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Andreas 2024-12-20 17:39:40 +01:00 committed by Andreas
parent 85395076be
commit b0cd910403
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(
self, individual: list[int]
) -> tuple[list[int], Optional[list[int]], Optional[int]]:
"""Split the individual solution into its components.
) -> Tuple[np.ndarray, Optional[np.ndarray], Optional[int]]:
"""
Split the individual solution into its components.
Components:
1. Discharge hours (binary),
2. Electric vehicle charge hours (float),
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_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 = (
individual[self.prediction_hours : self.prediction_hours * 2]
np.array(individual[self.prediction_hours : self.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:
@ -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
# )
# Penalty for charging EV, with battery full
len_soc = len(o["EAuto_SoC_pro_Stunde"])
eautocharge_hours = np.array(eautocharge_hours_index)
relevant_indices = eautocharge_hours[-len_soc:]
mask = (o["EAuto_SoC_pro_Stunde"] == 100) & (relevant_indices != 0)
gesamtbilanz += np.sum(mask) * 0.01
# if self.optimize_ev:
# # Penalty for charging EV, with battery full
# len_soc = len(o["EAuto_SoC_pro_Stunde"])
# eautocharge_hours = np.array(eautocharge_hours_index)
# relevant_indices = eautocharge_hours[-len_soc:]
# mask = (o["EAuto_SoC_pro_Stunde"] == 100) & (relevant_indices != 0)
# gesamtbilanz += np.sum(mask) * 0.1
individual.extra_data = ( # type: ignore[attr-defined]
o["Gesamtbilanz_Euro"],
@ -419,7 +426,7 @@ class optimization_problem:
# Insert the start solution into the population if provided
if start_solution is not None:
for _ in range(3):
for _ in range(10):
population.insert(0, creator.Individual(start_solution))
# Run the evolutionary algorithm
@ -451,7 +458,7 @@ class optimization_problem:
parameters: OptimizationParameters,
start_hour: int,
worst_case: bool = False,
ngen: int = 200,
ngen: int = 400,
) -> OptimizeResponse:
"""Perform EMS (Energy Management System) optimization and visualize results."""
einspeiseverguetung_euro_pro_wh = np.full(