bids are assigned, and how market prices are extracted.\n", - "\n", - "**We will cover the following topics:**\n", - "\n", - "1. **Introduction to Market Zone Coupling**\n", - "2. **Setting Up the ASSUME Framework for Market Zone Coupling**\n", - "3. **Understanding the Market Clearing Optimization**\n", - "4. **Creating Exemplary Input Files for Market Zone Coupling**\n", - " - 4.1. Defining Buses and Zones\n", - " - 4.2. Configuring Transmission Lines\n", - " - 4.3. Setting Up Power Plant and Demand Units\n", - " - 4.4. Preparing Demand Data\n", - "5. **Understanding the Market Clearing with Zone Coupling**\n", - " - 5.1. Calculating the Incidence Matrix\n", - " - 5.2. Implementing the Simplified Market Clearing Function\n", - " - 5.3. Running the Market Clearing Simulation\n", - " - 5.4. Extracting and Interpreting the Results\n", - " - 5.5. Comparing Simulations\n", - "6. **Execution with ASSUME**\n", - "7. **Analyzing the Results**\n", - "\n", - "Let's get started!" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: assume-framework in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (0.4.3)\n", + "Requirement already satisfied: argcomplete>=3.1.4 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from assume-framework) (3.5.1)\n", + "Requirement already satisfied: nest-asyncio>=1.5.6 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from assume-framework) (1.6.0)\n", + "Requirement already satisfied: mango-agents>=2.1.1 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from assume-framework) (2.1.1)\n", + "Requirement already satisfied: numpy>=1.26.4 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from assume-framework) (1.26.4)\n", + "Requirement already satisfied: tqdm>=4.64.1 in 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basic libraries that we will use throughout the tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a1543685", + "metadata": { + "id": "a1543685" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# import plotly for visualization\n", + "import plotly.graph_objects as go\n", + "\n", + "# import yaml for reading and writing YAML files\n", + "import yaml\n", + "\n", + "# Function to display DataFrame in Jupyter\n", + "from IPython.display import display" + ] + }, + { + "cell_type": "markdown", + "id": "902fc3a9", + "metadata": { + "id": "902fc3a9" + }, + "source": [ + "## 3. Understanding the Market Clearing Optimization\n", + "\n", + "Market clearing is a crucial component of electricity market simulations. It involves determining the optimal dispatch of supply and demand bids to maximize social welfare while respecting network constraints.\n", + "\n", + "In the context of market zone coupling, the market clearing process must account for:\n", + "\n", + "- **Connection Between Zones:** Transmission lines that allow energy flow between different market zones.\n", + "- **Constraints:** Limits on transmission capacities and ensuring energy balance within and across zones.\n", + "- **Bid Assignment:** Properly assigning bids to their respective zones and considering cross-zone trading.\n", + "- **Price Extraction:** Determining market prices for each zone based on the cleared bids and network constraints.\n", + "\n", + "The ASSUME framework uses Pyomo to formulate and solve the market clearing optimization problem. Below is a simplified version of the market clearing function, highlighting key components related to zone coupling." + ] + }, + { + "cell_type": "markdown", + "id": "4f874cfd", + "metadata": { + "id": "4f874cfd" + }, + "source": [ + "### Simplified Market Clearing Optimization Problem\n", + "\n", + "We consider a simplified market clearing optimization model focusing on zone coupling, aiming to minimize the total cost.\n", + "\n", + "#### Sets and Variables:\n", + "- $T$: Set of time periods.\n", + "- $N$: Set of nodes (zones).\n", + "- $L$: Set of lines.\n", + "- $x_o \\in [0, 1]$: Bid acceptance ratio for order $o$.\n", + "- $f_{t, l} \\in \\mathbb{R}$: Power flow on line $l$ at time $t$.\n", + "\n", + "#### Constants:\n", + "- $P_o$: Price of order $o$.\n", + "- $V_o$: Volume of order $o$.\n", + "- $S_l$: Nominal capacity of line $l$.\n", + "\n", + "#### Objective Function:\n", + "Minimize the total cost of accepted orders:\n", + "\n", + "$$\n", + "\\min \\sum_{o \\in O} P_o V_o x_o\n", + "$$\n", + "\n", + "#### Constraints:\n", + "\n", + "1. **Energy Balance for Each Node and Time Period**:\n", + "\n", + "$$\n", + "\\sum_{\\substack{o \\in O \\\\ \\text{node}(o) = n \\\\ \\text{time}(o) = t}} V_o x_o + \\sum_{l \\in L} I_{n, l} f_{t, l} = 0 \\quad \\forall n \\in N, \\, t \\in T\n", + "$$\n", + "\n", + "Where:\n", + "- $I_{n, l}$ is the incidence value for node $n$ and line $l$ (from the incidence matrix).\n", + "\n", + "2. **Transmission Capacity Constraints for Each Line and Time Period**:\n", + "\n", + "$$\n", + "-S_l \\leq f_{t, l} \\leq S_l \\quad \\forall l \\in L, \\, t \\in T\n", + "$$\n", + "\n", + "#### Summary:\n", + "The goal is to minimize the total cost while ensuring energy balance at each node and respecting transmission line capacity limits for each time period.\n", + "\n", + "In the actual ASSUME Framework, the optimization problem is more complex and includes additional constraints and variables, and supports also additional bid types such as block orders and linked orders. However, the simplified model above captures the essence of market clearing with zone coupling.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e2be3fe2", + "metadata": { + "id": "e2be3fe2" + }, + "outputs": [], + "source": [ + "import pyomo.environ as pyo\n", + "from pyomo.opt import SolverFactory, TerminationCondition\n", + "\n", + "\n", + "def simplified_market_clearing_opt(orders, incidence_matrix, lines):\n", + " \"\"\"\n", + " Simplified market clearing optimization focusing on zone coupling.\n", + "\n", + " Args:\n", + " orders (dict): Dictionary of orders with bid_id as keys.\n", + " lines (DataFrame): DataFrame containing information about the transmission lines.\n", + " incidence_matrix (DataFrame): Incidence matrix describing the network structure.\n", + "\n", + " Returns:\n", + " model (ConcreteModel): The solved Pyomo model.\n", + " results (SolverResults): The solver results.\n", + " \"\"\"\n", + " nodes = list(incidence_matrix.index)\n", + " line_ids = list(incidence_matrix.columns)\n", + "\n", + " model = pyo.ConcreteModel()\n", + " # Define dual suffix\n", + " model.dual = pyo.Suffix(direction=pyo.Suffix.IMPORT)\n", + "\n", + " # Define the set of time periods\n", + " model.T = pyo.Set(\n", + " initialize=sorted(set(order[\"time\"] for order in orders.values())),\n", + " doc=\"timesteps\",\n", + " )\n", + " # Define the set of nodes (zones)\n", + " model.nodes = pyo.Set(initialize=nodes, doc=\"nodes\")\n", + " # Define the set of lines\n", + " model.lines = pyo.Set(initialize=line_ids, doc=\"lines\")\n", + "\n", + " # Decision variables for bid acceptance ratios (0 to 1)\n", + " model.x = pyo.Var(\n", + " orders.keys(),\n", + " domain=pyo.NonNegativeReals,\n", + " bounds=(0, 1),\n", + " doc=\"bid_acceptance_ratio\",\n", + " )\n", + "\n", + " # Decision variables for power flows on each line at each time period\n", + " model.flows = pyo.Var(model.T, model.lines, domain=pyo.Reals, doc=\"power_flows\")\n", + "\n", + " # Energy balance constraint for each node and time period\n", + " def energy_balance_rule(model, node, t):\n", + " balance_expr = 0.0\n", + " # Add contributions from orders\n", + " for order_key, order in orders.items():\n", + " if order[\"node\"] == node and order[\"time\"] == t:\n", + " balance_expr += order[\"volume\"] * model.x[order_key]\n", + "\n", + " # Add contributions from line flows based on the incidence matrix\n", + " if incidence_matrix is not None:\n", + " for line in model.lines:\n", + " incidence_value = incidence_matrix.loc[node, line]\n", + " if incidence_value != 0:\n", + " balance_expr += incidence_value * model.flows[t, line]\n", + "\n", + " return balance_expr == 0\n", + "\n", + " model.energy_balance = pyo.Constraint(\n", + " model.nodes, model.T, rule=energy_balance_rule\n", + " )\n", + "\n", + " # Transmission capacity constraints for each line and time period\n", + " def transmission_capacity_rule(model, t, line):\n", + " \"\"\"\n", + " Limits the power flow on each line based on its capacity.\n", + " \"\"\"\n", + " capacity = lines.at[line, \"s_nom\"]\n", + " return (-capacity, model.flows[t, line], capacity)\n", + "\n", + " # Apply transmission capacity constraints to all lines and time periods\n", + " model.transmission_constraints = pyo.Constraint(\n", + " model.T, model.lines, rule=transmission_capacity_rule\n", + " )\n", + "\n", + " # Objective: Minimize total cost (sum of bid prices multiplied by accepted volumes)\n", + " model.objective = pyo.Objective(\n", + " expr=sum(orders[o][\"price\"] * orders[o][\"volume\"] * model.x[o] for o in orders),\n", + " sense=pyo.minimize,\n", + " doc=\"Total Cost Minimization\",\n", + " )\n", + "\n", + " # Choose the solver (HIGHS is used here)\n", + " solver = SolverFactory(\"appsi_highs\")\n", + " results = solver.solve(model)\n", + "\n", + " # Check if the solver found an optimal solution\n", + " if results.solver.termination_condition != TerminationCondition.optimal:\n", + " raise Exception(\"Solver did not find an optimal solution.\")\n", + "\n", + " return model, results" + ] + }, + { + "cell_type": "markdown", + "id": "8d42c532", + "metadata": { + "id": "8d42c532" + }, + "source": [ + "The above function is a simplified representation focusing on the essential aspects of market zone coupling. In the following sections, we will delve deeper into creating input files and mimicking the market clearing process using this function to understand the inner workings of the ASSUME framework." + ] + }, + { + "cell_type": "markdown", + "id": "11addaf0", + "metadata": { + "id": "11addaf0" + }, + "source": [ + "## 4. Creating Exemplary Input Files for Market Zone Coupling\n", + "\n", + "To implement market zone coupling, users need to prepare specific input files that define the network's structure, units, and demand profiles. Below, we will guide you through creating the necessary DataFrames for buses, transmission lines, power plant units, demand units, and demand profiles." + ] + }, + { + "cell_type": "markdown", + "id": "2a095ffb", + "metadata": { + "id": "2a095ffb" + }, + "source": [ + "### 4.1. Defining Buses and Zones\n", + "\n", + "**Buses** represent nodes in the network where energy can be injected or withdrawn. Each bus is assigned to a **zone**, which groups buses into market areas. This zoning facilitates market coupling by managing interactions between different market regions." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c1731cdc", + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 192 }, + "id": "c1731cdc", + "outputId": "0d0a8060-aa86-4ba8-a0b1-0e528bc9d0d2" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "76281e67", - "metadata": { - "id": "76281e67" - }, - "source": [ - "## 1. Introduction to Market Zone Coupling\n", - "\n", - "**Market Zone Coupling** is a mechanism that enables different geographical zones within an electricity market to interact and trade energy seamlessly. In the ASSUME framework, implementing market zone coupling is straightforward: by properly defining the input data and configuration files, the framework automatically manages the interactions between zones, including transmission constraints and cross-zone trading.\n", - "\n", - "This tutorial aims to provide a deeper understanding of how market zone coupling operates within ASSUME. While the framework handles much of the complexity internally, we will explore the underlying processes, such as the calculation of transmission capacities and the market clearing optimization. This detailed walkthrough is designed to enhance your comprehension of the framework's capabilities and the dynamics of multi-zone electricity markets.\n", - "\n", - "Throughout this tutorial, you will:\n", - "\n", - "- **Define Multiple Market Zones:** Segment the market into distinct zones based on geographical or operational criteria.\n", - "- **Configure Transmission Lines:** Establish connections that allow energy flow between different market zones.\n", - "- **Understand the Market Clearing Process:** Examine how the market clearing algorithm accounts for interactions and constraints across zones.\n", - "\n", - "By the end of this workshop, you will not only know how to set up market zone coupling in ASSUME but also gain insights into the internal mechanisms that drive market interactions and price formations across different zones." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Buses DataFrame:\n" + ] }, { - "cell_type": "markdown", - "id": "42ff364e", - "metadata": { - "id": "42ff364e" - }, - "source": [ - "## 2. Setting Up the ASSUME Framework for Market Zone Coupling\n", - "\n", - "Before diving into market zone coupling, ensure that you have the ASSUME framework installed and set up correctly. If you haven't done so already, follow the steps below to install the ASSUME core package and clone the repository containing predefined scenarios.\n", - "\n", - "**Note:** If you already have the ASSUME framework installed and the repository cloned, you can skip executing the following code cells." + "data": { + "text/html": [ + "
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v_nomzone_idxy
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north_1380.0DE_110.054.0
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south380.0DE_211.648.1
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" + ], + "text/plain": [ + " v_nom zone_id x y\n", + "name \n", + "north_1 380.0 DE_1 10.0 54.0\n", + "north_2 380.0 DE_1 9.5 53.5\n", + "south 380.0 DE_2 11.6 48.1" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# @title Define the buses DataFrame with three nodes and two zones\n", + "buses = pd.DataFrame(\n", + " {\n", + " \"name\": [\"north_1\", \"north_2\", \"south\"],\n", + " \"v_nom\": [380.0, 380.0, 380.0],\n", + " \"zone_id\": [\"DE_1\", \"DE_1\", \"DE_2\"],\n", + " \"x\": [10.0, 9.5, 11.6],\n", + " \"y\": [54.0, 53.5, 48.1],\n", + " }\n", + ").set_index(\"name\")\n", + "\n", + "# Display the buses DataFrame\n", + "print(\"Buses DataFrame:\")\n", + "display(buses)" + ] + }, + { + "cell_type": "markdown", + "id": "50a27c51", + "metadata": { + "id": "50a27c51" + }, + "source": [ + "**Explanation:**\n", + "\n", + "- **name:** Identifier for each bus (`north_1`, `north_2`, and `south`).\n", + "- **v_nom:** Nominal voltage level (in kV) for all buses.\n", + "- **zone_id:** Identifier for the market zone to which the bus belongs (`DE_1` for north buses and `DE_2` for the south bus).\n", + "- **x, y:** Geographical coordinates (optional, can be used for mapping or spatial analyses)." + ] + }, + { + "cell_type": "markdown", + "id": "1545e3bf", + "metadata": { + "id": "1545e3bf" + }, + "source": [ + "### 4.2. Configuring Transmission Lines\n", + "\n", + "**Transmission Lines** connect buses, allowing energy to flow between them. Each line has a specified capacity and electrical parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "64769ec7", + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 192 }, + "id": "64769ec7", + "outputId": "a47490cb-d06c-4152-8be6-64985a8dcbd0" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "0dd1c254", - "metadata": { - "id": "0dd1c254", - "vscode": { - "languageId": "shellscript" - } - }, - "outputs": [], - "source": [ - "# Install the ASSUME framework\n", - "!pip install assume-framework\n", - "\n", - "# Install Plotly if not already installed\n", - "!pip install plotly" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Transmission Lines DataFrame:\n" + ] }, { - "cell_type": "markdown", - "id": "4266c838", - "metadata": { - "id": "4266c838" - }, - "source": [ - "Let's also import some basic libraries that we will use throughout the tutorial." + "data": { + "text/html": [ + "
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bus0bus1s_nomxr
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Line_N1_Snorth_1south5000.00.010.001
Line_N2_Snorth_2south5000.00.010.001
Line_N1_N2north_1north_25000.00.010.001
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" + ], + "text/plain": [ + " bus0 bus1 s_nom x r\n", + "name \n", + "Line_N1_S north_1 south 5000.0 0.01 0.001\n", + "Line_N2_S north_2 south 5000.0 0.01 0.001\n", + "Line_N1_N2 north_1 north_2 5000.0 0.01 0.001" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# @title Define three transmission lines\n", + "lines = pd.DataFrame(\n", + " {\n", + " \"name\": [\"Line_N1_S\", \"Line_N2_S\", \"Line_N1_N2\"],\n", + " \"bus0\": [\"north_1\", \"north_2\", \"north_1\"],\n", + " \"bus1\": [\"south\", \"south\", \"north_2\"],\n", + " \"s_nom\": [5000.0, 5000.0, 5000.0],\n", + " \"x\": [0.01, 0.01, 0.01],\n", + " \"r\": [0.001, 0.001, 0.001],\n", + " }\n", + ").set_index(\"name\")\n", + "\n", + "print(\"Transmission Lines DataFrame:\")\n", + "display(lines)" + ] + }, + { + "cell_type": "markdown", + "id": "f2290793", + "metadata": { + "id": "f2290793" + }, + "source": [ + "**Explanation:**\n", + "\n", + "- **name:** Identifier for each transmission line (`Line_N1_S`, `Line_N2_S`, and `Line_N1_N2`).\n", + "- **bus0, bus1:** The two buses that the line connects.\n", + "- **s_nom:** Nominal apparent power capacity of the line (in MVA).\n", + "- **x:** Reactance of the line (in per unit).\n", + "- **r:** Resistance of the line (in per unit)." + ] + }, + { + "cell_type": "markdown", + "id": "c931cf9f", + "metadata": { + "id": "c931cf9f" + }, + "source": [ + "### 4.3. Setting Up Power Plant and Demand Units\n", + "\n", + "**Power Plant Units** represent energy generation sources, while **Demand Units** represent consumption. Each unit is associated with a specific bus (node) and has operational parameters that define its behavior in the market." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "8a1f9e35", + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 224 }, + "id": "8a1f9e35", + "outputId": "b7d43816-40af-4526-bb64-53d4a20ba911" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 2, - "id": "a1543685", - "metadata": { - "id": "a1543685" - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "# import plotly for visualization\n", - "import plotly.graph_objects as go\n", - "\n", - "# import yaml for reading and writing YAML files\n", - "import yaml\n", - "\n", - "# Function to display DataFrame in Jupyter\n", - "from IPython.display import display" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Power Plant Units DataFrame:\n" + ] }, { - "cell_type": "markdown", - "id": "902fc3a9", - "metadata": { - "id": "902fc3a9" - }, - "source": [ - "## 3. Understanding the Market Clearing Optimization\n", - "\n", - "Market clearing is a crucial component of electricity market simulations. It involves determining the optimal dispatch of supply and demand bids to maximize social welfare while respecting network constraints.\n", - "\n", - "In the context of market zone coupling, the market clearing process must account for:\n", - "\n", - "- **Connection Between Zones:** Transmission lines that allow energy flow between different market zones.\n", - "- **Constraints:** Limits on transmission capacities and ensuring energy balance within and across zones.\n", - "- **Bid Assignment:** Properly assigning bids to their respective zones and considering cross-zone trading.\n", - "- **Price Extraction:** Determining market prices for each zone based on the cleared bids and network constraints.\n", - "\n", - "The ASSUME framework uses Pyomo to formulate and solve the market clearing optimization problem. Below is a simplified version of the market clearing function, highlighting key components related to zone coupling." + "data": { + "text/html": [ + "
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nametechnologybidding_zonalfuel_typeemission_factormax_powermin_powerefficiencyadditional_costnodeunit_operator
0Unit 1nuclearnaive_eomuranium0.01000.00.00.35north_1Operator North
1Unit 2nuclearnaive_eomuranium0.01000.00.00.36north_1Operator North
2Unit 3nuclearnaive_eomuranium0.01000.00.00.37north_1Operator North
3Unit 4nuclearnaive_eomuranium0.01000.00.00.38north_1Operator North
4Unit 5nuclearnaive_eomuranium0.01000.00.00.39north_1Operator North
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" + ], + "text/plain": [ + " name technology bidding_zonal fuel_type emission_factor max_power \\\n", + "0 Unit 1 nuclear naive_eom uranium 0.0 1000.0 \n", + "1 Unit 2 nuclear naive_eom uranium 0.0 1000.0 \n", + "2 Unit 3 nuclear naive_eom uranium 0.0 1000.0 \n", + "3 Unit 4 nuclear naive_eom uranium 0.0 1000.0 \n", + "4 Unit 5 nuclear naive_eom uranium 0.0 1000.0 \n", + "\n", + " min_power efficiency additional_cost node unit_operator \n", + "0 0.0 0.3 5 north_1 Operator North \n", + "1 0.0 0.3 6 north_1 Operator North \n", + "2 0.0 0.3 7 north_1 Operator North \n", + "3 0.0 0.3 8 north_1 Operator North \n", + "4 0.0 0.3 9 north_1 Operator North " ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# @title Create the power plant units DataFrame\n", + "\n", + "# Define the total number of units\n", + "num_units = 30 # Reduced for simplicity\n", + "\n", + "# Generate the 'name' column: Unit 1 to Unit 30\n", + "names = [f\"Unit {i}\" for i in range(1, num_units + 1)]\n", + "\n", + "# All other columns with constant values\n", + "technology = [\"nuclear\"] * num_units\n", + "bidding_zonal = [\"naive_eom\"] * num_units\n", + "fuel_type = [\"uranium\"] * num_units\n", + "emission_factor = [0.0] * num_units\n", + "max_power = [1000.0] * num_units\n", + "min_power = [0.0] * num_units\n", + "efficiency = [0.3] * num_units\n", + "\n", + "# Generate 'additional_cost':\n", + "# - North units (1-15): 5 to 19\n", + "# - South units (16-30): 20 to 34\n", + "additional_cost = list(range(5, 5 + num_units))\n", + "\n", + "# Initialize 'node' and 'unit_operator' lists\n", + "node = []\n", + "unit_operator = []\n", + "\n", + "for i in range(1, num_units + 1):\n", + " if 1 <= i <= 8:\n", + " node.append(\"north_1\") # All north units connected to 'north_1'\n", + " unit_operator.append(\"Operator North\")\n", + " elif 9 <= i <= 15:\n", + " node.append(\"north_2\")\n", + " unit_operator.append(\"Operator North\")\n", + " else:\n", + " node.append(\"south\") # All south units connected to 'south'\n", + " unit_operator.append(\"Operator South\")\n", + "\n", + "# Create the DataFrame\n", + "powerplant_units = pd.DataFrame(\n", + " {\n", + " \"name\": names,\n", + " \"technology\": technology,\n", + " \"bidding_zonal\": bidding_zonal, # bidding strategy used to bid on the zonal market. Should be same name as in config file\n", + " \"fuel_type\": fuel_type,\n", + " \"emission_factor\": emission_factor,\n", + " \"max_power\": max_power,\n", + " \"min_power\": min_power,\n", + " \"efficiency\": efficiency,\n", + " \"additional_cost\": additional_cost,\n", + " \"node\": node,\n", + " \"unit_operator\": unit_operator,\n", + " }\n", + ")\n", + "\n", + "print(\"Power Plant Units DataFrame:\")\n", + "display(powerplant_units.head())" + ] + }, + { + "cell_type": "markdown", + "id": "Uwp8L0rombac", + "metadata": { + "id": "Uwp8L0rombac" + }, + "source": [ + "- **Power Plant Units:**\n", + " - **name:** Identifier for each power plant unit (`Unit 1` to `Unit 30`).\n", + " - **technology:** Type of technology (`nuclear` for all units).\n", + " - **bidding_nodal:** Bidding strategy used (`naive_eom` for all units).\n", + " - **fuel_type:** Type of fuel used (`uranium` for all units).\n", + " - **emission_factor:** Emissions per unit of energy produced (`0.0` for all units).\n", + " - **max_power, min_power:** Operational power limits (`1000.0` MW max, `0.0` MW min for all units).\n", + " - **efficiency:** Conversion efficiency (`0.3` for all units).\n", + " - **additional_cost:** Additional operational costs (`5` to `34`, with southern units being more expensive).\n", + " - **node:** The bus (zone) to which the unit is connected (`north_1` for units `1-15`, `south` for units `16-30`).\n", + " - **unit_operator:** Operator responsible for the unit (`Operator North` for northern units, `Operator South` for southern units)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "16f8a13c", + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 161 }, + "id": "16f8a13c", + "outputId": "aad8a140-a6ed-47fd-d06e-1e794aa1a829" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "4f874cfd", - "metadata": { - "id": "4f874cfd" - }, - "source": [ - "### Simplified Market Clearing Optimization Problem\n", - "\n", - "We consider a simplified market clearing optimization model focusing on zone coupling, aiming to minimize the total cost.\n", - "\n", - "#### Sets and Variables:\n", - "- $T$: Set of time periods.\n", - "- $N$: Set of nodes (zones).\n", - "- $L$: Set of lines.\n", - "- $x_o \\in [0, 1]$: Bid acceptance ratio for order $o$.\n", - "- $f_{t, l} \\in \\mathbb{R}$: Power flow on line $l$ at time $t$.\n", - "\n", - "#### Constants:\n", - "- $P_o$: Price of order $o$.\n", - "- $V_o$: Volume of order $o$.\n", - "- $S_l$: Nominal capacity of line $l$.\n", - "\n", - "#### Objective Function:\n", - "Minimize the total cost of accepted orders:\n", - "\n", - "$$\n", - "\\min \\sum_{o \\in O} P_o V_o x_o\n", - "$$\n", - "\n", - "#### Constraints:\n", - "\n", - "1. **Energy Balance for Each Node and Time Period**:\n", - "\n", - "$$\n", - "\\sum_{\\substack{o \\in O \\\\ \\text{node}(o) = n \\\\ \\text{time}(o) = t}} V_o x_o + \\sum_{l \\in L} I_{n, l} f_{t, l} = 0 \\quad \\forall n \\in N, \\, t \\in T\n", - "$$\n", - "\n", - "Where:\n", - "- $I_{n, l}$ is the incidence value for node $n$ and line $l$ (from the incidence matrix).\n", - "\n", - "2. **Transmission Capacity Constraints for Each Line and Time Period**:\n", - "\n", - "$$\n", - "-S_l \\leq f_{t, l} \\leq S_l \\quad \\forall l \\in L, \\, t \\in T\n", - "$$\n", - "\n", - "#### Summary:\n", - "The goal is to minimize the total cost while ensuring energy balance at each node and respecting transmission line capacity limits for each time period.\n", - "\n", - "In actual ASSUME Framework, the optimization problem is more complex and includes additional constraints and variables, and supports also additional bid types such as block and linked orders. However, the simplified model above captures the essence of market clearing with zone coupling.\n" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Demand Units DataFrame:\n" + ] }, { - "cell_type": "code", - "execution_count": 3, - "id": "e2be3fe2", - "metadata": { - "id": "e2be3fe2" - }, - "outputs": [], - "source": [ - "import pyomo.environ as pyo\n", - "from pyomo.opt import SolverFactory, TerminationCondition\n", - "\n", - "\n", - "def simplified_market_clearing_opt(orders, incidence_matrix, lines):\n", - " \"\"\"\n", - " Simplified market clearing optimization focusing on zone coupling.\n", - "\n", - " Args:\n", - " orders (dict): Dictionary of orders with bid_id as keys.\n", - " lines (DataFrame): DataFrame containing information about the transmission lines.\n", - " incidence_matrix (DataFrame): Incidence matrix describing the network structure.\n", - "\n", - " Returns:\n", - " model (ConcreteModel): The solved Pyomo model.\n", - " results (SolverResults): The solver results.\n", - " \"\"\"\n", - " nodes = list(incidence_matrix.index)\n", - " line_ids = list(incidence_matrix.columns)\n", - "\n", - " model = pyo.ConcreteModel()\n", - " # Define dual suffix\n", - " model.dual = pyo.Suffix(direction=pyo.Suffix.IMPORT)\n", - "\n", - " # Define the set of time periods\n", - " model.T = pyo.Set(\n", - " initialize=sorted(set(order[\"time\"] for order in orders.values())),\n", - " doc=\"timesteps\",\n", - " )\n", - " # Define the set of nodes (zones)\n", - " model.nodes = pyo.Set(initialize=nodes, doc=\"nodes\")\n", - " # Define the set of lines\n", - " model.lines = pyo.Set(initialize=line_ids, doc=\"lines\")\n", - "\n", - " # Decision variables for bid acceptance ratios (0 to 1)\n", - " model.x = pyo.Var(\n", - " orders.keys(),\n", - " domain=pyo.NonNegativeReals,\n", - " bounds=(0, 1),\n", - " doc=\"bid_acceptance_ratio\",\n", - " )\n", - "\n", - " # Decision variables for power flows on each line at each time period\n", - " model.flows = pyo.Var(model.T, model.lines, domain=pyo.Reals, doc=\"power_flows\")\n", - "\n", - " # Energy balance constraint for each node and time period\n", - " def energy_balance_rule(model, node, t):\n", - " balance_expr = 0.0\n", - " # Add contributions from orders\n", - " for order_key, order in orders.items():\n", - " if order[\"node\"] == node and order[\"time\"] == t:\n", - " balance_expr += order[\"volume\"] * model.x[order_key]\n", - "\n", - " # Add contributions from line flows based on the incidence matrix\n", - " if incidence_matrix is not None:\n", - " for line in model.lines:\n", - " incidence_value = incidence_matrix.loc[node, line]\n", - " if incidence_value != 0:\n", - " balance_expr += incidence_value * model.flows[t, line]\n", - "\n", - " return balance_expr == 0\n", - "\n", - " model.energy_balance = pyo.Constraint(\n", - " model.nodes, model.T, rule=energy_balance_rule\n", - " )\n", - "\n", - " # Transmission capacity constraints for each line and time period\n", - " def transmission_capacity_rule(model, t, line):\n", - " \"\"\"\n", - " Limits the power flow on each line based on its capacity.\n", - " \"\"\"\n", - " capacity = lines.at[line, \"s_nom\"]\n", - " return (-capacity, model.flows[t, line], capacity)\n", - "\n", - " # Apply transmission capacity constraints to all lines and time periods\n", - " model.transmission_constraints = pyo.Constraint(\n", - " model.T, model.lines, rule=transmission_capacity_rule\n", - " )\n", - "\n", - " # Objective: Minimize total cost (sum of bid prices multiplied by accepted volumes)\n", - " model.objective = pyo.Objective(\n", - " expr=sum(orders[o][\"price\"] * orders[o][\"volume\"] * model.x[o] for o in orders),\n", - " sense=pyo.minimize,\n", - " doc=\"Total Cost Minimization\",\n", - " )\n", - "\n", - " # Choose the solver (HIGHS is used here)\n", - " solver = SolverFactory(\"appsi_highs\")\n", - " results = solver.solve(model)\n", - "\n", - " # Check if the solver found an optimal solution\n", - " if results.solver.termination_condition != TerminationCondition.optimal:\n", - " raise Exception(\"Solver did not find an optimal solution.\")\n", - "\n", - " return model, results" + "data": { + "text/html": [ + "
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nametechnologybidding_zonalmax_powermin_powerunit_operatornode
0demand_north_1inflex_demandnaive_eom1000000eom_denorth_1
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2demand_southinflex_demandnaive_eom1000000eom_desouth
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" + ], + "text/plain": [ + " name technology bidding_zonal max_power min_power \\\n", + "0 demand_north_1 inflex_demand naive_eom 100000 0 \n", + "1 demand_north_2 inflex_demand naive_eom 100000 0 \n", + "2 demand_south inflex_demand naive_eom 100000 0 \n", + "\n", + " unit_operator node \n", + "0 eom_de north_1 \n", + "1 eom_de north_2 \n", + "2 eom_de south " ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# @title Define the demand units\n", + "demand_units = pd.DataFrame(\n", + " {\n", + " \"name\": [\"demand_north_1\", \"demand_north_2\", \"demand_south\"],\n", + " \"technology\": [\"inflex_demand\"] * 3,\n", + " \"bidding_zonal\": [\"naive_eom\"] * 3,\n", + " \"max_power\": [100000, 100000, 100000],\n", + " \"min_power\": [0, 0, 0],\n", + " \"unit_operator\": [\"eom_de\"] * 3,\n", + " \"node\": [\"north_1\", \"north_2\", \"south\"],\n", + " }\n", + ")\n", + "\n", + "# Display the demand_units DataFrame\n", + "print(\"Demand Units DataFrame:\")\n", + "display(demand_units)" + ] + }, + { + "cell_type": "markdown", + "id": "d847ac5f", + "metadata": { + "id": "d847ac5f" + }, + "source": [ + "- **Demand Units:**\n", + " - **name:** Identifier for each demand unit (`demand_north_1`, `demand_north_2`, and `demand_south`).\n", + " - **technology:** Type of demand (`inflex_demand` for all units).\n", + " - **bidding_zonal:** Bidding strategy used (`naive_eom` for all units).\n", + " - **max_power, min_power:** Operational power limits (`100000` MW max, `0` MW min for all units).\n", + " - **unit_operator:** Operator responsible for the unit (`eom_de` for all units).\n", + " - **node:** The bus (zone) to which the unit is connected (`north_1`, `north_2`, and `south`)." + ] + }, + { + "cell_type": "markdown", + "id": "8f1d684a", + "metadata": { + "id": "8f1d684a" + }, + "source": [ + "### 4.4. Preparing Demand Data\n", + "\n", + "**Demand Data** provides the expected electricity demand for each demand unit over time. This data is essential for simulating how demand varies and affects market dynamics." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "a0591f14", + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 255 }, + "id": "a0591f14", + "outputId": "d590647b-7522-4fce-bfe7-dc66b7b566e8" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "8d42c532", - "metadata": { - "id": "8d42c532" - }, - "source": [ - "The above function is a simplified representation focusing on the essential aspects of market zone coupling. In the following sections, we will delve deeper into creating input files and mimicking the market clearing process using this function to understand the inner workings of the ASSUME framework." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Demand DataFrame:\n" + ] }, { - "cell_type": "markdown", - "id": "11addaf0", - "metadata": { - "id": "11addaf0" - }, - "source": [ - "## 4. Creating Exemplary Input Files for Market Zone Coupling\n", - "\n", - "To implement market zone coupling, users need to prepare specific input files that define the network's structure, units, and demand profiles. Below, we will guide you through creating the necessary DataFrames for buses, transmission lines, power plant units, demand units, and demand profiles." + "data": { + "text/html": [ + "
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datetime
2019-01-01 00:00:002400240017400
2019-01-01 01:00:002800280016800
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" + ], + "text/plain": [ + " demand_north_1 demand_north_2 demand_south\n", + "datetime \n", + "2019-01-01 00:00:00 2400 2400 17400\n", + "2019-01-01 01:00:00 2800 2800 16800\n", + "2019-01-01 02:00:00 3200 3200 16200\n", + "2019-01-01 03:00:00 3600 3600 15600\n", + "2019-01-01 04:00:00 4000 4000 15000" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# @title Define the demand DataFrame\n", + "\n", + "# the demand for the north_1 and north_2 zones increases by 400 MW per hour\n", + "# while the demand for the south zone decreases by 600 MW per hour\n", + "# the demand starts at 2400 MW for the north zones and 17400 MW for the south zone\n", + "demand_df = pd.DataFrame(\n", + " {\n", + " \"datetime\": pd.date_range(start=\"2019-01-01\", periods=24, freq=\"h\"),\n", + " \"demand_north_1\": [2400 + i * 400 for i in range(24)],\n", + " \"demand_north_2\": [2400 + i * 400 for i in range(24)],\n", + " \"demand_south\": [17400 - i * 600 for i in range(24)],\n", + " }\n", + ")\n", + "\n", + "# Convert the 'datetime' column to datetime objects and set as index\n", + "demand_df.set_index(\"datetime\", inplace=True)\n", + "\n", + "# Display the demand_df DataFrame\n", + "print(\"Demand DataFrame:\")\n", + "display(demand_df.head())" + ] + }, + { + "cell_type": "markdown", + "id": "1756e6e3", + "metadata": { + "id": "1756e6e3" + }, + "source": [ + "**Explanation:**\n", + "\n", + "- **datetime:** Timestamp for each demand value.\n", + "- **demand_north_1, demand_north_2, demand_south:** Demand values for each respective demand unit.\n", + "\n", + "**Note:** The demand timeseries has been designed to be fulfillable by the defined power plants in both zones." + ] + }, + { + "cell_type": "markdown", + "id": "478211c6", + "metadata": { + "id": "478211c6" + }, + "source": [ + "## 5. Reproducing the Market Clearing Process\n", + "\n", + "With the input files prepared, we can now reproduce the market clearing process using the simplified market clearing function. This will help us understand how different market zones interact, how constraints are managed, how bids are assigned, and how market prices are extracted." + ] + }, + { + "cell_type": "markdown", + "id": "01680700", + "metadata": { + "id": "01680700" + }, + "source": [ + "### 5.1. Calculating the Incidence Matrix\n", + "\n", + "The **Incidence Matrix** represents the connection relationships between different nodes in a network. In the context of market zones, it indicates which transmission lines connect which zones. The incidence matrix is a binary matrix where each element denotes whether a particular node is connected to a line or not. This matrix is essential for understanding the structure of the transmission network and for formulating power flow equations during the market clearing process." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c9fb8458", + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 }, + "id": "c9fb8458", + "outputId": "380d3471-2a05-4cf2-bd37-77b944a6dc98" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "2a095ffb", - "metadata": { - "id": "2a095ffb" - }, - "source": [ - "### 4.1. Defining Buses and Zones\n", - "\n", - "**Buses** represent nodes in the network where energy can be injected or withdrawn. Each bus is assigned to a **zone**, which groups buses into market areas. This zoning facilitates market coupling by managing interactions between different market regions." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculated Incidence Matrix between Zones:\n" + ] }, { - "cell_type": "code", - "execution_count": 4, - "id": "c1731cdc", - "metadata": { - "cellView": "form", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 192 - }, - "id": "c1731cdc", - "outputId": "0d0a8060-aa86-4ba8-a0b1-0e528bc9d0d2" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Buses DataFrame:\n" - ] - }, - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "summary": "{\n \"name\": \"buses\",\n \"rows\": 3,\n \"fields\": [\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"north_1\",\n \"north_2\",\n \"south\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"v_nom\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 380.0,\n \"max\": 380.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 380.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"zone_id\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"DE_2\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"x\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0969655114602888,\n \"min\": 9.5,\n \"max\": 11.6,\n \"num_unique_values\": 3,\n \"samples\": [\n 10.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"y\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3.2715949219506575,\n \"min\": 48.1,\n \"max\": 54.0,\n \"num_unique_values\": 3,\n \"samples\": [\n 54.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", - "type": "dataframe", - "variable_name": "buses" - }, - "text/html": [ - "\n", - "
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" ], - "source": [ - "# @title Define the buses DataFrame with three nodes and two zones\n", - "buses = pd.DataFrame(\n", - " {\n", - " \"name\": [\"north_1\", \"north_2\", \"south\"],\n", - " \"v_nom\": [380.0, 380.0, 380.0],\n", - " \"zone_id\": [\"DE_1\", \"DE_1\", \"DE_2\"],\n", - " \"x\": [10.0, 9.5, 11.6],\n", - " \"y\": [54.0, 53.5, 48.1],\n", - " }\n", - ").set_index(\"name\")\n", - "\n", - "# Display the buses DataFrame\n", - "print(\"Buses DataFrame:\")\n", - "display(buses)" + "text/plain": [ + " Line_N1_S Line_N2_S Line_N1_N2\n", + "DE_1 1 1 0\n", + "DE_2 -1 -1 0" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# @title Create the incidence matrix\n", + "def create_incidence_matrix(lines, buses, zones_id=None):\n", + " # Determine nodes based on whether we're working with zones or individual buses\n", + " if zones_id:\n", + " nodes = buses[zones_id].unique() # Use zones as nodes\n", + " node_mapping = buses[zones_id].to_dict() # Map bus IDs to zones\n", + " else:\n", + " nodes = buses.index.values # Use buses as nodes\n", + " node_mapping = {bus: bus for bus in nodes} # Identity mapping for buses\n", + "\n", + " # Use the line indices as columns for the incidence matrix\n", + " line_indices = lines.index.values\n", + "\n", + " # Initialize incidence matrix as a DataFrame for easier label-based indexing\n", + " incidence_matrix = pd.DataFrame(0, index=nodes, columns=line_indices)\n", + "\n", + " # Fill in the incidence matrix by iterating over lines\n", + " for line_idx, line in lines.iterrows():\n", + " bus0 = line[\"bus0\"]\n", + " bus1 = line[\"bus1\"]\n", + "\n", + " # Retrieve mapped nodes (zones or buses)\n", + " node0 = node_mapping.get(bus0)\n", + " node1 = node_mapping.get(bus1)\n", + "\n", + " # Ensure both nodes are valid and part of the defined nodes\n", + " if (\n", + " node0 is not None\n", + " and node1 is not None\n", + " and node0 in nodes\n", + " and node1 in nodes\n", + " ):\n", + " if node0 != node1: # Only create incidence for different nodes\n", + " # Set incidence values: +1 for the \"from\" node and -1 for the \"to\" node\n", + " incidence_matrix.at[node0, line_idx] = (\n", + " 1 # Outgoing from bus0 (or zone0)\n", + " )\n", + " incidence_matrix.at[node1, line_idx] = -1 # Incoming to bus1 (or zone1)\n", + "\n", + " # Return the incidence matrix as a DataFrame\n", + " return incidence_matrix\n", + "\n", + "\n", + "# Calculate the incidence matrix\n", + "incidence_matrix = create_incidence_matrix(lines, buses, \"zone_id\")\n", + "\n", + "print(\"Calculated Incidence Matrix between Zones:\")\n", + "display(incidence_matrix)" + ] + }, + { + "cell_type": "markdown", + "id": "61e9050c", + "metadata": { + "id": "61e9050c" + }, + "source": [ + "**Explanation:**\n", + "\n", + "- **Nodes (Zones):** Extracted from the `buses` DataFrame (`DE_1` and `DE_2`).\n", + "- **Transmission Lines:** Iterated over to sum their capacities between different zones.\n", + "- **Bidirectional Flow Assumption:** Transmission capacities are added in both directions (`DE_1 -> DE_2` and `DE_2 -> DE_1`).\n", + "- **Lower Triangle Negative Values:** To indicate the opposite direction of power flow, capacities in the lower triangle of the matrix are converted to negative values." + ] + }, + { + "cell_type": "markdown", + "id": "12ccae5f", + "metadata": { + "id": "12ccae5f" + }, + "source": [ + "### 5.2. Creating and Mapping Market Orders\n", + "\n", + "We will construct a dictionary of market orders representing supply and demand bids from power plants and demand units.\n", + "The orders include details such as price, volume, location (node), and time. Once the orders are generated, they will be\n", + "mapped from nodes to corresponding zones using a pre-defined node-to-zone mapping." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "4f7366ae", + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 225 }, + "id": "4f7366ae", + "outputId": "1c291cb1-8e7b-4e36-cce9-ddd00735225d" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "50a27c51", - "metadata": { - "id": "50a27c51" - }, - "source": [ - "**Explanation:**\n", - "\n", - "- **name:** Identifier for each bus (`north_1`, `north_2`, and `south`).\n", - "- **v_nom:** Nominal voltage level (in kV) for all buses.\n", - "- **zone_id:** Identifier for the market zone to which the bus belongs (`DE_1` for north buses and `DE_2` for the south bus).\n", - "- **x, y:** Geographical coordinates (optional, can be used for mapping or spatial analyses)." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Sample Supply Order:\n" + ] }, { - "cell_type": "markdown", - "id": "1545e3bf", - "metadata": { - "id": "1545e3bf" - }, - "source": [ - "### 4.2. Configuring Transmission Lines\n", - "\n", - "**Transmission Lines** connect buses, allowing energy to flow between them. Each line has a specified capacity and electrical parameters." + "data": { + "text/plain": [ + "{'price': 5,\n", + " 'volume': 1000.0,\n", + " 'node': 'north_1',\n", + " 'time': Timestamp('2019-01-01 00:00:00')}" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": 5, - "id": "64769ec7", - "metadata": { - "cellView": "form", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 192 - }, - "id": "64769ec7", - "outputId": "a47490cb-d06c-4152-8be6-64985a8dcbd0" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Transmission Lines DataFrame:\n" - ] - }, - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "summary": "{\n \"name\": \"lines\",\n \"rows\": 3,\n \"fields\": [\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"Line_N1_S\",\n \"Line_N2_S\",\n \"Line_N1_N2\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"bus0\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"north_2\",\n \"north_1\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"bus1\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"north_2\",\n \"south\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"s_nom\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 5000.0,\n \"max\": 5000.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 5000.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"x\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.01,\n \"max\": 0.01,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.01\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"r\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.001,\n \"max\": 0.001,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.001\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", - "type": "dataframe", - "variable_name": "lines" - }, - "text/html": [ - "\n", - "
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bus0bus1s_nomxr
name
Line_N1_Snorth_1south5000.00.010.001
Line_N2_Snorth_2south5000.00.010.001
Line_N1_N2north_1north_25000.00.010.001
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\n" - ], - "text/plain": [ - " bus0 bus1 s_nom x r\n", - "name \n", - "Line_N1_S north_1 south 5000.0 0.01 0.001\n", - "Line_N2_S north_2 south 5000.0 0.01 0.001\n", - "Line_N1_N2 north_1 north_2 5000.0 0.01 0.001" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# @title Define three transmission lines\n", - "lines = pd.DataFrame(\n", - " {\n", - " \"name\": [\"Line_N1_S\", \"Line_N2_S\", \"Line_N1_N2\"],\n", - " \"bus0\": [\"north_1\", \"north_2\", \"north_1\"],\n", - " \"bus1\": [\"south\", \"south\", \"north_2\"],\n", - " \"s_nom\": [5000.0, 5000.0, 5000.0],\n", - " \"x\": [0.01, 0.01, 0.01],\n", - " \"r\": [0.001, 0.001, 0.001],\n", - " }\n", - ").set_index(\"name\")\n", - "\n", - "print(\"Transmission Lines DataFrame:\")\n", - "display(lines)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Sample Demand Order:\n" + ] }, { - "cell_type": "markdown", - "id": "f2290793", - "metadata": { - "id": "f2290793" - }, - "source": [ - "**Explanation:**\n", - "\n", - "- **name:** Identifier for each transmission line (`Line_N1_S`, `Line_N2_S`, and `Line_N1_N2`).\n", - "- **bus0, bus1:** The two buses that the line connects.\n", - "- **s_nom:** Nominal apparent power capacity of the line (in MVA).\n", - "- **x:** Reactance of the line (in per unit).\n", - "- **r:** Resistance of the line (in per unit)." + "data": { + "text/plain": [ + "{'price': 100,\n", + " 'volume': -2400,\n", + " 'node': 'north_1',\n", + " 'time': Timestamp('2019-01-01 00:00:00')}" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# @title Construct Orders and Map Nodes to Zones\n", + "# Initialize orders dictionary\n", + "orders = {}\n", + "\n", + "# Add power plant bids\n", + "for _, row in powerplant_units.iterrows():\n", + " bid_id = row[\"name\"]\n", + " for timestamp in demand_df.index:\n", + " orders[f\"{bid_id}_{timestamp}\"] = {\n", + " \"price\": row[\"additional_cost\"], # Assuming additional_cost as bid price\n", + " \"volume\": row[\"max_power\"], # Assuming max_power as bid volume\n", + " \"node\": row[\"node\"],\n", + " \"time\": timestamp,\n", + " }\n", + "\n", + "# Add demand bids\n", + "for _, row in demand_units.iterrows():\n", + " bid_id = row[\"name\"]\n", + " for timestamp in demand_df.index:\n", + " orders[f\"{bid_id}_{timestamp}\"] = {\n", + " \"price\": 100, # Demand bids with high price\n", + " \"volume\": -demand_df.loc[\n", + " timestamp, row[\"name\"]\n", + " ], # Negative volume for demand\n", + " \"node\": row[\"node\"],\n", + " \"time\": timestamp,\n", + " }\n", + "\n", + "# Display a sample order\n", + "print(\"\\nSample Supply Order:\")\n", + "display(orders[\"Unit 1_2019-01-01 00:00:00\"])\n", + "\n", + "print(\"\\nSample Demand Order:\")\n", + "display(orders[\"demand_north_1_2019-01-01 00:00:00\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e8b8a17f", + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 224 }, + "id": "e8b8a17f", + "outputId": "ae3db259-f2e7-4b60-91b1-ca130140fb30" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "c931cf9f", - "metadata": { - "id": "c931cf9f" - }, - "source": [ - "### 4.3. Setting Up Power Plant and Demand Units\n", - "\n", - "**Power Plant Units** represent energy generation sources, while **Demand Units** represent consumption. Each unit is associated with a specific bus (node) and has operational parameters that define its behavior in the market." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Mapped Orders:\n" + ] }, { - "cell_type": "code", - "execution_count": 6, - "id": "8a1f9e35", - "metadata": { - "cellView": "form", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 224 - }, - "id": "8a1f9e35", - "outputId": "b7d43816-40af-4526-bb64-53d4a20ba911" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Power Plant Units DataFrame:\n" - ] - }, - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "summary": "{\n \"name\": \"display(powerplant_units\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"Unit 2\",\n \"Unit 5\",\n \"Unit 3\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"technology\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"nuclear\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"bidding_zonal\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"naive_eom\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"fuel_type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"uranium\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"emission_factor\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"max_power\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 1000.0,\n \"max\": 1000.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 1000.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"min_power\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"efficiency\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.3,\n \"max\": 0.3,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"additional_cost\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 5,\n \"max\": 9,\n \"num_unique_values\": 5,\n \"samples\": [\n 6\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"node\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"north_1\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"unit_operator\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Operator North\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", - "type": "dataframe" - }, - "text/html": [ - "\n", - "
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" ], - "source": [ - "# @title Create the power plant units DataFrame\n", - "\n", - "# Define the total number of units\n", - "num_units = 30 # Reduced for simplicity\n", - "\n", - "# Generate the 'name' column: Unit 1 to Unit 30\n", - "names = [f\"Unit {i}\" for i in range(1, num_units + 1)]\n", - "\n", - "# All other columns with constant values\n", - "technology = [\"nuclear\"] * num_units\n", - "bidding_zonal = [\"naive_eom\"] * num_units\n", - "fuel_type = [\"uranium\"] * num_units\n", - "emission_factor = [0.0] * num_units\n", - "max_power = [1000.0] * num_units\n", - "min_power = [0.0] * num_units\n", - "efficiency = [0.3] * num_units\n", - "\n", - "# Generate 'additional_cost':\n", - "# - North units (1-15): 5 to 19\n", - "# - South units (16-30): 20 to 34\n", - "additional_cost = list(range(5, 5 + num_units))\n", - "\n", - "# Initialize 'node' and 'unit_operator' lists\n", - "node = []\n", - "unit_operator = []\n", - "\n", - "for i in range(1, num_units + 1):\n", - " if 1 <= i <= 8:\n", - " node.append(\"north_1\") # All north units connected to 'north_1'\n", - " unit_operator.append(\"Operator North\")\n", - " elif 9 <= i <= 15:\n", - " node.append(\"north_2\")\n", - " unit_operator.append(\"Operator North\")\n", - " else:\n", - " node.append(\"south\") # All south units connected to 'south'\n", - " unit_operator.append(\"Operator South\")\n", - "\n", - "# Create the DataFrame\n", - "powerplant_units = pd.DataFrame(\n", - " {\n", - " \"name\": names,\n", - " \"technology\": technology,\n", - " \"bidding_zonal\": bidding_zonal, # bidding strategy used to bid on the zonal market. Should be same name as in config file\n", - " \"fuel_type\": fuel_type,\n", - " \"emission_factor\": emission_factor,\n", - " \"max_power\": max_power,\n", - " \"min_power\": min_power,\n", - " \"efficiency\": efficiency,\n", - " \"additional_cost\": additional_cost,\n", - " \"node\": node,\n", - " \"unit_operator\": unit_operator,\n", - " }\n", - ")\n", - "\n", - "print(\"Power Plant Units DataFrame:\")\n", - "display(powerplant_units.head())" + "text/plain": [ + " price volume node time\n", + "Unit 1_2019-01-01 00:00:00 5 1000.0 DE_1 2019-01-01 00:00:00\n", + "Unit 1_2019-01-01 01:00:00 5 1000.0 DE_1 2019-01-01 01:00:00\n", + "Unit 1_2019-01-01 02:00:00 5 1000.0 DE_1 2019-01-01 02:00:00\n", + "Unit 1_2019-01-01 03:00:00 5 1000.0 DE_1 2019-01-01 03:00:00\n", + "Unit 1_2019-01-01 04:00:00 5 1000.0 DE_1 2019-01-01 04:00:00" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# @title Map the orders to zones\n", + "# Create a mapping from node_id to zone_id\n", + "node_mapping = buses[\"zone_id\"].to_dict()\n", + "\n", + "# Create a new dictionary with mapped zone IDs\n", + "orders_mapped = {}\n", + "for bid_id, bid in orders.items():\n", + " original_node = bid[\"node\"]\n", + " mapped_zone = node_mapping.get(\n", + " original_node, original_node\n", + " ) # Default to original_node if not found\n", + " orders_mapped[bid_id] = {\n", + " \"price\": bid[\"price\"],\n", + " \"volume\": bid[\"volume\"],\n", + " \"node\": mapped_zone, # Replace bus with zone ID\n", + " \"time\": bid[\"time\"],\n", + " }\n", + "\n", + "# Display the mapped orders\n", + "print(\"Mapped Orders:\")\n", + "display(pd.DataFrame(orders_mapped).T.head())" + ] + }, + { + "cell_type": "markdown", + "id": "1a5d589c", + "metadata": { + "id": "1a5d589c" + }, + "source": [ + "**Explanation:**\n", + "\n", + "- **Power Plant Bids:** Each power plant unit submits a bid for each time period with its `additional_cost` as the bid price and `max_power` as the bid volume.\n", + "- **Demand Bids:** Each demand unit submits a bid for each time period with a high price (set to 100) and a negative volume representing the demand.\n", + "- **Node to Zone Mapping:** After creating the bids, the node information is mapped to corresponding zones for further market clearing steps.\n", + " The mapping uses a pre-defined dictionary (`node_mapping`) to replace each node ID with the corresponding zone ID. In ASSUME, this mapping happens automatically on the market side, but we are simulating it here for educational purposes." + ] + }, + { + "cell_type": "markdown", + "id": "f11b487c", + "metadata": { + "id": "f11b487c" + }, + "source": [ + "### 5.3. Running the Market Clearing Simulation\n", + "\n", + "We will conduct three simulations:\n", + "\n", + "1. **Simulation 1:** Transmission capacities between `DE_1` (north) and `DE_2` (south) are **zero**.\n", + "2. **Simulation 2:** Transmission capacities between `DE_1` (north) and `DE_2` (south) are **medium**.\n", + "3. **Simulation 3:** Transmission capacities between `DE_1` (north) and `DE_2` (south) are **high**." + ] + }, + { + "cell_type": "markdown", + "id": "07082c73", + "metadata": { + "id": "07082c73" + }, + "source": [ + "#### Simulation 1: Zero Transmission Capacity Between Zones" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "1c7dfee2", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 210 }, + "id": "1c7dfee2", + "outputId": "86090b82-98e1-4d3b-bb1b-74b3c1c37e43" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "Uwp8L0rombac", - "metadata": { - "id": "Uwp8L0rombac" - }, - "source": [ - "- **Power Plant Units:**\n", - " - **name:** Identifier for each power plant unit (`Unit 1` to `Unit 30`).\n", - " - **technology:** Type of technology (`nuclear` for all units).\n", - " - **bidding_nodal:** Bidding strategy used (`naive_eom` for all units).\n", - " - **fuel_type:** Type of fuel used (`uranium` for all units).\n", - " - **emission_factor:** Emissions per unit of energy produced (`0.0` for all units).\n", - " - **max_power, min_power:** Operational power limits (`1000.0` MW max, `0.0` MW min for all units).\n", - " - **efficiency:** Conversion efficiency (`0.3` for all units).\n", - " - **additional_cost:** Additional operational costs (`5` to `34`, with southern units being more expensive).\n", - " - **node:** The bus (zone) to which the unit is connected (`north_1` for units `1-15`, `south` for units `16-30`).\n", - " - **unit_operator:** Operator responsible for the unit (`Operator North` for northern units, `Operator South` for southern units)." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "### Simulation 1: Zero Transmission Capacity Between Zones\n", + "Transmission Lines for Simulation 1:\n" + ] }, { - "cell_type": "code", - "execution_count": 7, - "id": "16f8a13c", - "metadata": { - "cellView": "form", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 161 - }, - "id": "16f8a13c", - "outputId": "aad8a140-a6ed-47fd-d06e-1e794aa1a829" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Demand Units DataFrame:\n" - ] - }, - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "summary": "{\n \"name\": \"demand_units\",\n \"rows\": 3,\n \"fields\": [\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"demand_north_1\",\n \"demand_north_2\",\n \"demand_south\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"technology\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"inflex_demand\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"bidding_zonal\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"naive_eom\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"max_power\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 100000,\n \"max\": 100000,\n \"num_unique_values\": 1,\n \"samples\": [\n 100000\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"min_power\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"unit_operator\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"eom_de\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"node\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"north_1\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", - "type": "dataframe", - "variable_name": "demand_units" - }, - "text/html": [ - "\n", - "
