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EV Bugs

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Andreas
2024-10-14 10:46:14 +02:00
committed by Andreas
parent 55842dba1d
commit 6238ba4daa
4 changed files with 68 additions and 91 deletions
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109
src/akkudoktoreos/class_optimize.py
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@@ -59,6 +59,43 @@ class optimization_problem:
discharge = np.where(discharge_hours_bin > 0, discharge_hours_bin, 0)
return charge, discharge
# Custom mutation function that applies type-specific mutations
def mutate(self,individual):
# Mutate the discharge state genes (-1, 0, 1)
individual[:self.prediction_hours], = self.toolbox.mutate_discharge(
individual[:self.prediction_hours]
)
if self.optimize_ev:
# Mutate the EV charging indices
ev_charge_part = individual[self.prediction_hours : self.prediction_hours * 2]
ev_charge_part_mutated, = self.toolbox.mutate_ev_charge_index(ev_charge_part)
ev_charge_part_mutated[self.prediction_hours - self.fixed_eauto_hours :] = [0] * self.fixed_eauto_hours
individual[self.prediction_hours : self.prediction_hours * 2] = ev_charge_part_mutated
# Mutate the appliance start hour if present
if self.opti_param["haushaltsgeraete"] > 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):
# Start with discharge states for the individual
individual_components = [self.toolbox.attr_discharge_state() for _ in range(self.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.prediction_hours)]
# Add the start time of the household appliance if it's being optimized
if self.opti_param["haushaltsgeraete"] > 0:
individual_components += [self.toolbox.attr_int()]
return creator.Individual(individual_components)
def split_individual(
self, individual: List[float]
@@ -100,43 +137,10 @@ class optimization_problem:
self.toolbox.register("attr_ev_charge_index", random.randint, 0, len(possible_ev_charge_currents) - 1)
self.toolbox.register("attr_int", random.randint, start_hour, 23)
# Function to create an individual based on the conditions
def create_individual():
# Start with discharge states for the individual
individual_components = [self.toolbox.attr_discharge_state() for _ in range(self.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.prediction_hours)]
# Add the start time of the household appliance if it's being optimized
if self.opti_param["haushaltsgeraete"] > 0:
individual_components += [self.toolbox.attr_int()]
return creator.Individual(individual_components)
# Register individual creation function
self.toolbox.register("individual", create_individual)
# # Register individual creation method based on household appliance parameter
# if opti_param["haushaltsgeraete"] > 0:
# self.toolbox.register(
# "individual",
# lambda: creator.Individual(
# [self.toolbox.attr_discharge_state() for _ in range(self.prediction_hours)]
# + [self.toolbox.attr_ev_charge_index() for _ in range(self.prediction_hours)]
# + [self.toolbox.attr_int()]
# ),
# )
# else:
# self.toolbox.register(
# "individual",
# lambda: creator.Individual(
# [self.toolbox.attr_discharge_state() for _ in range(self.prediction_hours)]
# + [self.toolbox.attr_ev_charge_index() for _ in range(self.prediction_hours)]
# ),
# )
self.toolbox.register("individual", self.create_individual)
# Register population, mating, mutation, and selection functions
self.toolbox.register("population", tools.initRepeat, list, self.toolbox.individual)
@@ -150,32 +154,8 @@ class optimization_problem:
# - Start hour mutation for household devices
self.toolbox.register("mutate_hour", tools.mutUniformInt, low=start_hour, up=23, indpb=0.1)
# Custom mutation function that applies type-specific mutations
def mutate(individual):
# Mutate the discharge state genes (-1, 0, 1)
individual[:self.prediction_hours], = self.toolbox.mutate_discharge(
individual[:self.prediction_hours]
)
if self.optimize_ev:
# Mutate the EV charging indices
ev_charge_part = individual[self.prediction_hours : self.prediction_hours * 2]
ev_charge_part_mutated, = self.toolbox.mutate_ev_charge_index(ev_charge_part)
ev_charge_part_mutated[self.prediction_hours - self.fixed_eauto_hours :] = [0] * self.fixed_eauto_hours
individual[self.prediction_hours : self.prediction_hours * 2] = ev_charge_part_mutated
# Mutate the appliance start hour if present
if self.opti_param["haushaltsgeraete"] > 0:
appliance_part = [individual[-1]]
appliance_part_mutated, = self.toolbox.mutate_hour(appliance_part)
individual[-1] = appliance_part_mutated[0]
return (individual,)
# Register custom mutation function
self.toolbox.register("mutate", mutate)
self.toolbox.register("mutate", self.mutate)
self.toolbox.register("select", tools.selTournament, tournsize=3)
@@ -198,13 +178,13 @@ class optimization_problem:
ems.set_akku_discharge_hours(discharge)
ems.set_akku_charge_hours(charge)
#print(charge)
eautocharge_hours_float = [
possible_ev_charge_currents[i] for i in eautocharge_hours_index
]
if self.optimize_ev:
eautocharge_hours_float = [
possible_ev_charge_currents[i] for i in eautocharge_hours_index
]
ems.set_eauto_charge_hours(eautocharge_hours_float)
return ems.simuliere(start_hour)
def evaluate(
@@ -226,7 +206,6 @@ class optimization_problem:
gesamtbilanz = o["Gesamtbilanz_Euro"] * (-1.0 if worst_case else 1.0)
discharge_hours_bin, eautocharge_hours_float, _ = self.split_individual(individual)
#max_ladeleistung = np.max(possible_ev_charge_currents)
# Small Penalty for not discharging
gesamtbilanz += sum(