diff --git a/assume/__init__.py b/assume/__init__.py
index 22583ff5..359e409b 100644
--- a/assume/__init__.py
+++ b/assume/__init__.py
@@ -5,6 +5,11 @@
 from importlib.metadata import version
 
 from assume.common import MarketConfig, MarketProduct
+from assume.scenario.loader_csv import (
+    load_custom_units,
+    load_scenario_folder,
+    run_learning,
+)
 from assume.world import World
 
 __version__ = version("assume-framework")
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 250933a1..97a50166 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -27,7 +27,7 @@
     "sphinx.ext.napoleon",
     "nbsphinx",
     "nbsphinx_link",
-    "sphinx.ext.imgconverter",  # for SVG conversion
+    # "sphinx.ext.imgconverter",  # for SVG conversion
 ]
 
 intersphinx_mapping = {
diff --git a/docs/source/examples/03_custom_unit_example.nblink b/docs/source/examples/03_custom_unit_example.nblink
new file mode 100644
index 00000000..c073f874
--- /dev/null
+++ b/docs/source/examples/03_custom_unit_example.nblink
@@ -0,0 +1 @@
+{"path": "../../../examples/notebooks/03_custom_unit_example.ipynb"}
diff --git a/docs/source/examples/06_market_comparison.nblink.license b/docs/source/examples/03_custom_unit_example.nblink.license
similarity index 100%
rename from docs/source/examples/06_market_comparison.nblink.license
rename to docs/source/examples/03_custom_unit_example.nblink.license
diff --git a/docs/source/examples/05_market_comparison.nblink b/docs/source/examples/05_market_comparison.nblink
new file mode 100644
index 00000000..c25a73ac
--- /dev/null
+++ b/docs/source/examples/05_market_comparison.nblink
@@ -0,0 +1 @@
+{"path": "../../../examples/notebooks/05_market_comparison.ipynb"}
diff --git a/examples/notebooks/06_market_comparison.ipynb.license b/docs/source/examples/05_market_comparison.nblink.license
similarity index 100%
rename from examples/notebooks/06_market_comparison.ipynb.license
rename to docs/source/examples/05_market_comparison.nblink.license
diff --git a/docs/source/examples/06_market_comparison.nblink b/docs/source/examples/06_market_comparison.nblink
deleted file mode 100644
index 3299ce21..00000000
--- a/docs/source/examples/06_market_comparison.nblink
+++ /dev/null
@@ -1 +0,0 @@
-{"path": "../../../examples/notebooks/06_market_comparison.ipynb"}
diff --git a/docs/source/examples_basic.rst b/docs/source/examples_basic.rst
index 5365b94f..089d1aa5 100644
--- a/docs/source/examples_basic.rst
+++ b/docs/source/examples_basic.rst
@@ -16,5 +16,6 @@ Here you can find several tutorials on how to use ASSUME framework to get you st
 
    examples/01_minimal_manual_example.nblink
    examples/02_automated_run_example.nblink
+   examples/03_custom_unit_example.nblink
    examples/04_reinforcement_learning_example.nblink
-   examples/06_market_comparison.nblink
+   examples/05_market_comparison.nblink
diff --git a/docs/source/img/Electrolyzer.png b/docs/source/img/Electrolyzer.png
new file mode 100644
index 00000000..b3c71281
Binary files /dev/null and b/docs/source/img/Electrolyzer.png differ
diff --git a/docs/source/img/Electrolyzer.png.license b/docs/source/img/Electrolyzer.png.license
new file mode 100644
index 00000000..a6ae0636
--- /dev/null
+++ b/docs/source/img/Electrolyzer.png.license
@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: ASSUME Developers
+
+SPDX-License-Identifier: AGPL-3.0-or-later
diff --git a/examples/inputs/example_03a/config.yaml b/examples/inputs/example_03a/config.yaml
index 955bf097..13e493f0 100644
--- a/examples/inputs/example_03a/config.yaml
+++ b/examples/inputs/example_03a/config.yaml
@@ -4,7 +4,7 @@
 
 base:
   start_date: 2019-01-01 00:00
-  end_date: 2019-01-31 00:00
+  end_date: 2019-01-30 00:00
   time_step: 1h
   save_frequency_hours: 24
 
diff --git a/examples/notebooks/01_minimal_manual_example.ipynb b/examples/notebooks/01_minimal_manual_example.ipynb
index 7678443f..0a5db7d4 100644
--- a/examples/notebooks/01_minimal_manual_example.ipynb
+++ b/examples/notebooks/01_minimal_manual_example.ipynb
@@ -447,7 +447,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.11.4"
+            "version": "3.11.3"
         },
         "nbsphinx": {
             "execute": "never"
diff --git a/examples/notebooks/02_automated_run_example.ipynb b/examples/notebooks/02_automated_run_example.ipynb
index 68d80468..af041644 100644
--- a/examples/notebooks/02_automated_run_example.ipynb
+++ b/examples/notebooks/02_automated_run_example.ipynb
@@ -289,7 +289,7 @@
     "config_data = {\n",
     "    \"hourly_market\": {\n",
     "        \"start_date\": \"2021-03-01 00:00\",\n",
-    "        \"end_date\": \"2021-03-08 00:00\",\n",
+    "        \"end_date\": \"2021-03-07 00:00\",\n",
     "        \"time_step\": \"1h\",\n",
     "        \"save_frequency_hours\": 24,\n",
     "        \"markets_config\": {\n",
@@ -409,7 +409,7 @@
     "new_market_config = {\n",
     "    \"daily_market\": {\n",
     "        \"start_date\": \"2021-03-01 00:00\",\n",
-    "        \"end_date\": \"2021-03-08 00:00\",\n",
+    "        \"end_date\": \"2021-03-07 00:00\",\n",
     "        \"time_step\": \"1h\",\n",
     "        \"save_frequency_hours\": 24,\n",
     "        \"markets_config\": {\n",
diff --git a/examples/notebooks/03_custom_unit_example.ipynb b/examples/notebooks/03_custom_unit_example.ipynb
new file mode 100644
index 00000000..50a25125
--- /dev/null
+++ b/examples/notebooks/03_custom_unit_example.ipynb
@@ -0,0 +1,760 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3. Implementation of a Demand Side Unit and Bidding Strategy\n",
+    "\n",
+    "This tutorial provides a step-by-step guide for implementing a custom Demand Side Unit with a Rule-Based Bidding Strategy in the ASSUME framework. By the end of this guide, you will be familiar with the process of creating and integrating a Demand Side Agent within the electricity market simulation environment provided by ASSUME.\n",
+    "\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"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Introduction to Unit Agents and Bidding Strategy\n",
+    "\n",
