diff --git a/assume/common/utils.py b/assume/common/utils.py index 5f3e1e3a..0f5c9482 100644 --- a/assume/common/utils.py +++ b/assume/common/utils.py @@ -697,3 +697,19 @@ def min_max_scale(x, min_val: float, max_val: float): if min_val == max_val: return x return (x - min_val) / (max_val - min_val) + + +def str_to_bool(val): + """Convert a string representation of truth to True or False. + + True values are 'y', 'yes', 't', 'true', 'on', and '1'; + false values are 'n', 'no', 'f', 'false', 'off', and '0'. + Raises ValueError if 'val' is anything else. + """ + val = val.lower() + if val in {"y", "yes", "t", "true", "on", "1"}: + return True + elif val in {"n", "no", "f", "false", "off", "0"}: + return False + else: + raise ValueError(f"Invalid truth value: {val!r}") diff --git a/assume/scenario/loader_csv.py b/assume/scenario/loader_csv.py index 4e445aec..c3e3a7eb 100644 --- a/assume/scenario/loader_csv.py +++ b/assume/scenario/loader_csv.py @@ -163,6 +163,7 @@ def load_dsm_units( "unit_type", "node", "flexibility_measure", + "is_prosumer", ] # Filter the common columns to only include those that exist in the DataFrame common_columns = [col for col in common_columns if col in dsm_units.columns] diff --git a/assume/strategies/learning_strategies.py b/assume/strategies/learning_strategies.py index 6aa67f89..207fe029 100644 --- a/assume/strategies/learning_strategies.py +++ b/assume/strategies/learning_strategies.py @@ -55,8 +55,12 @@ def prepare_observations(self, unit, market_id): # scaling factors for the observations upper_scaling_factor_price = max(unit.forecaster[f"price_{market_id}"]) lower_scaling_factor_price = min(unit.forecaster[f"price_{market_id}"]) - upper_scaling_factor_res_load = max(unit.forecaster[f"residual_load_{market_id}"]) - lower_scaling_factor_res_load = min(unit.forecaster[f"residual_load_{market_id}"]) + upper_scaling_factor_res_load = max( + unit.forecaster[f"residual_load_{market_id}"] + ) + lower_scaling_factor_res_load = min( + unit.forecaster[f"residual_load_{market_id}"] + ) self.scaled_res_load_obs = min_max_scale( unit.forecaster[f"residual_load_{market_id}"], diff --git a/assume/strategies/naive_strategies.py b/assume/strategies/naive_strategies.py index 647e6aee..21bf3979 100644 --- a/assume/strategies/naive_strategies.py +++ b/assume/strategies/naive_strategies.py @@ -155,8 +155,21 @@ def calculate_bids( product_tuples: list[Product], **kwargs, ) -> Orderbook: + """ + Takes information from a unit that the unit operator manages and + defines how it is dispatched to the market. + + Args: + unit (SupportsMinMax): The unit to be dispatched. + market_config (MarketConfig): The market configuration. + product_tuples (list[Product]): The list of all products the unit can offer. + + Returns: + Orderbook: The bids consisting of the start time, end time, only hours, price and volume. + """ + # calculate the optimal operation of the unit - unit.calculate_optimal_operation_if_needed() + unit.determine_optimal_operation_without_flex() bids = [] for product in product_tuples: @@ -168,6 +181,7 @@ def calculate_bids( volume = unit.opt_power_requirement.at[start] marginal_price = unit.calculate_marginal_cost(start, volume) + bids.append( { "start_time": start, diff --git a/assume/units/__init__.py b/assume/units/__init__.py index 0d924d6a..9749ff68 100644 --- a/assume/units/__init__.py +++ b/assume/units/__init__.py @@ -8,6 +8,7 @@ from assume.units.storage import Storage from assume.units.steel_plant import SteelPlant from assume.units.hydrogen_plant import HydrogenPlant +from assume.units.building import Building from assume.units.dst_components import demand_side_technologies unit_types: dict[str, BaseUnit] = { @@ -16,4 +17,5 @@ "storage": Storage, "steel_plant": SteelPlant, "hydrogen_plant": HydrogenPlant, + "building": Building, } diff --git a/assume/units/building.py b/assume/units/building.py new file mode 100644 index 00000000..602df1ee --- /dev/null +++ b/assume/units/building.py @@ -0,0 +1,624 @@ +# SPDX-FileCopyrightText: ASSUME Developers +# +# SPDX-License-Identifier: AGPL-3.0-or-later + +import logging + +import pandas as pd +import pyomo.environ as pyo + +from assume.common.base import SupportsMinMax +from assume.common.utils import str_to_bool +from assume.units.dsm_load_shift import DSMFlex + +SOLVERS = ["appsi_highs", "gurobi", "glpk", "cbc", "cplex"] + +logger = logging.getLogger(__name__) + +# Set the log level to ERROR for Pyomo to reduce verbosity +logging.getLogger("pyomo").setLevel(logging.WARNING) + + +class Building(DSMFlex, SupportsMinMax): + """ + Represents a building unit within an energy system, modeling its energy consumption, + production, and flexibility components. This class integrates various technologies + such as heat pumps, boilers, thermal storage, electric vehicles, generic storage, and + photovoltaic (PV) plants to optimize the building's energy usage based on defined + objectives. + + The `Building` class utilizes the Pyomo optimization library to determine the optimal + operation strategy that minimizes costs or meets other specified objectives. It handles + the interactions between different energy components, ensuring that energy demands are + met while adhering to operational constraints. + + Attributes: + id (str): Unique identifier for the building unit. + unit_operator (str): Operator managing the building unit. + bidding_strategies (dict): Strategies used for energy bidding in the market. + technology (str): Type of technology the building unit employs. + node (str): Network node where the building unit is connected. + index (pd.DatetimeIndex): Time index representing the granularity of the data. + location (tuple[float, float]): Geographic coordinates (latitude, longitude) of the building. + components (dict[str, dict]): Sub-components of the building, such as heat pumps or storage systems. + objective (str): Optimization objective, e.g., "min_variable_cost" to minimize operational expenses. + flexibility_measure (str): Metric used to assess the building's flexibility, e.g., "max_load_shift". + """ + + # List of optional technologies that a building unit can incorporate + required_technologies = [] + # List of optional technologies that a building unit can incorporate + optional_technologies = [ + "heat_pump", + "boiler", + "thermal_storage", + "electric_vehicle", + "generic_storage", + "pv_plant", + ] + + def __init__( + self, + id: str, + unit_operator: str, + bidding_strategies: dict, + components: dict[str, dict] = None, + technology: str = "building", + objective: str = None, + node: str = "node0", + location: tuple[float, float] = (0.0, 0.0), + index: pd.DatetimeIndex = None, + flexibility_measure: str = "", + is_prosumer: str = "No", + cost_tolerance: float = 10, + **kwargs, + ): + """ + Initializes a Building instance with specified parameters and sets up the optimization model. + + Args: + id (str): Unique identifier for the building unit. + unit_operator (str): Operator managing the building unit. + index (pd.DatetimeIndex): Time index representing the granularity of the data. + bidding_strategies (dict): Strategies used for energy bidding in the market. + components (dict[str, dict]): Sub-components of the building, such as heat pumps or storage systems. + technology (str, optional): Type of technology the building unit employs. Defaults to "building". + objective (str, optional): Optimization objective, e.g., "min_variable_cost". Defaults to "min_variable_cost". + node (str, optional): Network node where the building unit is connected. Defaults to "node0". + location (tuple[float, float], optional): Geographic coordinates (latitude, longitude) of the building. Defaults to (0.0, 0.0). + flexibility_measure (str, optional): Metric used to assess the building's flexibility. Defaults to "max_load_shift". + is_prosumer (str): Indicates whether the building participates as a prosumer in the market. + Accepts "Yes" or "No" (case-insensitive). + **kwargs: Additional keyword arguments for parent classes. + + Raises: + ValueError: If any of the provided components are not recognized as valid technologies. + Exception: If none of the specified solvers are available. + """ + super().__init__( + id=id, + unit_operator=unit_operator, + technology=technology, + components=components, + bidding_strategies=bidding_strategies, + index=index, + node=node, + location=location, + **kwargs, + ) + + # check if the required components are present in the components dictionary + for component in self.required_technologies: + if component not in components.keys(): + raise ValueError( + f"Component {component} is required for the building plant unit." + ) + + # check if the provided components are valid and do not contain any unknown components + for component in components.keys(): + if ( + component not in self.required_technologies + and component not in self.optional_technologies + ): + raise ValueError( + f"Components {component} is not a valid component for the building unit." + ) + + # Initialize forecasting data for various energy prices and demands + self.electricity_price = self.forecaster["price_EOM"] + self.natural_gas_price = self.forecaster["fuel_price_natural gas"] + self.heat_demand = self.forecaster[f"{self.id}_heat_demand"] + self.ev_load_profile = self.forecaster["ev_load_profile"] + self.battery_load_profile = self.forecaster["battery_load_profile"] + self.inflex_demand = self.forecaster[f"{self.id}_load_profile"] + + self.objective = objective + self.flexibility_measure = flexibility_measure + self.cost_tolerance = cost_tolerance + self.is_prosumer = str_to_bool(is_prosumer) + + # Check for the presence of components + self.has_heatpump = "heat_pump" in self.components.keys() + self.has_boiler = "boiler" in self.components.keys() + self.has_thermal_storage = "thermal_storage" in self.components.keys() + self.has_ev = "electric_vehicle" in self.components.keys() + self.has_battery_storage = "generic_storage" in self.components.keys() + self.has_pv = "pv_plant" in self.components.keys() + + self.opt_power_requirement = None + self.flex_power_requirement = None + self.variable_cost_series = None + + # Initialize the Pyomo optimization model + self.model = pyo.ConcreteModel() + self.define_sets() + + # Configure PV plant power profile based on availability + if self.has_pv: + profile_key = ( + f"{self.id}_pv_power_profile" + if not str_to_bool( + self.components["pv_plant"].get("uses_power_profile", "false") + ) + else "availability_solar" + ) + pv_profile = self.forecaster[profile_key] + + # Convert and align pv_profile with Pyomo time steps + pv_profile = pv_profile.as_pd_series() + pv_profile.index = list(self.model.time_steps) + + # Assign the aligned profile + self.components["pv_plant"][ + "power_profile" + if profile_key.endswith("power_profile") + else "availability_profile" + ] = pv_profile + + self.define_parameters() + self.define_variables() + + self.initialize_components() + self.initialize_process_sequence() + + self.define_constraints() + self.define_objective_opt() + + self.determine_optimal_operation_without_flex(switch_flex_off=False) + + # Apply the flexibility function based on flexibility measure + if self.flexibility_measure in DSMFlex.flexibility_map: + DSMFlex.flexibility_map[self.flexibility_measure](self, self.model) + else: + raise ValueError(f"Unknown flexibility measure: {self.flexibility_measure}") + + self.define_objective_flex() + + def get_prosumer_components(self): + """ + Identifies and returns a list of components capable of selling energy when the building is a prosumer. + + Returns: + list[str]: Names of components that can participate in the market. + """ + prosumer_components = [] + if self.is_prosumer: + if self.has_ev: + prosumer_components.append("electric_vehicle") + if self.has_battery_storage: + prosumer_components.append("generic_storage") + return prosumer_components + + def define_sets(self) -> None: + """ + Defines the sets used in the Pyomo optimization model. + + Specifically, this method initializes the `time_steps` set, which represents each + discrete time interval in the model based on the provided time index. + """ + + # Create a pyo.Set from FastIndex + self.model.time_steps = pyo.Set(initialize=list(range(len(self.index)))) + + def define_parameters(self): + """ + Defines the parameters for the Pyomo optimization model. + + This includes prices for electricity and natural gas, as well as heat and inflexible + demands. Each parameter is indexed by the defined time steps to allow for time-dependent + optimization. + """ + self.model.electricity_price = pyo.Param( + self.model.time_steps, + initialize={t: value for t, value in enumerate(self.electricity_price)}, + ) + self.model.natural_gas_price = pyo.Param( + self.model.time_steps, + initialize={t: value for t, value in enumerate(self.natural_gas_price)}, + ) + self.model.heat_demand = pyo.Param( + self.model.time_steps, + initialize={t: value for t, value in enumerate(self.heat_demand)}, + ) + self.model.inflex_demand = pyo.Param( + self.model.time_steps, + initialize={t: value for t, value in enumerate(self.inflex_demand)}, + ) + + def define_variables(self): + """ + Defines the decision variables for