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Andreas 2024-12-20 17:39:40 +01:00 committed by Andreas
parent 54f7989ec4
commit e1f4c75a5d
1 changed files with 21 additions and 14 deletions
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35
src/akkudoktoreos/optimization/genetic.py
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@ -225,26 +225,32 @@ class optimization_problem:
def split_individual( def split_individual(
self, individual: list[int] self, individual: list[int]
) -> tuple[list[int], Optional[list[int]], Optional[int]]: ) -> Tuple[np.ndarray, Optional[np.ndarray], Optional[int]]:
"""Split the individual solution into its components. """
Split the individual solution into its components.
Components: Components:
1. Discharge hours (binary), 1. Discharge hours (binary as int NumPy array),
2. Electric vehicle charge hours (float), 2. Electric vehicle charge hours (float as int NumPy array, if applicable),
3. Dishwasher start time (integer if applicable). 3. Dishwasher start time (integer if applicable).
""" """
discharge_hours_bin = individual[: self.prediction_hours] # Discharge hours as a NumPy array of ints
discharge_hours_bin = np.array(individual[: self.prediction_hours], dtype=int)
# EV charge hours as a NumPy array of ints (if optimize_ev is True)
eautocharge_hours_index = ( eautocharge_hours_index = (
individual[self.prediction_hours : self.prediction_hours * 2] np.array(individual[self.prediction_hours : self.prediction_hours * 2], dtype=int)
if self.optimize_ev if self.optimize_ev
else None else None
) )
# Washing machine start time as an integer (if applicable)
washingstart_int = ( washingstart_int = (
int(individual[-1]) int(individual[-1])
if self.opti_param and self.opti_param.get("home_appliance", 0) > 0 if self.opti_param and self.opti_param.get("home_appliance", 0) > 0
else None else None
) )
return discharge_hours_bin, eautocharge_hours_index, washingstart_int return discharge_hours_bin, eautocharge_hours_index, washingstart_int
def setup_deap_environment(self, opti_param: dict[str, Any], start_hour: int) -> None: def setup_deap_environment(self, opti_param: dict[str, Any], start_hour: int) -> None:
@ -371,12 +377,13 @@ class optimization_problem:
# 0.01 for i in range(start_hour, self.prediction_hours) if discharge_hours_bin[i] == 0.0 # 0.01 for i in range(start_hour, self.prediction_hours) if discharge_hours_bin[i] == 0.0
# ) # )
# Penalty for charging EV, with battery full # if self.optimize_ev:
len_soc = len(o["EAuto_SoC_pro_Stunde"]) # # Penalty for charging EV, with battery full
eautocharge_hours = np.array(eautocharge_hours_index) # len_soc = len(o["EAuto_SoC_pro_Stunde"])
relevant_indices = eautocharge_hours[-len_soc:] # eautocharge_hours = np.array(eautocharge_hours_index)
mask = (o["EAuto_SoC_pro_Stunde"] == 100) & (relevant_indices != 0) # relevant_indices = eautocharge_hours[-len_soc:]
gesamtbilanz += np.sum(mask) * 0.01 # mask = (o["EAuto_SoC_pro_Stunde"] == 100) & (relevant_indices != 0)
# gesamtbilanz += np.sum(mask) * 0.1
individual.extra_data = ( # type: ignore[attr-defined] individual.extra_data = ( # type: ignore[attr-defined]
o["Gesamtbilanz_Euro"], o["Gesamtbilanz_Euro"],
@ -419,7 +426,7 @@ class optimization_problem:
# Insert the start solution into the population if provided # Insert the start solution into the population if provided
if start_solution is not None: if start_solution is not None:
for _ in range(3): for _ in range(10):
population.insert(0, creator.Individual(start_solution)) population.insert(0, creator.Individual(start_solution))
# Run the evolutionary algorithm # Run the evolutionary algorithm
@ -451,7 +458,7 @@ class optimization_problem:
parameters: OptimizationParameters, parameters: OptimizationParameters,
start_hour: int, start_hour: int,
worst_case: bool = False, worst_case: bool = False,
ngen: int = 200, ngen: int = 400,
) -> OptimizeResponse: ) -> OptimizeResponse:
"""Perform EMS (Energy Management System) optimization and visualize results.""" """Perform EMS (Energy Management System) optimization and visualize results."""
einspeiseverguetung_euro_pro_wh = np.full( einspeiseverguetung_euro_pro_wh = np.full(