+    "The ASSUME framework is a versatile tool for simulating electricity markets, allowing researchers and industry professionals to analyze market dynamics and strategies.\n",
+    "\n",
+    "A **Unit** in ASSUME refers to an entity that participates in the market, either buying or selling electricity. Each unit operates based on a **Bidding Strategy**, which dictates its market behavior. For Demand Side Management (DSM) Units, this includes adjusting electricity demand in response to market conditions.\n",
+    "\n",
+    "In this tutorial, we will create a DSM Unit that represents an Electrolyser, capable of varying its demand to optimize for energy prices."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Understanding Demand Side Management (DSM)**\n",
+    "\n",
+    "Before we start coding, it's essential to understand what DSM is and why it's important in electricity market modeling. DSM allows for the dynamic adjustment of electricity demand, contributing to balanced grid operations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Understanding the Model**\n",
+    "\n",
+    "The image below illustrates the concept of a simple Electrolyser unit model: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# this cell is used to display the image in the notebook when using collab\n",
+    "# or running the notebook locally\n",
+    "\n",
+    "from IPython.display import Image, display\n",
+    "import os\n",
+    "\n",
+    "image_path = \"assume/docs/source/img/Electrolyzer.png\"\n",
+    "alt_image_path = \"../../docs/source/img/Electrolyzer.png\"\n",
+    "\n",
+    "if os.path.exists(image_path):\n",
+    "    display(Image(image_path))\n",
+    "elif os.path.exists(alt_image_path):\n",
+    "    display(Image(alt_image_path))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The image provides a visual representation of how dynamic efficiency varies based on different factors:\n",
+    "\n",
+    "- **X-Axis**: Represents the varying power input to the Electrolyser unit.\n",
+    "- **Y-Axis**: Indicates the efficiency levels that correspond to different power inputs.\n",
+    "- **Curve**: Shows that the efficiency is not constant and varies depending on the current power input to the unit."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Significance of this model**\n",
+    "\n",
+    "Understanding this model is crucial for several reasons:\n",
+    "\n",
+    "- **Adaptability**: The curve suggests that the unit can operate at different efficiency levels, allowing it to adapt to market conditions.\n",
+    "- **Optimization**: Knowing the efficiency levels at various power inputs allows the unit to operate at an optimal point, which is especially crucial in Demand Side Management (DSM) strategies.\n",
+    "- **Complexity**: The non-linear nature of the curve indicates that simple linear models may not be sufficient for capturing the unit's behavior, highlighting the need for a more complex model.\n",
+    "\n",
+    "By understanding this model, you'll gain valuable insights into how to calculate and utilize dynamic efficiency in the Electrolyser unit, a crucial aspect of DSM in the ASSUME framework."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Setting Up ASSUME\n",
+    "\n",
+    "Before we create our custom unit, let's set up the ASSUME framework. We'll install the ASSUME core package and clone the repository containing predefined scenarios."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!pip install assume-framework\n",
+    "!git clone https://github.com/assume-framework/assume.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that Google Colab does not support Docker functionalities, so features dependent on Docker will not be available here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Developing a New Demand Side Unit\n",
+    "\n",
+    "We will now develop a new unit that models an Electrolyser. This unit will be capable of adjusting its electricity consumption based on the market conditions, showcasing DSM capabilities."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1 Initializing Core Attributes\n",
+    "\n",
+    "We'll start by defining the core attributes of our Electrolyser class, such as its power capacity and operational parameters.\n",
+    "\n",
+    "- **ID**: A unique identifier for the unit.\n",
+    "- **Technology**: The type of technology used, which in this case is electrolysis for hydrogen production.\n",
+    "- **Unit Operator**: The entity responsible for operating the unit.\n",
+    "- **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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize the Electrolyser class with core attributes\n",
+    "\n",
+    "import pandas as pd\n",
+    "from assume.common.base import SupportsMinMax, BaseStrategy\n",
+    "from assume.common.market_objects import MarketConfig, Order, Orderbook, Product\n",
+    "\n",
+    "\n",
+    "class Electrolyser(SupportsMinMax):\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        id: str,\n",
+    "        technology: str,\n",
+    "        index: pd.DatetimeIndex,\n",
+    "        unit_operator: str,\n",
+    "        bidding_strategies: str,\n",
+    "        max_power: float,\n",
+    "        min_power: float,\n",
+    "        max_hydrogen: float,\n",
+    "        min_hydrogen: float,\n",
+    "        fixed_cost: float,\n",
+    "        **kwargs,\n",
+    "    ):\n",
+    "        super().__init__(\n",
+    "            id=id,\n",
+    "            unit_operator=unit_operator,\n",
+    "            technology=technology,\n",
+    "            bidding_strategies=bidding_strategies,\n",
+    "            index=index,\n",
+    "            **kwargs,\n",
+    "        )\n",
+    "\n",
+    "        self.min_hydrogen = min_hydrogen\n",
+    "        self.max_hydrogen = max_hydrogen\n",
+    "\n",
+    "        self.max_power = max_power\n",
+    "        self.min_power = min_power\n",
+    "        self.fixed_cost = fixed_cost\n",
+    "\n",
+    "        self.conversion_factors = self.get_conversion_factors()\n",