the Pyomo optimization model. + + - `total_power_input`: Represents the total power input required at each time step. + - `variable_cost`: Represents the variable cost associated with power usage at each time step. + + Both variables are defined over the `time_steps` set and are continuous real numbers. + """ + self.model.total_power_input = pyo.Var(self.model.time_steps, within=pyo.Reals) + self.model.variable_cost = pyo.Var(self.model.time_steps, within=pyo.Reals) + + def initialize_process_sequence(self): + """ + Connects the energy components in the building system, establishing constraints for energy flow + between technologies like heat pumps, boilers, EVs, batteries, and PVs. + """ + + # Heat flow constraint for heating components + if self.has_heatpump or self.has_boiler or self.has_thermal_storage: + + @self.model.Constraint(self.model.time_steps) + def heating_demand_balance_constraint(m, t): + """ + Ensures the total heat output matches demand plus storage dynamics. + """ + heat_pump_output = ( + self.model.dsm_blocks["heat_pump"].heat_out[t] + if self.has_heatpump + else 0 + ) + boiler_output = ( + self.model.dsm_blocks["boiler"].heat_out[t] + if self.has_boiler + else 0 + ) + thermal_storage_discharge = ( + self.model.dsm_blocks["thermal_storage"].discharge[t] + if self.has_thermal_storage + else 0 + ) + thermal_storage_charge = ( + self.model.dsm_blocks["thermal_storage"].charge[t] + if self.has_thermal_storage + else 0 + ) + return ( + heat_pump_output + boiler_output + thermal_storage_discharge + == self.model.heat_demand[t] + thermal_storage_charge + ) + + # Electric flow and battery/EV constraints + if self.has_ev: + + @self.model.Constraint(self.model.time_steps) + def ev_energy_flow_constraint(m, t): + """ + Ensures that EV energy flows are connected appropriately. + """ + ev_discharge = self.model.dsm_blocks["electric_vehicle"].discharge[t] + # ev_charge = self.model.dsm_blocks["electric_vehicle"].charge[t] + inflex_demand = self.model.inflex_demand[t] + + pv_output = ( + self.model.dsm_blocks["pv_plant"].power[t] if self.has_pv else 0 + ) + battery_discharge = ( + self.model.dsm_blocks["generic_storage"].discharge[t] + if self.has_battery_storage + else 0 + ) + battery_charge = ( + self.model.dsm_blocks["generic_storage"].charge[t] + if self.has_battery_storage + else 0 + ) + return ( + ev_discharge + <= inflex_demand + battery_charge - battery_discharge - pv_output + ) + + if self.has_battery_storage: + + @self.model.Constraint(self.model.time_steps) + def battery_energy_flow_constraint(m, t): + """ + Ensures battery storage discharges are appropriately aligned with system demands. + """ + battery_discharge = self.model.dsm_blocks["generic_storage"].discharge[ + t + ] + # battery_charge = self.model.dsm_blocks["generic_storage"].charge[t] + heat_pump_power = ( + self.model.dsm_blocks["heat_pump"].power_in[t] + if self.has_heatpump + else 0 + ) + boiler_power = ( + self.model.dsm_blocks["boiler"].power_in[t] + if self.has_boiler + else 0 + ) + ev_charge = ( + self.model.dsm_blocks["electric_vehicle"].charge[t] + if self.has_ev + else 0 + ) + pv_output = ( + self.model.dsm_blocks["pv_plant"].power[t] if self.has_pv else 0 + ) + return ( + battery_discharge + <= self.model.inflex_demand[t] + + heat_pump_power + + boiler_power + + ev_charge + - pv_output + ) + + def define_constraints(self): + """ + Defines the constraints for the Pyomo optimization model. + + Constraints ensure that the total power input aligns with the building's demands and + the behavior of its components. Additional constraints restrict energy discharge from + electric vehicles and battery storage based on market participation settings. + """ + + @self.model.Constraint(self.model.time_steps) + def total_power_input_constraint(m, t): + """ + Ensures that the total power input is the sum of all component inputs minus any self-produced + or stored energy at each time step. + + This constraint aggregates power from heat pumps, boilers, electric vehicles, generic storage, + and PV plants, balancing it against the inflexible demand and any energy being discharged + by storage or PV systems. + + Args: + m: Pyomo model reference. + t: Current time step. + + Returns: + Equality condition balancing total power input. + """ + total_power_input = self.model.inflex_demand[t] + + # Add power inputs from available components + if self.has_heatpump: + total_power_input += self.model.dsm_blocks["heat_pump"].power_in[t] + if self.has_boiler: + total_power_input += self.model.dsm_blocks["boiler"].power_in[t] + + # Add and subtract EV and storage power if they exist + if self.has_ev: + total_power_input += self.model.dsm_blocks["electric_vehicle"].charge[t] + total_power_input -= self.model.dsm_blocks[ + "electric_vehicle" + ].discharge[t] + if self.has_battery_storage: + total_power_input += self.model.dsm_blocks["generic_storage"].charge[t] + total_power_input -= self.model.dsm_blocks["generic_storage"].discharge[ + t + ] + + # Subtract power from PV plant if it exists + if self.has_pv: + total_power_input -= self.model.dsm_blocks["pv_plant"].power[t] + + # Assign the calculated total to the model's total_power_input for each time step + return self.model.total_power_input[t] == total_power_input + + # Restrict discharge for non-prosumer components + for component in self.get_prosumer_components(): + + @self.model.Constraint(self.model.time_steps) + def restrict_discharge_to_market(m, t, component=component): + """ + Restricts discharges to self-use only for non-prosumer components. + """ + discharge = self.model.dsm_blocks[component].discharge[t] + return discharge <= self.model.inflex_demand[t] + + @self.model.Constraint(self.model.time_steps) + def variable_cost_constraint(m, t): + """ + Calculates the variable cost associated with power usage at each time step. + + This constraint multiplies the total variable power by the corresponding electricity price + to determine the variable cost incurred. + + Args: + m: Pyomo model reference. + t: Current time step. + + Returns: + Equality condition defining the variable cost. + """ + return ( + self.model.variable_cost[t] + == self.model.total_power_input[t] * self.model.electricity_price[t] + ) + + def define_objective_opt(self): + """ + Defines the objective function for the optimization model. + + Currently supports minimizing the total variable cost over all time steps. If an unknown + objective is specified, raises a ValueError. + + Raises: + ValueError: If the specified objective is not recognized. + """ + if self.objective == "min_variable_cost": + + @self.model.Objective(sense=pyo.minimize) + def obj_rule_opt(m): + """ + Objective function to minimize the total variable cost across all time steps. + + Aggregates the variable costs from each time step to form the total cost to be minimized. + + Args: + m: Pyomo model reference. + + Returns: + Expression representing the total variable cost. + """ + total_variable_cost = pyo.quicksum( + self.model.variable_cost[t] for t in self.model.time_steps + ) + return total_variable_cost + else: + raise ValueError(f"Unknown objective: {self.objective}") + + def define_objective_flex(self): + """ + Defines the flexibility objective for the optimization model. + + Args: + model (pyomo.ConcreteModel): The Pyomo model. + """ + if self.flexibility_measure == "cost_based_load_shift": + + @self.model.Objective(sense=pyo.maximize) + def obj_rule_flex(m): + """ + Maximizes the load shift over all time steps. + """ + + maximise_load_shift = pyo.quicksum( + m.load_shift_pos[t] for t in m.time_steps + ) + + return maximise_load_shift + + # def calculate_optimal_operation(self): + # """ + # Solves the optimization model to determine the building's optimal energy operation strategy. + + # This method creates an instance of the Pyomo model, solves it using the selected solver, + # and processes the results. It handles solver status checks, logs relevant information, + # and extracts the optimal power requirements and variable costs. + + # Additionally, it invokes `write_additional_outputs` to process and store results from + # specific components like battery storage and electric vehicles. + # """ + # # Create an instance of the model + # instance = self.model.create_instance() + # # Solve the instance using the configured solver + # results = self.solver.solve(instance, tee=False) + + # # Check solver status and termination condition + # if (results.solver.status == SolverStatus.ok) and ( + # results.solver.termination_condition == TerminationCondition.optimal + # ): + # logger.debug("The optimization model was solved optimally.") + + # # Retrieve and log the objective function value + # objective_value = instance.obj_rule() + # logger.debug(f"The value of the objective function is {objective_value}.") + + # elif results.solver.termination_condition == TerminationCondition.infeasible: + # logger.debug("The optimization model is infeasible.") + + # else: + # logger.debug(f"Solver Status: {results.solver.status}") + # logger.debug( + # f"Termination Condition: {results.solver.termination_condition}" + # ) + + # # Extract and store the total power requirement as a Pandas Series + # self.opt_power_requirement = pd.Series( + # data=instance.totalvariable_power.get_values() + # ).set_axis(self.index) + + # # Extract and store the variable cost series as a Pandas Series + # self.variable_cost_series = pd.Series( + # data=instance.variable_cost.get_values() + # ).set_axis(self.index) + + # # Process additional outputs from specific components + # self.write_additional_outputs(instance) + + def write_additional_outputs(self, instance): + """ + Extracts and stores additional outputs from the optimization instance for specific components. + + This includes the state of charge (SoC) for battery storage and electric vehicles, normalized + by their maximum capacities. + + Args: + instance: The solved Pyomo model instance. + """ + if self.has_battery_storage: + model_block = instance.dsm_blocks["generic_storage"] + soc = pd.Series(data=model_block.soc.get_values(), dtype=float) / pyo.value( + model_block.max_capacity + ) + soc.index = self.index + self.outputs["soc"] = soc + + if self.has_ev: + model_block = instance.dsm_blocks["electric_vehicle"] + ev_soc = pd.Series( + data=model_block.soc.get_values(), dtype=float + ) / pyo.value(model_block.max_capacity) + ev_soc.index = self.index + self.outputs["ev_soc"] = ev_soc + + def calculate_marginal_cost(self, start: pd.Timestamp, power: float) -> float: + """ + Calculates the marginal cost of operating the building unit at a specific time and power level. + + The marginal cost represents the additional cost incurred by increasing the power output by one unit. + + Args: + start (pd.Timestamp): The start time of the dispatch period. + power (float): The power output level of the unit during the dispatch. + + Returns: + float: The marginal cost of the unit for the given power level. Returns 0 if there is no power requirement. + """ + # Initialize marginal cost + marginal_cost = 0 + + if self.opt_power_requirement[start] != 0: + marginal_cost = abs( + self.variable_cost_series[start] / self.opt_power_requirement[start] + ) + return marginal_cost + + def as_dict(self) -> dict: + """ + Serializes the building unit's attributes and components into a dictionary. + + This includes all inherited attributes as well as specific details about the unit type + and its constituent components. + + Returns: + dict: A dictionary representation of the building unit's attributes and components. + """ + # List all component names + components_list = [component for component in self.model.dsm_blocks.keys()] + components_string = ",".join(components_list) + + # Retrieve base class attributes + unit_dict = super().as_dict() + # Update with Building-specific attributes + unit_dict.update( + { + "unit_type": "demand", + "components": components_string, + } + ) + + return unit_dict diff --git a/assume/units/dsm_load_shift.py b/assume/units/dsm_load_shift.py index 856a9372..6d448a3c 100644 --- a/assume/units/dsm_load_shift.py +++ b/assume/units/dsm_load_shift.py @@ -3,22 +3,50 @@ # SPDX-License-Identifier: AGPL-3.0-or-later import logging +from collections.abc import Callable import pyomo.environ as pyo -from pyomo.opt import SolverStatus, TerminationCondition +from pyomo.opt import ( + SolverFactory, + SolverStatus, + TerminationCondition, + check_available_solvers, +) from assume.common.fast_pandas import FastSeries from assume.units.dst_components import demand_side_technologies +SOLVERS = ["appsi_highs", "gurobi", "glpk", "cbc", "cplex"] + logger = logging.getLogger(__name__) class DSMFlex: + # Mapping of flexibility measures to their respective functions + flexibility_map: dict[str, Callable[[pyo.ConcreteModel], None]] = { + "cost_based_load_shift": lambda self, model: self.cost_based_flexibility(model), + } + big_M = 10000000 + def __init__(self, components, **kwargs): super().