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bus0bus1s_nomxr
name
Line_N1_Snorth_1south00.010.001
Line_N2_Snorth_2south00.010.001
Line_N1_N2north_1north_200.010.001
\n", + "
" ], - "source": [ - "# @title Define the demand units\n", - "demand_units = pd.DataFrame(\n", - " {\n", - " \"name\": [\"demand_north_1\", \"demand_north_2\", \"demand_south\"],\n", - " \"technology\": [\"inflex_demand\"] * 3,\n", - " \"bidding_zonal\": [\"naive_eom\"] * 3,\n", - " \"max_power\": [100000, 100000, 100000],\n", - " \"min_power\": [0, 0, 0],\n", - " \"unit_operator\": [\"eom_de\"] * 3,\n", - " \"node\": [\"north_1\", \"north_2\", \"south\"],\n", - " }\n", - ")\n", - "\n", - "# Display the demand_units DataFrame\n", - "print(\"Demand Units DataFrame:\")\n", - "display(demand_units)" - ] - }, - { - "cell_type": "markdown", - "id": "d847ac5f", - "metadata": { - "id": "d847ac5f" - }, - "source": [ - "- **Demand Units:**\n", - " - **name:** Identifier for each demand unit (`demand_north_1`, `demand_north_2`, and `demand_south`).\n", - " - **technology:** Type of demand (`inflex_demand` for all units).\n", - " - **bidding_zonal:** Bidding strategy used (`naive_eom` for all units).\n", - " - **max_power, min_power:** Operational power limits (`100000` MW max, `0` MW min for all units).\n", - " - **unit_operator:** Operator responsible for the unit (`eom_de` for all units).\n", - " - **node:** The bus (zone) to which the unit is connected (`north_1`, `north_2`, and `south`)." + "text/plain": [ + " bus0 bus1 s_nom x r\n", + "name \n", + "Line_N1_S north_1 south 0 0.01 0.001\n", + "Line_N2_S north_2 south 0 0.01 0.001\n", + "Line_N1_N2 north_1 north_2 0 0.01 0.001" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"### Simulation 1: Zero Transmission Capacity Between Zones\")\n", + "\n", + "lines_sim1 = lines.copy()\n", + "lines_sim1[\"s_nom\"] = 0 # Set transmission capacity to zero for all lines\n", + "\n", + "print(\"Transmission Lines for Simulation 1:\")\n", + "display(lines_sim1)\n", + "\n", + "# Run the simplified market clearing for Simulation 1\n", + "model_sim1, results_sim1 = simplified_market_clearing_opt(\n", + " orders=orders_mapped,\n", + " incidence_matrix=incidence_matrix,\n", + " lines=lines_sim1,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "aef7c083", + "metadata": { + "id": "aef7c083" + }, + "source": [ + "#### Simulation 2: Medium Transmission Capacity Between Zones" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "86304253", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 210 }, + "id": "86304253", + "outputId": "3fa73e8b-d0e3-4fe8-d88c-1a896fb3e1ff" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "8f1d684a", - "metadata": { - "id": "8f1d684a" - }, - "source": [ - "### 4.4. Preparing Demand Data\n", - "\n", - "**Demand Data** provides the expected electricity demand for each demand unit over time. This data is essential for simulating how demand varies and affects market dynamics." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "### Simulation 2: Medium Transmission Capacity Between Zones\n", + "Transmission Lines for Simulation 2:\n" + ] }, { - "cell_type": "code", - "execution_count": 8, - "id": "a0591f14", - "metadata": { - "cellView": "form", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 255 - }, - "id": "a0591f14", - "outputId": "d590647b-7522-4fce-bfe7-dc66b7b566e8" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Demand DataFrame:\n" - ] - }, - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "summary": "{\n \"name\": \"display(demand_df\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"datetime\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2019-01-01 00:00:00\",\n \"max\": \"2019-01-01 04:00:00\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"2019-01-01 01:00:00\",\n \"2019-01-01 04:00:00\",\n \"2019-01-01 02:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"demand_north_1\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 632,\n \"min\": 2400,\n \"max\": 4000,\n \"num_unique_values\": 5,\n \"samples\": [\n 2800,\n 4000,\n 3200\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"demand_north_2\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 632,\n \"min\": 2400,\n \"max\": 4000,\n \"num_unique_values\": 5,\n \"samples\": [\n 2800,\n 4000,\n 3200\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"demand_south\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 948,\n \"min\": 15000,\n \"max\": 17400,\n \"num_unique_values\": 5,\n \"samples\": [\n 16800,\n 15000,\n 16200\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", - "type": "dataframe" - }, - "text/html": [ - "\n", - "
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bus0bus1s_nomxr
name
Line_N1_Snorth_1south3000.00.010.001
Line_N2_Snorth_2south3000.00.010.001
Line_N1_N2north_1north_23000.00.010.001
\n", + "
" ], - "source": [ - "# @title Define the demand DataFrame\n", - "\n", - "# the demand for the north_1 and north_2 zones increases by 400 MW per hour\n", - "# while the demand for the south zone decreases by 600 MW per hour\n", - "# the demand starts at 2400 MW for the north zones and 17400 MW for the south zone\n", - "demand_df = pd.DataFrame(\n", - " {\n", - " \"datetime\": pd.date_range(start=\"2019-01-01\", periods=24, freq=\"h\"),\n", - " \"demand_north_1\": [2400 + i * 400 for i in range(24)],\n", - " \"demand_north_2\": [2400 + i * 400 for i in range(24)],\n", - " \"demand_south\": [17400 - i * 600 for i in range(24)],\n", - " }\n", - ")\n", - "\n", - "# Convert the 'datetime' column to datetime objects and set as index\n", - "demand_df.set_index(\"datetime\", inplace=True)\n", - "\n", - "# Display the demand_df DataFrame\n", - "print(\"Demand DataFrame:\")\n", - "display(demand_df.head())" + "text/plain": [ + " bus0 bus1 s_nom x r\n", + "name \n", + "Line_N1_S north_1 south 3000.0 0.01 0.001\n", + "Line_N2_S north_2 south 3000.0 0.01 0.001\n", + "Line_N1_N2 north_1 north_2 3000.0 0.01 0.001" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"### Simulation 2: Medium Transmission Capacity Between Zones\")\n", + "\n", + "# Define the lines for Simulation 2 with medium transmission capacity\n", + "lines_sim2 = lines.copy()\n", + "lines_sim2[\"s_nom\"] = 3000.0 # Set transmission capacity to 3000 MW for all lines\n", + "\n", + "# Display the incidence matrix for Simulation 2\n", + "print(\"Transmission Lines for Simulation 2:\")\n", + "display(lines_sim2)\n", + "\n", + "# Run the simplified market clearing for Simulation 2\n", + "model_sim2, results_sim2 = simplified_market_clearing_opt(\n", + " orders=orders_mapped,\n", + " incidence_matrix=incidence_matrix,\n", + " lines=lines_sim2,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "5c721991", + "metadata": { + "id": "5c721991" + }, + "source": [ + "#### Simulation 3: High Transmission Capacity Between Zones" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "a1c7f344", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 210 }, + "id": "a1c7f344", + "lines_to_end_of_cell_marker": 0, + "lines_to_next_cell": 1, + "outputId": "78e208e2-81f7-4678-9adc-bbdddd2802ea" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "1756e6e3", - "metadata": { - "id": "1756e6e3" - }, - "source": [ - "**Explanation:**\n", - "\n", - "- **datetime:** Timestamp for each demand value.\n", - "- **demand_north_1, demand_north_2, demand_south:** Demand values for each respective demand unit.\n", - "\n", - "**Note:** The demand timeseries has been designed to be fulfillable by the defined power plants in both zones." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "### Simulation 3: High Transmission Capacity Between Zones\n", + "Transmission Lines for Simulation 3:\n" + ] }, { - "cell_type": "markdown", - "id": "478211c6", - "metadata": { - "id": "478211c6" - }, - "source": [ - "## 5. Reproducing the Market Clearing Process\n", - "\n", - "With the input files prepared, we can now reproduce the market clearing process using the simplified market clearing function. This will help us understand how different market zones interact, how constraints are managed, how bids are assigned, and how market prices are extracted." + "data": { + "text/html": [ + "
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bus0bus1s_nomxr
name
Line_N1_Snorth_1south5000.00.010.001
Line_N2_Snorth_2south5000.00.010.001
Line_N1_N2north_1north_25000.00.010.001
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" + ], + "text/plain": [ + " bus0 bus1 s_nom x r\n", + "name \n", + "Line_N1_S north_1 south 5000.0 0.01 0.001\n", + "Line_N2_S north_2 south 5000.0 0.01 0.001\n", + "Line_N1_N2 north_1 north_2 5000.0 0.01 0.001" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"### Simulation 3: High Transmission Capacity Between Zones\")\n", + "\n", + "# Define the lines for Simulation 3 with high transmission capacity\n", + "lines_sim3 = lines.copy()\n", + "lines_sim3[\"s_nom\"] = 5000.0 # Set transmission capacity to 5000 MW for all lines\n", + "\n", + "# Display the line capacities for Simulation 3\n", + "print(\"Transmission Lines for Simulation 3:\")\n", + "display(lines_sim3)\n", + "\n", + "# Run the simplified market clearing for Simulation 3\n", + "model_sim3, results_sim3 = simplified_market_clearing_opt(\n", + " orders=orders_mapped,\n", + " incidence_matrix=incidence_matrix,\n", + " lines=lines_sim3,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "661e6c30", + "metadata": { + "id": "661e6c30" + }, + "source": [ + "### 5.4. Extracting and Interpreting the Results\n", + "\n", + "After running all three simulations, we can extract the results to understand how the presence or absence of transmission capacity affects bid acceptances and power flows between zones.\n", + "\n", + "#### Extracting Clearing Prices\n", + "\n", + "The **clearing prices** for each market zone and time period are extracted using the dual variables associated with the energy balance constraints in the optimization model. Specifically, the dual variable of the energy balance constraint for a given zone and time period represents the marginal price of electricity in that zone at that time.\n", + "\n", + "In the `extract_results` function, the following steps are performed to obtain the clearing prices:\n", + "\n", + "1. **Energy Balance Constraints:** For each zone and time period, the energy balance equation ensures that the total supply plus imports minus exports equals the demand.\n", + "2. **Dual Variables:** The dual variable (`model.dual[model.energy_balance[node, t]]`) associated with each energy balance constraint captures the sensitivity of the objective function (total cost) to a marginal increase in demand or supply.\n", + "3. **Clearing Price Interpretation:** The value of the dual variable corresponds to the clearing price in the respective zone and time period, reflecting the cost of supplying an additional unit of electricity.\n", + "\n", + "This method leverages the duality in optimization to efficiently extract market prices resulting from the optimal dispatch of bids under the given constraints." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "bdcc49e7", + "metadata": { + "cellView": "form", + "id": "bdcc49e7" + }, + "outputs": [], + "source": [ + "# @title Function to extract market clearing results from the optimization model\n", + "def extract_results(model, incidence_matrix):\n", + " nodes = list(incidence_matrix.index)\n", + " lines = list(incidence_matrix.columns)\n", + "\n", + " # Extract accepted bid ratios using a dictionary comprehension\n", + " accepted_bids = {\n", + " o: pyo.value(model.x[o]) for o in model.x if pyo.value(model.x[o]) > 0\n", + " }\n", + "\n", + " # Extract power flows on each line for each time period\n", + " power_flows = [\n", + " {\"time\": t, \"line\": line, \"flow_MW\": pyo.value(model.flows[t, line])}\n", + " for t in model.T\n", + " for line in lines\n", + " if pyo.value(model.flows[t, line]) != 0\n", + " ]\n", + " power_flows_df = pd.DataFrame(power_flows)\n", + "\n", + " # Extract market clearing prices from dual variables\n", + " clearing_prices = [\n", + " {\n", + " \"zone\": node,\n", + " \"time\": t,\n", + " \"clearing_price\": pyo.value(model.dual[model.energy_balance[node, t]]),\n", + " }\n", + " for node in nodes\n", + " for t in model.T\n", + " ]\n", + " clearing_prices_df = pd.DataFrame(clearing_prices)\n", + "\n", + " return accepted_bids, power_flows_df, clearing_prices_df" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "512ed95f", + "metadata": { + "id": "512ed95f" + }, + "outputs": [], + "source": [ + "# Extract results for Simulation 1\n", + "accepted_bids_sim1, power_flows_df_sim1, clearing_prices_df_sim1 = extract_results(\n", + " model_sim1, incidence_matrix\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "7b32b7c3", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 70 }, + "id": "7b32b7c3", + "outputId": "7d56dd2f-8ab9-4a95-df0b-dbd6aac660e4" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "01680700", - "metadata": { - "id": "01680700" - }, - "source": [ - "### 5.1. Calculating the Incidence Matrix\n", - "\n", - "The **Incidence Matrix** represents the connection relationships between different nodes in a network. In the context of market zones, it indicates which transmission lines connect which zones. The incidence matrix is a binary matrix where each element denotes whether a particular node is connected to a line or not. This matrix is essential for understanding the structure of the transmission network and for formulating power flow equations during the market clearing process." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Simulation 1: Power Flows Between Zones\n" + ] }, { - "cell_type": "code", - "execution_count": 10, - "id": "c9fb8458", - "metadata": { - "cellView": "form", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 142 - }, - "id": "c9fb8458", - "outputId": "380d3471-2a05-4cf2-bd37-77b944a6dc98" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculated Incidence Matrix between Zones:\n" - ] - }, - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "summary": "{\n \"name\": \"incidence_matrix\",\n \"rows\": 2,\n \"fields\": [\n {\n \"column\": \"Line_N1_S\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": -1,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n -1,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Line_N2_S\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": -1,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n -1,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Line_N1_N2\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", - "type": "dataframe", - "variable_name": "incidence_matrix" - }, - "text/html": [ - "\n", - "
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" ], - "source": [ - "# @title Create the incidence matrix\n", - "def create_incidence_matrix(lines, buses, zones_id=None):\n", - " # Determine nodes based on whether we're working with zones or individual buses\n", - " if zones_id:\n", - " nodes = buses[zones_id].unique() # Use zones as nodes\n", - " node_mapping = buses[zones_id].to_dict() # Map bus IDs to zones\n", - " else:\n", - " nodes = buses.index.values # Use buses as nodes\n", - " node_mapping = {bus: bus for bus in nodes} # Identity mapping for buses\n", - "\n", - " # Use the line indices as columns for the incidence matrix\n", - " line_indices = lines.index.values\n", - "\n", - " # Initialize incidence matrix as a DataFrame for easier label-based indexing\n", - " incidence_matrix = pd.DataFrame(0, index=nodes, columns=line_indices)\n", - "\n", - " # Fill in the incidence matrix by iterating over lines\n", - " for line_idx, line in lines.iterrows():\n", - " bus0 = line[\"bus0\"]\n", - " bus1 = line[\"bus1\"]\n", - "\n", - " # Retrieve mapped nodes (zones or buses)\n", - " node0 = node_mapping.get(bus0)\n", - " node1 = node_mapping.get(bus1)\n", - "\n", - " # Ensure both nodes are valid and part of the defined nodes\n", - " if (\n", - " node0 is not None\n", - " and node1 is not None\n", - " and node0 in nodes\n", - " and node1 in nodes\n", - " ):\n", - " if node0 != node1: # Only create incidence for different nodes\n", - " # Set incidence values: +1 for the \"from\" node and -1 for the \"to\" node\n", - " incidence_matrix.at[node0, line_idx] = (\n", - " 1 # Outgoing from bus0 (or zone0)\n", - " )\n", - " incidence_matrix.at[node1, line_idx] = -1 # Incoming to bus1 (or zone1)\n", - "\n", - " # Return the incidence matrix as a DataFrame\n", - " return incidence_matrix\n", - "\n", - "\n", - "# Calculate the incidence matrix\n", - "incidence_matrix = create_incidence_matrix(lines, buses, \"zone_id\")\n", - "\n", - "print(\"Calculated Incidence Matrix between Zones:\")\n", - "display(incidence_matrix)" - ] - }, - { - "cell_type": "markdown", - "id": "61e9050c", - "metadata": { - "id": "61e9050c" - }, - "source": [ - "**Explanation:**\n", - "\n", - "- **Nodes (Zones):** Extracted from the `buses` DataFrame (`DE_1` and `DE_2`).\n", - "- **Transmission Lines:** Iterated over to sum their capacities between different zones.\n", - "- **Bidirectional Flow Assumption:** Transmission capacities are added in both directions (`DE_1 -> DE_2` and `DE_2 -> DE_1`).\n", - "- **Lower Triangle Negative Values:** To indicate the opposite direction of power flow, capacities in the lower triangle of the matrix are converted to negative values." + "text/plain": [ + "Empty DataFrame\n", + "Columns: []\n", + "Index: []" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Simulation 1: Power Flows Between Zones\")\n", + "display(power_flows_df_sim1.head())" + ] + }, + { + "cell_type": "markdown", + "id": "Q37fGve_m7sf", + "metadata": { + "id": "Q37fGve_m7sf" + }, + "source": [ + "As it is to be expected, there are no flows printed since there is no transfer capacity available." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "2d386677", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 413 }, + "id": "2d386677", + "outputId": "7062cc2c-e168-45a6-9294-5ea193ad78c2" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "12ccae5f", - "metadata": { - "id": "12ccae5f" - }, - "source": [ - "### 5.2. Creating and Mapping Market Orders\n", - "\n", - "We will construct a dictionary of market orders representing supply and demand bids from power plants and demand units.\n", - "The orders include details such as price, volume, location (node), and time. Once the orders are generated, they will be\n", - "mapped from nodes to corresponding zones using a pre-defined node-to-zone mapping." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Simulation 1: Clearing Prices per Zone and Time\n" + ] }, { - "cell_type": "code", - "execution_count": 11, - "id": "4f7366ae", - "metadata": { - "cellView": "form", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 225 - }, - "id": "4f7366ae", - "outputId": "1c291cb1-8e7b-4e36-cce9-ddd00735225d" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Sample Supply Order:\n" - ] - }, - { - "data": { - "text/plain": [ - "{'price': 5,\n", - " 'volume': 1000.0,\n", - " 'node': 'north_1',\n", - " 'time': Timestamp('2019-01-01 00:00:00')}" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Sample Demand Order:\n" - ] - }, - { - "data": { - "text/plain": [ - "{'price': 100,\n", - " 'volume': -2400,\n", - " 'node': 'north_1',\n", - " 'time': Timestamp('2019-01-01 00:00:00')}" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + "data": { + "text/html": [ + "
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zonetimeclearing_price
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3DE_12019-01-01 03:00:0012.0
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" ], - "source": [ - "# @title Construct Orders and Map Nodes to Zones\n", - "# Initialize orders dictionary\n", - "orders = {}\n", - "\n", - "# Add power plant bids\n", - "for _, row in powerplant_units.iterrows():\n", - " bid_id = row[\"name\"]\n", - " for timestamp in demand_df.index:\n", - " orders[f\"{bid_id}_{timestamp}\"] = {\n", - " \"price\": row[\"additional_cost\"], # Assuming additional_cost as bid price\n", - " \"volume\": row[\"max_power\"], # Assuming max_power as bid volume\n", - " \"node\": row[\"node\"],\n", - " \"time\": timestamp,\n", - " }\n", - "\n", - "# Add demand bids\n", - "for _, row in demand_units.iterrows():\n", - " bid_id = row[\"name\"]\n", - " for timestamp in demand_df.index:\n", - " orders[f\"{bid_id}_{timestamp}\"] = {\n", - " \"price\": 100, # Demand bids with high price\n", - " \"volume\": -demand_df.loc[\n", - " timestamp, row[\"name\"]\n", - " ], # Negative volume for demand\n", - " \"node\": row[\"node\"],\n", - " \"time\": timestamp,\n", - " }\n", - "\n", - "# Display a sample order\n", - "print(\"\\nSample Supply Order:\")\n", - "display(orders[\"Unit 1_2019-01-01 00:00:00\"])\n", - "\n", - "print(\"\\nSample Demand Order:\")\n", - "display(orders[\"demand_north_1_2019-01-01 00:00:00\"])" + "text/plain": [ + " zone time clearing_price\n", + "0 DE_1 2019-01-01 00:00:00 9.0\n", + "1 DE_1 2019-01-01 01:00:00 10.0\n", + "2 DE_1 2019-01-01 02:00:00 11.0\n", + "3 DE_1 2019-01-01 03:00:00 12.0\n", + "4 DE_1 2019-01-01 04:00:00 12.0" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": 12, - "id": "e8b8a17f", - "metadata": { - "cellView": "form", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 224 - }, - "id": "e8b8a17f", - "outputId": "ae3db259-f2e7-4b60-91b1-ca130140fb30" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mapped Orders:\n" - ] - }, - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "summary": "{\n \"name\": \"display(pd\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": 5,\n \"max\": 5,\n \"num_unique_values\": 1,\n \"samples\": [\n 5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"volume\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": 1000.0,\n \"max\": 1000.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 1000.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"node\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"DE_1\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"time\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2019-01-01 00:00:00\",\n \"max\": \"2019-01-01 04:00:00\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"2019-01-01 01:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", - "type": "dataframe" - }, - "text/html": [ - "\n", - "
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" ], - "source": [ - "# @title Map the orders to zones\n", - "# Create a mapping from node_id to zone_id\n", - "node_mapping = buses[\"zone_id\"].to_dict()\n", - "\n", - "# Create a new dictionary with mapped zone IDs\n", - "orders_mapped = {}\n", - "for bid_id, bid in orders.items():\n", - " original_node = bid[\"node\"]\n", - " mapped_zone = node_mapping.get(\n", - " original_node, original_node\n", - " ) # Default to original_node if not found\n", - " orders_mapped[bid_id] = {\n", - " \"price\": bid[\"price\"],\n", - " \"volume\": bid[\"volume\"],\n", - " \"node\": mapped_zone, # Replace bus with zone ID\n", - " \"time\": bid[\"time\"],\n", - " }\n", - "\n", - "# Display the mapped orders\n", - "print(\"Mapped Orders:\")\n", - "display(pd.DataFrame(orders_mapped).T.head())" + "text/plain": [ + " zone time clearing_price\n", + "24 DE_2 2019-01-01 00:00:00 100.0\n", + "25 DE_2 2019-01-01 01:00:00 100.0\n", + "26 DE_2 2019-01-01 02:00:00 100.0\n", + "27 DE_2 2019-01-01 03:00:00 100.0\n", + "28 DE_2 2019-01-01 04:00:00 100.0" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Simulation 1: Clearing Prices per Zone and Time\")\n", + "display(clearing_prices_df_sim1.loc[clearing_prices_df_sim1[\"zone\"] == \"DE_1\"].head())\n", + "display(clearing_prices_df_sim1.loc[clearing_prices_df_sim1[\"zone\"] == \"DE_2\"].head())" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "d8327407", + "metadata": { + "id": "d8327407" + }, + "outputs": [], + "source": [ + "# Extract results for Simulation 2\n", + "accepted_bids_sim2, power_flows_df_sim2, clearing_prices_df_sim2 = extract_results(\n", + " model_sim2, incidence_matrix\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "9b5fc1de", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 224 }, + "id": "9b5fc1de", + "outputId": "25af541d-12cb-47d6-bc08-92ee847cd820" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "1a5d589c", - "metadata": { - "id": "1a5d589c" - }, - "source": [ - "**Explanation:**\n", - "\n", - "- **Power Plant Bids:** Each power plant unit submits a bid for each time period with its `additional_cost` as the bid price and `max_power` as the bid volume.\n", - "- **Demand Bids:** Each demand unit submits a bid for each time period with a high price (set to 100) and a negative volume representing the demand.\n", - "- **Node to Zone Mapping:** After creating the bids, the node information is mapped to corresponding zones for further market clearing steps.\n", - " The mapping uses a pre-defined dictionary (`node_mapping`) to replace each node ID with the corresponding zone ID. In ASSUME, this mapping happens automatically on the market side, but we are simulating it here for educational purposes." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Simulation 2: Power Flows Between Zones\n" + ] }, { - "cell_type": "markdown", - "id": "f11b487c", - "metadata": { - "id": "f11b487c" - }, - "source": [ - "### 5.3. Running the Market Clearing Simulation\n", - "\n", - "We will conduct three simulations:\n", - "\n", - "1. **Simulation 1:** Transmission capacities between `DE_1` (north) and `DE_2` (south) are **zero**.\n", - "2. **Simulation 2:** Transmission capacities between `DE_1` (north) and `DE_2` (south) are **medium**.\n", - "3. **Simulation 3:** Transmission capacities between `DE_1` (north) and `DE_2` (south) are **high**." + "data": { + "text/html": [ + "
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timelineflow_MW
02019-01-01 00:00:00Line_N1_S-3000.0
12019-01-01 00:00:00Line_N2_S-3000.0
22019-01-01 00:00:00Line_N1_N2-3000.0
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" + ], + "text/plain": [ + " time line flow_MW\n", + "0 2019-01-01 00:00:00 Line_N1_S -3000.0\n", + "1 2019-01-01 00:00:00 Line_N2_S -3000.0\n", + "2 2019-01-01 00:00:00 Line_N1_N2 -3000.0\n", + "3 2019-01-01 01:00:00 Line_N1_S -3000.0\n", + "4 2019-01-01 01:00:00 Line_N2_S -3000.0" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Simulation 2: Power Flows Between Zones\")\n", + "display(power_flows_df_sim2.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "b7c5d148", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 413 }, + "id": "b7c5d148", + "outputId": "4abfe739-2b01-485c-cde7-e385debad088" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "07082c73", - "metadata": { - "id": "07082c73" - }, - "source": [ - "#### Simulation 1: Zero Transmission Capacity Between Zones" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Simulation 2: Clearing Prices per Zone and Time\n" + ] }, { - "cell_type": "code", - "execution_count": 36, - "id": "1c7dfee2", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 210 - }, - "id": "1c7dfee2", - "outputId": "86090b82-98e1-4d3b-bb1b-74b3c1c37e43" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### Simulation 1: Zero Transmission Capacity Between Zones\n", - "Transmission Lines for Simulation 1:\n" - ] - }, - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "summary": "{\n \"name\": \"lines_sim1\",\n \"rows\": 3,\n \"fields\": [\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"Line_N1_S\",\n \"Line_N2_S\",\n \"Line_N1_N2\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"bus0\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"north_2\",\n \"north_1\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"bus1\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"north_2\",\n \"south\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"s_nom\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"x\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.01,\n \"max\": 0.01,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.01\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"r\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.001,\n \"max\": 0.001,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.001\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", - "type": "dataframe", - "variable_name": "lines_sim1" - }, - "text/html": [ - "\n", - "
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bus0bus1s_nomxr
name
Line_N1_Snorth_1south00.010.001
Line_N2_Snorth_2south00.010.001
Line_N1_N2north_1north_200.010.001
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zonetimeclearing_price
0DE_12019-01-01 00:00:0015.0
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2DE_12019-01-01 02:00:0017.0
3DE_12019-01-01 03:00:0018.0
4DE_12019-01-01 04:00:0018.0
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" ], - "source": [ - "print(\"### Simulation 1: Zero Transmission Capacity Between Zones\")\n", - "\n", - "lines_sim1 = lines.copy()\n", - "lines_sim1[\"s_nom\"] = 0 # Set transmission capacity to zero for all lines\n", - "\n", - "print(\"Transmission Lines for Simulation 1:\")\n", - "display(lines_sim1)\n", - "\n", - "# Run the simplified market clearing for Simulation 1\n", - "model_sim1, results_sim1 = simplified_market_clearing_opt(\n", - " orders=orders_mapped,\n", - " incidence_matrix=incidence_matrix,\n", - " lines=lines_sim1,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "aef7c083", - "metadata": { - "id": "aef7c083" - }, - "source": [ - "#### Simulation 2: Medium Transmission Capacity Between Zones" + "text/plain": [ + " zone time clearing_price\n", + "0 DE_1 2019-01-01 00:00:00 15.0\n", + "1 DE_1 2019-01-01 01:00:00 16.0\n", + "2 DE_1 2019-01-01 02:00:00 17.0\n", + "3 DE_1 2019-01-01 03:00:00 18.0\n", + "4 DE_1 2019-01-01 04:00:00 18.0" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": 15, - "id": "86304253", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 210 - }, - "id": "86304253", - "outputId": "3fa73e8b-d0e3-4fe8-d88c-1a896fb3e1ff" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### Simulation 2: Medium Transmission Capacity Between Zones\n", - "Transmission Lines for Simulation 2:\n" - ] - }, - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "summary": "{\n \"name\": \"lines_sim2\",\n \"rows\": 3,\n \"fields\": [\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"Line_N1_S\",\n \"Line_N2_S\",\n \"Line_N1_N2\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"bus0\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"north_2\",\n \"north_1\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"bus1\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"north_2\",\n \"south\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"s_nom\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 3000.0,\n \"max\": 3000.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 3000.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"x\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.01,\n \"max\": 0.01,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.01\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"r\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.001,\n \"max\": 0.001,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.001\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", - "type": "dataframe", - "variable_name": "lines_sim2" - }, - "text/html": [ - "\n", - "
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bus0bus1s_nomxr
name
Line_N1_Snorth_1south3000.00.010.001
Line_N2_Snorth_2south3000.00.010.001
Line_N1_N2north_1north_23000.00.010.001
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zonetimeclearing_price
24DE_22019-01-01 00:00:0031.0
25DE_22019-01-01 01:00:0030.0
26DE_22019-01-01 02:00:0030.0
27DE_22019-01-01 03:00:0029.0
28DE_22019-01-01 04:00:0028.0
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" ], - "source": [ - "print(\"### Simulation 2: Medium Transmission Capacity Between Zones\")\n", - "\n", - "# Define the lines for Simulation 2 with medium transmission capacity\n", - "lines_sim2 = lines.copy()\n", - "lines_sim2[\"s_nom\"] = 3000.0 # Set transmission capacity to 3000 MW for all lines\n", - "\n", - "# Display the incidence matrix for Simulation 2\n", - "print(\"Transmission Lines for Simulation 2:\")\n", - "display(lines_sim2)\n", - "\n", - "# Run the simplified market clearing for Simulation 2\n", - "model_sim2, results_sim2 = simplified_market_clearing_opt(\n", - " orders=orders_mapped,\n", - " incidence_matrix=incidence_matrix,\n", - " lines=lines_sim2,\n", - ")" + "text/plain": [ + " zone time clearing_price\n", + "24 DE_2 2019-01-01 00:00:00 31.0\n", + "25 DE_2 2019-01-01 01:00:00 30.0\n", + "26 DE_2 2019-01-01 02:00:00 30.0\n", + "27 DE_2 2019-01-01 03:00:00 29.0\n", + "28 DE_2 2019-01-01 04:00:00 28.0" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Simulation 2: Clearing Prices per Zone and Time\")\n", + "display(clearing_prices_df_sim2.loc[clearing_prices_df_sim2[\"zone\"] == \"DE_1\"].head())\n", + "display(clearing_prices_df_sim2.loc[clearing_prices_df_sim2[\"zone\"] == \"DE_2\"].head())" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "7f850cf5", + "metadata": { + "id": "7f850cf5" + }, + "outputs": [], + "source": [ + "# Extract results for Simulation 3\n", + "accepted_bids_sim3, power_flows_df_sim3, clearing_prices_df_sim3 = extract_results(\n", + " model_sim3, incidence_matrix\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "3b2528a2", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 224 }, + "id": "3b2528a2", + "outputId": "f97d364c-890e-40b7-aeb9-691052170a64" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "5c721991", - "metadata": { - "id": "5c721991" - }, - "source": [ - "#### Simulation 3: High Transmission Capacity Between Zones" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Simulation 3: Power Flows Between Zones\n" + ] }, { - "cell_type": "code", - "execution_count": 16, - "id": "a1c7f344", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 210 - }, - "id": "a1c7f344", - "lines_to_end_of_cell_marker": 0, - "lines_to_next_cell": 1, - "outputId": "78e208e2-81f7-4678-9adc-bbdddd2802ea" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### Simulation 3: High Transmission Capacity Between Zones\n", - "Transmission Lines for Simulation 3:\n" - ] - }, - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "summary": "{\n \"name\": \"lines_sim3\",\n \"rows\": 3,\n \"fields\": [\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"Line_N1_S\",\n \"Line_N2_S\",\n \"Line_N1_N2\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"bus0\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"north_2\",\n \"north_1\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"bus1\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"north_2\",\n \"south\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"s_nom\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 5000.0,\n \"max\": 5000.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 5000.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"x\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.01,\n \"max\": 0.01,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.01\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"r\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.001,\n \"max\": 0.001,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.001\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", - "type": "dataframe", - "variable_name": "lines_sim3" - }, - "text/html": [ - "\n", - "
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bus0bus1s_nomxr
name
Line_N1_Snorth_1south5000.00.010.001
Line_N2_Snorth_2south5000.00.010.001
Line_N1_N2north_1north_25000.00.010.001
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" ], - "source": [ - "print(\"### Simulation 3: High Transmission Capacity Between Zones\")\n", - "\n", - "# Define the lines for Simulation 3 with high transmission capacity\n", - "lines_sim3 = lines.copy()\n", - "lines_sim3[\"s_nom\"] = 5000.0 # Set transmission capacity to 5000 MW for all lines\n", - "\n", - "# Display the line capacities for Simulation 3\n", - "print(\"Transmission Lines for Simulation 3:\")\n", - "display(lines_sim3)\n", - "\n", - "# Run the simplified market clearing for Simulation 3\n", - "model_sim3, results_sim3 = simplified_market_clearing_opt(\n", - " orders=orders_mapped,\n", - " incidence_matrix=incidence_matrix,\n", - " lines=lines_sim3,\n", - ")" + "text/plain": [ + " time line flow_MW\n", + "0 2019-01-01 00:00:00 Line_N1_S -5000.0\n", + "1 2019-01-01 00:00:00 Line_N2_S -5000.0\n", + "2 2019-01-01 00:00:00 Line_N1_N2 -5000.0\n", + "3 2019-01-01 01:00:00 Line_N1_S -5000.0\n", + "4 2019-01-01 01:00:00 Line_N2_S -4400.0" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Simulation 3: Power Flows Between Zones\")\n", + "display(power_flows_df_sim3.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "05961462", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 413 }, + "id": "05961462", + "outputId": "d6e9c38d-ab03-4828-e243-181791179ead" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "661e6c30", - "metadata": { - "id": "661e6c30" - }, - "source": [ - "### 5.4. Extracting and Interpreting the Results\n", - "\n", - "After running all three simulations, we can extract the results to understand how the presence or absence of transmission capacity affects bid acceptances and power flows between zones.\n", - "\n", - "#### Extracting Clearing Prices\n", - "\n", - "The **clearing prices** for each market zone and time period are extracted using the dual variables associated with the energy balance constraints in the optimization model. Specifically, the dual variable of the energy balance constraint for a given zone and time period represents the marginal price of electricity in that zone at that time.\n", - "\n", - "In the `extract_results` function, the following steps are performed to obtain the clearing prices:\n", - "\n", - "1. **Energy Balance Constraints:** For each zone and time period, the energy balance equation ensures that the total supply plus imports minus exports equals the demand.\n", - "2. **Dual Variables:** The dual variable (`model.dual[model.energy_balance[node, t]]`) associated with each energy balance constraint captures the sensitivity of the objective function (total cost) to a marginal increase in demand or supply.\n", - "3. **Clearing Price Interpretation:** The value of the dual variable corresponds to the clearing price in the respective zone and time period, reflecting the cost of supplying an additional unit of electricity.\n", - "\n", - "This method leverages the duality in optimization to efficiently extract market prices resulting from the optimal dispatch of bids under the given constraints." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Simulation 3: Clearing Prices per Zone and Time\n" + ] }, { - "cell_type": "code", - "execution_count": 17, - "id": "bdcc49e7", - "metadata": { - "cellView": "form", - "id": "bdcc49e7" - }, - "outputs": [], - "source": [ - "# @title Function to extract market clearing results from the optimization model\n", - "def extract_results(model, incidence_matrix):\n", - " nodes = list(incidence_matrix.index)\n", - " lines = list(incidence_matrix.columns)\n", - "\n", - " # Extract accepted bid ratios using a dictionary comprehension\n", - " accepted_bids = {\n", - " o: pyo.value(model.x[o]) for o in model.x if pyo.value(model.x[o]) > 0\n", - " }\n", - "\n", - " # Extract power flows on each line for each time period\n", - " power_flows = [\n", - " {\"time\": t, \"line\": line, \"flow_MW\": pyo.value(model.flows[t, line])}\n", - " for t in model.T\n", - " for line in lines\n", - " if pyo.value(model.flows[t, line]) != 0\n", - " ]\n", - " power_flows_df = pd.DataFrame(power_flows)\n", - "\n", - " # Extract market clearing prices from dual variables\n", - " clearing_prices = [\n", - " {\n", - " \"zone\": node,\n", - " \"time\": t,\n", - " \"clearing_price\": pyo.value(model.dual[model.energy_balance[node, t]]),\n", - " }\n", - " for node in nodes\n", - " for t in model.T\n", - " ]\n", - " clearing_prices_df = pd.DataFrame(clearing_prices)\n", - "\n", - " return accepted_bids, power_flows_df, clearing_prices_df" + "data": { + "text/html": [ + "
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zonetimeclearing_price
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" + ], + "text/plain": [ + " zone time clearing_price\n", + "0 DE_1 2019-01-01 00:00:00 19.0\n", + "1 DE_1 2019-01-01 01:00:00 27.0\n", + "2 DE_1 2019-01-01 02:00:00 27.0\n", + "3 DE_1 2019-01-01 03:00:00 27.0\n", + "4 DE_1 2019-01-01 04:00:00 27.0" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": 18, - "id": "512ed95f", - "metadata": { - "id": "512ed95f" - }, - "outputs": [], - "source": [ - "# Extract results for Simulation 1\n", - "accepted_bids_sim1, power_flows_df_sim1, clearing_prices_df_sim1 = extract_results(\n", - " model_sim1, incidence_matrix\n", - ")" + "data": { + "text/html": [ + "
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zonetimeclearing_price
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" + ], + "text/plain": [ + " zone time clearing_price\n", + "24 DE_2 2019-01-01 00:00:00 27.0\n", + "25 DE_2 2019-01-01 01:00:00 27.0\n", + "26 DE_2 2019-01-01 02:00:00 27.0\n", + "27 DE_2 2019-01-01 03:00:00 27.0\n", + "28 DE_2 2019-01-01 04:00:00 27.0" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Simulation 3: Clearing Prices per Zone and Time\")\n", + "display(clearing_prices_df_sim3.loc[clearing_prices_df_sim3[\"zone\"] == \"DE_1\"].head())\n", + "display(clearing_prices_df_sim3.loc[clearing_prices_df_sim3[\"zone\"] == \"DE_2\"].head())" + ] + }, + { + "cell_type": "markdown", + "id": "fb62e2fd", + "metadata": { + "id": "fb62e2fd" + }, + "source": [ + "**Explanation:**\n", + "\n", + "- **Accepted Bids:** Shows which bids were accepted in each simulation and the ratio at which they were accepted.\n", + "- **Power Flows:** Indicates the amount of energy transmitted between zones. In Simulation 1, with zero transmission capacity, there should be no power flows between `DE_1` and `DE_2`. In Simulation 2 and 3, with medium and high transmission capacities, power flows can occur between zones.\n", + "- **Clearing Prices:** Represents the average bid price in each zone at each time period. Comparing prices across simulations can reveal the impact of transmission capacity on market prices." + ] + }, + { + "cell_type": "markdown", + "id": "3dbd64e0", + "metadata": { + "id": "3dbd64e0" + }, + "source": [ + "### 5.5. Comparing Simulations\n", + "\n", + "To better understand the impact of transmission capacity, let's compare the key results from all three simulations." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "0ffe7033", + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 617 }, + "id": "0ffe7033", + "outputId": "b0b4295a-095b-4871-aeef-d5aa44f866f8" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 19, - "id": "7b32b7c3", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 70 + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "dash": "dash" + }, + "mode": "lines", + "name": "DE_1 - Sim1 (Zero Capacity)", + "type": "scatter", + "x": [ + "2019-01-01T00:00:00", + "2019-01-01T01:00:00", + "2019-01-01T02:00:00", + "2019-01-01T03:00:00", + "2019-01-01T04:00:00", + "2019-01-01T05:00:00", + "2019-01-01T06:00:00", + "2019-01-01T07:00:00", + "2019-01-01T08:00:00", + "2019-01-01T09:00:00", + "2019-01-01T10:00:00", + "2019-01-01T11:00:00", + "2019-01-01T12:00:00", + "2019-01-01T13:00:00", + "2019-01-01T14:00:00", + "2019-01-01T15:00:00", + "2019-01-01T16:00:00", + "2019-01-01T17:00:00", + "2019-01-01T18:00:00", + "2019-01-01T19:00:00", + "2019-01-01T20:00:00", + "2019-01-01T21:00:00", + "2019-01-01T22:00:00", + "2019-01-01T23:00:00" + ], + "y": [ + 9, + 10, + 11, + 12, + 12, + 13, + 14, + 15, + 16, + 16, + 17, + 18, + 19, + 100, + 100, + 100, + 100, + 100, + 100, + 100, + 100, + 100, + 100, + 100 + ] }, - 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zonetimeclearing_price
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zonetimeclearing_price
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The dashed lines represent Simulation 1 (no transmission capacity), dotted lines represent Simulation 2 (medium transmission capacity), and solid lines represent Simulation 3 (high transmission capacity). This visualization helps in observing how the presence of transmission capacity affects price convergence or divergence between zones." - ] - }, - { - "cell_type": "markdown", - "id": "fb8f157c", - "metadata": { - "id": "fb8f157c" - }, - "source": [ - "## 6. Execution with ASSUME\n", - "\n", - "In a real-world scenario, the ASSUME framework handles the reading of CSV files and the configuration of the simulation through configuration files. For the purpose of this tutorial, we'll integrate our prepared data and configuration into ASSUME to execute the simulation seamlessly.\n", - "\n", - "### Step 1: Saving Input Files\n", - "\n", - "We will save the generated input DataFrames to the `inputs/tutorial_08` folder. The required files are:\n", - "- `demand_units.csv`\n", - "- `demand_df.csv`\n", - "- `powerplant_units.csv`\n", - "- `buses.csv`\n", - "- `lines.csv`\n", - "\n", - "Additionally, we'll create a new file `fuel_prices.csv`.\n", - "\n", - "**Note:** The demand timeseries has been extended to cover 48 hours as ASSUME always requires an additional day of data for the market simulation.\n", - "\n", - "#### Create the Inputs Directory and Save CSV Files" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "531a7a24", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "white", + "showlakes": true, + "showland": true, + "subunitcolor": "#C8D4E3" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "white", + "polar": { + "angularaxis": { + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "" + }, + "bgcolor": "white", + "radialaxis": { + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", + "gridwidth": 2, + "linecolor": "#EBF0F8", + "showbackground": true, + "ticks": "", + "zerolinecolor": "#EBF0F8" + }, + "yaxis": { + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", + "gridwidth": 2, + "linecolor": "#EBF0F8", + "showbackground": true, + "ticks": "", + "zerolinecolor": "#EBF0F8" + }, + "zaxis": { + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", + "gridwidth": 2, + "linecolor": "#EBF0F8", + "showbackground": true, + "ticks": "", + "zerolinecolor": "#EBF0F8" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + }, + "baxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + }, + "bgcolor": "white", + "caxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + } + } }, - "id": "531a7a24", - "outputId": "abc151f4-2f50-4ebd-b405-49f0340cd96d" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input CSV files have been saved to 'inputs/tutorial_08'.\n" - ] - } - ], - "source": [ - "import os\n", - "\n", - "# Define the input directory\n", - "input_dir = \"inputs/tutorial_08\"\n", - "\n", - "# Create the directory if it doesn't exist\n", - "os.makedirs(input_dir, exist_ok=True)\n", - "\n", - "# extend demand_df for another day with the same demand profile\n", - "demand_df = pd.concat([demand_df, demand_df])\n", - "demand_df.index = pd.date_range(start=\"2019-01-01\", periods=48, freq=\"h\")\n", - "\n", - "# Save the DataFrames to CSV files\n", - "buses.to_csv(os.path.join(input_dir, \"buses.csv\"), index=True)\n", - "lines.to_csv(os.path.join(input_dir, \"lines.csv\"), index=True)\n", - "powerplant_units.to_csv(os.path.join(input_dir, \"powerplant_units.csv\"), index=False)\n", - "demand_units.to_csv(os.path.join(input_dir, \"demand_units.csv\"), index=False)\n", - "demand_df.to_csv(os.path.join(input_dir, \"demand_df.csv\"))\n", - "\n", - "print(\"Input CSV files have been saved to 'inputs/tutorial_08'.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "2d61a40b", - "metadata": { - "cellView": "form", - "colab": { - "base_uri": "https://localhost:8080/" + "title": { + "text": "Clearing Prices per Zone Over Time: Sim1, Sim2, & Sim3" }, - "id": "2d61a40b", - "outputId": "8ce46e76-c462-4c8e-db62-8f787b354403" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fuel Prices CSV file has been saved to 'inputs/tutorial_08/fuel_prices.csv'.\n" - ] - } - ], - "source": [ - "# @title Create fuel prices\n", - "fuel_prices = {\n", - " \"fuel\": [\"uranium\", \"co2\"],\n", - " \"price\": [5, 25],\n", - "}\n", - "\n", - "# Convert to DataFrame and save as CSV\n", - "fuel_prices_df = pd.DataFrame(fuel_prices).T\n", - "fuel_prices_df.to_csv(\n", - " os.path.join(input_dir, \"fuel_prices_df.csv\"), index=True, header=False\n", - ")\n", - "\n", - "print(\"Fuel Prices CSV file has been saved to 'inputs/tutorial_08/fuel_prices.csv'.\")" - ] - }, - { - "cell_type": "markdown", - "id": "e0e47625", - "metadata": { - "id": "e0e47625" - }, - "source": [ - "### Step 2: Creating the Configuration YAML File\n", - "\n", - "The configuration file defines the simulation parameters, including market settings and network configurations. Below is the YAML configuration tailored for our tutorial." - ] - }, - { - "cell_type": "markdown", - "id": "44e22a14", - "metadata": { - "id": "44e22a14" - }, - "source": [ - "#### Create `config.yaml`" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "821a4002", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "width": 1000, + "xaxis": { + "tickangle": 45, + "title": { + "text": "Time" + }, + "type": "date" }, - "id": "821a4002", - "outputId": "ac8bf62b-8e38-4199-a45a-5c5397342bef" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Configuration YAML file has been saved to 'inputs/tutorial_08/config.yaml'.\n" - ] + "yaxis": { + "title": { + "text": "Clearing Price" + } } - ], - "source": [ - "config = {\n", - " \"zonal_case\": {\n", - " \"start_date\": \"2019-01-01 00:00\",\n", - " \"end_date\": \"2019-01-01 23:00\",\n", - " \"time_step\": \"1h\",\n", - " \"save_frequency_hours\": 24,\n", - " \"markets_config\": {\n", - " \"zonal\": {\n", - " \"operator\": \"EOM_operator\",\n", - " \"product_type\": \"energy\",\n", - " \"products\": [{\"duration\": \"1h\", \"count\": 1, \"first_delivery\": \"1h\"}],\n", - " \"opening_frequency\": \"1h\",\n", - " \"opening_duration\": \"1h\",\n", - " \"volume_unit\": \"MWh\",\n", - " \"maximum_bid_volume\": 100000,\n", - " \"maximum_bid_price\": 3000,\n", - " \"minimum_bid_price\": -500,\n", - " \"price_unit\": \"EUR/MWh\",\n", - " \"market_mechanism\": \"pay_as_clear_complex\",\n", - " \"additional_fields\": [\"bid_type\", \"node\"],\n", - " \"param_dict\": {\"network_path\": \".\", \"zones_identifier\": \"zone_id\"},\n", - " }\n", - " },\n", - " }\n", - "}\n", - "\n", - "# Define the path for the config file\n", - "config_path = os.path.join(input_dir, \"config.yaml\")\n", - "\n", - "# Save the configuration to a YAML file\n", - "with open(config_path, \"w\") as file:\n", - " yaml.dump(config, file, sort_keys=False)\n", - "\n", - "print(f\"Configuration YAML file has been saved to '{config_path}'.\")" - ] + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# @title Plot the market clearing prices for each zone and simulation run\n", + "# Initialize the Plotly figure\n", + "fig = go.Figure()\n", + "\n", + "# Iterate over each zone to plot clearing prices for all three simulations\n", + "for zone in incidence_matrix.index:\n", + " # Filter data for the current zone and Simulation 1\n", + " zone_prices_sim1 = clearing_prices_df_sim1[clearing_prices_df_sim1[\"zone\"] == zone]\n", + " # Filter data for the current zone and Simulation 2\n", + " zone_prices_sim2 = clearing_prices_df_sim2[clearing_prices_df_sim2[\"zone\"] == zone]\n", + " # Filter data for the current zone and Simulation 3\n", + " zone_prices_sim3 = clearing_prices_df_sim3[clearing_prices_df_sim3[\"zone\"] == zone]\n", + "\n", + " # Add trace for Simulation 1\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=zone_prices_sim1[\"time\"],\n", + " y=zone_prices_sim1[\"clearing_price\"],\n", + " mode=\"lines\",\n", + " name=f\"{zone} - Sim1 (Zero Capacity)\",\n", + " line=dict(dash=\"dash\"), # Dashed line for Simulation 1\n", + " )\n", + " )\n", + "\n", + " # Add trace for Simulation 2\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=zone_prices_sim2[\"time\"],\n", + " y=zone_prices_sim2[\"clearing_price\"],\n", + " mode=\"lines\",\n", + " name=f\"{zone} - Sim2 (Medium Capacity)\",\n", + " line=dict(dash=\"dot\"), # Dotted line for Simulation 2\n", + " )\n", + " )\n", + "\n", + " # Add trace for Simulation 3\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=zone_prices_sim3[\"time\"],\n", + " y=zone_prices_sim3[\"clearing_price\"],\n", + " mode=\"lines\",\n", + " name=f\"{zone} - Sim3 (High Capacity)\",\n", + " line=dict(dash=\"solid\"), # Solid line for Simulation 3\n", + " )\n", + " )\n", + "\n", + "# Update layout for better aesthetics and interactivity\n", + "fig.update_layout(\n", + " title=\"Clearing Prices per Zone Over Time: Sim1, Sim2, & Sim3\",\n", + " xaxis_title=\"Time\",\n", + " yaxis_title=\"Clearing Price\",\n", + " legend_title=\"Simulations\",\n", + " xaxis=dict(\n", + " tickangle=45,\n", + " type=\"date\", # Ensure the x-axis is treated as dates\n", + " ),\n", + " hovermode=\"x unified\", # Unified hover for better comparison\n", + " template=\"plotly_white\", # Clean white background\n", + " width=1000,\n", + " height=600,\n", + ")\n", + "\n", + "# Display the interactive plot\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7ee17c77", + "metadata": { + "id": "7ee17c77" + }, + "source": [ + "**Explanation:**\n", + "\n", + "- **Clearing Prices Plot:** Shows how market prices vary over time for each zone across all three simulations. The dashed lines represent Simulation 1 (no transmission capacity), dotted lines represent Simulation 2 (medium transmission capacity), and solid lines represent Simulation 3 (high transmission capacity). This visualization helps in observing how the presence of transmission capacity affects price convergence or divergence between zones." + ] + }, + { + "cell_type": "markdown", + "id": "fb8f157c", + "metadata": { + "id": "fb8f157c" + }, + "source": [ + "## 6. Execution with ASSUME\n", + "\n", + "In a real-world scenario, the ASSUME framework handles the reading of CSV files and the configuration of the simulation through configuration files. For the purpose of this tutorial, we'll integrate our prepared data and configuration into ASSUME to execute the simulation seamlessly.\n", + "\n", + "### Step 1: Saving Input Files\n", + "\n", + "We will save the generated input DataFrames to the `inputs/tutorial_08` folder. The required files are:\n", + "- `demand_units.csv`\n", + "- `demand_df.csv`\n", + "- `powerplant_units.csv`\n", + "- `buses.csv`\n", + "- `lines.csv`\n", + "\n", + "Additionally, we'll create a new file `fuel_prices.csv`.