+    "\n",
+    "    # this function is a must be part of any unit class\n",
+    "    # as it controls how the unit is dispatched after market clearings\n",
+    "    # and is executed after each market clearing\n",
+    "    def execute_current_dispatch(\n",
+    "        self,\n",
+    "        start: pd.Timestamp,\n",
+    "        end: pd.Timestamp,\n",
+    "    ):\n",
+    "        end_excl = end - self.index.freq\n",
+    "\n",
+    "        # Calculate mean power for this time period\n",
+    "        avg_power = abs(self.outputs[\"energy\"].loc[start:end_excl]).mean()\n",
+    "\n",
+    "        # Decide which efficiency point to use\n",
+    "        if avg_power < self.min_power:\n",
+    "            self.outputs[\"energy\"].loc[start:end_excl] = 0\n",
+    "            self.outputs[\"hydrogen\"].loc[start:end_excl] = 0\n",
+    "        else:\n",
+    "            if avg_power <= 0.35 * self.max_power:\n",
+    "                dynamic_conversion_factor = self.conversion_factors[0]\n",
+    "            else:\n",
+    "                dynamic_conversion_factor = self.conversion_factors[1]\n",
+    "\n",
+    "            self.outputs[\"energy\"].loc[start:end_excl] = avg_power\n",
+    "            self.outputs[\"hydrogen\"].loc[start:end_excl] = (\n",
+    "                avg_power / dynamic_conversion_factor\n",
+    "            )\n",
+    "\n",
+    "        return self.outputs[\"energy\"].loc[start:end_excl]\n",
+    "\n",
+    "    # this function is a must be part of each unit class\n",
+    "    # as it dictates which parameters of the unit we would like to save to the databse\n",
+    "    # or csv files\n",
+    "    def as_dict(self) -> dict:\n",
+    "        unit_dict = super().as_dict()\n",
+    "        unit_dict.update(\n",
+    "            {\n",
+    "                \"max_power\": self.max_power,\n",
+    "                \"min_power\": self.min_power,\n",
+    "                \"min_hydrogen\": self.min_hydrogen,\n",
+    "                \"max_hydrogen\": self.max_hydrogen,\n",
+    "                \"unit_type\": \"electrolyzer\",\n",
+    "            }\n",
+    "        )\n",
+    "        return unit_dict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Why Are These Attributes Important?**\n",
+    "\n",
+    "Understanding these attributes is crucial for the following reasons:\n",
+    "\n",
+    "- They define the **range of actions** the Electrolyser unit can perform in the electricity market.\n",
+    "- They are used to **calculate dynamic efficiency** and **power input**, as we'll see in the later sections.\n",
+    "- They can be crucial for implementing **demand-side management strategies**, where the unit adjusts its operations based on market signals."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2 Power Calculation Function\n",
+    "\n",
+    "Next, we'll implement a function to calculate the power input for the Electrolyser based on its demand profile. This function calculates the amount of electrical power that should be supplied to the unit, taking into consideration several attributes like maximum capacity, demand profile, and dynamic efficiency.\n",
+    "\n",
+    "**This function is integral for:**\n",
+    "- **Optimizing Resource Utilization**: Ensuring that the Electrolyser operates within its optimal efficiency range.\n",
+    "- **Demand-Side Management (DSM)**: Allowing the unit to adapt its power consumption in response to market signals and constraints, thereby contributing to grid stability.\n",
+    "\n",
+    "**Key Parameters Involved**\n",
+    "- **Maximum and Minimum Capacity**: The upper and lower bounds for power input to the Electrolyser.\n",
+    "- **Demand Profile**: The expected hydrogen production rates, which influence the power requirements.\n",
+    "- **Dynamic Efficiency**: The efficiency of converting power to hydrogen at different levels of power input, as discussed in the previous section."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 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 Electrolyser(Electrolyser):\n",
+    "    def calculate_min_max_power(\n",
+    "        self,\n",
+    "        start: pd.Timestamp,\n",
+    "        end: pd.Timestamp,\n",
+    "        hydrogen_demand=0,\n",
+    "    ):\n",
+    "        # check if hydrogen_demand is within min and max hydrogen production\n",
+    "        # and adjust accordingly\n",
+    "        if hydrogen_demand < self.min_hydrogen:\n",
+    "            hydrogen_production = self.min_hydrogen\n",
+    "\n",
+    "        elif hydrogen_demand > self.max_hydrogen:\n",
+    "            hydrogen_production = self.max_hydrogen\n",
+    "\n",
+    "        else:\n",
+    "            hydrogen_production = hydrogen_demand\n",
+    "\n",
+    "        # get dynamic conversion factor\n",
+    "        dynamic_conversion_factor = self.get_dynamic_conversion_factor(\n",
+    "            hydrogen_production\n",
+    "        )\n",
+    "        power = hydrogen_production * dynamic_conversion_factor\n",
+    "\n",
+    "        return power, hydrogen_production"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.3 Developing Advanced Attributes\n",
+    "\n",
+    "We will enhance our Electrolyser class by adding advanced attributes like dynamic efficiency, which varies with power input.\n",
+    "\n",
+    "**Dynamic efficiency** refers to the Electrolyser unit's ability to convert electrical power into hydrogen gas at varying rates of efficiency, depending on its current power input. This attribute is crucial for the following reasons:\n",
+    "\n",
+    "- It allows the unit to **adapt to fluctuating market conditions**, optimizing its operation for price signals.\n",
+    "- It provides a **quantitative measure for decision-making**, particularly in DSM where the unit may need to adjust its demand profile.\n",
+    "\n",
+    "**Key Parameters Involved**\n",
+    "\n",
+    "- **Average Power**: The mean power consumed during a specific time period.\n",