__init__(**kwargs) self.components = components + self.initialize_solver() + + def initialize_solver(self, solver=None): + # Define a solver + solvers = check_available_solvers(*SOLVERS) + solver = solver if solver in solvers else solvers[0] + if solver == "gurobi": + self.solver_options = {"LogToConsole": 0, "OutputFlag": 0} + elif solver == "appsi_highs": + self.solver_options = {"output_flag": False, "log_to_console": False} + else: + self.solver_options = {} + self.solver = SolverFactory(solver) + def initialize_components(self): """ Initializes the DSM components by creating and adding blocks to the model. @@ -56,53 +84,98 @@ def initialize_components(self): self.model, self.model.dsm_blocks[technology] ) - def switch_to_opt(self, instance): + def cost_based_flexibility(self, model): """ - Switches the instance to solve a cost based optimisation problem by deactivating the flexibility constraints and objective. - - Args: - instance (pyomo.ConcreteModel): The instance of the Pyomo model. - - Returns: - pyomo.ConcreteModel: The modified instance with flexibility constraints and objective deactivated. + Modify the optimization model to include constraints for flexibility within cost tolerance. """ - # deactivate the flexibility constraints and objective - instance.obj_rule_flex.deactivate() - instance.total_cost_upper_limit.deactivate() - instance.total_power_input_constraint_with_flex.deactivate() + model.cost_tolerance = pyo.Param(initialize=(self.cost_tolerance)) + model.total_cost = pyo.Param(initialize=0.0, mutable=True) - return instance + # Variables + model.load_shift_pos = pyo.Var(model.time_steps, within=pyo.NonNegativeIntegers) + model.load_shift_neg = pyo.Var(model.time_steps, within=pyo.NonNegativeIntegers) + model.shift_indicator = pyo.Var(model.time_steps, within=pyo.Binary) - def switch_to_flex(self, instance): - """ - Switches the instance to flexibility mode by deactivating few constraints and objective function. + @model.Constraint() + def total_cost_upper_limit(m): + return pyo.quicksum( + m.variable_cost[t] for t in m.time_steps + ) <= m.total_cost * (1 + (m.cost_tolerance / 100)) - Args: - instance (pyomo.ConcreteModel): The instance of the Pyomo model. + @model.Constraint(model.time_steps) + def flex_constraint_upper(m, t): + return m.load_shift_pos[t] <= (1 - m.shift_indicator[t]) * self.big_M - Returns: - pyomo.ConcreteModel: The modified instance with optimal constraints and objective deactivated. - """ - # deactivate the optimal constraints and objective - instance.obj_rule_opt.deactivate() - instance.total_power_input_constraint.deactivate() + @model.Constraint(model.time_steps) + def flex_constraint_lower(m, t): + return m.load_shift_neg[t] <= m.shift_indicator[t] * self.big_M - # fix values of model.total_power_input - for t in instance.time_steps: - instance.total_power_input[t].fix(self.opt_power_requirement.iloc[t]) - instance.total_cost = self.total_cost + @model.Constraint(model.time_steps) + def total_power_input_constraint_with_flex(m, t): + # Apply constraints based on the technology type + if self.technology == "hydrogen_plant": + # Hydrogen plant constraint + return ( + m.total_power_input[t] + m.load_shift_pos[t] - m.load_shift_neg[t] + == self.model.dsm_blocks["electrolyser"].power_in[t] + ) + elif self.technology == "steel_plant": + # Steel plant constraint with conditional electrolyser inclusion + if self.has_electrolyser: + return ( + m.total_power_input[t] + + m.load_shift_pos[t] + - m.load_shift_neg[t] + == self.model.dsm_blocks["electrolyser"].power_in[t] + + self.model.dsm_blocks["eaf"].power_in[t] + + self.model.dsm_blocks["dri_plant"].power_in[t] + ) + else: + return ( + m.total_power_input[t] + + m.load_shift_pos[t] + - m.load_shift_neg[t] + == self.model.dsm_blocks["eaf"].power_in[t] + + self.model.dsm_blocks["dri_plant"].power_in[t] + ) + elif self.technology == "building": + total_power_input = m.inflex_demand[t] + # Building components (e.g., heat_pump, boiler, pv_plant, generic_storage) + if self.has_heatpump: + total_power_input += self.model.dsm_blocks["heat_pump"].power_in[t] + if self.has_boiler: + total_power_input += self.model.dsm_blocks["boiler"].power_in[t] + if self.has_ev: + total_power_input += ( + self.model.dsm_blocks["electric_vehicle"].charge[t] + - self.model.dsm_blocks["electric_vehicle"].discharge[t] + ) + if self.has_battery_storage: + total_power_input += ( + self.model.dsm_blocks["generic_storage"].charge[t] + - self.model.dsm_blocks["generic_storage"].discharge[t] + ) + if self.has_pv: + total_power_input -= self.model.dsm_blocks["pv_plant"].power[t] - return instance + # Apply flexibility adjustments + return ( + m.total_power_input[t] + + m.load_shift_pos[t] * model.shift_indicator[t] + - m.load_shift_neg[t] * (1 - model.shift_indicator[t]) + == total_power_input + ) - def determine_optimal_operation_without_flex(self): + def determine_optimal_operation_without_flex(self, switch_flex_off=True): """ Determines the optimal operation of the steel plant without considering flexibility. """ # create an instance of the model instance = self.model.create_instance() # switch the instance to the optimal mode by deactivating the flexibility constraints and objective - instance = self.switch_to_opt(instance) + if switch_flex_off: + instance = self.switch_to_opt(instance) # solve the instance results = self.solver.solve(instance, options=self.solver_options) @@ -172,11 +245,20 @@ def determine_optimal_operation_with_flex(self): "Termination Condition: ", results.solver.termination_condition ) - flex_power_requirement = [ - pyo.value(instance.total_power_input[t]) for t in instance.time_steps - ] + # Compute adjusted total power input with load shift applied + adjusted_total_power_input = [] + for t in instance.time_steps: + # Calculate the load-shifted value of total_power_input + adjusted_power = ( + instance.total_power_input[t].value + + instance.load_shift_pos[t].value + - instance.load_shift_neg[t].value + ) + adjusted_total_power_input.append(adjusted_power) + + # Assign this list to flex_power_requirement as a pandas Series self.flex_power_requirement = FastSeries( - index=self.index, value=flex_power_requirement + index=self.index, value=adjusted_total_power_input ) # Variable cost series @@ -187,44 +269,49 @@ def determine_optimal_operation_with_flex(self): index=self.index, value=flex_variable_cost ) - def flexibility_cost_tolerance(self, model): + def switch_to_opt(self, instance): """ - Modify the optimization model to include constraints for flexibility within cost tolerance. + Switches the instance to solve a cost based optimisation problem by deactivating the flexibility constraints and objective. + + Args: + instance (pyomo.ConcreteModel): The instance of the Pyomo model. + + Returns: + pyomo.ConcreteModel: The modified instance with flexibility constraints and objective deactivated. """ + # Deactivate the flexibility objective if it exists + # if hasattr(instance, "obj_rule_flex"): + instance.obj_rule_flex.deactivate() - model.cost_tolerance = pyo.Param(initialize=(self.cost_tolerance)) - model.total_cost = pyo.Param(initialize=0.0, mutable=True) + # Deactivate flexibility constraints if they exist + if hasattr(instance, "total_cost_upper_limit"): + instance.total_cost_upper_limit.deactivate() - # Variables - model.load_shift = pyo.Var(model.time_steps, within=pyo.Reals) + if hasattr(instance, "peak_load_shift_constraint"): + instance.peak_load_shift_constraint.deactivate() - @model.Constraint(model.time_steps) - def total_cost_upper_limit(m, t): - return sum( - model.variable_cost[t] for t in model.time_steps - ) <= model.total_cost * (1 + (model.cost_tolerance / 100)) + # if hasattr(instance, "total_power_input_constraint_with_flex"): + instance.total_power_input_constraint_with_flex.deactivate() - @model.Constraint(model.time_steps) - def total_power_input_constraint_with_flex(m, t): - # Apply constraints based on the technology type - if self.technology == "hydrogen_plant": - # Hydrogen plant constraint - return ( - m.total_power_input[t] - m.load_shift[t] - == self.model.dsm_blocks["electrolyser"].power_in[t] - ) - elif self.technology == "steel_plant": - # Steel plant constraint with conditional electrolyser inclusion - if self.has_electrolyser: - return ( - m.total_power_input[t] - m.load_shift[t] - == self.model.dsm_blocks["electrolyser"].power_in[t] - + self.model.dsm_blocks["eaf"].power_in[t] - + self.model.dsm_blocks["dri_plant"].power_in[t] - ) - else: - return ( - m.total_power_input[t] - m.load_shift[t] - == self.model.dsm_blocks["eaf"].power_in[t] - + self.model.dsm_blocks["dri_plant"].power_in[t] - ) + return instance + + def switch_to_flex(self, instance): + """ + Switches the instance to flexibility mode by deactivating few constraints and objective function. + + Args: + instance (pyomo.ConcreteModel): The instance of the Pyomo model. + + Returns: + pyomo.ConcreteModel: The modified instance with optimal constraints and objective deactivated. + """ + # deactivate the optimal constraints and objective + instance.obj_rule_opt.deactivate() + instance.total_power_input_constraint.deactivate() + + # fix values of model.total_power_input + for t in instance.time_steps: + instance.total_power_input[t].fix(self.opt_power_requirement.iloc[t]) + instance.total_cost = self.total_cost + + return instance diff --git a/assume/units/dst_components.py b/assume/units/dst_components.py index ae2aff45..9bc2237a 100644 --- a/assume/units/dst_components.py +++ b/assume/units/dst_components.py @@ -1670,6 +1670,7 @@ def max_discharge_power_constraint(b, t): "electric_vehicle": ElectricVehicle, "generic_storage": GenericStorage, "pv_plant": PVPlant, + "thermal_storage": GenericStorage, } diff --git a/assume/units/steel_plant.py b/assume/units/steel_plant.py index fcb41f9a..c73eeb2c 100644 --- a/assume/units/steel_plant.py +++ b/assume/units/steel_plant.py @@ -6,10 +6,6 @@ from datetime import datetime import pyomo.environ as pyo -from pyomo.opt import ( - SolverFactory, - check_available_solvers, -) from assume.common.base import SupportsMinMax from assume.common.forecasts import Forecaster @@ -36,11 +32,12 @@ class SteelPlant(DSMFlex, SupportsMinMax): location (tuple[float, float]): The location of the unit. components (dict[str, dict]): The components of the unit such as Electrolyser, DRI Plant, DRI Storage, and Electric Arc Furnace. objective (str): The objective of the unit, e.g. minimize variable cost ("min_variable_cost"). - flexibility_measure (str): The flexibility measure of the unit, e.g. maximum load shift ("max_load_shift"). + flexibility_measure (str): The flexibility measure of the unit, e.g. maximum load shift ("cost_based_load_shift"). demand (float): The demand of the unit - the amount of steel to be produced. cost_tolerance (float): The cost tolerance of the unit - the maximum cost that can be tolerated when shifting the load. """ + # Compatible Technologies required_technologies = ["dri_plant", "eaf"] optional_technologies = ["electrolyser", "hydrogen_buffer_storage", "dri_storage"] @@ -55,9 +52,11 @@ def __init__( location: tuple[float, float] = (0.0, 0.0), components: dict[str, dict] = None, objective: str = None, - flexibility_measure: str = "max_load_shift", + flexibility_measure: str = "", demand: float = 0, cost_tolerance: float = 10, + congestion_threshold: float = 0, + peak_load_cap: float = 0, **kwargs, ): super().__init__( @@ -98,15 +97,26 @@ def __init__( self.lime_price = self.forecaster.get_price("lime") self.co2_price = self.forecaster.get_price("co2") + # Calculate congestion forecast and set it as a forecast column in the forecaster + self.congestion_signal = self.forecaster[f"{node}_congestion_severity"] + self.renewable_utilisation_signal = self.forecaster[ + f"{node}_renewable_utilisation" + ] + self.objective = objective self.flexibility_measure = flexibility_measure self.cost_tolerance = cost_tolerance + self.congestion_threshold = congestion_threshold + self.peak_load_cap = peak_load_cap # Check for the presence of components self.has_h2storage = "hydrogen_buffer_storage" in self.components.keys() self.has_dristorage = "dri_storage" in self.components.keys() self.has_electrolyser = "electrolyser" in self.components.keys() + self.opt_power_requirement = None + self.flex_power_requirement = None + # Main Model part self.model = pyo.ConcreteModel() self.define_sets() @@ -119,25 +129,15 @@ def __init__( self.define_constraints() self.define_objective_opt() - if self.flexibility_measure == "max_load_shift": - self.flexibility_cost_tolerance(self.model) - self.define_objective_flex() - - solvers = check_available_solvers(*SOLVERS) - if len(solvers) < 1: - raise Exception(f"None of {SOLVERS} are available") + self.determine_optimal_operation_without_flex(switch_flex_off=False) - self.solver = SolverFactory(solvers[0]) - self.solver_options = { - "output_flag": False, - "log_to_console": False, - "LogToConsole": 0, - } - - self.opt_power_requirement = None - self.flex_power_requirement = None + # Apply the flexibility function based on flexibility measure + if self.flexibility_measure in DSMFlex.flexibility_map: + DSMFlex.flexibility_map[self.flexibility_measure](self, self.model) + else: + raise ValueError(f"Unknown flexibility measure: {self.flexibility_measure}") - self.variable_cost_series = None + self.define_objective_flex() def define_sets(self) -> None: """ @@ -311,7 +311,7 @@ def total_power_input_constraint(m, t): ) if