\n", + "\n", + "**Note:** The demand timeseries has been extended to cover 48 hours as ASSUME always requires an additional day of data for the market simulation.\n", + "\n", + "#### Create the Inputs Directory and Save CSV Files" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "531a7a24", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "531a7a24", + "outputId": "abc151f4-2f50-4ebd-b405-49f0340cd96d" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "e2e9403a", - "metadata": { - "id": "e2e9403a" - }, - "source": [ - "### Detailed Configuration Explanation\n", - "\n", - "The `config.yaml` file plays a key role in defining the simulation parameters. Below is a detailed explanation of each configuration parameter:\n", - "\n", - "- **zonal_case:**\n", - " - **start_date:** The start date and time for the simulation (`2019-01-01 00:00`).\n", - " - **end_date:** The end date and time for the simulation (`2019-01-02 00:00`).\n", - " - **time_step:** The simulation time step (`1h`), indicating hourly intervals.\n", - " - **save_frequency_hours:** How frequently the simulation results are saved (`24` hours).\n", - "\n", - "- **markets_config:**\n", - " - **zonal:** The name of the market. Remember, that our power plant units had a column named bidding_zonal. This is how a particluar bidding strategy is assigned to a particluar market.\n", - " - **operator:** The market operator (`EOM_operator`).\n", - " - **product_type:** Type of market product (`energy`).\n", - " - **products:** List defining the market products:\n", - " - **duration:** Duration of the product (`1h`).\n", - " - **count:** Number of products (`1`).\n", - " - **first_delivery:** When the first delivery occurs (`1h`).\n", - " - **opening_frequency:** Frequency of market openings (`1h`).\n", - " - **opening_duration:** Duration of market openings (`1h`).\n", - " - **volume_unit:** Unit of volume measurement (`MWh`).\n", - " - **maximum_bid_volume:** Maximum volume allowed per bid (`100000` MWh).\n", - " - **maximum_bid_price:** Maximum price allowed per bid (`3000` EUR/MWh).\n", - " - **minimum_bid_price:** Minimum price allowed per bid (`-500` EUR/MWh).\n", - " - **price_unit:** Unit of price measurement (`EUR/MWh`).\n", - " - **market_mechanism:** The market clearing mechanism (`pay_as_clear_complex`).\n", - " - **additional_fields:** Additional fields required for bids:\n", - " - **bid_type:** Type of bid (e.g., supply or demand).\n", - " - **node:** The market zone associated with the bid.\n", - " - **param_dict:**\n", - " - **network_path:** Path to the network files (`.` indicates current directory).\n", - " - **zones_identifier:** Identifier used for market zones (`zone_id`).\n", - "\n", - "This configuration ensures that the simulation accurately represents the zonal market dynamics, including bid restrictions and market operations." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Input CSV files have been saved to 'inputs/tutorial_08'.\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "# Define the input directory\n", + "input_dir = \"inputs/tutorial_08\"\n", + "\n", + "# Create the directory if it doesn't exist\n", + "os.makedirs(input_dir, exist_ok=True)\n", + "\n", + "# extend demand_df for another day with the same demand profile\n", + "demand_df = pd.concat([demand_df, demand_df])\n", + "demand_df.index = pd.date_range(start=\"2019-01-01\", periods=48, freq=\"h\")\n", + "\n", + "# Save the DataFrames to CSV files\n", + "buses.to_csv(os.path.join(input_dir, \"buses.csv\"), index=True)\n", + "lines.to_csv(os.path.join(input_dir, \"lines.csv\"), index=True)\n", + "powerplant_units.to_csv(os.path.join(input_dir, \"powerplant_units.csv\"), index=False)\n", + "demand_units.to_csv(os.path.join(input_dir, \"demand_units.csv\"), index=False)\n", + "demand_df.to_csv(os.path.join(input_dir, \"demand_df.csv\"))\n", + "\n", + "print(\"Input CSV files have been saved to 'inputs/tutorial_08'.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "2d61a40b", + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "2d61a40b", + "outputId": "8ce46e76-c462-4c8e-db62-8f787b354403" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "6fd79730", - "metadata": { - "id": "6fd79730" - }, - "source": [ - "### Step 3: Running the Simulation\n", - "\n", - "With the input files and configuration in place, we can now run the simulation using ASSUME's built-in functions." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Fuel Prices CSV file has been saved to 'inputs/tutorial_08/fuel_prices.csv'.\n" + ] + } + ], + "source": [ + "# @title Create fuel prices\n", + "fuel_prices = {\n", + " \"fuel\": [\"uranium\", \"co2\"],\n", + " \"price\": [5, 25],\n", + "}\n", + "\n", + "# Convert to DataFrame and save as CSV\n", + "fuel_prices_df = pd.DataFrame(fuel_prices).T\n", + "fuel_prices_df.to_csv(\n", + " os.path.join(input_dir, \"fuel_prices_df.csv\"), index=True, header=False\n", + ")\n", + "\n", + "print(\"Fuel Prices CSV file has been saved to 'inputs/tutorial_08/fuel_prices.csv'.\")" + ] + }, + { + "cell_type": "markdown", + "id": "e0e47625", + "metadata": { + "id": "e0e47625" + }, + "source": [ + "### Step 2: Creating the Configuration YAML File\n", + "\n", + "The configuration file defines the simulation parameters, including market settings and network configurations. Below is the YAML configuration tailored for our tutorial." + ] + }, + { + "cell_type": "markdown", + "id": "44e22a14", + "metadata": { + "id": "44e22a14" + }, + "source": [ + "#### Create `config.yaml`" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "821a4002", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "821a4002", + "outputId": "ac8bf62b-8e38-4199-a45a-5c5397342bef" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "33ff62b1", - "metadata": { - "id": "33ff62b1" - }, - "source": [ - "#### Example Simulation Code" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Configuration YAML file has been saved to 'inputs/tutorial_08\\config.yaml'.\n" + ] + } + ], + "source": [ + "config = {\n", + " \"zonal_case\": {\n", + " \"start_date\": \"2019-01-01 00:00\",\n", + " \"end_date\": \"2019-01-01 23:00\",\n", + " \"time_step\": \"1h\",\n", + " \"save_frequency_hours\": 24,\n", + " \"markets_config\": {\n", + " \"zonal\": {\n", + " \"operator\": \"EOM_operator\",\n", + " \"product_type\": \"energy\",\n", + " \"products\": [{\"duration\": \"1h\", \"count\": 1, \"first_delivery\": \"1h\"}],\n", + " \"opening_frequency\": \"1h\",\n", + " \"opening_duration\": \"1h\",\n", + " \"volume_unit\": \"MWh\",\n", + " \"maximum_bid_volume\": 100000,\n", + " \"maximum_bid_price\": 3000,\n", + " \"minimum_bid_price\": -500,\n", + " \"price_unit\": \"EUR/MWh\",\n", + " \"market_mechanism\": \"pay_as_clear_complex\",\n", + " \"additional_fields\": [\"bid_type\", \"node\"],\n", + " \"param_dict\": {\"network_path\": \".\", \"zones_identifier\": \"zone_id\"},\n", + " }\n", + " },\n", + " }\n", + "}\n", + "\n", + "# Define the path for the config file\n", + "config_path = os.path.join(input_dir, \"config.yaml\")\n", + "\n", + "# Save the configuration to a YAML file\n", + "with open(config_path, \"w\") as file:\n", + " yaml.dump(config, file, sort_keys=False)\n", + "\n", + "print(f\"Configuration YAML file has been saved to '{config_path}'.\")" + ] + }, + { + "cell_type": "markdown", + "id": "e2e9403a", + "metadata": { + "id": "e2e9403a" + }, + "source": [ + "### Detailed Configuration Explanation\n", + "\n", + "The `config.yaml` file plays a key role in defining the simulation parameters. Below is a detailed explanation of each configuration parameter:\n", + "\n", + "- **zonal_case:**\n", + " - **start_date:** The start date and time for the simulation (`2019-01-01 00:00`).\n", + " - **end_date:** The end date and time for the simulation (`2019-01-02 00:00`).\n", + " - **time_step:** The simulation time step (`1h`), indicating hourly intervals.\n", + " - **save_frequency_hours:** How frequently the simulation results are saved (`24` hours).\n", + "\n", + "- **markets_config:**\n", + " - **zonal:** The name of the market. Remember, that our power plant units had a column named bidding_zonal. This is how a particluar bidding strategy is assigned to a particluar market.\n", + " - **operator:** The market operator (`EOM_operator`).\n", + " - **product_type:** Type of market product (`energy`).\n", + " - **products:** List defining the market products:\n", + " - **duration:** Duration of the product (`1h`).\n", + " - **count:** Number of products (`1`).\n", + " - **first_delivery:** When the first delivery occurs (`1h`).\n", + " - **opening_frequency:** Frequency of market openings (`1h`).\n", + " - **opening_duration:** Duration of market openings (`1h`).\n", + " - **volume_unit:** Unit of volume measurement (`MWh`).\n", + " - **maximum_bid_volume:** Maximum volume allowed per bid (`100000` MWh).\n", + " - **maximum_bid_price:** Maximum price allowed per bid (`3000` EUR/MWh).\n", + " - **minimum_bid_price:** Minimum price allowed per bid (`-500` EUR/MWh).\n", + " - **price_unit:** Unit of price measurement (`EUR/MWh`).\n", + " - **market_mechanism:** The market clearing mechanism (`pay_as_clear_complex`).\n", + " - **additional_fields:** Additional fields required for bids:\n", + " - **bid_type:** Type of bid (e.g., supply or demand).\n", + " - **node:** The market zone associated with the bid.\n", + " - **param_dict:**\n", + " - **network_path:** Path to the network files (`.` indicates current directory).\n", + " - **zones_identifier:** Identifier used for market zones (`zone_id`).\n", + "\n", + "This configuration ensures that the simulation accurately represents the zonal market dynamics, including bid restrictions and market operations." + ] + }, + { + "cell_type": "markdown", + "id": "6fd79730", + "metadata": { + "id": "6fd79730" + }, + "source": [ + "### Step 3: Running the Simulation\n", + "\n", + "With the input files and configuration in place, we can now run the simulation using ASSUME's built-in functions." + ] + }, + { + "cell_type": "markdown", + "id": "33ff62b1", + "metadata": { + "id": "33ff62b1" + }, + "source": [ + "#### Example Simulation Code" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "3a79848a", + "metadata": { + "id": "3a79848a" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "3a79848a", - "metadata": { - "id": "3a79848a" - }, - "outputs": [], - "source": [ - "# import the main World class and the load_scenario_folder functions from assume\n", - "from assume import World\n", - "from assume.scenario.loader_csv import load_scenario_folder\n", - "\n", - "# Define paths for input and output data\n", - "csv_path = \"outputs\"\n", - "\n", - "# Define the data format and database URI\n", - "# Use \"local_db\" for SQLite database or \"timescale\" for TimescaleDB in Docker\n", - "\n", - "# Create directories if they don't exist\n", - "os.makedirs(csv_path, exist_ok=True)\n", - "os.makedirs(\"local_db\", exist_ok=True)\n", - "\n", - "data_format = \"local_db\" # \"local_db\" or \"timescale\"\n", - "\n", - "if data_format == \"local_db\":\n", - " db_uri = \"sqlite:///local_db/assume_db.db\"\n", - "elif data_format == \"timescale\":\n", - " db_uri = \"postgresql://assume:assume@localhost:5432/assume\"\n", - "\n", - "# Create the World instance\n", - "world = World(database_uri=db_uri, export_csv_path=csv_path)\n", - "\n", - "# Load the scenario by providing the world instance\n", - "# The path to the inputs folder and the scenario name (subfolder in inputs)\n", - "# and the study case name (which config to use for the simulation)\n", - "load_scenario_folder(\n", - " world,\n", - " inputs_path=\"inputs\",\n", - " scenario=\"tutorial_08\",\n", - " study_case=\"zonal_case\",\n", - ")\n", - "\n", - "# Run the simulation\n", - "world.run()" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.world:connected to db\n", + "INFO:assume.scenario.loader_csv:Starting Scenario tutorial_08/zonal_case from inputs\n", + "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", + "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", + "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", + "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", + "INFO:assume.scenario.loader_csv:save_frequency_hours is disabled due to CSV export being enabled. Data will be stored in the CSV files at the end of the simulation.\n", + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", + "INFO:assume.world:activating container\n", + "INFO:assume.common.outputs:tried writing grid data to non postGIS database\n", + "INFO:assume.world:all agents up - starting simulation\n" + ] }, { - "cell_type": "markdown", - "id": "be819122", - "metadata": { - "id": "be819122" - }, - "source": [ - "## 7. Analyzing the Results\n", - "\n", - "After running the simulation, you can analyze the results using the methods demonstrated in section 5. This integration with ASSUME allows for more extensive and scalable simulations, leveraging the framework's capabilities for handling complex market scenarios.\n", - "\n", - "In this section, we will:\n", - "\n", - "1. **Locate the Simulation Output Files:** Understand where the simulation results are saved.\n", - "2. **Load and Inspect the Output Data:** Read the output CSV files and examine their structure.\n", - "3. **Plot Clearing Prices:** Visualize the market clearing prices to compare with our previous manual simulations." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 23:00:00: : 82801it [00:15, 5451.10it/s] \n" + ] + } + ], + "source": [ + "# import the main World class and the load_scenario_folder functions from assume\n", + "from assume import World\n", + "from assume.scenario.loader_csv import load_scenario_folder\n", + "\n", + "# Define paths for input and output data\n", + "csv_path = \"outputs\"\n", + "\n", + "# Define the data format and database URI\n", + "# Use \"local_db\" for SQLite database or \"timescale\" for TimescaleDB in Docker\n", + "\n", + "# Create directories if they don't exist\n", + "os.makedirs(csv_path, exist_ok=True)\n", + "os.makedirs(\"local_db\", exist_ok=True)\n", + "\n", + "data_format = \"local_db\" # \"local_db\" or \"timescale\"\n", + "\n", + "if data_format == \"local_db\":\n", + " db_uri = \"sqlite:///local_db/assume_db.db\"\n", + "elif data_format == \"timescale\":\n", + " db_uri = \"postgresql://assume:assume@localhost:5432/assume\"\n", + "\n", + "# Create the World instance\n", + "world = World(database_uri=db_uri, export_csv_path=csv_path)\n", + "\n", + "# Load the scenario by providing the world instance\n", + "# The path to the inputs folder and the scenario name (subfolder in inputs)\n", + "# and the study case name (which config to use for the simulation)\n", + "load_scenario_folder(\n", + " world,\n", + " inputs_path=\"inputs\",\n", + " scenario=\"tutorial_08\",\n", + " study_case=\"zonal_case\",\n", + ")\n", + "\n", + "# Run the simulation\n", + "world.run()" + ] + }, + { + "cell_type": "markdown", + "id": "be819122", + "metadata": { + "id": "be819122" + }, + "source": [ + "## 7. Analyzing the Results\n", + "\n", + "After running the simulation, you can analyze the results using the methods demonstrated in section 5. This integration with ASSUME allows for more extensive and scalable simulations, leveraging the framework's capabilities for handling complex market scenarios.\n", + "\n", + "In this section, we will:\n", + "\n", + "1. **Locate the Simulation Output Files:** Understand where the simulation results are saved.\n", + "2. **Load and Inspect the Output Data:** Read the output CSV files and examine their structure.\n", + "3. **Plot Clearing Prices:** Visualize the market clearing prices to compare with our previous manual simulations." + ] + }, + { + "cell_type": "markdown", + "id": "5ca43ca3", + "metadata": { + "id": "5ca43ca3" + }, + "source": [ + "### 7.1. Locating the Simulation Output Files\n", + "\n", + "The simulation outputs are saved in the `outputs/tutorial_08_zonal_case` directory. Specifically, the key output file we'll work with is `market_meta.csv`, which contains detailed information about the market outcomes for each zone and time period." + ] + }, + { + "cell_type": "markdown", + "id": "78707ac9", + "metadata": { + "id": "78707ac9" + }, + "source": [ + "### 7.2. Loading and Inspecting the Output Data" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "6e71a328", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 255 }, + "id": "6e71a328", + "outputId": "738e1589-5d53-4831-cbcf-4fefca4f7860" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "5ca43ca3", - "metadata": { - "id": "5ca43ca3" - }, - "source": [ - "### 7.1. Locating the Simulation Output Files\n", - "\n", - "The simulation outputs are saved in the `outputs/tutorial_08_zonal_case` directory. Specifically, the key output file we'll work with is `market_meta.csv`, which contains detailed information about the market outcomes for each zone and time period." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Sample of market_meta.csv:\n" + ] }, { - "cell_type": "markdown", - "id": "78707ac9", - "metadata": { - "id": "78707ac9" - }, - "source": [ - "### 7.2. Loading and Inspecting the Output Data" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "6e71a328", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 255 - }, - "id": "6e71a328", - "outputId": "738e1589-5d53-4831-cbcf-4fefca4f7860" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sample of market_meta.csv:\n" - ] - }, - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "summary": "{\n \"name\": \"display(market_meta\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"time\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2019-01-01 01:00:00\",\n \"max\": \"2019-01-01 03:00:00\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"2019-01-01 01:00:00\",\n \"2019-01-01 02:00:00\",\n \"2019-01-01 03:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"supply_volume\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4108,\n \"min\": 7400,\n \"max\": 15000,\n \"num_unique_values\": 3,\n \"samples\": [\n 15000,\n 7400,\n 7600\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"demand_volume\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5564,\n \"min\": 5600,\n \"max\": 16800,\n \"num_unique_values\": 5,\n \"samples\": [\n 16800,\n 7200,\n 6400\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"demand_volume_energy\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5564,\n \"min\": 5600,\n \"max\": 16800,\n \"num_unique_values\": 5,\n \"samples\": [\n 16800,\n 7200,\n 6400\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"supply_volume_energy\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4108,\n \"min\": 7400,\n \"max\": 15000,\n \"num_unique_values\": 3,\n \"samples\": [\n 15000,\n 7400,\n 7600\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 43.667,\n \"max\": 43.667,\n \"num_unique_values\": 1,\n \"samples\": [\n 43.667\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"max_price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 43.667,\n \"max\": 43.667,\n \"num_unique_values\": 1,\n \"samples\": [\n 43.667\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"min_price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 43.667,\n \"max\": 43.667,\n \"num_unique_values\": 1,\n \"samples\": [\n 43.667\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"node\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"DE_2\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"product_start\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"2019-01-01 01:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"product_end\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"2019-01-01 02:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"only_hours\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": null,\n \"max\": null,\n \"num_unique_values\": 0,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"market_id\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"simulation\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", - "type": "dataframe" - }, - "text/html": [ - "\n", - "
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supply_volumedemand_volumedemand_volume_energysupply_volume_energypricemax_pricemin_pricenodeproduct_startproduct_endonly_hoursmarket_idsimulation
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" ], - "source": [ - "# Define the path to the simulation output\n", - "output_dir = \"outputs/tutorial_08_zonal_case\"\n", - "market_meta_path = os.path.join(output_dir, \"market_meta.csv\")\n", - "\n", - "# Load the market_meta.csv file\n", - "market_meta = pd.read_csv(market_meta_path, index_col=\"time\", parse_dates=True)\n", - "# drop the first column\n", - "market_meta = market_meta.drop(columns=market_meta.columns[0])\n", - "\n", - "# Display a sample of the data\n", - "print(\"Sample of market_meta.csv:\")\n", - "display(market_meta.head())" - ] - }, - { - "cell_type": "markdown", - "id": "870b1c74", - "metadata": { - "id": "870b1c74" - }, - "source": [ - "**Explanation:**\n", - "\n", - "- **market_meta.csv:** This file contains the market outcomes for each zone and time period, including supply and demand volumes, clearing prices, and other relevant metrics.\n", - "- **Columns:**\n", - " - `supply_volume`: Total volume supplied in the zone.\n", - " - `demand_volume`: Total volume demanded in the zone.\n", - " - `demand_volume_energy`: Energy demand volume (same as `demand_volume` for energy markets).\n", - " - `supply_volume_energy`: Energy supply volume (same as `supply_volume` for energy markets).\n", - " - `price`: Clearing price in the zone for the time period.\n", - " - `max_price`: Maximum bid price accepted.\n", - " - `min_price`: Minimum bid price accepted.\n", - " - `node`: Identifier for the market zone (`DE_1` or `DE_2`).\n", - " - `product_start`: Start time of the market product.\n", - " - `product_end`: End time of the market product.\n", - " - `only_hours`: Indicator flag (not used in this context).\n", - " - `market_id`: Identifier for the market (`zonal`).\n", - " - `time`: Timestamp for the market product.\n", - " - `simulation`: Identifier for the simulation case (`tutorial_08_zonal_case`)." + "text/plain": [ + " supply_volume demand_volume demand_volume_energy \\\n", + "time \n", + "2019-01-01 01:00:00 15000 5600 5600 \n", + "2019-01-01 01:00:00 7400 16800 16800 \n", + "2019-01-01 02:00:00 15000 6400 6400 \n", + "2019-01-01 02:00:00 7600 16200 16200 \n", + "2019-01-01 03:00:00 15000 7200 7200 \n", + "\n", + " supply_volume_energy price max_price min_price node \\\n", + "time \n", + "2019-01-01 01:00:00 15000 43.667 43.667 43.667 DE_1 \n", + "2019-01-01 01:00:00 7400 43.667 43.667 43.667 DE_2 \n", + "2019-01-01 02:00:00 15000 43.667 43.667 43.667 DE_1 \n", + "2019-01-01 02:00:00 7600 43.667 43.667 43.667 DE_2 \n", + "2019-01-01 03:00:00 15000 43.667 43.667 43.667 DE_1 \n", + "\n", + " product_start product_end only_hours \\\n", + "time \n", + "2019-01-01 01:00:00 2019-01-01 01:00:00 2019-01-01 02:00:00 NaN \n", + "2019-01-01 01:00:00 2019-01-01 01:00:00 2019-01-01 02:00:00 NaN \n", + "2019-01-01 02:00:00 2019-01-01 02:00:00 2019-01-01 03:00:00 NaN \n", + "2019-01-01 02:00:00 2019-01-01 02:00:00 2019-01-01 03:00:00 NaN \n", + "2019-01-01 03:00:00 2019-01-01 03:00:00 2019-01-01 04:00:00 NaN \n", + "\n", + " market_id simulation \n", + "time \n", + "2019-01-01 01:00:00 zonal tutorial_08_zonal_case \n", + "2019-01-01 01:00:00 zonal tutorial_08_zonal_case \n", + "2019-01-01 02:00:00 zonal tutorial_08_zonal_case \n", + "2019-01-01 02:00:00 zonal tutorial_08_zonal_case \n", + "2019-01-01 03:00:00 zonal tutorial_08_zonal_case " ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define the path to the simulation output\n", + "output_dir = \"outputs/tutorial_08_zonal_case\"\n", + "market_meta_path = os.path.join(output_dir, \"market_meta.csv\")\n", + "\n", + "# Load the market_meta.csv file\n", + "market_meta = pd.read_csv(market_meta_path, index_col=\"time\", parse_dates=True)\n", + "# drop the first column\n", + "market_meta = market_meta.drop(columns=market_meta.columns[0])\n", + "\n", + "# Display a sample of the data\n", + "print(\"Sample of market_meta.csv:\")\n", + "display(market_meta.head())" + ] + }, + { + "cell_type": "markdown", + "id": "870b1c74", + "metadata": { + "id": "870b1c74" + }, + "source": [ + "**Explanation:**\n", + "\n", + "- **market_meta.csv:** This file contains the market outcomes for each zone and time period, including supply and demand volumes, clearing prices, and other relevant metrics.\n", + "- **Columns:**\n", + " - `supply_volume`: Total volume supplied in the zone.\n", + " - `demand_volume`: Total volume demanded in the zone.\n", + " - `demand_volume_energy`: Energy demand volume (same as `demand_volume` for energy markets).\n", + " - `supply_volume_energy`: Energy supply volume (same as `supply_volume` for energy markets).\n", + " - `price`: Clearing price in the zone for the time period.\n", + " - `max_price`: Maximum bid price accepted.\n", + " - `min_price`: Minimum bid price accepted.\n", + " - `node`: Identifier for the market zone (`DE_1` or `DE_2`).\n", + " - `product_start`: Start time of the market product.\n", + " - `product_end`: End time of the market product.\n", + " - `only_hours`: Indicator flag (not used in this context).\n", + " - `market_id`: Identifier for the market (`zonal`).\n", + " - `time`: Timestamp for the market product.\n", + " - `simulation`: Identifier for the simulation case (`tutorial_08_zonal_case`)." + ] + }, + { + "cell_type": "markdown", + "id": "d0fd6e1b", + "metadata": { + "id": "d0fd6e1b" + }, + "source": [ + "### 7.3. Plotting Clearing Prices\n", + "\n", + "To verify that the simulation results align with our previous manual demonstrations, we'll plot the clearing prices for each zone over time. This will help us observe how transmission capacities influence price convergence or divergence between zones." + ] + }, + { + "cell_type": "markdown", + "id": "934872ad", + "metadata": { + "id": "934872ad" + }, + "source": [ + "#### Processing the Market Meta Data" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "fd2e3048", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 255 }, + "id": "fd2e3048", + "outputId": "7d9d0dc5-7042-488f-93d9-655bf4139807" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "d0fd6e1b", - "metadata": { - "id": "d0fd6e1b" - }, - "source": [ - "### 7.3. Plotting Clearing Prices\n", - "\n", - "To verify that the simulation results align with our previous manual demonstrations, we'll plot the clearing prices for each zone over time. This will help us observe how transmission capacities influence price convergence or divergence between zones." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Sample of Processed Clearing Prices:\n" + ] }, { - "cell_type": "markdown", - "id": "934872ad", - "metadata": { - "id": "934872ad" - }, - "source": [ - "#### Processing the Market Meta Data" + "data": { + "text/html": [ + "
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"metadata": {}, - "output_type": "display_data" + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Clearing Prices per Zone Over Time: Simulation Results" + }, + "width": 1000, + "xaxis": { + "tickangle": 45, + "title": { + "text": "Time" + }, + "type": "date" + }, + "yaxis": { + "title": { + "text": "Clearing Price (EUR/MWh)" + } } - ], - "source": [ - "# @title Plot market clearing prices\n", - "# Initialize the Plotly figure\n", - "fig = go.Figure()\n", - "\n", - "# Iterate over each zone to plot clearing prices\n", - "for zone in zones:\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=clearing_prices_df.index,\n", - " y=clearing_prices_df[f\"{zone}_price\"],\n", - " mode=\"lines\",\n", - " name=f\"{zone} - Simulation\",\n", - " line=dict(width=2),\n", - " )\n", - " )\n", - "\n", - "# Update layout for better aesthetics and interactivity\n", - "fig.update_layout(\n", - " title=\"Clearing Prices per Zone Over Time: Simulation Results\",\n", - " xaxis_title=\"Time\",\n", - " yaxis_title=\"Clearing Price (EUR/MWh)\",\n", - " legend_title=\"Market Zones\",\n", - " xaxis=dict(\n", - " tickangle=45,\n", - " type=\"date\", # Ensure the x-axis is treated as dates\n", - " ),\n", - " hovermode=\"x unified\", # Unified hover for better comparison\n", - " template=\"plotly_white\", # Clean white background\n", - " width=1000,\n", - " height=600,\n", - ")\n", - "\n", - "# Display the interactive plot\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "id": "b34407b1", - "metadata": { - "id": "b34407b1" - }, - "source": [ - "**Explanation:**\n", - "\n", - "- **Plot Details:**\n", - " - **Lines:** Each zone's clearing price over time is represented by a distinct line.\n", - " - **Interactivity:** The Plotly plot allows for interactive exploration of the data, such as zooming and hovering for specific values.\n", - " - **Aesthetics:** The clean white template and clear labels enhance readability.\n", - "\n", - "- **Interpretation:**\n", - " - **Price Trends:** Observing how clearing prices fluctuate over time within each zone.\n", - " - **Impact of Transmission Capacity:** Comparing price levels between zones can reveal the effects of transmission capacities on market equilibrium. For instance, higher transmission capacity might lead to more price convergence between zones, while zero capacity could result in divergent price levels due to isolated supply and demand dynamics." - ] - }, - { - "cell_type": "markdown", - "id": "3f448fb4", - "metadata": { - "id": "3f448fb4" - }, - "source": [ - "## **Conclusion**\n", - "\n", - "Congratulations! You've successfully navigated through the **Market Zone Coupling** process using the **ASSUME Framework**. Here's a quick recap of what you've accomplished:\n", - "\n", - "### **Key Achievements:**\n", - "\n", - "1. **Market Setup:**\n", - " - **Defined Zones and Buses:** Established distinct market zones and configured their connections through transmission lines.\n", - " - **Configured Units:** Set up power plant and demand units within each zone, detailing their operational parameters.\n", - "\n", - "2. **Market Clearing Optimization:**\n", - " - **Implemented Optimization Model:** Utilized a simplified Pyomo-based model to perform market clearing, accounting for bid acceptances and power flows.\n", - " - **Simulated Transmission Scenarios:** Ran simulations with varying transmission capacities to observe their impact on energy distribution and pricing.\n", - "\n", - "3. **Result Analysis:**\n", - " - **Extracted Clearing Prices:** Retrieved and interpreted market prices from the optimization results.\n", - " - **Visualized Outcomes:** Created interactive plots to compare how different transmission capacities influence market dynamics across zones.\n", - "\n", - "### **Key Takeaways:**\n", - "\n", - "- **Impact of Transmission Capacity:** Transmission limits play a crucial role in determining energy flows and price convergence between market zones.\n", - "- **ASSUME Framework Efficiency:** ASSUME streamlines complex market simulations, making it easier to model and analyze multi-zone interactions.\n", - "\n", - "### **Next Steps:**\n", - "\n", - "- **Integrate Renewable Sources:** Expand the model to include renewable energy units and assess their impact on market dynamics.\n", - "- **Scale Up Simulations:** Apply the framework to larger, more complex market scenarios to further test its capabilities.\n", - "\n", - "Thank you for participating in this tutorial! With the foundational knowledge gained, you're now equipped to delve deeper into energy market simulations and leverage the ASSUME framework for more advanced analyses." - ] - } - ], - "metadata": { - "colab": { - "provenance": [], - "toc_visible": true - }, - "jupytext": { - "cell_metadata_filter": "-all", - "main_language": "python", - "notebook_metadata_filter": "-all" - }, - "kernelspec": { - "display_name": "assume-framework", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.11.7" + } + } + }, + "metadata": {}, + "output_type": "display_data" } + ], + "source": [ + "# @title Plot market clearing prices\n", + "# Initialize the Plotly figure\n", + "fig = go.Figure()\n", + "\n", + "# Iterate over each zone to plot clearing prices\n", + "for zone in zones:\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=clearing_prices_df.index,\n", + " y=clearing_prices_df[f\"{zone}_price\"],\n", + " mode=\"lines\",\n", + " name=f\"{zone} - Simulation\",\n", + " line=dict(width=2),\n", + " )\n", + " )\n", + "\n", + "# Update layout for better aesthetics and interactivity\n", + "fig.update_layout(\n", + " title=\"Clearing Prices per Zone Over Time: Simulation Results\",\n", + " xaxis_title=\"Time\",\n", + " yaxis_title=\"Clearing Price (EUR/MWh)\",\n", + " legend_title=\"Market Zones\",\n", + " xaxis=dict(\n", + " tickangle=45,\n", + " type=\"date\", # Ensure the x-axis is treated as dates\n", + " ),\n", + " hovermode=\"x unified\", # Unified hover for better comparison\n", + " template=\"plotly_white\", # Clean white background\n", + " width=1000,\n", + " height=600,\n", + ")\n", + "\n", + "# Display the interactive plot\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b34407b1", + "metadata": { + "id": "b34407b1" + }, + "source": [ + "**Explanation:**\n", + "\n", + "- **Plot Details:**\n", + " - **Lines:** Each zone's clearing price over time is represented by a distinct line.\n", + " - **Interactivity:** The Plotly plot allows for interactive exploration of the data, such as zooming and hovering for specific values.\n", + " - **Aesthetics:** The clean white template and clear labels enhance readability.\n", + "\n", + "- **Interpretation:**\n", + " - **Price Trends:** Observing how clearing prices fluctuate over time within each zone.\n", + " - **Impact of Transmission Capacity:** Comparing price levels between zones can reveal the effects of transmission capacities on market equilibrium. For instance, higher transmission capacity might lead to more price convergence between zones, while zero capacity could result in divergent price levels due to isolated supply and demand dynamics." + ] + }, + { + "cell_type": "markdown", + "id": "3f448fb4", + "metadata": { + "id": "3f448fb4" + }, + "source": [ + "## **Conclusion**\n", + "\n", + "Congratulations! You've successfully navigated through the **Market Zone Coupling** process using the **ASSUME Framework**. Here's a quick recap of what you've accomplished:\n", + "\n", + "### **Key Achievements:**\n", + "\n", + "1. **Market Setup:**\n", + " - **Defined Zones and Buses:** Established distinct market zones and configured their connections through transmission lines.\n", + " - **Configured Units:** Set up power plant and demand units within each zone, detailing their operational parameters.\n", + "\n", + "2. **Market Clearing Optimization:**\n", + " - **Implemented Optimization Model:** Utilized a simplified Pyomo-based model to perform market clearing, accounting for bid acceptances and power flows.\n", + " - **Simulated Transmission Scenarios:** Ran simulations with varying transmission capacities to observe their impact on energy distribution and pricing.\n", + "\n", + "3. **Result Analysis:**\n", + " - **Extracted Clearing Prices:** Retrieved and interpreted market prices from the optimization results.\n", + " - **Visualized Outcomes:** Created interactive plots to compare how different transmission capacities influence market dynamics across zones.\n", + "\n", + "### **Key Takeaways:**\n", + "\n", + "- **Impact of Transmission Capacity:** Transmission limits play a crucial role in determining energy flows and price convergence between market zones.\n", + "- **ASSUME Framework Efficiency:** ASSUME streamlines complex market simulations, making it easier to model and analyze multi-zone interactions.\n", + "\n", + "### **Next Steps:**\n", + "\n", + "- **Integrate Renewable Sources:** Expand the model to include renewable energy units and assess their impact on market dynamics.\n", + "- **Scale Up Simulations:** Apply the framework to larger, more complex market scenarios to further test its capabilities.\n", + "\n", + "Thank you for participating in this tutorial! With the foundational knowledge gained, you're now equipped to delve deeper into energy market simulations and leverage the ASSUME framework for more advanced analyses." + ] + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": "assume", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 5 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/examples/notebooks/09_example_Sim_and_xRL.ipynb b/examples/notebooks/09_example_Sim_and_xRL.ipynb index 79d468970..9257a9ea4 100644 --- a/examples/notebooks/09_example_Sim_and_xRL.ipynb +++ b/examples/notebooks/09_example_Sim_and_xRL.ipynb @@ -43,9 +43,9 @@ "\n", "2. [Explainable AI and SHAP Values](#2-explainable-ai-and-shap-values)\n", "\n", - " 2.1 Understanding Explainable AI\n", + " 2.1 [Understanding Explainable AI](#21-understanding-explainable-ai)\n", "\n", - " 2.2 Introduction to SHAP Values\n", + " 2.2 [Introduction to SHAP Values](#22-introduction-to-shap-values)\n", "\n", "3. [Calculating SHAP Values](#3-calculating-shap-values)\n", "\n", @@ -118,7 +118,7 @@ "- **Rewards**: The agents are rewarded based on profits and opportunity costs, helping them learn optimal bidding strategies.\n", "- **Algorithm**: We utilize a multi-agent version of the TD3 (Twin Delayed Deep Deterministic Policy Gradient) algorithm, which ensures stable learning even in non-stationary environments.\n", "\n", - "For a more detailed explanation of the RL configurations, refer to the [Deep Reinforcement Learning Tutorial](https://example.com/deep-rl-tutorial).\n", + "For a more detailed explanation of the RL configurations, refer to the [Deep Reinforcement Learning Tutorial](04_reinforcement_learning_example.ipynb).\n", "\n", "### Key Aspects of the Simulation\n", "\n", @@ -163,17 +163,19 @@ "height": 1000 }, "id": "ee220130", - "outputId": "ffd98b47-2b07-41cd-dfe4-ff0381571825", - "vscode": { - "languageId": "shellscript" - } + "outputId": "ffd98b47-2b07-41cd-dfe4-ff0381571825" }, "outputs": [], "source": [ - "!pip install 'assume-framework[learning]'\n", + "import importlib.util\n", + "# Check if 'google.colab' is available\n", + "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", + "\n", + "if IN_COLAB:\n", + " !pip install 'assume-framework[learning]'\n", + " !git clone --depth=1 https://github.com/assume-framework/assume.git assume-repo\n", "!pip install plotly\n", - "!pip install nbconvert\n", - "!git clone --depth=1 https://github.com/assume-framework/assume.git assume-repo" + "!pip install nbconvert" ] }, { @@ -195,8 +197,6 @@ }, "outputs": [], "source": [ - "import importlib.util\n", - "\n", "import pandas as pd\n", "\n", "# import plotly for visualization\n", @@ -205,9 +205,6 @@ "# import yaml for reading and writing YAML files\n", "import yaml\n", "\n", - "# Check if 'google.colab' is available\n", - "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", - "\n", "colab_inputs_path = \"inputs\"\n", "local_inputs_path = \"../inputs\"\n", "\n", @@ -237,27 +234,25 @@ "execution_count": null, "id": "85fdfe19", "metadata": { - "lines_to_next_cell": 2, - "vscode": { - "languageId": "shellscript" - } + "lines_to_next_cell": 2 }, "outputs": [], "source": [ "# For local execution:\n", "%cd assume/examples/notebooks/\n", "\n", - "# For execution in Google Colab:\n", - "%cd assume-repo/examples/notebooks/\n", + "if IN_COLAB:\n", + " # For execution in Google Colab:\n", + " %cd assume-repo/examples/notebooks/\n", "\n", - "# Execute the Market Zone Splitting tutorial:\n", - "!jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=60 --output output.ipynb 08_market_zone_coupling.ipynb\n", + " # Execute the Market Zone Splitting tutorial:\n", + " !jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=60 --output output.ipynb 08_market_zone_coupling.ipynb\n", "\n", - "# Return to content folder (for Colab):\n", - "%cd /content\n", + " # Return to content folder (for Colab):\n", + " %cd /content\n", "\n", - "# Copy inputs directory to the working folder (for Colab):\n", - "!cp -r assume-repo/examples/notebooks/inputs ." + " # Copy inputs directory to the working folder (for Colab):\n", + " !cp -r assume-repo/examples/notebooks/inputs ." ] }, { @@ -278,7 +273,6 @@ "# Define the input directory\n", "input_dir = os.path.join(inputs_path, \"tutorial_08\")\n", "\n", - "\n", "# Read the DataFrames from CSV files\n", "powerplant_units = pd.read_csv(os.path.join(input_dir, \"powerplant_units.csv\"))\n", "demand_df = pd.read_csv(os.path.join(input_dir, \"demand_df.csv\"))\n", @@ -1467,13 +1461,21 @@ "notebook_metadata_filter": "-all" }, "kernelspec": { - "display_name": "assume-framework", + "display_name": "assume", "language": "python", "name": "python3" }, "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", "name": "python", - "version": "3.11.9" + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" } }, "nbformat": 4, diff --git a/examples/notebooks/11_redispatch.ipynb b/examples/notebooks/11_redispatch.ipynb index 212dc51b1..e387c900c 100644 --- a/examples/notebooks/11_redispatch.ipynb +++ b/examples/notebooks/11_redispatch.ipynb @@ -62,11 +62,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "vscode": { - "languageId": "shellscript" - } - }, + "metadata": {}, "outputs": [], "source": [ "import os\n", @@ -107,11 +103,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "vscode": { - "languageId": "shellscript" - } - }, + "metadata": {}, "outputs": [], "source": [ "# Simplified function to add generators to the grid network\n", @@ -154,11 +146,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "vscode": { - "languageId": "shellscript" - } - }, + "metadata": {}, "outputs": [], "source": [ "# Simplified function to add loads to the grid network\n", @@ -200,11 +188,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "vscode": { - "languageId": "shellscript" - } - }, + "metadata": {}, "outputs": [], "source": [ "# Simplified function to add loads to the redispatch network\n", @@ -246,11 +230,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "vscode": { - "languageId": "shellscript" - } - }, + "metadata": {}, "outputs": [], "source": [ "# Simplified function to add grid buses and lines to the redispatch network\n", @@ -299,11 +279,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "vscode": { - "languageId": "shellscript" - } - }, + "metadata": {}, "outputs": [], "source": [ "from assume.common.market_objects import MarketConfig, Orderbook\n", From fe22e12b0e799171decea7868fc80d3ae2aa36ba Mon Sep 17 00:00:00 2001 From: AndreasEppler Date: Wed, 20 Nov 2024 08:57:30 +0100 Subject: [PATCH 04/22] Added if-query for colab specific shell commands, typo fixes. --- ...forcement_learning_algorithm_example.ipynb | 65 +- .../notebooks/09_example_Sim_and_xRL.ipynb | 4398 ++++++++++++++++- 2 files changed, 4407 insertions(+), 56 deletions(-) diff --git a/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb b/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb index bccbc6339..6955e07ca 100644 --- a/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb +++ b/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "d2e2b8fe", "metadata": { "colab": { @@ -39,7 +39,13 @@ }, "outputs": [], "source": [ - "!pip install assume-framework[learning]" + "import importlib.util\n", + "\n", + "# Check whether notebook is run in google colab\n", + "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", + "\n", + "if IN_COLAB:\n", + " !pip install assume-framework[learning]" ] }, { @@ -56,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "d5e77f71", "metadata": { "colab": { @@ -67,7 +73,8 @@ }, "outputs": [], "source": [ - "!git clone https://github.com/assume-framework/assume.git assume-repo" + "if IN_COLAB:\n", + " !git clone https://github.com/assume-framework/assume.git assume-repo" ] }, { @@ -84,7 +91,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "de097384", "metadata": { "colab": { @@ -95,7 +102,8 @@ }, "outputs": [], "source": [ - "!cd assume-repo && assume -s example_01b -db \"sqlite:///./examples/local_db/assume_db_example_01b.db\"" + "if IN_COLAB:\n", + " !cd assume-repo && assume -s example_01b -db \"sqlite:///./examples/local_db/assume_db_example_01b.db\"" ] }, { @@ -110,16 +118,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "7d9899ff", "metadata": {}, "outputs": [], "source": [ - "import importlib.util\n", - "\n", - "# Check if 'google.colab' is available\n", - "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", - "\n", "colab_inputs_path = \"assume-repo/examples/inputs\"\n", "local_inputs_path = \"../inputs\"\n", "\n", @@ -135,7 +138,7 @@ "source": [ "## 1. From one simulation year to learning episodes\n", "\n", - "In a normal simulation without reinforcement learning, we only run the time horizon of the simulation once. For RL the agents need to learn their strategy based on interactions. For that to work an RL agent has to see a situation, aka a simulation hour, multiple times, and hence we need to run the entire simulation horizon multiple times as well. \n", + "In a normal simulation without reinforcement learning, we only run the time horizon of the simulation once. For RL the agents need to learn their strategy based on interactions. For that to work, a RL agent has to see a situation, aka a simulation hour, multiple times, and hence we need to run the entire simulation horizon multiple times as well. \n", "\n", "To enable this we define a run learning function that will be called if the simulation is started and we defined in our config that we want to activate learning. " ] @@ -152,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "ade14744", "metadata": { "id": "xUsbeZdPJ_2Q" @@ -209,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "94517a3e", "metadata": { "id": "UXYSesx4Ifp5" @@ -403,7 +406,7 @@ "source": [ "## 2. What role has a learning role\n", "\n", - "The LearningRole class in learning_role.py is a central component of the reinforcement learning framework. It manages configurations, device settings, early stopping of the learning process, and initializes various RL strategies the algorithm and buffers. This class ensures that the RL agent can be trained or evaluated effectively, leveraging the available hardware and adhering to the specified configurations. The parameters of the learning process are also described in the read-the-docs under learning_algorithms.\n", + "The LearningRole class in learning_role.py is a central component of the reinforcement learning framework. It manages configurations, device settings, early stopping of the learning process, and initializes various RL strategies, the algorithm and buffers. This class ensures that the RL agent can be trained or evaluated effectively, leveraging the available hardware and adhering to the specified configurations. The parameters of the learning process are also described in the read-the-docs under learning_algorithms.\n", "\n", "### 2.1 Learning Data Management\n", "\n", @@ -412,7 +415,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "daed035c", "metadata": {}, "outputs": [], @@ -499,12 +502,12 @@ "\n", "### 2.2 Learning Algorithm\n", "\n", - "If learning is used, then the learning role initializes a learning algorithm which is the heart of the learning progress. Currently, only the MATD3 is implemented, but we are working on different PPO implementations as well. If you would like to add an algorithm it woulb be integrated here." + "If learning is used, then the learning role initializes a learning algorithm which is the heart of the learning progress. Currently, only the MATD3 is implemented, but we are working on different PPO implementations as well. If you would like to add an algorithm it would be integrated here." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "632844c2", "metadata": { "id": "0ww-L9fABnw3" @@ -563,7 +566,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "c715f90e", "metadata": {}, "outputs": [], @@ -631,7 +634,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "753dbab1", "metadata": {}, "outputs": [], @@ -797,7 +800,7 @@ "source": [ "The other functions within the reinforcement learning algorithm are primarily there to store, update, and save the new policies. These functions either write the updated policies to a designated location or save them into the `inter_episodic_data`.\n", "\n", - "If you would like to make a change to this algorithm, the most likely modification would be to the `update_policy` function, as it plays a central role in the learning process. The other functions would only need adjustments if the different algorithm features vary likethe target critics or critic architectures.\n" + "If you would like to make a change to this algorithm, the most likely modification would be to the `update_policy` function, as it plays a central role in the learning process. The other functions would only need adjustments if the different algorithm features vary like the target critics or critic architectures.\n" ] }, { @@ -825,7 +828,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "6bb09b5b", "metadata": { "id": "moZ_UD7FfkOh" @@ -854,7 +857,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "5cff2f6a", "metadata": { "id": "iPz8v4N5hpfr" @@ -969,9 +972,21 @@ ], "metadata": { "kernelspec": { - "display_name": "assume-framework", + "display_name": "assume", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" } }, "nbformat": 4, diff --git a/examples/notebooks/09_example_Sim_and_xRL.ipynb b/examples/notebooks/09_example_Sim_and_xRL.ipynb index 9257a9ea4..a58be6358 100644 --- a/examples/notebooks/09_example_Sim_and_xRL.ipynb +++ b/examples/notebooks/09_example_Sim_and_xRL.ipynb @@ -155,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "647079b9", "metadata": { "colab": { @@ -165,7 +165,48 @@ "id": "ee220130", "outputId": "ffd98b47-2b07-41cd-dfe4-ff0381571825" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: plotly in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (5.24.1)\n", + "Requirement already satisfied: tenacity>=6.2.0 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from plotly) (9.0.0)\n", + "Requirement already satisfied: packaging in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from plotly) (24.1)\n", + "Requirement already satisfied: nbconvert in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (7.16.4)\n", + "Requirement already 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c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert) (2.9.0)\n", + "Requirement already satisfied: pyzmq>=23.0 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert) (25.1.2)\n", + "Requirement already satisfied: tornado>=6.2 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert) (6.4.1)\n", + "Requirement already satisfied: six>=1.5 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from python-dateutil>=2.8.2->jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert) (1.16.0)\n" + ] + } + ], "source": [ "import importlib.util\n", "# Check if 'google.colab' is available\n", @@ -190,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "b7c91474", "metadata": { "id": "e62e00c9" @@ -231,12 +272,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "id": "85fdfe19", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[WinError 3] Das System kann den angegebenen Pfad nicht finden: 'assume/examples/notebooks/'\n", + "c:\\Users\\AEppl\\OneDrive\\Dokumente\\Studium\\2024-25 Winersemester\\Hiwi IISM\\assume\\examples\\notebooks\n" + ] + } + ], "source": [ "# For local execution:\n", "%cd assume/examples/notebooks/\n", @@ -257,7 +307,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "id": "1ca7eab9", "metadata": { "colab": { @@ -271,7 +321,7 @@ "import os\n", "\n", "# Define the input directory\n", - "input_dir = os.path.join(inputs_path, \"tutorial_08\")\n", + "input_dir = os.path.join(\"inputs\", \"tutorial_08\")\n", "\n", "# Read the DataFrames from CSV files\n", "powerplant_units = pd.read_csv(os.path.join(input_dir, \"powerplant_units.csv\"))\n", @@ -298,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "id": "8c4153fa", "metadata": { "colab": { @@ -308,7 +358,221 @@ "id": "b205256f", "outputId": "b9bb887b-f534-4a50-dd5b-229be1012600" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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technologybidding_zonalfuel_typeemission_factormax_powermin_powerefficiencyadditional_costnodeunit_operator
name
Unit 11nuclearnaive_eomuranium0.01000.00.00.315north_2Operator North
Unit 12nuclearnaive_eomuranium0.01000.00.00.316north_2Operator North
Unit 13nuclearnaive_eomuranium0.01000.00.00.317north_2Operator North
Unit 14nuclearnaive_eomuranium0.01000.00.00.318north_2Operator North
Unit 15nuclearnaive_eomuranium0.01000.00.00.319north_2Operator North
Unit 16nuclearnaive_eomuranium0.01000.00.00.320southOperator South
Unit 17nuclearnaive_eomuranium0.01000.00.00.321southOperator South
Unit 18nuclearnaive_eomuranium0.01000.00.00.322southOperator South
Unit 19nuclearnaive_eomuranium0.01000.00.00.323southOperator South
Unit 20nuclearpp_learninguranium0.05000.00.00.324southOperator-RL
\n", + "
" + ], + "text/plain": [ + " technology bidding_zonal fuel_type emission_factor max_power \\\n", + "name \n", + "Unit 11 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 12 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 13 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 14 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 15 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 16 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 17 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 18 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 19 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 20 nuclear pp_learning uranium 0.0 5000.0 \n", + "\n", + " min_power efficiency additional_cost node unit_operator \n", + "name \n", + "Unit 11 0.0 0.3 15 north_2 Operator North \n", + "Unit 12 0.0 0.3 16 north_2 Operator North \n", + "Unit 13 0.0 0.3 17 north_2 Operator North \n", + "Unit 14 0.0 0.3 18 north_2 Operator North \n", + "Unit 15 0.0 0.3 19 north_2 Operator North \n", + "Unit 16 0.0 0.3 20 south Operator South \n", + "Unit 17 0.0 0.3 21 south Operator South \n", + "Unit 18 0.0 0.3 22 south Operator South \n", + "Unit 19 0.0 0.3 23 south Operator South \n", + "Unit 20 0.0 0.3 24 south Operator-RL " + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Create scarcity in southern Germany by limiting the number of power plants\n", "powerplant_units = powerplant_units[:20]\n", @@ -386,7 +650,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "id": "f6c64dc2", "metadata": { "colab": { @@ -465,7 +729,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "id": "a01977d5", "metadata": { "cellView": "form", @@ -689,7 +953,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "id": "0c1c9334", "metadata": { "colab": { @@ -699,7 +963,541 @@ "id": "bfadf522", "outputId": "7c91ab13-a3c2-4e89-d8ac-d20be95391f6" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.world:connected to db\n", + "INFO:assume.scenario.loader_csv:Starting Scenario tutorial_08/zonal_case from inputs\n", + "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", + "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", + "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", + "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", + "INFO:assume.scenario.loader_csv:save_frequency_hours is disabled due to CSV export being enabled. Data will be stored in the CSV files at the end of the simulation.\n", + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", + "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", + "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", + "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", + "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training Episodes: 0%| | 0/15 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
price forecast t+1price forecast t+2price forecast t+3price forecast t+4price forecast t+5price forecast t+6price forecast t+7price forecast t+8price forecast t+9price forecast t+10...residual load forecast t+17residual load forecast t+18residual load forecast t+19residual load forecast t+20residual load forecast t+21residual load forecast t+22residual load forecast t+23residual load forecast t+24total capacity t-1marginal costs t-1
02.242.262.282.302.322.342.362.382.402.42...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.4066670.000.406667
12.262.282.302.322.342.362.382.402.422.44...0.0000000.0000000.0000000.0000000.0000000.0000000.4066670.4066670.680.406667
22.282.302.322.342.362.382.402.422.442.46...0.0000000.0000000.0000000.0000000.0000000.4066670.4066670.4066671.000.406667
32.302.322.342.362.382.402.422.442.462.48...0.0000000.0000000.0000000.0000000.4066670.4066670.4066670.4066670.760.406667
42.322.342.362.382.402.422.442.462.482.50...0.0000000.0000000.0000000.4066670.4066670.4066670.4066670.4066670.800.406667
..................................................................