+    "- **Conversion Factors**: These are factors used to convert the average power into hydrogen production, and they can vary based on the power level."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 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 Electrolyser(Electrolyser):\n",
+    "    def get_dynamic_conversion_factor(self, hydrogen_demand=None):\n",
+    "        # Adjust efficiency based on power\n",
+    "        if hydrogen_demand <= 0.35 * self.max_hydrogen:\n",
+    "            return self.conversion_factors[0]\n",
+    "        else:\n",
+    "            return self.conversion_factors[1]\n",
+    "\n",
+    "    def get_conversion_factors(self):\n",
+    "        # Calculate the conversion factor for the two efficiency points\n",
+    "        conversion_point_1 = (0.3 * self.max_power) / (\n",
+    "            0.35 * self.max_hydrogen\n",
+    "        )  # MWh / Tonne\n",
+    "        conversion_point_2 = self.max_power / self.max_hydrogen  # MWh / Tonne\n",
+    "\n",
+    "        return [conversion_point_1, conversion_point_2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Rule-Based Bidding Strategy\n",
+    "\n",
+    "Now, we'll define a rule-based bidding strategy for our Electrolyser unit. This strategy will use market information to place bids.\n",
+    "\n",
+    "**Key components of a Rule-Based Bidding Strategy**\n",
+    "1. **Product Type**: This sets the stage for the kind of products (e.g., energy, ancillary services) that the unit will bid for in the electricity market.\n",
+    "2. **Bid Volume**: The quantity of the product that the unit offers in its bid.\n",
+    "3. **Marginal Revenue**: Used to determine the price at which the unit should make its bid. This involves calculating the unit's incremental revenue for additional units of electricity sold or consumed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "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",
+    "\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",
+    "$$\\text{Marginal Revenue} = \\left( \\text{Hydrogen Price} - \\text{Fixed Cost} \\right) \\times \\frac{\\text{Hydrogen Production}}{\\text{Power}}$$\n",
+    "\n",
+    "where:\n",
+    "- **Hydrogen Price**: The price of hydrogen at the specific time frame, fetched from the unit's forecaster.\n",
+    "- **Fixed Cost**: The constant cost associated with the unit, not varying with the amount of power or hydrogen produced.\n",
+    "- **Hydrogen Production**: The amount of hydrogen produced during the given time frame, calculated based on the hydrogen demand.\n",
+    "- **Power**: The electrical power consumed by the Electrolyser unit to produce the given amount of hydrogen.\n",
+    "\n",
+    "This mathematical equation provides just a mere example for understanding and implementing an effective rule-based bidding strategy for the Electrolyser unit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class NaiveStrategyElectrolyser(BaseStrategy):\n",
+    "    def calculate_bids(\n",
+    "        self,\n",
+    "        unit: SupportsMinMax,\n",
+    "        market_config: MarketConfig,\n",
+    "        product_tuples: list[Product],\n",
+    "        **kwargs,\n",
+    "    ) -> Orderbook:\n",
+    "        \"\"\"\n",
+    "        Takes information from a unit that the unit operator manages and\n",
+    "        defines how it is dispatched to the market\n",
+    "\n",
+    "        :param unit: the unit to be dispatched\n",
+    "        :type unit: SupportsMinMax\n",
+    "        :param market_config: the market configuration\n",
+    "        :type market_config: MarketConfig\n",
+    "        :param product_tuples: list of all products the unit can offer\n",
+    "        :type product_tuples: list[Product]\n",
+    "        :return: the bids\n",
+    "        :rtype: Orderbook\n",
+    "        \"\"\"\n",
+    "        start = product_tuples[0][0]  # start time of the first product\n",
+    "        bids = []\n",
+    "\n",
+    "        # iterate over all products\n",
+    "        # to create a bid for each product\n",
+    "        # in this case it would be 24 bids for each hour of the day-ahead market\n",
+    "        for product in product_tuples:\n",
+    "            \"\"\"\n",
+    "            for each product, calculate the marginal revenue of the unit at the start time of the product\n",
+    "            and the volume of the product. Dispatch the order to the market.\n",
+    "            \"\"\"\n",
+    "\n",
+    "            # get the start and end time of the product\n",
+    "            # for example 1 AM to 2 AM\n",
+    "            start = product[0]\n",
+    "            end = product[1]\n",
+    "\n",
+    "            # Get hydrogen demand and price for the product start time\n",
+    "            # in this case for the start hour of the product\n",
+    "            hydrogen_demand = unit.forecaster[f\"{unit.id}_h2demand\"].loc[start]\n",
+    "            hydrogen_price = unit.forecaster[f\"{unit.id}_h2price\"].loc[start]\n",
+    "\n",
+    "            # Calculate the required power and the actual possible hydrogen production\n",
+    "            # given the hydrogen demand\n",
+    "            power, hydrogen_production = unit.calculate_min_max_power(\n",
+    "                start=start,\n",
+    "                end=end,\n",
+    "                hydrogen_demand=hydrogen_demand,\n",
+    "            )\n",
+    "\n",
+    "            # Calculate the marginal revenue of producing hydrogen\n",
+    "            # as described in the equation above\n",
+    "            marginal_revenue = (\n",
+    "                (hydrogen_price - unit.fixed_cost) * hydrogen_production / power\n",
+    "            )\n",
+    "\n",
+    "            # set the bid price to the marginal revenue\n",
+    "            bid_price = marginal_revenue\n",
+    "\n",
+    "            # formulate the order\n",
+    "            # start, end, price, and volume are required\n",
+    "            order: Order = {\n",
+    "                \"start_time\": product[0],\n",
+    "                \"end_time\": product[1],\n",
+    "                \"price\": bid_price,\n",