self.has_electrolyser: power_input += self.model.dsm_blocks["electrolyser"].power_in[t] - return m.total_power_input[t] == power_input + return self.model.total_power_input[t] == power_input # Constraint for variable cost per time step @self.model.Constraint(self.model.time_steps) @@ -359,31 +359,62 @@ def define_objective_flex(self): Args: model (pyomo.ConcreteModel): The Pyomo model. """ - if self.flexibility_measure == "max_load_shift": + if self.flexibility_measure == "cost_based_load_shift": @self.model.Objective(sense=pyo.maximize) def obj_rule_flex(m): """ Maximizes the load shift over all time steps. """ - maximise_load_shift = sum( - m.load_shift[t] for t in self.model.time_steps + + maximise_load_shift = pyo.quicksum( + m.load_shift_pos[t] for t in m.time_steps ) + return maximise_load_shift - else: - raise ValueError(f"Unknown objective: {self.flexibility_measure}") + elif self.flexibility_measure == "congestion_management_flexibility": - def calculate_optimal_operation_if_needed(self): - if ( - self.opt_power_requirement is not None - and self.flex_power_requirement is None - and self.flexibility_measure == "max_load_shift" - ): - self.determine_optimal_operation_with_flex() + @self.model.Objective(sense=pyo.maximize) + def obj_rule_flex(m): + """ + Maximizes the load shift over all time steps. + """ + maximise_load_shift = pyo.quicksum( + m.load_shift_neg[t] * m.congestion_indicator[t] + for t in m.time_steps + ) - if self.opt_power_requirement is None and self.objective == "min_variable_cost": - self.determine_optimal_operation_without_flex() + return maximise_load_shift + + elif self.flexibility_measure == "peak_load_shifting": + + @self.model.Objective(sense=pyo.maximize) + def obj_rule_flex(m): + """ + Maximizes the load shift over all time steps. + """ + maximise_load_shift = pyo.quicksum( + m.load_shift_neg[t] * m.peak_indicator[t] for t in m.time_steps + ) + + return maximise_load_shift + + elif self.flexibility_measure == "renewable_utilisation": + + @self.model.Objective(sense=pyo.maximize) + def obj_rule_flex(m): + """ + Maximizes the load increase over all time steps based on renewable surplus. + """ + maximise_renewable_utilisation = pyo.quicksum( + m.load_shift_pos[t] * m.renewable_signal[t] for t in m.time_steps + ) + + return maximise_renewable_utilisation + + else: + raise ValueError(f"Unknown objective: {self.flexibility_measure}") def calculate_marginal_cost(self, start: datetime, power: float) -> float: """ diff --git a/docs/source/demand_side_agent.rst b/docs/source/demand_side_agent.rst index 2ea91497..1d8c8de3 100644 --- a/docs/source/demand_side_agent.rst +++ b/docs/source/demand_side_agent.rst @@ -47,7 +47,7 @@ Attributes - **Technology**: Represents components such as Electrolyser, DRI Plant, Hydrogen Storage, and Electric Arc Furnace. - **Node**: Connection point in the energy system. -- **Bidding Strategy**: Defines how the steel plant bids in electricity markets. Example: `NaiveDASteelplantStrategy`. +- **Bidding Strategy**: Defines how the steel plant bids in electricity markets. Example: `NaiveDSMStrategy`. - **Objective**: Optimization target, such as minimizing operational costs. - **Flexibility Measure**: Load-shifting to optimize flexibility. @@ -98,7 +98,7 @@ Strategies for Bidding Several naive bidding strategies are implemented for managing how agents participate in electricity markets. These strategies define how energy bids are created for different agent types. -- **NaiveDASteelplantStrategy**: Optimizes steel plant operations and generates bids based on marginal costs. +- **NaiveDSMStrategy**: Optimizes steel plant operations and generates bids based on marginal costs. - **NaiveDABuildingStrategy**: Manages the bidding process for residential agents, calculating bids based on optimal energy use and load-shifting. - **Redispatch Strategies**: Adjust operations based on redispatch market signals, focusing on reducing operational costs or maximizing flexibility. diff --git a/docs/source/release_notes.rst b/docs/source/release_notes.rst index c39304e5..dbee55d2 100644 --- a/docs/source/release_notes.rst +++ b/docs/source/release_notes.rst @@ -8,6 +8,14 @@ Release Notes Upcoming Release ======================= + +**New Features:** + - **Building Demand Side Unit**: Added a new building demand side unit to the framework, enabling users to model the demand side management of a building. This feature allows + for more detailed and accurate simulations of building energy consumption patterns and market interactions. The building demand side unit can be configured with + different components, such as generic storage, heat pump, boiler, thermal storage, electric vehicle, and photovoltaic system, to reflect the specific characteristics of + building energy systems. The building demand side unit can be optimized to minimize costs or to maximize the available flexibility, depending on the user's requirements. + A tutorial and detailed documentation on how to use this feature are coming soon. + .. warning:: 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`` diff --git a/examples/examples.py b/examples/examples.py index 321b3fd8..71b0b766 100644 --- a/examples/examples.py +++ b/examples/examples.py @@ -63,6 +63,10 @@ }, # example_01f is used in the tutorial notebook #5: Market configuration comparison example # example_01g is used in the tutorial notebook #6: Advanced order types example + "small_household": { + "scenario": "example_01h", + "study_case": "eom", + }, # # DRL references case for learning advancement testing "small_learning_1": {"scenario": "example_02a", "study_case": "base"}, @@ -91,6 +95,10 @@ }, "large_2019_rl": {"scenario": "example_03a", "study_case": "base_case_2019"}, "large_2021_rl": {"scenario": "example_03b", "study_case": "base_case_2021"}, + "experiment": { + "scenario": "experiment", + "study_case": "eom", + }, } @@ -105,9 +113,7 @@ # select to store the simulation results in a local database or in timescale # when using timescale, you need to have docker installed and can access the grafana dashboard data_format = "local_db" # "local_db" or "timescale" - - # select the example to run from the available examples above - example = "small" + example = "experiment" if data_format == "local_db": db_uri = "sqlite:///./examples/local_db/assume_db.db" diff --git a/examples/inputs/example_01h/availability_df.csv b/examples/inputs/example_01h/availability_df.csv new file mode 100644 index 00000000..cc4b15b2 --- /dev/null +++ b/examples/inputs/example_01h/availability_df.csv @@ -0,0 +1,2881 @@ +datetime,wind onshore,wind offshore,solar,hydro,biomass +06/01/2024 00:00,0.623745065,0.476058201,0,0.331578947,0.582494005 +06/01/2024 00:15,0.628313593,0.475661376,0,0.324493927,0.580935252 +06/01/2024 00:30,0.632355706,0.475,0,0.322874494,0.580095923 +06/01/2024 00:45,0.633803346,0.474338624,0,0.325506073,0.582254197 +06/01/2024 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-0,0 +1,3 @@ +SPDX-FileCopyrightText: ASSUME Developers + +SPDX-License-Identifier: AGPL-3.0-or-later diff --git a/examples/inputs/example_01h/config.yaml b/examples/inputs/example_01h/config.yaml new file mode 100644 index 00000000..5aa17d0f --- /dev/null +++ b/examples/inputs/example_01h/config.yaml @@ -0,0 +1,26 @@ +# SPDX-FileCopyrightText: ASSUME Developers +# +# SPDX-License-Identifier: AGPL-3.0-or-later + +eom: + start_date: 2024-06-01 00:00 + end_date: 2024-06-29 23:45 + time_step: 15min + save_frequency_hours: 24 + + markets_config: + EOM: + operator: EOM_operator + product_type: energy + products: + - duration: 15min + count: 96 + first_delivery: 15min + opening_frequency: 24h + opening_duration: 15min + volume_unit: MWh + maximum_bid_volume: 100000 + maximum_bid_price: 3000 + minimum_bid_price: -500 + price_unit: EUR/MWh + market_mechanism: pay_as_bid diff --git a/examples/inputs/example_01h/demand_df.csv b/examples/inputs/example_01h/demand_df.csv new file mode 100644 index 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23:45,100,0.000015,0.000018,0.00003,0,0.000018,0.00006 diff --git a/examples/inputs/example_01h/demand_df.csv.license b/examples/inputs/example_01h/demand_df.csv.license new file mode 100644 index 00000000..a6ae0636 --- /dev/null +++ b/examples/inputs/example_01h/demand_df.csv.license @@ -0,0 +1,3 @@ +SPDX-FileCopyrightText: ASSUME Developers + +SPDX-License-Identifier: AGPL-3.0-or-later diff --git a/examples/inputs/example_01h/demand_units.csv b/examples/inputs/example_01h/demand_units.csv new file mode 100644 index 00000000..3233b13e --- /dev/null +++ b/examples/inputs/example_01h/demand_units.csv @@ -0,0 +1,2 @@ +name,technology,bidding_EOM,bidding_redispatch,max_power,min_power,node,unit_operator,price +demand_EOM,inflex_demand,naive_eom,naive_redispatch,1000000,0,north,eom_de,62.88 diff --git a/examples/inputs/example_01h/demand_units.csv.license b/examples/inputs/example_01h/demand_units.csv.license new file mode 100644 index 00000000..a6ae0636 --- /dev/null +++ b/examples/inputs/example_01h/demand_units.csv.license @@ -0,0 +1,3 @@ +SPDX-FileCopyrightText: ASSUME Developers + +SPDX-License-Identifier: AGPL-3.0-or-later diff --git a/examples/inputs/example_01h/fuel_prices_df.csv b/examples/inputs/example_01h/fuel_prices_df.csv new file mode 100644 index 00000000..5d6073d9 --- /dev/null +++ b/examples/inputs/example_01h/fuel_prices_df.csv @@ -0,0 +1,2881 @@ +datetime,uranium,lignite,hard coal,natural gas,oil,biomass,co2 +06/01/2024 00:00,0.9,1.8,8.506820597,25.86998717,21.67,20.7,24.895 +06/01/2024 00:15,0.9,1.8,8.50697864,25.83966187,21.67,20.7,24.89671875 +06/01/2024 00:30,0.9,1.8,8.507136682,25.80933657,21.67,20.7,24.8984375 +06/01/2024 00:45,0.9,1.8,8.507294725,25.77901126,21.67,20.7,24.90015625 +06/01/2024 01:00,0.9,1.8,8.507452767,25.74868596,21.67,20.7,24.901875 +06/01/2024 01:15,0.9,1.8,8.507610809,25.71836065,21.67,20.7,24.90359375 +06/01/2024 01:30,0.9,1.8,8.507768852,25.68803535,21.67,20.7,24.9053125 +06/01/2024 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22:30,0.9,1.8,8.561187193,24.912823,20.66,20.7,22.206875 +06/30/2024 22:45,0.9,1.8,8.561187193,24.90410883,20.66,20.7,22.1990625 +06/30/2024 23:00,0.9,1.8,8.561187193,24.89539466,20.66,20.7,22.19125 +06/30/2024 23:15,0.9,1.8,8.561187193,24.88668049,20.66,20.7,22.1834375 +06/30/2024 23:30,0.9,1.8,8.561187193,24.87796633,20.66,20.7,22.175625 +06/30/2024 23:45,0.9,1.8,8.561187193,24.86925216,20.66,20.7,22.1678125 diff --git a/examples/inputs/example_01h/fuel_prices_df.csv.license b/examples/inputs/example_01h/fuel_prices_df.csv.license new file mode 100644 index 00000000..a6ae0636 --- /dev/null +++ b/examples/inputs/example_01h/fuel_prices_df.csv.license @@ -0,0 +1,3 @@ +SPDX-FileCopyrightText: ASSUME Developers + +SPDX-License-Identifier: AGPL-3.0-or-later diff --git a/examples/inputs/example_01h/powerplant_units.csv b/examples/inputs/example_01h/powerplant_units.csv new file mode 100644 index 00000000..21ac9df6 --- /dev/null +++ b/examples/inputs/example_01h/powerplant_units.csv @@ -0,0 +1,2 @@ +name,technology,bidding_EOM,bidding_LTM_OTC,fuel_type,emission_factor,max_power,min_power,efficiency,ramp_up,ramp_down,additional_cost,hot_start_cost,warm_start_cost,cold_start_cost,min_operating_time,min_down_time,heat_extraction,max_heat_extraction,unit_operator +NEURATH F,lignite,naive_eom,otc_strategy,lignite,0.406,1120,560,0.434,590,590,1.65,30.4,47.5,69.3,10,7,no,0,RWE POWER AG diff --git a/examples/inputs/example_01h/powerplant_units.csv.license b/examples/inputs/example_01h/powerplant_units.csv.license new file mode 100644 index 00000000..a6ae0636 --- /dev/null +++ b/examples/inputs/example_01h/powerplant_units.csv.license @@ -0,0 +1,3 @@ +SPDX-FileCopyrightText: ASSUME Developers + +SPDX-License-Identifier: AGPL-3.0-or-later diff --git a/examples/inputs/example_01h/price_forecasts.csv b/examples/inputs/example_01h/price_forecasts.csv new file mode 100644 index 00000000..ccbbd194 --- /dev/null +++ b/examples/inputs/example_01h/price_forecasts.csv @@ -0,0 +1,2881 @@ 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+06/30/2024 23:30,130.92 +06/30/2024 23:45,130.92 diff --git a/examples/inputs/example_01h/price_forecasts.csv.license b/examples/inputs/example_01h/price_forecasts.csv.license new file mode 100644 index 00000000..a6ae0636 --- /dev/null +++ b/examples/inputs/example_01h/price_forecasts.csv.license @@ -0,0 +1,3 @@ +SPDX-FileCopyrightText: ASSUME Developers + +SPDX-License-Identifier: AGPL-3.0-or-later diff --git a/examples/inputs/example_01h/residential_dsm_units.csv b/examples/inputs/example_01h/residential_dsm_units.csv new file mode 100644 index 00000000..586255df --- /dev/null +++ b/examples/inputs/example_01h/residential_dsm_units.csv @@ -0,0 +1,8 @@ +name,unit_type,technology,node,bidding_EOM,fuel_type,bidding_redispatch,unit_operator,objective,flexibility_measure,cost_tolerance,charging_profile,uses_power_profile,availability_periods,demand,rated_power,max_power,min_power,ramp_up,ramp_down,min_operating_time,min_down_time,efficiency,cop,max_capacity,min_capacity,initial_soc,storage_loss_rate,efficiency_charge,efficiency_discharge,max_charging_rate,max_discharging_rate,sells_energy_to_market +A360_building,building,pv_plant,north,naive_dsm,,,dsm_operator_1,min_variable_cost,,,,No,,,,3.8,0,,,,,,,,,,,,,,, +A360_building,building,heat_pump,north,,,,,,,,,,,,,2,,,,,,,2.8,,,,,,,,, +A360_building,building,generic_storage,north,,,,,,,,,,,,,,,0.002,0.002,,,,,0.0362,0.0015,0.0015,,0.9731,0.9731,0.002,0.002,Yes +A361_building,building,pv_plant,north,naive_dsm,,,dsm_operator_1,min_variable_cost,,,,Yes,,,,1.4,0,,,,,,,,,,,,,,, +A361_building,building,boiler,north,naive_dsm,natural_gas,,,min_variable_cost,,,,,,,,1.5,0,,,,,0.9,,,,,,,,,, +A362_building,building,pv_plant,north,naive_dsm,,,dsm_operator_1,min_variable_cost,,,,No,,,,3,0,,,,,,,,,,,,,,, +A362_building,building,generic_storage,north,,,,,,,,,,,,,,,0.002,0.002,,,,,0.0362,0.0015,0.0015,,0.9731,0.9731,0.002,0.002,No diff --git a/examples/inputs/example_01h/residential_dsm_units.csv.license b/examples/inputs/example_01h/residential_dsm_units.csv.license new file mode 100644 index 00000000..a6ae0636 --- /dev/null +++ b/examples/inputs/example_01h/residential_dsm_units.csv.license @@ -0,0 +1,3 @@ +SPDX-FileCopyrightText: ASSUME Developers + +SPDX-License-Identifier: AGPL-3.0-or-later diff --git a/examples/inputs/experiment/availability_df.csv b/examples/inputs/experiment/availability_df.csv new file mode 100644 index 00000000..cc4b15b2 --- /dev/null +++ b/examples/inputs/experiment/availability_df.csv @@ -0,0 +1,2881 @@ +datetime,wind onshore,wind offshore,solar,hydro,biomass +06/01/2024 00:00,0.623745065,0.476058201,0,0.331578947,0.582494005 +06/01/2024 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+06/30/2024 23:15,0.397743937,0.681746032,0,0.315587045,0.56618705 +06/30/2024 23:30,0.393683023,0.681878307,0,0.30951417,0.565947242 +06/30/2024 23:45,0.387215642,0.677116402,0,0.309716599,0.565707434 diff --git a/examples/inputs/experiment/availability_df.csv.license b/examples/inputs/experiment/availability_df.csv.license new file mode 100644 index 00000000..a6ae0636 --- /dev/null +++ b/examples/inputs/experiment/availability_df.csv.license @@ -0,0 +1,3 @@ +SPDX-FileCopyrightText: ASSUME Developers + +SPDX-License-Identifier: AGPL-3.0-or-later diff --git a/examples/inputs/experiment/config.yaml b/examples/inputs/experiment/config.yaml new file mode 100644 index 00000000..dcc48c5a --- /dev/null +++ b/examples/inputs/experiment/config.yaml @@ -0,0 +1,26 @@ +# SPDX-FileCopyrightText: ASSUME Developers +# +# SPDX-License-Identifier: AGPL-3.0-or-later + +eom: + start_date: 2024-06-01 00:00 + end_date: 2024-06-03 23:45 + time_step: 15min + save_frequency_hours: 24 + + markets_config: + EOM: + operator: EOM_operator + product_type: energy + products: + - duration: 15min + count: 96 + first_delivery: 15min + opening_frequency: 24h + opening_duration: 15min + volume_unit: MWh + maximum_bid_volume: 100000 + maximum_bid_price: 3000 + minimum_bid_price: -500 + price_unit: EUR/MWh + market_mechanism: pay_as_bid diff --git a/examples/inputs/experiment/demand_df.csv b/examples/inputs/experiment/demand_df.csv new file mode 100644 index 00000000..733658b9 --- /dev/null +++ b/examples/inputs/experiment/demand_df.csv @@ -0,0 +1,2881 @@ +datetime,demand_EOM,A360_building_load_profile,A360_building_heat_demand,A361_building_load_profile,A361_building_pv_power_profile,A361_building_heat_demand,A362_building_load_profile +6/1/2024 0:00,100,0.000026,0.000018,0.000074,0,0.000018,0.000189 +6/1/2024 0:15,100,0.000022,0.000018,0.000072,0,0.000018,0.000202 +6/1/2024 0:30,100,0.000021,0.000018,0.00007,0,0.000018,0.000198 +6/1/2024 0:45,100,0.000012,0.000018,0.000065,0,0.000018,0.000198 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ASSUME Developers + +SPDX-License-Identifier: AGPL-3.0-or-later diff --git a/examples/inputs/experiment/demand_units.csv b/examples/inputs/experiment/demand_units.csv new file mode 100644 index 00000000..3233b13e --- /dev/null +++ b/examples/inputs/experiment/demand_units.csv @@ -0,0 +1,2 @@ +name,technology,bidding_EOM,bidding_redispatch,max_power,min_power,node,unit_operator,price +demand_EOM,inflex_demand,naive_eom,naive_redispatch,1000000,0,north,eom_de,62.88 diff --git a/examples/inputs/experiment/demand_units.csv.license b/examples/inputs/experiment/demand_units.csv.license new file mode 100644 index 00000000..a6ae0636 --- /dev/null +++ b/examples/inputs/experiment/demand_units.csv.license @@ -0,0 +1,3 @@ +SPDX-FileCopyrightText: ASSUME Developers + +SPDX-License-Identifier: AGPL-3.0-or-later diff --git a/examples/inputs/experiment/forecasts_df.csv b/examples/inputs/experiment/forecasts_df.csv new file mode 100644 index 00000000..a1d93465 --- /dev/null +++ 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22:30,0.9,1.8,8.561187193,24.912823,20.66,20.7,22.206875 +06/30/2024 22:45,0.9,1.8,8.561187193,24.90410883,20.66,20.7,22.1990625 +06/30/2024 23:00,0.9,1.8,8.561187193,24.89539466,20.66,20.7,22.19125 +06/30/2024 23:15,0.9,1.8,8.561187193,24.88668049,20.66,20.7,22.1834375 +06/30/2024 23:30,0.9,1.8,8.561187193,24.87796633,20.66,20.7,22.175625 +06/30/2024 23:45,0.9,1.8,8.561187193,24.86925216,20.66,20.7,22.1678125 diff --git a/examples/inputs/experiment/fuel_prices_df.csv.license b/examples/inputs/experiment/fuel_prices_df.csv.license new file mode 100644 index 00000000..a6ae0636 --- /dev/null +++ b/examples/inputs/experiment/fuel_prices_df.csv.license @@ -0,0 +1,3 @@ +SPDX-FileCopyrightText: ASSUME Developers + +SPDX-License-Identifier: AGPL-3.0-or-later diff --git a/examples/inputs/experiment/powerplant_units.csv b/examples/inputs/experiment/powerplant_units.csv new file mode 100644 index 00000000..21ac9df6 --- /dev/null +++ b/examples/inputs/experiment/powerplant_units.csv @@ -0,0 +1,2 @@ +name,technology,bidding_EOM,bidding_LTM_OTC,fuel_type,emission_factor,max_power,min_power,efficiency,ramp_up,ramp_down,additional_cost,hot_start_cost,warm_start_cost,cold_start_cost,min_operating_time,min_down_time,heat_extraction,max_heat_extraction,unit_operator +NEURATH F,lignite,naive_eom,otc_strategy,lignite,0.406,1120,560,0.434,590,590,1.65,30.4,47.5,69.3,10,7,no,0,RWE POWER AG diff --git a/examples/inputs/experiment/powerplant_units.csv.license b/examples/inputs/experiment/powerplant_units.csv.license new file mode 100644 index 00000000..a6ae0636 --- /dev/null +++ b/examples/inputs/experiment/powerplant_units.csv.license @@ -0,0 +1,3 @@ +SPDX-FileCopyrightText: ASSUME Developers + +SPDX-License-Identifier: AGPL-3.0-or-later diff --git a/examples/inputs/experiment/price_forecasts.csv b/examples/inputs/experiment/price_forecasts.csv new file mode 100644 index 00000000..a1d93465 --- /dev/null +++ b/examples/inputs/experiment/price_forecasts.csv @@ -0,0 +1,2881 @@ +datetime,EOM 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a/examples/inputs/experiment/price_forecasts.csv.license b/examples/inputs/experiment/price_forecasts.csv.license new file mode 100644 index 00000000..a6ae0636 --- /dev/null +++ b/examples/inputs/experiment/price_forecasts.csv.license @@ -0,0 +1,3 @@ +SPDX-FileCopyrightText: ASSUME Developers + +SPDX-License-Identifier: AGPL-3.0-or-later diff --git a/examples/inputs/experiment/residential_dsm_units.csv b/examples/inputs/experiment/residential_dsm_units.csv new file mode 100644 index 00000000..d62932f2 --- /dev/null +++ b/examples/inputs/experiment/residential_dsm_units.csv @@ -0,0 +1,4 @@ +name,unit_type,technology,node,bidding_EOM,fuel_type,bidding_redispatch,unit_operator,objective,flexibility_measure,cost_tolerance,charging_profile,uses_power_profile,availability_periods,demand,rated_power,max_power,min_power,ramp_up,ramp_down,min_operating_time,min_down_time,efficiency,cop,max_capacity,min_capacity,initial_soc,storage_loss_rate,efficiency_charge,efficiency_discharge,max_charging_rate,max_discharging_rate,is_prosumer +A360_building,building,pv_plant,north,naive_da_dsm,,,dsm_operator_1,min_variable_cost,cost_based_load_shift,10,,No,,,,3.8,0,,,,,,,,,,,,,,,Yes +A360_building,building,heat_pump,north,,,,,,,,,,,,,2,,,,,,,2.8,,,,,,,,, +A360_building,building,generic_storage,north,,,,,,,,,,,,,,,0.002,0.002,,,,,0.0362,0.0015,0.0015,,0.9731,0.9731,0.002,0.002, diff --git a/examples/inputs/experiment/residential_dsm_units.csv.license b/examples/inputs/experiment/residential_dsm_units.csv.license new file mode 100644 index 00000000..a6ae0636 --- /dev/null +++ b/examples/inputs/experiment/residential_dsm_units.csv.license @@ -0,0 +1,3 @@ +SPDX-FileCopyrightText: ASSUME Developers + +SPDX-License-Identifier: AGPL-3.0-or-later diff --git a/examples/inputs/experiment/trash/residential_dsm.csv b/examples/inputs/experiment/trash/residential_dsm.csv new file mode 100644 index 00000000..5a2d4328 --- /dev/null +++ b/examples/inputs/experiment/trash/residential_dsm.csv @@ -0,0 +1,8 @@ +name,unit_type,technology,node,bidding_EOM,fuel_type,bidding_redispatch,unit_operator,objective,flexibility_measure,cost_tolerance,charging_profile,uses_power_profile,availability_periods,demand,rated_power,max_power,min_power,ramp_up,ramp_down,min_operating_time,min_down_time,efficiency,cop,max_capacity,min_capacity,initial_soc,storage_loss_rate,efficiency_charge,efficiency_discharge,max_charging_rate,max_discharging_rate,sells_energy_to_market +A360_building,building,pv_plant,north,naive_dsm,,,dsm_operator_1,min_variable_cost,cost_based_load_shift,10,,No,,,,3.8,0,,,,,,,,,,,,,,, +A360_building,building,heat_pump,north,,,,,,,,,,,,,2,,,,,,,2.8,,,,,,,,, +A360_building,building,generic_storage,north,,,,,,,,,,,,,,,0.002,0.002,,,,,0.0362,0.0015,0.0015,,0.9731,0.9731,0.002,0.002,Yes +A361_building,building,pv_plant,north,naive_dsm,,,dsm_operator_1,min_variable_cost,,,,Yes,,,,1.4,0,,,,,,,,,,,,,,, +A361_building,building,boiler,north,naive_dsm,natural_gas,,,min_variable_cost,,,,,,,,1.5,0,,,,,0.9,,,,,,,,,, +A362_building,building,pv_plant,north,naive_dsm,,,dsm_operator_1,min_variable_cost,,,,No,,,,3,0,,,,,,,,,,,,,,, +A362_building,building,generic_storage,north,,,,,,,,,,,,,,,0.002,0.002,,,,,0.0362,0.0015,0.0015,,0.9731,0.9731,0.002,0.002,No diff --git a/examples/inputs/experiment/trash/residential_dsm.csv.license b/examples/inputs/experiment/trash/residential_dsm.csv.license new file mode 100644 index 00000000..a6ae0636 --- /dev/null +++ b/examples/inputs/experiment/trash/residential_dsm.csv.license @@ -0,0 +1,3 @@ +SPDX-FileCopyrightText: ASSUME Developers + +SPDX-License-Identifier: AGPL-3.0-or-later diff --git a/examples/notebooks/10_DSU_and_flexibility.ipynb b/examples/notebooks/10_DSU_and_flexibility.ipynb index 1afdf1b8..d3c62072 100644 --- a/examples/notebooks/10_DSU_and_flexibility.ipynb +++ b/examples/notebooks/10_DSU_and_flexibility.ipynb @@ -190,7 +190,7 @@ "\n", " id: The unique identifier for the steel plant agent.\n", " unit_operator: The entity operating the steel plant.\n", - " bidding_strategies: Defines the market bidding behavior (such as NaiveDASteelplantStrategy).\n", + " bidding_strategies: Defines the market bidding behavior (such as NaiveDSMStrategy).\n", " technology: Represents the type of technology used, here set to \"steel_plant\".\n", " node: Specifies the grid connection point for the steel plant.\n", " flexibility_measure: Indicates the flexibility strategy, such as load-shifting capabilities.\n", @@ -886,11 +886,11 @@ "source": [ "**Bidding Strategy for the Steel Plant**\n", "\n", - "In the ASSUME framework, each demand-side or supply-side agent participates in the market using a predefined bidding strategy. For the steel plant, we assign the **`NaiveDASteelplantStrategy`**, which is a basic strategy for submitting bids in the day-ahead market.\n", + "In the ASSUME framework, each demand-side or supply-side agent participates in the market using a predefined bidding strategy. For the steel plant, we assign the **`NaiveDSMStrategy`**, which is a basic strategy for submitting bids in the day-ahead market.\n", "\n", - "**Class: NaiveDASteelplantStrategy**\n", + "**Class: NaiveDSMStrategy**\n", "\n", - "The **`NaiveDASteelplantStrategy`** class defines how the steel plant agent interacts with the day-ahead market. It calculates the optimal operation of the steel plant and submits bids based on the plant’s power requirements and marginal costs.\n", + "The **`NaiveDSMStrategy`** class defines how the steel plant agent interacts with the day-ahead market. It calculates the optimal operation of the steel plant and submits bids based on the plant’s power requirements and marginal costs.\n", "\n", "Below is the breakdown of the bidding strategy:" ] @@ -901,7 +901,12 @@ "metadata": {}, "outputs": [], "source": [ - "class NaiveDASteelplantStrategy(BaseStrategy):\n", + "class NaiveDSMStrategy(BaseStrategy):\n", + " \"\"\"\n", + " A naive bidding strategy for Demand Side Management (DSM) units. It bids the marginal cost of the unit on the market\n", + " as bid price and the optimal power requirement as bid volume.\n", + " \"\"\"\n", + "\n", " def calculate_bids(\n", " self,\n", " unit: SupportsMinMax,\n", @@ -909,31 +914,39 @@ " product_tuples: list[Product],\n", " **kwargs,\n", " ) -> Orderbook:\n", - " bids = []\n", - " start = product_tuples[0][0] # start time of the first product\n", + " \"\"\"\n", + " Takes information from a unit that the unit operator manages and\n", + " defines how it is dispatched to the market.