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EV possible currents -> Work now (new Mutate Function)
-> Some Penalties are removed (deprecated) AC Charge -> Parameter in Optimization -1,0,1 -> Missing integration in class_ems
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Andreas
Andreas
parent
e65eb6b6dd
commit
6881710295
@ -1,6 +1,7 @@
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#!/usr/bin/env python3
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import json
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import time
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# Import necessary modules from the project
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from akkudoktoreos.class_optimize import optimization_problem
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@ -241,7 +242,7 @@ parameter = {
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# Electricity price forecast (48 hours)
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"strompreis_euro_pro_wh": strompreis_euro_pro_wh,
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# Minimum SOC for electric car
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"eauto_min_soc": 80,
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"eauto_min_soc": 20,
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# Electric car battery capacity (Wh)
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"eauto_cap": 60000,
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# Charging efficiency of the electric car
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@ -262,6 +263,9 @@ parameter = {
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"min_soc_prozent": 15,
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}
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# Startzeit nehmen
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start_time = time.time()
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# Initialize the optimization problem
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opt_class = optimization_problem(
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prediction_hours=48, strafe=10, optimization_hours=24, verbose=True, fixed_seed=42
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@ -270,8 +274,16 @@ opt_class = optimization_problem(
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# Perform the optimisation based on the provided parameters and start hour
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ergebnis = opt_class.optimierung_ems(parameter=parameter, start_hour=start_hour)
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# Endzeit nehmen
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end_time = time.time()
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# Berechnete Zeit ausgeben
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elapsed_time = end_time - start_time
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print(f"Elapsed time: {elapsed_time:.4f} seconds")
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# Print or visualize the result
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# pprint(ergebnis)
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json_data = json.dumps(ergebnis)
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print(json_data)
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#json_data = json.dumps(ergebnis)
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#print(json_data)
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@ -84,7 +84,7 @@ class PVAkku:
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return (self.soc_wh / self.kapazitaet_wh) * 100
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def energie_abgeben(self, wh, hour):
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if self.discharge_array[hour] == 0:
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if self.discharge_array[hour] == 0 and self.discharge_array[hour] == -1:
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return 0.0, 0.0 # No energy discharge and no losses
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# Calculate the maximum energy that can be discharged considering min_soc and efficiency
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@ -28,6 +28,7 @@ class EnergieManagementSystem:
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self.akku.set_discharge_per_hour(ds)
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def set_eauto_charge_hours(self, ds: List[int]) -> None:
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self.eauto.set_charge_per_hour(ds)
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def set_haushaltsgeraet_start(self, ds: List[int], global_start_hour: int = 0) -> None:
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@ -39,7 +39,7 @@ class optimization_problem:
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) -> Tuple[List[int], List[float], Optional[int]]:
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"""
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Split the individual solution into its components:
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1. Discharge hours (binary),
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1. Discharge hours (-1 (Charge),0 (Nothing),1 (Discharge)),
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2. Electric vehicle charge hours (float),
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3. Dishwasher start time (integer if applicable).
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"""
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@ -69,8 +69,8 @@ class optimization_problem:
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# Initialize toolbox with attributes and operations
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self.toolbox = base.Toolbox()
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self.toolbox.register("attr_bool", random.randint, 0, 1)
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self.toolbox.register("attr_float", random.uniform, 0, 1)
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self.toolbox.register("attr_discharge_state", random.randint, -1, 1)
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self.toolbox.register("attr_ev_charge_index", random.randint, 0, len(moegliche_ladestroeme_in_prozent) - 1)
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self.toolbox.register("attr_int", random.randint, start_hour, 23)
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# Register individual creation method based on household appliance parameter
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@ -78,8 +78,8 @@ class optimization_problem:
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self.toolbox.register(
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"individual",
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lambda: creator.Individual(
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[self.toolbox.attr_bool() for _ in range(self.prediction_hours)]
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+ [self.toolbox.attr_float() for _ in range(self.prediction_hours)]
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[self.toolbox.attr_discharge_state() for _ in range(self.prediction_hours)]
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+ [self.toolbox.attr_ev_charge_index() for _ in range(self.prediction_hours)]
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+ [self.toolbox.attr_int()]
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),
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)
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@ -87,15 +87,48 @@ class optimization_problem:
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self.toolbox.register(
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"individual",
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lambda: creator.Individual(
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[self.toolbox.attr_bool() for _ in range(self.prediction_hours)]
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+ [self.toolbox.attr_float() for _ in range(self.prediction_hours)]
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[self.toolbox.attr_discharge_state() for _ in range(self.prediction_hours)]
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+ [self.toolbox.attr_ev_charge_index() for _ in range(self.prediction_hours)]
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),
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)