+    "                \"volume\": -power,\n",
+    "            }\n",
+    "\n",
+    "            # append the order to the list of bids\n",
+    "            bids.append(order)\n",
+    "\n",
+    "        return bids"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Integrating the New Unit and Strategy into ASSUME\n",
+    "\n",
+    "Finally, we'll integrate our new unit and bidding strategy into the ASSUME simulation environment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "electrolyser_demo 2019-01-30 00:00:00: : 2505601.0it [00:05, 463038.19it/s]                         \n"
+     ]
+    }
+   ],
+   "source": [
+    "# import packages\n",
+    "import logging\n",
+    "import os\n",
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "import pandas as pd\n",
+    "from dateutil import rrule as rr\n",
+    "\n",
+    "from assume import World\n",
+    "from assume.common.forecasts import CsvForecaster, NaiveForecast\n",
+    "from assume.common.market_objects import MarketConfig, MarketProduct\n",
+    "\n",
+    "logger = logging.getLogger(__name__)\n",
+    "\n",
+    "# define output path\n",
+    "csv_path = \"./outputs\"\n",
+    "os.makedirs(\"./local_db\", exist_ok=True)\n",
+    "\n",
+    "# create world isntance\n",
+    "world = World(export_csv_path=csv_path)\n",
+    "\n",
+    "# add new unit type to world\n",
+    "world.unit_types[\"electrolyser\"] = Electrolyser\n",
+    "# add new bidding strategy to world\n",
+    "world.bidding_strategies[\"electrolyser_naive\"] = NaiveStrategyElectrolyser\n",
+    "\n",
+    "\n",
+    "async def init():\n",
+    "    # define simulation period and ID\n",
+    "    start = datetime(2019, 1, 1)\n",
+    "    end = datetime(2019, 1, 30)\n",
+    "    index = pd.date_range(\n",
+    "        start=start,\n",
+    "        end=end + timedelta(hours=24),\n",
+    "        freq=\"H\",\n",
+    "    )\n",
+    "    sim_id = \"electrolyser_demo\"\n",
+    "\n",
+    "    # run world setup to create simulation and different roles\n",
+    "    # this creates the clock and the outputs role\n",
+    "    await world.setup(\n",
+    "        start=start,\n",
+    "        end=end,\n",
+    "        save_frequency_hours=None,\n",
+    "        simulation_id=sim_id,\n",
+    "        index=index,\n",
+    "    )\n",
+    "\n",
+    "    # define market design and add it to a market\n",
+    "    marketdesign = [\n",
+    "        MarketConfig(\n",
+    "            name=\"EOM\",\n",
+    "            opening_hours=rr.rrule(rr.HOURLY, interval=1, dtstart=start, until=end),\n",
+    "            opening_duration=timedelta(hours=1),\n",
+    "            market_mechanism=\"pay_as_clear\",\n",
+    "            market_products=[\n",
+    "                MarketProduct(\n",
+    "                    duration=timedelta(hours=1),\n",
+    "                    count=1,\n",
+    "                    first_delivery=timedelta(hours=1),\n",
+    "                )\n",
+    "            ],\n",
+    "        )\n",
+    "    ]\n",
+    "\n",
+    "    mo_id = \"market_operator\"\n",
+    "    world.add_market_operator(id=mo_id)\n",
+    "    for market_config in marketdesign:\n",
+    "        world.add_market(\n",
+    "            market_operator_id=mo_id,\n",
+    "            market_config=market_config,\n",
+    "        )\n",
+    "\n",
+    "    # add unit operator\n",
+    "    world.add_unit_operator(id=\"power_plant_operator\")\n",
+    "\n",
+    "    # define a simple forecaster\n",
+    "    simple_forecaster = NaiveForecast(index, availability=1, fuel_price=0, co2_price=50)\n",
+    "\n",
+    "    # add a unit to the world\n",
+    "    world.add_unit(\n",
+    "        id=\"power_plant_01\",\n",
+    "        unit_type=\"power_plant\",\n",
+    "        unit_operator_id=\"power_plant_operator\",\n",
+    "        unit_params={\n",
+    "            \"min_power\": 0,\n",
+    "            \"max_power\": 100,\n",
+    "            \"bidding_strategies\": {\"energy\": \"naive\"},\n",
+    "            \"fixed_cost\": 5,\n",
+    "            \"technology\": \"wind turbine\",\n",
+    "        },\n",
+    "        forecaster=simple_forecaster,\n",
+    "    )\n",
+    "\n",
+    "    # repeat for demand unit\n",
+    "    world.add_unit_operator(\"demand_operator\")\n",
+    "    world.add_unit(\n",
+    "        id=\"demand_unit_1\",\n",
+    "        unit_type=\"demand\",\n",
+    "        unit_operator_id=\"demand_operator\",\n",
+    "        unit_params={\n",
+    "            \"min_power\": 0,\n",
+    "            \"max_power\": 1000,\n",
+    "            \"bidding_strategies\": {\"energy\": \"naive\"},\n",
+    "            \"technology\": \"demand\",\n",
+    "        },\n",
+    "        forecaster=NaiveForecast(index, demand=50),\n",
+    "    )\n",
+    "\n",
+    "    # load forecasts for hydrogen demand and hydrogen price\n",
+    "    hydrogen_forecasts = pd.read_csv(\n",
+    "        \"assume/examples/inputs/example_03a/forecasts_df.csv\",\n",
+    "        index_col=0,\n",
+    "        parse_dates=True,\n",
+    "    )\n",
+    "\n",
+    "    # add the electrolyser unit to the world\n",
+    "    world.add_unit_operator(id=\"electrolyser_operator\")\n",
+    "    hydrogen_plant_forecaster = CsvForecaster(index=index)\n",
+    "    hydrogen_plant_forecaster.set_forecast(data=hydrogen_forecasts)\n",
+    "\n",
+    "    world.add_unit(\n",
+    "        id=\"elektrolyser_01\",\n",
+    "        unit_type=\"electrolyser\",\n",
+    "        unit_operator_id=\"electrolyser_operator\",\n",
+    "        unit_params={\n",
+    "            \"min_power\": 7,\n",
+    "            \"max_power\": 52.25,\n",
+    "            \"min_hydrogen\": 0.15,\n",
+    "            \"max_hydrogen\": 0.95,\n",
+    "            \"bidding_strategies\": {\"energy\": \"electrolyser_naive\"},\n",
+    "            \"technology\": \"electrolyser\",\n",
+    "            \"fixed_cost\": 10,\n",
+    "        },\n",
+    "        forecaster=hydrogen_plant_forecaster,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# run the simulation\n",