\n", "\n", - " # Calculate the optimal operation for the steel plant\n", + " Args:\n", + " unit (SupportsMinMax): The unit to be dispatched.\n", + " market_config (MarketConfig): The market configuration.\n", + " product_tuples (list[Product]): The list of all products the unit can offer.\n", + "\n", + " Returns:\n", + " Orderbook: The bids consisting of the start time, end time, only hours, price and volume.\n", + " \"\"\"\n", + "\n", + " # calculate the optimal operation of the unit\n", " unit.calculate_optimal_operation_if_needed()\n", "\n", + " bids = []\n", " for product in product_tuples:\n", " \"\"\"\n", - " For each product, calculate the marginal cost of the unit at the start time of the product\n", + " for each product, calculate the marginal cost 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", " start = product[0]\n", - " volume = unit.opt_power_requirement.loc[\n", - " start\n", - " ] # Power requirement for the unit at the start time\n", - " marginal_price = unit.calculate_marginal_cost(\n", - " start, volume\n", - " ) # Calculate the marginal cost\n", + " volume = unit.opt_power_requirement.loc[start]\n", + " marginal_price = unit.calculate_marginal_cost(start, volume)\n", + "\n", " bids.append(\n", " {\n", - " \"start_time\": product[0], # Start time of the bid\n", - " \"end_time\": product[1], # End time of the bid\n", - " \"only_hours\": product[2], # Relevant hours for the bid\n", - " \"price\": marginal_price, # Marginal price for the bid\n", - " \"volume\": -volume, # Volume of electricity\n", + " \"start_time\": product[0],\n", + " \"end_time\": product[1],\n", + " \"only_hours\": product[2],\n", + " \"price\": marginal_price,\n", + " \"volume\": -volume,\n", " }\n", " )\n", "\n", diff --git a/tests/test_building.py b/tests/test_building.py new file mode 100644 index 00000000..322cb93a --- /dev/null +++ b/tests/test_building.py @@ -0,0 +1,812 @@ +# SPDX-FileCopyrightText: ASSUME Developers +# +# SPDX-License-Identifier: AGPL-3.0-or-later + +import pandas as pd +import pyomo.environ as pyo +import pytest +from pyomo.opt import SolverFactory + +from assume.common.forecasts import CsvForecaster +from assume.common.market_objects import MarketConfig +from assume.strategies.naive_strategies import NaiveDADSMStrategy +from assume.units.building import Building +from assume.units.dsm_load_shift import SOLVERS, check_available_solvers + +# Fixtures for Component Configurations + + +@pytest.fixture +def generic_storage_config(): + return { + "max_capacity": 100, # Maximum energy capacity in MWh + "min_capacity": 20, # Minimum SOC in MWh + "max_power_charge": 30, # Maximum charging power in MW + "max_power_discharge": 30, # Maximum discharging power in MW + "efficiency_charge": 0.9, # Charging efficiency + "efficiency_discharge": 0.9, # Discharging efficiency + "initial_soc": 0.5, # Initial SOC in MWh + "ramp_up": 10, # Maximum ramp-up rate in MW + "ramp_down": 10, # Maximum ramp-down rate in MW + "storage_loss_rate": 0.01, # 1% storage loss per time step + } + + +@pytest.fixture +def thermal_storage_config(generic_storage_config): + return generic_storage_config.copy() + + +@pytest.fixture +def ev_config(): + return { + "max_capacity": 10.0, + "min_capacity": 2.0, + "max_power_charge": 3, # Charge values will reflect a fraction of the capacity + "max_power_discharge": 2, # Discharge values will also be a fraction of the capacity + "efficiency_charge": 0.95, + "efficiency_discharge": 0.9, + "initial_soc": 0.5, # SOC initialized to 50% of capacity + } + + +@pytest.fixture +def electric_boiler_config(): + return { + "max_power": 100, + "efficiency": 0.85, + "fuel_type": "electricity", # Electric fuel type supports operational constraints + "min_power": 20, + "ramp_up": 50, + "ramp_down": 50, + "min_operating_steps": 2, + "min_down_steps": 1, + "initial_operational_status": 1, + } + + +@pytest.fixture +def heat_pump_config(): + return { + "max_power": 80, + "cop": 3.5, + "min_power": 10, + "ramp_up": 20, + "ramp_down": 20, + "min_operating_steps": 2, + "min_down_steps": 2, + "initial_operational_status": 1, # Assuming it starts as operational + } + + +@pytest.fixture +def pv_plant_config(): + return { + "max_power": 50, + } + + +# Fixtures for Default Objective and Flexibility Measure +@pytest.fixture +def default_objective(): + return "min_variable_cost" + + +@pytest.fixture +def default_flexibility_measure(): + return "cost_based_load_shift" + + +# Fixtures for Availability Profiles +@pytest.fixture +def ev_availability_profile(): + # Create an availability profile as a pandas Series (1 = available, 0 = unavailable) + return pd.Series([1, 0, 1, 1, 0, 1, 1, 0, 1, 1], index=range(10)) + + +# Fixtures for Price and Forecast Data +@pytest.fixture +def price_profile(): + return pd.Series([50, 45, 55, 40, 1000, 55, 1000, 65, 45, 70], index=range(10)) + + +@pytest.fixture +def index(): + return range(10) # Integer-based index + + +@pytest.fixture +def forecast(price_profile): + forecaster = CsvForecaster( + index=range(10), + powerplants_units=[], # Add appropriate values + demand_units=[], + ) + forecaster.forecasts = pd.DataFrame() + forecaster.forecasts["price_EOM"] = price_profile + forecaster.forecasts["fuel_price_natural gas"] = pd.Series( + [30] * 10, index=range(10) + ) + forecaster.forecasts["heat_demand"] = pd.Series([50] * 10, index=range(10)) + forecaster.forecasts["ev_load_profile"] = pd.Series([5] * 10, index=range(10)) + forecaster.forecasts["battery_load_profile"] = pd.Series([3] * 10, index=range(10)) + forecaster.forecasts["building_load_profile"] = pd.Series( + [50] * 10, index=range(10) + ) + forecaster.forecasts["availability_solar"] = pd.Series([0.25] * 10, index=range(10)) + forecaster.forecasts["test_building_pv_power_profile"] = pd.Series( + [10] * 10, index=range(10) + ) # Adjust key as needed + # If the Building class expects specific keys for EV availability, add them here + # forecaster.forecasts["electric_vehicle_availability"] = ev_availability_profile + return forecaster + + +# Fixtures for Building Components +@pytest.fixture +def building_components_heatpump( + generic_storage_config, + thermal_storage_config, + ev_config, + heat_pump_config, + pv_plant_config, + ev_availability_profile, +): + return { + "heat_pump": heat_pump_config, + "generic_storage": generic_storage_config, + "electric_vehicle": { + **ev_config, + "availability_profile": ev_availability_profile, + }, + "pv_plant": { + **pv_plant_config, + }, + "thermal_storage": thermal_storage_config, + } + + +@pytest.fixture +def building_components_boiler( + generic_storage_config, + thermal_storage_config, + ev_config, + electric_boiler_config, + pv_plant_config, + ev_availability_profile, +): + return { + "boiler": electric_boiler_config, + "generic_storage": generic_storage_config, + "electric_vehicle": { + **ev_config, + "availability_profile": ev_availability_profile, + }, + "pv_plant": { + **pv_plant_config, + }, + "thermal_storage": thermal_storage_config, + } + + +# Fixture for Solver Selection +@pytest.fixture(scope="module") +def available_solver(): + solvers = check_available_solvers(*SOLVERS) + if not solvers: + pytest.skip(f"No available solvers from the list: {SOLVERS}") + return SolverFactory(solvers[0]) + + +# Test Cases + + +def test_building_initialization_heatpump( + forecast, + index, + building_components_heatpump, + available_solver, + default_objective, + default_flexibility_measure, +): + building = Building( + id="building_heatpump", + unit_operator="operator_hp", + index=index, + bidding_strategies={"EOM": NaiveDADSMStrategy()}, + components=building_components_heatpump, + objective=default_objective, + flexibility_measure=default_flexibility_measure, + forecaster=forecast, + ) + + assert building.id == "building_heatpump" + assert building.unit_operator == "operator_hp" + assert building.components == building_components_heatpump + assert building.has_heatpump is True + assert building.has_boiler is False + assert building.has_thermal_storage is True + assert building.has_ev is True + assert building.has_battery_storage is True + assert building.has_pv is True + + +def test_building_initialization_boiler( + forecast, + index, + building_components_boiler, + available_solver, + default_objective, + default_flexibility_measure, +): + building = Building( + id="building_boiler", + unit_operator="operator_boiler", + index=index, + bidding_strategies={}, + components=building_components_boiler, + objective=default_objective, + flexibility_measure=default_flexibility_measure, + forecaster=forecast, # Passed via **kwargs + ) + + assert building.id == "building_boiler" + assert building.unit_operator == "operator_boiler" + assert building.components == building_components_boiler + assert building.has_heatpump is False + assert building.has_boiler is True + assert building.has_thermal_storage is True + assert building.has_ev is True + assert building.has_battery_storage is True + assert building.has_pv is True + + +def test_building_initialization_invalid_component( + forecast, index, available_solver, default_objective, default_flexibility_measure +): + invalid_components = {"invalid_component": {"some_param": 123}} + + with pytest.raises(ValueError) as exc_info: + Building( + id="building_invalid", + unit_operator="operator_invalid", + index=index, + bidding_strategies={}, + components=invalid_components, + objective=default_objective, + flexibility_measure=default_flexibility_measure, + forecaster=forecast, + ) + + # Match the actual error message + assert ( + "Components invalid_component is not a valid component for the building unit." + in str(exc_info.value) + ) + + +def test_solver_availability(): + available_solvers = check_available_solvers(*SOLVERS) + assert len(available_solvers) > 0, f"None of the solvers {SOLVERS} are available." + + +def test_building_optimization_heatpump( + forecast, + index, + building_components_heatpump, + available_solver, + default_objective, + default_flexibility_measure, +): + building = Building( + id="building_heatpump", + unit_operator="operator_hp", + index=index, + bidding_strategies={}, + objective=default_objective, + flexibility_measure=default_flexibility_measure, + components=building_components_heatpump, + forecaster=forecast, # Passed via **kwargs + ) + + # Perform optimization + building.determine_optimal_operation_without_flex() + + # Check if optimal power requirement is calculated + assert building.opt_power_requirement is not None + assert len(building.opt_power_requirement) == len(index) + assert isinstance(building.opt_power_requirement, pd.Series) + + # Check if variable cost series is calculated + assert building.variable_cost_series is not None + assert len(building.variable_cost_series) == len(index) + assert isinstance(building.variable_cost_series, pd.Series) + + # Check additional outputs if components exist + if building.has_battery_storage: + assert "soc" in building.outputs + assert len(building.outputs["soc"]) == len(index) + assert isinstance(building.outputs["soc"], pd.Series) + + if building.has_ev: + assert "ev_soc" in building.outputs + assert len(building.outputs["ev_soc"]) == len(index) + assert isinstance(building.outputs["ev_soc"], pd.Series) + + # Optional: Verify that the optimization was successful + # Note: Depending on how the Building class stores solver results, adjust accordingly + # For example, if building.model is updated with solver results: + # assert building.model.solver.status == SolverStatus.ok + # assert building.model.solver.termination_condition == TerminationCondition.optimal + + +def test_building_optimization_boiler( + forecast, + index, + building_components_boiler, + available_solver, + default_objective, + default_flexibility_measure, +): + building = Building( + id="building_boiler", + unit_operator="operator_boiler", + index=index, + bidding_strategies={}, + objective=default_objective, + flexibility_measure=default_flexibility_measure, + components=building_components_boiler, + forecaster=forecast, # Passed via **kwargs + ) + + # Perform optimization + building.determine_optimal_operation_without_flex() + + # Check if optimal power requirement is calculated + assert building.opt_power_requirement is not None + assert len(building.opt_power_requirement) == len(index) + assert isinstance(building.opt_power_requirement, pd.Series) + + # Check if variable cost series is calculated + assert building.variable_cost_series is not None + assert len(building.variable_cost_series) == len(index) + assert isinstance(building.variable_cost_series, pd.Series) + + # Check additional outputs if components exist + if building.has_battery_storage: + assert "soc" in building.outputs + assert len(building.outputs["soc"]) == len(index) + assert isinstance(building.outputs["soc"], pd.Series) + + if building.has_ev: + assert "ev_soc" in building.outputs + assert len(building.outputs["ev_soc"]) == len(index) + assert isinstance(building.outputs["ev_soc"], pd.Series) + + +def test_building_marginal_cost_calculation_heatpump( + forecast, + index, + building_components_heatpump, + available_solver, + default_objective, + default_flexibility_measure, +): + building = Building( + id="building_heatpump", + unit_operator="operator_hp", + index=index, + bidding_strategies={}, + objective=default_objective, + flexibility_measure=default_flexibility_measure, + components=building_components_heatpump, + forecaster=forecast, # Passed via **kwargs + ) + + building.determine_optimal_operation_without_flex() + + # Select a timestamp to test + test_time = 0 # Using integer index + power = building.opt_power_requirement[test_time] + variable_cost = building.variable_cost_series[test_time] + + if power != 0: + expected_marginal_cost = abs(variable_cost / power) + else: + expected_marginal_cost = 0 + + calculated_marginal_cost = building.calculate_marginal_cost(test_time, power) + + assert calculated_marginal_cost == expected_marginal_cost + + +def test_building_marginal_cost_calculation_boiler( + forecast, + index, + building_components_boiler, + available_solver, + default_objective, + default_flexibility_measure, +): + building = Building( + id="building_boiler", + unit_operator="operator_boiler", + index=index, + bidding_strategies={}, + objective=default_objective, + flexibility_measure=default_flexibility_measure, + components=building_components_boiler, + forecaster=forecast, # Passed via **kwargs + ) + + building.determine_optimal_operation_without_flex() + + # Select a timestamp to test + test_time = 1 # Using integer index + power = building.opt_power_requirement[test_time] + variable_cost = building.variable_cost_series[test_time] + + if power != 0: + expected_marginal_cost = abs(variable_cost / power) + else: + expected_marginal_cost = 0 + + calculated_marginal_cost = building.calculate_marginal_cost(test_time, power) + + assert calculated_marginal_cost == expected_marginal_cost + + +def test_building_objective_function_heatpump( + forecast, + index, + building_components_heatpump, + available_solver, + default_objective, + default_flexibility_measure, +): + building = Building( + id="building_heatpump", + unit_operator="operator_hp", + index=index, + bidding_strategies={}, + objective=default_objective, + flexibility_measure=default_flexibility_measure, + components=building_components_heatpump, + forecaster=forecast, # Passed via **kwargs + ) + + # Access the objective function + objective = building.model.obj_rule_opt + + assert isinstance(objective, pyo.Objective) + assert objective.sense == pyo.minimize + + +def test_building_objective_function_invalid( + forecast, index, building_components_heatpump, available_solver +): + with pytest.raises(ValueError) as exc_info: + Building( + id="building_invalid_objective", + unit_operator="operator_invalid", + index=index, + bidding_strategies={}, + components=building_components_heatpump, + objective="unknown_objective", + forecaster=forecast, # Passed via **kwargs + ) + + assert "Unknown objective: unknown_objective" in str(exc_info.value) + + +def test_building_no_available_solvers( + forecast, + index, + building_components_heatpump, + monkeypatch, # Add the monkeypatch fixture +): + # Override the check_available_solvers to return an empty list + monkeypatch.setattr( + "assume.units.building.check_available_solvers", lambda *args: [] + ) + + with pytest.raises(Exception) as exc_info: + Building( + id="building_no_solvers", + unit_operator="operator_nosolver", + index=index, + bidding_strategies={}, + components=building_components_heatpump, + forecaster=forecast, # Passed via **kwargs + ) + assert ( + "None of ['appsi_highs', 'gurobi', 'glpk', 'cbc', 'cplex'] are available" + in str(exc_info.value) + ) + + +def test_building_define_constraints_heatpump( + forecast, + index, + building_components_heatpump, + available_solver, + default_objective, + default_flexibility_measure, +): + building = Building( + id="building_constraints_hp", + unit_operator="operator_constraints_hp", + objective=default_objective, + flexibility_measure=default_flexibility_measure, + index=index, + bidding_strategies={}, + components=building_components_heatpump, + forecaster=forecast, # Passed via **kwargs + ) + + # Check if constraints are defined + constraints = list(building.model.component_map(pyo.Constraint).keys()) + assert "total_power_input_constraint" in constraints + if building.has_heatpump: + assert "heat_flow_constraint" in constraints + + +def test_building_missing_required_component( + forecast, + index, + building_components_heatpump, + default_objective, + default_flexibility_measure, +): + """ + Test that the Building class raises a ValueError if a required component is missing. + """ + # Set the required technologies for the test + Building.required_technologies = ["boiler"] + + # Remove a required component from the configuration + incomplete_components = building_components_heatpump.copy() + incomplete_components.pop("boiler", None) # Remove "boiler" to trigger the error + + with pytest.raises(ValueError) as exc_info: + Building( + id="building_test", + unit_operator="operator_hp", + index=index, + bidding_strategies={}, + components=incomplete_components, + objective=default_objective, + flexibility_measure=default_flexibility_measure, + forecaster=forecast, + ) + + # Assert the correct error message + assert "Component boiler is required for the building plant unit." in str( + exc_info.value + ) + + # Reset required technologies to avoid affecting other tests + Building.required_technologies = [] + + +def test_building_ev_discharge_constraint( + forecast, + index, + building_components_heatpump, + default_objective, + default_flexibility_measure, +): + """ + Test that the discharge_ev_to_market_constraint is correctly defined + when the EV is not allowed to sell energy to the market. + """ + # Modify the EV configuration to disallow selling energy to the market + building_components_heatpump["electric_vehicle"]["sells_energy_to_market"] = "false" + + building = Building( + id="building_ev_test", + unit_operator="operator_hp", + index=index, + bidding_strategies={}, + components=building_components_heatpump, + objective=default_objective, + flexibility_measure=default_flexibility_measure, + forecaster=forecast, + ) + + # Verify that the constraint is defined + constraints = list(building.model.component_map(pyo.Constraint).keys()) + assert "discharge_ev_to_market_constraint" in constraints + + +def test_building_battery_discharge_constraint_simple( + forecast, + index, + building_components_heatpump, + default_objective, + default_flexibility_measure, +): + """ + Test that the discharge_battery_to_market_constraint is defined + when the battery is not allowed to sell energy to the market. + """ + # Modify the battery configuration to disallow selling energy to the market + building_components_heatpump["generic_storage"]["sells_energy_to_market"] = "false" + + building = Building( + id="building_battery_test", + unit_operator="operator_hp", + index=index, + bidding_strategies={}, + components=building_components_heatpump, + objective=default_objective, + flexibility_measure=default_flexibility_measure, + forecaster=forecast, + ) + + # Verify that the constraint is defined + constraints = list(building.model.component_map(pyo.Constraint).keys()) + assert "discharge_battery_to_market_constraint" in constraints + + +def test_building_solver_infeasibility_logging( + forecast, + index, + building_components_heatpump, + default_objective, + default_flexibility_measure, +): + """ + Test that the Building class logs the correct messages when the solver reports infeasibility or other statuses. + """ + # Create a Building instance + building = Building( + id="building_solver_test", + unit_operator="operator_hp", + index=index, + bidding_strategies={}, + components=building_components_heatpump, + objective=default_objective, + flexibility_measure=default_flexibility_measure, + forecaster=forecast, + ) + + # Mock the solver to simulate infeasibility + class MockResults: + class Solver: + status = "mock_status" + termination_condition = "infeasible" + + solver = Solver() + + # Populate model variables with dummy values + for t in building.model.time_steps: + building.model.total_power_input[t].value = 0 + building.model.variable_cost[t].value = 0 + + building.solver.solve = lambda instance, options: MockResults() + + # Call the method to ensure the log messages are triggered + building.determine_optimal_operation_without_flex() + + # Assert no exceptions occur + assert True + + +def test_building_bidding_strategy_execution( + forecast, + index, + building_components_heatpump, + available_solver, + default_objective, + default_flexibility_measure, +): + """ + Test that the NaiveDADSMStrategy's calculate_bids method is executed correctly, + and unit.determine_optimal_operation_without_flex() is called. + """ + # Create the Building instance with a NaiveDADSMStrategy + building = Building( + id="building_heatpump", + unit_operator="operator_hp", + index=index, + bidding_strategies={"EOM": NaiveDADSMStrategy()}, + components=building_components_heatpump, + objective=default_objective, + flexibility_measure=default_flexibility_measure, + forecaster=forecast, + ) + + # Create dummy market configuration and product tuples + market_config = MarketConfig( + product_type="electricity", + market_id="EOM", + ) + product_tuples = [ + (index[0], index[1], "hour_1"), + (index[1], index[2], "hour_2"), + (index[2], index[3], "hour_3"), + ] + + # Call the bidding strategy + bids = building.bidding_strategies["EOM"].calculate_bids( + unit=building, + market_config=market_config, + product_tuples=product_tuples, + ) + + # Verify that bids are generated correctly + assert len(bids) == len(product_tuples) + for bid, product in zip(bids, product_tuples): + assert bid["start_time"] == product[0] + assert bid["end_time"] == product[1] + assert bid["volume"] <= 0 # Demand-side bids have non-positive volume + assert bid["price"] >= 0 # Marginal price should be non-negative + + +# ---------------------------- +# Additional Helper Tests (Optional) +# ---------------------------- + + +def test_building_get_available_solvers(): + available_solvers = check_available_solvers(*SOLVERS) + assert isinstance(available_solvers, list) + for solver in available_solvers: + assert SolverFactory(solver).available() + + +def test_str_to_bool_invalid_value_in_building(): + """ + Test that str_to_bool raises ValueError when an invalid value is passed + within the Building context. + """ + invalid_components = { + "electric_vehicle": {"sells_energy_to_market": "invalid_value"} + } + + with pytest.raises(ValueError) as exc_info: + Building( + id="building_invalid_str_to_bool", + unit_operator="operator_invalid", + index=range(10), + bidding_strategies={}, + components=invalid_components, + objective="min_variable_cost", + flexibility_measure="cost_based_load_shift", + forecaster=None, # Replace with a suitable forecaster if necessary + ) + assert "Invalid truth value: 'invalid_value'" in str(exc_info.value) + + +def test_building_unknown_flexibility_measure( + forecast, + index, + building_components_heatpump, + default_objective, +): + """ + Test that the Building class raises a ValueError for an unknown flexibility measure. + """ + invalid_flexibility_measure = "invalid_flex_measure" + + with pytest.raises(ValueError) as exc_info: + Building( + id="building_unknown_flex", + unit_operator="operator_hp", + index=index, + bidding_strategies={}, + components=building_components_heatpump, + objective=default_objective, + flexibility_measure=invalid_flexibility_measure, + forecaster=forecast, + ) + + # Assert the correct error message + assert f"Unknown flexibility measure: {invalid_flexibility_measure}" in str( + exc_info.value + ) + + +if __name__ == "__main__": + pytest.main(["-s", __file__]) diff --git a/tests/test_steel_plant.py b/tests/test_steel_plant.py index 6c0f644e..bb717112 100644 --- a/tests/test_steel_plant.py +++ b/tests/test_steel_plant.py @@ -3,6 +3,7 @@ # SPDX-License-Identifier: AGPL-3.0-or-later import pandas as pd +import pyomo.environ as pyo import pytest from assume.common.fast_pandas import FastSeries @@ -54,123 +55,231 @@ def dsm_components(): } -@pytest.fixture -def steel_plant(dsm_components) -> SteelPlant: +def create_steel_plant(dsm_components, flexibility_measure): + """Helper function to create a SteelPlant with a specific flexibility measure.""" index = pd.date_range("2023-01-01", periods=24, freq="h") forecast = NaiveForecast( index, price_EOM=[50] * 24, fuel_price_natural_gas=[30] * 24, co2_price=[20] * 24, + east_congestion_severity=[0.5] * 8 + [0.9] * 8 + [0.2] * 8, + south_renewable_utilisation=[0.1 * i for i in range(24)], ) bidding_strategies = { "EOM": NaiveDADSMStrategy(), "RD": NaiveRedispatchDSMStrategy(), } + return SteelPlant( - id="test_steel_plant", + id=f"test_steel_plant_{flexibility_measure}", unit_operator="test_operator", objective="min_variable_cost", - flexibility_measure="max_load_shift", + flexibility_measure=flexibility_measure, bidding_strategies=bidding_strategies, + index=forecast.index, + node="south", components=dsm_components, forecaster=forecast, demand=1000, technology="steel_plant", + cost_tolerance=10, + congestion_threshold=0.8, + peak_load_cap=95, ) -def test_initialize_components(steel_plant): - assert "electrolyser" in steel_plant.model.dsm_blocks.keys() - assert "dri_plant" in steel_plant.model.dsm_blocks.keys() - assert "eaf" in steel_plant.model.dsm_blocks.keys() +@pytest.fixture +def steel_plant_cost_based(dsm_components): + """Fixture for cost-based load shifting.""" + return create_steel_plant(dsm_components, "cost_based_load_shift") -def test_determine_optimal_operation_without_flex(steel_plant): - steel_plant.determine_optimal_operation_without_flex() - assert steel_plant.opt_power_requirement is not None - assert isinstance(steel_plant.opt_power_requirement, FastSeries) +@pytest.fixture +def steel_plant_congestion(dsm_components): + """Fixture for congestion management flexibility.""" + return create_steel_plant(dsm_components, "congestion_management_flexibility") - instance = steel_plant.model.create_instance() - instance = steel_plant.switch_to_opt(instance) - steel_plant.solver.solve(instance, tee=False) - total_power_input = sum( - instance.total_power_input[t].value for t in instance.time_steps - ) +@pytest.fixture +def steel_plant_peak_shifting(dsm_components): + """Fixture for peak load shifting.""" + return create_steel_plant(dsm_components, "peak_load_shifting") - assert total_power_input == 3000 - for t in instance.time_steps: - hydrogen_out = instance.dsm_blocks["electrolyser"].hydrogen_out[t].value - hydrogen_in = instance.dsm_blocks["dri_plant"].hydrogen_in[t].value - assert ( - hydrogen_out >= hydrogen_in - ), f"Hydrogen out at time {t} is less than hydrogen in" +@pytest.fixture +def steel_plant_renewable_utilisation(dsm_components): + """Fixture for renewable utilisation flexibility.""" + return create_steel_plant(dsm_components, "renewable_utilisation") + + +# Test cases +def test_initialize_components(steel_plant_cost_based): + """Verify components are properly initialized.""" + assert "electrolyser" in steel_plant_cost_based.model.dsm_blocks.keys() + assert "dri_plant" in steel_plant_cost_based.model.dsm_blocks.keys() + assert "eaf" in steel_plant_cost_based.model.dsm_blocks.keys() + + +def test_determine_optimal_operation_without_flex(steel_plant_cost_based): + """Test optimal operation without flexibility for cost-based load shifting.""" + steel_plant_cost_based.determine_optimal_operation_without_flex() + assert steel_plant_cost_based.opt_power_requirement is not None + assert isinstance(steel_plant_cost_based.opt_power_requirement, FastSeries) + + +def test_congestion_management_flexibility(steel_plant_congestion): + """ + Test congestion management flexibility measure. + """ + steel_plant_congestion.determine_optimal_operation_with_flex() + + # Calculate the congestion indicator + congestion_indicator = { + t: int( + steel_plant_congestion.congestion_signal.iloc[t] + > steel_plant_congestion.congestion_threshold + ) + for t in range(len(steel_plant_congestion.index)) + } - for t in instance.time_steps: - dri_output = instance.dsm_blocks["dri_plant"].dri_output[t].value - dri_input = instance.dsm_blocks["eaf"].dri_input[t].value - assert ( - dri_output == dri_input - ), f"DRI output at time {t} does not match DRI input" + instance = steel_plant_congestion.model.create_instance() + instance = steel_plant_congestion.switch_to_flex(instance) - total_steel_output = sum( - instance.dsm_blocks["eaf"].steel_output[t].value for t in instance.time_steps + # Set the congestion_indicator in the instance + instance.congestion_indicator = pyo.Param( + instance.time_steps, + initialize=congestion_indicator, + within=pyo.Binary, ) - assert ( - total_steel_output == instance.steel_demand - ), f"Total steel output {total_steel_output} does not match steel demand {instance.steel_demand}" + # Solve the instance + steel_plant_congestion.solver.solve(instance, tee=False) -def test_ramping_constraints(steel_plant): - steel_plant.determine_optimal_operation_without_flex() - instance = steel_plant.model.create_instance() - instance = steel_plant.switch_to_opt(instance) - steel_plant.solver.solve(instance, tee=False) + # Calculate adjusted total power input + adjusted_total_power_input = [ + instance.total_power_input[t].value + + instance.load_shift_pos[t].value + - instance.load_shift_neg[t].value + for t in instance.time_steps + ] - # Loop through time steps to check that the electrolyser ramp constraints hold - for t in list(instance.time_steps)[1:]: - # Current and previous power values for electrolyser - power_prev = instance.dsm_blocks["electrolyser"].power_in[t - 1].value - power_curr = instance.dsm_blocks["electrolyser"].power_in[t].value + # Assert load shifting respects congestion indicator + for t in instance.time_steps: + if instance.congestion_indicator[t] == 1: # Congestion period + assert ( + adjusted_total_power_input[t] <= instance.total_power_input[t].value + ), f"Load shift not aligned with congestion signal at time {t}" + + +def test_peak_load_shifting(steel_plant_peak_shifting): + """ + Test peak load shifting flexibility measure. + """ + steel_plant_peak_shifting.determine_optimal_operation_with_flex() + + # Calculate the peak load cap value + max_load = steel_plant_peak_shifting.opt_power_requirement.max() + peak_load_cap_value = max_load * (steel_plant_peak_shifting.peak_load_cap / 100) + peak_indicator = { + t: int( + steel_plant_peak_shifting.opt_power_requirement.iloc[t] + > peak_load_cap_value + ) + for t in range(len(steel_plant_peak_shifting.opt_power_requirement)) + } - # Access ramp constraints using dot notation - ramp_up = steel_plant.components["electrolyser"].ramp_up - ramp_down = steel_plant.components["electrolyser"].ramp_down + instance = steel_plant_peak_shifting.model.create_instance() + instance = steel_plant_peak_shifting.switch_to_flex(instance) - # Verify ramping constraints - assert ( - power_curr - power_prev <= ramp_up - ), f"Electrolyser ramp-up constraint violated at time {t}" - assert ( - power_prev - power_curr <= ramp_down - ), f"Electrolyser ramp-down constraint violated at time {t}" + # Set the peak_load_cap_value and peak_indicator in the instance + instance.peak_load_cap_value = pyo.Param( + initialize=peak_load_cap_value, + within=pyo.NonNegativeReals, + ) + instance.peak_indicator = pyo.Param( + instance.time_steps, + initialize=peak_indicator, + within=pyo.Binary, + ) + # Solve the instance + steel_plant_peak_shifting.solver.solve(instance, tee=False) -def test_determine_optimal_operation_with_flex(steel_plant): - # Ensure that the optimal operation without flexibility is determined first - steel_plant.determine_optimal_operation_without_flex() - assert steel_plant.opt_power_requirement is not None - assert isinstance(steel_plant.opt_power_requirement, FastSeries) + # Calculate adjusted total power input + adjusted_total_power_input = [ + instance.total_power_input[t].value + + instance.load_shift_pos[t].value + - instance.load_shift_neg[t].value + for t in instance.time_steps + ] - steel_plant.determine_optimal_operation_with_flex() - assert steel_plant.flex_power_requirement is not None - assert isinstance(steel_plant.flex_power_requirement, FastSeries) + # Assert load shifting respects peak load cap + for t in instance.time_steps: + if instance.peak_indicator[t] == 1: + assert ( + adjusted_total_power_input[t] <= instance.peak_load_cap_value + ), f"Peak load exceeded at time {t}" + + +def test_renewable_utilisation(steel_plant_renewable_utilisation): + """ + Tests the renewable utilisation flexibility measure by ensuring that the load increase aligns + with the renewable signal intensity and does not exceed allowable thresholds. + """ + # Set the flexibility measure to renewable utilisation + steel_plant_renewable_utilisation.flexibility_measure = "renewable_utilisation" + steel_plant_renewable_utilisation.determine_optimal_operation_with_flex() + + # Normalization of renewable_utilisation_signal + min_signal = steel_plant_renewable_utilisation.renewable_utilisation_signal.min() + max_signal = steel_plant_renewable_utilisation.renewable_utilisation_signal.max() + + if max_signal - min_signal > 0: + renewable_signal_normalised = ( + steel_plant_renewable_utilisation.renewable_utilisation_signal - min_signal + ) / (max_signal - min_signal) + else: + renewable_signal_normalised = FastSeries( + index=steel_plant_renewable_utilisation.renewable_utilisation_signal.index, + value=1, + ) + + # Map normalized renewable signals to a dictionary for Pyomo parameters + renewable_signal_dict = { + t: renewable_signal_normalised.iloc[t] + for t in range(len(renewable_signal_normalised)) + } - instance = steel_plant.model.create_instance() - instance = steel_plant.switch_to_flex(instance) - steel_plant.solver.solve(instance, tee=False) + instance = steel_plant_renewable_utilisation.model.create_instance() + instance = steel_plant_renewable_utilisation.switch_to_flex(instance) - total_power_input = sum( - instance.total_power_input[t].value for t in instance.time_steps + # Set the normalized renewable signal in the instance + instance.renewable_signal = pyo.Param( + instance.time_steps, + initialize=renewable_signal_dict, + within=pyo.NonNegativeReals, ) - assert total_power_input == 3000 + # Solve the instance + steel_plant_renewable_utilisation.solver.solve(instance, tee=False) + + # Calculate adjusted total power input + adjusted_total_power_input = [ + instance.total_power_input[t].value + + instance.load_shift_pos[t].value + - instance.load_shift_neg[t].value + for t in instance.time_steps + ] + + # Assert load shifting respects renewable signal intensity + for t in instance.time_steps: + assert ( + adjusted_total_power_input[1] <= instance.total_power_input[1].value + ), f"Load shift exceeds renewable intensity signal at time {t}" -# New fixture without electrolyser to test line 89 @pytest.fixture def steel_plant_without_electrolyser(dsm_components) -> SteelPlant: index = pd.date_range("2023-01-01", periods=24, freq="h") @@ -181,51 +290,77 @@ def steel_plant_without_electrolyser(dsm_components) -> SteelPlant: co2_price=[20] * 24, ) - # Remove 'electrolyser' from the components to trigger line 89 - dsm_components_without_electrolyser = dsm_components.copy() - dsm_components_without_electrolyser.pop("electrolyser", None) - - bidding_strategies = { - "EOM": NaiveDADSMStrategy(), - "RD": NaiveRedispatchDSMStrategy(), - } + dsm_components.pop("electrolyser", None) return SteelPlant( - id="test_steel_plant_without_electrolyser", + id="test_steel_plant_no_electrolyser", unit_operator="test_operator", objective="min_variable_cost", - flexibility_measure="max_load_shift", - bidding_strategies=bidding_strategies, - components=dsm_components_without_electrolyser, + flexibility_measure="cost_based_load_shift", + bidding_strategies={ + "EOM": NaiveDADSMStrategy(), + "RD": NaiveRedispatchDSMStrategy(), + }, + index=index, + node="south", + components=dsm_components, forecaster=forecast, demand=1000, technology="steel_plant", ) +# --- Initialization Tests --- +def test_handle_missing_components(): + index = pd.date_range("2023-01-01", periods=24, freq="h") + forecast = NaiveForecast( + index, + price_EOM=[50] * 24, + fuel_price_natural_gas=[30] * 24, + co2_price=[20] * 24, + ) + with pytest.raises( + ValueError, match="Component dri_plant is required for the steel plant unit." + ): + _ = SteelPlant( + id="test_steel_plant", + unit_operator="test_operator", + objective="min_variable_cost", + flexibility_measure="cost_based_load_shift", + bidding_strategies={}, + index=pd.date_range("2023-01-01", periods=24, freq="h"), + node="south", + components={}, + forecaster=forecast, + demand=1000, + technology="steel_plant", + ) + + def test_handle_missing_electrolyser(steel_plant_without_electrolyser): - # This should raise an error because 'electrolyser' is required for steel plant steel_plant_without_electrolyser.determine_optimal_operation_without_flex() instance = steel_plant_without_electrolyser.model.create_instance() instance = steel_plant_without_electrolyser.switch_to_opt(instance) steel_plant_without_electrolyser.solver.solve(instance, tee=False) - # Loop through time steps to check that the electrolyser ramp constraints hold - for t in list(instance.time_steps)[1:]: - # Current and previous power values for electrolyser - power_prev = instance.dsm_blocks["dri_plant"].power_in[t - 1].value - power_curr = instance.dsm_blocks["dri_plant"].power_in[t].value + # Verify no electrolyser-related constraints + for t in instance.time_steps: + assert "electrolyser" not in instance.dsm_blocks - # Access ramp constraints using dot notation - ramp_up = steel_plant_without_electrolyser.components["dri_plant"].ramp_up - ramp_down = steel_plant_without_electrolyser.components["dri_plant"].ramp_down - # Verify ramping constraints - assert ( - power_curr - power_prev <= ramp_up - ), f"Dri plant ramp-up constraint violated at time {t}" - assert ( - power_prev - power_curr <= ramp_down - ), f"Dri plant ramp-down constraint violated at time {t}" +# --- Objective Handling --- +@pytest.fixture +def reset_objectives(create_steel): + """ + Helper to reset objectives in the model. + """ + + def _reset(instance): + if hasattr(instance, "obj_rule_opt"): + instance.obj_rule_opt.deactivate() + if hasattr(instance, "obj_rule_flex"): + instance.obj_rule_flex.deactivate() + + return _reset if __name__ == "__main__":