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# Register population, mating, mutation, and selection functions
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self.toolbox.register("population", tools.initRepeat, list, self.toolbox.individual)
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self.toolbox.register("mate", tools.cxTwoPoint)
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self.toolbox.register("mutate", tools.mutFlipBit, indpb=0.1)
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#self.toolbox.register("mutate", tools.mutFlipBit, indpb=0.1)
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# Register separate mutation functions for each type of value:
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# - Discharge state mutation (-1, 0, 1)
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self.toolbox.register("mutate_discharge", tools.mutUniformInt, low=0, up=1, indpb=0.1)
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# - Float mutation for EV charging values
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self.toolbox.register("mutate_ev_charge_index", tools.mutUniformInt, low=0, up=len(moegliche_ladestroeme_in_prozent) - 1, indpb=0.1)
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# - Start hour mutation for household devices
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self.toolbox.register("mutate_hour", tools.mutUniformInt, low=start_hour, up=23, indpb=0.3)
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# Custom mutation function that applies type-specific mutations
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def mutate(individual):
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# Mutate the discharge state genes (-1, 0, 1)
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individual[:self.prediction_hours], = self.toolbox.mutate_discharge(
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individual[:self.prediction_hours]
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)
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# Mutate the EV charging indices
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ev_charge_part = individual[self.prediction_hours : self.prediction_hours * 2]
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ev_charge_part_mutated, = self.toolbox.mutate_ev_charge_index(ev_charge_part)
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individual[self.prediction_hours : self.prediction_hours * 2] = ev_charge_part_mutated
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# Mutate the appliance start hour if present
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if len(individual) > self.prediction_hours * 2:
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appliance_part = [individual[-1]]
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appliance_part_mutated, = self.toolbox.mutate_hour(appliance_part)
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individual[-1] = appliance_part_mutated[0]
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return (individual,)
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# Register custom mutation function
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self.toolbox.register("mutate", mutate)
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self.toolbox.register("select", tools.selTournament, tournsize=3)
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def evaluate_inner(
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@ -106,16 +139,22 @@ class optimization_problem:
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using the provided individual solution.
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"""
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ems.reset()
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discharge_hours_bin, eautocharge_hours_float, spuelstart_int = self.split_individual(
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discharge_hours_bin, eautocharge_hours_index, spuelstart_int = self.split_individual(
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individual
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)
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if self.opti_param.get("haushaltsgeraete", 0) > 0:
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ems.set_haushaltsgeraet_start(spuelstart_int, global_start_hour=start_hour)
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ems.set_akku_discharge_hours(discharge_hours_bin)
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eautocharge_hours_float[self.prediction_hours - self.fixed_eauto_hours :] = [
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0.0
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eautocharge_hours_index[self.prediction_hours - self.fixed_eauto_hours :] = [
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0
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] * self.fixed_eauto_hours
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eautocharge_hours_float = [
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moegliche_ladestroeme_in_prozent[i] for i in eautocharge_hours_index
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]
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ems.set_eauto_charge_hours(eautocharge_hours_float)
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return ems.simuliere(start_hour)
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@ -134,16 +173,17 @@ class optimization_problem:
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o = self.evaluate_inner(individual, ems, start_hour)
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except Exception as e:
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return (100000.0,) # Return a high penalty in case of an exception
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gesamtbilanz = o["Gesamtbilanz_Euro"] * (-1.0 if worst_case else 1.0)
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discharge_hours_bin, eautocharge_hours_float, _ = self.split_individual(individual)
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max_ladeleistung = np.max(moegliche_ladestroeme_in_prozent)
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# Penalty for not discharging
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# Small Penalty for not discharging
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gesamtbilanz += sum(
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0.01 for i in range(self.prediction_hours) if discharge_hours_bin[i] == 0.0
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)
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# Penalty for charging the electric vehicle during restricted hours
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gesamtbilanz += sum(
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self.strafe
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@ -151,13 +191,6 @@ class optimization_problem:
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if eautocharge_hours_float[i] != 0.0
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)
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# Penalty for exceeding maximum charge power
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gesamtbilanz += sum(
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self.strafe * 10
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for ladeleistung in eautocharge_hours_float
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if ladeleistung > max_ladeleistung
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)
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# Penalty for not meeting the minimum SOC (State of Charge) requirement
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if parameter["eauto_min_soc"] - ems.eauto.ladezustand_in_prozent() <= 0.0:
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gesamtbilanz += sum(
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@ -205,7 +238,7 @@ class optimization_problem:
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self.toolbox,
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mu=100,
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lambda_=200,
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cxpb=0.5,
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cxpb=0.7,
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mutpb=0.3,
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ngen=ngen,
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stats=stats,
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