+    "world.loop.run_until_complete(init())\n",
+    "world.run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above code block is more in line with Tutorial 1, where we manually defined the simulation and executed it. It shows all inner working and serves better understanding. \n",
+    "\n",
+    "For more practical applications you should use the construct from Tutorial 2 to have an automated loading and execution of the scenario, where all parameters are defined in a configuration file, as in the following code block.\n",
+    "\n",
+    "For more details on the configuration and input files structure check out the example_03a folder in examples folder."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "example_03a_base 2019-01-30 00:00:00: : 2505601.0it [00:03, 629000.05it/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",
+    "# Set up logging\n",
+    "log = logging.getLogger(__name__)\n",
+    "\n",
+    "# Define paths for input and output data\n",
+    "csv_path = \"outputs\"\n",
+    "\n",
+    "# Create directories if they don't exist\n",
+    "os.makedirs(\"local_db\", exist_ok=True)\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",
+    "# but you can use a file-based sqlite database too:\n",
+    "data_format = \"local_db\"  # \"local_db\" or \"timescale\"\n",
+    "\n",
+    "if data_format == \"local_db\":\n",
+    "    db_uri = f\"sqlite:///./local_db/assume_db_example_03.db\"\n",
+    "elif data_format == \"timescale\":\n",
+    "    db_uri = \"postgresql://assume:assume@localhost:5432/assume\"\n",
+    "\n",
+    "# create world\n",
+    "world = World(database_uri=db_uri, export_csv_path=csv_path)\n",
+    "\n",
+    "# load 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=\"assume/examples/inputs\",\n",
+    "    scenario=\"example_03a\",\n",
+    "    study_case=\"base\",\n",
+    ")\n",
+    "\n",
+    "# run the simulation\n",
+    "world.run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "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."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  },
+  "nbsphinx": {
+   "execute": "never"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/notebooks/03_custom_unit_example.ipynb.license b/examples/notebooks/03_custom_unit_example.ipynb.license
new file mode 100644
index 00000000..a6ae0636
--- /dev/null
+++ b/examples/notebooks/03_custom_unit_example.ipynb.license
@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: ASSUME Developers
+
+SPDX-License-Identifier: AGPL-3.0-or-later
diff --git a/examples/notebooks/04_reinforcement_learning_example.ipynb b/examples/notebooks/04_reinforcement_learning_example.ipynb
index 4f156fcb..981cd87b 100644
--- a/examples/notebooks/04_reinforcement_learning_example.ipynb
+++ b/examples/notebooks/04_reinforcement_learning_example.ipynb
@@ -238,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "id": "xUsbeZdPJ_2Q"
    },
@@ -264,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
     "id": "UXYSesx4Ifp5"
    },
@@ -355,11 +355,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "id": "iApbQsg5x_u2"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "UsageError: Line magic function `%%add_to` not found.\n"
+     ]
+    }
+   ],
    "source": [
     "# magic to enable class definitions across colab cells\n",
     "%%add_to RLStrategy\n",
diff --git a/examples/notebooks/06_market_comparison.ipynb b/examples/notebooks/05_market_comparison.ipynb
similarity index 84%
rename from examples/notebooks/06_market_comparison.ipynb
rename to examples/notebooks/05_market_comparison.ipynb
index 5b571cad..2875e8bd 100644
--- a/examples/notebooks/06_market_comparison.ipynb
+++ b/examples/notebooks/05_market_comparison.ipynb
@@ -137,9 +137,10 @@
    "outputs": [],
    "source": [
     "import yaml\n",
+    "\n",
     "# lets look at the scenarios we have:\n",
-    "config_file =\"assume/examples/inputs/example_02c/config.yaml\"\n",
-    "config_file =\"examples/inputs/example_02c/config.yaml\"\n",
+    "config_file = \"assume/examples/inputs/example_02c/config.yaml\"\n",
+    "config_file = \"examples/inputs/example_02c/config.yaml\"\n",
     "with open(str(config_file), \"r\") as f:\n",
     "    config = yaml.safe_load(f)\n",
     "\n",
@@ -302,718 +303,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  0%|          | 1/1728000 [00:00<397:40:56,  1.21it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:assume.markets.base_market:1546304399 Market result [(datetime.datetime(2019, 1, 1, 1, 59, 59), datetime.datetime(2019, 1, 8, 1, 59, 59), None)] for market LTM_OTC are empty!\n",
-      "WARNING:assume.markets.base_market:1546304399 Market result [(datetime.datetime(2019, 1, 1, 0, 59, 59), datetime.datetime(2019, 1, 1, 1, 59, 59), None)] for market EOM are empty!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "example_02c_ltm_case 2019-01-01 00:00:00:   0%|          | 3600/1728000 [00:01<09:17, 3094.22it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:assume.markets.base_market:1546304400 Market result [(datetime.datetime(2019, 1, 1, 1, 0), datetime.datetime(2019, 1, 1, 2, 0), None)] for market EOM are empty!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "example_02c_ltm_case 2019-01-11 22:00:00:  55%|█████▍    | 946801.0/1728000 [06:37<04:57, 2628.26it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:assume.markets.base_market:1547251200.0 Market result [(datetime.datetime(2019, 1, 12, 0, 0), datetime.datetime(2019, 1, 12, 1, 0), None)] for market EOM are empty!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "example_02c_ltm_case 2019-01-11 23:00:00:  55%|█████▌    | 950401.0/1728000 [06:38<04:53, 2649.05it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:assume.markets.base_market:1547254800.0 Market result [(datetime.datetime(2019, 1, 12, 1, 0), datetime.datetime(2019, 1, 12, 2, 0), None)] for market EOM are empty!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "example_02c_ltm_case 2019-01-12 00:00:00:  55%|█████▌    | 954001.0/1728000 [06:40<04:59, 2588.26it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:assume.markets.base_market:1547258400.0 Market result [(datetime.datetime(2019, 1, 12, 2, 0), datetime.datetime(2019, 1, 12, 3, 0), None)] for market EOM are empty!