2652.502.522.542.562.582.602.622.642.662.68...0.4066670.4066670.4066670.0000000.0000000.0000000.0000000.0000001.000.406667
2662.522.542.562.582.602.622.642.662.682.22...0.4066670.4066670.0000000.0000000.0000000.0000000.0000000.0000001.000.406667
2672.542.562.582.602.622.642.662.682.222.24...0.4066670.0000000.0000000.0000000.0000000.0000000.0000000.0000001.000.406667
2682.562.582.602.622.642.662.682.222.242.26...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000001.000.406667
2692.582.602.622.642.662.682.222.242.262.28...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000001.000.406667
\n", + "

270 rows × 50 columns

\n", + "" + ], + "text/plain": [ + " price forecast t+1 price forecast t+2 price forecast t+3 \\\n", + "0 2.24 2.26 2.28 \n", + "1 2.26 2.28 2.30 \n", + "2 2.28 2.30 2.32 \n", + "3 2.30 2.32 2.34 \n", + "4 2.32 2.34 2.36 \n", + ".. ... ... ... \n", + "265 2.50 2.52 2.54 \n", + "266 2.52 2.54 2.56 \n", + "267 2.54 2.56 2.58 \n", + "268 2.56 2.58 2.60 \n", + "269 2.58 2.60 2.62 \n", + "\n", + " price forecast t+4 price forecast t+5 price forecast t+6 \\\n", + "0 2.30 2.32 2.34 \n", + "1 2.32 2.34 2.36 \n", + "2 2.34 2.36 2.38 \n", + "3 2.36 2.38 2.40 \n", + "4 2.38 2.40 2.42 \n", + ".. ... ... ... \n", + "265 2.56 2.58 2.60 \n", + "266 2.58 2.60 2.62 \n", + "267 2.60 2.62 2.64 \n", + "268 2.62 2.64 2.66 \n", + "269 2.64 2.66 2.68 \n", + "\n", + " price forecast t+7 price forecast t+8 price forecast t+9 \\\n", + "0 2.36 2.38 2.40 \n", + "1 2.38 2.40 2.42 \n", + "2 2.40 2.42 2.44 \n", + "3 2.42 2.44 2.46 \n", + "4 2.44 2.46 2.48 \n", + ".. ... ... ... \n", + "265 2.62 2.64 2.66 \n", + "266 2.64 2.66 2.68 \n", + "267 2.66 2.68 2.22 \n", + "268 2.68 2.22 2.24 \n", + "269 2.22 2.24 2.26 \n", + "\n", + " price forecast t+10 ... residual load forecast t+17 \\\n", + "0 2.42 ... 0.000000 \n", + "1 2.44 ... 0.000000 \n", + "2 2.46 ... 0.000000 \n", + "3 2.48 ... 0.000000 \n", + "4 2.50 ... 0.000000 \n", + ".. ... ... ... \n", + "265 2.68 ... 0.406667 \n", + "266 2.22 ... 0.406667 \n", + "267 2.24 ... 0.406667 \n", + "268 2.26 ... 0.000000 \n", + "269 2.28 ... 0.000000 \n", + "\n", + " residual load forecast t+18 residual load forecast t+19 \\\n", + "0 0.000000 0.000000 \n", + "1 0.000000 0.000000 \n", + "2 0.000000 0.000000 \n", + "3 0.000000 0.000000 \n", + "4 0.000000 0.000000 \n", + ".. ... ... \n", + "265 0.406667 0.406667 \n", + "266 0.406667 0.000000 \n", + "267 0.000000 0.000000 \n", + "268 0.000000 0.000000 \n", + "269 0.000000 0.000000 \n", + "\n", + " residual load forecast t+20 residual load forecast t+21 \\\n", + "0 0.000000 0.000000 \n", + "1 0.000000 0.000000 \n", + "2 0.000000 0.000000 \n", + "3 0.000000 0.406667 \n", + "4 0.406667 0.406667 \n", + ".. ... ... \n", + "265 0.000000 0.000000 \n", + "266 0.000000 0.000000 \n", + "267 0.000000 0.000000 \n", + "268 0.000000 0.000000 \n", + "269 0.000000 0.000000 \n", + "\n", + " residual load forecast t+22 residual load forecast t+23 \\\n", + "0 0.000000 0.000000 \n", + "1 0.000000 0.406667 \n", + "2 0.406667 0.406667 \n", + "3 0.406667 0.406667 \n", + "4 0.406667 0.406667 \n", + ".. ... ... \n", + "265 0.000000 0.000000 \n", + "266 0.000000 0.000000 \n", + "267 0.000000 0.000000 \n", + "268 0.000000 0.000000 \n", + "269 0.000000 0.000000 \n", + "\n", + " residual load forecast t+24 total capacity t-1 marginal costs t-1 \n", + "0 0.406667 0.00 0.406667 \n", + "1 0.406667 0.68 0.406667 \n", + "2 0.406667 1.00 0.406667 \n", + "3 0.406667 0.76 0.406667 \n", + "4 0.406667 0.80 0.406667 \n", + ".. ... ... ... \n", + "265 0.000000 1.00 0.406667 \n", + "266 0.000000 1.00 0.406667 \n", + "267 0.000000 1.00 0.406667 \n", + "268 0.000000 1.00 0.406667 \n", + "269 0.000000 1.00 0.406667 \n", + "\n", + "[270 rows x 50 columns]" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# path to extra loggedobservation values\n", "path = input_dir + \"/learned_strategies/zonal_case/buffer_obs\"\n", @@ -1149,7 +3354,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "id": "cca85e13", "metadata": { "id": "4da4de57" @@ -1166,12 +3371,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "id": "1cd3b7e6", "metadata": { "id": "37adecfa" }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# which actor is the RL actor\n", "ACTOR_NUM = len(powerplant_units) # 20\n", @@ -1199,7 +3422,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "id": "c507d331", "metadata": { "id": "e6460cfb" @@ -1229,7 +3452,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "id": "b0758eb5", "metadata": {}, "outputs": [], @@ -1262,7 +3485,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "id": "40e12192", "metadata": { "id": "6d9be211" @@ -1279,7 +3502,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "id": "56a32f41", "metadata": { "id": "84bb96cf" @@ -1292,12 +3515,2070 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "id": "4279910b", "metadata": { "id": "2a7929e4" }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/41 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No data for colormapping provided via 'c'. Parameters 'vmin', 'vmax' will be ignored\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Summary plot for the first output dimension\n", "shap.summary_plot(shap_values[0], X_test, feature_names=feature_names, show=False)\n", From 37643e7d35153a239ec44b4fbb7ad0b1802bb43f Mon Sep 17 00:00:00 2001 From: AndreasEppler Date: Wed, 20 Nov 2024 10:48:14 +0100 Subject: [PATCH 05/22] Cleared notebooks --- examples/amiris-examples | 1 + .../notebooks/01_minimal_manual_example.ipynb | 57 +- .../notebooks/02_automated_run_example.ipynb | 86 +- .../notebooks/03_custom_unit_example.ipynb | 75 +- ...forcement_learning_algorithm_example.ipynb | 2 +- examples/notebooks/05_market_comparison.ipynb | 278 +- .../07_interoperability_example.ipynb | 2 +- .../notebooks/08_market_zone_coupling.ipynb | 4246 +--------------- .../notebooks/09_example_Sim_and_xRL.ipynb | 4396 +---------------- 9 files changed, 152 insertions(+), 8991 deletions(-) create mode 160000 examples/amiris-examples diff --git a/examples/amiris-examples b/examples/amiris-examples new file mode 160000 index 000000000..4687f1fc3 --- /dev/null +++ b/examples/amiris-examples @@ -0,0 +1 @@ +Subproject commit 4687f1fc3c8bc8522fe9743b77bc21a0328d8143 diff --git a/examples/notebooks/01_minimal_manual_example.ipynb b/examples/notebooks/01_minimal_manual_example.ipynb index fb3585ed0..c6fa06e58 100644 --- a/examples/notebooks/01_minimal_manual_example.ipynb +++ b/examples/notebooks/01_minimal_manual_example.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -48,17 +48,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.world:connected to db\n" - ] - } - ], + "outputs": [], "source": [ "import logging\n", "import os\n", @@ -108,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -131,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -173,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -211,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -259,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -316,17 +308,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "world_script_simulation 2023-12-05 00:00:00: : 5356801.0it [00:06, 860204.20it/s] \n" - ] - } - ], + "outputs": [], "source": [ "world.run()" ] @@ -349,26 +333,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.world:connected to db\n", - "INFO:assume.world:activating container\n", - "INFO:assume.world:all agents up - starting simulation\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "world_script_simulation 2023-03-31 00:00:00: : 7689601.0it [00:09, 796703.78it/s] \n" - ] - } - ], + "outputs": [], "source": [ "import logging\n", "from datetime import datetime, timedelta\n", diff --git a/examples/notebooks/02_automated_run_example.ipynb b/examples/notebooks/02_automated_run_example.ipynb index cecfcea7c..69e9a2104 100644 --- a/examples/notebooks/02_automated_run_example.ipynb +++ b/examples/notebooks/02_automated_run_example.ipynb @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -68,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -116,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -179,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -205,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -258,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -284,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -369,40 +369,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.world:connected to db\n", - "INFO:assume.scenario.loader_csv:Starting Scenario example_01/hourly_market from inputs\n", - "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", - "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", - "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", - "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", - "INFO:assume.scenario.loader_csv:save_frequency_hours is disabled due to CSV export being enabled. Data will be stored in the CSV files at the end of the simulation.\n", - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01_hourly_market 2021-03-07 00:00:00: : 518401it [00:04, 124410.59it/s] \n" - ] - } - ], + "outputs": [], "source": [ "# define the database uri. In this case we are using a local sqlite database\n", "db_uri = \"sqlite:///local_db/assume_db.db\"\n", @@ -435,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -480,40 +449,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.world:connected to db\n", - "INFO:assume.scenario.loader_csv:Starting Scenario example_01/daily_market from inputs\n", - "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", - "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", - "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", - "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", - "INFO:assume.scenario.loader_csv:save_frequency_hours is disabled due to CSV export being enabled. Data will be stored in the CSV files at the end of the simulation.\n", - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01_daily_market 2021-03-07 00:00:00: : 518401it [00:00, 659017.51it/s] \n" - ] - } - ], + "outputs": [], "source": [ "data_format = \"local_db\" # \"local_db\" or \"timescale\"\n", "\n", diff --git a/examples/notebooks/03_custom_unit_example.ipynb b/examples/notebooks/03_custom_unit_example.ipynb index c5d543aab..0820c9a62 100644 --- a/examples/notebooks/03_custom_unit_example.ipynb +++ b/examples/notebooks/03_custom_unit_example.ipynb @@ -56,18 +56,7 @@ "languageId": "shellscript" } }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# this cell is used to display the image in the notebook when using colab\n", "# or running the notebook locally\n", @@ -129,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -143,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -169,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -207,7 +196,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -332,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -390,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -455,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -538,17 +527,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "electrolyser_demo 2019-01-30 00:00:00: : 2505601it [00:12, 203669.90it/s] \n" - ] - } - ], + "outputs": [], "source": [ "# import packages\n", "import logging\n", @@ -701,41 +682,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.world:connected to db\n", - "INFO:assume.scenario.loader_csv:Starting Scenario example_01e/base from ../inputs\n", - "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Downsampling demand_df successful.\n", - "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", - "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", - "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", - "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", - "INFO:assume.scenario.loader_csv:save_frequency_hours is disabled due to CSV export being enabled. Data will be stored in the CSV files at the end of the simulation.\n", - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", - "INFO:assume.scenario.loader_csv:Adding electrolyser units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01e_base 2019-01-30 00:00:00: : 2505601it [00:11, 220050.96it/s] \n" - ] - } - ], + "outputs": [], "source": [ "# import the main World class and the load_scenario_folder functions from assume\n", "# import the function to load custom units\n", diff --git a/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb b/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb index 6955e07ca..7e543a938 100644 --- a/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb +++ b/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb @@ -9,7 +9,7 @@ "source": [ "# 4.1 RL Algorithm tutorial\n", "\n", - "This tutorial will introduce users into the MATD3 implementation in ASSUME and hence how we use reinforcement learning (RL). The main objective of this tutorial is to ensure participants grasp the steps required to equip ASSUME with a RL algorithm. It therefore starts one level deeper than the RL_application example and the knowledge from this tutorial is not required if the already per-configured algorithm in Assume is to be used. The algorithm explained here is usable as a plug and play solution in the framework. The following coding snippets will highlight the key in the algorithm class and will explain the interactions with the learning role and other classes along the way. \n", + "This tutorial will introduce users into the MATD3 implementation in ASSUME and hence how we use reinforcement learning (RL). The main objective of this tutorial is to ensure participants grasp the steps required to equip ASSUME with a RL algorithm. It therefore starts one level deeper than the RL_application example and the knowledge from this tutorial is not required if the already pre-configured algorithm in Assume is to be used. The algorithm explained here is usable as a plug and play solution in the framework. The following coding snippets will highlight the key in the algorithm class and will explain the interactions with the learning role and other classes along the way. \n", "\n", "The outline of this tutorial is as follows. We will start with an introduction to the changed simulation flow when we use reinforcement learning (1. From one simulation year to learning episodes). If you need a refresher on RL in general, please visit our readthedocs (https://assume.readthedocs.io/en/latest/). Afterwards, we dive into the tasks and reason behind a learning role (2. What role has a learning role) and then dive into the characteristics of the algorithm (3. The MATD3).\n", "\n", diff --git a/examples/notebooks/05_market_comparison.ipynb b/examples/notebooks/05_market_comparison.ipynb index facadc21e..890a41795 100644 --- a/examples/notebooks/05_market_comparison.ipynb +++ b/examples/notebooks/05_market_comparison.ipynb @@ -64,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -72,33 +72,7 @@ "id": "m0DaRwFA7VgW", "outputId": "5655adad-5b7a-4fe3-9067-6b502a06136b" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting matplotlib\n", - " Using cached matplotlib-3.9.2-cp312-cp312-win_amd64.whl.metadata (11 kB)\n", - "Collecting contourpy>=1.0.1 (from matplotlib)\n", - " Using cached contourpy-1.3.0-cp312-cp312-win_amd64.whl.metadata (5.4 kB)\n", - "Collecting cycler>=0.10 (from matplotlib)\n", - " Using cached cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)\n", - "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from matplotlib) (4.54.1)\n", - "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from matplotlib) (1.4.7)\n", - "Requirement already satisfied: numpy>=1.23 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from matplotlib) (2.1.3)\n", - "Requirement already satisfied: packaging>=20.0 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from matplotlib) (24.1)\n", - "Requirement already satisfied: pillow>=8 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from matplotlib) (11.0.0)\n", - "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from matplotlib) (3.2.0)\n", - "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from matplotlib) (2.9.0)\n", - "Requirement already satisfied: six>=1.5 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", - "Using cached matplotlib-3.9.2-cp312-cp312-win_amd64.whl (7.8 MB)\n", - "Using cached contourpy-1.3.0-cp312-cp312-win_amd64.whl (218 kB)\n", - "Using cached cycler-0.12.1-py3-none-any.whl (8.3 kB)\n", - "Installing collected packages: cycler, contourpy, matplotlib\n", - "Successfully installed contourpy-1.3.0 cycler-0.12.1 matplotlib-3.9.2\n" - ] - } - ], + "outputs": [], "source": [ "import importlib.util\n", "\n", @@ -119,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -138,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -161,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -188,20 +162,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['eom_case', 'ltm_case']" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import yaml\n", "\n", @@ -243,31 +206,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'start_date': '2019-01-01 00:00',\n", - " 'end_date': '2019-01-21 00:00',\n", - " 'time_step': '1h',\n", - " 'markets_config': {'EOM': {'operator': 'EOM_operator',\n", - " 'product_type': 'energy',\n", - " 'products': [{'duration': '1h', 'count': 24, 'first_delivery': '1h'}],\n", - " 'opening_frequency': '1d',\n", - " 'opening_duration': '1h',\n", - " 'volume_unit': 'MWh',\n", - " 'maximum_volume': 1000000,\n", - " 'price_unit': 'EUR/MWh',\n", - " 'market_mechanism': 'pay_as_clear'}}}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# let us take a look at the configuration we are about to run:\n", "config[\"eom_case\"]" @@ -283,20 +224,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'duration': '1h', 'count': 24, 'first_delivery': '1h'}]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "config[\"eom_case\"][\"markets_config\"][\"EOM\"][\"products\"]" ] @@ -317,43 +247,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.world:connected to db\n", - "INFO:assume.scenario.loader_csv:Starting Scenario example_01f/eom_case from ../inputs\n", - "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Downsampling demand_df successful.\n", - "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Downsampling availability_df successful.\n", - "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", - "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Downsampling fuel_prices_df successful.\n", - "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", - "INFO:assume.world:activating container\n", - "INFO:assume.world:all agents up - starting simulation\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01f_eom_case 2019-01-21 00:00:00: : 1728001.0it [04:09, 6937.39it/s] \n" - ] - } - ], + "outputs": [], "source": [ "world = World(database_uri=DB_URI)\n", "load_scenario_folder(\n", @@ -376,15 +272,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Das System kann den angegebenen Pfad nicht finden.\n" - ] - } - ], + "outputs": [], "source": [ "if IN_COLAB:\n", " !cd assume-repo && assume -s example_01f -c eom_case -db \"sqlite:///./examples/local_db/assume_db_example_01f.db\"" @@ -401,42 +289,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'start_date': '2019-01-01 00:00',\n", - " 'end_date': '2019-01-21 00:00',\n", - " 'time_step': '1h',\n", - " 'markets_config': {'EOM': {'operator': 'EOM_operator',\n", - " 'product_type': 'energy',\n", - " 'start_date': '2019-01-01 00:00',\n", - " 'products': [{'duration': '1h', 'count': 24, 'first_delivery': '1h'}],\n", - " 'opening_frequency': '1d',\n", - " 'opening_duration': '1h',\n", - " 'volume_unit': 'MWh',\n", - " 'maximum_volume': 1000000,\n", - " 'price_unit': 'EUR/MWh',\n", - " 'market_mechanism': 'pay_as_clear'},\n", - " 'LTM_OTC': {'operator': 'LTM_operator',\n", - " 'product_type': 'energy',\n", - " 'start_date': '2019-01-01 00:00',\n", - " 'products': [{'duration': '7d', 'count': 1, 'first_delivery': '2h'}],\n", - " 'opening_frequency': '7d',\n", - " 'opening_duration': '1h',\n", - " 'volume_unit': 'MW',\n", - " 'maximum_volume': 1000000,\n", - " 'price_unit': 'EUR/MW',\n", - " 'market_mechanism': 'pay_as_bid'}}}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "config[\"ltm_case\"]" ] @@ -455,20 +310,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'duration': '7d', 'count': 1, 'first_delivery': '2h'}]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "config[\"ltm_case\"][\"markets_config\"][\"LTM_OTC\"][\"products\"]" ] @@ -485,43 +329,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.world:connected to db\n", - "INFO:assume.scenario.loader_csv:Starting Scenario example_01f/ltm_case from ../inputs\n", - "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Downsampling demand_df successful.\n", - "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Downsampling availability_df successful.\n", - "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", - "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Downsampling fuel_prices_df successful.\n", - "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", - "INFO:assume.world:activating container\n", - "INFO:assume.world:all agents up - starting simulation\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01f_ltm_case 2019-01-21 00:00:00: : 1728001.0it [03:37, 7957.47it/s] \n" - ] - } - ], + "outputs": [], "source": [ "world = World(database_uri=DB_URI)\n", "load_scenario_folder(\n", @@ -544,15 +354,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Das System kann den angegebenen Pfad nicht finden.\n" - ] - } - ], + "outputs": [], "source": [ "if IN_COLAB:\n", " !cd assume-repo && assume -s example_01f -c ltm_case -db \"sqlite:///./examples/local_db/assume_db_example_01f.db\"" @@ -571,20 +373,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import os\n", "\n", @@ -657,7 +448,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -665,26 +456,7 @@ "id": "qoWI_agIJOE4", "outputId": "9b40e670-bfef-4560-d6e8-61a1b29d1975" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\AEppl\\AppData\\Local\\Temp\\ipykernel_27556\\1389063350.py:30: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", - " ddf = ddf.T.fillna(method=\"ffill\")\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# second plot\n", "sql = \"\"\"\n", diff --git a/examples/notebooks/07_interoperability_example.ipynb b/examples/notebooks/07_interoperability_example.ipynb index 8452b4af3..db11f78ef 100644 --- a/examples/notebooks/07_interoperability_example.ipynb +++ b/examples/notebooks/07_interoperability_example.ipynb @@ -429,7 +429,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.6" + "version": "3.12.7" }, "nbsphinx": { "execute": "never" diff --git a/examples/notebooks/08_market_zone_coupling.ipynb b/examples/notebooks/08_market_zone_coupling.ipynb index 28dca59a4..608f187fe 100644 --- a/examples/notebooks/08_market_zone_coupling.ipynb +++ b/examples/notebooks/08_market_zone_coupling.ipynb @@ -71,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "0dd1c254", "metadata": { "id": "0dd1c254", @@ -79,43 +79,7 @@ "languageId": "shellscript" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: assume-framework in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (0.4.3)\n", - "Requirement already satisfied: argcomplete>=3.1.4 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from assume-framework) (3.5.1)\n", - "Requirement already satisfied: nest-asyncio>=1.5.6 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from assume-framework) (1.6.0)\n", - "Requirement already satisfied: mango-agents>=2.1.1 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from assume-framework) (2.1.1)\n", - "Requirement already satisfied: numpy>=1.26.4 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from assume-framework) (1.26.4)\n", - "Requirement already satisfied: tqdm>=4.64.1 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from assume-framework) (4.66.6)\n", - "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from assume-framework) (2.9.0)\n", - 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"Requirement already satisfied: packaging in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from plotly) (24.1)\n" - ] - } - ], + "outputs": [], "source": [ "# Install the ASSUME framework\n", "!pip install assume-framework\n", @@ -136,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "a1543685", "metadata": { "id": "a1543685" @@ -231,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "e2be3fe2", "metadata": { "id": "e2be3fe2" @@ -371,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "c1731cdc", "metadata": { "cellView": "form", @@ -382,86 +346,7 @@ "id": "c1731cdc", "outputId": "0d0a8060-aa86-4ba8-a0b1-0e528bc9d0d2" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Buses DataFrame:\n" - ] - }, - { - "data": { - "text/html": [ - "
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v_nomzone_idxy
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" - ], - "text/plain": [ - " v_nom zone_id x y\n", - "name \n", - "north_1 380.0 DE_1 10.0 54.0\n", - "north_2 380.0 DE_1 9.5 53.5\n", - "south 380.0 DE_2 11.6 48.1" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# @title Define the buses DataFrame with three nodes and two zones\n", "buses = pd.DataFrame(\n", @@ -508,7 +393,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "64769ec7", "metadata": { "cellView": "form", @@ -519,91 +404,7 @@ "id": "64769ec7", "outputId": "a47490cb-d06c-4152-8be6-64985a8dcbd0" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Transmission Lines DataFrame:\n" - ] - }, - { - "data": { - "text/html": [ - "
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bus0bus1s_nomxr
name
Line_N1_Snorth_1south5000.00.010.001
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nametechnologybidding_zonalfuel_typeemission_factormax_powermin_powerefficiencyadditional_costnodeunit_operator
0Unit 1nuclearnaive_eomuranium0.01000.00.00.35north_1Operator North
1Unit 2nuclearnaive_eomuranium0.01000.00.00.36north_1Operator North
2Unit 3nuclearnaive_eomuranium0.01000.00.00.37north_1Operator North
3Unit 4nuclearnaive_eomuranium0.01000.00.00.38north_1Operator North
4Unit 5nuclearnaive_eomuranium0.01000.00.00.39north_1Operator North
\n", - "
" - ], - "text/plain": [ - " name technology bidding_zonal fuel_type emission_factor max_power \\\n", - "0 Unit 1 nuclear naive_eom uranium 0.0 1000.0 \n", - "1 Unit 2 nuclear naive_eom uranium 0.0 1000.0 \n", - "2 Unit 3 nuclear naive_eom uranium 0.0 1000.0 \n", - "3 Unit 4 nuclear naive_eom uranium 0.0 1000.0 \n", - "4 Unit 5 nuclear naive_eom uranium 0.0 1000.0 \n", - "\n", - " min_power efficiency additional_cost node unit_operator \n", - "0 0.0 0.3 5 north_1 Operator North \n", - "1 0.0 0.3 6 north_1 Operator North \n", - "2 0.0 0.3 7 north_1 Operator North \n", - "3 0.0 0.3 8 north_1 Operator North \n", - "4 0.0 0.3 9 north_1 Operator North " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# @title Create the power plant units DataFrame\n", "\n", @@ -880,7 +545,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "16f8a13c", "metadata": { "cellView": "form", @@ -891,95 +556,7 @@ "id": "16f8a13c", "outputId": "aad8a140-a6ed-47fd-d06e-1e794aa1a829" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Demand Units DataFrame:\n" - ] - }, - { - "data": { - "text/html": [ - "
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nametechnologybidding_zonalmax_powermin_powerunit_operatornode
0demand_north_1inflex_demandnaive_eom1000000eom_denorth_1
1demand_north_2inflex_demandnaive_eom1000000eom_denorth_2
2demand_southinflex_demandnaive_eom1000000eom_desouth
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" - ], - "text/plain": [ - " name technology bidding_zonal max_power min_power \\\n", - "0 demand_north_1 inflex_demand naive_eom 100000 0 \n", - "1 demand_north_2 inflex_demand naive_eom 100000 0 \n", - "2 demand_south inflex_demand naive_eom 100000 0 \n", - "\n", - " unit_operator node \n", - "0 eom_de north_1 \n", - "1 eom_de north_2 \n", - "2 eom_de south " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# @title Define the demand units\n", "demand_units = pd.DataFrame(\n", @@ -1029,7 +606,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "a0591f14", "metadata": { "cellView": "form", @@ -1040,95 +617,7 @@ "id": "a0591f14", "outputId": "d590647b-7522-4fce-bfe7-dc66b7b566e8" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Demand DataFrame:\n" - ] - }, - { - "data": { - "text/html": [ - "
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demand_north_1demand_north_2demand_south
datetime
2019-01-01 00:00:002400240017400
2019-01-01 01:00:002800280016800
2019-01-01 02:00:003200320016200
2019-01-01 03:00:003600360015600
2019-01-01 04:00:004000400015000
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" - ], - "text/plain": [ - " demand_north_1 demand_north_2 demand_south\n", - "datetime \n", - "2019-01-01 00:00:00 2400 2400 17400\n", - "2019-01-01 01:00:00 2800 2800 16800\n", - "2019-01-01 02:00:00 3200 3200 16200\n", - "2019-01-01 03:00:00 3600 3600 15600\n", - "2019-01-01 04:00:00 4000 4000 15000" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# @title Define the demand DataFrame\n", "\n", @@ -1193,7 +682,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "c9fb8458", "metadata": { "cellView": "form", @@ -1204,67 +693,7 @@ "id": "c9fb8458", "outputId": "380d3471-2a05-4cf2-bd37-77b944a6dc98" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculated Incidence Matrix between Zones:\n" - ] - }, - { - "data": { - "text/html": [ - "
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Line_N1_SLine_N2_SLine_N1_N2
DE_1110
DE_2-1-10
\n", - "
" - ], - "text/plain": [ - " Line_N1_S Line_N2_S Line_N1_N2\n", - "DE_1 1 1 0\n", - "DE_2 -1 -1 0" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# @title Create the incidence matrix\n", "def create_incidence_matrix(lines, buses, zones_id=None):\n", @@ -1347,7 +776,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "4f7366ae", "metadata": { "cellView": "form", @@ -1358,48 +787,7 @@ "id": "4f7366ae", "outputId": "1c291cb1-8e7b-4e36-cce9-ddd00735225d" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Sample Supply Order:\n" - ] - }, - { - "data": { - "text/plain": [ - "{'price': 5,\n", - " 'volume': 1000.0,\n", - " 'node': 'north_1',\n", - " 'time': Timestamp('2019-01-01 00:00:00')}" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Sample Demand Order:\n" - ] - }, - { - "data": { - "text/plain": [ - "{'price': 100,\n", - " 'volume': -2400,\n", - " 'node': 'north_1',\n", - " 'time': Timestamp('2019-01-01 00:00:00')}" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# @title Construct Orders and Map Nodes to Zones\n", "# Initialize orders dictionary\n", @@ -1439,7 +827,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "e8b8a17f", "metadata": { "cellView": "form", @@ -1450,94 +838,7 @@ "id": "e8b8a17f", "outputId": "ae3db259-f2e7-4b60-91b1-ca130140fb30" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mapped Orders:\n" - ] - }, - { - "data": { - "text/html": [ - "
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pricevolumenodetime
Unit 1_2019-01-01 00:00:0051000.0DE_12019-01-01 00:00:00
Unit 1_2019-01-01 01:00:0051000.0DE_12019-01-01 01:00:00
Unit 1_2019-01-01 02:00:0051000.0DE_12019-01-01 02:00:00
Unit 1_2019-01-01 03:00:0051000.0DE_12019-01-01 03:00:00
Unit 1_2019-01-01 04:00:0051000.0DE_12019-01-01 04:00:00
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" - ], - "text/plain": [ - " price volume node time\n", - "Unit 1_2019-01-01 00:00:00 5 1000.0 DE_1 2019-01-01 00:00:00\n", - "Unit 1_2019-01-01 01:00:00 5 1000.0 DE_1 2019-01-01 01:00:00\n", - "Unit 1_2019-01-01 02:00:00 5 1000.0 DE_1 2019-01-01 02:00:00\n", - "Unit 1_2019-01-01 03:00:00 5 1000.0 DE_1 2019-01-01 03:00:00\n", - "Unit 1_2019-01-01 04:00:00 5 1000.0 DE_1 2019-01-01 04:00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# @title Map the orders to zones\n", "# Create a mapping from node_id to zone_id\n", @@ -1605,7 +906,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "1c7dfee2", "metadata": { "colab": { @@ -1615,92 +916,7 @@ "id": "1c7dfee2", "outputId": "86090b82-98e1-4d3b-bb1b-74b3c1c37e43" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### Simulation 1: Zero Transmission Capacity Between Zones\n", - "Transmission Lines for Simulation 1:\n" - ] - }, - { - "data": { - "text/html": [ - "
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bus0bus1s_nomxr
name
Line_N1_Snorth_1south00.010.001
Line_N2_Snorth_2south00.010.001
Line_N1_N2north_1north_200.010.001
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bus0bus1s_nomxr
name
Line_N1_Snorth_1south3000.00.010.001
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bus0bus1s_nomxr
name
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zonetimeclearing_price
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zonetimeclearing_price
24DE_22019-01-01 00:00:00100.0
25DE_22019-01-01 01:00:00100.0
26DE_22019-01-01 02:00:00100.0
27DE_22019-01-01 03:00:00100.0
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timelineflow_MW
02019-01-01 00:00:00Line_N1_S-3000.0
12019-01-01 00:00:00Line_N2_S-3000.0
22019-01-01 00:00:00Line_N1_N2-3000.0
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zonetimeclearing_price
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zonetimeclearing_price
24DE_22019-01-01 00:00:0031.0
25DE_22019-01-01 01:00:0030.0
26DE_22019-01-01 02:00:0030.0
27DE_22019-01-01 03:00:0029.0
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timelineflow_MW
02019-01-01 00:00:00Line_N1_S-5000.0
12019-01-01 00:00:00Line_N2_S-5000.0
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"tickangle": 45, - "title": { - "text": "Time" - }, - "type": "date" - }, - "yaxis": { - "title": { - "text": "Clearing Price" - } - } - } - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# @title Plot the market clearing prices for each zone and simulation run\n", "# Initialize the Plotly figure\n", @@ -4240,7 +1402,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "531a7a24", "metadata": { "colab": { @@ -4249,15 +1411,7 @@ "id": "531a7a24", "outputId": "abc151f4-2f50-4ebd-b405-49f0340cd96d" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input CSV files have been saved to 'inputs/tutorial_08'.\n" - ] - } - ], + "outputs": [], "source": [ "import os\n", "\n", @@ -4283,7 +1437,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "2d61a40b", "metadata": { "cellView": "form", @@ -4293,15 +1447,7 @@ "id": "2d61a40b", "outputId": "8ce46e76-c462-4c8e-db62-8f787b354403" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fuel Prices CSV file has been saved to 'inputs/tutorial_08/fuel_prices.csv'.\n" - ] - } - ], + "outputs": [], "source": [ "# @title Create fuel prices\n", "fuel_prices = {\n", @@ -4342,7 +1488,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "821a4002", "metadata": { "colab": { @@ -4351,15 +1497,7 @@ "id": "821a4002", "outputId": "ac8bf62b-8e38-4199-a45a-5c5397342bef" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Configuration YAML file has been saved to 'inputs/tutorial_08\\config.yaml'.\n" - ] - } - ], + "outputs": [], "source": [ "config = {\n", " \"zonal_case\": {\n", @@ -4464,46 +1602,12 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "3a79848a", "metadata": { "id": "3a79848a" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.world:connected to db\n", - "INFO:assume.scenario.loader_csv:Starting Scenario tutorial_08/zonal_case from inputs\n", - "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", - "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", - "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", - "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", - "INFO:assume.scenario.loader_csv:save_frequency_hours is disabled due to CSV export being enabled. Data will be stored in the CSV files at the end of the simulation.\n", - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", - "INFO:assume.world:activating container\n", - "INFO:assume.common.outputs:tried writing grid data to non postGIS database\n", - "INFO:assume.world:all agents up - starting simulation\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 23:00:00: : 82801it [00:15, 5451.10it/s] \n" - ] - } - ], + "outputs": [], "source": [ "# import the main World class and the load_scenario_folder functions from assume\n", "from assume import World\n", @@ -4585,7 +1689,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "6e71a328", "metadata": { "colab": { @@ -4595,189 +1699,7 @@ "id": "6e71a328", "outputId": "738e1589-5d53-4831-cbcf-4fefca4f7860" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sample of market_meta.csv:\n" - ] - }, - { - "data": { - "text/html": [ - "
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}, - "xaxis": { - "automargin": true, - "gridcolor": "#EBF0F8", - "linecolor": "#EBF0F8", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "#EBF0F8", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "#EBF0F8", - "linecolor": "#EBF0F8", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "#EBF0F8", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Clearing Prices per Zone Over Time: Simulation Results" - }, - "width": 1000, - "xaxis": { - "tickangle": 45, - "title": { - "text": "Time" - }, - "type": "date" - }, - "yaxis": { - "title": { - "text": "Clearing Price (EUR/MWh)" - } - } - } - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# @title Plot market clearing prices\n", "# Initialize the Plotly figure\n", diff --git a/examples/notebooks/09_example_Sim_and_xRL.ipynb b/examples/notebooks/09_example_Sim_and_xRL.ipynb index a58be6358..d55c7678c 100644 --- a/examples/notebooks/09_example_Sim_and_xRL.ipynb +++ b/examples/notebooks/09_example_Sim_and_xRL.ipynb @@ -155,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "647079b9", "metadata": { "colab": { @@ -165,48 +165,7 @@ "id": "ee220130", "outputId": "ffd98b47-2b07-41cd-dfe4-ff0381571825" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: plotly in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (5.24.1)\n", - "Requirement already satisfied: tenacity>=6.2.0 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from plotly) (9.0.0)\n", - "Requirement already satisfied: packaging in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from plotly) (24.1)\n", - "Requirement already satisfied: nbconvert in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (7.16.4)\n", - "Requirement already satisfied: beautifulsoup4 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from nbconvert) (4.12.3)\n", - "Requirement already satisfied: bleach!=5.0.0 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from nbconvert) (6.2.0)\n", - 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] - } - ], + "outputs": [], "source": [ "import importlib.util\n", "# Check if 'google.colab' is available\n", @@ -231,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "b7c91474", "metadata": { "id": "e62e00c9" @@ -272,21 +231,12 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "85fdfe19", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[WinError 3] Das System kann den angegebenen Pfad nicht finden: 'assume/examples/notebooks/'\n", - "c:\\Users\\AEppl\\OneDrive\\Dokumente\\Studium\\2024-25 Winersemester\\Hiwi IISM\\assume\\examples\\notebooks\n" - ] - } - ], + "outputs": [], "source": [ "# For local execution:\n", "%cd assume/examples/notebooks/\n", @@ -307,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "1ca7eab9", "metadata": { "colab": { @@ -348,7 +298,7 @@ }, { "cell_type": "code", - 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technologybidding_zonalfuel_typeemission_factormax_powermin_powerefficiencyadditional_costnodeunit_operator
name
Unit 11nuclearnaive_eomuranium0.01000.00.00.315north_2Operator North
Unit 12nuclearnaive_eomuranium0.01000.00.00.316north_2Operator North
Unit 13nuclearnaive_eomuranium0.01000.00.00.317north_2Operator North
Unit 14nuclearnaive_eomuranium0.01000.00.00.318north_2Operator North
Unit 15nuclearnaive_eomuranium0.01000.00.00.319north_2Operator North
Unit 16nuclearnaive_eomuranium0.01000.00.00.320southOperator South
Unit 17nuclearnaive_eomuranium0.01000.00.00.321southOperator South
Unit 18nuclearnaive_eomuranium0.01000.00.00.322southOperator South
Unit 19nuclearnaive_eomuranium0.01000.00.00.323southOperator South
Unit 20nuclearpp_learninguranium0.05000.00.00.324southOperator-RL
\n", - "
" - ], - "text/plain": [ - " technology bidding_zonal fuel_type emission_factor max_power \\\n", - "name \n", - "Unit 11 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 12 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 13 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 14 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 15 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 16 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 17 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 18 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 19 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 20 nuclear pp_learning uranium 0.0 5000.0 \n", - "\n", - " min_power efficiency additional_cost node unit_operator \n", - "name \n", - "Unit 11 0.0 0.3 15 north_2 Operator North \n", - "Unit 12 0.0 0.3 16 north_2 Operator North \n", - "Unit 13 0.0 0.3 17 north_2 Operator North \n", - "Unit 14 0.0 0.3 18 north_2 Operator North \n", - "Unit 15 0.0 0.3 19 north_2 Operator North \n", - "Unit 16 0.0 0.3 20 south Operator South \n", - "Unit 17 0.0 0.3 21 south Operator South \n", - "Unit 18 0.0 0.3 22 south Operator South \n", - "Unit 19 0.0 0.3 23 south Operator South \n", - "Unit 20 0.0 0.3 24 south Operator-RL " - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Create scarcity in southern Germany by limiting the number of power plants\n", "powerplant_units = powerplant_units[:20]\n", @@ -650,7 +386,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "id": "f6c64dc2", "metadata": { "colab": { @@ -729,7 +465,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "id": "a01977d5", "metadata": { "cellView": "form", @@ -953,7 +689,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "id": "0c1c9334", "metadata": { "colab": { @@ -963,541 +699,7 @@ "id": "bfadf522", "outputId": "7c91ab13-a3c2-4e89-d8ac-d20be95391f6" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.world:connected to db\n", - "INFO:assume.scenario.loader_csv:Starting Scenario tutorial_08/zonal_case from inputs\n", - "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", - "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", - "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", - "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", - "INFO:assume.scenario.loader_csv:save_frequency_hours is disabled due to CSV export being enabled. Data will be stored in the CSV files at the end of the simulation.\n", - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", - "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", - "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", - "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", - "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Episodes: 0%| | 0/15 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
price forecast t+1price forecast t+2price forecast t+3price forecast t+4price forecast t+5price forecast t+6price forecast t+7price forecast t+8price forecast t+9price forecast t+10...residual load forecast t+17residual load forecast t+18residual load forecast t+19residual load forecast t+20residual load forecast t+21residual load forecast t+22residual load forecast t+23residual load forecast t+24total capacity t-1marginal costs t-1
02.242.262.282.302.322.342.362.382.402.42...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.4066670.000.406667
12.262.282.302.322.342.362.382.402.422.44...0.0000000.0000000.0000000.0000000.0000000.0000000.4066670.4066670.680.406667
22.282.302.322.342.362.382.402.422.442.46...0.0000000.0000000.0000000.0000000.0000000.4066670.4066670.4066671.000.406667
32.302.322.342.362.382.402.422.442.462.48...0.0000000.0000000.0000000.0000000.4066670.4066670.4066670.4066670.760.406667
42.322.342.362.382.402.422.442.462.482.50...0.0000000.0000000.0000000.4066670.4066670.4066670.4066670.4066670.800.406667
..................................................................
2652.502.522.542.562.582.602.622.642.662.68...0.4066670.4066670.4066670.0000000.0000000.0000000.0000000.0000001.000.406667
2662.522.542.562.582.602.622.642.662.682.22...0.4066670.4066670.0000000.0000000.0000000.0000000.0000000.0000001.000.406667
2672.542.562.582.602.622.642.662.682.222.24...0.4066670.0000000.0000000.0000000.0000000.0000000.0000000.0000001.000.406667
2682.562.582.602.622.642.662.682.222.242.26...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000001.000.406667
2692.582.602.622.642.662.682.222.242.262.28...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000001.000.406667
\n", - "

270 rows × 50 columns

\n", - "" - ], - "text/plain": [ - " price forecast t+1 price forecast t+2 price forecast t+3 \\\n", - "0 2.24 2.26 2.28 \n", - "1 2.26 2.28 2.30 \n", - "2 2.28 2.30 2.32 \n", - "3 2.30 2.32 2.34 \n", - "4 2.32 2.34 2.36 \n", - ".. ... ... ... \n", - "265 2.50 2.52 2.54 \n", - "266 2.52 2.54 2.56 \n", - "267 2.54 2.56 2.58 \n", - "268 2.56 2.58 2.60 \n", - "269 2.58 2.60 2.62 \n", - "\n", - " price forecast t+4 price forecast t+5 price forecast t+6 \\\n", - "0 2.30 2.32 2.34 \n", - "1 2.32 2.34 2.36 \n", - "2 2.34 2.36 2.38 \n", - "3 2.36 2.38 2.40 \n", - "4 2.38 2.40 2.42 \n", - ".. ... ... ... \n", - "265 2.56 2.58 2.60 \n", - "266 2.58 2.60 2.62 \n", - "267 2.60 2.62 2.64 \n", - "268 2.62 2.64 2.66 \n", - "269 2.64 2.66 2.68 \n", - "\n", - " price forecast t+7 price forecast t+8 price forecast t+9 \\\n", - "0 2.36 2.38 2.40 \n", - "1 2.38 2.40 2.42 \n", - "2 2.40 2.42 2.44 \n", - "3 2.42 2.44 2.46 \n", - "4 2.44 2.46 2.48 \n", - ".. ... ... ... \n", - "265 2.62 2.64 2.66 \n", - "266 2.64 2.66 2.68 \n", - "267 2.66 2.68 2.22 \n", - "268 2.68 2.22 2.24 \n", - "269 2.22 2.24 2.26 \n", - "\n", - " price forecast t+10 ... residual load forecast t+17 \\\n", - "0 2.42 ... 0.000000 \n", - "1 2.44 ... 0.000000 \n", - "2 2.46 ... 0.000000 \n", - "3 2.48 ... 0.000000 \n", - "4 2.50 ... 0.000000 \n", - ".. ... ... ... \n", - "265 2.68 ... 0.406667 \n", - "266 2.22 ... 0.406667 \n", - "267 2.24 ... 0.406667 \n", - "268 2.26 ... 0.000000 \n", - "269 2.28 ... 0.000000 \n", - "\n", - " residual load forecast t+18 residual load forecast t+19 \\\n", - "0 0.000000 0.000000 \n", - "1 0.000000 0.000000 \n", - "2 0.000000 0.000000 \n", - "3 0.000000 0.000000 \n", - "4 0.000000 0.000000 \n", - ".. ... ... \n", - "265 0.406667 0.406667 \n", - "266 0.406667 0.000000 \n", - "267 0.000000 0.000000 \n", - "268 0.000000 0.000000 \n", - "269 0.000000 0.000000 \n", - "\n", - " residual load forecast t+20 residual load forecast t+21 \\\n", - "0 0.000000 0.000000 \n", - "1 0.000000 0.000000 \n", - "2 0.000000 0.000000 \n", - "3 0.000000 0.406667 \n", - "4 0.406667 0.406667 \n", - ".. ... ... \n", - "265 0.000000 0.000000 \n", - "266 0.000000 0.000000 \n", - "267 0.000000 0.000000 \n", - "268 0.000000 0.000000 \n", - "269 0.000000 0.000000 \n", - "\n", - " residual load forecast t+22 residual load forecast t+23 \\\n", - "0 0.000000 0.000000 \n", - "1 0.000000 0.406667 \n", - "2 0.406667 0.406667 \n", - "3 0.406667 0.406667 \n", - "4 0.406667 0.406667 \n", - ".. ... ... \n", - "265 0.000000 0.000000 \n", - "266 0.000000 0.000000 \n", - "267 0.000000 0.000000 \n", - "268 0.000000 0.000000 \n", - "269 0.000000 0.000000 \n", - "\n", - " residual load forecast t+24 total capacity t-1 marginal costs t-1 \n", - "0 0.406667 0.00 0.406667 \n", - "1 0.406667 0.68 0.406667 \n", - "2 0.406667 1.00 0.406667 \n", - "3 0.406667 0.76 0.406667 \n", - "4 0.406667 0.80 0.406667 \n", - ".. ... ... ... \n", - "265 0.000000 1.00 0.406667 \n", - "266 0.000000 1.00 0.406667 \n", - "267 0.000000 1.00 0.406667 \n", - "268 0.000000 1.00 0.406667 \n", - "269 0.000000 1.00 0.406667 \n", - "\n", - "[270 rows x 50 columns]" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# path to extra loggedobservation values\n", "path = input_dir + \"/learned_strategies/zonal_case/buffer_obs\"\n", @@ -3354,7 +1149,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "id": "cca85e13", "metadata": { "id": "4da4de57" @@ -3371,30 +1166,12 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "id": "1cd3b7e6", "metadata": { "id": "37adecfa" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# which actor is the RL actor\n", "ACTOR_NUM = len(powerplant_units) # 20\n", @@ -3422,7 +1199,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "id": "c507d331", "metadata": { "id": "e6460cfb" @@ -3452,7 +1229,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "id": "b0758eb5", "metadata": {}, "outputs": [], @@ -3485,7 +1262,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "id": "40e12192", "metadata": { "id": "6d9be211" @@ -3502,7 +1279,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "id": "56a32f41", "metadata": { "id": "84bb96cf" @@ -3515,2070 +1292,12 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "id": "4279910b", "metadata": { "id": "2a7929e4" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/41 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No data for colormapping provided via 'c'. Parameters 'vmin', 'vmax' will be ignored\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Summary plot for the first output dimension\n", "shap.summary_plot(shap_values[0], X_test, feature_names=feature_names, show=False)\n", From df098c711c72383c34c0a1e7549067b532f985b0 Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Wed, 20 Nov 2024 11:00:00 +0100 Subject: [PATCH 06/22] - fixed tutorial 06 to align with clearing changes with regard to node and incidence matrix defintion --- .../06_advanced_orders_example.ipynb | 4036 ++++++++++++++++- 1 file changed, 3906 insertions(+), 130 deletions(-) diff --git a/examples/notebooks/06_advanced_orders_example.ipynb b/examples/notebooks/06_advanced_orders_example.ipynb index 25d8d87c2..fc8627b14 100644 --- a/examples/notebooks/06_advanced_orders_example.ipynb +++ b/examples/notebooks/06_advanced_orders_example.ipynb @@ -125,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -152,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -171,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ @@ -194,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -240,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -273,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -281,20 +281,21 @@ "EPS = 1e-4\n", "\n", "\n", - "def market_clearing_opt(orders, market_products, mode, with_linked_bids):\n", + "def market_clearing_opt(orders: Orderbook, market_products, mode, with_linked_bids, incidence_matrix: pd.DataFrame = None,\n", + " lines: pd.DataFrame = None, solver: str = \"appsi_highs\", solver_options: dict = {},\n", + "):\n", " \"\"\"\n", " Sets up and solves the market clearing optimization problem.\n", "\n", - " Args:\n", - " orders (Orderbook): The list of the orders.\n", - " market_products (list[MarketProduct]): The products to be traded.\n", - " mode (str): The mode of the market clearing determining whether the minimum acceptance ratio is considered.\n", - " with_linked_bids (bool): Whether the market clearing should include linked bids.\n", - "\n", - " Returns:\n", - " tuple[pyomo.ConcreteModel, pyomo.opt.results.SolverResults]: The solved pyomo model and the solver results.\n", " \"\"\"\n", - " # initiate the pyomo model\n", + " # Set nodes and lines based on the incidence matrix and lines DataFrame\n", + " if incidence_matrix is not None:\n", + " nodes = list(incidence_matrix.index)\n", + " line_ids = list(incidence_matrix.columns)\n", + " else:\n", + " nodes = [\"node0\"]\n", + " line_ids = [\"line0\"]\n", + "\n", " model = pyo.ConcreteModel()\n", "\n", " # add dual suffix to the model (we need this to extract the market clearing prices later)\n", @@ -302,11 +303,11 @@ " if mode != \"with_min_acceptance_ratio\":\n", " model.dual = pyo.Suffix(direction=pyo.Suffix.IMPORT_EXPORT)\n", "\n", - " # add sets for the orders, timesteps, and bids, to specify the indexes for the decision variables\n", " model.T = pyo.Set(\n", " initialize=[market_product[0] for market_product in market_products],\n", " doc=\"timesteps\",\n", " )\n", + "\n", " model.sBids = pyo.Set(\n", " initialize=[order[\"bid_id\"] for order in orders if order[\"bid_type\"] == \"SB\"],\n", " doc=\"simple_bids\",\n", @@ -317,16 +318,17 @@ " ],\n", " doc=\"block_bids\",\n", " )\n", - " model.nodes = pyo.Set(initialize=[\"node0\"], doc=\"nodes\")\n", "\n", - " # decision variables: the acceptance ratio of simple bids\n", + " model.nodes = pyo.Set(initialize=nodes, doc=\"nodes\")\n", + " model.lines = pyo.Set(initialize=line_ids, doc=\"lines\")\n", + "\n", + " # decision variables for the acceptance ratio of simple and block bids (including linked bids)\n", " model.xs = pyo.Var(\n", " model.sBids,\n", " domain=pyo.NonNegativeReals,\n", " bounds=(0, 1),\n", " doc=\"simple_bid_acceptance\",\n", " )\n", - " # decision variables: the acceptance ratio of block bids (including linked bids)\n", " model.xb = pyo.Var(\n", " model.bBids,\n", " domain=pyo.NonNegativeReals,\n", @@ -334,10 +336,13 @@ " doc=\"block_bid_acceptance\",\n", " )\n", "\n", - " # if the mode is 'with_min_acceptance_ratio', add the binary decision variables for the acceptance\n", - " # and the minimum acceptance ratio constraints\n", + " # decision variables that define flows between nodes\n", + " # assuming the orders contain the node and are collected in nodes\n", + " if incidence_matrix is not None:\n", + " # Decision variables for flows on each line at each timestep\n", + " model.flows = pyo.Var(model.T, model.lines, domain=pyo.Reals, doc=\"power_flows\")\n", + "\n", " if mode == \"with_min_acceptance_ratio\":\n", - " # add set for all bids, since the minimum acceptance ratio constraints are defined for all bids\n", " model.Bids = pyo.Set(\n", " initialize=[order[\"bid_id\"] for order in orders], doc=\"all_bids\"\n", " )\n", @@ -349,16 +354,10 @@ " )\n", "\n", " # add minimum acceptance ratio constraints\n", - " \"\"\"\n", - " Minimum acceptance constraints are defined as:\n", - " acceptance ratio (decision variable) >= min_acceptance_ratio * acceptance (binary decision variable)\n", - " acceptance ratio (decision variable) <= acceptance (binary decision variable)\n", - " \"\"\"\n", " model.mar_constr = pyo.ConstraintList()\n", " for order in orders:\n", " if order[\"min_acceptance_ratio\"] is None:\n", " continue\n", - "\n", " elif order[\"bid_type\"] == \"SB\":\n", " model.mar_constr.add(\n", " model.xs[order[\"bid_id\"]]\n", @@ -376,31 +375,8 @@ " model.mar_constr.add(\n", " model.xb[order[\"bid_id\"]] <= model.x[order[\"bid_id\"]]\n", " )\n", - " # add energy balance constraint\n", - " \"\"\"\n", - " Energy balance is defined as:\n", - " sum over all orders of (acceptance reatio (decision variable) * offered volume) = 0\n", - " \"\"\"\n", - " balance_expr = {t: 0.0 for t in model.T}\n", - " for order in orders:\n", - " if order[\"bid_type\"] == \"SB\":\n", - " balance_expr[order[\"start_time\"]] += (\n", - " order[\"volume\"] * model.xs[order[\"bid_id\"]]\n", - " )\n", - " elif order[\"bid_type\"] in [\"BB\", \"LB\"]:\n", - " for start_time, volume in order[\"volume\"].items():\n", - " balance_expr[start_time] += volume * model.xb[order[\"bid_id\"]]\n", - "\n", - " def energy_balance_rule(m, t):\n", - " return balance_expr[t] == 0\n", - "\n", - " model.energy_balance = pyo.Constraint(model.T, rule=energy_balance_rule)\n", "\n", " # limit the acceptance of child bids by the acceptance of their parent bid\n", - " \"\"\"\n", - " The linked bid constraints are defined as:\n", - " acceptance ratio of child bid (decision variable) <= acceptance ratio of parent bid (decision variable)\n", - " \"\"\"\n", " if with_linked_bids:\n", " model.linked_bid_constr = pyo.ConstraintList()\n", " for order in orders:\n", @@ -410,12 +386,60 @@ " model.xb[order[\"bid_id\"]] <= model.xb[parent_bid_id]\n", " )\n", "\n", + " # Function to calculate the balance for each node and time\n", + " def energy_balance_rule(model, node, t):\n", + " \"\"\"\n", + " Calculate the energy balance for a given node and time.\n", + "\n", + " This function calculates the energy balance for a specific node and time in a complex clearing algorithm. It iterates over the orders and adjusts the balance expression based on the bid type. It also adjusts the flow subtraction to account for actual connections if an incidence matrix is provided.\n", + "\n", + " Args:\n", + " model: The complex clearing model.\n", + " node: The node for which to calculate the energy balance.\n", + " t: The time for which to calculate the energy balance.\n", + "\n", + " Returns:\n", + " bool: True if the energy balance is zero, False otherwise.\n", + " \"\"\"\n", + " balance_expr = 0.0 # Initialize the balance expression\n", + " # Iterate over orders to adjust the balance expression based on bid type\n", + " for order in orders:\n", + " if (\n", + " order[\"bid_type\"] == \"SB\"\n", + " and order[\"node\"] == node\n", + " and order[\"start_time\"] == t\n", + " ):\n", + " balance_expr += order[\"volume\"] * model.xs[order[\"bid_id\"]]\n", + " elif order[\"bid_type\"] in [\"BB\", \"LB\"] and order[\"node\"] == node:\n", + " for start_time, volume in order[\"volume\"].items():\n", + " if start_time == t:\n", + " balance_expr += volume * model.xb[order[\"bid_id\"]]\n", + "\n", + " # Add contributions from line flows based on the incidence matrix\n", + " if incidence_matrix is not None:\n", + " for line in model.lines:\n", + " incidence_value = incidence_matrix.loc[node, line]\n", + " if incidence_value != 0:\n", + " balance_expr += incidence_value * model.flows[t, line]\n", + "\n", + " return balance_expr == 0\n", + "\n", + " # Add the energy balance constraints for each node and time period using the rule\n", + " # Define the energy balance constraint using two indices (node and time)\n", + " model.energy_balance = pyo.Constraint(\n", + " model.nodes, model.T, rule=energy_balance_rule\n", + " )\n", + "\n", + " if incidence_matrix is not None:\n", + " model.transmission_constr = pyo.ConstraintList()\n", + " for t in model.T:\n", + " for line in model.lines:\n", + " capacity = lines.at[line, \"s_nom\"]\n", + " # Limit the flow on each line\n", + " model.transmission_constr.add(model.flows[t, line] <= capacity)\n", + " model.transmission_constr.add(model.flows[t, line] >= -capacity)\n", + "\n", " # define the objective function as cost minimization\n", - " \"\"\"\n", - " The objective function is defined as:\n", - " sum over all orders of (price * volume * acceptance ratio (decision variable))\n", - " The sense of the objective function is minimize.\n", - " \"\"\"\n", " obj_expr = 0\n", " for order in orders:\n", " if order[\"bid_type\"] == \"SB\":\n", @@ -426,45 +450,24 @@ "\n", " model.objective = pyo.Objective(expr=obj_expr, sense=pyo.minimize)\n", "\n", - " # check available solvers, gurobi is preferred\n", - " solvers = check_available_solvers(*SOLVERS)\n", - " if len(solvers) < 1:\n", - " raise Exception(f\"None of {SOLVERS} are available\")\n", - "\n", - " solver = SolverFactory(solvers[0])\n", - "\n", - " if solver.name == \"gurobi\":\n", - " options = {\"cutoff\": -1.0, \"MIPGap\": EPS}\n", - " elif solver.name == \"cplex\":\n", - " options = {\n", - " \"mip.tolerances.lowercutoff\": -1.0,\n", - " \"mip.tolerances.absmipgap\": EPS,\n", - " }\n", - " elif solver.name == \"cbc\":\n", - " options = {\"sec\": 60, \"ratio\": 0.1}\n", - " else:\n", - " options = {}\n", - "\n", + " solver = SolverFactory(solver)\n", " # Solve the model\n", " instance = model.create_instance()\n", - " results = solver.solve(instance, options=options)\n", + " results = solver.solve(instance, options=solver_options)\n", "\n", - " \"\"\"\n", - " After solving the model, \n", - " fix the acceptance of each order to the value in the solution and \n", - " solve the model again as simple linear problem.\n", - " This is necessary to get dual variables.\n", - " \"\"\"\n", - " # fix all model.x to the values in the solution\n", + " # Fix all model.x to the values in the solution\n", " if mode == \"with_min_acceptance_ratio\":\n", - " # add dual suffix to the model (we need this to extract the market clearing prices later)\n", + " # Add dual suffix to the model (needed to extract duals later)\n", " instance.dual = pyo.Suffix(direction=pyo.Suffix.IMPORT_EXPORT)\n", "\n", " for bid_id in instance.Bids:\n", + " # Fix the binary variable to its value\n", " instance.x[bid_id].fix(instance.x[bid_id].value)\n", + " # Change the domain to Reals (or appropriate continuous domain)\n", + " instance.x[bid_id].domain = pyo.Reals\n", "\n", - " # resolve the model\n", - " results = solver.solve(instance, options=options)\n", + " # Resolve the model\n", + " results = solver.solve(instance, options=solver_options)\n", "\n", " return instance, results" ] @@ -494,19 +497,53 @@ " def __init__(self, marketconfig: MarketConfig):\n", " super().__init__(marketconfig)\n", "\n", - " def validate_orderbook(self, orderbook: Orderbook, agent_addr) -> None:\n", + " def validate_orderbook(\n", + " self, orderbook: Orderbook, agent_addr\n", + " ) -> None:\n", " \"\"\"\n", - " This function ensures that the bid type is in ['SB', 'BB', 'LB'] and that the order volume is not larger than the maximum bid volume.\n", + " Checks whether the bid types are valid and whether the volumes are within the maximum bid volume.\n", "\n", " Args:\n", - " orderbook: The orderbook\n", - " agent_addr: The agent address\n", + " orderbook (Orderbook): The orderbook to be validated.\n", + " agent_addr (AgentAddress): The agent address of the market.\n", "\n", " Raises:\n", - " AssertionError: If the bid type is not in ['SB', 'BB', 'LB'] or the order volume is larger than the maximum bid volume\n", + " ValueError: If the bid type is invalid.