"""Genetic algorithm.""" import random import time from typing import Any, Optional import numpy as np from deap import algorithms, base, creator, tools from loguru import logger from numpydantic import NDArray, Shape from pydantic import ConfigDict, Field from akkudoktoreos.core.pydantic import PydanticBaseModel from akkudoktoreos.devices.genetic.battery import Battery from akkudoktoreos.devices.genetic.homeappliance import HomeAppliance from akkudoktoreos.devices.genetic.inverter import Inverter from akkudoktoreos.optimization.genetic.geneticparams import ( GeneticEnergyManagementParameters, GeneticOptimizationParameters, ) from akkudoktoreos.optimization.genetic.geneticsolution import ( GeneticSimulationResult, GeneticSolution, ) from akkudoktoreos.optimization.optimizationabc import OptimizationBase class GeneticSimulation(PydanticBaseModel): """Device simulation for GENETIC optimization algorithm.""" # Disable validation on assignment to speed up simulation runs. model_config = ConfigDict( validate_assignment=False, ) start_hour: int = Field( default=0, ge=0, le=23, json_schema_extra={"description": "Starting hour on day for optimizations."}, ) optimization_hours: Optional[int] = Field( default=24, ge=0, json_schema_extra={"description": "Number of hours into the future for optimizations."}, ) prediction_hours: Optional[int] = Field( default=48, ge=0, json_schema_extra={"description": "Number of hours into the future for predictions"}, ) load_energy_array: Optional[NDArray[Shape["*"], float]] = Field( default=None, json_schema_extra={ "description": "An array of floats representing the total load (consumption) in watts for different time intervals." }, ) pv_prediction_wh: Optional[NDArray[Shape["*"], float]] = Field( default=None, json_schema_extra={ "description": "An array of floats representing the forecasted photovoltaic output in watts for different time intervals." }, ) elect_price_hourly: Optional[NDArray[Shape["*"], float]] = Field( default=None, json_schema_extra={ "description": "An array of floats representing the electricity price in euros per watt-hour for different time intervals." }, ) elect_revenue_per_hour_arr: Optional[NDArray[Shape["*"], float]] = Field( default=None, json_schema_extra={ "description": "An array of floats representing the feed-in compensation in euros per watt-hour." }, ) battery: Optional[Battery] = Field(default=None, json_schema_extra={"description": "TBD."}) ev: Optional[Battery] = Field(default=None, json_schema_extra={"description": "TBD."}) home_appliance: Optional[HomeAppliance] = Field( default=None, json_schema_extra={"description": "TBD."} ) inverter: Optional[Inverter] = Field(default=None, json_schema_extra={"description": "TBD."}) ac_charge_hours: Optional[NDArray[Shape["*"], float]] = Field( default=None, json_schema_extra={"description": "TBD"} ) dc_charge_hours: Optional[NDArray[Shape["*"], float]] = Field( default=None, json_schema_extra={"description": "TBD"} ) bat_discharge_hours: Optional[NDArray[Shape["*"], float]] = Field( default=None, json_schema_extra={"description": "TBD"} ) ev_charge_hours: Optional[NDArray[Shape["*"], float]] = Field( default=None, json_schema_extra={"description": "TBD"} ) ev_discharge_hours: Optional[NDArray[Shape["*"], float]] = Field( default=None, json_schema_extra={"description": "TBD"} ) home_appliance_start_hour: Optional[int] = Field( default=None, json_schema_extra={"description": "Home appliance start hour - None denotes no start."}, ) def prepare( self, parameters: GeneticEnergyManagementParameters, optimization_hours: int, prediction_hours: int, ev: Optional[Battery] = None, home_appliance: Optional[HomeAppliance] = None, inverter: Optional[Inverter] = None, ) -> None: self.optimization_hours = optimization_hours self.prediction_hours = prediction_hours self.load_energy_array = np.array(parameters.gesamtlast, float) self.pv_prediction_wh = np.array(parameters.pv_prognose_wh, float) self.elect_price_hourly = np.array(parameters.strompreis_euro_pro_wh, float) self.elect_revenue_per_hour_arr = ( parameters.einspeiseverguetung_euro_pro_wh if isinstance(parameters.einspeiseverguetung_euro_pro_wh, list) else np.full( len(self.load_energy_array), parameters.einspeiseverguetung_euro_pro_wh, float ) ) if inverter: self.battery = inverter.battery else: self.battery = None self.ev = ev self.home_appliance = home_appliance self.inverter = inverter self.ac_charge_hours = np.full(self.prediction_hours, 0.0) self.dc_charge_hours = np.full(self.prediction_hours, 0.0) self.bat_discharge_hours = np.full(self.prediction_hours, 0.0) self.ev_charge_hours = np.full(self.prediction_hours, 0.0) self.ev_discharge_hours = np.full(self.prediction_hours, 0.0) self.home_appliance_start_hour = None """Prepare simulation runs.""" self.load_energy_array = np.array(parameters.gesamtlast, float) self.pv_prediction_wh = np.array(parameters.pv_prognose_wh, float) self.elect_price_hourly = np.array(parameters.strompreis_euro_pro_wh, float) self.elect_revenue_per_hour_arr = ( parameters.einspeiseverguetung_euro_pro_wh if