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "example_02c_ltm_case 2019-01-12 01:00:00:  55%|█████▌    | 957601.0/1728000 [06:41<05:00, 2567.75it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:assume.markets.base_market:1547262000.0 Market result [(datetime.datetime(2019, 1, 12, 3, 0), datetime.datetime(2019, 1, 12, 4, 0), None)] for market EOM are empty!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "example_02c_ltm_case 2019-01-12 02:00:00:  56%|█████▌    | 961201.0/1728000 [06:42<04:56, 2582.30it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:assume.markets.base_market:1547265600.0 Market result [(datetime.datetime(2019, 1, 12, 4, 0), datetime.datetime(2019, 1, 12, 5, 0), None)] for market EOM are empty!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "example_02c_ltm_case 2019-01-12 03:00:00:  56%|█████▌    | 964801.0/1728000 [06:44<04:52, 2608.49it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:assume.markets.base_market:1547269200.0 Market result [(datetime.datetime(2019, 1, 12, 5, 0), datetime.datetime(2019, 1, 12, 6, 0), None)] for market EOM are empty!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "example_02c_ltm_case 2019-01-12 04:00:00:  56%|█████▌    | 968401.0/1728000 [06:45<04:52, 2601.07it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:assume.markets.base_market:1547272800.0 Market result [(datetime.datetime(2019, 1, 12, 6, 0), datetime.datetime(2019, 1, 12, 7, 0), None)] for market EOM are empty!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "example_02c_ltm_case 2019-01-12 05:00:00:  56%|█████▋    | 972001.0/1728000 [06:47<04:54, 2570.75it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:assume.markets.base_market:1547276400.0 Market result [(datetime.datetime(2019, 1, 12, 7, 0), datetime.datetime(2019, 1, 12, 8, 0), None)] for market EOM are empty!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "example_02c_ltm_case 2019-01-12 21:00:00:  60%|█████▉    | 1029601.0/1728000 [07:09<04:25, 2628.15it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:assume.markets.base_market:1547334000.0 Market result [(datetime.datetime(2019, 1, 12, 23, 0), datetime.datetime(2019, 1, 13, 0, 0), None)] for market EOM are empty!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "example_02c_ltm_case 2019-01-13 04:00:00:  61%|██████    | 1054801.0/1728000 [07:22<04:48, 2336.23it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:assume.markets.base_market:1547359200.0 Market result [(datetime.datetime(2019, 1, 13, 6, 0), datetime.datetime(2019, 1, 13, 7, 0), None)] for market EOM are empty!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "example_02c_ltm_case 2019-01-13 05:00:00:  61%|██████▏   | 1058401.0/1728000 [07:24<04:39, 2396.35it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:assume.markets.base_market:1547362800.0 Market result [(datetime.datetime(2019, 1, 13, 7, 0), datetime.datetime(2019, 1, 13, 8, 0), None)] for market EOM are empty!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
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-      "example_02c_ltm_case 2019-01-13 06:00:00:  61%|██████▏   | 1062001.0/1728000 [07:25<04:36, 2412.05it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:assume.markets.base_market:1547366400.0 Market result [(datetime.datetime(2019, 1, 13, 8, 0), datetime.datetime(2019, 1, 13, 9, 0), None)] for market EOM are empty!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
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-      "example_02c_ltm_case 2019-01-13 07:00:00:  62%|██████▏   | 1065601.0/1728000 [07:27<04:43, 2334.42it/s]"
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-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
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-      "WARNING:assume.markets.base_market:1547370000.0 Market result [(datetime.datetime(2019, 1, 13, 9, 0), datetime.datetime(2019, 1, 13, 10, 0), None)] for market EOM are empty!\n"
-     ]
-    },
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-     "name": "stderr",
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-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
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-    },
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
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-    },
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-      "WARNING:assume.markets.base_market:1547427600.0 Market result [(datetime.datetime(2019, 1, 14, 1, 0), datetime.datetime(2019, 1, 14, 2, 0), None)] for market EOM are empty!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
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-    },
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-     ]
-    },
-    {
-     "name": "stderr",
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-    },
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-     "output_type": "stream",
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-      "WARNING:assume.markets.base_market:1547434800.0 Market result [(datetime.datetime(2019, 1, 14, 3, 0), datetime.datetime(2019, 1, 14, 4, 0), None)] for market EOM are empty!\n"
-     ]
-    },
-    {
-     "name": "stderr",
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-     ]
-    },
-    {
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-    },
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-     ]
-    },
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-     "name": "stderr",
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-    },
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-      "WARNING:assume.markets.base_market:1547780400.0 Market result [(datetime.datetime(2019, 1, 18, 3, 0), datetime.datetime(2019, 1, 18, 4, 0), None)] for market EOM are empty!\n"
-     ]
-    },
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-     "name": "stdout",