\n", " \"\"\"\n", + " market_id = self.marketconfig.market_id\n", + " max_volume = self.marketconfig.maximum_bid_volume\n", + "\n", + " for order in orderbook:\n", + " # if bid_type is None, set to default bid_type\n", + " if order[\"bid_type\"] is None:\n", + " order[\"bid_type\"] = \"SB\"\n", + " # Validate bid_type\n", + " elif order[\"bid_type\"] not in [\"SB\", \"BB\", \"LB\"]:\n", + " order[\"bid_type\"] = \"SB\" # Set to default bid_type\n", + "\n", " super().validate_orderbook(orderbook, agent_addr)\n", "\n", + " for order in orderbook:\n", + " # Validate volumes\n", + " if order[\"bid_type\"] in [\"BB\", \"LB\"]:\n", + " for key, volume in order.get(\"volume\", {}).items():\n", + " if abs(volume) > max_volume:\n", + " order[\"volume\"][key] = max_volume if volume > 0 else -max_volume\n", + "\n", + " # Node validation\n", + " node = order.get(\"node\")\n", + " if node:\n", + " if self.zones_id:\n", + " node = self.node_to_zone.get(node, self.nodes[0])\n", + " order[\"node\"] = node\n", + " if node not in self.nodes:\n", + " order[\"node\"] = self.nodes[0]\n", + " else:\n", + " if self.incidence_matrix is not None:\n", + " order[\"node\"] = self.nodes[0]\n", + " else:\n", + " order[\"node\"] = \"node0\"\n", + "\n", " def clear(\n", " self, orderbook: Orderbook, market_products\n", " ) -> tuple[Orderbook, Orderbook, list[dict]]:\n", @@ -524,6 +561,13 @@ " accepted_orders (Orderbook): The accepted orders.\n", " rejected_orders (Orderbook): The rejected orders.\n", " meta (list[dict]): The market clearing results.\n", + "\n", + " Notes:\n", + " First the market clearing is solved using the cost minimization with the pyomo model market_clearing_opt.\n", + " Then the market clearing prices are extracted from the solved model as dual variables of the energy balance constraint.\n", + " Next the surplus of each order and its children is calculated and orders with negative surplus are removed from the orderbook.\n", + " This is repeated until all orders remaining in the orderbook have positive surplus.\n", + " Optional additional fields are: min_acceptance_ratio, parent_bid_id, node\n", " \"\"\"\n", "\n", " if len(orderbook) == 0:\n", @@ -531,8 +575,9 @@ "\n", " orderbook.sort(key=itemgetter(\"start_time\", \"end_time\", \"only_hours\"))\n", "\n", + " orderbook = check_for_tensors(orderbook)\n", + "\n", " # create a list of all orders linked as child to a bid\n", - " # this helps to later check the surplus for linked bids\n", " child_orders = []\n", " for order in orderbook:\n", " order[\"accepted_price\"] = {}\n", @@ -546,14 +591,14 @@ " )\n", " if parent_bid is None:\n", " order[\"parent_bid_id\"] = None\n", - " log.warning(f\"Parent bid {parent_bid_id} not in orderbook\")\n", + " logger.warning(f\"Parent bid {parent_bid_id} not in orderbook\")\n", " else:\n", " child_orders.append(order)\n", "\n", " with_linked_bids = bool(child_orders)\n", + "\n", " rejected_orders: Orderbook = []\n", "\n", - " # check whether the minimum acceptance ratio is specified\n", " mode = \"default\"\n", " if \"min_acceptance_ratio\" in self.marketconfig.additional_fields:\n", " mode = \"with_min_acceptance_ratio\"\n", @@ -566,18 +611,21 @@ " market_products=market_products,\n", " mode=mode,\n", " with_linked_bids=with_linked_bids,\n", + " incidence_matrix=self.incidence_matrix,\n", + " lines=self.lines,\n", + " solver=self.solver,\n", + " solver_options=self.solver_options,\n", " )\n", "\n", " if results.solver.termination_condition == TerminationCondition.infeasible:\n", " raise Exception(\"infeasible\")\n", "\n", " # extract dual from model.energy_balance\n", - " # we iterate over nodes to make a nested dictionary since this\n", - " # is the format the later functions expect\n", " market_clearing_prices = {}\n", " for node in self.nodes:\n", " market_clearing_prices[node] = {\n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " t: instance.dual[instance.energy_balance[node, t]]\n", + " for t in instance.T\n", " }\n", "\n", " # check the surplus of each order and remove those with negative surplus\n", @@ -585,16 +633,12 @@ " for order in orderbook:\n", " children = []\n", " if with_linked_bids:\n", - " # get all children of the current order\n", " children = [\n", " child\n", " for child in child_orders\n", " if child[\"parent_bid_id\"] == order[\"bid_id\"]\n", " ]\n", "\n", - " # here we use the predefined fluction calculate_order_surplus,\n", - " # the surplus is given as (market_clearing_price - order_price) * order_volume\n", - " # the surplus of children is added to the surplus of the parent bid if positive\n", " order_surplus = calculate_order_surplus(\n", " order, market_clearing_prices, instance, children\n", " )\n", @@ -617,20 +661,20 @@ " if all(order_surplus >= 0 for order_surplus in orders_surplus):\n", " break\n", "\n", - " # here we use the predefined function extract_results,\n", - " # it returns the accepted and rejected orders, and the market meta data for each timestep\n", - " # if you want to take a closer look, please refer to our documentation.\n", - " accepted_orders, rejected_orders, meta = extract_results(\n", + " log_flows = True\n", + "\n", + " accepted_orders, rejected_orders, meta, flows = extract_results(\n", " model=instance,\n", " orders=orderbook,\n", " rejected_orders=rejected_orders,\n", " market_products=market_products,\n", " market_clearing_prices=market_clearing_prices,\n", + " log_flows=log_flows,\n", " )\n", "\n", " self.all_orders = []\n", "\n", - " return accepted_orders, rejected_orders, meta" + " return accepted_orders, rejected_orders, meta, flows" ] }, { @@ -642,9 +686,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.world:connected to db\n", + "INFO:assume.scenario.loader_csv:Starting Scenario example_01g/sho_case from ../inputs\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", + "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", + "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Downsampling fuel_prices_df successful.\n", + "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding storage units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" + ] + }, + { + "data": { + "text/plain": [ + "MarketConfig(market_id='EOM', opening_hours=, opening_duration=Timedelta('1 days 00:00:00'), market_mechanism='pay_as_clear_advanced', market_products=[MarketProduct(duration=Timedelta('0 days 01:00:00'), count=24, first_delivery=Timedelta('1 days 00:00:00'), only_hours=None, eligible_lambda_function=None)], product_type='energy', maximum_bid_volume=100000, maximum_bid_price=3000, minimum_bid_price=-500, maximum_gradient=None, additional_fields=['bid_type', 'min_acceptance_ratio', 'parent_bid_id'], volume_unit='MWh', volume_tick=None, price_unit='EUR/MWh', price_tick=None, supports_get_unmatched=False, eligible_obligations_lambda= at 0x000001B47FDFB560>, param_dict={}, addr=AgentAddress(protocol_addr='world', aid='EOM_operator'), aid=' ')" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "world = World(database_uri=DB_URI)\n", "\n", @@ -685,9 +768,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "world.unit_operators[\"coal_unit_operator\"].units[\"coal_unit\"].bidding_strategies[\"EOM\"]" ] @@ -701,9 +795,1200 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.world:activating container\n", + "INFO:assume.world:all agents up - starting simulation\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 1/2588400 [00:00<105:28:36, 6.82it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-02 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-02 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-02 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-01 23:00:00: 3%|▎ | 86401.0/2588400 [00:00<00:07, 323006.69it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-03 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-03 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-03 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-02 23:00:00: 7%|▋ | 172801.0/2588400 [00:00<00:08, 272146.58it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-04 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-04 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-04 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-03 23:00:00: 10%|█ | 259201.0/2588400 [00:00<00:06, 355423.16it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-05 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-05 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-05 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-04 23:00:00: 13%|█▎ | 345601.0/2588400 [00:00<00:05, 405621.68it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-06 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-06 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-06 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-05 23:00:00: 17%|█▋ | 432001.0/2588400 [00:01<00:04, 435628.36it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-07 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-07 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-07 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-06 23:00:00: 20%|██ | 518401.0/2588400 [00:01<00:04, 434181.36it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-08 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-08 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-08 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-07 23:00:00: 23%|██▎ | 604801.0/2588400 [00:01<00:04, 431253.72it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-09 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-09 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-09 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-08 23:00:00: 27%|██▋ | 691201.0/2588400 [00:01<00:04, 418821.17it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-10 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-10 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-10 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-09 23:00:00: 30%|███ | 777601.0/2588400 [00:01<00:04, 421711.32it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-11 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-11 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-11 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-10 23:00:00: 33%|███▎ | 864001.0/2588400 [00:02<00:04, 417440.48it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-12 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-12 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-12 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-11 23:00:00: 37%|███▋ | 950401.0/2588400 [00:02<00:05, 319370.91it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-13 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-13 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-13 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-12 23:00:00: 40%|████ | 1036801.0/2588400 [00:02<00:04, 329788.53it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-14 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-14 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-14 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-13 23:00:00: 43%|████▎ | 1123201.0/2588400 [00:03<00:04, 333316.01it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-15 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-15 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-15 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-14 23:00:00: 47%|████▋ | 1209601.0/2588400 [00:03<00:04, 337007.62it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-16 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-16 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-16 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-15 23:00:00: 50%|█████ | 1296001.0/2588400 [00:03<00:03, 335680.15it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-17 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-17 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-17 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-16 23:00:00: 53%|█████▎ | 1382401.0/2588400 [00:04<00:04, 272159.11it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-18 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-18 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-18 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-17 23:00:00: 57%|█████▋ | 1468801.0/2588400 [00:04<00:03, 282855.58it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-19 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-19 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-19 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-18 23:00:00: 60%|██████ | 1555201.0/2588400 [00:04<00:03, 288447.60it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-20 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-20 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-20 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-19 23:00:00: 63%|██████▎ | 1641601.0/2588400 [00:04<00:03, 284299.66it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-21 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-21 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-21 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-20 23:00:00: 67%|██████▋ | 1728001.0/2588400 [00:05<00:03, 283293.98it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-22 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-22 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-22 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-21 23:00:00: 70%|███████ | 1814401.0/2588400 [00:05<00:03, 232216.45it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-23 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-23 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-23 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-22 23:00:00: 73%|███████▎ | 1900801.0/2588400 [00:06<00:02, 236896.66it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-24 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-24 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-24 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-23 23:00:00: 77%|███████▋ | 1987201.0/2588400 [00:06<00:02, 239133.32it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-25 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-25 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-25 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-24 23:00:00: 80%|████████ | 2073601.0/2588400 [00:07<00:02, 202251.29it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-26 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-26 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-26 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-25 23:00:00: 83%|████████▎ | 2160001.0/2588400 [00:07<00:02, 200147.72it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-27 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-27 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-27 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-26 23:00:00: 87%|████████▋ | 2246401.0/2588400 [00:08<00:01, 181515.42it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-28 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-28 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-28 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-27 23:00:00: 90%|█████████ | 2332801.0/2588400 [00:08<00:01, 187927.15it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-29 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-29 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-29 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-28 23:00:00: 93%|█████████▎| 2419201.0/2588400 [00:08<00:00, 192462.56it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-30 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-30 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-30 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-29 23:00:00: 97%|█████████▋| 2505601.0/2588400 [00:09<00:00, 167151.79it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:mango.util.distributed_clock:agent0: no new events, time stands still\n", + "WARNING:mango.util.distributed_clock:agent0: no new events, time stands still\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_sho_case 2020-01-30 00:00:00: 97%|█████████▋| 2505601.0/2588400 [00:09<00:00, 260735.92it/s]\n" + ] + } + ], "source": [ "world.run()" ] @@ -734,7 +2019,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -752,7 +2037,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -894,6 +2179,7 @@ " \"price\": bid_price_flex, # ====== new\n", " \"volume\": bid_quantity_flex, # ====== new\n", " \"bid_type\": \"SB\", # ====== new\n", + " \"node\": unit.node,\n", " },\n", " )\n", " # calculate previous power with planned dispatch (bid_quantity)\n", @@ -925,6 +2211,7 @@ " \"accepted_volume\": {\n", " product[0]: 0 for product in product_tuples\n", " }, # ====== new\n", + " \"node\": unit.node,\n", " }\n", " )\n", "\n", @@ -968,9 +2255,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.world:connected to db\n", + "INFO:assume.scenario.loader_csv:Starting Scenario example_01g/bo_case from ../inputs\n", + "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", + "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", + "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Downsampling fuel_prices_df successful.\n", + "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding storage units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" + ] + }, + { + "data": { + "text/plain": [ + "<__main__.blockStrategy at 0x1b42a17cdd0>" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "world = World(database_uri=DB_URI)\n", "\n", @@ -1000,9 +2320,1200 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.world:activating container\n", + "INFO:assume.world:all agents up - starting simulation\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 1/2588400 [00:00<88:05:28, 8.16it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-02 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-02 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-02 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-01 23:00:00: 3%|▎ | 86401.0/2588400 [00:00<00:07, 333275.70it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-03 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-03 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-03 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-02 23:00:00: 7%|▋ | 172801.0/2588400 [00:00<00:06, 400126.60it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-04 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-04 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-04 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-03 23:00:00: 10%|█ | 259201.0/2588400 [00:00<00:05, 428157.55it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-05 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-05 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-05 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-04 23:00:00: 13%|█▎ | 345601.0/2588400 [00:00<00:05, 447515.33it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-06 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-06 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-06 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-05 23:00:00: 17%|█▋ | 432001.0/2588400 [00:01<00:04, 446061.80it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-07 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-07 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-07 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-06 23:00:00: 20%|██ | 518401.0/2588400 [00:01<00:05, 408592.71it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-08 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-08 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-08 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-07 23:00:00: 23%|██▎ | 604801.0/2588400 [00:01<00:05, 389582.05it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-09 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-09 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-09 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-08 23:00:00: 27%|██▋ | 691201.0/2588400 [00:01<00:06, 297799.98it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-10 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-10 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-10 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-09 23:00:00: 30%|███ | 777601.0/2588400 [00:02<00:06, 289999.10it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-11 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-11 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-11 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-10 23:00:00: 33%|███▎ | 864001.0/2588400 [00:02<00:06, 284331.76it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-12 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-12 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-12 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-11 23:00:00: 37%|███▋ | 950401.0/2588400 [00:02<00:05, 300337.41it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-13 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-13 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-13 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-12 23:00:00: 40%|████ | 1036801.0/2588400 [00:03<00:05, 307983.42it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-14 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-14 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-14 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-13 23:00:00: 43%|████▎ | 1123201.0/2588400 [00:03<00:04, 312309.94it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-15 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-15 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-15 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-14 23:00:00: 47%|████▋ | 1209601.0/2588400 [00:03<00:05, 261932.41it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-16 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-16 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-16 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-15 23:00:00: 50%|█████ | 1296001.0/2588400 [00:04<00:04, 272530.73it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-17 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-17 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-17 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-16 23:00:00: 53%|█████▎ | 1382401.0/2588400 [00:04<00:04, 275450.36it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-18 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-18 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-18 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-17 23:00:00: 57%|█████▋ | 1468801.0/2588400 [00:04<00:04, 277675.95it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-19 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-19 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-19 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-18 23:00:00: 60%|██████ | 1555201.0/2588400 [00:05<00:04, 233918.88it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-20 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-20 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-20 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-19 23:00:00: 63%|██████▎ | 1641601.0/2588400 [00:05<00:03, 242350.54it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-21 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-21 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-21 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-20 23:00:00: 67%|██████▋ | 1728001.0/2588400 [00:05<00:03, 245615.38it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-22 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-22 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-22 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-21 23:00:00: 70%|███████ | 1814401.0/2588400 [00:06<00:03, 211066.81it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-23 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-23 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-23 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-22 23:00:00: 73%|███████▎ | 1900801.0/2588400 [00:06<00:03, 218535.11it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-24 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-24 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-24 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-23 23:00:00: 77%|███████▋ | 1987201.0/2588400 [00:07<00:02, 223091.16it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-25 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-25 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-25 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-24 23:00:00: 80%|████████ | 2073601.0/2588400 [00:07<00:02, 192415.04it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-26 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-26 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-26 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-25 23:00:00: 83%|████████▎ | 2160001.0/2588400 [00:08<00:02, 192497.28it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-27 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-27 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-27 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-26 23:00:00: 87%|████████▋ | 2246401.0/2588400 [00:08<00:01, 196470.00it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-28 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-28 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-28 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-27 23:00:00: 90%|█████████ | 2332801.0/2588400 [00:09<00:01, 200149.14it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-29 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-29 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-29 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-28 23:00:00: 93%|█████████▎| 2419201.0/2588400 [00:09<00:00, 176061.63it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-30 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-30 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-30 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-29 23:00:00: 97%|█████████▋| 2505601.0/2588400 [00:10<00:00, 194742.17it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:mango.util.distributed_clock:agent0: no new events, time stands still\n", + "WARNING:mango.util.distributed_clock:agent0: no new events, time stands still\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_bo_case 2020-01-30 00:00:00: 97%|█████████▋| 2505601.0/2588400 [00:10<00:00, 250101.10it/s]\n" + ] + } + ], "source": [ "world.run()" ] @@ -1018,7 +3529,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -1167,6 +3678,7 @@ " \"volume\": {start: bid_quantity_flex}, # ====== new\n", " \"bid_type\": \"LB\", # ====== new\n", " \"parent_bid_id\": parent_id, # ====== new\n", + " \"node\": unit.node,\n", " },\n", " )\n", " # calculate previous power with planned dispatch (bid_quantity)\n", @@ -1194,6 +3706,7 @@ " \"min_acceptance_ratio\": 1,\n", " \"accepted_volume\": {product[0]: 0 for product in product_tuples},\n", " \"bid_id\": block_id,\n", + " \"node\": unit.node,\n", " }\n", " )\n", "\n", @@ -1232,9 +3745,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 68, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.world:connected to db\n", + "INFO:assume.scenario.loader_csv:Starting Scenario example_01g/lo_case from ../inputs\n", + "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", + "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", + "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Downsampling fuel_prices_df successful.\n", + "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding storage units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" + ] + }, + { + "data": { + "text/plain": [ + "<__main__.linkedStrategy at 0x1b42c0bc810>" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "world = World(database_uri=DB_URI)\n", "\n", @@ -1264,9 +3810,1202 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 69, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.world:activating container\n", + "INFO:assume.world:all agents up - starting simulation\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 1/2588400 [00:00<106:44:55, 6.74it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-02 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-02 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-02 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-01 23:00:00: 3%|▎ | 86401.0/2588400 [00:00<00:08, 304069.54it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-03 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-03 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-03 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-02 23:00:00: 7%|▋ | 172801.0/2588400 [00:00<00:07, 327669.82it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-04 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-04 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-04 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-03 23:00:00: 10%|█ | 259201.0/2588400 [00:00<00:06, 346103.47it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-05 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-05 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-05 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-04 23:00:00: 13%|█▎ | 345601.0/2588400 [00:01<00:06, 347361.41it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-06 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-06 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-06 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-05 23:00:00: 17%|█▋ | 432001.0/2588400 [00:01<00:06, 332355.19it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-07 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-07 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-07 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-06 23:00:00: 20%|██ | 518401.0/2588400 [00:01<00:08, 249126.61it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-08 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-08 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-08 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-07 23:00:00: 23%|██▎ | 604801.0/2588400 [00:02<00:07, 259670.77it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-09 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-09 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-09 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-08 23:00:00: 27%|██▋ | 691201.0/2588400 [00:02<00:07, 263069.09it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-10 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-10 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-10 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-09 23:00:00: 30%|███ | 777601.0/2588400 [00:02<00:06, 259792.09it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-11 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-11 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-11 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-10 23:00:00: 33%|███▎ | 864001.0/2588400 [00:03<00:06, 254545.16it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-12 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-12 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-12 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-11 23:00:00: 37%|███▋ | 950401.0/2588400 [00:03<00:07, 212249.92it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-13 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-13 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-13 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-12 23:00:00: 40%|████ | 1036801.0/2588400 [00:04<00:07, 212292.44it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-14 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-14 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-14 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-13 23:00:00: 43%|████▎ | 1123201.0/2588400 [00:04<00:07, 207474.60it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:pyomo.core:Setting Var 'x[lignite_unit_block]' to a value `0.999999999999998` (float) not in domain Binary.\n", + "WARNING:pyomo.core:Setting Var 'x[combined_gas_unit_block]' to a value `1.000000000000001` (float) not in domain Binary.\n", + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-15 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-15 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-15 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-14 23:00:00: 47%|████▋ | 1209601.0/2588400 [00:05<00:08, 170711.92it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-16 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-16 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-16 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-15 23:00:00: 50%|█████ | 1296001.0/2588400 [00:05<00:07, 170074.37it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-17 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-17 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-17 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-16 23:00:00: 53%|█████▎ | 1382401.0/2588400 [00:06<00:07, 168116.94it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-18 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-18 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-18 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-17 23:00:00: 57%|█████▋ | 1468801.0/2588400 [00:07<00:07, 142806.39it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-19 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-19 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-19 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-18 23:00:00: 60%|██████ | 1555201.0/2588400 [00:07<00:07, 142674.15it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-20 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-20 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-20 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-19 23:00:00: 63%|██████▎ | 1641601.0/2588400 [00:08<00:07, 132561.49it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-21 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-21 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-21 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-20 23:00:00: 67%|██████▋ | 1728001.0/2588400 [00:09<00:06, 136773.45it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-22 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-22 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-22 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-21 23:00:00: 70%|███████ | 1814401.0/2588400 [00:09<00:06, 127180.97it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-23 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-23 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-23 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-22 23:00:00: 73%|███████▎ | 1900801.0/2588400 [00:10<00:05, 126821.13it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-24 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-24 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-24 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-23 23:00:00: 77%|███████▋ | 1987201.0/2588400 [00:11<00:05, 117727.28it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-25 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-25 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-25 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-24 23:00:00: 80%|████████ | 2073601.0/2588400 [00:12<00:04, 113235.10it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-26 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-26 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-26 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-25 23:00:00: 83%|████████▎ | 2160001.0/2588400 [00:12<00:03, 117259.62it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-27 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-27 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-27 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-26 23:00:00: 87%|████████▋ | 2246401.0/2588400 [00:13<00:03, 107206.93it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-28 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-28 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-28 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-27 23:00:00: 90%|█████████ | 2332801.0/2588400 [00:14<00:02, 110554.35it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-29 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-29 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-29 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-28 23:00:00: 93%|█████████▎| 2419201.0/2588400 [00:15<00:01, 102712.31it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-30 00:00:00' is not valid for indexed component 'energy_balance'\"\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=KeyError(\"Index '2020-01-30 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", + " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", + " ^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", + " market_clearing_prices[node] = {\n", + " ^\n", + " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", + " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", + " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", + " validated_index = self._validate_index(index)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", + " raise KeyError(\n", + "KeyError: \"Index '2020-01-30 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-29 23:00:00: 97%|█████████▋| 2505601.0/2588400 [00:16<00:00, 102238.94it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:mango.util.distributed_clock:agent0: no new events, time stands still\n", + "WARNING:mango.util.distributed_clock:agent0: no new events, time stands still\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_01g_lo_case 2020-01-30 00:00:00: 97%|█████████▋| 2505601.0/2588400 [00:16<00:00, 152149.65it/s]\n" + ] + } + ], "source": [ "world.run()" ] @@ -1282,7 +5021,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ @@ -1299,9 +5038,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "TypeError", + "evalue": "no numeric data to plot", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[71], line 36\u001b[0m\n\u001b[0;32m 32\u001b[0m ax2 \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39mtwinx() \u001b[38;5;66;03m# Create another axes that shares the same x-axis as ax.\u001b[39;00m\n\u001b[0;32m 34\u001b[0m width \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.4\u001b[39m\n\u001b[1;32m---> 36\u001b[0m \u001b[43mkpis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtotal_volume\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbar\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwidth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwidth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mposition\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mroyalblue\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 37\u001b[0m kpis\u001b[38;5;241m.\u001b[39mtotal_cost\u001b[38;5;241m.\u001b[39mplot(kind\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbar\u001b[39m\u001b[38;5;124m\"\u001b[39m, ax\u001b[38;5;241m=\u001b[39max2, width\u001b[38;5;241m=\u001b[39mwidth, position\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgreen\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 39\u001b[0m \u001b[38;5;66;03m# set x-achxis limits\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\plotting\\_core.py:1030\u001b[0m, in \u001b[0;36mPlotAccessor.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1027\u001b[0m label_name \u001b[38;5;241m=\u001b[39m label_kw \u001b[38;5;129;01mor\u001b[39;00m data\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[0;32m 1028\u001b[0m data\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m=\u001b[39m label_name\n\u001b[1;32m-> 1030\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mplot_backend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkind\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\plotting\\_matplotlib\\__init__.py:71\u001b[0m, in \u001b[0;36mplot\u001b[1;34m(data, kind, **kwargs)\u001b[0m\n\u001b[0;32m 69\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124max\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(ax, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mleft_ax\u001b[39m\u001b[38;5;124m\"\u001b[39m, ax)\n\u001b[0;32m 70\u001b[0m plot_obj \u001b[38;5;241m=\u001b[39m PLOT_CLASSES[kind](data, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m---> 71\u001b[0m \u001b[43mplot_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 72\u001b[0m plot_obj\u001b[38;5;241m.\u001b[39mdraw()\n\u001b[0;32m 73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m plot_obj\u001b[38;5;241m.\u001b[39mresult\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:499\u001b[0m, in \u001b[0;36mMPLPlot.generate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 497\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m 498\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgenerate\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 499\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compute_plot_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 500\u001b[0m fig \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfig\n\u001b[0;32m 501\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_plot(fig)\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:698\u001b[0m, in \u001b[0;36mMPLPlot._compute_plot_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 696\u001b[0m \u001b[38;5;66;03m# no non-numeric frames or series allowed\u001b[39;00m\n\u001b[0;32m 697\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_empty:\n\u001b[1;32m--> 698\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mno numeric data to plot\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 700\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;241m=\u001b[39m numeric_data\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m_convert_to_ndarray)\n", + "\u001b[1;31mTypeError\u001b[0m: no numeric data to plot" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sql = \"\"\"\n", "SELECT ident, simulation,\n", @@ -1386,7 +5151,18 @@ "id": "qoWI_agIJOE4", "outputId": "9b40e670-bfef-4560-d6e8-61a1b29d1975" }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# second plot for the accepted volume of the coal unit\n", "sql = \"\"\"\n", @@ -1473,7 +5249,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "assume", + "display_name": "assume-framework", "language": "python", "name": "python3" }, @@ -1487,7 +5263,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.11.9" }, "nbsphinx": { "execute": "never" From d938f9d755578959a4d282e90e37b275182708e8 Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Wed, 20 Nov 2024 11:00:27 +0100 Subject: [PATCH 07/22] - cleared notebooks outputs --- .../06_advanced_orders_example.ipynb | 3582 +---------------- 1 file changed, 45 insertions(+), 3537 deletions(-) diff --git a/examples/notebooks/06_advanced_orders_example.ipynb b/examples/notebooks/06_advanced_orders_example.ipynb index fc8627b14..725d11a4c 100644 --- a/examples/notebooks/06_advanced_orders_example.ipynb +++ b/examples/notebooks/06_advanced_orders_example.ipynb @@ -125,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 91, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -152,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 92, "metadata": {}, "outputs": [], "source": [ @@ -171,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 93, "metadata": {}, "outputs": [], "source": [ @@ -194,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 94, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -240,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 95, "metadata": {}, "outputs": [], "source": [ @@ -273,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 96, "metadata": {}, "outputs": [], "source": [ @@ -484,7 +484,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 97, "metadata": {}, "outputs": [], "source": [ @@ -575,8 +575,6 @@ "\n", " orderbook.sort(key=itemgetter(\"start_time\", \"end_time\", \"only_hours\"))\n", "\n", - " orderbook = check_for_tensors(orderbook)\n", - "\n", " # create a list of all orders linked as child to a bid\n", " child_orders = []\n", " for order in orderbook:\n", @@ -591,7 +589,6 @@ " )\n", " if parent_bid is None:\n", " order[\"parent_bid_id\"] = None\n", - " logger.warning(f\"Parent bid {parent_bid_id} not in orderbook\")\n", " else:\n", " child_orders.append(order)\n", "\n", @@ -686,7 +683,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 98, "metadata": {}, "outputs": [ { @@ -694,13 +691,7 @@ "output_type": "stream", "text": [ "INFO:assume.world:connected to db\n", - "INFO:assume.scenario.loader_csv:Starting Scenario example_01g/sho_case from ../inputs\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "INFO:assume.scenario.loader_csv:Starting Scenario example_01g/sho_case from ../inputs\n", "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", @@ -720,10 +711,10 @@ { "data": { "text/plain": [ - "MarketConfig(market_id='EOM', opening_hours=, opening_duration=Timedelta('1 days 00:00:00'), market_mechanism='pay_as_clear_advanced', market_products=[MarketProduct(duration=Timedelta('0 days 01:00:00'), count=24, first_delivery=Timedelta('1 days 00:00:00'), only_hours=None, eligible_lambda_function=None)], product_type='energy', maximum_bid_volume=100000, maximum_bid_price=3000, minimum_bid_price=-500, maximum_gradient=None, additional_fields=['bid_type', 'min_acceptance_ratio', 'parent_bid_id'], volume_unit='MWh', volume_tick=None, price_unit='EUR/MWh', price_tick=None, supports_get_unmatched=False, eligible_obligations_lambda= at 0x000001B47FDFB560>, param_dict={}, addr=AgentAddress(protocol_addr='world', aid='EOM_operator'), aid=' ')" + "MarketConfig(market_id='EOM', opening_hours=, opening_duration=Timedelta('1 days 00:00:00'), market_mechanism='pay_as_clear_advanced', market_products=[MarketProduct(duration=Timedelta('0 days 01:00:00'), count=24, first_delivery=Timedelta('1 days 00:00:00'), only_hours=None, eligible_lambda_function=None)], product_type='energy', maximum_bid_volume=100000, maximum_bid_price=3000, minimum_bid_price=-500, maximum_gradient=None, additional_fields=['bid_type', 'min_acceptance_ratio', 'parent_bid_id'], volume_unit='MWh', volume_tick=None, price_unit='EUR/MWh', price_tick=None, supports_get_unmatched=False, eligible_obligations_lambda= at 0x000001B47FDFB560>, param_dict={}, addr=AgentAddress(protocol_addr='world', aid='EOM_operator'), aid=' ')" ] }, - "execution_count": 60, + "execution_count": 98, "metadata": {}, "output_type": "execute_result" } @@ -768,16 +759,16 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 99, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 61, + "execution_count": 99, "metadata": {}, "output_type": "execute_result" } @@ -795,7 +786,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 100, "metadata": {}, "outputs": [ { @@ -810,1182 +801,22 @@ "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 1/2588400 [00:00<105:28:36, 6.82it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-02 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-02 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-02 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-01 23:00:00: 3%|▎ | 86401.0/2588400 [00:00<00:07, 323006.69it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-03 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-03 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-03 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-02 23:00:00: 7%|▋ | 172801.0/2588400 [00:00<00:08, 272146.58it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-04 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-04 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-04 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-03 23:00:00: 10%|█ | 259201.0/2588400 [00:00<00:06, 355423.16it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-05 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-05 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-05 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-04 23:00:00: 13%|█▎ | 345601.0/2588400 [00:00<00:05, 405621.68it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-06 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-06 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-06 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-05 23:00:00: 17%|█▋ | 432001.0/2588400 [00:01<00:04, 435628.36it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-07 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-07 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-07 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-06 23:00:00: 20%|██ | 518401.0/2588400 [00:01<00:04, 434181.36it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-08 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-08 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-08 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-07 23:00:00: 23%|██▎ | 604801.0/2588400 [00:01<00:04, 431253.72it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-09 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-09 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-09 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-08 23:00:00: 27%|██▋ | 691201.0/2588400 [00:01<00:04, 418821.17it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-10 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-10 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-10 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-09 23:00:00: 30%|███ | 777601.0/2588400 [00:01<00:04, 421711.32it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-11 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-11 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-11 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-10 23:00:00: 33%|███▎ | 864001.0/2588400 [00:02<00:04, 417440.48it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-12 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-12 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-12 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-11 23:00:00: 37%|███▋ | 950401.0/2588400 [00:02<00:05, 319370.91it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-13 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-13 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-13 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-12 23:00:00: 40%|████ | 1036801.0/2588400 [00:02<00:04, 329788.53it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-14 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-14 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-14 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-13 23:00:00: 43%|████▎ | 1123201.0/2588400 [00:03<00:04, 333316.01it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-15 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-15 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-15 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-14 23:00:00: 47%|████▋ | 1209601.0/2588400 [00:03<00:04, 337007.62it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-16 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-16 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-16 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-15 23:00:00: 50%|█████ | 1296001.0/2588400 [00:03<00:03, 335680.15it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-17 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-17 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-17 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-16 23:00:00: 53%|█████▎ | 1382401.0/2588400 [00:04<00:04, 272159.11it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-18 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-18 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-18 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-17 23:00:00: 57%|█████▋ | 1468801.0/2588400 [00:04<00:03, 282855.58it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-19 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-19 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-19 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-18 23:00:00: 60%|██████ | 1555201.0/2588400 [00:04<00:03, 288447.60it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-20 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-20 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-20 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-19 23:00:00: 63%|██████▎ | 1641601.0/2588400 [00:04<00:03, 284299.66it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-21 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-21 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-21 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-20 23:00:00: 67%|██████▋ | 1728001.0/2588400 [00:05<00:03, 283293.98it/s]" + "example_01g_sho_case 2020-01-29 23:00:00: 97%|█████████▋| 2505601.0/2588400 [00:24<00:00, 105845.94it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-22 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-22 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-22 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + "WARNING:mango.util.distributed_clock:agent0: no new events, time stands still\n", + "WARNING:mango.util.distributed_clock:agent0: no new events, time stands still\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "example_01g_sho_case 2020-01-21 23:00:00: 70%|███████ | 1814401.0/2588400 [00:05<00:03, 232216.45it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-23 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-23 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-23 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-22 23:00:00: 73%|███████▎ | 1900801.0/2588400 [00:06<00:02, 236896.66it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-24 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-24 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-24 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-23 23:00:00: 77%|███████▋ | 1987201.0/2588400 [00:06<00:02, 239133.32it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-25 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-25 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-25 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-24 23:00:00: 80%|████████ | 2073601.0/2588400 [00:07<00:02, 202251.29it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-26 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-26 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-26 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-25 23:00:00: 83%|████████▎ | 2160001.0/2588400 [00:07<00:02, 200147.72it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-27 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-27 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-27 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-26 23:00:00: 87%|████████▋ | 2246401.0/2588400 [00:08<00:01, 181515.42it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-28 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-28 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-28 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-27 23:00:00: 90%|█████████ | 2332801.0/2588400 [00:08<00:01, 187927.15it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-29 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-29 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-29 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-28 23:00:00: 93%|█████████▎| 2419201.0/2588400 [00:08<00:00, 192462.56it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-30 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-30 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-30 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-29 23:00:00: 97%|█████████▋| 2505601.0/2588400 [00:09<00:00, 167151.79it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:mango.util.distributed_clock:agent0: no new events, time stands still\n", - "WARNING:mango.util.distributed_clock:agent0: no new events, time stands still\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-30 00:00:00: 97%|█████████▋| 2505601.0/2588400 [00:09<00:00, 260735.92it/s]\n" + "example_01g_sho_case 2020-01-30 00:00:00: 97%|█████████▋| 2505601.0/2588400 [00:24<00:00, 101569.79it/s]\n" ] } ], @@ -2019,7 +850,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 101, "metadata": {}, "outputs": [], "source": [ @@ -2037,7 +868,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 102, "metadata": {}, "outputs": [], "source": [ @@ -2255,7 +1086,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 103, "metadata": {}, "outputs": [ { @@ -2283,10 +1114,10 @@ { "data": { "text/plain": [ - "<__main__.blockStrategy at 0x1b42a17cdd0>" + "<__main__.blockStrategy at 0x1b42e272210>" ] }, - "execution_count": 65, + "execution_count": 103, "metadata": {}, "output_type": "execute_result" } @@ -2320,7 +1151,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 104, "metadata": {}, "outputs": [ { @@ -2335,1182 +1166,22 @@ "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 1/2588400 [00:00<88:05:28, 8.16it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-02 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-02 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-02 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-01 23:00:00: 3%|▎ | 86401.0/2588400 [00:00<00:07, 333275.70it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-03 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-03 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-03 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-02 23:00:00: 7%|▋ | 172801.0/2588400 [00:00<00:06, 400126.60it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-04 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-04 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-04 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-03 23:00:00: 10%|█ | 259201.0/2588400 [00:00<00:05, 428157.55it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-05 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-05 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-05 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-04 23:00:00: 13%|█▎ | 345601.0/2588400 [00:00<00:05, 447515.33it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-06 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-06 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-06 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-05 23:00:00: 17%|█▋ | 432001.0/2588400 [00:01<00:04, 446061.80it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-07 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-07 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-07 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-06 23:00:00: 20%|██ | 518401.0/2588400 [00:01<00:05, 408592.71it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-08 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-08 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-08 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-07 23:00:00: 23%|██▎ | 604801.0/2588400 [00:01<00:05, 389582.05it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-09 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-09 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-09 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-08 23:00:00: 27%|██▋ | 691201.0/2588400 [00:01<00:06, 297799.98it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-10 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-10 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-10 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-09 23:00:00: 30%|███ | 777601.0/2588400 [00:02<00:06, 289999.10it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-11 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-11 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-11 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-10 23:00:00: 33%|███▎ | 864001.0/2588400 [00:02<00:06, 284331.76it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-12 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-12 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-12 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-11 23:00:00: 37%|███▋ | 950401.0/2588400 [00:02<00:05, 300337.41it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-13 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-13 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-13 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-12 23:00:00: 40%|████ | 1036801.0/2588400 [00:03<00:05, 307983.42it/s]" + "example_01g_bo_case 2020-01-29 00:00:00: 97%|█████████▋| 2502001.0/2588400 [00:22<00:00, 108881.80it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-14 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-14 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-14 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + "WARNING:mango.util.distributed_clock:agent0: no new events, time stands still\n", + "WARNING:mango.util.distributed_clock:agent0: no new events, time stands still\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "example_01g_bo_case 2020-01-13 23:00:00: 43%|████▎ | 1123201.0/2588400 [00:03<00:04, 312309.94it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-15 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-15 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-15 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-14 23:00:00: 47%|████▋ | 1209601.0/2588400 [00:03<00:05, 261932.41it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-16 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-16 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-16 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-15 23:00:00: 50%|█████ | 1296001.0/2588400 [00:04<00:04, 272530.73it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-17 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-17 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-17 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-16 23:00:00: 53%|█████▎ | 1382401.0/2588400 [00:04<00:04, 275450.36it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-18 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-18 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-18 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-17 23:00:00: 57%|█████▋ | 1468801.0/2588400 [00:04<00:04, 277675.95it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-19 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-19 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-19 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-18 23:00:00: 60%|██████ | 1555201.0/2588400 [00:05<00:04, 233918.88it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-20 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-20 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-20 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-19 23:00:00: 63%|██████▎ | 1641601.0/2588400 [00:05<00:03, 242350.54it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-21 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-21 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-21 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-20 23:00:00: 67%|██████▋ | 1728001.0/2588400 [00:05<00:03, 245615.38it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-22 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-22 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-22 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-21 23:00:00: 70%|███████ | 1814401.0/2588400 [00:06<00:03, 211066.81it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-23 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-23 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-23 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-22 23:00:00: 73%|███████▎ | 1900801.0/2588400 [00:06<00:03, 218535.11it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-24 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-24 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-24 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-23 23:00:00: 77%|███████▋ | 1987201.0/2588400 [00:07<00:02, 223091.16it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-25 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-25 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-25 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-24 23:00:00: 80%|████████ | 2073601.0/2588400 [00:07<00:02, 192415.04it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-26 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-26 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-26 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-25 23:00:00: 83%|████████▎ | 2160001.0/2588400 [00:08<00:02, 192497.28it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-27 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-27 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-27 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-26 23:00:00: 87%|████████▋ | 2246401.0/2588400 [00:08<00:01, 196470.00it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-28 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-28 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-28 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-27 23:00:00: 90%|█████████ | 2332801.0/2588400 [00:09<00:01, 200149.14it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-29 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-29 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-29 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-28 23:00:00: 93%|█████████▎| 2419201.0/2588400 [00:09<00:00, 176061.63it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-30 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-30 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-30 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-29 23:00:00: 97%|█████████▋| 2505601.0/2588400 [00:10<00:00, 194742.17it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:mango.util.distributed_clock:agent0: no new events, time stands still\n", - "WARNING:mango.util.distributed_clock:agent0: no new events, time stands still\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-30 00:00:00: 97%|█████████▋| 2505601.0/2588400 [00:10<00:00, 250101.10it/s]\n" + "example_01g_bo_case 2020-01-30 00:00:00: 97%|█████████▋| 2505601.0/2588400 [00:22<00:00, 109186.98it/s]\n" ] } ], @@ -3529,7 +1200,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 105, "metadata": {}, "outputs": [], "source": [ @@ -3745,7 +1416,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 106, "metadata": {}, "outputs": [ { @@ -3773,10 +1444,10 @@ { "data": { "text/plain": [ - "<__main__.linkedStrategy at 0x1b42c0bc810>" + "<__main__.linkedStrategy at 0x1b42eea6ed0>" ] }, - "execution_count": 68, + "execution_count": 106, "metadata": {}, "output_type": "execute_result" } @@ -3810,7 +1481,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 107, "metadata": {}, "outputs": [ { @@ -3825,1184 +1496,36 @@ "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 1/2588400 [00:00<106:44:55, 6.74it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-02 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-02 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-02 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-01 23:00:00: 3%|▎ | 86401.0/2588400 [00:00<00:08, 304069.54it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-03 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-03 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-03 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-02 23:00:00: 7%|▋ | 172801.0/2588400 [00:00<00:07, 327669.82it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-04 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-04 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-04 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-03 23:00:00: 10%|█ | 259201.0/2588400 [00:00<00:06, 346103.47it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-05 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-05 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-05 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-04 23:00:00: 13%|█▎ | 345601.0/2588400 [00:01<00:06, 347361.41it/s]" + "example_01g_lo_case 2020-01-12 23:00:00: 40%|████ | 1036801.0/2588400 [00:10<00:16, 94622.67it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-06 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-06 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-06 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + "WARNING:pyomo.core:Setting Var 'x[combined_gas_unit_block]' to a value `1.0000000000000016` (float) not in domain Binary.