isinstance(parameters.einspeiseverguetung_euro_pro_wh, list) else np.full( len(self.load_energy_array), parameters.einspeiseverguetung_euro_pro_wh, float ) ) def reset(self) -> None: if self.ev: self.ev.reset() if self.battery: self.battery.reset() self.home_appliance_start_hour = None def simulate(self, start_hour: int) -> dict[str, Any]: """Simulate energy usage and costs for the given start hour. akku_soc_pro_stunde begin of the hour, initial hour state! last_wh_pro_stunde integral of last hour (end state) """ # Remember start hour self.start_hour = start_hour # Provide fast (3x..5x) local read access (vs. self.xxx) for repetitive read access load_energy_array_fast = self.load_energy_array ev_charge_hours_fast = self.ev_charge_hours ev_discharge_hours_fast = self.ev_discharge_hours ac_charge_hours_fast = self.ac_charge_hours dc_charge_hours_fast = self.dc_charge_hours bat_discharge_hours_fast = self.bat_discharge_hours elect_price_hourly_fast = self.elect_price_hourly elect_revenue_per_hour_arr_fast = self.elect_revenue_per_hour_arr pv_prediction_wh_fast = self.pv_prediction_wh battery_fast = self.battery ev_fast = self.ev home_appliance_fast = self.home_appliance inverter_fast = self.inverter # Check for simulation integrity (in a way that mypy understands) if ( load_energy_array_fast is None or pv_prediction_wh_fast is None or elect_price_hourly_fast is None or ev_charge_hours_fast is None or ac_charge_hours_fast is None or dc_charge_hours_fast is None or elect_revenue_per_hour_arr_fast is None or bat_discharge_hours_fast is None or ev_discharge_hours_fast is None ): missing = [] if load_energy_array_fast is None: missing.append("Load Energy Array") if pv_prediction_wh_fast is None: missing.append("PV Prediction Wh") if elect_price_hourly_fast is None: missing.append("Electricity Price Hourly") if ev_charge_hours_fast is None: missing.append("EV Charge Hours") if ac_charge_hours_fast is None: missing.append("AC Charge Hours") if dc_charge_hours_fast is None: missing.append("DC Charge Hours") if elect_revenue_per_hour_arr_fast is None: missing.append("Electricity Revenue Per Hour") if bat_discharge_hours_fast is None: missing.append("Battery Discharge Hours") if ev_discharge_hours_fast is None: missing.append("EV Discharge Hours") msg = ", ".join(missing) logger.error("Mandatory data missing - %s", msg) raise ValueError(f"Mandatory data missing: {msg}") if not ( len(load_energy_array_fast) == len(pv_prediction_wh_fast) == len(elect_price_hourly_fast) ): error_msg = f"Array sizes do not match: Load Curve = {len(load_energy_array_fast)}, PV Forecast = {len(pv_prediction_wh_fast)}, Electricity Price = {len(elect_price_hourly_fast)}" logger.error(error_msg) raise ValueError(error_msg) end_hour = len(load_energy_array_fast) total_hours = end_hour - start_hour # Pre-allocate arrays for the results, optimized for speed loads_energy_per_hour = np.full((total_hours), np.nan) feedin_energy_per_hour = np.full((total_hours), np.nan) consumption_energy_per_hour = np.full((total_hours), np.nan) costs_per_hour = np.full((total_hours), np.nan) revenue_per_hour = np.full((total_hours), np.nan) losses_wh_per_hour = np.full((total_hours), np.nan) electricity_price_per_hour = np.full((total_hours), np.nan) # Set initial state if battery_fast: # Pre-allocate arrays for the results, optimized for speed soc_per_hour = np.full((total_hours), np.nan) soc_per_hour[0] = battery_fast.current_soc_percentage() # Determine AC charging availability from inverter parameters if inverter_fast: ac_to_dc_eff_fast = inverter_fast.ac_to_dc_efficiency dc_to_ac_eff_fast = inverter_fast.dc_to_ac_efficiency max_ac_charge_w_fast = inverter_fast.max_ac_charge_power_w else: ac_to_dc_eff_fast = 1.0 dc_to_ac_eff_fast = 1.0 max_ac_charge_w_fast = None ac_charging_possible = ac_to_dc_eff_fast > 0 and ( max_ac_charge_w_fast is None or max_ac_charge_w_fast > 0 ) # If AC charging is disabled via inverter, zero out AC charge hours if not ac_charging_possible: ac_charge_hours_fast = np.zeros_like(ac_charge_hours_fast) # Fill the charge array of the battery dc_charge_hours_fast[0:start_hour] = 0 dc_charge_hours_fast[end_hour:] = 0 ac_charge_hours_fast[0:start_hour] = 0 ac_charge_hours_fast[end_hour:] = 0 battery_fast.charge_array = np.where( ac_charge_hours_fast != 0, ac_charge_hours_fast, dc_charge_hours_fast ) # Fill the discharge array of the battery bat_discharge_hours_fast[0:start_hour] = 0 bat_discharge_hours_fast[end_hour:] = 0 battery_fast.discharge_array = bat_discharge_hours_fast else: # Default return if no battery is available soc_per_hour = np.full((total_hours), 0) ac_to_dc_eff_fast = 1.0 dc_to_ac_eff_fast = 1.0 max_ac_charge_w_fast = None ac_charging_possible = False if ev_fast: # Pre-allocate arrays for the results, optimized for speed soc_ev_per_hour = np.full((total_hours), np.nan) soc_ev_per_hour[0] = ev_fast.current_soc_percentage() # Fill the charge array of the ev ev_charge_hours_fast[0:start_hour] = 0 ev_charge_hours_fast[end_hour:] = 0 ev_fast.charge_array = ev_charge_hours_fast # Fill the discharge array of the ev ev_discharge_hours_fast[0:start_hour] = 0 