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-      "WARNING:assume.markets.base_market:1547784000.0 Market result [(datetime.datetime(2019, 1, 18, 4, 0), datetime.datetime(2019, 1, 18, 5, 0), None)] for market EOM are empty!\n"
-     ]
-    },
-    {
-     "name": "stderr",
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-     ]
-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:assume.markets.base_market:1547856000.0 Market result [(datetime.datetime(2019, 1, 19, 0, 0), datetime.datetime(2019, 1, 19, 1, 0), None)] for market EOM are empty!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
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-      "example_02c_ltm_case 2019-01-18 23:00:00:  90%|█████████ | 1555201.0/1728000 [10:59<01:35, 1817.41it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:assume.markets.base_market:1547859600.0 Market result [(datetime.datetime(2019, 1, 19, 1, 0), datetime.datetime(2019, 1, 19, 2, 0), None)] for market EOM are empty!\n"
-     ]
-    },
-    {
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-      "WARNING:assume.markets.base_market:1547863200.0 Market result [(datetime.datetime(2019, 1, 19, 2, 0), datetime.datetime(2019, 1, 19, 3, 0), None)] for market EOM are empty!\n"
-     ]
-    },
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-      "WARNING:assume.markets.base_market:1547866800.0 Market result [(datetime.datetime(2019, 1, 19, 3, 0), datetime.datetime(2019, 1, 19, 4, 0), None)] for market EOM are empty!\n"
-     ]
-    },
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-     ]
-    },
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-      "WARNING:assume.markets.base_market:1547874000.0 Market result [(datetime.datetime(2019, 1, 19, 5, 0), datetime.datetime(2019, 1, 19, 6, 0), None)] for market EOM are empty!\n"
-     ]
-    },
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-     ]
-    },
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-      "WARNING:assume.markets.base_market:1547953200.0 Market result [(datetime.datetime(2019, 1, 20, 3, 0), datetime.datetime(2019, 1, 20, 4, 0), None)] for market EOM are empty!\n"
-     ]
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-      "WARNING:assume.markets.base_market:1547956800.0 Market result [(datetime.datetime(2019, 1, 20, 4, 0), datetime.datetime(2019, 1, 20, 5, 0), None)] for market EOM are empty!\n"
-     ]
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-     ]
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-      "WARNING:assume.markets.base_market:1547964000.0 Market result [(datetime.datetime(2019, 1, 20, 6, 0), datetime.datetime(2019, 1, 20, 7, 0), None)] for market EOM are empty!\n"
-     ]
-    },
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-     ]
-    },
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-      "WARNING:assume.markets.base_market:1547971200.0 Market result [(datetime.datetime(2019, 1, 20, 8, 0), datetime.datetime(2019, 1, 20, 9, 0), None)] for market EOM are empty!\n"
-     ]
-    },
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-     "name": "stderr",
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-    },
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-      "WARNING:assume.markets.base_market:1547974800.0 Market result [(datetime.datetime(2019, 1, 20, 9, 0), datetime.datetime(2019, 1, 20, 10, 0), None)] for market EOM are empty!\n"
-     ]
-    },
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-     "name": "stderr",
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-    },
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-      "WARNING:assume.markets.base_market:1547992800.0 Market result [(datetime.datetime(2019, 1, 20, 14, 0), datetime.datetime(2019, 1, 20, 15, 0), None)] for market EOM are empty!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
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-      "example_02c_ltm_case 2019-01-20 13:00:00:  98%|█████████▊| 1692001.0/1728000 [11:51<00:13, 2767.25it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:assume.markets.base_market:1547996400.0 Market result [(datetime.datetime(2019, 1, 20, 15, 0), datetime.datetime(2019, 1, 20, 16, 0), None)] for market EOM are empty!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "example_02c_ltm_case 2019-01-20 14:00:00:  98%|█████████▊| 1695601.0/1728000 [11:52<00:11, 2728.46it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:assume.markets.base_market:1548000000.0 Market result [(datetime.datetime(2019, 1, 20, 16, 0), datetime.datetime(2019, 1, 20, 17, 0), None)] for market EOM are empty!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "example_02c_ltm_case 2019-01-20 21:00:00: 100%|█████████▉| 1720801.0/1728000 [12:02<00:02, 2640.73it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:assume.markets.base_market:1548025200.0 Market result [(datetime.datetime(2019, 1, 20, 23, 0), datetime.datetime(2019, 1, 21, 0, 0), None)] for market EOM are empty!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "example_02c_ltm_case 2019-01-20 22:00:00: 100%|█████████▉| 1724401.0/1728000 [12:06<00:02, 1706.54it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:assume.markets.base_market:1548028800.0 Market result [(datetime.datetime(2019, 1, 21, 0, 0), datetime.datetime(2019, 1, 21, 1, 0), None)] for market EOM are empty!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "example_02c_ltm_case 2019-01-21 00:00:00: : 1728001.0it [12:07, 2376.68it/s]                           \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "world = World(database_uri=DB_URI)\n",
     "load_scenario_folder(\n",
@@ -1241,7 +533,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.3"
   },
   "nbsphinx": {
    "execute": "never"
diff --git a/examples/notebooks/05_market_comparison.ipynb.license b/examples/notebooks/05_market_comparison.ipynb.license
new file mode 100644
index 00000000..a6ae0636
--- /dev/null
+++ b/examples/notebooks/05_market_comparison.ipynb.license
@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: ASSUME Developers
+
+SPDX-License-Identifier: AGPL-3.0-or-later