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "example_01g_lo_case 2020-01-05 23:00:00: 17%|█▋ | 432001.0/2588400 [00:01<00:06, 332355.19it/s]" + "example_01g_lo_case 2020-01-29 23:00:00: 97%|█████████▋| 2505601.0/2588400 [00:25<00:00, 100321.53it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-07 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-07 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-07 00:00:00' is not valid for indexed component 'energy_balance'\"\n" + "WARNING:mango.util.distributed_clock:agent0: no new events, time stands still\n", + "WARNING:mango.util.distributed_clock:agent0: no new events, time stands still\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "example_01g_lo_case 2020-01-06 23:00:00: 20%|██ | 518401.0/2588400 [00:01<00:08, 249126.61it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-08 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-08 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-08 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-07 23:00:00: 23%|██▎ | 604801.0/2588400 [00:02<00:07, 259670.77it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-09 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-09 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-09 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-08 23:00:00: 27%|██▋ | 691201.0/2588400 [00:02<00:07, 263069.09it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-10 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-10 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-10 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-09 23:00:00: 30%|███ | 777601.0/2588400 [00:02<00:06, 259792.09it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-11 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-11 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-11 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-10 23:00:00: 33%|███▎ | 864001.0/2588400 [00:03<00:06, 254545.16it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-12 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-12 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-12 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-11 23:00:00: 37%|███▋ | 950401.0/2588400 [00:03<00:07, 212249.92it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-13 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-13 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-13 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-12 23:00:00: 40%|████ | 1036801.0/2588400 [00:04<00:07, 212292.44it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-14 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-14 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-14 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-13 23:00:00: 43%|████▎ | 1123201.0/2588400 [00:04<00:07, 207474.60it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:pyomo.core:Setting Var 'x[lignite_unit_block]' to a value `0.999999999999998` (float) not in domain Binary.\n", - "WARNING:pyomo.core:Setting Var 'x[combined_gas_unit_block]' to a value `1.000000000000001` (float) not in domain Binary.\n", - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-15 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-15 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-15 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-14 23:00:00: 47%|████▋ | 1209601.0/2588400 [00:05<00:08, 170711.92it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-16 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-16 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-16 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-15 23:00:00: 50%|█████ | 1296001.0/2588400 [00:05<00:07, 170074.37it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-17 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-17 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-17 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-16 23:00:00: 53%|█████▎ | 1382401.0/2588400 [00:06<00:07, 168116.94it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-18 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-18 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-18 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-17 23:00:00: 57%|█████▋ | 1468801.0/2588400 [00:07<00:07, 142806.39it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-19 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-19 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-19 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-18 23:00:00: 60%|██████ | 1555201.0/2588400 [00:07<00:07, 142674.15it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-20 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-20 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-20 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-19 23:00:00: 63%|██████▎ | 1641601.0/2588400 [00:08<00:07, 132561.49it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-21 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-21 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-21 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-20 23:00:00: 67%|██████▋ | 1728001.0/2588400 [00:09<00:06, 136773.45it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-22 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-22 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-22 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-21 23:00:00: 70%|███████ | 1814401.0/2588400 [00:09<00:06, 127180.97it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-23 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-23 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-23 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-22 23:00:00: 73%|███████▎ | 1900801.0/2588400 [00:10<00:05, 126821.13it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-24 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-24 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-24 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-23 23:00:00: 77%|███████▋ | 1987201.0/2588400 [00:11<00:05, 117727.28it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-25 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-25 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-25 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-24 23:00:00: 80%|████████ | 2073601.0/2588400 [00:12<00:04, 113235.10it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-26 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-26 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-26 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-25 23:00:00: 83%|████████▎ | 2160001.0/2588400 [00:12<00:03, 117259.62it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-27 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-27 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-27 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-26 23:00:00: 87%|████████▋ | 2246401.0/2588400 [00:13<00:03, 107206.93it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-28 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-28 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-28 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-27 23:00:00: 90%|█████████ | 2332801.0/2588400 [00:14<00:02, 110554.35it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-29 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-29 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-29 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-28 23:00:00: 93%|█████████▎| 2419201.0/2588400 [00:15<00:01, 102712.31it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: \"Index '2020-01-30 00:00:00' is not valid for indexed component 'energy_balance'\"\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=KeyError(\"Index '2020-01-30 00:00:00' is not valid for indexed component 'energy_balance'\")>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 193, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 612, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 607, in clear_market\n", - " (accepted_orderbook, rejected_orderbook, market_meta, flows) = self.clear(\n", - " ^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 92, in clear\n", - " market_clearing_prices[node] = {\n", - " ^\n", - " File \"C:\\Users\\tg3533\\AppData\\Local\\Temp\\ipykernel_24072\\3874917237.py\", line 93, in \n", - " t: instance.dual[instance.energy_balance[t]] for t in instance.T\n", - " ~~~~~~~~~~~~~~~~~~~~~~~^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 644, in __getitem__\n", - " validated_index = self._validate_index(index)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\core\\base\\indexed_component.py\", line 866, in _validate_index\n", - " raise KeyError(\n", - "KeyError: \"Index '2020-01-30 00:00:00' is not valid for indexed component 'energy_balance'\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-29 23:00:00: 97%|█████████▋| 2505601.0/2588400 [00:16<00:00, 102238.94it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:mango.util.distributed_clock:agent0: no new events, time stands still\n", - "WARNING:mango.util.distributed_clock:agent0: no new events, time stands still\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-30 00:00:00: 97%|█████████▋| 2505601.0/2588400 [00:16<00:00, 152149.65it/s]\n" + "example_01g_lo_case 2020-01-30 00:00:00: 97%|█████████▋| 2505601.0/2588400 [00:25<00:00, 97388.82it/s] \n" ] } ], @@ -5021,7 +1544,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 108, "metadata": {}, "outputs": [], "source": [ @@ -5038,27 +1561,12 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 109, "metadata": {}, "outputs": [ - { - "ename": "TypeError", - "evalue": "no numeric data to plot", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[71], line 36\u001b[0m\n\u001b[0;32m 32\u001b[0m ax2 \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39mtwinx() \u001b[38;5;66;03m# Create another axes that shares the same x-axis as ax.\u001b[39;00m\n\u001b[0;32m 34\u001b[0m width \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.4\u001b[39m\n\u001b[1;32m---> 36\u001b[0m \u001b[43mkpis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtotal_volume\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbar\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwidth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwidth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mposition\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mroyalblue\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 37\u001b[0m kpis\u001b[38;5;241m.\u001b[39mtotal_cost\u001b[38;5;241m.\u001b[39mplot(kind\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbar\u001b[39m\u001b[38;5;124m\"\u001b[39m, ax\u001b[38;5;241m=\u001b[39max2, width\u001b[38;5;241m=\u001b[39mwidth, position\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgreen\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 39\u001b[0m \u001b[38;5;66;03m# set x-achxis limits\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\plotting\\_core.py:1030\u001b[0m, in \u001b[0;36mPlotAccessor.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1027\u001b[0m label_name \u001b[38;5;241m=\u001b[39m label_kw \u001b[38;5;129;01mor\u001b[39;00m data\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[0;32m 1028\u001b[0m data\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m=\u001b[39m label_name\n\u001b[1;32m-> 1030\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mplot_backend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkind\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\plotting\\_matplotlib\\__init__.py:71\u001b[0m, in \u001b[0;36mplot\u001b[1;34m(data, kind, **kwargs)\u001b[0m\n\u001b[0;32m 69\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124max\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(ax, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mleft_ax\u001b[39m\u001b[38;5;124m\"\u001b[39m, ax)\n\u001b[0;32m 70\u001b[0m plot_obj \u001b[38;5;241m=\u001b[39m PLOT_CLASSES[kind](data, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m---> 71\u001b[0m \u001b[43mplot_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 72\u001b[0m plot_obj\u001b[38;5;241m.\u001b[39mdraw()\n\u001b[0;32m 73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m plot_obj\u001b[38;5;241m.\u001b[39mresult\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:499\u001b[0m, in \u001b[0;36mMPLPlot.generate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 497\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m 498\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgenerate\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 499\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compute_plot_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 500\u001b[0m fig \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfig\n\u001b[0;32m 501\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_plot(fig)\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:698\u001b[0m, in \u001b[0;36mMPLPlot._compute_plot_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 696\u001b[0m \u001b[38;5;66;03m# no non-numeric frames or series allowed\u001b[39;00m\n\u001b[0;32m 697\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_empty:\n\u001b[1;32m--> 698\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mno numeric data to plot\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 700\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;241m=\u001b[39m numeric_data\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m_convert_to_ndarray)\n", - "\u001b[1;31mTypeError\u001b[0m: no numeric data to plot" - ] - }, { "data": { - "image/png": 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", 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3nTp16qXPPX78uAYOHKjjx48rderUypMnjzJnziwp/vfXqlevnoYNG6Zbt24pffr0WrdunSpXrixXV1dJTyfgcHFxiRVuMmTIIEmJ2mGUXjwT6qsmnon+ubVq1eqFy2/evPnS5/78888aO3asrl+/LmdnZxUoUECpUqWKtd7zp9JFH22IDkfRP5Nn2djYxGqLj/v378toNL70/fCqn/O///6r/v37a/fu3bK1tVWuXLlMR5Li+n6Ifv9cvXr1peuEhITIwcFBdnZ2unv3ruk98Hyt0tMwkzZtWi1ZskTTpk3Tpk2btGLFCqVKlUqfffaZvv/++zjNevoi0dfxxfVnlTZt2ldu786dOwoKClLBggVfuDwoKEjp0qV77fsn+r2ZPn36176GZ9/j0e+vuPyubG1tVa5cOdMsnzdu3NCQIUO0efNmbd++/aWn8MZ3nCXUjh07NGzYMF24cEFp06ZV/vz5Tf3E9b149+7dGME02rPvrWgvGqvcYxBAUiAAAkhyzs7OkvTCCQuCgoJeGiair6vy8PDQL7/8oly5csnKykp//PGHNm/eHO86Pv30U40cOVKbNm1SqVKldPz4cXXu3Nm0PF26dLp9+7YiIyNjhMDooPWiOqOPQjx/NOnhw4ev3Rl/nehbEIwePTrGbIjRXhQKJOnAgQPq2bOnmjZtqubNm5uO6o0aNUoHDx6MVw3Ozs6xfm9Go/GlE4zEhaOjowwGw0vfD9Hvl+dFRUWpVatWsrW11Y8//qgCBQrIxsZG58+fj9fpfAUKFJCbm5v+/PPPGEfPnvX999/ryJEj2r59u9KlS6d//vnnhbVK/3tf5MqVS/7+/oqMjNSxY8f0008/admyZcqePbtatGgR5/qeFX0kKzg4WLly5YrV/4vCw6s4OjoqR44cGj169AuXZ82aNU7vn+j3ZkhISIzn3759W6dOnYp1tDQ+GjVqpJw5c8a6/Ye7u7uGDh2qX3/9VefPn3/lNZwJ8fzESo8ePXrpuv/++6/at29vuhY1W7ZsMhgMWrJkiWnW27hIly6d6X30rOffWwDwJjEJDIAklzNnTmXIkEEbNmyI0X758mUdOXLEdM3U80feLly4oDt37uirr75Snjx5TEcP/vzzT0lxP4UvmpOTk6pWrarNmzdr06ZNypw5s8qUKWNa7u3trYiIiFjX1URPxlC8ePFY24w+2vDsBCR3795VYGBgvGp7EU9PT9na2urGjRsqXLiw6Z+NjY3Gjh370tkJDx8+rKioKHXs2NG08x4ZGWmavTM+P7dSpUrpzz//jHGq2Y4dO2Jc4xVfadKkUaFChbRp06YYO93379/X9u3bTT/n598Pt2/f1sWLF1W/fn3Tz0GK//vByspKzZo10/bt27Vt27ZYy/fs2aM//vhDNWrUkJ2dnT788ENdvXo11v3cfv75Z9na2qpIkSIKCAhQyZIlFRQUJGtra3l5eemHH36Qk5OTrl27Zuo3vjw9PWVnZxdr7Bw4cEDXrl0zjZ248vb21vXr15U+ffoY76m//vpLs2fPlrW1dZzeP7ly5ZKLi4vplMdoP/30k1q1apWo90eWLFkUEBCgy5cvx1p28eJFSU+vh0tKDg4OsSYRetWXJSdOnFBYWJhatWql7Nmzm74Iig5/0UfmXvc7//DDD3X48OFYR6N//vlnZciQQe+//368XwsAxBdHAAEkOSsrK3Xt2lW9e/eWn5+fateurdu3b5smavjmm28kyXQtXPRRl5w5c8rBwUHTp0+XjY2NbGxstHnzZtP0+wm5/qVevXpq0aKFrl+/Ll9f3xg7aB999JF8fHz0/fff68aNG8qfP7/27dunWbNmqW7dujFuCxDNw8NDmTJl0pQpU+Tg4CCDwaAZM2a8cpbCuHJxcVGLFi00YcIEPXjwQD4+Prpx44bp+qeXTaJRpEgRSdKgQYNUr1493b17V0uWLDHNvPjo0aMXnib3Iu3bt9fWrVvVvHlztWjRQiEhIRo/fnyMawITws/PT82bN1erVq3UuHFjPXnyRDNnzlR4eLjat28v6X/vh927dyt37tzy9PRUlixZtGTJEr333ntycnLSjh07tHDhQknxez80a9ZM+/fvV8eOHfX555+rfPnysrKy0v79+7Vo0SIVKFDAdM2qr6+vli5dqvbt26tTp07KmjWrtm3bptWrV6tDhw5ycnJSsWLFFBUVpfbt26tVq1ZKmzatNm3apPv376tatWqS/nfUbMOGDfL09IzT0TtnZ2e1atVKU6ZMka2trSpWrKgrV65owoQJypMnj+rWrRv3H/r/v5bFixfrm2++UZs2bZQpUybt2rVLs2bN0pdffmkKtNLr3z8dO3bUoEGDlD59elWqVEkXL17UxIkT1aRJkxfOQBpXXbp00d69e1W/fn199dVX8vLykpWVlY4fP266fUf0pDJJpWLFipoxY4ZmzJghT09Pbdu27ZW3milYsKBsbGzk7++vb7/9VuHh4VqzZo22b98u6X9HD1/3O//mm2/0888/q1mzZurQoYOcnZ21bt067dmzR8OGDUvQlwYAEF/8pQHwRvj6+mrixIm6ePGi2rdvrxEjRsjLy0s//vij6fqqvHnz6pNPPtGSJUvUrVs3OTo6aurUqTIajercubN69Oiha9euafHixUqbNq0OHDgQ7zpKlSql9957T1euXJGvr2+MZdHhrVGjRpo/f75atWqlgIAAde3a9aXTrVtbW2vixIlyc3NT165dNXToUH388cemnf7E+u6779SrVy9t2bJFLVu2lL+/v4oXL67FixfHmDzmWT4+Purfv78OHz6sli1basSIEcqcObPpFgPxOQ00R44cWrx4saytrdWlSxdNnTpVPXv2TNQOvvT09zBv3jyFhoaqa9eu6tevn9zd3bVy5UrT0R0HBwd988032rp1q1q2bKknT55o6tSpcnd3V69evfTdd9/p6NGjmjZtmnLlyhWv94Otra2mTp2qPn366OTJk+rZs6c6d+6sP/74Q+3atdPChQtNp/CmTp1aixYtUsWKFTVhwgS1bdtWBw8e1NChQ9WxY0dJUsaMGTV79mw5Ojqqb9++at26tU6ePKlJkyapZMmSkp5ObFK4cGH16tXLNIFIXHTs2FEDBgzQnj171KZNG02ePFk1atTQ0qVL431tW5o0abRkyRIVL15c/v7+atmypX799Vf5+fmZbnof1/dPkyZNNGLECO3du1etW7fW/Pnz1bJlS/Xo0SNeNT0va9asWrt2rT799FOtX79e7dq1U+vWrbV+/Xo1b95cU6ZMeekEMAnVunVrNWjQQHPmzFHbtm0VFBSkoUOHvnT9999/X2PGjNGNGzfUtm1b0wytixYtksFgML0XX/c7z5Ahg5YtW6aCBQtqyJAh6ty5s65fv66pU6eqXr16SfoaAeBlDEauKAYAAAAAi8ARQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEDbmLuBtCgq6b+4SkIJZWRnk6ppWISEPFRXF7TOBlIhxDKR8jGMklQwZHM1dgllwBBCIIysrgwwGg6ysDOYuBUACMY6BlI9xDCQOARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEwhRo69AeVLVvipf8OHTrwyudv27ZVt2+HxKmvDh1aac6cGbHaN2xYp0qVSuvRo0exloWFhalatfL67bctr9z2nDkz1KFDqzjVAQAAgFdLDvuIzwoI+EUtW36tqlXL6bPPamjIkAG6ceO/OL+eV7l69Yp27/4rSbZlSWzMXUBy1nDA7bfa34qBLnFet3PnbmrTpoMk6bfftmj58sWaNWuBabmTU7qXPve//66rf/9eWrXq54QXK+mjjyppzJiR2r17pypXrhZj2Z49uyRJZcuWS1QfAAAAyU3GqU5vtb+b7e7Fed3ksI8YbdKksQoI+EVt2nSUl1dx3b17V7NnT1OHDq01c+Z8ubjEfd/3RUaMGKyiRYupVKkySVKvpeAIYArl4OCg9OndlD69mxwcHGRlZWV6nD69m2xtbV/6XKPRmCQ1ODk5ycenlLZv3xZr2bZtW1SuXHnZ26dKkr4AAADweslhH1GSjh49opUrl2nYsDH69NM6ypo1mwoWLKThw8coMjJCK1cuTXQfSVmvJSEAvqNu3ryhfv16qWbNSvr448oaP95f4eHhkqQGDWqb/rtx43oZjUYtXDhXDRrUVoUKJfXZZzU0d+7MOPVTtWoN7dmzS2FhYaa2sLBQ7dq1U1Wr1pAkXbp0UV27dlS1auVVp05NzZs3S1FRUbG2tXHjetWv/2mMtmdPLRg69AdNnTpR/fv3VuXKZfTllw109uxpzZw5VTVqVFDdurW0bdtW03Nv3PhPPXt2UeXKZVS//qeaO3emIiMj4/FTBAAAeLe8rX3EgIANKlCgoDw9i8ZoT5UqlUaMGKN69Rqa2jZuXK8mTeqrUqUyat68qY4cOWRadvDgfjVr1liVKpVWgwafad261ZKe7hceOXJI8+bN4nKieCIAvoOePHmiTp3aKjT0sSZPnqlBg0Zo166dmjp1oiSZTgOYNWuBKleuqoCAX7Ry5TL17Pm9li1bo2++aaG5c2fqzJnTr+2rTJmPJEn79u02te3e/ZdSpUqlEiW8defOHbVv30Jubm6aOXO+/Px6avXqFVq1almCXtuqVcvk5VVcCxYsl5NTOnXq1Fa3b4doxoz5KlPmI/n7D1NUVJSMRqP69u0hFxdXzZu3RH36DNCWLQFatGhegvoFAABI6d7mPuL582dVoMAHL1yWL19+ubm5SXoa/saNG6Uvv2ym+fOXqEQJb3Xv3llBQTcVGRmpfv16qWLFylqy5Ee1bNlGY8eO1MWLF9S5czcVKlREjRp9qWHD/JPoJ2QZCIDvoL17dyk4+Kb69Rus3LnzqHjxD9W1a0+tXbtKjx49krPz0/OtnZ1dZG+fSu7u76lPnwEqUcJbmTJlVp069ZU+fXpdvBj42r5SpUqlcuXK648/fje1bdu2VZUqVZGNjY22bAmQvX0q9ejRVzly5FS5chXUokUbLV26MEGvzcOjgOrWra+sWbOpatUaCgsL1XffddP77+dQ/foNdf/+PYWEhOjgwf3677/r6tGjr7Jnz6FixUqoffvvtHJlwoInAABASvc29xHv33+gtGkdXrvejz8uV/36jVSz5ifKnj2H2rbtqFy58mj16pV6+PCB7t27K1fX9MqUKbOqVaup8eOnmk5vtbGxUerUqV95XSNiYxKYd9ClSxeVLVt2OTn97wLlwoWLKDIyUlevXpaDg2OM9YsVK6GTJ09o+vTJ+uefizp79oxu3br1wtM0X6RKleoaPLi/IiIiFBERod27d2rcuCmSpH/+uSgPjwKysfnfW61QIU/dunVL9+/fj/dry5w5i+n/7e3t5eLiarrO0N7eXpL05Em4/vnnou7du6vq1cub1o+KilJYWJju3r2jdOmc4903AABASvY29xHTpUsXp329S5cu6ZtvWsZoK1SosP7556KcnNKpTp36GjlyiObPn60yZcrp448/i1E/4o8jgO8gOzv7WG2RkVEx/vus9evX6bvv2ik8PEzly1fShAnTlDGje5z78/YuKSsrgw4dOqBdu3bK2dlVhQoV+f9a7GKtHxUVGeO/0QwGwwvqjrmOtbV1jMdWVi9+C0dGRip79hyaN2+p6d+CBcu1fPnaOH0bBQAA8K55m/uIHh75debM3y9ctnLlMk2fPvn/a4q9rxgZGWWqp1u3Xlq4cIVq166rU6dOqlWrr7n1QyIRAN9B2bO/r8uX/9W9e3dNbSdPHpO1tbWyZMkaK2itW7da33zTQp06+alGjY+VLp2zQkJuxXlmJRsbG1WoUFk7dvyhP//8XVWq/O+WENmzv68zZ/5WRESEqe3EieNydnaJdbjexsYmxj0FjUajrl+/Fq/XHi1btvd148Z/cnZ2Udas2ZQ1azZdv35Vc+bMeGHQBAAAeNe9zX3EatVq6u+/T+rYsSMx2h89eqSVK5eaDgRkz/6+Tp48EWOdkyePK3v293XrVrDGjBmprFmz6euvm2v27IUqXtxbf/31p6QXHzzA6xEA30EffuijzJmzaPDg/goMPK9Dhw5o3Dh/Va1aQ46OjkqVKrWkpxfnPnr0SOnSpdOBA/v077//6PTpvzVgQG9FREToyZPwOPdZtWoN7dq1Q3v37jbN/ik9HfxPnjzRqFFDdenSRe3YsV1z585Q3br1Yw3a/Pk/0L17d/Xjj8t19eoVTZo0Vvfuxf2+N8/y9i6p9957T4MG9VNg4HkdPXpYo0YNU6pUqWIdRQQAALAEb3MfsVChIvrkkzrq1ctPGzb8pKtXr+jw4YPq1q2TrKys1KTJ15Kkhg2baPXqFQoI+EX//vuPpk2bpMDAc/r00zpyckqnP//cpokTx+rq1Ss6cuSQzp8/q7x5PSRJqVOn1pUrl+N843o8xTWA7yBra2uNGDFW48aNUqtWXytNmrSqVq2GWrVqL0lydnZW9eo11b9/b7Vt21GdO3fTsGED1axZY7m4uKhy5apKlSq1zp49E+c+PT29ZDQalTGju3Llym1qT5MmrcaMmagJE8bo22+byNnZRQ0afKGmTb+JtY1s2bKrffvvtGDBXM2aNU21atVWxYqVEvUzGD/eX61afa3UqdOoYsUq6tChc4K2BwAAkNK97X3E7t17K2fOnFq5cqnGj/eXo6OTPvzQRwMHDjPNx1C5clWFhNzS7NnTFRJyS3ny5NPYsZP1/vs5JEkjRozVhAlj9PXXjZQmTVp9/HFtffppHUnSJ5/U0fDhg/TPPxc1d+6SpP5xvbMMRgu6g2JQUPwnHQGi2dhYycUlrW7ffqiIiLhNkAMgeWEcAykf4xhJJUMGx9ev9A7iCCAAAAAAxEF4eLh8fX3Vr18/+fj4qFevXlq7dm2s9Xx8fLRw4dPbnpUoUSLWjKiHDh1S2rRp30rNzyMAAgAAAMBrhIWFyc/PT+fOnTO19e3bV35+fqbHV69eVdOmTfXVV19Jkm7cuKH79+9r69atSpUqlWm9NGnSvL3Cn0MABAAAAIBXOH/+vPz8/GLNgOro6ChHx/+dStqrVy/VqFFDVapUkSQFBgYqQ4YMypYt21ut91WYBRQAAAAAXmHfvn3y8fHRihUrXrrO7t27tX//fnXt2tXUdv78eeXMmfNtlBhnFnUE0MrKICsr7heChLG2torxXwApD+MYSPkYxzCHxo0bv3admTNnqm7dusqUKZOpLTAwUI8fP1bTpk118eJFFShQQH369DFrKLSoAOjqmpYbRiLRnJxSm7sEAInEOAZSPsYxkpPLly9rz5496tu3b4z2Cxcu6O7du+ratascHBw0a9YsNWvWTL/88oscHBzMUqtFBcCQkIccAUSCWVtbyckpte7de6zISKadBlIixjGQ8jGOkVRcXJJuFs7NmzerQIECypMnT4z2OXPm6MmTJ6YZP0ePHq3y5cvr999/16effppk/ceHRQXAqCijoqIs5raHeEMiI6O47xCQwjGOgZSPcYzkZMeOHapcuXKsdjs7O9nZ2Zke29vbK2vWrLpx48bbLC8GTp4GAAAAgAQyGo06fvy4ihUrFqu9SpUqWrNmjant0aNH+ueff5QrV663XaaJRR0BBAAAAICkdPXqVT18+DDW6Z8Gg0EVKlTQpEmTlCVLFrm6umrChAl67733VL58eTNVSwAEAAAAgAS7deuWJCldunSxlnXv3l02Njby8/PTgwcPVLJkSc2cOVPW1tZvu0wTg/H5uxm+w4KC7pu7BKRgNjZWcnFJq9u3H3LNAZBCMY6BlI9xjKSSIYPj61d6B3ENIAAAAABYCAIgAAAAAFgIAiAAAAAAWAgCIAAAAABYCAIgAAAAAFgIbgMBAACAFMUw0GDuEizezXb3zF0CEogjgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIWzMXQDiruGA2+YuweJtm5rW3CUghWMcmx/jGInFOE4G3M1dAJBycQQQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALIRZA+CNGzfUqVMneXt7q1y5cho+fLjCwsJeuO6pU6fUoEEDeXp6ql69ejpx4sRbrhYAAAAAUjazBUCj0ahOnTrp8ePHWrJkicaNG6fff/9d48ePj7Xuo0eP1KpVK5UoUUJr1qyRl5eXWrdurUePHr39wgEAAAAghTJbALxw4YKOHDmi4cOHK2/evCpRooQ6deqkDRs2xFp348aNsre3V48ePZQ7d2717dtXadOmVUBAgBkqBwAAAICUyWwBMEOGDJo9e7bc3NxitD948CDWukePHlXx4sVlMBgkSQaDQcWKFdORI0feRqkAAAAA8E6wMVfHTk5OKleunOlxVFSUFi9erJIlS8ZaNygoSHny5InRlj59ep07dy5efVpZGWRlZUhYwcD/s7Zm7iQgpWMcA0Di2NhY5t/R8PBw+fr6ql+/fvLx8ZEkDRkyRIsWLYqxXr9+/fTll19KkjZs2KDx48crKChIZcuW1eDBg+Xq6vrWa49mtgD4PH9/f506dUo//vhjrGWPHz+WnZ1djDY7OzuFh4fHqw9X17Smo4gp0y1zFwBJTk6pzV0CUjTGcXLAOEbiMI4BF5e05i7hrQsLC5Ofn1+sg1CBgYHy8/NT3bp1TW0ODg6SpGPHjqlv374aOHCg8ufPr6FDh6p3796aMWPGW639WckiAPr7+2vBggUaN26c8uXLF2u5vb19rLAXHh6uVKlSxaufkJCHHAFEot2791iRkVHmLgNAIjCOASBxbt9+aO4SEi0+Ifb8+fPy8/OT0WiMtSwwMFDNmzdXhgwZYi1bvHixatasqTp16kiSRo0apYoVK+ry5cvKli1bgmtPDLMHwMGDB2vZsmXy9/dX9erVX7iOu7u7goODY7QFBwcrY8aM8eorKsqoqKjYvzQgPiIjoxQRwY4jkJIxjgEgcSztb+i+ffvk4+OjLl26qGjRoqb2Bw8e6MaNG8qRI8cLn3f06FG1bNnS9DhTpkzKnDmzjh49apkBcPLkyVq+fLnGjh2rGjVqvHQ9T09PzZo1S0ajUQaDQUajUYcOHVKbNm3i1V9KvwYwbzZbc5cAce0QEodxnDwwjpEYjGPz+z1+VwHhDbC0awAbN278wvbAwEAZDAZNnz5df/75p5ydnfXNN9+YTge9efNmrINW6dOn13///ffGa34ZswXAwMBATZ06Va1atVLx4sUVFBRkWpYhQwYFBQXJ0dFRqVKlUo0aNTRmzBgNHTpUjRo10vLly/X48WPVrFkzXn2m9GsAZ/S2vHOtkyOuHUJiMI6TB8YxEoNxbH4zB5q7AljiNYAvcuHCBRkMBuXKlUtffvml9u/fr379+snBwUFVq1ZVaGhoksxlkpTMFgB/++03RUZGatq0aZo2bVqMZWfOnFHZsmU1fPhw+fr6ysHBQTNmzNCAAQO0cuVKeXh4aObMmUqTJk28+kzp1wD2mn7P3CVYvBm9M3HtEBKFcWx+jGMkFuMYsLxrAF+mTp06qlixopydnSVJ+fPn16VLl7Rs2TJVrVr1pXOZpE5tvi8izRYAW7VqpVatWr10+ZkzZ2I8LlKkiNauXZuoPlP6NYDnLj8xdwkQ1w4hcRjHyQPjGInBOE4G3M1dAPgb+pTBYDCFv2i5cuXSnj17JL18LpMXTRjztljWybsAAAAAkEQmTJigZs2axWg7ffq0cuXKJenpXCYHDx40Lbt+/bquX78uT0/Pt1lmDARAAAAAAEiAihUrav/+/ZozZ47+/fdfLV26VOvWrdO3334rSfriiy/0008/adWqVTp9+rR69OihChUqmG0GUCkZ3AYCAAAAAFKiIkWKaMKECZo4caImTJigLFmyaMyYMfLy8pIkeXl5adCgQZo4caLu3r2rMmXKaPDgwWatmQAIAAAAAHH0/FwlVapUUZUqVV66vq+vr3x9fd90WXHGKaAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAAAQB+Hh4frkk0+0d+9eU9uRI0fUqFEjeXl5qXr16lq1alWM59SuXVseHh4x/p09e/Ztl25iY7aeAQAAACCFCAsLk5+fn86dO2dqCwoKUsuWLfXFF19oxIgROnnypHr37q0MGTKoQoUKioyM1KVLl7R48WLlyJHD9DwXFxczvIKnCIAAAAAA8Arnz5+Xn5+fjEZjjPatW7fKzc1NXbt2lSTlyJFDe/fu1fr161WhQgVduXJFT548UZEiRWRvb2+O0mMhAAIAAADAK+zbt08+Pj7q0qWLihYtamovV66cChQoEGv9Bw8eSHoaHDNlypRswp9EAAQAAACAV2rcuPEL27NmzaqsWbOaHt+6dUu//PKLOnbsKEkKDAyUra2tWrdurRMnTihnzpzq0aOHihQp8lbqfhGLCoBWVgZZWRnMXUaC5c1ma+4SIMnamrmTkHCM4+SBcYzEYByb3+/h5q4ANjb8HX1eaGioOnbsKDc3NzVs2FCSdPHiRd29e1cNGjRQp06dtHLlSn399dfauHGjMmXKZJY6DcbnT2R9hxmNRhkMKTcAAgAAQDIMZH/O3IwDLCZCxOLh4aGFCxfKx8fH1Pbw4UO1a9dO586d09KlS00TvkRERCg0NFQODg6SnuaR2rVr6+OPP1abNm3MUb5lHQEMCXmYoo8A9pp+z9wlWLwZvTPp3r3HioyMMncpSKEYx+bHOEZiMY4B6fbth+YuIdFcXNImyXYePHigFi1a6N9//9WCBQtizPZpY2NjCn+SZDAYlCtXLt24cSNJ+k4IiwqAUVFGRUWl3G8rzl1+Yu4SICkyMkoREew4ImEYx8kD4xiJwThOBtzNXQD4G/pUVFSUOnTooCtXrmjRokXKnTt3jOVNmzaVj4+POnToYFr/zJkzatKkiTnKlWRhARAAAAAAksqPP/6ovXv3atq0aXJyclJQUJAkydbWVs7OzqpUqZKmTJmiAgUKKGfOnFq4cKHu37+vunXrmq1mAiAAAAAAJMDmzZsVFRWl1q1bx2j39vbWokWL1KxZM4WFhWnIkCEKDg6Wp6en5s2bF+O00LeNAAgAAAAAcXTmzBnT/8+ZM+eV6xoMBrVp08ZsE768CPO3AgAAAICFIAACAAAAgIUgAAIAAACAhSAAAgAAAICFiPckMEFBQfrjjz905MgRBQcHy2AwKGPGjPL09FSFChXk6ur6JuoEAAAAACRSnAPg5cuXNXnyZP3yyy9ydnZW3rx55erqqsjISF24cEGbNm1S//79VatWLXXo0EHZs2d/k3UDAAAAAOIpTgFw/vz5mjt3rj7++GMtX75chQoVirWO0WjUyZMntXbtWjVu3FgtWrRQs2bNkrpeAAAAAEACxSkAXr9+Xb/88oscHR1fuo7BYFChQoVUqFAhdejQQVOnTk2yIgEAAAAAiRenANi7d+94bdTFxUV9+/ZNUEEAAAAAgDcj3pPASNLjx491/vx5hYWFyWg0xlj24YcfJklhAAAAAICkFe8A+Mcff+i7775TaGhorPBnMBj0999/J1lxAAAAAICkE+8A6O/vrzJlyqh9+/ZycnJ6EzUBAAAAAN6AeAfAf/75R1OmTNH777//JuoBAAAAALwhVvF9Qo4cORQUFPQmagEAAAAAvEFxOgJ47do10/83atRI/fr1U9++ffX+++/L2to6xrqZM2dO2goBAAAAAEkiTgGwUqVKMhgMpsdGo1EtWrSI1cYkMAAAAACQfMUpAC5cuPCNFhEeHi5fX1/169dPPj4+L1ynbdu22rZtW4y26dOnq2LFim+0NgAAAAB4V8QpAJ44cUJly5ZVvnz5kryAsLAw+fn56dy5c69cLzAwUP7+/ipVqpSpLV26dEleDwAAAAC8q+IUACdNmiR/f3+lT59epUuXVpkyZVS6dGllyJAhUZ2fP39efn5+se4n+Lzw8HBduXJFhQsXTnSfAAAAAGCp4hQA9+/fr+PHj+vgwYM6cOCAhg4dqvv37ytPnjwqW7asSpcuLW9vb9nb28er83379snHx0ddunRR0aJFX7rehQsXZDAYlC1btnhtHwAAAADwP3EKgDY2NvLy8pKXl5datGgho9GoM2fO6MCBAzpw4ID69u2rO3fuqHjx4po3b16cO2/cuHGc1rtw4YIcHBzUo0cP7du3T++99546duyo8uXLx7kvAAAAALB08b4RvCQZDAblyJFDISEhCg4O1qNHj7R//36dPXs2qeuT9DQAhoaGqmzZsmrVqpW2bNmitm3basWKFSpcuHCct2NlZZCVleH1KwKvYG0d79tnAkhmGMcAkDg2NvwdfdPCw8N18OBBHTlyRMHBwTIYDMqYMaM8PT314YcfysoqYb+DeAXAM2fOaOfOndqxY4cOHTokSSpatKjKli2r7777Th988EGCiniddu3aqWnTpqZJX/Lnz6+TJ09q5cqV8QqArq5pY9y6IuW5Ze4CIMnJKbW5S0CKxjhODhjHSBzGMeDiktbcJbyz7t27pwULFmjJkiW6d++esmbNKldXV0VGRurWrVsaN26cnJyc1KRJEzVr1kxOTk7x2n6cAmCfPn20c+dOBQUF6f3331fZsmXVrFkzeXt7K02aNAl6YfFhZWUVa8bPXLly6fz58/HaTkjIQ44AItHu3XusyMgoc5cBIBEYxwCQOLdvPzR3CYmWHEPs1q1bNXjwYHl6emrgwIEqX768UqVKFWOdO3fuaO/evVq7dq1q1aqlAQMGqGrVqnHuI04BcM2aNcqcObMGDRqkzz77THZ2dvF7JYnUq1cvGQwGDR8+3NR2+vTpeN+WIirKqKioV884CrxOZGSUIiLYcQRSMsYxACQOf0PfjJ9++kmLFi1S9uzZX7qOs7OzqlevrurVqyswMFBjxoyJVwCM04mjAwYMUIECBTRixAh5e3urefPmmj9//mvv3ZcYQUFBCg0NlSRVqlRJ69ev17p16/TPP/9o8uTJOnjwoL788ss31j8AAAAAvE2TJk16Zfh7Xu7cuTV16tR49RGnI4BffPGFvvjiC0VEROjQoUP666+/9PPPP2vkyJHKmDGjSpcurXLlyql06dJydnaOVwEvU7ZsWQ0fPly+vr6qVq2aBgwYoGnTpunatWvKmzevZs+eraxZsyZJXwAAAABgbsOGDZOTk5M6dOggSXr06FGsS+7WrFmje/fuqVmzZgnqI16TwNjY2Mjb21ve3t7q0qWLQkJC9Ndff2nPnj3q16+fQkNDdfLkyQQVcubMmVc+btCggRo0aJCgbQMAAABAcrdx40aNGDHC9Pijjz7SunXrNH36dHXv3l3p0qVTlixZNHbs2LcTAKPduXNHhw8f1qFDh3TkyBGdOHFCdnZ2qlSpUoKKAAAAAABLd+/ePeXIkcP0ODIyUlFRUdq4caNat26tdOnSKWvWrLp3716C+4hTALxw4YIp8B06dEiXLl2Svb29ihUrpnLlyqlnz54qWLBgCr/FAgAAAACYj7u7uy5fvmy61C06XxmN/5vI8vr160qfPn2C+4hTAKxVq5asra1VqFAhVatWTaVLl5aXl9dbnw0UAAAAAN5V5cuX1+zZs1WqVKkXLjcajZo7d668vb0T3EecAuDUqVPl7e0tBweHBHcEAAAAAHi5li1b6pNPPlGLFi3UuXNnSf87Cnj48GGNHDlSu3fv1qpVqxLcR5wCYPS1fffv35ejo6MkacOGDYqIiDCtkzdvXhUsWDDBhQAAAACAJXN3d9ecOXPk5+enzz//XJJUv359hYaGqmfPnsqcObOmT5+uPHnyJLiPOE8CM2PGDE2ZMkWbN29WpkyZ1K9fPz1+/Ni03M3NTQEBARwlBAAAAIAEKlKkiDZu3KidO3eqU6dOqlKlin766ScNGTJEH3/8sWxtbRO1/TgFwF9//VVTpkxR3759lSFDBlP7li1blC1bNl2/fl2fffaZVq5cqW+//TZRBQEAAACAJbO1tVXFihU1YMAAVa1aVV5eXqpUqVKiw58UxwC4bNkytWvXTg0bNjS1PTvjZ6ZMmdSsWTP9+uuvBEAAAAAASIDJkyfHalu0aJEkaeHChTHao28WH19xCoAnT55U3759Y7Q9OxWpJFWuXFlz585NUBEAAAAAYOkmT54sKysrvffee69cz2AwvNkAGBYWprRp08Zomzdvntzd3U2P06ZNq8jIyAQVAQAAAACW7vPPP9eWLVskSR9//LE+/vhj5c+fP0n7sIrLShkzZtSlS5ditBUtWjTGfQDPnTunzJkzJ2lxAAAAAGApBg0apJ07d2rw4MEKCQnR119/rVq1amnKlCmx8lhCxSkAlitXTgsWLHjlOosWLVKFChWSoiYAAAAAsEjW1tYqU6aMhg4dqp07d6p79+76559/VK9ePdWtW1ezZs3StWvXErz9OAXAb775RgcOHNB3332n//77L8ay4OBg9ejRQ6dOndLXX3+d4EIAAAAAAP8TPRvoqFGjtHv3bn3++eeaMWOGKleunOBtxukawGzZsmnKlCnq3r27KlasqPfff1+urq66e/euLl26JFdXV02ePFkZM2ZMcCEAAAAAgJhu3rypX3/9VQEBATp48KDef/99NW3aNMHbi/ON4H18fLRp0yZt2rRJ+/fvV3BwsDJnzqwvv/xSn3zyiRwdHRNcBAAAAADgqRs3bmjz5s0KCAjQ4cOHlS1bNtWsWVPff/99oieFiXMAlJ7O9Fm/fn3Vr18/UZ0CAAAAAGKaP3++Nm/erKNHjypz5syqWbOm+vbtq4IFCyZZH3EKgO3bt1evXr2ULVu2OG304sWLGjVqlKZNm5ao4gAAAADAUowYMUK2trYqV66cChcuLEn6/fff9fvvv8da943eB7Bu3bpq2rSpihYtqlq1aqlcuXJKnTp1jHXu3bunvXv3avXq1Tpx4oT69euXoIIAAAAAwBJF31bv3LlzOnfu3EvXe+M3gq9SpYq8vb01f/589e/fX/fv31eWLFnk6uqqqKgohYSE6Nq1a3J0dFSjRo00cuRIpUuXLkEFAQAAAIAl2rZt2xvvI87XADo5OalTp05q06aN9u/fr6NHjyo4OFhWVlbKkCGDihQpIm9vb1lbW7/JegEAAADgnXTq1Cl98MEH8XrOiRMnVKhQoTivH69JYCTJzs5OZcqUUZkyZeL7VAAAAADASwwYMEB58uRRixYtlDt37leue/LkSc2bN08XL17U6tWr49xHvAMgAAAAACDpLV++XLNnz1bDhg2VKVMmlS9fXvny5VP69OkVGRmpkJAQnTp1Snv27NG1a9f0zTffaPjw4fHqgwAIAAAAAHEQHh4uX19f9evXTz4+PpKky5cvq1+/fjpy5IgyZ86sPn36qGzZsqbn7Nq1S8OGDdPly5fl6empoUOHvvTuCtbW1mrdurWaNGmi5cuX67ffftP8+fMVEREhSbK1tVWRIkVUt25d+fr6JmjeFQIgAAAAALxGWFiY/Pz8YszOaTQa1b59e+XLl0+rV6/W1q1b1aFDB23cuFGZM2fWtWvX1L59e3Xs2FHlypXTlClT1K5dO/38888yGAwv7cvBwUEtWrRQixYtZDQadfv2bVlZWcnZ2TnRr8Mq0VsAAAAAgHfY+fPn9fnnn+vff/+N0b5nzx5dvnxZgwYNUu7cudW6dWsVLVrUdE3eqlWrVKhQIX377bfKmzevhg8frqtXr2rfvn1x7ttgMMjV1TVJwp+UwAAYGhqqdevWacyYMbpz54727dun27dvJ0lBAAAAAJCc7Nu3Tz4+PlqxYkWM9qNHj+qDDz5QmjRpTG3FixfXkSNHTMtLlChhWpY6dWoVLFjQtNwc4n0KaHBwsBo2bKhbt24pPDxcn3/+uebOnasTJ05owYIFr52txpysrAyysnr5odbkLm82W3OXAEnW1hw4R8IxjpMHxjESg3Fsfr+Hm7sC2NhY1t/Rxo0bv7A9KChIGTNmjNGWPn16/ffff3Fabg7xDoAjRoxQ3rx5tWHDBpUuXVqSNHLkSH333Xfy9/fX9OnTk7zIpOLqmvaV59omdzN6pzV3CZDk5JTa3CUgBWMcJw+MYyQG49j8Zg40dwVwcWEcSNLjx49lZ2cXo83Ozk7h4eFxWm4O8Q6Ae/bs0cyZM5U69f8+PNOlS6eePXvqq6++StLiklpIyMMUfQSw1/R75i7B4s3onUn37j1WZGSUuUtBCsU4Nj/GMRKLcQxIt28/NHcJiZYUIdbe3l537tyJ0RYeHq5UqVKZlj8f9sLDw+Xk5PTabU+ePFnNmzePkbsk6cGDB5owYYL69u2boJrjHQAfPnwY4xzXZ0VPT5pcRUUZFRVlNHcZCXbu8hNzlwBJkZFRiohgxxEJwzhOHhjHSAzGcTLgbu4CwN/Qp9zd3XX+/PkYbcHBwabTPt3d3RUcHBxreYECBV64vcDAQIWEhEiSpkyZovz588e61cPZs2e1cuXKtxcAP/zwQy1btky9e/c2tT158kTTpk1TsWLFElQEAAAAAKQ0np6emjlzpkJDQ01H/Q4ePKjixYublh88eNC0/uPHj3Xq1Cl16NDhhdu7fPmy2rRpY7ps7WXr1atXL8E1xzsA9uzZU02aNNG+ffv05MkT/fDDD7pw4YLu37+vxYsXJ7gQAAAAAEhJvL29lSlTJvXu3Vvt2rXT77//rmPHjmn48OGSnga1OXPmaObMmapYsaKmTJmirFmzmm4i/7wKFSpo27ZtioqKUpUqVbRq1Sq5urqalhsMBqVJkyZRt4SIdwDMnTu3fv75Zy1dulQZM2ZUVFSUatasqcaNGytr1qwJLgQAAAAAUhJra2tNnTpVffv2la+vr95//31NmTJFmTNnliRlzZpVkyZN0rBhwzRlyhR5eXlpypQpr5yYMvq5v/32mzJnzpzkk1jGOwBKUsaMGfXdd98laSEAAAAAkNydOXMmxuP333//lWdCli9fXuXLl493PxkzZtSMGTNUs2ZNvf/+++rbt682btyoYsWKafTo0XJxcYn3NqUEBMCQkBDNmjVL586de+H0pQsXLkxQIQAAAACAp/z9/fXTTz+pXLly+vPPP7V27Vp16tRJ27dv16hRo0ynmcZXvANgjx49dPz4cZUuXdp0oSMAAAAAIOkEBARo7NixKliwoAYMGCBvb2+1adNGZcuWVcuWLRO83XgHwIMHD2rGjBny9vZOcKcAAAAAgJe7c+eOcufOLUn666+/1LBhQ0mSs7OzQkNDE7zdeAdAd3d3pU2b+JsmAgAAAABeLHv27Dp+/Lhu3bqlK1euqFy5cpKkrVu3JmryzXgHwO7du2vgwIHq0qWLsmXLJisrqxjLo2etAQAAAAAkTIsWLdS1a1dZWVmpZMmSyp8/v6ZMmaIpU6Zo2LBhCd5uvAOg0WhUYGCgvv3221jtBoNBf//9d4KLAQAAAABIderUUf78+XXlyhV99NFHkqTChQtrzpw5KlWqVIK3G+8AOGzYMJUsWVKff/65UqdOneCOAQAAAAAvlz9/fmXNmlWnT5+Wra2tihUrJgcHh0RtM0G3gejVq5eyZcuWqI4BAAAAAC8WFRWlkSNHaunSpYqIiJDRaJSdnZ0aNmyoPn36JPgG8fEOgD4+Pjp8+DABEAAAAADekBkzZmj16tXq3r27vL29FRUVpf3792vKlClyd3dXixYtErTdeAfAEiVKaMCAAdq+fbuyZ88uG5uYm+jQoUOCCgEAAAAAPLVq1SoNGDBAn376qantgw8+kKurqyZNmvT2AuCyZcvk4uKiI0eO6MiRIzGWGQwGAiAAAAAAJNKtW7fk6ekZq93T01PXr19P8HbjHQC3bduW4M4AAAAAAK+XI0cO7dq1S9mzZ4/R/tdffylLliwJ3m68AyAAAAAA4M365ptv1L9/f12+fFnFihWTJB08eFBLlixRjx49ErzdeAfA/Pnzv3LGGe4DCAAAAACJU6dOHd25c0ezZ8/WnDlzJElubm767rvv1KRJkwRvN0H3AXw2AEZEROjSpUtat25dopIoAAAAAOB/mjVrpmbNmikkJERGo1Hp06dP9DbjHQB9fX1f2F6oUCGtWrVKn332WaKLAgAAAABLtXHjRn300Uemm767urpq+fLlcnR0VK1atRJ8D0BJskqqIosUKaKDBw8m1eYAAAAAwKI8efJEbdq0kZ+fn06ePBlj2cGDB+Xn56fOnTsrIiIiwX0kSQB8+PChFi9eLDc3t6TYHAAAAABYnIULF+r48eNatGiRfHx8Yizz9/fXggULtHfvXi1btizBfSTZJDAGg0EDBw5McCEAAAAAYMnWrVunXr16qUSJEi9c7uPjo86dO2vFihVq2rRpgvpI9CQwkmRraytPT09ly5YtQUUAAAAAgKW7cuWKvLy8XrlO6dKl5e/vn+A+kmwSGAAAAABAwqVNm1b3799/5TqhoaFKnTp1gvuIUwCcPHlynDfYoUOHBBcDAAAAAJaqaNGi+uWXX1SgQIGXrrN+/Xp5eHgkuI84BcA1a9bEaWMGg4EACAAAAAAJEH3fv/fee0+NGzeWldX/5uw0Go1atGiR5s+fr0mTJiW4jzgFwG3btiW4AwAAAADA65UoUUI9e/bUiBEjNHXqVBUpUkROTk66c+eOjh49qocPH+q7775TpUqVEtxHvK8BlJ6mzx07dujs2bOysbFR3rx5VbJkSVlbWye4EAAAAACwdE2bNtWHH36oVatW6eTJk7p06ZJcXV1Vv359+fr6Knfu3InafrwD4J07d9S8eXOdPHlSjo6OMhqNevDggQoWLKh58+bJyckpUQUBAAAAgCXLnz+/+vXr90a2He8bwY8cOVKhoaFat26d9u/frwMHDmjdunUKDw/XmDFj3kSNAAAAAIAkEO8A+Pvvv2vAgAHKnz+/qS1//vz6/vvvtXXr1iQtDgAAAACQdOIdACMiIuTm5har3c3NTQ8ePEiSogAAAAAASS/eAbBgwYJatmxZrPZly5a98n4VAAAAAADzivckMN99952++uorHTlyRMWKFZMkHTx4UKdPn9bs2bOTvEAAAAAAQNKIUwA8cuSIihYtKkny8vLSkiVLNHfuXO3cuVNGo1EeHh7q37+/ihQp8iZrBQAAAIB3Vv78+WUwGOK07t9//52gPuIUABs1aqQ8efKofv36+uyzz1SkSBGNHz8+QR0CAAAAAGIbNmxYnANgQsUpAC5ZskTr1q3T1KlTNWbMGFWqVEkNGjRQ2bJl32hxAAAAAGApfH1933gfcQqAxYsXV/HixdWvXz9t3bpVP/30k1q3bq2MGTPK19dXvr6+ypIly5uuFQAAAAAsxm+//aazZ88qMjLS1BYeHq7jx49r3rx5CdpmvCaBsbOzU61atVSrVi3dunVLP//8s9atW6dp06apZMmSql+/vmrVqpWgQgAAAAAAT40ePVqzZ8+Wm5ubbt26JXd3dwUHBysyMlIff/xxgrcb79tAREufPr2++eYb/fTTT1q5cqXu3bsnPz+/BBcCAAAAAHhq/fr16tOnj3bu3KmMGTNq6dKl2rlzp4oVK6Zs2bIleLsJDoARERHatm2bunTpoqZNm+rmzZtq1apVggsBAAAAADx169YtVapUSZLk4eGhY8eOydnZWV26dNHGjRsTvN143wfwwIEDWr9+vQICAvTw4UNVqlRJ48ePV7ly5WRlleA8CQAAAAD4f05OTnr06JEkKXv27Dp//rwkKXPmzLpx40aCtxunAHju3DmtX79eGzZs0PXr15UnTx61bdtWtWvXlqura4I7BwAAAADE5uPjo9GjR2vw4MHy9PTUjBkz1LhxY23evDlRGSxOAfDTTz+Vg4ODatWqpfr163PDdwAAAAB4g7p376527dpp06ZNaty4sebNm6cyZcpIknr27Jng7cYpAI4YMUI1atRQqlSpEtwRAAAAACBuMmfOrHXr1iksLEx2dnZasmSJdu7cKXd3dxUuXDjB243TRXt16tQh/AEAAADAW1K5cmXduXNH9vb2kqTUqVOratWqcnd3V8mSJRO83XhPAgMAAAAASHobN27Ujh07JElXr17VoEGDTAEw2tWrV2UwGBLcBwEQAAAAAJIBLy8vLV++XEajUZJ07do12drampYbDAalSZNGI0eOTHAfBEAAAAAASAYyZcqkhQsXSpKaNm2qyZMnK126dEnaBwEQAAAAAJKZRYsWSZICAwN19uxZ2draKnfu3MqZM2eithunAJg/f/44n2f6999/J6ogAAAAALB04eHh6tq1q7Zu3WpqMxgMqlixosaPHy87O7sEbTdOAXDYsGGJutDwdcLDw+Xr66t+/frJx8fnheucOnVKAwYM0NmzZ5UnTx4NHDhQhQoVemM1AQAAAIC5jB07VseOHdOUKVPk7e2tqKgo7d+/X0OGDNGkSZPk5+eXoO3GKQD6+vomaONxERYWJj8/P507d+6l6zx69EitWrXSp59+qhEjRmjZsmVq3bq1tmzZojRp0ryx2gAAAADAHDZs2KDBgwerYsWKprYqVarI2tpaAwcOfLMB8Hm//fabzp49q8jISFNbeHi4jh8/rnnz5sV5O+fPn5efn59plpuX2bhxo+zt7dWjRw8ZDAb17dtXf/75pwICAt5oOAUAAABg2dasWaPevXvHajcYDDp9+rTatm2rbdu2xVg2ffr0GMEtIR4+fKhcuXLFas+ZM6dCQkISvN14B8DRo0dr9uzZcnNz061bt+Tu7q7g4GBFRkbq448/jte29u3bJx8fH3Xp0kVFixZ96XpHjx5V8eLFTaehGgwGFStWTEeOHCEAAgAAAHhjatWqpXLlypkeR0RE6Ouvv1aFChUkPZ2kxd/fX6VKlTKtkxQzd+bLl08BAQFq3bp1jPZNmzYlaiKYeAfA9evXq0+fPvrqq69Uvnx5LV26VGnSpFH79u2VLVu2eG2rcePGcVovKChIefLkidGWPn36V542+iJWVgZZWb25axlhGaytrcxdAoBEYhwDQOLY2FjO39FUqVIpVapUpsczZsyQ0WhUt27dFB4eritXrqhw4cLKkCFDkvbbtm1btWvXTn///beKFSsmSTp48KC2bNmiMWPGJHi78Q6At27dUqVKlSRJHh4eOnbsmGrUqKEuXbqob9++6ty5c4KLeZnHjx/HmuXGzs5O4eHh8dqOq2vaNzqZzZt3y9wFQJKTU2pzl4AUjXGcHDCOkTiMY8DFJa25SzCLO3fuaNasWRoyZIjs7Ox0+vRpGQyGeB8Ie5kCBQpo586dSp8+vSpUqKAJEyZo1qxZ2r59u4xGozw8PDR+/HhVq1YtwX3EOwA6OTnp0aNHkqTs2bPr/PnzkqTMmTPrxo0bCS7kVezt7WOFvfDw8BhJPC7u3HmUor/1zZvN1twlQNKDB6GKinr1davAyzCOkwfGMRKDcWx+v8fvGADegHv3Hpu7hERLyJeBy5YtU8aMGVWjRg1J0oULF+Tg4KAePXpo3759eu+999SxY0eVL18+QTU9PzdK1apVVbVq1QRt62XiHQB9fHw0evRoDR48WJ6enpoxY4YaN26szZs3y9XVNUmLixZ9neGzgoODlTFjxnhtx9k5TYo+AjijN99YJwcODvH74gF4FuM4eWAcIzEYx+Y3c6C5K4AlnklhNBq1atUqtWjRwtR24cIFhYaGqmzZsmrVqpW2bNmitm3basWKFSpcuLAZq325eAfA7t27q127dtq0aZMaN26sefPmqUyZMpKknj17JnmBkuTp6alZs2bJaDTKYDDIaDTq0KFDatOmTby2ExLyMEVfA9hr+j1zl2DxZvTOpHv3HisyMsrcpSCFYhybH+MYicU4BqTbtx+au4REi+9prMePH9eNGzdiTHzZrl07NW3a1DTpS/78+XXy5EmtXLkywQFw06ZNcnBweO16derUSdD24x0AM2fOrHXr1iksLEx2dnZasmSJdu7cKXd39yRNuUFBQXJ0dFSqVKlUo0YNjRkzRkOHDlWjRo20fPlyPX78WDVr1ozXNqOijCn6lJ9zl5+YuwRIioyMUkQEO45IGMZx8sA4RmIwjpMBd3MXAEv8G7pjxw6VKFEixgyfVlZWsWb8zJUrl+kyuYQYMmTIa9cxGAxvLwBWrlxZq1evlrOzsyQpderUqlq1qm7cuKGSJUtq7969CSrkeWXLltXw4cPl6+srBwcHzZgxQwMGDNDKlSvl4eGhmTNnchN4AEC8GQam3DNB3hU323EEDUDKc+zYMdNsnNF69eolg8Gg4cOHm9pOnz6tfPnyJbifv/76S+nTp0/w818nTgFw48aN2rFjhyTp6tWrGjRokOzt7WOsc/Xq1URdX3fmzJlXPi5SpIjWrl2b4O0DAAAAQEKdO3dOtWvXjtFWqVIlde3aVT4+PvLy8tL69et18OBBDRo0KEF9vI35SuIUAL28vLR8+XLTrDTXrl2Tre3/ZsAyGAxKkyaNRo4c+WaqBAAAAAAzCg4OlpOTU4y2atWqacCAAZo2bZquXbumvHnzavbs2cqaNWuC+nh+FtA3IU4BMFOmTFq4cKEkqWnTppo8eXKS3N0eAAAAAFKCY8eOvbC9QYMGatCgQZL0Ubdu3VhnWia1eF8DuGjRIklSYGCgzp49K1tbW+XOnVs5c+ZM8uIAAAAAwFI8ey3hmxLvABgeHq6uXbtq69atpjaDwaCKFStq/PjxsrOzS9ICgeSEySPMj8kjAAAAEs4qvk8YO3asjh07pilTpmj//v3au3evJk2apFOnTmnSpElvokYAAAAAQBKIdwDcsGGDBg4cqMqVK8vR0VHp0qVTlSpVNGDAAK1fv/5N1AgAAAAASALxDoAPHz5Urly5YrXnzJlTISEhSVIUAAAAACDpxTsA5suXTwEBAbHaN23axEQwAAAAAJCMxXsSmLZt26pdu3b6+++/VaxYMUnSwYMHtWXLFo0ZMybJCwQAAAAAJI04BcACBQpo586dSp8+vSpUqKAJEyZo1qxZ2r59u4xGozw8PDR+/HhVq1btTdcLAAAAAEigOAXA5+9IX7VqVVWtWvWNFAQAAAAAeDPifQ0gAAAAACBlivM1gJs2bZKDg8Nr16tTp05i6gEAAAAAvCFxDoBDhgx57ToGg4EACAAAAADJVJwD4F9//aX06dO/yVoAAAAAAG9QnK4BNBgMb7oOAAAAAMAbFqcA+PwsoAAAAACAlCdOAbBu3bqyt7d/07UAAAAAAN6gOF0DOHz48DddBwAAAADgDeM+gAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAAYCEIgAAAAABgIQiAAAAAAGAhCIAAAAAA8ApbtmyRh4dHjH+dOnWSJJ06dUoNGjSQp6en6tWrpxMnTpi52lcjAAIAAADAK5w/f14VK1bUzp07Tf+GDBmiR48eqVWrVipRooTWrFkjLy8vtW7dWo8ePTJ3yS9FAAQAAACAVwgMDFS+fPmUIUMG0z8nJydt3LhR9vb26tGjh3Lnzq2+ffsqbdq0CggIMHfJL0UABAAAAIBXCAwMVI4cOWK1Hz16VMWLF5fBYJAkGQwGFStWTEeOHHm7BcYDARAAAAAAXsJoNOrixYvauXOnqlevripVqmj06NEKDw9XUFCQMmbMGGP99OnT67///jNTta9nY+4C3iYrK4OsrAzmLiPB8mazNXcJFu/3cHNXABublP29FePY/BjH5sc4RmIxjs0vpY/j+Lh27ZoeP34sOzs7jR8/XleuXNGQIUMUGhpqan+WnZ2dwsOT75vUogKgq2ta0+HZlGhG77TmLsHizRxo7grg4pKyxwHj2PwYx+bHOEZiMY7NL6WP4/jIkiWL9u7dq3Tp0slgMKhAgQKKiopS9+7d5e3tHSvshYeHK1WqVGaq9vUsKgCGhDxM0UcAe02/Z+4SALO7ffuhuUtIFMYxwDgG3gUpfRxL8Quxzs7OMR7nzp1bYWFhypAhg4KDg2MsCw4OjnVaaHJiUQEwKsqoqCijuctIsHOXn5i7BLibuwBERESZu4REYRwnA4xjs2McI9EYx2aX0sdxfOzYsUPdunXT9u3blTp1aknS33//LWdnZxUvXlyzZs2S0WiUwWCQ0WjUoUOH1KZNGzNX/XKWc/IuAAAAAMSTl5eX7O3t9f333+vChQv6448/NGrUKLVo0UI1atTQvXv3NHToUJ0/f15Dhw7V48ePVbNmTXOX/VIEQAAAAAB4CQcHB82ZM0chISGqV6+e+vbtq4YNG6pFixZycHDQjBkzdPDgQfn6+uro0aOaOXOm0qRJY+6yX8qiTgEFAAAAgPjKmzev5s2b98JlRYoU0dq1a99yRQnHEUAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQZg2AYWFh6tOnj0qUKKGyZctq7ty5L123bdu28vDwiPHv999/f4vVAgAAAEDKZmPOzkeNGqUTJ05owYIFunbtmnr27KnMmTOrRo0asdYNDAyUv7+/SpUqZWpLly7d2ywXAAAAAFI0swXAR48eadWqVZo1a5YKFiyoggUL6ty5c1qyZEmsABgeHq4rV66ocOHCypAhg5kqBgAAAICUzWyngJ4+fVoRERHy8vIytRUvXlxHjx5VVFRUjHUvXLggg8GgbNmyve0yAQAAAOCdYbYAGBQUJBcXF9nZ2Zna3NzcFBYWpjt37sRY98KFC3JwcFCPHj1UtmxZ1a9fX3/88cdbrhgAAAAAUjaznQL6+PHjGOFPkulxeHh4jPYLFy4oNDRUZcuWVatWrbRlyxa1bdtWK1asUOHChePcp5WVQVZWhsQXD8BsbGyYvBhI6RjHQMrHOE65zBYA7e3tYwW96MepUqWK0d6uXTs1bdrUNOlL/vz5dfLkSa1cuTJeAdDVNa0MhpQcAG+ZuwDA7Fxc0pq7hERiHAOMYyDlS/nj2HKZLQC6u7vr9u3bioiIkI3N0zKCgoKUKlUqOTk5xVjXysoq1oyfuXLl0vnz5+PVZ0jIQ44AAinc7dsPzV0CgERiHAMp37swji01xJotABYoUEA2NjY6cuSISpQoIUk6ePCgChcuLCurmIeUe/XqJYPBoOHDh5vaTp8+rXz58sWrz6goo6KijIkvHoDZREREvX4lAMka4xhI+RjHKZfZTt5NnTq16tSpox9++EHHjh3T1q1bNXfuXH311VeSnh4NDA0NlSRVqlRJ69ev17p16/TPP/9o8uTJOnjwoL788ktzlQ8AAAAAKY5Zr97s3bu3ChYsqK+//loDBw5Ux44dVa1aNUlS2bJltXHjRklStWrVNGDAAE2bNk2ffPKJtm3bptmzZytr1qzmLB8AAAAAUhSznQIqPT0KOHLkSI0cOTLWsjNnzsR43KBBAzVo0OBtlQYAAAAA7xzmbwUAAAAAC0EABAAAAAALQQAEAAAAAAtBAAQAAAAAC0EABAAAAAALQQAEAAAAAAtBAAQAAAAAC0EABAAAAAALQQAEAAAAAAtBAAQAAAAAC0EABAAAAAALQQAEAAAAAAtBAAQAAAAAC0EABAAAAAALQQAEAAAAAAtBAAQAAAAAC0EABAAAAAALQQAEAAAAAAtBAAQAAAAAC0EABAAAAAALQQAEAAAAgFe4ceOGOnXqJG9vb5UrV07Dhw9XWFiYJGnIkCHy8PCI8W/x4sVmrvjlbMxdAAAAAAAkV0ajUZ06dZKTk5OWLFmiu3fvqk+fPrKyslLPnj0VGBgoPz8/1a1b1/QcBwcHM1b8ahwBBAAAAICXuHDhgo4cOaLhw4crb968KlGihDp16qQNGzZIkgIDA/XBBx8oQ4YMpn+pU6c2c9UvRwAEAAAAgJfIkCGDZs+eLTc3txjtDx480IMHD3Tjxg3lyJHDPMUlAAEQAAAAAF7CyclJ5cqVMz2OiorS4sWLVbJkSQUGBspgMGj69On66KOPVLt2ba1du9aM1b6eRV0DaGVlkJWVwdxlJFjebLbmLsHi/R5u7gpgY5Oyv7diHJsf49j8GMdILMax+aX0cZwY/v7+OnXqlH788UedPHlSBoNBuXLl0pdffqn9+/erX79+cnBwUNWqVc1d6gsZjEaj0dxFvC1Go1EGQ8oNgDA/w0DeP+ZmHGAxf7LwhjCOzY9xjMRiHJufpY5jf39/zZs3T+PGjVP16tVlNBp19+5dOTs7m9YZPHiwLl68qLlz55qv0FewqCOAISEPU/QRwF7T75m7BMDsbt9+aO4SEoVxDDCOgXdBSh/HkuTikjZe6w8ePFjLli2Tv7+/qlevLkkyGAwxwp8k5cqVS3v27EmqMpOcRQXAqCijoqJS7rcV5y4/MXcJcDd3AYiIiDJ3CYnCOE4GGMdmxzhGojGOzS6lj+P4mjx5spYvX66xY8eqRo0apvYJEybo8OHDmj9/vqnt9OnTypUrlxmqjBvLPXkXAAAAAF4jMDBQU6dOVcuWLVW8eHEFBQWZ/lWsWFH79+/XnDlz9O+//2rp0qVat26dvv32W3OX/VIWdQQQAAAAAOLjt99+U2RkpKZNm6Zp06bFWHbmzBlNmDBBEydO1IQJE5QlSxaNGTNGXl5eZqr29QiAAAAAAPASrVq1UqtWrV66vEqVKqpSpcpbrChxOAUUAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALAQBEAAAAAAsBAEQAAAAACwEARAAAAAALIRZA2BYWJj69OmjEiVKqGzZspo7d+5L1z116pQaNGggT09P1atXTydOnHiLlQIAAACwVPHJLcmdWQPgqFGjdOLECS1YsEADBgzQ5MmTFRAQEGu9R48eqVWrVipRooTWrFkjLy8vtW7dWo8ePTJD1QAAAAAsSVxzS0pgtgD46NEjrVq1Sn379lXBggVVtWpVtWjRQkuWLIm17saNG2Vvb68ePXood+7c6tu3r9KmTZtif+gAAAAAUob45JaUwGwB8PTp04qIiJCXl5eprXjx4jp69KiioqJirHv06FEVL15cBoNBkmQwGFSsWDEdOXLkbZYMAAAAwMLEJ7ekBDbm6jgoKEguLi6ys7Mztbm5uSksLEx37tyRq6trjHXz5MkT4/np06fXuXPn4tWnjY2VrK1T7rw3ebPZmrsEi/d7uLkrgL292f5sJQnGsfkxjs2PcYzEYhybX0ofx/ERn9ySEpjtN/f48eMYP0RJpsfh4eFxWvf59V7HxSVtAipNPmb0Tm3uEizeDBnNXQJSOMax+TGOkViMY/NjHONtik9uSQnMdjjM3t4+1g8s+nGqVKnitO7z6wEAAABAUopPbkkJzBYA3d3ddfv2bUVERJjagoKClCpVKjk5OcVaNzg4OEZbcHCwMmbM+FZqBQAAAGCZ4pNbUgKzBcACBQrIxsYmxkQuBw8eVOHChWVlFbMsT09PHT58WEbj08P9RqNRhw4dkqen59ssGQAAAICFiU9uSQnMVnHq1KlVp04d/fDDDzp27Ji2bt2quXPn6quvvpL0NFWHhoZKkmrUqKF79+5p6NChOn/+vIYOHarHjx+rZs2a5iofAAAAgAV4XW5JaQzG6MNqZvD48WP98MMP+vXXX+Xg4KDmzZurWbNmkiQPDw8NHz5cvr6+kqRjx45pwIABCgwMlIeHhwYOHKgPPvjAXKUDAAAAsBCvyi0pjVkDIAAAAADg7Ul5J60CAAAAABKEAAgAAAAAFoIACAAAAAAWggAIAAAAABaCAAgAAAAAFoIACAAAAAAWggAIAEAKwZ2bgJQlKirK3CUAsdiYuwAAb1ZUVJSsrPiuB0iJnh+/BoPBjNUAiI9nx++hQ4cUGRkpa2trFS1alM9lmBU3ggfeYc9++Pz111+6du2aXFxclDNnTuXOnVtGo5EdSiCZenb8bty4URcuXJCtra0KFiyosmXLmrk6AHE1evRobdy4UW5ubrp8+bJKlSqlr776SkWLFjV3abBQfP0AvMOidx5HjRql/v37a82aNVq3bp0aNWqk/fv3E/6AZOzZ8Ttq1CgFBgbqxo0batGihVatWmXm6gDExaZNm7Ru3TqNHz9eK1eu1Lfffqtff/1VdnZ2evz4sbnLg4UiAALvuJ07d+rnn3/WuHHjtGzZMpUrV07379/Xo0ePdO/ePXOXB+AVjh8/roCAAI0fP17jxo1T6dKlJUmurq66ceOGmasD8DrXrl1T4cKFVaRIEQUEBGjGjBnq37+/Hj16pBUrVpi7PFgoAiDwjnv06JE++OADFSlSRL/++qtGjBihIUOGKGfOnBo1ahQ7kUAyZjQa5ebmpqJFi2rz5s3q3r27Bg0aJE9PT33//fc6fvy4uUsE8P9eNOFLunTplD59em3evFm9e/dWt27d9Pnnn+vx48caP368/v33XzNUCktHAATeIS/68LGxsdE///yjFStWqE+fPurevbvq16+v//77T9u3b1dwcLAZKgXwvBeN31SpUunq1auaMmWKvv/+e3Xv3l2ff/65Hj16ZDolFID5PXvNbmBgoC5fvqz79++rYMGC2rBhgzp37qw+ffqoUaNGkiRra2vlypVLjo6O5iwbFooACLwjnv3wOXXqlP766y89ePBAZcuWlYeHhwYOHKivv/5ajRs3liRlzpxZ6dKlM2fJAP7fs+N3165d+vHHH/X3338rX758ql27tqZNm6Y6deqYxq+rq6vSpEkjW1tbc5YNQE+P1EeP3zFjxqhdu3Zq1KiRdu7cqQIFCmjEiBGSpNu3b+vYsWO6cuWKFi5cKGdnZzk7O5uxclgqbgMBvCOiP3z8/f21YcMGGQwG+fr6qlOnTvrss8/04MEDHTt2TDt27JC9vb1mzZolR0dHFShQwMyVA3h2wpeffvpJrq6uKlGihPr166e6devq7t27+v3335UtWzY5Ojpqw4YNsrOzYzZQIBmInlBt3rx5Wr16taZPn64nT54oZ86cCg8PV9WqVTVjxgwNHDhQixcvloODgxwdHbV48WIZDAZu14S3jgAIvENWr16tdevWacKECcqbN68iIiIUGhoqb29vGY1G/fHHH2rXrp3y5s0rR0dHLVq0SFZWVnz4AMnA1q1b9fPPP2vq1Kny9PTUrVu3FBQUJGtra9WrV0/58uXT/PnzlSlTJrm4uGj69OmytrY23VsMgHldvHhRdevWVZEiRXTu3DktWbJEv/76qxwcHNStWzdt2LBB//77r8LCwlS4cGFZWVkpIiJCNjbsjuPt4j6AwDtk9OjRunv3rgYPHqwjR45o/fr12rJli2xsbNSuXTvVr19f//77r9KkSSNXV1c+fIBkZOnSpdq2bZtmz56tffv2aeXKlfrrr78UFRWlzz77TH369NH9+/dlb28vW1tbGQwGxi9gJs9/cRoREaEffvhBp06dUv78+fXbb78pd+7cypUrl6ysrHTu3DnNmDFDTk5OL90G8LbwqQGkUC/64MiSJYtmz56tBw8eaPv27fL09NS3336rmzdvasSIEapUqZKyZ88eYxvsPAJv34vGb968eTVo0CB98cUXOnbsmMqVKyc/Pz/Z2tpqwIABqlevnjw8PEzrG41Gxi9gBs9fc+/g4KDs2bOrefPmGjNmjE6fPq3WrVurXLlyyps3r3766Sf9888/sre3j7Edwh/MhU8OIAV69sPn4MGDCg8Pl4eHh7744guFhIRo165d6tWrlypWrKiMGTPq8uXLOnjwoEJDQ2Nshw8f4O17dvxu375dQUFBKly4sD788ENNmTJFW7duVbNmzVS+fHmlSpVKoaGhWrBggcLCwmJsJ/q6IwBvz/MTvmzYsEEPHjxQ7dq11bJlS02ePFmS9PjxY4WEhOjx48fauHGj0qZNKzs7O3OWDphwCiiQwhiNRtOOn7+/v1atWiUHBwdFRUVp2bJlypQpk4xGo+7evat///1XmTJlUq9evRQeHq6FCxey0wiY0bPjd+TIkVq+fLkyZsyof//9VxMnTlTVqlUlPb159KlTp+Tu7q4JEybozp07WrlyJV/aAMnExIkTtXjxYvn7++vBgweaPHmyihcvriZNmqhAgQIaMmSI9u7dq6ioKNnb22vFihWytbWN8TcAMBeOAAIpTPQHx6xZs0wTvmTOnFnfffedvvjiCy1atEjZsmXTli1bNGjQIOXNm1fW1tZaunQps40BZhY9fpcsWaINGzZo7ty58vLyUufOndWjRw+NHDlS1apVU2BgoHr37q3s2bMrbdq0WrZsGRM2AcnErVu3tH//fo0bN05lypTR6dOn9d9//+no0aMyGAzq2LGjWrZsKW9vbxkMBlWqVEnW1tZcs4tkg3chkAI9efJE+/fvV7t27VSqVCkdOHBAd+7ckYuLi+rXr6+ffvpJDRo0UN68eWVnZ6f8+fMz4QuQjBw7dkyfffaZvLy8dPjwYZ05c0YlS5ZUly5dNGHCBFWpUkUbNmyQlZWV3NzcmPAFMKPnj9pFRETo7Nmzevz4sa5cuaKpU6eqT58+cnd3V+fOnRUeHq6PP/5Y1apVMz0nMjKS8Ytkg68RgRQgKioqxuPw8HAFBwfLyclJ169f17Jly9SwYUPNmTNHzs7O+vLLL7V9+3YVLVpUH3zwgenIAR8+wNv3/PiVpLCwMDk5Oem///7TqlWr9Mknn2jatGn66KOP1LNnT02bNk02NjbKkCGD6cg94xd4+6Kiokzh7/bt27px44ZcXV3Vv39/5cyZU/v27ZODg4M8PT310UcfKWvWrDp27Ji2bt0qo9Go6CutuFULkhMCIJDMPXvKV2BgoP79919ZW1ura9euypYtmw4fPqxHjx6pQoUKcnFxUZYsWXT//n3NmzdPz17iy2ljwNv37Pg9fPiw9u/fr8uXL6tjx44qU6aMAgMDdfXqVdMN3V1dXeXm5qYTJ07I1dXVtB3GL2Ae0WNv3Lhxat++vT755BOtX79eXl5eypkzp1avXi07Ozvly5dPd+/elbu7u1q2bKmBAwfKYDBwvR+SJb5OBJK56A+fsWPHKiAgQAaDQcWKFVPfvn3l4OCg5cuXK1OmTMqfP78iIiJka2urqVOnqlixYnzwAGYWPX79/f21YcMGOTo6ytXVVSNHjlTu3Lk1efJkpU6dWkWLFpUkBQcHq3v37qpcubIMBgMTRgDJwIIFC7Ry5Ur5+/vr7t27cnV1VebMmSVJH3zwgR48eKANGzZow4YNCg0NVZ06dbjmHskaARBIAdauXas1a9Zo4sSJkqQ0adLIwcFBkpQ9e3bNmTNH+fPn1/r16/Xw4UMVLVqUDx8gmfjtt9+0fv16TZ06VRkyZFBkZKQyZcokScqaNasmT56swYMH6/jx4woNDVWFChUYv0AycvbsWTVs2NB0pD7a0aNHlTp1ap0/f14zZsyQi4uL5s2bx4RNSPYIgEAKcPbsWX344YcqVqxYjPY1a9aoWLFiunPnjtasWaP06dNr7ty5sra2VmRkJNccAMnA5cuXlSVLFhUqVChG+6pVq+Tk5KShQ4fqjz/+UN68efXDDz/IxsaG8QskE5GRkQoMDIxxH93w8HCFhYVp1KhRqlOnjlq1aqXHjx8rffr0TLiGFIF3J5CMPXv616NHj/TkyRPZ2tqalh89elQBAQGaOXOmbt++LWdnZ2YLBJIZBwcHhYWF6erVq8qSJYup/dGjR5o2bZp+++03ffLJJ7K3t5ckxi+QjFhbW6tRo0aaNWuWtm7dqipVqsjOzk52dnbKlSuXDh06pPr165vOymHCJqQEHJsGkrHo8Fe0aFHt3r1b27Zti7E8U6ZMSp06tSTJxcXFdM0QHz5A8lGgQAHdunVLq1ev1t27d03t7733nrJly6aIiAhT+GP8AsmPp6enChUqpJUrVyogIECS9ODBA125ckXu7u4xrtPltE+kBHzKAClA9erVdfLkSXXr1k1hYWHy9PRUxowZdfjwYaVPnz7GukwYASQvBQsWVPfu3dWvXz9FRETIx8dHuXPn1tq1a5U2bdoYgY/xCyQ/OXPmVNOmTbV69WoNGzZMU6ZMka2trZ48eaIOHTqYuzwg3gzGZ+eJB5DsPHsa6LRp07R06VKlSpVKqVKlkrW1tVatWiVbW1tmCwSSoWfH5ebNm7VgwQJdvXpVTk5OSp06tZYsWcL4BVKIkJAQ3bx5U7t375abm5tq1qwpGxsbTttGikMABFKYv//+W/fu3dPjx49Vrlw5WVtb8+EDJGPPhrvg4GA9ePBADx480AcffMCEEUAKx4RNSIkIgEAK8bIjBOw8AuaX0CN4jF/APF50m4a43Lrh+XUIgEiJuFIVMJOoqKgXtkdGRr6wPXrn8vnvbNh5BN6+58dv9Ph82biOxvgFzO/ZEHf+/HmdOHFCd+7cidP4jX7evn37JInwhxSJI4CAGTz74XPgwAHdu3dPDg4OKlq0qOzs7F76LeSzRxl27typNGnSxLo3IIA369nxuXXrVgUFBclgMKhixYpyd3d/6fOeHb8BAQEKDw9X7dq130rNAGIbM2aMNm7cqMjISD158kR169bVp59+Kg8Pj1jrPjt+ly9frh9++EE//vhjrPt7AikBXz0CZhC98zhq1CgFBATIaDQqTZo0MhqNmjNnjjJlyhQrBD774bNkyRKNHTtWc+bMMUv9gCV7dvyuX79e7u7uioiI0PDhwzV27FhVrlw51nOeHb/Lli3TqFGjNHXq1LdaN2DJnj9Vc82aNVq7dq38/f2VK1cu/fHHH9q4caNu3bqlFi1aKHfu3KZ1nw9/Y8eO1YQJEwh/SLE4BRQwk02bNmnDhg2aOHGiVq5cqYkTJypLlixq2LChgoKCZGVlZTod5fkPn/Hjx2vIkCEqWrSoGV8BYLl27dqlgIAAzZo1S0uWLNGaNWvUpEkTde3aVbt27ZKkl47fsWPHavjw4SpVqpTZ6gcsib+/v7Zt26YnT56Y2v7++29VrVpVpUqVkru7uz7//HM1atRIp06d0o4dOyQ9HbvPj19/f38NHjxY1atXN8trAZICARAwk6CgIBUtWlSFChVShgwZlDt3bo0dO1Y5cuRQjx49FBERISsrqxd++AwZMkQ1a9Y08ysALNe9e/fk5uamHDlyyMbGRlZWVurRo4fq1Kmjvn376u7du68cvzVq1DDzKwAsx8GDBzVr1izt2rVL4eHhkqSHDx/q9u3bkv53bW6NGjVUqlQpLVmyRGFhYZL+d33vihUr5O/vr2HDhhH+kOIRAIG34EWX2l67dk0nT540PY6IiJCjo6Pq1q2r69evmz6Ynt15HD16NB8+wFv2ookh7ty5o3PnzsloNMra2tq0U1mvXj1FRETo+vXrkmKO3zFjxjB+gbcoeuwuX75cbm5umjx5snbv3i1JKliwoP766y8dO3Ysxgy+uXLlUvbs2WVnZ2dqnz9/vvz9/TV8+HDGL94JBEDgLYj+ELlz544ePHggSfL19VXq1Kk1ceLEGFPBp0+fXmnSpIlx/d+6des0ZMgQDR06lA8f4C2JnpE3eiyGhISYvpipU6eOChQoID8/Pz148EB2dnaSJHt7ezk7O8vW1ta0nYCAAA0aNEhDhgxh/AJvkZWVlWkcT506VenTp9eECRO0a9cuNWnSROXKlVObNm20Z88eXb9+XQ8fPlRAQICcnZ1Nn9uRkZE6efKk+vfvr2rVqpnz5QBJhllAgTdk4sSJqlOnjrJnzy5JmjBhgrZu3SpnZ2dVqlRJ33zzjaZNm6Zdu3bJw8ND7dq10927dzV8+HBZWVlp2rRpMhgMCgsL07Rp01S0aFFVqFDBvC8KsBD+/v7y9PRUlSpVZGVlpXHjxun333+X0WhUlixZ1K1bN506dUo///yzwsLC1KNHD4WGhmrOnDl6+PChFixYYNr5/OWXX5QhQwau+QPM5NkJYNq0aaObN2/Kz89PpUqVUv/+/fXnn3/KyspK6dKlk9Fo1OrVq2Vra2uajC0u9wcEUhICIPAGPHr0SN7e3ipRooRGjx6tzZs3a9q0afr222916dIl7dmzR/Xq1VPr1q21bNkyLV++XJcuXVL27NmVKlUqLV26VLa2tqYPrfDwcNMRBgBv3hdffKHIyEh17txZ165d08SJE9WtWzelSpVKc+fO1ZMnT9SlSxfZ2tpq7ty5OnTokDJnzqx06dJp7ty5McYvO4+A+b0oBHbv3l2lSpXS4cOHdfPmTUlSlSpVZG1tHePMHOBdQwAEklj0zt7du3dVt25dZcuWTfny5VPhwoVVu3Zt3b17V5s3b9b06dNVv359tWvXTpK0Y8cOubm5KV++fHz4AGbybFhr166dQkJClDNnTmXJkkUdOnQwrde2bVtdv35dixYtkqOjo86fPy9HR0dlyJBBVlZWjF8gGXrZkUBvb+8Yp20/f8sI4F3DV5JAEove+UuXLp3WrFmjq1evatGiRaZJIdKlS6fq1aurTZs2WrNmjcaMGSNJKleunAoUKCBra2tFRkay8wiYwfPXDLm5uWnt2rWmCV+iTZs2TY8ePdLcuXMlPZ04wt3d3XS6GOMXSH6iP18lafr06cqUKZP69eunEydOxFoPeJcRAIEkFD3jmI2NjSIiIuTs7Ky1a9cqZ86c+v3333X58mVJ/wuBrVu31uLFi7Vs2bIY2+HDBzCfZ3cSJ0+erI8//lgXLlzQgQMHYtxHrECBAqYx/+wpnpzuCbx9z5/Q9rIT3J4f3zVr1lSRIkXeeH1AcsIpoEASefbUsTVr1ujUqVPKkCGDWrdurbt376pOnTrKli2bhg0bpqxZs0qSbt++rUOHDqlChQqEPiCZef50sQcPHqh9+/YqUKCA0qRJoyZNmqh06dLq0qWLmSsFLNuz99u8dOmScuTIEav9eZymDUtGAASSmL+/v1avXq2KFSvK3t5efn5+cnR01L1791SvXj299957GjFihLJkyRLjeVxzACQ/z4fAM2fOyN7eXlmzZtXNmze1Zs0adiIBM3r2y9cDBw5o6tSpat26tXx8fCS9PAQ+HxozZMigtGnTvr3CATPiPBUgCZ04cUKbN2/W5MmTNXz4cLVr104XLlzQsmXLdOfOHf3888+6efOm2rRpo6CgoBjPJfwByc/z1wyVLFlS//zzjxo0aKC1a9eaTvcG8PYZjUZT+FuyZIlmz56tvXv3asaMGdq1a5ekp/fhfdHpodHhb9GiReratavu3bv3dosHzIivLYEkFBoaKmtra6VNm1Zbt27V4sWLFRgYqPDwcP38888aOXKkVqxYoe+//16urq7mLhewaM8fGXjZkYLoEGhtba3hw4fL2dlZVatWNU0YwxFAwDyix+uUKVO0aNEide7cWUWKFNH+/fu1dOlSPXnyROXLlzeFwGf/K0nLly/XxIkT9cMPPyhTpkzmfCnAW8UpoEASioqKUtWqVRUWFqbg4GB9+umnqlatmgoVKqQmTZqoQ4cO8vX1Na3PaZ+AeXDNEPBuCA8PV6dOnVSxYkU1bNhQknTy5EmtXr1a169f19dff62SJUtKivmZu3z5cvn7+2v48OGqVq2a2eoHzIFPMiCJRH+wrFu3Tr/++qs8PDxUqFAh03I3NzfTjmX0NQuEP+Dte9U1Q88fIXjWs+OVa4aAt2/dunUqXbq0MmbMaGqLjIzUuXPnlD9/flNbwYIFJUndunXTtGnTJEklS5aMFf6GDRtG+INF4hpAIIlYW1srKipKjo6OqlevntKmTatJkyZp2bJl+uabb/T48WPVrl1bEtPEA+bCNUNAynTv3j398MMPmjdvnm7dumVqT506terVq6cTJ07o1KlTpvaCBQsqV65cunnzpn788UcdP35c0tPwN3LkSA0fPlzVq1d/668DSA7YCwWS0LPBzsHBQQcOHNCWLVvk4uKiNWvWxJhQAsDb9+w1Q5MmTVL58uXVvn17GY1GLV26VH/88YdpvegQ+KJrhpo3b841Q8Bb8uTJEzk5Ocnd3V3z5s3TlClTdPfuXdPyYsWKKSwsTCtWrDAFvQcPHigyMlKVK1dWSEiIDh06pP/++08rV67UyJEjOfIHi8Y1gEAcxXXCiOc9e80B1xAB5sc1Q0DKExUVJV9fX5UrV06LFy9WrVq11K1bN7m4uEiSNm/erDVr1ujKlSvKmDGj7t69qydPnmj9+vUaOXKkzp49qzlz5ujGjRtyd3c386sBzIs9USAOXjRhxKuuFYp+jhTzuiHCH/B2cc0QkPJFRkbqypUrsra21rfffquaNWuqUaNGkiQ/Pz+5urqqevXqyp07ty5duqT9+/fL3d1dzZo1k/T0aGD27NllNBoJf4A4BRR4raioKFPIO3DggAYNGqS9e/dKevG1Qs96NjQ+fPjwzRcLwIRrhoCU6/r167p165aMRqOsra3l6Oiohg0bytbWVh988IGWLFmi9evXa8yYMQoJCZEk5cmTR1WqVJGfn9//tXfvUVXV6R/H3xwuGuANBZXSMMxcmkRpY2qCoqbSmJnoaJqJ5gVNRReNKYY4EgXqiUTRGMxLcjFMHRs1J80oWk6JNkmpaKCGhiYi4l0u5/eHP86Al2Yak8OBz+sf45y9z/nu1tprn+f7/T7PQ9euXdm4cSPR0dFs27aN4cOH/1e7dkRqAy1HiPyKmwtGfPnll+bgr7S0lK5du952JfDmghEbN25k6dKlqhgoUkVuzhm6du0a06ZNo0GDBsCNnKHdu3ezbt06AgIC6NChQ6WcoQMHDrBv3z5cXV2VMyRSxSZNmsRPP/2EjY0NDRs2JCIiggcffJChQ4cCN7Zxd+jQgcTEREaMGIGNjQ0hISE0bNgQuNGTd9++fSQmJtKiRQvWrl1LmzZtLHhFItWLcgBF/gsVm8yeO3eOPXv24OTkxJAhQ/D19QW4Y5PZRYsWER4ezrPPPmvJSxCpdZQzJGJ93nzzTfbu3cvs2bO5evUqiYmJfPfdd8yZMwcfHx+cnZ2Bf+fnZmZmMmrUKLp3705kZKT5/eLiYvNxdevWtdj1iFRHWgEU+Q+uX79OZmYm06dPv6VgREpKCnXq1OGpp57CxsZGBSNEqgnlDIlYn9LSUk6ePIm/vz+dOnUC4OmnnyYiIgKj0cj169fp168fdevWNVfV7tChAytWrCAmJgZHR0fzZ9nZ2WFjY4O9vb2lLkek2lIAKFKBCkaIWK+8vDwcHBxwcXG5Y87QiBEjgH8Hga1bt6Z169b4+PiQk5PDxo0byc7OZtu2bSQlJSlnSKQK2dra0qRJE44dO0ZxcTG2trYYDAbmzJmDvb09ixcvpnHjxnTv3t084VpaWsoTTzzBmjVrgBsr/waDQfeuyK9QERiR/6eCESLWa9KkSYwbN47Ro0czatQojh8/jouLC0OHDsXZ2blSztDHH3+M0WiksLDQfH55ztD7779PTk6OcoZELKRt27ZkZGRw8OBBDAYDJSUlAMycOZMuXbowa9Ysrly5Yp5wLf+3PKOpYj9eEbk93SUiqMmsiDV78803OXXqFOHh4cycORNnZ2eGDx/O1q1buXjxIgAODg7m7WKJiYls2bKFsLAw8/v169dnyJAhbNq0iZiYmEor/iJSdV588UU8PDwICQkhLy8POzs7SktLgRv3erNmzcyrfRXLWGjFT+S/pwBQBLC3t6esrIz77ruPcePGmUtHnzt3DrixvXPkyJGcOnWK119/ncDAQEaNGsXJkycJCQnhkUce4YsvvqBZs2YsW7ZMwZ9IFbk5Z+jpp59m2bJl+Pv7YzQa2bFjB1evXgW4JWeosLDwlpwhe3t7FYwQsZDyQG/58uW4ubkxZswYcnNzzat8paWluLm5ceXKFUBBn8j/SjmAIqhghIi1Us6QSM1ha2trrqQdHx9PUFAQL7/8MnPnzuWhhx6iadOmXLhwQfeqyF1SGwiptSoWjLCxsaGgoIAdO3bg7++Ps7MzmZmZjBgxggEDBpiDwHLXr18nJyeHH374gezsbD788EOSkpKUMyRiAUlJSaxZs4bo6Gi8vLwoKSnBzu7G/GZoaChpaWl8+umn3HfffZXOu7l/p4hUP7NnzyYnJ4effvqJpk2bUlxczKZNm8z3uIj8dgoApVa6U5PZctevX8fBwcEcBD733HOVmswWFRXx97//3dxkNjg4WDlDIhY0ceJEcnJyWL16Nc2bN6/UkiUgIIA+ffowYcIEBX0iVqLiPXzo0CFOnjyJyWSiZ8+e5pX88vdF5LdRACi1jprMitQcFX8Ejhw5krNnzxIfH0+LFi3M70+ZMoU2bdoQHBxswZGKyG9Vvj37Zgr+RO6OisBIraKCESI1S3nOEEB8fDxubm68/PLLpKWlkZubS2lpqXKGRKqJO6053On18uCvrKys0usK/kTujlYApdYJCwujrKyMuXPnmgtGAERFRbF9+3bmzZt3S8GIig+bO81Iikj1oJwhkeqn4rOzoKCAa9eu0bhxYxwcHG55v6KK27azsrJwdXWtlJMvIr+dAkCpdVQwQqRmUs6QSPUXExNDRkYGhw8fxtfXFw8PDyZPnnzbYys+dz/44ANWr17N+++/T8uWLatyyCI1jgJAqZVUMEKkZlLOkEj1tWLFCuLj4zEajbi5uREbG8vu3btZv359pUJsUDn4S0lJwWg0Eh4ejr+/vyWGLlKjaB+b1CpqMitiXZQzJGKd3nnnHdLS0sx/m0wmcnJyCA4Oplu3bpw+fZovv/yS8PBwioqK+OKLLyodWzH4W7BgAfPnz1fwJ/I7UQAotYoKRohYj7KyMvO9WFBQQF5eHtevXwduTM7cHOSVM5lM5kAwKyuLgoKCqhmwiABw7Ngxjhw5wnvvvcfu3buBG/dldnY258+f5+uvv2bq1KmEhITw7LPPsnXrVrZs2UJpaWml+748+IuMjKRv376WvCSRGkVbQKXWU8EIkepNOUMi1mf//v0kJydz7NgxXn31Vbp168aGDRtISkri0KFDzJ07lyFDhgCwZMkSvvnmG1atWmWevFm3bh1vv/02UVFRPPPMM5a8FJEaR79wpdYqzwmKjIxUwQiRamrFihUkJydXyhlatWoVf/zjH/9jzlBsbCzh4eEK/kSqUHkerpeXF6WlpXz44YfExsZSp04devTowfbt23n00Udxd3cHoKioiH379uHh4YHBYMBkMvHLL7+wdetWoqOj6dOnj4WvSKTm0Qqg1GoqGCFSfbzzzjs88cQT+Pr6AjcCujlz5vDoo48yfPhw0tPTmTJlChEREbRs2ZJz587h4+NjPlbbxkQs63bP1KysLP76179y4sQJ5syZQ8OGDXn77bf58ccfAXB0dKSkpISPPvoIe3t7871cUFCgdg8i94hWAKVGuVPVzju9XrFgRMWHloI/kapVnjO0Z88eHBwc6NKlizlnqEWLFrfkDEVFRVFQUEC3bt2wsbEx378K/kQso2Lu7SeffMKpU6dwdnbGz8+P6dOnExMTQ0REBGFhYRiNRg4dOsTu3btxd3enf//+2NnZUVJSYn7+KvgTuXe0Aig1hprMilg35QyJWKeKz9eoqCg2bdpEixYtuHbtGr/88gtxcXE0aNCAuLg4Tpw4wbRp0+jSpUulz9DOG5GqoyqgUmOUP3xiYmKYOnUqAwcOJDQ0lKVLl1Z6v6KbC0ZMnjyZixcvVt2gRcRczdPLy4uhQ4fi4eFBbGwsGRkZ9OjRg8aNG//HnKHTp0+bc4YU/IlUrfLna25uLoWFhSxdupSUlBRWrFhB//79CQwMpLi4mBkzZuDh4UF4eDiZmZmVPkPBn0jV0Qqg1ChqMitiXZQzJFIzpKWlMWHCBBo2bEh8fDxeXl4AlJSUEBYWRlZWFsnJyWRmZrJnzx7GjRunoE/EQrQCKFZLTWZFrNvNOUOrVq1i/fr1uLq6Mn36dFq0aEFERARFRUUYjUaio6MZNGgQgYGBbNiwAXt7e0pKSsyfp+BPpOrc3IfT19eXwMBACgsLyc/PB24Ef3Z2dvTv39/8eseOHZk4caK52raIVD0VgRGrpIIRItbt13KGFi1aRFxcHEFBQcTFxfGXv/zFnDNUvqoAN3KG1K9TpOpVvH/37dvHtWvX6NKlCzNnzuTy5cuEhISQkpJCmzZtAHB3d8fR0ZErV65U+hytAIpYhlYAxSp5eHgwceJEHnzwQRYvXsxXX32FwWBg6NCh7Nixg7FjxzJr1ixGjBgBgJOTE3l5eZWCv3Xr1hEVFcVbb72l4E+kiilnSMR6VZy8GTt2LNOmTWPQoEEUFBQwb948nn32WYYNG8aqVav429/+RmRkJM7OzrRq1crCIxcRUAAoVkgFI0RqhrS0NPr06cOuXbuws7PDYDDQpEkTZs+ejb+/P7Nnz6ZJkyYMGTKEQYMG0a5dO0sPWaRWq7jt89ixY/zrX/8iLi6OhIQE6tSpw0svvcSZM2eYP38+L7zwAm+//Tapqak89thjrF69GoPBoG2fItWAisCIVVHBCBHrdbv7NyoqipUrVxIXF4efn585Z6g8f/eDDz4wT+aASsWLWErF3Pm9e/dy/vx5kpKSiI+Px2AwcP78ecaPH8/FixdZtWoVrq6uREVFmY/p3LkzxcXF2NvbW/hKREQBoFiNig+fm5vMXrlyhZiYGHJzcwkLC6N169a/2mT2dk3hReTeuVPOEMDcuXP5+OOPK+UMZWdnExwcTExMDJ6enhYbt4hUfv4uXLiQFStW0LhxY/Lz89m2bZt5a+f58+eZMGECly5dIiEhgaZNmzJ37ly2bduG0Wjk6aeftuRliMj/UwAoVkFNZkVqhqioKFJSUrC3t+f+++9nxYoVuLi48MYbb7BlyxamTp1Ko0aN2Lx5M5cvXyYxMfG2PTxFpOplZGSwZMkSZsyYQWFhIampqRw5coTFixebJ2+KiooICAjAy8uLhQsXAvDaa6/xzTffsH37durWrWvJSxARFACKlcnNzSUuLo4hQ4bg7e1NQUEBcXFxbNiwgXXr1lGvXj0WL17Mt99+y8KFC+nQoYOlhyxSq1WcvDl27BizZs1i6tSpODk5ERkZyYULF8zbxSIiIli7di2dOnXiD3/4AxMnTsTBwUGTNyLVwM6dO9m0aRMXL15k5cqVABw4cICEhAQOHTrEu+++y8MPPwzApUuXqFu3bqX79syZM7i6ulpk7CJSmaZVxWqoYISIdanY52/v3r3k5OTg5ORE586d8fLy4r333sPZ2ZnRo0dz5swZ5syZQ2BgIJmZmXTu3BkHBweKi4sV/IlYwM19/uzs7Lh8+TIHDhww9+Bt164dY8eOpW3btsyYMYODBw8CNypv39znT8GfSPWhAFCqLTWZFbFeN+cMjRw5krCwMNLT0zl+/DgADRo0ID4+nnr16jFmzBhOnz7NzJkzef7555kyZQrp6ekqGCFiARVX7o8fP87ly5fx9fXl9ddf54knniApKYn09HQA2rdvz7hx43BzcyMhIaHS52jyRqR60hZQqZZUMEKkZlDOkIh1qTh58+6777J161ZMJhMvvvgiw4YNIzs7m2XLllFaWsrIkSPp1q0bcGOLd8uWLZWzK2IFFABKtaaCESLWSzlDItbLaDSSnJzMG2+8weHDh9m1axf+/v6MHTuWnJwcli1bhslk4oUXXsDPz8983u3avYhI9aI7VKoVNZkVsV7KGRKpGXJzc0lPT2f58uU899xz9O7dm6NHj/KPf/yDhIQEHn74YYKCgigsLGTfvn2VzlXwJ1L9aQVQqg01mRWxXjfnDLm6uuLo6MiRI0cwGo2UlZXx0ksvmfuAHTx4kIULF9KwYUMWLVpkyaGL1Ho3r9odOnSI4cOHk5qaiq2tLVFRUfj5+VFUVER8fDyjRo1i6NChlJSU0KxZMwV9IlZGAaBUC2oyK2K9lDMkUjOsXLmSnj170rx5c1avXk3v3r354osvyMnJYfLkyTRu3Jju3btTr149fHx8mDNnDqBtnyLWRnerVAvlPx4zMjL4/vvvWbduHZGRkfTp04egoCAOHz4M/Ltq4LVr11iwYAEA8+bNw9fXl9DQUK5evWqxaxCprcrvX6PRyNq1a5k8eTLPPPMMqamprFixAk9PTyZNmoSdnR3Jycl89tlnAHh4eGAwGG7ZOioiVa+wsJDExESysrKoU6cOAQEBPPDAA3z00Uc88MADNG3alLy8PNq2bcukSZOYPXu2+VwFfyLWRXesVBs7d+5k9erV2NjY4OXlhY+PD0FBQbRr147g4GCOHDkCQP369dm4cSNRUVHmcxcsWMD69etVLVDEQpQzJGJdbp54cXJywsXFhe+++w4AFxcXTCYTLVu25MKFC+zfv5+33noLe3t7nnvuOU3eiFgxPXXFYlQwQsR63Xz/Xrp0iaNHj9KgQQOOHj3K8uXLCQ8PZ8CAAaxZs4b33nuPJk2aEB0dzYwZMyw0ahEpVz7xkpqaytatWzl37hx+fn5cuHDBfEydOnXw9vYmLS2N4OBgzp49y9KlS83BnyZvRKyTcgDFIlQwQqRmUM6QiHUpz9k1mUxkZ2fz5z//maNHj+Lm5sbPP/+Mk5MTs2bNokGDBvTo0QO4scJ//fp1WrVqhcFgoKSkBDs7O8teiIj8zxQASpVTwQiRmqGwsJCAgABee+01+vbtS0FBAc7OzgwePJgBAwYwfvx4cnNzCQsLY+DAgeZtYyJiGRUnXnJzc3F0dKSkpITGjRuTlZXFrl27WLJkCZ06deLo0aPUr1+f4uJiwsPDzROypaWllfp1ioj10fSNVLmKBSMqNplNTU3l0qVLjB07lkmTJrFs2TKSk5O5du0afn5+eHh4AFo5ELGUm++9ijlDffv2xcXFhWvXrlXKGVq+fPktOUO6f0Uso/zee+edd/jyyy+5cOECTk5OPPPMM0yaNIn27dvzz3/+kyeffBKj0UhGRgZZWVk89dRT5s9Q8Cdi/fQUFotQwQgR66OcIRHr98EHH5CSkkJoaChr167Fy8uLxYsXk52dDUDz5s0pKCjAzc0Nf39/pk+fjp2dHSUlJRYeuYj8XrQCKFXi5h9+dyoYUd5kFmDo0KFER0fTrFkzSw1bRLg1Zyg5OfmWnKFOnTqZc4bGjRtHv379lDMkUg0dO3aMYcOG0bFjR3bs2MG2bdt46623yM/PJz8/n759+xIZGUlBQQEuLi7m83T/itQcmoqVKlEe/K1cuZJjx47RqlUrgoKCMBgMpKWl4ebmhq+vL6NHj8bW1pbNmzcTHx+Pu7u7Sk2LWFBZWZl52/aJEydo1KgRy5YtY8+ePRiNRiZMmEBhYSGpqamEhobSv39/evfuzfHjx/H09MRgMFBaWqofjyIWZjKZMJlM5OXl4eTkxPfff89rr71GcHAwgwYN4ocffmDp0qXk5+fTqlUrGjVqZOkhi8g9oieyVJnyJrPu7u54eHgQEBCAs7MzH330EQMGDKBp06bk5ubStm1bc8GIcto2JmIZyhkSqRnKJ3IGDhxIaGgoRqOR6Oho87O2fIvnsGHD+NOf/oSNjY22bYvUULqr5Z5Rk1mRmkE5QyI1R/fu3Rk8eDAtW7akQYMGAJw/f569e/ea++mWb/lW8CdSM2kFUO6ZigUjynOE/Pz8OHnypPmY8oIRH3/8MVu2bMHV1ZW1a9eqYIRINaKcIZGaw9HRkfHjx2MwGJg2bRr3338/JpMJe3t71q9fD1Ru1yQiNY/6AMrvTk1mRWqG8sfD5MmT8fb2pmvXrrz00kuEhIQwYsQI3n//fT7//HP8/f359NNPSUhI0I9GEStRUlJCVlYWBw8epF69evTu3RtbW1s9f0VqAQWA8rtSk1mRmmf79u2EhoZy8eLFSjlD8fHxpKens2bNGvPEj1buRayXnr8itYOmeOR3pYIRIjVPec7Qrl27lDMkUoPp+StSOygAlN9decGIuLg4HnjgAZYuXcrixYvp27cvnp6etxSM8Pf3B9C2E5FqSjlDIiIiNYe2gMrvbv78+Tg7OzN9+nR27NjBrFmzmD17Nu7u7gBcuHCByMhI1q9fX6lghIhUb8oZEhERsX56YsvvpnwuIS8vD29vb3OT2ZCQEAYNGlSpYISazIpYHzs7O9q3b0/79u3Nr6nJu4iIiHXRCqD87lQwQkRERESketIvb/ndqcmsiIiIiEj1pBVAuSfOnj1LQkICycnJtxSMsLe3V8EIERERERELUAAo94wKRoiIiIiIVC8KAKVKqcmsiIiIiIjlKAAUERERERGpJVSBQ0REREREpJZQACgiIiIiIlJLKAAUERERERGpJRQAioiIiIiI1BIKAEVERERERGoJBYAiIiIiIiK1hAJAERGpMps3b2bo0KF4e3vz+OOPM3jwYFJSUszv+/n5ERsbe8++f8OGDTzyyCO/6Zxdu3bx448/AvD111/zyCOPcOLEiXsxPBERkXvOztIDEBGR2mH9+vW8+eabhIaG0rFjR0wmE1999RURERHk5+fz6quvsn79eurUqWPpoZqdPHmSiRMnsmbNGlq3bs3jjz9Oeno6Li4ulh6aiIjI/0QBoIiIVImkpCQGDx5MQECA+bWHHnqI06dPs2bNGl599dVqF1iZTKZKfzs4OODq6mqh0YiIiNw9bQEVEZEqYTAY+Pbbbzl//nyl18ePH8+6deuAyltAY2NjGT16NEuWLKFr1648/vjjhIWFkZeXx4QJE3jsscfo06cPn3/+ufmzbreF9Ne2lf78889Mnz6dLl260L59e3x8fFiwYAFlZWWcOHGCXr16ATBq1ChiY2Nv2QJ69epVYmJi6NWrFx06dGDgwIFs377d/PkbNmygT58+5n8fffRRXnjhBfbu3Xt3/zNFRET+RwoARUSkSrzyyiscOHAAHx8fxo8fT3x8PPv376devXq0atXqtudkZGRw9OhREhMTmTNnDuvWrSMgIID+/fuzYcMGPD09ef31129ZqftvBQUFceHCBVauXMknn3zCmDFjSEhI4LPPPqN58+akpqYCN4LRMWPG3HL+jBkz2LRpE2+88QabN2+md+/eTJs2jR07dpiPycvLIyUlhQULFrBx40buu+++uxqziIjI3VAAKCIiVaJfv34kJyfTq1cvvvvuOxYtWsSQIUPo16/fHVfEysrKmDdvHq1atWLw4ME0atSIp556iueffx5PT0+GDx/OuXPnOHPmzG8ez9WrVxk4cCDz58+nbdu2tGjRgtGjR9OkSROysrKwtbU1b0lt0KABTk5Olc7Pzs5m586dzJ07lx49etCqVSumTJlCr169WL58ufm44uJi5s2bh7e3Nw8//DCBgYH89NNP/9OYRURE7pZyAEVEpMp4e3vj7e1NWVkZhw4dIi0tjbVr1zJu3Dg+/fTTW45v3Lgxzs7O5r8dHR1p2bKl+e+6desCcP369d88lrp16zJy5Eg++eQT9u/fz/Hjx8nKyiI/P5+ysrL/eH5WVhYAHTt2rPT6k08+idForPSap6en+b/r1asH3AgMRUREqppWAEVE5J47deoU8+bN49SpU8CNfMB27doRFBTEqlWruHTpEnv27LnlPHt7+1teMxh+26OrpKTktq9fvnyZYcOGsXz5curXr8+gQYNISkqiWbNmv+nzb2YymbCzqzy/6uDgcNvjREREqppWAEVE5J5zcHAgNTWV5s2bM378+Erv1a9fH4AmTZrc9ffY29tz8eJF898XL17k7Nmztz02PT2dH374ga+++sr83YWFhZw9e9YcnNnY2Nzxu8r7Ce7du5eePXuaX8/IyKB169Z3fS0iIiL3ggJAERG551xcXHjllVd49913uXTpEv369cPZ2Zkff/yRuLg4OnfuTKdOne76e7y9vdm6dSt9+/alfv36LF68GFtb29seW77St3nzZvr27UteXh5Go5Hi4mLzllJHR0cADh8+TLt27Sqd7+npSc+ePZk3bx42NjY8+OCDbNmyhZ07dxITE3PX1yIiInIvKAAUEZEqERwcjIeHBx9++CGJiYlcvXoVd3d3+vfvz4QJE36X75gxYwaFhYUEBgZSr149xowZQ1FR0W2P9fLyYtasWaxatYqYmBiaNm2Kv78/zZs3JzMzE4BGjRoxePBgoqOjOX78OH369Kn0GUajEaPRSGhoKEVFRbRp04bY2NhbjhMREakubExKQhAREREREakVVARGRERERESkllAAKCIiIiIiUksoABQREREREaklFACKiIiIiIjUEgoARUREREREagkFgCIiIiIiIrWEAkAREREREZFaQgGgiIiIiIhILaEAUEREREREpJZQACgiIiIiIlJLKAAUERERERGpJRQAioiIiIiI1BL/B/01qH+M8mvIAAAAAElFTkSuQmCC", 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" ] @@ -5143,7 +1651,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 110, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -5154,7 +1662,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] From 74835738b781c8c5fd69769e717a4c055e04b565 Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Wed, 20 Nov 2024 11:01:13 +0100 Subject: [PATCH 08/22] - somehow outputs were not cleared as intended --- .../06_advanced_orders_example.ipynb | 297 ++---------------- 1 file changed, 29 insertions(+), 268 deletions(-) diff --git a/examples/notebooks/06_advanced_orders_example.ipynb b/examples/notebooks/06_advanced_orders_example.ipynb index 725d11a4c..f68c55346 100644 --- a/examples/notebooks/06_advanced_orders_example.ipynb +++ b/examples/notebooks/06_advanced_orders_example.ipynb @@ -125,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -152,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -171,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -194,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -240,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -273,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -484,7 +484,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -683,42 +683,9 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.world:connected to db\n", - "INFO:assume.scenario.loader_csv:Starting Scenario example_01g/sho_case from ../inputs\n", - "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", - "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", - "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Downsampling fuel_prices_df successful.