ev_discharge_hours_fast[end_hour:] = 0 ev_fast.discharge_array = ev_discharge_hours_fast else: # Default return if no electric vehicle is available soc_ev_per_hour = np.full((total_hours), 0) if home_appliance_fast and self.home_appliance_start_hour: home_appliance_enabled = True # Pre-allocate arrays for the results, optimized for speed home_appliance_wh_per_hour = np.full((total_hours), np.nan) self.home_appliance_start_hour = home_appliance_fast.set_starting_time( self.home_appliance_start_hour, start_hour ) else: home_appliance_enabled = False # Default return if no home appliance is available home_appliance_wh_per_hour = np.full((total_hours), 0) for hour in range(start_hour, end_hour): hour_idx = hour - start_hour # Accumulate loads and PV generation consumption = load_energy_array_fast[hour] losses_wh_per_hour[hour_idx] = 0.0 # Home appliances if home_appliance_enabled: ha_load = home_appliance_fast.get_load_for_hour(hour) # type: ignore[union-attr] consumption += ha_load home_appliance_wh_per_hour[hour_idx] = ha_load # E-Auto handling if ev_fast: soc_ev_per_hour[hour_idx] = ev_fast.current_soc_percentage() # save begin state if ev_charge_hours_fast[hour] > 0: loaded_energy_ev, verluste_eauto = ev_fast.charge_energy( wh=None, hour=hour, charge_factor=ev_charge_hours_fast[hour] ) consumption += loaded_energy_ev losses_wh_per_hour[hour_idx] += verluste_eauto # Process inverter logic energy_feedin_grid_actual = energy_consumption_grid_actual = losses = eigenverbrauch = ( 0.0 ) if inverter_fast: energy_produced = pv_prediction_wh_fast[hour] ( energy_feedin_grid_actual, energy_consumption_grid_actual, losses, eigenverbrauch, ) = inverter_fast.process_energy(energy_produced, consumption, hour) # AC PV Battery Charge if battery_fast: soc_per_hour[hour_idx] = battery_fast.current_soc_percentage() # save begin state hour_ac_charge = ac_charge_hours_fast[hour] if hour_ac_charge > 0.0 and ac_charging_possible: # Cap charge factor by max_ac_charge_power_w if set effective_charge_factor = hour_ac_charge if max_ac_charge_w_fast is not None and battery_fast.max_charge_power_w > 0: # DC power = max_charge_power_w * factor # AC power = DC power / ac_to_dc_eff # AC power must be <= max_ac_charge_power_w max_dc_factor = ( max_ac_charge_w_fast * ac_to_dc_eff_fast ) / battery_fast.max_charge_power_w effective_charge_factor = min(effective_charge_factor, max_dc_factor) if effective_charge_factor > 0: battery_charged_energy_actual, battery_losses_actual = ( battery_fast.charge_energy( None, hour, charge_factor=effective_charge_factor ) ) # DC energy entering the battery (before battery internal efficiency) dc_energy = battery_charged_energy_actual + battery_losses_actual # AC energy consumed from grid (accounts for AC→DC conversion loss) ac_energy = dc_energy / ac_to_dc_eff_fast # Inverter AC→DC conversion losses inverter_charge_losses = ac_energy - dc_energy consumption += ac_energy energy_consumption_grid_actual += ac_energy losses_wh_per_hour[hour_idx] += ( battery_losses_actual + inverter_charge_losses ) # Update hourly arrays feedin_energy_per_hour[hour_idx] = energy_feedin_grid_actual consumption_energy_per_hour[hour_idx] = energy_consumption_grid_actual losses_wh_per_hour[hour_idx] += losses loads_energy_per_hour[hour_idx] = consumption hourly_electricity_price = elect_price_hourly_fast[hour] electricity_price_per_hour[hour_idx] = hourly_electricity_price # Financial calculations costs_per_hour[hour_idx] = energy_consumption_grid_actual * hourly_electricity_price revenue_per_hour[hour_idx] = ( energy_feedin_grid_actual * elect_revenue_per_hour_arr_fast[hour] ) total_cost = np.nansum(costs_per_hour) total_losses = np.nansum(losses_wh_per_hour) total_revenue = np.nansum(revenue_per_hour) # Prepare output dictionary return { "Last_Wh_pro_Stunde": loads_energy_per_hour, "Netzeinspeisung_Wh_pro_Stunde": feedin_energy_per_hour, "Netzbezug_Wh_pro_Stunde": consumption_energy_per_hour, "Kosten_Euro_pro_Stunde": costs_per_hour, "akku_soc_pro_stunde": soc_per_hour, "Einnahmen_Euro_pro_Stunde": revenue_per_hour, "Gesamtbilanz_Euro": total_cost - total_revenue, # Fitness score ("FitnessMin") "EAuto_SoC_pro_Stunde": soc_ev_per_hour, "Gesamteinnahmen_Euro": total_revenue, "Gesamtkosten_Euro": total_cost, "Verluste_Pro_Stunde": losses_wh_per_hour, "Gesamt_Verluste": total_losses, "Home_appliance_wh_per_hour": home_appliance_wh_per_hour, "Electricity_price": electricity_price_per_hour, } class GeneticOptimization(OptimizationBase): """GENETIC algorithm to solve energy optimization.""" def __init__( self, verbose: bool = False, fixed_seed: Optional[int] = None, ): """Initialize the optimization problem with the required parameters.""" self.opti_param: dict[str, Any] = {} self.fixed_eauto_hours = ( self.config.prediction.hours - self.config.optimization.horizon_hours ) self.ev_possible_charge_values: list[float] = [1.0] self.verbose = verbose self.fix_seed = fixed_seed self.optimize_ev = True self.optimize_dc_charge = False self.fitness_history: dict[str, Any] = {} # Set a fixed seed for random operations if provided or in debug mode if self.fix_seed is not None: random.seed(self.fix_seed) elif logger.level == "DEBUG": self.fix_seed = random.randint(1, 100000000000) # noqa: S311 random.seed(self.fix_seed) # Create Simulation self.simulation = GeneticSimulation() def decode_charge_discharge( self, discharge_hours_bin: np.ndarray ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: """Decode the input array into ac_charge, dc_charge, and discharge arrays.""" discharge_hours_bin_np = np.array(discharge_hours_bin) len_ac = len(self.ev_possible_charge_values) # Categorization: # Idle: 0 .. len_ac-1 # Discharge: len_ac .. 2*len_ac - 1 # AC Charge: 2*len_ac .. 3*len_ac - 1 # DC optional: 3*len_ac (not allowed), 3*len_ac + 1 (allowed) # Idle has no charge, Discharge has binary 1, AC Charge has corresponding values # Idle states idle_mask = (discharge_hours_bin_np >= 0) & (discharge_hours_bin_np < len_ac) # Discharge states discharge_mask = (discharge_hours_bin_np >= len_ac) & (discharge_hours_bin_np < 2 * len_ac) # AC states ac_mask = (discharge_hours_bin_np >= 2 * len_ac) & (discharge_hours_bin_np < 3 * len_ac) ac_indices = (discharge_hours_bin_np[ac_mask] - 2 * len_ac).astype(int) # DC states (if enabled) if self.optimize_dc_charge: dc_not_allowed_state = 3 * len_ac dc_allowed_state = 3 * len_ac + 1 dc_charge = np.where(discharge_hours_bin_np == dc_allowed_state, 1, 0) else: dc_charge = np.ones_like(discharge_hours_bin_np, dtype=float) # Generate the result arrays discharge = np.zeros_like(discharge_hours_bin_np, dtype=int) discharge[discharge_mask] = 1 # Set Discharge states to 1 ac_charge = np.zeros_like(discharge_hours_bin_np, dtype=float) ac_charge[ac_mask] = [self.ev_possible_charge_values[i] for i in ac_indices] # Idle is just 0, already default. return ac_charge, dc_charge, discharge def mutate(self, individual: list[int]) -> tuple[list[int]]: """Custom mutation function for the individual.""" # Calculate the number of states len_ac = len(self.ev_possible_charge_values) if self.optimize_dc_charge: total_states = 3 * len_ac + 2 else: total_states = 3 * len_ac # 1. Mutating the charge_discharge part charge_discharge_part = individual[: self.config.prediction.hours] (charge_discharge_mutated,) = self.toolbox.mutate_charge_discharge(charge_discharge_part) # Instead of a fixed clamping to 0..8 or 0..6 dynamically: charge_discharge_mutated = np.clip(charge_discharge_mutated, 0, total_states - 1) individual[: self.config.prediction.hours] = charge_discharge_mutated # 2. Mutating the EV charge part, if active if self.optimize_ev: ev_charge_part = individual[ self.config.prediction.hours : self.config.prediction.hours * 2 ] (ev_charge_part_mutated,) = self.toolbox.mutate_ev_charge_index(ev_charge_part) ev_charge_part_mutated[self.config.prediction.hours - self.fixed_eauto_hours :] = [ 0 ] * self.fixed_eauto_hours individual[self.config.prediction.hours : self.config.prediction.hours * 2] = ( ev_charge_part_mutated ) # 3. Mutating the appliance start time, if applicable if self.opti_param["home_appliance"] > 0: appliance_part = [individual[-1]] (appliance_part_mutated,) = self.toolbox.mutate_hour(appliance_part) individual[-1] = appliance_part_mutated[0] return (individual,) # Method to create an individual based on the conditions def create_individual(self) -> list[int]: # Start with discharge states for the individual individual_components = [ self.toolbox.attr_discharge_state() for _ in range(self.config.prediction.hours) ] # Add EV charge index values if optimize_ev is True if self.optimize_ev: individual_components += [ self.toolbox.attr_ev_charge_index() for _ in range(self.config.prediction.hours) ] # Add the start time of the household appliance if it's being optimized if self.opti_param["home_appliance"] > 0: individual_components += [self.toolbox.attr_int()] return creator.Individual(individual_components) def merge_individual( self, discharge_hours_bin: np.ndarray, eautocharge_hours_index: Optional[np.ndarray], washingstart_int: Optional[int], ) -> list[int]: """Merge the individual components back into a single solution list. Parameters: discharge_hours_bin (np.ndarray): Binary discharge hours. eautocharge_hours_index (Optional[np.ndarray]): EV charge hours as integers, or None. washingstart_int (Optional[int]): Dishwasher start time as integer, or None. Returns: list[int]: The merged individual solution as a list of integers. """ # Start with the discharge hours individual = discharge_hours_bin.tolist() # Add EV charge hours if applicable if self.optimize_ev and eautocharge_hours_index is not None: individual.extend(eautocharge_hours_index.tolist()) elif self.optimize_ev: # Falls optimize_ev aktiv ist, aber keine EV-Daten vorhanden sind, fügen wir Nullen hinzu individual.extend([0] * self.config.prediction.hours) # Add dishwasher start time if applicable if self.opti_param.get("home_appliance", 0) > 0 and washingstart_int is not None: individual.append(washingstart_int) elif self.opti_param.get("home_appliance", 0) > 0: # Falls ein Haushaltsgerät optimiert wird, aber kein Startzeitpunkt vorhanden ist individual.append(0) return individual def