\n", - "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding storage units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "data": { - "text/plain": [ - "MarketConfig(market_id='EOM', opening_hours=, opening_duration=Timedelta('1 days 00:00:00'), market_mechanism='pay_as_clear_advanced', market_products=[MarketProduct(duration=Timedelta('0 days 01:00:00'), count=24, first_delivery=Timedelta('1 days 00:00:00'), only_hours=None, eligible_lambda_function=None)], product_type='energy', maximum_bid_volume=100000, maximum_bid_price=3000, minimum_bid_price=-500, maximum_gradient=None, additional_fields=['bid_type', 'min_acceptance_ratio', 'parent_bid_id'], volume_unit='MWh', volume_tick=None, price_unit='EUR/MWh', price_tick=None, supports_get_unmatched=False, eligible_obligations_lambda= at 0x000001B47FDFB560>, param_dict={}, addr=AgentAddress(protocol_addr='world', aid='EOM_operator'), aid=' ')" - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "world = World(database_uri=DB_URI)\n", "\n", @@ -759,20 +726,9 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "world.unit_operators[\"coal_unit_operator\"].units[\"coal_unit\"].bidding_strategies[\"EOM\"]" ] @@ -786,40 +742,9 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.world:activating container\n", - "INFO:assume.world:all agents up - starting simulation\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-29 23:00:00: 97%|█████████▋| 2505601.0/2588400 [00:24<00:00, 105845.94it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:mango.util.distributed_clock:agent0: no new events, time stands still\n", - "WARNING:mango.util.distributed_clock:agent0: no new events, time stands still\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_sho_case 2020-01-30 00:00:00: 97%|█████████▋| 2505601.0/2588400 [00:24<00:00, 101569.79it/s]\n" - ] - } - ], + "outputs": [], "source": [ "world.run()" ] @@ -850,7 +775,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -868,7 +793,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1086,42 +1011,9 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.world:connected to db\n", - "INFO:assume.scenario.loader_csv:Starting Scenario example_01g/bo_case from ../inputs\n", - "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", - "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", - "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Downsampling fuel_prices_df successful.\n", - "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding storage units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "data": { - "text/plain": [ - "<__main__.blockStrategy at 0x1b42e272210>" - ] - }, - "execution_count": 103, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "world = World(database_uri=DB_URI)\n", "\n", @@ -1151,40 +1043,9 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.world:activating container\n", - "INFO:assume.world:all agents up - starting simulation\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-29 00:00:00: 97%|█████████▋| 2502001.0/2588400 [00:22<00:00, 108881.80it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:mango.util.distributed_clock:agent0: no new events, time stands still\n", - "WARNING:mango.util.distributed_clock:agent0: no new events, time stands still\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_bo_case 2020-01-30 00:00:00: 97%|█████████▋| 2505601.0/2588400 [00:22<00:00, 109186.98it/s]\n" - ] - } - ], + "outputs": [], "source": [ "world.run()" ] @@ -1200,7 +1061,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1416,42 +1277,9 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.world:connected to db\n", - "INFO:assume.scenario.loader_csv:Starting Scenario example_01g/lo_case from ../inputs\n", - "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", - "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", - "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Downsampling fuel_prices_df successful.\n", - "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding storage units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "data": { - "text/plain": [ - "<__main__.linkedStrategy at 0x1b42eea6ed0>" - ] - }, - "execution_count": 106, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "world = World(database_uri=DB_URI)\n", "\n", @@ -1481,54 +1309,9 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.world:activating container\n", - "INFO:assume.world:all agents up - starting simulation\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-12 23:00:00: 40%|████ | 1036801.0/2588400 [00:10<00:16, 94622.67it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:pyomo.core:Setting Var 'x[combined_gas_unit_block]' to a value `1.0000000000000016` (float) not in domain Binary.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-29 23:00:00: 97%|█████████▋| 2505601.0/2588400 [00:25<00:00, 100321.53it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:mango.util.distributed_clock:agent0: no new events, time stands still\n", - "WARNING:mango.util.distributed_clock:agent0: no new events, time stands still\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_01g_lo_case 2020-01-30 00:00:00: 97%|█████████▋| 2505601.0/2588400 [00:25<00:00, 97388.82it/s] \n" - ] - } - ], + "outputs": [], "source": [ "world.run()" ] @@ -1544,7 +1327,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1561,20 +1344,9 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sql = \"\"\"\n", "SELECT ident, simulation,\n", @@ -1651,7 +1423,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1659,18 +1431,7 @@ "id": "qoWI_agIJOE4", "outputId": "9b40e670-bfef-4560-d6e8-61a1b29d1975" }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# second plot for the accepted volume of the coal unit\n", "sql = \"\"\"\n", From 5c54510c063ac0cfb0f76a65bedf9afe6305b01e Mon Sep 17 00:00:00 2001 From: AndreasEppler Date: Wed, 20 Nov 2024 11:03:38 +0100 Subject: [PATCH 09/22] Changed amiris-examples folders location to be in inputs in order to be ignored by git. --- examples/amiris-examples | 1 - examples/notebooks/07_interoperability_example.ipynb | 4 ++-- 2 files changed, 2 insertions(+), 3 deletions(-) delete mode 160000 examples/amiris-examples diff --git a/examples/amiris-examples b/examples/amiris-examples deleted file mode 160000 index 4687f1fc3..000000000 --- a/examples/amiris-examples +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 4687f1fc3c8bc8522fe9743b77bc21a0328d8143 diff --git a/examples/notebooks/07_interoperability_example.ipynb b/examples/notebooks/07_interoperability_example.ipynb index db11f78ef..999c11b88 100644 --- a/examples/notebooks/07_interoperability_example.ipynb +++ b/examples/notebooks/07_interoperability_example.ipynb @@ -257,7 +257,7 @@ }, "outputs": [], "source": [ - "!cd .. && git clone https://gitlab.com/dlr-ve/esy/amiris/examples.git amiris-examples" + "!cd inputs && git clone https://gitlab.com/dlr-ve/esy/amiris/examples.git amiris-examples" ] }, { @@ -277,7 +277,7 @@ "from assume.scenario.loader_amiris import load_amiris\n", "\n", "scenario = \"Simple\" # Germany20{15-19}, Austria2019 or Simple\n", - "base_path = f\"../amiris-examples/{scenario}/\"\n", + "base_path = f\"inputs/amiris-examples/{scenario}/\"\n", "\n", "# make sure that you have a database server up and running - preferabely in docker\n", "# DB_URI = \"postgresql://assume:assume@localhost:5432/assume\"\n", From 08973204de2a6364632b00caa962f2eb255e821d Mon Sep 17 00:00:00 2001 From: AndreasEppler Date: Wed, 20 Nov 2024 11:13:37 +0100 Subject: [PATCH 10/22] Fixed outline links. --- examples/notebooks/05_market_comparison.ipynb | 2 +- .../notebooks/06_advanced_orders_example.ipynb | 16 ++++++++-------- .../notebooks/07_interoperability_example.ipynb | 8 ++++---- 3 files changed, 13 insertions(+), 13 deletions(-) diff --git a/examples/notebooks/05_market_comparison.ipynb b/examples/notebooks/05_market_comparison.ipynb index 890a41795..1d413d834 100644 --- a/examples/notebooks/05_market_comparison.ipynb +++ b/examples/notebooks/05_market_comparison.ipynb @@ -366,7 +366,7 @@ "id": "zMyZhaNM7NRP" }, "source": [ - "## 3. Visualize the results\n", + "## 5. Visualize the results\n", "\n", "We can visualize the results using the following functions" ] diff --git a/examples/notebooks/06_advanced_orders_example.ipynb b/examples/notebooks/06_advanced_orders_example.ipynb index f68c55346..4bce42aec 100644 --- a/examples/notebooks/06_advanced_orders_example.ipynb +++ b/examples/notebooks/06_advanced_orders_example.ipynb @@ -19,17 +19,17 @@ "\n", "**As a whole, this tutorial covers the following**\n", "\n", - "1. Explain the basic rules of block and linked orders.\n", + "1. [Explain the basic rules of block and linked orders.](#1-basics)\n", "\n", - "2. Run a small example with single hourly orders.\n", + "2. [Run a small example with single hourly orders.](#2-get-assume-running)\n", "\n", - "3. Create the new market clearing algorithm.\n", + "3. [Create the new market clearing algorithm.](#3-market-clearing-algorithm)\n", "\n", - "4. Adjust a given strategy to integrate block orders.\n", + "4. [Adjust a given strategy to integrate block orders.](#4-block-orders)\n", "\n", - "5. Adjust a given strategy to integrate linked orders.\n", + "5. [Adjust a given strategy to integrate linked orders.](#5-linked-orders)\n", "\n", - "6. Extract graphs from the simulation run and interpret results." + "6. [Extract graphs from the simulation run and interpret results.](#6-using-advanced-orders-effects-of-regular-block-orders-and-linked-orders)" ] }, { @@ -1518,7 +1518,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "assume-framework", + "display_name": "assume", "language": "python", "name": "python3" }, @@ -1532,7 +1532,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.7" }, "nbsphinx": { "execute": "never" diff --git a/examples/notebooks/07_interoperability_example.ipynb b/examples/notebooks/07_interoperability_example.ipynb index 999c11b88..f779de121 100644 --- a/examples/notebooks/07_interoperability_example.ipynb +++ b/examples/notebooks/07_interoperability_example.ipynb @@ -16,13 +16,13 @@ "\n", "**As a whole, this tutorial covers the following**\n", "\n", - "1. running a small scenario from CSV folder with the CLI\n", + "1. [running a small scenario from CSV folder with the CLI](#1-scenario-from-cli)\n", "\n", - "2. creating a small simulation from scratch as shown in tutorial 01\n", + "2. [creating a small simulation from scratch as shown in tutorial 01](#2-run-from-a-script-to-customize-scenario-yourself)\n", "\n", - "3. load a scenario from an AMIRIS scenario.yaml\n", + "3. [load a scenario from an AMIRIS scenario.yaml](#3-load-amiris-scenario)\n", "\n", - "4. load a scenario from a pypsa network" + "4. [load a scenario from a pypsa network](#4-load-pypsa-scenario)" ] }, { From 0b1e5b77478cb4d95876fffee25f6322510146ea Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Wed, 20 Nov 2024 11:31:49 +0100 Subject: [PATCH 11/22] - added release notes --- docs/source/release_notes.rst | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/source/release_notes.rst b/docs/source/release_notes.rst index 9c43725cc..48a3774c9 100644 --- a/docs/source/release_notes.rst +++ b/docs/source/release_notes.rst @@ -13,6 +13,9 @@ Upcoming Release The features in this section are not released yet, but will be part of the next release! To use the features already you have to install the main branch, e.g. ``pip install git+https://github.com/assume-framework/assume`` +**Bugfixes:** + - **Tutorials**: General fixes of the tutorials, to align with updated functionalitites of Assume + v0.4.3 - (11th November 2024) =========================================== From 8d7d78838f6dc018958571266b826960a721f053 Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Wed, 20 Nov 2024 13:50:59 +0100 Subject: [PATCH 12/22] - run pre commit --- docs/source/learning.rst | 2 +- docs/source/learning_algorithm.rst | 10 ++++---- .../04_reinforcement_learning_example.ipynb | 1 + .../06_advanced_orders_example.ipynb | 17 +++++++++----- .../notebooks/09_example_Sim_and_xRL.ipynb | 1 + examples/notebooks/11_redispatch.ipynb | 23 ++++++------------- 6 files changed, 26 insertions(+), 28 deletions(-) diff --git a/docs/source/learning.rst b/docs/source/learning.rst index acbd083d2..38070aeca 100644 --- a/docs/source/learning.rst +++ b/docs/source/learning.rst @@ -136,7 +136,7 @@ The Actor We will explain the way learning works in ASSUME starting from the interface to the simulation, namely the bidding strategy of the power plants. The bidding strategy, per definition in ASSUME, defines the way we formulate bids based on the technical restrictions of the unit. In a learning setting, this is done by the actor network. Which maps the observation to an action. The observation thereby is managed and collected by the units operator as -summarized in the following picture. As you can see in the current working version, the observation space contains a residual load forecast for the next 24 hours and a price +summarized in the following picture. As you can see in the current working version, the observation space contains a residual load forecast for the next 24 hours and a price forecast for 24 hours, as well as the current capacity of the power plant and its marginal costs. .. image:: img/ActorTask.jpg diff --git a/docs/source/learning_algorithm.rst b/docs/source/learning_algorithm.rst index 61a0e4714..32c664d88 100644 --- a/docs/source/learning_algorithm.rst +++ b/docs/source/learning_algorithm.rst @@ -6,10 +6,10 @@ Reinforcement Learning Algorithms ################################## -In the chapter :doc:`learning` we got a general overview of how RL is implemented for a multi-agent setting in Assume. -If you want to apply these RL algorithms to a new problem, you do not necessarily need to understand how the RL algorithms work in detail. -All that is needed is to adapt the bidding strategies, which is covered in the tutorial. -However, for the interested reader, we will give a brief overview of the RL algorithms used in Assume. +In the chapter :doc:`learning` we got a general overview of how RL is implemented for a multi-agent setting in Assume. +If you want to apply these RL algorithms to a new problem, you do not necessarily need to understand how the RL algorithms work in detail. +All that is needed is to adapt the bidding strategies, which is covered in the tutorial. +However, for the interested reader, we will give a brief overview of the RL algorithms used in Assume. We start with the learning role, which is the core of the learning implementation. The Learning Role @@ -17,7 +17,7 @@ The Learning Role The learning role orchestrates the learning process. It initializes the training process and manages the experience gained in a buffer. It also schedules policy updates, thus bringing critic and actor together during the learning process. -Specifically, this means that at the beginning of the simulation we schedule recurrent policy updates, where the output of the critic +Specifically, this means that at the beginning of the simulation we schedule recurrent policy updates, where the output of the critic is used as a loss for the actor, which then updates its weights using backward propagation. With the learning role, we can also choose which RL algorithm should be used. The algorithm and the buffer have base classes and can be customized if needed. diff --git a/examples/notebooks/04_reinforcement_learning_example.ipynb b/examples/notebooks/04_reinforcement_learning_example.ipynb index 89680eacb..c7a54bb10 100644 --- a/examples/notebooks/04_reinforcement_learning_example.ipynb +++ b/examples/notebooks/04_reinforcement_learning_example.ipynb @@ -50,6 +50,7 @@ "# or running the notebook locally\n", "\n", "import os\n", + "\n", "from IPython.display import SVG, display\n", "\n", "image_path = \"assume-repo/docs/source/img/architecture.svg\"\n", diff --git a/examples/notebooks/06_advanced_orders_example.ipynb b/examples/notebooks/06_advanced_orders_example.ipynb index f68c55346..1ab1d6c4d 100644 --- a/examples/notebooks/06_advanced_orders_example.ipynb +++ b/examples/notebooks/06_advanced_orders_example.ipynb @@ -248,7 +248,7 @@ "from operator import itemgetter\n", "\n", "import pyomo.environ as pyo\n", - "from pyomo.opt import SolverFactory, TerminationCondition, check_available_solvers\n", + "from pyomo.opt import SolverFactory, TerminationCondition\n", "\n", "from assume.common.market_objects import MarketConfig, Orderbook\n", "from assume.markets.clearing_algorithms.complex_clearing import (\n", @@ -281,8 +281,15 @@ "EPS = 1e-4\n", "\n", "\n", - "def market_clearing_opt(orders: Orderbook, market_products, mode, with_linked_bids, incidence_matrix: pd.DataFrame = None,\n", - " lines: pd.DataFrame = None, solver: str = \"appsi_highs\", solver_options: dict = {},\n", + "def market_clearing_opt(\n", + " orders: Orderbook,\n", + " market_products,\n", + " mode,\n", + " with_linked_bids,\n", + " incidence_matrix: pd.DataFrame = None,\n", + " lines: pd.DataFrame = None,\n", + " solver: str = \"appsi_highs\",\n", + " solver_options: dict = {},\n", "):\n", " \"\"\"\n", " Sets up and solves the market clearing optimization problem.\n", @@ -497,9 +504,7 @@ " def __init__(self, marketconfig: MarketConfig):\n", " super().__init__(marketconfig)\n", "\n", - " def validate_orderbook(\n", - " self, orderbook: Orderbook, agent_addr\n", - " ) -> None:\n", + " def validate_orderbook(self, orderbook: Orderbook, agent_addr) -> None:\n", " \"\"\"\n", " Checks whether the bid types are valid and whether the volumes are within the maximum bid volume.\n", "\n", diff --git a/examples/notebooks/09_example_Sim_and_xRL.ipynb b/examples/notebooks/09_example_Sim_and_xRL.ipynb index d55c7678c..757d923c2 100644 --- a/examples/notebooks/09_example_Sim_and_xRL.ipynb +++ b/examples/notebooks/09_example_Sim_and_xRL.ipynb @@ -168,6 +168,7 @@ "outputs": [], "source": [ "import importlib.util\n", + "\n", "# Check if 'google.colab' is available\n", "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", "\n", diff --git a/examples/notebooks/11_redispatch.ipynb b/examples/notebooks/11_redispatch.ipynb index e387c900c..3916b9c89 100644 --- a/examples/notebooks/11_redispatch.ipynb +++ b/examples/notebooks/11_redispatch.ipynb @@ -65,17 +65,12 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", - "import matplotlib.pyplot as plt\n", + "from pathlib import Path\n", + "\n", "import numpy as np\n", "import pandas as pd\n", - "import plotly.graph_objects as go\n", - "import pyomo as pyo\n", - "import seaborn as sns\n", - "import yaml\n", "import pypsa\n", - "from assume import World\n", - "from pathlib import Path\n", + "\n", "\n", "# Simplified function to add read required CSV files\n", "def read_grid(network_path: str | Path) -> dict[str, pd.DataFrame]:\n", @@ -282,12 +277,12 @@ "metadata": {}, "outputs": [], "source": [ - "from assume.common.market_objects import MarketConfig, Orderbook\n", + "from assume.common.market_objects import Orderbook\n", + "\n", "\n", "def clear(\n", " self, orderbook: Orderbook, market_products\n", ") -> tuple[Orderbook, Orderbook, list[dict]]:\n", - "\n", " orderbook_df = pd.DataFrame(orderbook)\n", " orderbook_df[\"accepted_volume\"] = 0.0\n", " orderbook_df[\"accepted_price\"] = 0.0\n", @@ -345,9 +340,7 @@ "\n", " # Update p_max_pu for generators with _up and _down suffixes\n", " redispatch_network.generators_t.p_max_pu.update(p_max_pu_up.add_suffix(\"_up\"))\n", - " redispatch_network.generators_t.p_max_pu.update(\n", - " p_max_pu_down.add_suffix(\"_down\")\n", - " )\n", + " redispatch_network.generators_t.p_max_pu.update(p_max_pu_down.add_suffix(\"_down\"))\n", "\n", " # Add _up and _down suffix to costs and update the network\n", " redispatch_network.generators_t.marginal_cost.update(costs.add_suffix(\"_up\"))\n", @@ -359,9 +352,7 @@ " redispatch_network.lpf()\n", "\n", " # check lines for congestion where power flow is larget than s_nom\n", - " line_loading = (\n", - " redispatch_network.lines_t.p0.abs() / redispatch_network.lines.s_nom\n", - " )\n", + " line_loading = redispatch_network.lines_t.p0.abs() / redispatch_network.lines.s_nom\n", "\n", " # if any line is congested, perform redispatch\n", " if line_loading.max().max() > 1:\n", From 66b81a52e8d05507357dc8362cec7f71e42e32b2 Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Wed, 20 Nov 2024 13:58:13 +0100 Subject: [PATCH 13/22] - fix tests --- examples/notebooks/11_redispatch.ipynb | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/examples/notebooks/11_redispatch.ipynb b/examples/notebooks/11_redispatch.ipynb index 3916b9c89..af8eb41e0 100644 --- a/examples/notebooks/11_redispatch.ipynb +++ b/examples/notebooks/11_redispatch.ipynb @@ -277,7 +277,8 @@ "metadata": {}, "outputs": [], "source": [ - "from assume.common.market_objects import Orderbook\n", + "from assume.common.market_objects import MarketConfig, Orderbook\n", + "from assume.common.grid_utils import calculate_network_meta\n", "\n", "\n", "def clear(\n", @@ -356,15 +357,13 @@ "\n", " # if any line is congested, perform redispatch\n", " if line_loading.max().max() > 1:\n", - " log.debug(\"Congestion detected\")\n", - "\n", + " \n", " status, termination_condition = redispatch_network.optimize(\n", " solver_name=self.solver,\n", " env=self.env,\n", " )\n", "\n", " if status != \"ok\":\n", - " log.error(f\"Solver exited with {termination_condition}\")\n", " raise Exception(\"Solver in redispatch market did not converge\")\n", "\n", " # process dispatch data\n", @@ -372,9 +371,6 @@ " network=redispatch_network, orderbook_df=orderbook_df\n", " )\n", "\n", - " # if no congestion is detected set accepted volume and price to 0\n", - " else:\n", - " log.debug(\"No congestion detected\")\n", "\n", " # return orderbook_df back to orderbook format as list of dicts\n", " accepted_orders = orderbook_df.to_dict(\"records\")\n", From 236274c25372db799f31deebc442ab25763cf8d4 Mon Sep 17 00:00:00 2001 From: AndreasEppler Date: Wed, 20 Nov 2024 16:02:10 +0100 Subject: [PATCH 14/22] Removed unused old code lines from before previous bug fix. --- examples/notebooks/06_advanced_orders_example.ipynb | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/examples/notebooks/06_advanced_orders_example.ipynb b/examples/notebooks/06_advanced_orders_example.ipynb index 4bce42aec..fe5ddf7f2 100644 --- a/examples/notebooks/06_advanced_orders_example.ipynb +++ b/examples/notebooks/06_advanced_orders_example.ipynb @@ -248,7 +248,7 @@ "from operator import itemgetter\n", "\n", "import pyomo.environ as pyo\n", - "from pyomo.opt import SolverFactory, TerminationCondition, check_available_solvers\n", + "from pyomo.opt import SolverFactory, TerminationCondition\n", "\n", "from assume.common.market_objects import MarketConfig, Orderbook\n", "from assume.markets.clearing_algorithms.complex_clearing import (\n", @@ -277,10 +277,8 @@ "metadata": {}, "outputs": [], "source": [ - "SOLVERS = [\"appsi_highs\", \"gurobi\"]\n", "EPS = 1e-4\n", "\n", - "\n", "def market_clearing_opt(orders: Orderbook, market_products, mode, with_linked_bids, incidence_matrix: pd.DataFrame = None,\n", " lines: pd.DataFrame = None, solver: str = \"appsi_highs\", solver_options: dict = {},\n", "):\n", @@ -478,7 +476,7 @@ "source": [ "So this function defines how the objective is solved. Let's create the market clearing algorithm as a MarketRole inheriting from the ComplexClearingRole in the ASSUME framework.\n", "\n", - "First, we define the class ComplexClearRole and initiate it.\n", + "First, we define the class ComplexClearingRole and initiate it.\n", "Then, we specify the main function to clear the market using the function market_clearing_opt() to calculate the market outcome as optimization:" ] }, From abfe7849bfc96d2140b7732a8c9e70c4880742a1 Mon Sep 17 00:00:00 2001 From: AndreasEppler Date: Wed, 20 Nov 2024 16:02:49 +0100 Subject: [PATCH 15/22] Fixed colab RL bug caused by the most recent pyomo version. --- ...forcement_learning_algorithm_example.ipynb | 7 ++++-- .../04_reinforcement_learning_example.ipynb | 5 +++- .../notebooks/09_example_Sim_and_xRL.ipynb | 3 +++ .../notebooks/10_DSU_and_flexibility.ipynb | 23 +++++++++++-------- 4 files changed, 26 insertions(+), 12 deletions(-) diff --git a/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb b/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb index 7e543a938..00325c98a 100644 --- a/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb +++ b/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "d2e2b8fe", "metadata": { "colab": { @@ -45,7 +45,10 @@ "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", "\n", "if IN_COLAB:\n", - " !pip install assume-framework[learning]" + " !pip install assume-framework[learning]\n", + " # Colab currently has issues with pyomo version 6.8.2, causing the notebook to crash\n", + " # Installing an older version resolves this issue. This should only be considered a temporary fix.\n", + " !pip install pyomo==6.8.0" ] }, { diff --git a/examples/notebooks/04_reinforcement_learning_example.ipynb b/examples/notebooks/04_reinforcement_learning_example.ipynb index 89680eacb..796e819a9 100644 --- a/examples/notebooks/04_reinforcement_learning_example.ipynb +++ b/examples/notebooks/04_reinforcement_learning_example.ipynb @@ -175,7 +175,10 @@ "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", "\n", "if IN_COLAB:\n", - " !pip install 'assume-framework[learning]'" + " !pip install 'assume-framework[learning]'\n", + " # Colab currently has issues with pyomo version 6.8.2, causing the notebook to crash\n", + " # Installing an older version resolves this issue. This should only be considered a temporary fix.\n", + " !pip install pyomo==6.8.0" ] }, { diff --git a/examples/notebooks/09_example_Sim_and_xRL.ipynb b/examples/notebooks/09_example_Sim_and_xRL.ipynb index d55c7678c..fa3c47743 100644 --- a/examples/notebooks/09_example_Sim_and_xRL.ipynb +++ b/examples/notebooks/09_example_Sim_and_xRL.ipynb @@ -174,6 +174,9 @@ "if IN_COLAB:\n", " !pip install 'assume-framework[learning]'\n", " !git clone --depth=1 https://github.com/assume-framework/assume.git assume-repo\n", + " # Colab currently has issues with pyomo version 6.8.2, causing the notebook to crash\n", + " # Installing an older version resolves this issue. This should only be considered a temporary fix.\n", + " !pip install pyomo==6.8.0\n", "!pip install plotly\n", "!pip install nbconvert" ] diff --git a/examples/notebooks/10_DSU_and_flexibility.ipynb b/examples/notebooks/10_DSU_and_flexibility.ipynb index d7e07d594..41fbfbe48 100644 --- a/examples/notebooks/10_DSU_and_flexibility.ipynb +++ b/examples/notebooks/10_DSU_and_flexibility.ipynb @@ -47,15 +47,20 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "vscode": { - "languageId": "shellscript" - } - }, + "metadata": {}, "outputs": [], "source": [ - "# Install the ASSUME framework with the PyPSA library for network optimization\n", - "!pip install assume-framework[network]\n", + "import importlib.util\n", + "\n", + "# Check whether notebook is run in google colab\n", + "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", + "\n", + "if IN_COLAB:\n", + " # Install the ASSUME framework with the PyPSA library for network optimization\n", + " !pip install assume-framework[network]\n", + " # Colab currently has issues with pyomo version 6.8.2, causing the notebook to crash\n", + " # Installing an older version resolves this issue. This should only be considered a temporary fix.\n", + " !pip install pyomo==6.8.0\n", "\n", "#Install some additional packages for plotting\n", "!pip install plotly\n", @@ -2948,7 +2953,7 @@ ], "metadata": { "kernelspec": { - "display_name": "assume-framework", + "display_name": "assume", "language": "python", "name": "python3" }, @@ -2962,7 +2967,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.12.7" } }, "nbformat": 4, From b1b6ffa9f67a4c26ae3784bb5cced614a64f22a7 Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Wed, 20 Nov 2024 16:27:48 +0100 Subject: [PATCH 16/22] - added requests installation to tutorial 10 --- .../notebooks/10_DSU_and_flexibility.ipynb | 33 ++++++++++++++++--- 1 file changed, 29 insertions(+), 4 deletions(-) diff --git a/examples/notebooks/10_DSU_and_flexibility.ipynb b/examples/notebooks/10_DSU_and_flexibility.ipynb index 41fbfbe48..224e053d4 100644 --- a/examples/notebooks/10_DSU_and_flexibility.ipynb +++ b/examples/notebooks/10_DSU_and_flexibility.ipynb @@ -48,7 +48,15 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "^C\n" + ] + } + ], "source": [ "import importlib.util\n", "\n", @@ -65,7 +73,8 @@ "#Install some additional packages for plotting\n", "!pip install plotly\n", "!pip install cartopy\n", - "!pip install seaborn" + "!pip install seaborn\n", + "!pip install requests==2.32.2" ] }, { @@ -77,9 +86,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ImportError", + "evalue": "DLL load failed while importing _context: Das angegebene Modul wurde nicht gefunden.", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[5], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcartopy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcrs\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mccrs\u001b[39;00m\n\u001b[0;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\cartopy\\__init__.py:106\u001b[0m\n\u001b[0;32m 102\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m 104\u001b[0m \u001b[38;5;66;03m# Commonly used sub-modules. Imported here to provide end-user\u001b[39;00m\n\u001b[0;32m 105\u001b[0m \u001b[38;5;66;03m# convenience.\u001b[39;00m\n\u001b[1;32m--> 106\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcartopy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcrs\u001b[39;00m \u001b[38;5;66;03m# noqa: E402 module-level imports\u001b[39;00m\n\u001b[0;32m 107\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcartopy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfeature\u001b[39;00m \u001b[38;5;66;03m# noqa: E402,F401 (unused import)\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\cartopy\\crs.py:21\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m\n\u001b[0;32m 20\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m---> 21\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpyproj\u001b[39;00m\n\u001b[0;32m 22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyproj\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Transformer\n\u001b[0;32m 23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyproj\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexceptions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ProjError\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyproj\\__init__.py:47\u001b[0m\n\u001b[0;32m 43\u001b[0m \u001b[38;5;66;03m# end delvewheel patch\u001b[39;00m\n\u001b[0;32m 45\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m\n\u001b[1;32m---> 47\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpyproj\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnetwork\u001b[39;00m\n\u001b[0;32m 48\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyproj\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_context\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ( \u001b[38;5;66;03m# noqa: F401 pylint: disable=unused-import\u001b[39;00m\n\u001b[0;32m 49\u001b[0m set_use_global_context,\n\u001b[0;32m 50\u001b[0m )\n\u001b[0;32m 51\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyproj\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_show_versions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ( \u001b[38;5;66;03m# noqa: F401 pylint: disable=unused-import\u001b[39;00m\n\u001b[0;32m 52\u001b[0m show_versions,\n\u001b[0;32m 53\u001b[0m )\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyproj\\network.py:10\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpathlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Path\n\u001b[0;32m 8\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcertifi\u001b[39;00m\n\u001b[1;32m---> 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyproj\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_context\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _set_context_ca_bundle_path\n\u001b[0;32m 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyproj\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_network\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ( \u001b[38;5;66;03m# noqa: F401 pylint: disable=unused-import\u001b[39;00m\n\u001b[0;32m 12\u001b[0m is_network_enabled,\n\u001b[0;32m 13\u001b[0m set_network_enabled,\n\u001b[0;32m 14\u001b[0m )\n\u001b[0;32m 17\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mset_ca_bundle_path\u001b[39m(ca_bundle_path: Path \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[1;31mImportError\u001b[0m: DLL load failed while importing _context: Das angegebene Modul wurde nicht gefunden." + ] + } + ], "source": [ "import os\n", "\n", From f0595ee5fdf92c99cc03bd916c1d3e4b55da1627 Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Wed, 20 Nov 2024 16:34:23 +0100 Subject: [PATCH 17/22] - ruff formatting alignment --- examples/notebooks/11_redispatch.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/notebooks/11_redispatch.ipynb b/examples/notebooks/11_redispatch.ipynb index af8eb41e0..610d2bb9e 100644 --- a/examples/notebooks/11_redispatch.ipynb +++ b/examples/notebooks/11_redispatch.ipynb @@ -277,7 +277,7 @@ "metadata": {}, "outputs": [], "source": [ - "from assume.common.market_objects import MarketConfig, Orderbook\n", + "from assume.common.market_objects import Orderbook\n", "from assume.common.grid_utils import calculate_network_meta\n", "\n", "\n", From c716aa8f40ddd975b9fab863dbc20975389e886b Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Wed, 20 Nov 2024 16:35:23 +0100 Subject: [PATCH 18/22] - clear outputs tutorial 10 --- .../notebooks/10_DSU_and_flexibility.ipynb | 30 ++----------------- 1 file changed, 3 insertions(+), 27 deletions(-) diff --git a/examples/notebooks/10_DSU_and_flexibility.ipynb b/examples/notebooks/10_DSU_and_flexibility.ipynb index 224e053d4..c9b18ed59 100644 --- a/examples/notebooks/10_DSU_and_flexibility.ipynb +++ b/examples/notebooks/10_DSU_and_flexibility.ipynb @@ -48,15 +48,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "^C\n" - ] - } - ], + "outputs": [], "source": [ "import importlib.util\n", "\n", @@ -86,25 +78,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "ImportError", - "evalue": "DLL load failed while importing _context: Das angegebene Modul wurde nicht gefunden.", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[5], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcartopy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcrs\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mccrs\u001b[39;00m\n\u001b[0;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\cartopy\\__init__.py:106\u001b[0m\n\u001b[0;32m 102\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m 104\u001b[0m \u001b[38;5;66;03m# Commonly used sub-modules. Imported here to provide end-user\u001b[39;00m\n\u001b[0;32m 105\u001b[0m \u001b[38;5;66;03m# convenience.\u001b[39;00m\n\u001b[1;32m--> 106\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcartopy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcrs\u001b[39;00m \u001b[38;5;66;03m# noqa: E402 module-level imports\u001b[39;00m\n\u001b[0;32m 107\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcartopy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfeature\u001b[39;00m \u001b[38;5;66;03m# noqa: E402,F401 (unused import)\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\cartopy\\crs.py:21\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m\n\u001b[0;32m 20\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m---> 21\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpyproj\u001b[39;00m\n\u001b[0;32m 22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyproj\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Transformer\n\u001b[0;32m 23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyproj\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexceptions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ProjError\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyproj\\__init__.py:47\u001b[0m\n\u001b[0;32m 43\u001b[0m \u001b[38;5;66;03m# end delvewheel patch\u001b[39;00m\n\u001b[0;32m 45\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m\n\u001b[1;32m---> 47\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpyproj\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnetwork\u001b[39;00m\n\u001b[0;32m 48\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyproj\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_context\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ( \u001b[38;5;66;03m# noqa: F401 pylint: disable=unused-import\u001b[39;00m\n\u001b[0;32m 49\u001b[0m set_use_global_context,\n\u001b[0;32m 50\u001b[0m )\n\u001b[0;32m 51\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyproj\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_show_versions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ( \u001b[38;5;66;03m# noqa: F401 pylint: disable=unused-import\u001b[39;00m\n\u001b[0;32m 52\u001b[0m show_versions,\n\u001b[0;32m 53\u001b[0m )\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyproj\\network.py:10\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpathlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Path\n\u001b[0;32m 8\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcertifi\u001b[39;00m\n\u001b[1;32m---> 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyproj\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_context\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _set_context_ca_bundle_path\n\u001b[0;32m 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyproj\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_network\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ( \u001b[38;5;66;03m# noqa: F401 pylint: disable=unused-import\u001b[39;00m\n\u001b[0;32m 12\u001b[0m is_network_enabled,\n\u001b[0;32m 13\u001b[0m set_network_enabled,\n\u001b[0;32m 14\u001b[0m )\n\u001b[0;32m 17\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mset_ca_bundle_path\u001b[39m(ca_bundle_path: Path \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[1;31mImportError\u001b[0m: DLL load failed while importing _context: Das angegebene Modul wurde nicht gefunden." - ] - } - ], + "outputs": [], "source": [ "import os\n", "\n", From bfcf8f8cba601eebc7ac7b6a48544dd31643ea32 Mon Sep 17 00:00:00 2001 From: Florian Maurer Date: Wed, 20 Nov 2024 16:43:22 +0100 Subject: [PATCH 19/22] fix typo --- docs/source/learning_algorithm.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/learning_algorithm.rst b/docs/source/learning_algorithm.rst index 32c664d88..210745d14 100644 --- a/docs/source/learning_algorithm.rst +++ b/docs/source/learning_algorithm.rst @@ -89,7 +89,7 @@ TD3 is summarized in the following picture from the authors of the original pape The steps in the algorithm are translated to implementations in ASSUME in the following way. The initialization of the actors and critics is done by the :func:`assume.reinforcement_learning.algorithms.matd3.TD3.initialize_policy` function, which is called in the learning role. The replay buffer needs to be stable across different episodes, which corresponds to runs of the entire simulation, hence it needs to be detached from the -entities of the simualtion that are killed after each episode, like the learning role. Therefore, it is initialized independently and given to the learning role +entities of the simulation that are killed after each episode, like the learning role. Therefore, it is initialized independently and given to the learning role at the beginning of each episode. For more information regarding the buffer see :doc:`buffers`. The core of the algorithm is embodied by the :func:`assume.reinforcement_learning.algorithms.matd3.TD3.update_policy` in the learning algorithms. Here, the critic and the actor are updated according to the algorithm. From bdf701648c9b832dbd98e3cefbf9ad449f206f16 Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Wed, 20 Nov 2024 16:49:16 +0100 Subject: [PATCH 20/22] - ruff formatting --- examples/notebooks/10_DSU_and_flexibility.ipynb | 2 +- examples/notebooks/11_redispatch.ipynb | 4 +--- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/examples/notebooks/10_DSU_and_flexibility.ipynb b/examples/notebooks/10_DSU_and_flexibility.ipynb index c9b18ed59..9761f6e2a 100644 --- a/examples/notebooks/10_DSU_and_flexibility.ipynb +++ b/examples/notebooks/10_DSU_and_flexibility.ipynb @@ -62,7 +62,7 @@ " # Installing an older version resolves this issue. This should only be considered a temporary fix.\n", " !pip install pyomo==6.8.0\n", "\n", - "#Install some additional packages for plotting\n", + "# Install some additional packages for plotting\n", "!pip install plotly\n", "!pip install cartopy\n", "!pip install seaborn\n", diff --git a/examples/notebooks/11_redispatch.ipynb b/examples/notebooks/11_redispatch.ipynb index 610d2bb9e..d940a3477 100644 --- a/examples/notebooks/11_redispatch.ipynb +++ b/examples/notebooks/11_redispatch.ipynb @@ -277,8 +277,8 @@ "metadata": {}, "outputs": [], "source": [ - "from assume.common.market_objects import Orderbook\n", "from assume.common.grid_utils import calculate_network_meta\n", + "from assume.common.market_objects import Orderbook\n", "\n", "\n", "def clear(\n", @@ -357,7 +357,6 @@ "\n", " # if any line is congested, perform redispatch\n", " if line_loading.max().max() > 1:\n", - " \n", " status, termination_condition = redispatch_network.optimize(\n", " solver_name=self.solver,\n", " env=self.env,\n", @@ -371,7 +370,6 @@ " network=redispatch_network, orderbook_df=orderbook_df\n", " )\n", "\n", - "\n", " # return orderbook_df back to orderbook format as list of dicts\n", " accepted_orders = orderbook_df.to_dict(\"records\")\n", " rejected_orders = []\n", From b07da00103f317b7910342d818a3eac9f239594e Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Wed, 20 Nov 2024 16:52:36 +0100 Subject: [PATCH 21/22] - cleared output that was accidentally merged back --- ...forcement_learning_algorithm_example.ipynb | 124 +- .../04_reinforcement_learning_example.ipynb | 4 +- .../notebooks/09_example_Sim_and_xRL.ipynb | 4479 +---------------- 3 files changed, 53 insertions(+), 4554 deletions(-) diff --git a/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb b/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb index 03548e2a6..291fcf344 100644 --- a/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb +++ b/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb @@ -37,47 +37,7 @@ "id": "m0DaRwFA7VgW", "outputId": "5655adad-5b7a-4fe3-9067-6b502a06136b" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: assume-framework[learning] in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (0.4.3)\n", - "Requirement already satisfied: argcomplete>=3.1.4 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from assume-framework[learning]) (3.5.1)\n", - "Requirement already satisfied: nest-asyncio>=1.5.6 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from assume-framework[learning]) (1.6.0)\n", - "Requirement already satisfied: mango-agents>=2.1.1 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from assume-framework[learning]) (2.1.1)\n", - "Requirement already satisfied: numpy>=1.26.4 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from assume-framework[learning]) (1.26.4)\n", - "Requirement already satisfied: tqdm>=4.64.1 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from assume-framework[learning]) (4.66.6)\n", - 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"Requirement already satisfied: colorama in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from tqdm>=4.64.1->assume-framework[learning]) (0.4.6)\n", - "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from jinja2->torch>=2.0.1->assume-framework[learning]) (3.0.2)\n" - ] - } - ], + "outputs": [], "source": [ "import importlib.util\n", "\n", @@ -105,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "d5e77f71", "metadata": { "colab": { @@ -134,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "de097384", "metadata": { "colab": { @@ -161,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "7d9899ff", "metadata": {}, "outputs": [], @@ -198,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "ade14744", "metadata": { "id": "xUsbeZdPJ_2Q" @@ -255,7 +215,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "94517a3e", "metadata": { "id": "UXYSesx4Ifp5" @@ -458,7 +418,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "daed035c", "metadata": {}, "outputs": [], @@ -550,7 +510,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "632844c2", "metadata": { "id": "0ww-L9fABnw3" @@ -609,7 +569,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "c715f90e", "metadata": {}, "outputs": [], @@ -677,7 +637,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "753dbab1", "metadata": {}, "outputs": [], @@ -871,7 +831,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "6bb09b5b", "metadata": { "id": "moZ_UD7FfkOh" @@ -900,7 +860,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "5cff2f6a", "metadata": { "id": "iPz8v4N5hpfr" @@ -943,61 +903,7 @@ "lines_to_next_cell": 0, "outputId": "e30f4279-7a4e-4efc-9cfb-61416e4fe2f1" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.world:connected to db\n", - "INFO:assume.scenario.loader_csv:Starting Scenario example_02a/base from ../inputs\n", - "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Downsampling demand_df successful.\n", - "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", - "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", - "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", - "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", - "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Downsampling demand_df successful.\n", - "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", - "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", - "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", - "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Episodes: 0%| | 0/100 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
technologybidding_zonalfuel_typeemission_factormax_powermin_powerefficiencyadditional_costnodeunit_operator
name
Unit 11nuclearnaive_eomuranium0.01000.00.00.315north_2Operator North
Unit 12nuclearnaive_eomuranium0.01000.00.00.316north_2Operator North
Unit 13nuclearnaive_eomuranium0.01000.00.00.317north_2Operator North
Unit 14nuclearnaive_eomuranium0.01000.00.00.318north_2Operator North
Unit 15nuclearnaive_eomuranium0.01000.00.00.319north_2Operator North
Unit 16nuclearnaive_eomuranium0.01000.00.00.320southOperator South
Unit 17nuclearnaive_eomuranium0.01000.00.00.321southOperator South
Unit 18nuclearnaive_eomuranium0.01000.00.00.322southOperator South
Unit 19nuclearnaive_eomuranium0.01000.00.00.323southOperator South
Unit 20nuclearpp_learninguranium0.05000.00.00.324southOperator-RL
\n", - "" - ], - "text/plain": [ - " technology bidding_zonal fuel_type emission_factor max_power \\\n", - "name \n", - "Unit 11 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 12 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 13 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 14 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 15 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 16 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 17 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 18 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 19 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 20 nuclear pp_learning uranium 0.0 5000.0 \n", - "\n", - " min_power efficiency additional_cost node unit_operator \n", - "name \n", - "Unit 11 0.0 0.3 15 north_2 Operator North \n", - "Unit 12 0.0 0.3 16 north_2 Operator North \n", - "Unit 13 0.0 0.3 17 north_2 Operator North \n", - "Unit 14 0.0 0.3 18 north_2 Operator North \n", - "Unit 15 0.0 0.3 19 north_2 Operator North \n", - "Unit 16 0.0 0.3 20 south Operator South \n", - "Unit 17 0.0 0.3 21 south Operator South \n", - "Unit 18 0.0 0.3 22 south Operator South \n", - "Unit 19 0.0 0.3 23 south Operator South \n", - "Unit 20 0.0 0.3 24 south Operator-RL " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Create scarcity in southern Germany by limiting the number of power plants\n", "powerplant_units = powerplant_units[:20]\n", @@ -630,7 +383,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "f6c64dc2", "metadata": { "colab": { @@ -639,15 +392,7 @@ "id": "9c555ce9", "outputId": "473126ae-3c3e-4698-e3a5-347cc00e5108" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Configuration YAML file has been saved to 'inputs\\tutorial_08\\config.yaml'.\n" - ] - } - ], + "outputs": [], "source": [ "# YAML configuration for the RL training\n", "config = {\n", @@ -717,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "a01977d5", "metadata": { "cellView": "form", @@ -941,7 +686,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "0c1c9334", "metadata": { "colab": { @@ -951,555 +696,7 @@ "id": "bfadf522", "outputId": "7c91ab13-a3c2-4e89-d8ac-d20be95391f6" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.world:connected to db\n", - "INFO:assume.scenario.loader_csv:Starting Scenario tutorial_08/zonal_case from inputs\n", - "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", - "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", - "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", - "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", - "INFO:assume.scenario.loader_csv:save_frequency_hours is disabled due to CSV export being enabled. Data will be stored in the CSV files at the end of the simulation.\n", - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", - "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:residential_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", - "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", - "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", - "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Episodes: 0%| | 0/15 [00:00=1.0.1 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from matplotlib) (1.3.0)\n", - "Requirement already satisfied: cycler>=0.10 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from matplotlib) (0.12.1)\n", - "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from matplotlib) (4.54.1)\n", - "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from matplotlib) (1.4.7)\n", - "Requirement already satisfied: numpy>=1.23 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from matplotlib) (1.26.4)\n", - "Requirement already satisfied: packaging>=20.0 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from matplotlib) (24.1)\n", - "Requirement already satisfied: pillow>=8 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from matplotlib) (11.0.0)\n", - "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from matplotlib) (3.2.0)\n", - "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from matplotlib) (2.9.0)\n", - "Requirement already satisfied: six>=1.5 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", - "Requirement already satisfied: shap==0.42.1 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (0.42.1)\n", - "Requirement already satisfied: numpy in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from shap==0.42.1) (1.26.4)\n", - "Requirement already satisfied: scipy in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from shap==0.42.1) (1.14.1)\n", - "Requirement already satisfied: scikit-learn in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from shap==0.42.1) (1.3.0)\n", - "Requirement already satisfied: pandas in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from shap==0.42.1) (2.2.3)\n", - "Requirement already satisfied: tqdm>=4.27.0 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from shap==0.42.1) (4.66.6)\n", - "Requirement already satisfied: packaging>20.9 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from shap==0.42.1) (24.1)\n", - "Requirement already satisfied: slicer==0.0.7 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from shap==0.42.1) (0.0.7)\n", - "Requirement already satisfied: numba in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from shap==0.42.1) (0.60.0)\n", - "Requirement already satisfied: cloudpickle in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from shap==0.42.1) (3.1.0)\n", - "Requirement already satisfied: colorama in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from tqdm>=4.27.0->shap==0.42.1) (0.4.6)\n", - "Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from numba->shap==0.42.1) (0.43.0)\n", - 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"Requirement already satisfied: numpy>=1.17.3 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from scikit-learn==1.3.0) (1.26.4)\n", - "Requirement already satisfied: scipy>=1.5.0 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from scikit-learn==1.3.0) (1.14.1)\n", - "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from scikit-learn==1.3.0) (1.4.2)\n", - "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\aeppl\\.conda\\envs\\assume\\lib\\site-packages (from scikit-learn==1.3.0) (3.5.0)\n" - ] - } - ], + "outputs": [], "source": [ "!pip install matplotlib\n", "!pip install shap==0.42.1\n", @@ -2836,20 +1021,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "26c6f33b", "metadata": { "id": "b6ee4f28" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n" - ] - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", @@ -2870,7 +1047,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "ab89d972", "metadata": { "id": "44862f06" @@ -2926,448 +1103,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "6e142be2", "metadata": { "id": "d522969d" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "inputs\\tutorial_08/learned_strategies/zonal_case/buffer_obs/buffer_obs.json\n", - "500000\n", - "270\n" - ] - }, - { - "data": { - "text/html": [ - "
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price forecast t+1price forecast t+2price forecast t+3price forecast t+4price forecast t+5price forecast t+6price forecast t+7price forecast t+8price forecast t+9price forecast t+10...residual load forecast t+17residual load forecast t+18residual load forecast t+19residual load forecast t+20residual load forecast t+21residual load forecast t+22residual load forecast t+23residual load forecast t+24total capacity t-1marginal costs t-1
02.242.262.282.302.322.342.362.382.402.42...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.4066670.000.406667
12.262.282.302.322.342.362.382.402.422.44...0.0000000.0000000.0000000.0000000.0000000.0000000.4066670.4066670.680.406667
22.282.302.322.342.362.382.402.422.442.46...0.0000000.0000000.0000000.0000000.0000000.4066670.4066670.4066670.720.406667
32.302.322.342.362.382.402.422.442.462.48...0.0000000.0000000.0000000.0000000.4066670.4066670.4066670.4066670.760.406667
42.322.342.362.382.402.422.442.462.482.50...0.0000000.0000000.0000000.4066670.4066670.4066670.4066670.4066670.800.406667
..................................................................