split_individual( self, individual: list[int] ) -> tuple[np.ndarray, Optional[np.ndarray], Optional[int]]: """Split the individual solution into its components. Components: 1. Discharge hours (binary as int NumPy array), 2. Electric vehicle charge hours (float as int NumPy array, if applicable), 3. Dishwasher start time (integer if applicable). """ # Discharge hours as a NumPy array of ints discharge_hours_bin = np.array(individual[: self.config.prediction.hours], dtype=int) # EV charge hours as a NumPy array of ints (if optimize_ev is True) eautocharge_hours_index = ( # append ev charging states to individual np.array( individual[self.config.prediction.hours : self.config.prediction.hours * 2], dtype=int, ) if self.optimize_ev else None ) # Washing machine start time as an integer (if applicable) washingstart_int = ( int(individual[-1]) if self.opti_param and self.opti_param.get("home_appliance", 0) > 0 else None ) return discharge_hours_bin, eautocharge_hours_index, washingstart_int def setup_deap_environment(self, opti_param: dict[str, Any], start_hour: int) -> None: """Set up the DEAP environment with fitness and individual creation rules.""" self.opti_param = opti_param # Remove existing definitions if any for attr in ["FitnessMin", "Individual"]: if attr in creator.__dict__: del creator.__dict__[attr] creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", list, fitness=creator.FitnessMin) self.toolbox = base.Toolbox() len_ac = len(self.ev_possible_charge_values) # Total number of states without DC: # Idle: len_ac states # Discharge: len_ac states # AC-Charge: len_ac states # Total without DC: 3 * len_ac # With DC: + 2 states if self.optimize_dc_charge: total_states = 3 * len_ac + 2 else: total_states = 3 * len_ac # State space: 0 .. (total_states - 1) self.toolbox.register("attr_discharge_state", random.randint, 0, total_states - 1) # EV attributes if self.optimize_ev: self.toolbox.register( "attr_ev_charge_index", random.randint, 0, len_ac - 1, ) # Household appliance start time self.toolbox.register("attr_int", random.randint, start_hour, 23) self.toolbox.register("individual", self.create_individual) self.toolbox.register("population", tools.initRepeat, list, self.toolbox.individual) self.toolbox.register("mate", tools.cxTwoPoint) # Mutation operator for charge/discharge states self.toolbox.register( "mutate_charge_discharge", tools.mutUniformInt, low=0, up=total_states - 1, indpb=0.2 ) # Mutation operator for EV states self.toolbox.register( "mutate_ev_charge_index", tools.mutUniformInt, low=0, up=len_ac - 1, indpb=0.2, ) # Mutation for household appliance self.toolbox.register("mutate_hour", tools.mutUniformInt, low=start_hour, up=23, indpb=0.2) # Custom mutate function remains unchanged self.toolbox.register("mutate", self.mutate) self.toolbox.register("select", tools.selTournament, tournsize=3) def evaluate_inner(self, individual: list[int]) -> dict[str, Any]: """Simulates the energy management system (EMS) using the provided individual solution. This is an internal function. """ self.simulation.reset() discharge_hours_bin, eautocharge_hours_index, washingstart_int = self.split_individual( individual ) if self.opti_param.get("home_appliance", 0) > 0 and washingstart_int: # Set start hour for appliance self.simulation.home_appliance_start_hour = washingstart_int ac_charge_hours, dc_charge_hours, discharge = self.decode_charge_discharge( discharge_hours_bin ) self.simulation.bat_discharge_hours = discharge # Set DC charge hours only if DC optimization is enabled if self.optimize_dc_charge: self.simulation.dc_charge_hours = dc_charge_hours else: self.simulation.dc_charge_hours = np.full(self.config.prediction.hours, 1) self.simulation.ac_charge_hours = ac_charge_hours if eautocharge_hours_index is not None: eautocharge_hours_float = np.array( [self.ev_possible_charge_values[i] for i in eautocharge_hours_index], float, ) # discharge is set to 0 by default self.simulation.ev_charge_hours = eautocharge_hours_float else: # discharge is set to 0 by default self.simulation.ev_charge_hours = np.full(self.config.prediction.hours, 0) # Do the simulation and return result. return self.simulation.simulate(self.ems.start_datetime.hour) def evaluate( self, individual: list[int], parameters: GeneticOptimizationParameters, start_hour: int, worst_case: bool, ) -> tuple[float]: """Evaluate the fitness score of a single individual in the DEAP genetic algorithm. This method runs a simulation based on the provided individual genome and optimization parameters. The resulting performance is converted into a fitness score compatible with DEAP (i.e., returned as a 1-tuple). Args: individual (list[int]): The genome representing one candidate solution. parameters (GeneticOptimizationParameters): Optimization parameters that influence simulation behavior, constraints, and scoring logic. start_hour (int): The simulation start hour (0–23 or domain-specific). Used to initialize time-based scheduling or constraints. worst_case (bool): If True, evaluates the solution under worst-case assumptions (e.g., pessimistic forecasts or boundary conditions). If False, uses nominal assumptions. Returns: tuple[float]: A single-element tuple containing the computed fitness score. Lower score is better: "FitnessMin". Raises: ValueError: If input arguments are invalid or the individual structure is not compatible with the simulation. RuntimeError: If the simulation fails or cannot produce results. Notes: The resulting score should match DEAP's expected format: a tuple, even if only a single scalar fitness value is returned. """ try: simulation_result = self.evaluate_inner(individual) except Exception as e: # Return bad fitness score ("FitnessMin") in case of an exception return (100000.0,) gesamtbilanz = simulation_result["Gesamtbilanz_Euro"] * (-1.0 if worst_case else 1.0) # EV 100% & charge not allowed if self.optimize_ev: discharge_hours_bin, eautocharge_hours_index, washingstart_int = self.split_individual( individual ) eauto_soc_per_hour = np.array( simulation_result.get("EAuto_SoC_pro_Stunde", []) ) # Beispielkey if eauto_soc_per_hour is None or eautocharge_hours_index is None: raise ValueError("eauto_soc_per_hour or eautocharge_hours_index is None") min_length = min(eauto_soc_per_hour.size, eautocharge_hours_index.size) eauto_soc_per_hour_tail = eauto_soc_per_hour[-min_length:] eautocharge_hours_index_tail = eautocharge_hours_index[-min_length:] # Mask invalid_charge_mask = (eauto_soc_per_hour_tail == 100) & ( eautocharge_hours_index_tail > 0 ) if np.any(invalid_charge_mask): invalid_indices = np.where(invalid_charge_mask)[0] if len(invalid_indices) > 1: eautocharge_hours_index_tail[invalid_indices[1:]] = 0 eautocharge_hours_index[-min_length:] = eautocharge_hours_index_tail.tolist() adjusted_individual = self.merge_individual( discharge_hours_bin, eautocharge_hours_index, washingstart_int ) individual[:] = adjusted_individual # New check: Activate discharge when battery SoC is 0 # battery_soc_per_hour = np.array( # o.get("akku_soc_pro_stunde", []) # ) # Example key for battery SoC # if battery_soc_per_hour is not None: # if battery_soc_per_hour is None or discharge_hours_bin is None: # raise ValueError("battery_soc_per_hour or discharge_hours_bin is None") # min_length = min(battery_soc_per_hour.size, discharge_hours_bin.size) # battery_soc_per_hour_tail = battery_soc_per_hour[-min_length:] # discharge_hours_bin_tail = discharge_hours_bin[-min_length:] # len_ac = len(self.config.optimization.ev_available_charge_rates_percent) # # # Find hours where battery SoC is 0 # # zero_soc_mask = battery_soc_per_hour_tail == 0 # # discharge_hours_bin_tail[zero_soc_mask] = ( # # len_ac + 2 # # ) # Activate discharge for these hours # # When Battery SoC then set the Discharge randomly to 0 or 1. otherwise it's very # # unlikely to get a state where a battery can store energy for a longer time # # Find hours where battery SoC is 0 # zero_soc_mask = battery_soc_per_hour_tail == 0 # # discharge_hours_bin_tail[zero_soc_mask] = ( # # len_ac + 2 # # ) # Activate discharge for these hours # set_to_len_ac_plus_2 = np.random.rand() < 0.5 # True mit 50% Wahrscheinlichkeit # # Werte setzen basierend auf der zufälligen Entscheidung # value_to_set = len_ac + 2 if set_to_len_ac_plus_2 else 0 # discharge_hours_bin_tail[zero_soc_mask] = value_to_set # # Merge the updated discharge_hours_bin back into the individual # adjusted_individual = self.merge_individual( # discharge_hours_bin, eautocharge_hours_index, washingstart_int # ) # individual[:] = adjusted_individual # More metrics individual.extra_data = ( # type: ignore[attr-defined] simulation_result["Gesamtbilanz_Euro"], simulation_result["Gesamt_Verluste"], parameters.eauto.min_soc_percentage - self.simulation.ev.current_soc_percentage() if parameters.eauto and self.simulation.ev else 0, ) # Adjust total balance with battery value and penalties for unmet SOC if self.simulation.battery: battery_energy_content = self.simulation.battery.current_energy_content() # Apply DC→AC inverter efficiency to residual battery value # (stored DC energy must pass through inverter to be usable as AC) if self.simulation.inverter: battery_energy_content *= self.simulation.inverter.dc_to_ac_efficiency restwert_akku = battery_energy_content * parameters.ems.preis_euro_pro_wh_akku gesamtbilanz += -restwert_akku # --- AC charging break-even penalty --- # Penalise AC charging decisions that cannot be economically justified given the # round-trip losses (AC→DC charge conversion, battery internal, DC→AC discharge # conversion) and the best available future electricity prices. # # Key insight: energy already stored in the battery (from PV, zero grid cost) covers # the most expensive future hours first. AC charging from the grid only makes sense # for the hours that remain uncovered, and only when the discharge price exceeds # P_charge / η_round_trip. # # This penalty does not double-count the simulation result – it amplifies the "bad # decision" signal so that the genetic algorithm converges faster away from # unprofitable charging regions. if ( self.simulation.battery and self.simulation.inverter and self.simulation.ac_charge_hours is not None