2652.502.522.542.562.582.602.622.642.662.68...0.4066670.4066670.4066670.0000000.0000000.0000000.0000000.0000001.000.406667
2662.522.542.562.582.602.622.642.662.682.22...0.4066670.4066670.0000000.0000000.0000000.0000000.0000000.0000001.000.406667
2672.542.562.582.602.622.642.662.682.222.24...0.4066670.0000000.0000000.0000000.0000000.0000000.0000000.0000001.000.406667
2682.562.582.602.622.642.662.682.222.242.26...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000001.000.406667
2692.582.602.622.642.662.682.222.242.262.28...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000001.000.406667
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270 rows × 50 columns

\n", - "
" - ], - "text/plain": [ - " price forecast t+1 price forecast t+2 price forecast t+3 \\\n", - "0 2.24 2.26 2.28 \n", - "1 2.26 2.28 2.30 \n", - "2 2.28 2.30 2.32 \n", - "3 2.30 2.32 2.34 \n", - "4 2.32 2.34 2.36 \n", - ".. ... ... ... \n", - "265 2.50 2.52 2.54 \n", - "266 2.52 2.54 2.56 \n", - "267 2.54 2.56 2.58 \n", - "268 2.56 2.58 2.60 \n", - "269 2.58 2.60 2.62 \n", - "\n", - " price forecast t+4 price forecast t+5 price forecast t+6 \\\n", - "0 2.30 2.32 2.34 \n", - "1 2.32 2.34 2.36 \n", - "2 2.34 2.36 2.38 \n", - "3 2.36 2.38 2.40 \n", - "4 2.38 2.40 2.42 \n", - ".. ... ... ... \n", - "265 2.56 2.58 2.60 \n", - "266 2.58 2.60 2.62 \n", - "267 2.60 2.62 2.64 \n", - "268 2.62 2.64 2.66 \n", - "269 2.64 2.66 2.68 \n", - "\n", - " price forecast t+7 price forecast t+8 price forecast t+9 \\\n", - "0 2.36 2.38 2.40 \n", - "1 2.38 2.40 2.42 \n", - "2 2.40 2.42 2.44 \n", - "3 2.42 2.44 2.46 \n", - "4 2.44 2.46 2.48 \n", - ".. ... ... ... \n", - "265 2.62 2.64 2.66 \n", - "266 2.64 2.66 2.68 \n", - "267 2.66 2.68 2.22 \n", - "268 2.68 2.22 2.24 \n", - "269 2.22 2.24 2.26 \n", - "\n", - " price forecast t+10 ... residual load forecast t+17 \\\n", - "0 2.42 ... 0.000000 \n", - "1 2.44 ... 0.000000 \n", - "2 2.46 ... 0.000000 \n", - "3 2.48 ... 0.000000 \n", - "4 2.50 ... 0.000000 \n", - ".. ... ... ... \n", - "265 2.68 ... 0.406667 \n", - "266 2.22 ... 0.406667 \n", - "267 2.24 ... 0.406667 \n", - "268 2.26 ... 0.000000 \n", - "269 2.28 ... 0.000000 \n", - "\n", - " residual load forecast t+18 residual load forecast t+19 \\\n", - "0 0.000000 0.000000 \n", - "1 0.000000 0.000000 \n", - "2 0.000000 0.000000 \n", - "3 0.000000 0.000000 \n", - "4 0.000000 0.000000 \n", - ".. ... ... \n", - "265 0.406667 0.406667 \n", - "266 0.406667 0.000000 \n", - "267 0.000000 0.000000 \n", - "268 0.000000 0.000000 \n", - "269 0.000000 0.000000 \n", - "\n", - " residual load forecast t+20 residual load forecast t+21 \\\n", - "0 0.000000 0.000000 \n", - "1 0.000000 0.000000 \n", - "2 0.000000 0.000000 \n", - "3 0.000000 0.406667 \n", - "4 0.406667 0.406667 \n", - ".. ... ... \n", - "265 0.000000 0.000000 \n", - "266 0.000000 0.000000 \n", - "267 0.000000 0.000000 \n", - "268 0.000000 0.000000 \n", - "269 0.000000 0.000000 \n", - "\n", - " residual load forecast t+22 residual load forecast t+23 \\\n", - "0 0.000000 0.000000 \n", - "1 0.000000 0.406667 \n", - "2 0.406667 0.406667 \n", - "3 0.406667 0.406667 \n", - "4 0.406667 0.406667 \n", - ".. ... ... \n", - "265 0.000000 0.000000 \n", - "266 0.000000 0.000000 \n", - "267 0.000000 0.000000 \n", - "268 0.000000 0.000000 \n", - "269 0.000000 0.000000 \n", - "\n", - " residual load forecast t+24 total capacity t-1 marginal costs t-1 \n", - "0 0.406667 0.00 0.406667 \n", - "1 0.406667 0.68 0.406667 \n", - "2 0.406667 0.72 0.406667 \n", - "3 0.406667 0.76 0.406667 \n", - "4 0.406667 0.80 0.406667 \n", - ".. ... ... ... \n", - "265 0.000000 1.00 0.406667 \n", - "266 0.000000 1.00 0.406667 \n", - "267 0.000000 1.00 0.406667 \n", - "268 0.000000 1.00 0.406667 \n", - "269 0.000000 1.00 0.406667 \n", - "\n", - "[270 rows x 50 columns]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# path to extra loggedobservation values\n", "path = input_dir + \"/learned_strategies/zonal_case/buffer_obs\"\n", @@ -3405,7 +1146,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "cca85e13", "metadata": { "id": "4da4de57" @@ -3422,30 +1163,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "1cd3b7e6", "metadata": { "id": "37adecfa" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# which actor is the RL actor\n", "ACTOR_NUM = len(powerplant_units) # 20\n", @@ -3473,7 +1196,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "c507d331", "metadata": { "id": "e6460cfb" @@ -3503,19 +1226,10 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "b0758eb5", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", - "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n" - ] - } - ], + "outputs": [], "source": [ "# @ Title Split the data into training and testing sets\n", "X_train, X_test, y_train, y_test = train_test_split(\n", @@ -3545,7 +1259,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "40e12192", "metadata": { "id": "6d9be211" @@ -3562,20 +1276,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "56a32f41", "metadata": { "id": "84bb96cf" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:shap:Using 229 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.\n" - ] - } - ], + "outputs": [], "source": [ "# Create the SHAP Kernel Explainer\n", "explainer = shap.KernelExplainer(model_predict, X_train)" @@ -3583,2070 +1289,12 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "4279910b", "metadata": { "id": "2a7929e4" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/41 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No data for colormapping provided via 'c'. Parameters 'vmin', 'vmax' will be ignored\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Summary plot for the first output dimension\n", "shap.summary_plot(shap_values[0], X_test, feature_names=feature_names, show=False)\n", @@ -5865,7 +1458,7 @@ "notebook_metadata_filter": "-all" }, "kernelspec": { - "display_name": "assume", + "display_name": "assume-framework", "language": "python", "name": "python3" }, @@ -5879,7 +1472,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.11.9" } }, "nbformat": 4, From 2bd2ca77a4ddd3f0b1625ac7bda988e2295976d0 Mon Sep 17 00:00:00 2001 From: AndreasEppler Date: Wed, 20 Nov 2024 18:01:15 +0100 Subject: [PATCH 22/22] Added final cell with all code for execution without pasting the quiz solutions. --- .../04_reinforcement_learning_example.ipynb | 895 +++++++++++++++++- 1 file changed, 888 insertions(+), 7 deletions(-) diff --git a/examples/notebooks/04_reinforcement_learning_example.ipynb b/examples/notebooks/04_reinforcement_learning_example.ipynb index d395bbaeb..353cf9fca 100644 --- a/examples/notebooks/04_reinforcement_learning_example.ipynb +++ b/examples/notebooks/04_reinforcement_learning_example.ipynb @@ -300,6 +300,7 @@ "source": [ "import logging\n", "import os\n", + "import yaml\n", "from datetime import datetime, timedelta\n", "from pathlib import Path\n", "\n", @@ -564,18 +565,18 @@ " # =============================================================================\n", " # 1.1 Get the Observations, which are the basis of the action decision\n", " # =============================================================================\n", + " \n", " # residual load forecast\n", - " # residual load forecast\n", - " scaling_factor_res_load = self.max_demand\n", + " scaling_factor_res_load = None #TODO\n", "\n", " # price forecast\n", - " scaling_factor_price = self.max_bid_price\n", + " scaling_factor_price = None #TODO\n", "\n", " # total capacity\n", - " scaling_factor_total_capacity = unit.max_power\n", + " scaling_factor_total_capacity = None #TODO\n", "\n", " # marginal cost\n", - " scaling_factor_marginal_cost = self.max_bid_price\n", + " scaling_factor_marginal_cost = None #TODO\n", "\n", " # checks if we are at the end of the simulation horizon, since we need to change the forecast then\n", " # for residual load and price forecast and scale them\n", @@ -775,7 +776,7 @@ " # =============================================================================\n", " # ==> YOUR CODE HERE\n", " base_bid = None # TODO\n", - " # add niose to the last dimension of the observation\n", + " # add noise to the last dimension of the observation\n", " # needs to be adjusted if observation space is changed, because only makes sense\n", " # if the last dimension of the observation space are the marginal cost\n", " curr_action = noise + base_bid.clone().detach()\n", @@ -1287,7 +1288,7 @@ "source": [ "learning_config = {\n", " \"continue_learning\": False,\n", - " \"trained_policies_save_path\": \"null\",\n", + " \"trained_policies_save_path\": None,\n", " \"max_bid_price\": 100,\n", " \"algorithm\": \"matd3\",\n", " \"learning_rate\": 0.001,\n", @@ -1305,6 +1306,26 @@ "}" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "bac01731", + "metadata": {}, + "outputs": [], + "source": [ + "# Read the YAML file\n", + "with open(f\"{inputs_path}/example_02a/config.yaml\") as file:\n", + " data = yaml.safe_load(file)\n", + "\n", + "# store our modifications to the config file\n", + "data[\"base\"][\"learning_mode\"] = True\n", + "data[\"base\"][\"learning_config\"] = learning_config\n", + "\n", + "# Write the modified data back to the file\n", + "with open(f\"{inputs_path}/example_02a/config.yaml\", \"w\") as file:\n", + " yaml.safe_dump(data, file)" + ] + }, { "cell_type": "markdown", "id": "132f9429", @@ -1662,6 +1683,866 @@ "lines_to_next_cell": 2 }, "outputs": [], + "source": [ + "# @title Complete notebook code with tasks already filled in\n", + "\n", + "# this cell is used to display the image in the notebook when using colab\n", + "# or running the notebook locally\n", + "\n", + "import os\n", + "\n", + "import importlib.util\n", + "\n", + "# Check if 'google.colab' is available\n", + "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", + "\n", + "if IN_COLAB:\n", + " !pip install 'assume-framework[learning]'\n", + " # Colab currently has issues with pyomo version 6.8.2, causing the notebook to crash\n", + " # Installing an older version resolves this issue. This should only be considered a temporary fix.\n", + " !pip install pyomo==6.8.0\n", + " !git clone --depth=1 https://github.com/assume-framework/assume.git assume-repo\n", + " !cd assume-repo && assume -s example_01b -db \"sqlite:///./examples/local_db/assume_db_example_01b.db\"\n", + "\n", + "colab_inputs_path = \"assume-repo/examples/inputs\"\n", + "local_inputs_path = \"../inputs\"\n", + "\n", + "inputs_path = colab_inputs_path if IN_COLAB else local_inputs_path \n", + "\n", + "import logging\n", + "import os\n", + "import yaml\n", + "from datetime import datetime, timedelta\n", + "from pathlib import Path\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import torch as th\n", + "\n", + "from assume import World\n", + "from assume.common.base import LearningStrategy, SupportsMinMax\n", + "from assume.common.market_objects import MarketConfig, Orderbook, Product\n", + "from assume.reinforcement_learning.algorithms import actor_architecture_aliases\n", + "from assume.reinforcement_learning.learning_utils import NormalActionNoise\n", + "from assume.scenario.loader_csv import load_scenario_folder, run_learning\n", + "\n", + "class RLStrategy(LearningStrategy):\n", + " \"\"\"\n", + " Reinforcement Learning Strategy\n", + " \"\"\"\n", + "\n", + " def __init__(self, *args, **kwargs):\n", + " super().__init__(obs_dim=50, act_dim=2, unique_obs_dim=2, *args, **kwargs)\n", + "\n", + " self.unit_id = kwargs[\"unit_id\"]\n", + "\n", + " # defines bounds of actions space\n", + " self.max_bid_price = kwargs.get(\"max_bid_price\", 100)\n", + " self.max_demand = kwargs.get(\"max_demand\", 10e3)\n", + "\n", + " # tells us whether we are training the agents or just executing per-learnind strategies\n", + " self.learning_mode = kwargs.get(\"learning_mode\", False)\n", + " self.perform_evaluation = kwargs.get(\"perform_evaluation\", False)\n", + "\n", + " # based on learning config define algorithm configuration\n", + " self.algorithm = kwargs.get(\"algorithm\", \"matd3\")\n", + " actor_architecture = kwargs.get(\"actor_architecture\", \"mlp\")\n", + "\n", + " # define the architecture of the actor neural network\n", + " # if you use many time series niputs you might want to use the LSTM instead of the MLP for example\n", + " if actor_architecture in actor_architecture_aliases.keys():\n", + " self.actor_architecture_class = actor_architecture_aliases[\n", + " actor_architecture\n", + " ]\n", + " else:\n", + " raise ValueError(\n", + " f\"Policy '{actor_architecture}' unknown. Supported architectures are {list(actor_architecture_aliases.keys())}\"\n", + " )\n", + "\n", + " # sets the devide of the actor network\n", + " device = kwargs.get(\"device\", \"cpu\")\n", + " self.device = th.device(device if th.cuda.is_available() else \"cpu\")\n", + " if not self.learning_mode:\n", + " self.device = th.device(\"cpu\")\n", + "\n", + " # future: add option to choose between float16 and float32\n", + " # float_type = kwargs.get(\"float_type\", \"float32\")\n", + " self.float_type = th.float\n", + "\n", + " # for definition of observation space\n", + " self.foresight = kwargs.get(\"foresight\", 24)\n", + "\n", + " if self.learning_mode:\n", + " self.learning_role = None\n", + " self.collect_initial_experience_mode = kwargs.get(\n", + " \"episodes_collecting_initial_experience\", True\n", + " )\n", + "\n", + " self.action_noise = NormalActionNoise(\n", + " mu=0.0,\n", + " sigma=kwargs.get(\"noise_sigma\", 0.1),\n", + " action_dimension=self.act_dim,\n", + " scale=kwargs.get(\"noise_scale\", 1.0),\n", + " dt=kwargs.get(\"noise_dt\", 1.0),\n", + " )\n", + "\n", + " elif Path(load_path=kwargs[\"trained_policies_save_path\"]).is_dir():\n", + " self.load_actor_params(load_path=kwargs[\"trained_policies_save_path\"])\n", + "\n", + "# we define the class again and inherit from the initial class just to add the additional method to the original class\n", + "# this is a workaround to have different methods of the class in different cells\n", + "# which is good for the purpose of this tutorial\n", + "# however, you should have all functions in a single class when using this example in .py files\n", + "\n", + "\n", + "class RLStrategy(RLStrategy):\n", + " def calculate_bids(\n", + " self,\n", + " unit: SupportsMinMax,\n", + " market_config: MarketConfig,\n", + " product_tuples: list[Product],\n", + " **kwargs,\n", + " ) -> Orderbook:\n", + " \"\"\"\n", + " Calculate bids for a unit -> STEP 1 & 2\n", + " \"\"\"\n", + "\n", + " start = product_tuples[0][0]\n", + " end = product_tuples[0][1]\n", + " # get technical bounds for the unit output from the unit\n", + " min_power, max_power = unit.calculate_min_max_power(start, end)\n", + " min_power = min_power[start]\n", + " max_power = max_power[start]\n", + "\n", + " # =============================================================================\n", + " # 1. Get the Observations, which are the basis of the action decision\n", + " # =============================================================================\n", + " next_observation = self.create_observation(\n", + " unit=unit,\n", + " market_id=market_config.market_id,\n", + " start=start,\n", + " end=end,\n", + " )\n", + "\n", + " # =============================================================================\n", + " # 2. Get the Actions, based on the observations\n", + " # =============================================================================\n", + " actions, noise = self.get_actions(next_observation)\n", + "\n", + " bids = actions\n", + "\n", + " bids = self.remove_empty_bids(bids)\n", + "\n", + " return bids\n", + " \n", + "# we define the class again and inherit from the initial class just to add the additional method to the original class\n", + "# this is a workaround to have different methods of the class in different cells\n", + "# which is good for the purpose of this tutorial\n", + "# however, you should have all functions in a single class when using this example in .py files\n", + "\n", + "\n", + "class RLStrategy(RLStrategy):\n", + " def calculate_reward(\n", + " self,\n", + " unit,\n", + " marketconfig: MarketConfig,\n", + " orderbook: Orderbook,\n", + " ):\n", + " \"\"\"\n", + " Calculate reward\n", + " \"\"\"\n", + "\n", + " return None\n", + " \n", + "# we define the class again and inherit from the initial class just to add the additional method to the original class\n", + "# this is a workaround to have different methods of the class in different cells\n", + "# which is good for the purpose of this tutorial\n", + "# however, you should have all functions in a single class when using this example in .py files\n", + "\n", + "class RLStrategy(RLStrategy):\n", + " def create_observation(\n", + " self,\n", + " unit: SupportsMinMax,\n", + " market_id: str,\n", + " start: datetime,\n", + " end: datetime,\n", + " ):\n", + " \"\"\"\n", + " Create observation\n", + " \"\"\"\n", + "\n", + " end_excl = end - unit.index.freq\n", + "\n", + " # get the forecast length depending on the time unit considered in the modelled unit\n", + " forecast_len = pd.Timedelta((self.foresight - 1) * unit.index.freq)\n", + "\n", + " # =============================================================================\n", + " # 1.1 Get the Observations, which are the basis of the action decision\n", + " # =============================================================================\n", + " \n", + " # residual load forecast\n", + " scaling_factor_res_load = self.max_demand\n", + "\n", + " # price forecast\n", + " scaling_factor_price = self.max_bid_price\n", + "\n", + " # total capacity\n", + " scaling_factor_total_capacity = unit.max_power\n", + "\n", + " # marginal cost\n", + " scaling_factor_marginal_cost = self.max_bid_price\n", + "\n", + " # checks if we are at the end of the simulation horizon, since we need to change the forecast then\n", + " # for residual load and price forecast and scale them\n", + " if (\n", + " end_excl + forecast_len\n", + " > unit.forecaster[f\"residual_load_{market_id}\"].index[-1]\n", + " ):\n", + " scaled_res_load_forecast = (\n", + " unit.forecaster[f\"residual_load_{market_id}\"].loc[start:].values\n", + " / scaling_factor_res_load\n", + " )\n", + " scaled_res_load_forecast = np.concatenate(\n", + " [\n", + " scaled_res_load_forecast,\n", + " unit.forecaster[f\"residual_load_{market_id}\"].iloc[\n", + " : self.foresight - len(scaled_res_load_forecast)\n", + " ],\n", + " ]\n", + " )\n", + "\n", + " else:\n", + " scaled_res_load_forecast = (\n", + " unit.forecaster[f\"residual_load_{market_id}\"]\n", + " .loc[start : end_excl + forecast_len]\n", + " .values\n", + " / scaling_factor_res_load\n", + " )\n", + "\n", + " if end_excl + forecast_len > unit.forecaster[f\"price_{market_id}\"].index[-1]:\n", + " scaled_price_forecast = (\n", + " unit.forecaster[f\"price_{market_id}\"].loc[start:].values\n", + " / scaling_factor_price\n", + " )\n", + " scaled_price_forecast = np.concatenate(\n", + " [\n", + " scaled_price_forecast,\n", + " unit.forecaster[f\"price_{market_id}\"].iloc[\n", + " : self.foresight - len(scaled_price_forecast)\n", + " ],\n", + " ]\n", + " )\n", + "\n", + " else:\n", + " scaled_price_forecast = (\n", + " unit.forecaster[f\"price_{market_id}\"]\n", + " .loc[start : end_excl + forecast_len]\n", + " .values\n", + " / scaling_factor_price\n", + " )\n", + "\n", + " # get last accepted bid volume and the current marginal costs of the unit\n", + " current_volume = unit.get_output_before(start)\n", + " current_costs = unit.calc_marginal_cost_with_partial_eff(current_volume, start)\n", + "\n", + " # scale unit outputs\n", + " scaled_total_capacity = current_volume / scaling_factor_total_capacity\n", + " scaled_marginal_cost = current_costs / scaling_factor_marginal_cost\n", + "\n", + " # concat all obsverations into one array\n", + " observation = np.concatenate(\n", + " [\n", + " scaled_res_load_forecast,\n", + " scaled_price_forecast,\n", + " np.array([scaled_total_capacity, scaled_marginal_cost]),\n", + " ]\n", + " )\n", + "\n", + " # transfer array to GPU for NN processing\n", + " observation = (\n", + " th.tensor(observation, dtype=self.float_type)\n", + " .to(self.device, non_blocking=True)\n", + " .view(-1)\n", + " )\n", + "\n", + " return observation.detach().clone()\n", + "\n", + "# we define the class again and inherit from the initial class just to add the additional method to the original class\n", + "# this is a workaround to have different methods of the class in different cells\n", + "# which is good for the purpose of this tutorial\n", + "# however, you should have all functions in a single class when using this example in .py files\n", + "\n", + "\n", + "class RLStrategy(RLStrategy):\n", + " def get_actions(self, next_observation):\n", + " \"\"\"\n", + " Get actions\n", + " \"\"\"\n", + "\n", + " # distinction whether we are in learning mode or not to handle exploration realised with noise\n", + " if self.learning_mode:\n", + " # if we are in learning mode, the first x episodes we want to explore the entire action space\n", + " # to get a good initial experience in the area around the costs of the agent\n", + " if self.collect_initial_experience_mode:\n", + " # define current action as solely noise\n", + " noise = (\n", + " th.normal(\n", + " mean=0.0, std=0.2, size=(1, self.act_dim), dtype=self.float_type\n", + " )\n", + " .to(self.device)\n", + " .squeeze()\n", + " )\n", + "\n", + " # =============================================================================\n", + " # 2.1 Get Actions and handle exploration\n", + " # =============================================================================\n", + " # ==> YOUR CODE HERE\n", + " base_bid = next_observation[-1] # = marginal_costs\n", + " # add noise to the last dimension of the observation\n", + " # needs to be adjusted if observation space is changed, because only makes sense\n", + " # if the last dimension of the observation space are the marginal cost\n", + " curr_action = noise + base_bid.clone().detach()\n", + "\n", + " else:\n", + " # if we are not in the initial exploration phase we chose the action with the actor neuronal net\n", + " # and add noise to the action\n", + " curr_action = self.actor(next_observation).detach()\n", + " noise = th.tensor(\n", + " self.action_noise.noise(), device=self.device, dtype=self.float_type\n", + " )\n", + " curr_action += noise\n", + " else:\n", + " # if we are not in learning mode we just use the actor neuronal net to get the action without adding noise\n", + "\n", + " curr_action = self.actor(next_observation).detach()\n", + " noise = tuple(0 for _ in range(self.act_dim))\n", + "\n", + " curr_action = curr_action.clamp(-1, 1)\n", + "\n", + " return curr_action, noise\n", + " \n", + "# we define the class again and inherit from the initial class just to add the additional method to the original class\n", + "# this is a workaround to have different methods of the class in different cells\n", + "# which is good for the purpose of this tutorial\n", + "# however, you should have all functions in a single class when using this example in .py files\n", + "\n", + "\n", + "class RLStrategy(RLStrategy):\n", + " def calculate_bids(\n", + " self,\n", + " unit: SupportsMinMax,\n", + " market_config: MarketConfig,\n", + " product_tuples: list[Product],\n", + " **kwargs,\n", + " ) -> Orderbook:\n", + " \"\"\"\n", + " Calculate bids for a unit\n", + " \"\"\"\n", + "\n", + " bid_quantity_inflex, bid_price_inflex = 0, 0\n", + " bid_quantity_flex, bid_price_flex = 0, 0\n", + "\n", + " start = product_tuples[0][0]\n", + " end = product_tuples[0][1]\n", + " # get technical bounds for the unit output from the unit\n", + " min_power, max_power = unit.calculate_min_max_power(start, end)\n", + " min_power = min_power[start]\n", + " max_power = max_power[start]\n", + "\n", + " # =============================================================================\n", + " # 1. Get the Observations, which are the basis of the action decision\n", + " # =============================================================================\n", + " next_observation = self.create_observation(\n", + " unit=unit,\n", + " market_id=market_config.market_id,\n", + " start=start,\n", + " end=end,\n", + " )\n", + "\n", + " # =============================================================================\n", + " # 2. Get the Actions, based on the observations\n", + " # =============================================================================\n", + " actions, noise = self.get_actions(next_observation)\n", + "\n", + " bids = actions\n", + "\n", + " # =============================================================================\n", + " # 3.2 Transform Actions into bids\n", + " # =============================================================================\n", + " # ==> YOUR CODE HERE\n", + " # actions are in the range [0,1], we need to transform them into actual bids\n", + " # we can use our domain knowledge to guide the bid formulation\n", + "\n", + " #calculate actual bids\n", + " #rescale actions to actual prices\n", + " bid_prices = actions * self.max_bid_price\n", + "\n", + " #calculate inflexible part of the bid\n", + " bid_quantity_inflex = min_power\n", + " bid_price_inflex = min(bid_prices)\n", + "\n", + " #calculate flexible part of the bid\n", + " bid_quantity_flex = max_power - bid_quantity_inflex\n", + " bid_price_flex = max(bid_prices)\n", + "\n", + " # actually formulate bids in orderbook format\n", + " bids = [\n", + " {\n", + " \"start_time\": start,\n", + " \"end_time\": end,\n", + " \"only_hours\": None,\n", + " \"price\": bid_price_inflex,\n", + " \"volume\": bid_quantity_inflex,\n", + " },\n", + " {\n", + " \"start_time\": start,\n", + " \"end_time\": end,\n", + " \"only_hours\": None,\n", + " \"price\": bid_price_flex,\n", + " \"volume\": bid_quantity_flex,\n", + " },\n", + " ]\n", + "\n", + " # store results in unit outputs as lists to be written to the buffer for learning\n", + " unit.outputs[\"rl_observations\"].append(next_observation)\n", + " unit.outputs[\"rl_actions\"].append(actions)\n", + "\n", + " # store results in unit outputs as series to be written to the database by the unit operator\n", + " unit.outputs[\"actions\"][start] = actions\n", + " unit.outputs[\"exploration_noise\"][start] = noise\n", + "\n", + " bids = self.remove_empty_bids(bids)\n", + "\n", + " return bids\n", + " \n", + "# we define the class again and inherit from the initial class just to add the additional method to the original class\n", + "# this is a workaround to have different methods of the class in different cells\n", + "# which is good for the purpose of this tutorial\n", + "# however, you should have all functions in a single class when using this example in .py files\n", + "\n", + "\n", + "class RLStrategy(RLStrategy):\n", + " def calculate_reward(\n", + " self,\n", + " unit,\n", + " marketconfig: MarketConfig,\n", + " orderbook: Orderbook,\n", + " ):\n", + " \"\"\"\n", + " Calculate reward\n", + " \"\"\"\n", + "\n", + " # =============================================================================\n", + " # 3. Calculate Reward\n", + " # =============================================================================\n", + " # function is called after the market is cleared and we get the market feedback,\n", + " # so we can calculate the profit\n", + "\n", + " product_type = marketconfig.product_type\n", + "\n", + " profit = 0\n", + " reward = 0\n", + " opportunity_cost = 0\n", + "\n", + " # iterate over all orders in the orderbook, to calculate order specific profit\n", + " for order in orderbook:\n", + " start = order[\"start_time\"]\n", + " end = order[\"end_time\"]\n", + " end_excl = end - unit.index.freq\n", + "\n", + " # depending on whether the unit calaculates marginal costs we take costs\n", + " if unit.marginal_cost is not None:\n", + " marginal_cost = (\n", + " unit.marginal_cost[start]\n", + " if len(unit.marginal_cost) > 1\n", + " else unit.marginal_cost\n", + " )\n", + " else:\n", + " marginal_cost = unit.calc_marginal_cost_with_partial_eff(\n", + " power_output=unit.outputs[product_type].loc[start:end_excl],\n", + " timestep=start,\n", + " )\n", + "\n", + " duration = (end - start) / timedelta(hours=1)\n", + "\n", + " # calculate profit as income - running_cost from this event\n", + " price_difference = order[\"accepted_price\"] - marginal_cost\n", + " order_profit = price_difference * order[\"accepted_volume\"] * duration\n", + "\n", + " # calculate opportunity cost\n", + " # as the loss of income we have because we are not running at full power\n", + " order_opportunity_cost = (\n", + " price_difference\n", + " * (\n", + " unit.max_power - unit.outputs[product_type].loc[start:end_excl]\n", + " ).sum()\n", + " * duration\n", + " )\n", + "\n", + " # if our opportunity costs are negative, we did not miss an opportunity to earn money and we set them to 0\n", + " order_opportunity_cost = max(order_opportunity_cost, 0)\n", + "\n", + " # collect profit and opportunity cost for all orders\n", + " opportunity_cost += order_opportunity_cost\n", + " profit += order_profit\n", + "\n", + " # consideration of start-up costs, which are evenly divided between the\n", + " # upward and downward regulation events\n", + " if (\n", + " unit.outputs[product_type].loc[start] != 0\n", + " and unit.outputs[product_type].loc[start - unit.index.freq] == 0\n", + " ):\n", + " profit = profit - unit.hot_start_cost / 2\n", + " elif (\n", + " unit.outputs[product_type].loc[start] == 0\n", + " and unit.outputs[product_type].loc[start - unit.index.freq] != 0\n", + " ):\n", + " profit = profit - unit.hot_start_cost / 2\n", + "\n", + " # =============================================================================\n", + " # =============================================================================\n", + " # ==> YOUR CODE HERE\n", + " # The straight forward implementation would be reward = profit, yet we would like to give the agent more guidance\n", + " # in the learning process, so we add a regret term to the reward, which is the opportunity cost\n", + " # define the reward and scale it\n", + "\n", + " scaling = 0.1 / unit.max_power\n", + " regret_scale = 0.2\n", + " reward = float(profit - regret_scale * opportunity_cost) * scaling\n", + "\n", + " # store results in unit outputs which are written to database by unit operator\n", + " unit.outputs[\"profit\"].loc[start:end_excl] += profit\n", + " unit.outputs[\"reward\"].loc[start:end_excl] = reward\n", + " unit.outputs[\"regret\"].loc[start:end_excl] = opportunity_cost\n", + "\n", + "# we define the class again and inherit from the initial class just to add the additional method to the original class\n", + "# this is a workaround to have different methods of the class in different cells\n", + "# which is good for the purpose of this tutorial\n", + "# however, you should have all functions in a single class when using this example in .py files\n", + "\n", + "\n", + "class RLStrategy(RLStrategy):\n", + " def load_actor_params(self, load_path):\n", + " \"\"\"\n", + " Load actor parameters\n", + " \"\"\"\n", + " directory = f\"{load_path}/actors/actor_{self.unit_id}.pt\"\n", + "\n", + " params = th.load(directory, map_location=self.device)\n", + "\n", + " self.actor = self.actor_architecture_class(\n", + " obs_dim=self.obs_dim,\n", + " act_dim=self.act_dim,\n", + " float_type=self.float_type,\n", + " unique_obs_dim=self.unique_obs_dim,\n", + " num_timeseries_obs_dim=self.num_timeseries_obs_dim,\n", + " ).to(self.device)\n", + "\n", + " self.actor.load_state_dict(params[\"actor\"])\n", + "\n", + " if self.learning_mode:\n", + " self.actor_target = self.actor_architecture_class(\n", + " obs_dim=self.obs_dim,\n", + " act_dim=self.act_dim,\n", + " float_type=self.float_type,\n", + " unique_obs_dim=self.unique_obs_dim,\n", + " num_timeseries_obs_dim=self.num_timeseries_obs_dim,\n", + " ).to(self.device)\n", + " self.actor_target.load_state_dict(params[\"actor_target\"])\n", + " self.actor_target.eval()\n", + " self.actor.optimizer.load_state_dict(params[\"actor_optimizer\"])\n", + "\n", + "learning_config = {\n", + " \"continue_learning\": False,\n", + " \"trained_policies_save_path\": None,\n", + " \"max_bid_price\": 100,\n", + " \"algorithm\": \"matd3\",\n", + " \"learning_rate\": 0.001,\n", + " \"training_episodes\": 2,\n", + " \"episodes_collecting_initial_experience\": 1,\n", + " \"train_freq\": \"24h\",\n", + " \"gradient_steps\": -1,\n", + " \"batch_size\": 256,\n", + " \"gamma\": 0.99,\n", + " \"device\": \"cpu\",\n", + " \"noise_sigma\": 0.1,\n", + " \"noise_scale\": 1,\n", + " \"noise_dt\": 1,\n", + " \"validation_episodes_interval\": 5,\n", + "}\n", + "\n", + "# Read the YAML file\n", + "with open(f\"{inputs_path}/example_02a/config.yaml\") as file:\n", + " data = yaml.safe_load(file)\n", + "\n", + "# store our modifications to the config file\n", + "data[\"base\"][\"learning_mode\"] = True\n", + "data[\"base\"][\"learning_config\"] = learning_config\n", + "\n", + "# Write the modified data back to the file\n", + "with open(f\"{inputs_path}/example_02a/config.yaml\", \"w\") as file:\n", + " yaml.safe_dump(data, file)\n", + "\n", + "# Read the YAML file\n", + "with open(f\"{inputs_path}/example_02b/config.yaml\") as file:\n", + " data = yaml.safe_load(file)\n", + "\n", + "# store our modifications to the config file\n", + "data[\"base\"][\"learning_mode\"] = True\n", + "data[\"base\"][\"learning_config\"] = learning_config\n", + "\n", + "# Write the modified data back to the file\n", + "with open(f\"{inputs_path}/example_02b/config.yaml\", \"w\") as file:\n", + " yaml.safe_dump(data, file)\n", + "\n", + "# Read the YAML file\n", + "with open(f\"{inputs_path}/example_02c/config.yaml\") as file:\n", + " data = yaml.safe_load(file)\n", + "\n", + "# store our modifications to the config file\n", + "data[\"base\"][\"learning_mode\"] = True\n", + "data[\"base\"][\"learning_config\"] = learning_config\n", + "\n", + "# Write the modified data back to the file\n", + "with open(f\"{inputs_path}/example_02c/config.yaml\", \"w\") as file:\n", + " yaml.safe_dump(data, file)\n", + "\n", + "log = logging.getLogger(__name__)\n", + "\n", + "csv_path = \"outputs\"\n", + "os.makedirs(\"local_db\", exist_ok=True)\n", + "\n", + "if __name__ == \"__main__\":\n", + " db_uri = \"sqlite:///local_db/assume_db.db\"\n", + "\n", + " scenario = \"example_02a\"\n", + " study_case = \"base\"\n", + "\n", + " # create world\n", + " world = World(database_uri=db_uri, export_csv_path=csv_path)\n", + "\n", + " # we import our defined bidding strategey class including the learning into the world bidding strategies\n", + " # in the example files we provided the name of the learning bidding strategies in the input csv \"pp_learning\"\n", + " # hence we define this strategey to be the one of the learning class\n", + " world.bidding_strategies[\"pp_learning\"] = RLStrategy\n", + "\n", + " # then we load the scenario specified above from the respective input files\n", + " load_scenario_folder(\n", + " world,\n", + " inputs_path=inputs_path,\n", + " scenario=scenario,\n", + " study_case=study_case,\n", + " )\n", + "\n", + " # run learning if learning mode is enabled\n", + " # needed as we simulate the modelling horizon multiple times to train reinforcement learning run_learning( world, inputs_path=input_path, scenario=scenario, study_case=study_case, )\n", + "\n", + " if world.learning_config.get(\"learning_mode\", False):\n", + " run_learning(\n", + " world,\n", + " inputs_path=inputs_path,\n", + " scenario=scenario,\n", + " study_case=study_case,\n", + " )\n", + "\n", + " # after the learning is done we make a normal run of the simulation, which equals a test run\n", + " world.run()\n", + "\n", + "log = logging.getLogger(__name__)\n", + "\n", + "csv_path = \"outputs\"\n", + "os.makedirs(\"local_db\", exist_ok=True)\n", + "\n", + "if __name__ == \"__main__\":\n", + " db_uri = \"sqlite:///local_db/assume_db.db\"\n", + "\n", + " scenario = \"example_02b\"\n", + " study_case = \"base\"\n", + "\n", + " # create world\n", + " world = World(database_uri=db_uri, export_csv_path=csv_path)\n", + "\n", + " # we import our defined bidding strategey class including the learning into the world bidding strategies\n", + " # in the example files we provided the name of the learning bidding strategeis in the input csv is \"pp_learning\"\n", + " # hence we define this strategey to be one of the learning class\n", + " world.bidding_strategies[\"pp_learning\"] = RLStrategy\n", + "\n", + " # then we load the scenario specified above from the respective input files\n", + " load_scenario_folder(\n", + " world,\n", + " inputs_path=inputs_path,\n", + " scenario=scenario,\n", + " study_case=study_case,\n", + " )\n", + "\n", + " # run learning if learning mode is enabled\n", + " # needed as we simulate the modelling horizon multiple times to train reinforcement learning run_learning( world, inputs_path=input_path, scenario=scenario, study_case=study_case, )\n", + "\n", + " if world.learning_config.get(\"learning_mode\", False):\n", + " run_learning(\n", + " world,\n", + " inputs_path=inputs_path,\n", + " scenario=scenario,\n", + " study_case=study_case,\n", + " )\n", + "\n", + " # after the learning is done we make a normal run of the simulation, which equals a test run\n", + " world.run()\n", + "\n", + "log = logging.getLogger(__name__)\n", + "\n", + "csv_path = \"outputs\"\n", + "os.makedirs(\"local_db\", exist_ok=True)\n", + "\n", + "if __name__ == \"__main__\":\n", + " db_uri = \"sqlite:///local_db/assume_db.db\"\n", + "\n", + " scenario = \"example_02c\"\n", + " study_case = \"base\"\n", + "\n", + " # create world\n", + " world = World(database_uri=db_uri, export_csv_path=csv_path)\n", + "\n", + " # we import our defined bidding strategey class including the learning into the world bidding strategies\n", + " # in the example files we provided the name of the learning bidding strategeis in the input csv is \"pp_learning\"\n", + " # hence we define this strategey to be one of the learning class\n", + " world.bidding_strategies[\"pp_learning\"] = RLStrategy\n", + "\n", + " # then we load the scenario specified above from the respective input files\n", + " load_scenario_folder(\n", + " world,\n", + " inputs_path=inputs_path,\n", + " scenario=scenario,\n", + " study_case=study_case,\n", + " )\n", + "\n", + " # run learning if learning mode is enabled\n", + " # needed as we simulate the modelling horizon multiple times to train reinforcement learning run_learning( world, inputs_path=input_path, scenario=scenario, study_case=study_case, )\n", + "\n", + " if world.learning_config.get(\"learning_mode\", False):\n", + " run_learning(\n", + " world,\n", + " inputs_path=inputs_path,\n", + " scenario=scenario,\n", + " study_case=study_case,\n", + " )\n", + "\n", + " # after the learning is done we make a normal run of the simulation, which equals a test run\n", + " world.run()\n", + "\n", + "!pip install matplotlib\n", + "\n", + "import os\n", + "from functools import partial\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from sqlalchemy import create_engine\n", + "\n", + "os.makedirs(\"outputs\", exist_ok=True)\n", + "\n", + "db_uri = \"sqlite:///local_db/assume_db.db\"\n", + "\n", + "engine = create_engine(db_uri)\n", + "\n", + "\n", + "sql = \"\"\"\n", + "SELECT ident, simulation,\n", + "sum(round(CAST(value AS numeric), 2)) FILTER (WHERE variable = 'total_cost') as total_cost,\n", + "sum(round(CAST(value AS numeric), 2)*1000) FILTER (WHERE variable = 'total_volume') as total_volume,\n", + "sum(round(CAST(value AS numeric), 2)) FILTER (WHERE variable = 'avg_price') as average_cost\n", + "FROM kpis\n", + "where variable in ('total_cost', 'total_volume', 'avg_price')\n", + "and simulation in ('example_02a_base', 'example_02b_base', 'example_02c_base')\n", + "group by simulation, ident ORDER BY simulation\n", + "\"\"\"\n", + "\n", + "\n", + "kpis = pd.read_sql(sql, engine)\n", + "\n", + "# sort the dataframe to have sho, bo and lo case in the right order\n", + "\n", + "# sort kpis in the order sho, bo, lo\n", + "\n", + "kpis = kpis.sort_values(\n", + " by=\"simulation\",\n", + " # key=lambda x: x.map({\"example_02a\": 1, \"example_02b\": 2, \"example_02c\": 3}),\n", + ")\n", + "\n", + "\n", + "kpis[\"total_volume\"] /= 1e9\n", + "kpis[\"total_cost\"] /= 1e6\n", + "savefig = partial(plt.savefig, transparent=False, bbox_inches=\"tight\")\n", + "\n", + "xticks = kpis[\"simulation\"].unique()\n", + "plt.style.use(\"seaborn-v0_8\")\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(10, 6))\n", + "\n", + "ax2 = ax.twinx() # Create another axes that shares the same x-axis as ax.\n", + "\n", + "width = 0.4\n", + "\n", + "kpis.total_volume.plot(kind=\"bar\", ax=ax, width=width, position=1, color=\"royalblue\")\n", + "kpis.total_cost.plot(kind=\"bar\", ax=ax2, width=width, position=0, color=\"green\")\n", + "\n", + "# set x-achxis limits\n", + "ax.set_xlim(-0.6, len(kpis[\"simulation\"]) - 0.4)\n", + "\n", + "# set y-achxis limits\n", + "ax.set_ylim(0, max(kpis.total_volume) * 1.1 + 0.1)\n", + "ax2.set_ylim(0, max(kpis.total_cost) * 1.1 + 0.1)\n", + "\n", + "ax.set_ylabel(\"Total Volume (GWh)\")\n", + "ax2.set_ylabel(\"Total Cost (M€)\")\n", + "\n", + "ax.set_xticklabels(xticks, rotation=45)\n", + "ax.set_xlabel(\"Simulation\")\n", + "\n", + "ax.legend([\"Total Volume\"], loc=\"upper left\")\n", + "ax2.legend([\"Total Cost\"], loc=\"upper right\")\n", + "\n", + "plt.title(\"Total Volume and Total Cost for each Simulation\")\n", + "\n", + "sql = \"\"\"\n", + "SELECT\n", + " product_start AS \"time\",\n", + " price AS \"Price\",\n", + " simulation AS \"simulation\",\n", + " node\n", + "FROM market_meta\n", + "WHERE simulation in ('example_02a_base', 'example_02b_base', 'example_02c_base') AND market_id in ('EOM') \n", + "GROUP BY market_id, simulation, product_start, price, node\n", + "ORDER BY product_start, node\n", + "\n", + "\"\"\"\n", + "\n", + "df = pd.read_sql(sql, engine)\n", + "\n", + "df\n", + "\n", + "# Convert the 'time' column to datetime\n", + "df[\"time\"] = pd.to_datetime(df[\"time\"])\n", + "\n", + "# Plot the data\n", + "plt.figure(figsize=(14, 7))\n", + "# Loop through each simulation and plot\n", + "for simulation in df[\"simulation\"].unique():\n", + " subset = df[df[\"simulation\"] == simulation]\n", + " plt.plot(subset[\"time\"], subset[\"Price\"], label=simulation)\n", + "\n", + "plt.title(\"Price over Time for Different Simulations\")\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Price\")\n", + "plt.legend(title=\"Simulation\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "97f4d181", + "metadata": {}, + "outputs": [], "source": [] } ],