and self.simulation.elect_price_hourly is not None and self.simulation.load_energy_array is not None ): inv = self.simulation.inverter bat = self.simulation.battery # Full round-trip efficiency: 1 Wh drawn from grid → η Wh delivered to AC load round_trip_eff = ( inv.ac_to_dc_efficiency * bat.charging_efficiency * bat.discharging_efficiency * inv.dc_to_ac_efficiency ) if round_trip_eff > 0: ac_charge_arr = self.simulation.ac_charge_hours prices_arr = self.simulation.elect_price_hourly load_arr = self.simulation.load_energy_array n = len(prices_arr) # Usable AC energy already in battery from prior PV charging (zero grid cost). # This covers the most expensive future hours first, pushing AC charging demand # to cheaper hours where the break-even hurdle may not be met. initial_soc_wh = (bat.initial_soc_percentage / 100.0) * bat.capacity_wh free_ac_wh = ( max(0.0, initial_soc_wh - bat.min_soc_wh) * bat.discharging_efficiency * inv.dc_to_ac_efficiency ) # Configurable penalty multiplier (default 1 = economic loss in €) try: ac_penalty_factor = float( self.config.optimization.genetic.penalties["ac_charge_break_even"] ) except Exception: ac_penalty_factor = 1.0 for hour in range(start_hour, min(len(ac_charge_arr), n)): ac_factor = ac_charge_arr[hour] if ac_factor <= 0.0: continue charge_price = prices_arr[hour] if charge_price <= 0: continue # Price that a future discharge hour must reach to break even break_even_price = charge_price / round_trip_eff # Build list of (price, load_wh) for all future hours in the horizon future = [ (float(prices_arr[h]), float(load_arr[h])) for h in range(hour + 1, n) ] # Sort descending by price so we "use" the most expensive hours first future.sort(key=lambda x: -x[0]) # Consume free PV energy against the highest-price future hours. # The first uncovered (partially or fully) hour defines the best # price still available for the new AC charge. remaining_free = free_ac_wh best_uncovered_price = 0.0 for fp, fl in future: if remaining_free >= fl: # Entire expensive hour is already covered by free PV energy remaining_free -= fl else: # First hour not (fully) covered: this is where new charge goes best_uncovered_price = fp break if best_uncovered_price < break_even_price: # AC charging at this hour is economically unjustified. # Penalty = excess cost per Wh × DC energy requested this hour. dc_wh = bat.max_charge_power_w * ac_factor ac_wh = dc_wh / max(inv.ac_to_dc_efficiency, 1e-9) excess_cost_per_wh = break_even_price - best_uncovered_price gesamtbilanz += ac_wh * excess_cost_per_wh * ac_penalty_factor if self.optimize_ev and parameters.eauto and self.simulation.ev: try: penalty = self.config.optimization.genetic.penalties["ev_soc_miss"] except: # Use default penalty = 10 logger.error( "Penalty function parameter `ev_soc_miss` not configured, using {}.", penalty ) ev_soc_percentage = self.simulation.ev.current_soc_percentage() if ( ev_soc_percentage < parameters.eauto.min_soc_percentage or ev_soc_percentage > parameters.eauto.max_soc_percentage ): gesamtbilanz += ( abs(parameters.eauto.min_soc_percentage - ev_soc_percentage) * penalty ) return (gesamtbilanz,) def optimize( self, start_solution: Optional[list[float]] = None, ngen: int = 200, ) -> tuple[Any, dict[str, list[Any]]]: """Run the optimization process using a genetic algorithm. @TODO: optimize() ngen default (200) is different from optimierung_ems() ngen default (400). """ # Set the number of inviduals in a generation try: individuals = self.config.optimization.genetic.individuals if individuals is None: raise except: individuals = 300 logger.error("Individuals not configured. Using {}.", individuals) population = self.toolbox.population(n=individuals) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("min", np.min) stats.register("avg", np.mean) stats.register("max", np.max) logger.debug("Start optimize: {}", start_solution) # Insert the start solution into the population if provided if start_solution is not None: for _ in range(10): population.insert(0, creator.Individual(start_solution)) # Run the evolutionary algorithm pop, log = algorithms.eaMuPlusLambda( population, self.toolbox, mu=100, lambda_=150, cxpb=0.6, mutpb=0.4, ngen=ngen, stats=stats, halloffame=hof, verbose=self.verbose, ) # Store fitness history self.fitness_history = { "gen": log.select("gen"), # Generation numbers (X-axis) "avg": log.select("avg"), # Average fitness for each generation (Y-axis) "max": log.select("max"), # Maximum fitness for each generation (Y-axis) "min": log.select("min"), # Minimum fitness for each generation (Y-axis) } member: dict[str, list[float]] = {"bilanz": [], "verluste": [], "nebenbedingung": []} for ind in population: if hasattr(ind, "extra_data"): extra_value1, extra_value2, extra_value3 = ind.extra_data member["bilanz"].append(extra_value1) member["verluste"].append(extra_value2) member["nebenbedingung"].append(extra_value3) return hof[0], member def optimierung_ems( self, parameters: GeneticOptimizationParameters, start_hour: Optional[int] = None, worst_case: bool = False, ngen: Optional[int] = None, ) -> GeneticSolution: """Perform EMS (Energy Management System) optimization and visualize results.""" if start_hour is None: start_hour = self.ems.start_datetime.hour # Start hour has to be in sync with energy management if start_hour != self.ems.start_datetime.hour: raise ValueError( f"Start hour not synced. EMS {self.ems.start_datetime.hour} vs. GENETIC {start_hour}." ) # Set the number of generations generations = ngen if generations is None: try: generations = self.config.optimization.genetic.generations except: generations = 400 logger.error("Generations not configured. Using {}.", generations) einspeiseverguetung_euro_pro_wh = np.full( self.config.prediction.hours, parameters.ems.einspeiseverguetung_euro_pro_wh ) self.simulation.reset() # Initialize PV and EV batteries akku: Optional[Battery] = None if parameters.pv_akku: akku = Battery( parameters.pv_akku, prediction_hours=self.config.prediction.hours, ) akku.set_charge_per_hour(np.full(self.config.prediction.hours, 0)) eauto: Optional[Battery] = None if parameters.eauto: eauto = Battery( parameters.eauto, prediction_hours=self.config.prediction.hours, ) eauto.set_charge_per_hour(np.full(self.config.prediction.hours, 1)) self.optimize_ev = ( parameters.eauto.min_soc_percentage - parameters.eauto.initial_soc_percentage >= 0 ) # electrical vehicle charge rates if parameters.eauto.charge_rates is not None: self.ev_possible_charge_values = parameters.eauto.charge_rates elif ( self.config.devices.electric_vehicles and self.config.devices.electric_vehicles[0] and self.config.devices.electric_vehicles[0].charge_rates is not None ): self.ev_possible_charge_values = self.config.devices.electric_vehicles[ 0 ].charge_rates else: warning_msg = "No charge rates provided for electric vehicle - using default." logger.warning(warning_msg) self.ev_possible_charge_values = [ 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, ] else: self.optimize_ev = False # Initialize household appliance if applicable dishwasher = ( HomeAppliance( parameters=parameters.dishwasher, optimization_hours=self.config.optimization.horizon_hours, prediction_hours=self.config.prediction.hours, ) if parameters.dishwasher is not None else None ) # Initialize the inverter and energy management system inverter: Optional[Inverter] = None if parameters.inverter: inverter = Inverter( parameters.inverter, battery=akku, ) # Prepare device simulation self.simulation.prepare( parameters=parameters.ems, optimization_hours=self.config.optimization.horizon_hours, prediction_hours=self.config.prediction.hours, inverter=inverter, # battery is part of inverter ev=eauto, home_appliance=dishwasher, ) # Setup the DEAP environment and optimization process self.setup_deap_environment({"home_appliance": 1 if dishwasher else 0}, start_hour) self.toolbox.register( "evaluate", lambda ind: self.evaluate(ind, parameters, start_hour, worst_case), ) start_time = time.time() start_solution, extra_data = self.optimize(parameters.start_solution, ngen=generations) elapsed_time = time.time() - start_time logger.debug(f"Time evaluate inner: {elapsed_time:.4f} sec.") # Perform final evaluation on the best solution simulation_result = self.evaluate_inner(start_solution) # Prepare results discharge_hours_bin, eautocharge_hours_index, washingstart_int = self.split_individual( start_solution ) # home appliance may have choosen a different appliance start hour if self.simulation.home_appliance: washingstart_int = self.simulation.home_appliance_start_hour eautocharge_hours_float = ( [self.ev_possible_charge_values[i] for i in eautocharge_hours_index] if eautocharge_hours_index is not None else None ) # Simulation may have changed something, use simulation values ac_charge_hours = self.simulation.ac_charge_hours if ac_charge_hours is None: ac_charge_hours = [] else: ac_charge_hours = ac_charge_hours.tolist() dc_charge_hours = self.simulation.dc_charge_hours if dc_charge_hours is None: dc_charge_hours = [] else: dc_charge_hours = dc_charge_hours.tolist() discharge = self.simulation.bat_discharge_hours if discharge is None: discharge = [] else: discharge = discharge.tolist() # Visualize the results in PDF try: from akkudoktoreos.utils.visualize import prepare_visualize visualize = { "ac_charge": ac_charge_hours, "dc_charge": dc_charge_hours, "discharge_allowed": discharge, "eautocharge_hours_float": eautocharge_hours_float, "result": simulation_result, "eauto_obj": self.simulation.ev.to_dict() if self.simulation.ev else None, "start_solution": start_solution, "spuelstart": washingstart_int, "extra_data": extra_data, "fitness_history": self.fitness_history, "fixed_seed": self.fix_seed, } prepare_visualize(parameters, visualize, start_hour=start_hour) except Exception as ex: error_msg = f"Visualization failed: {ex}" logger.error(error_msg) return GeneticSolution( **{ "ac_charge": ac_charge_hours, "dc_charge": dc_charge_hours, "discharge_allowed": discharge, "eautocharge_hours_float": eautocharge_hours_float, "result": GeneticSimulationResult(**simulation_result), "eauto_obj": self.simulation.ev, "start_solution": start_solution, "washingstart": washingstart_int, } )