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Verluste stündlich / gesamt werden ausgegeben und mit minimiert
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99
test.py
99
test.py
@@ -19,7 +19,7 @@ import random
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import os
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start_hour = 11
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prediction_hours = 24
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date = (datetime.now().date() + timedelta(hours = prediction_hours)).strftime("%Y-%m-%d")
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date_now = datetime.now().strftime("%Y-%m-%d")
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@@ -36,8 +36,9 @@ discharge_array = np.full(prediction_hours,1) #np.array([1, 0, 1, 0, 1, 1, 1, 1,
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laden_moeglich = np.full(prediction_hours,1) # np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0])
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#np.full(prediction_hours,1)
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eauto = EAuto(soc=10, capacity = 60000, power_charge = 7000, load_allowed = laden_moeglich)
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min_soc_eauto = 10
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eauto = PVAkku(kapazitaet_wh=60000, hours=prediction_hours, lade_effizienz=0.95, entlade_effizienz=1.0, max_ladeleistung_w=10000 ,start_soc_prozent=10)
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eauto.set_charge_per_hour(laden_moeglich)
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min_soc_eauto = 80
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hohe_strafe = 10.0
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@@ -82,24 +83,25 @@ gesamtlast.hinzufuegen("Heatpump", leistung_wp)
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# EAuto
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######################
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leistung_eauto = eauto.get_stuendliche_last()
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soc_eauto = eauto.get_stuendlicher_soc()
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gesamtlast.hinzufuegen("eauto", leistung_eauto)
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# leistung_eauto = eauto.get_stuendliche_last()
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# soc_eauto = eauto.get_stuendlicher_soc()
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# gesamtlast.hinzufuegen("eauto", leistung_eauto)
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# print(gesamtlast.gesamtlast_berechnen())
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# EMS / Stromzähler Bilanz
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ems = EnergieManagementSystem(akku, gesamtlast.gesamtlast_berechnen(), pv_forecast, specific_date_prices, einspeiseverguetung_cent_pro_wh)
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#akku=None, pv_prognose_wh=None, strompreis_cent_pro_wh=None, einspeiseverguetung_cent_pro_wh=None, eauto=None, gesamtlast=None
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ems = EnergieManagementSystem(akku=akku, gesamtlast = gesamtlast, pv_prognose_wh=pv_forecast, strompreis_cent_pro_wh=specific_date_prices, einspeiseverguetung_cent_pro_wh=einspeiseverguetung_cent_pro_wh, eauto=eauto)
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o = ems.simuliere(0)#ems.simuliere_ab_jetzt()
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o = ems.simuliere(start_hour)#ems.simuliere_ab_jetzt()
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#pprint(o)
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#pprint(o["Gesamtbilanz_Euro"])
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#visualisiere_ergebnisse(gesamtlast,leistung_haushalt,leistung_wp, pv_forecast, specific_date_prices, o, soc_eauto)
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#visualisiere_ergebnisse(gesamtlast, pv_forecast, specific_date_prices, o,discharge_array,laden_moeglich, temperature_forecast, start_hour, prediction_hours)
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#sys.exit()
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# Optimierung
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@@ -111,17 +113,18 @@ def evaluate_inner(individual):
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#print(discharge_hours_bin)
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#print(len(eautocharge_hours_float))
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ems.reset()
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eauto.reset()
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#eauto.reset()
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ems.set_akku_discharge_hours(discharge_hours_bin)
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eauto.set_laden_moeglich(eautocharge_hours_float)
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eauto.berechne_ladevorgang()
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leistung_eauto = eauto.get_stuendliche_last()
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gesamtlast.hinzufuegen("eauto", leistung_eauto)
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ems.set_eauto_charge_hours(eautocharge_hours_float)
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ems.set_gesamtlast(gesamtlast.gesamtlast_berechnen())
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#eauto.set_laden_moeglich(eautocharge_hours_float)
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#eauto.berechne_ladevorgang()
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#leistung_eauto = eauto.get_stuendliche_last()
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#gesamtlast.hinzufuegen("eauto", leistung_eauto)
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o = ems.simuliere(0)
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#ems.set_gesamtlast(gesamtlast.gesamtlast_berechnen())
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o = ems.simuliere(start_hour)
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return o, eauto
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# Fitness-Funktion (muss Ihre EnergieManagementSystem-Logik integrieren)
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@@ -130,12 +133,13 @@ def evaluate(individual):
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gesamtbilanz = o["Gesamtbilanz_Euro"]
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# Überprüfung, ob der Mindest-SoC erreicht wird
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final_soc = eauto.get_stuendlicher_soc()[-1] # Nimmt den SoC am Ende des Optimierungszeitraums
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final_soc = eauto.ladezustand_in_prozent() # Nimmt den SoC am Ende des Optimierungszeitraums
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strafe = 0.0
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#if final_soc < min_soc_eauto:
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# Fügt eine Strafe hinzu, wenn der Mindest-SoC nicht erreicht wird
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strafe = max(0,(min_soc_eauto - final_soc) * hohe_strafe ) # `hohe_strafe` ist ein vorher festgelegter Strafwert
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gesamtbilanz += strafe
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gesamtbilanz += o["Gesamt_Verluste"]/1000.0
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return (gesamtbilanz,)
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@@ -229,65 +233,14 @@ print("Beste Lösung:", best_solution)
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#ems.set_akku_discharge_hours(best_solution)
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o,eauto = evaluate_inner(best_solution)
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soc_eauto = eauto.get_stuendlicher_soc()
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print(soc_eauto)
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pprint(o)
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pprint(eauto.get_stuendlicher_soc())
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# # Werkzeug-Setup
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# creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
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# creator.create("Individual", list, fitness=creator.FitnessMin)
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# toolbox = base.Toolbox()
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# toolbox.register("attr_bool", random.randint, 0, 1)
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# toolbox.register("attr_float", random.uniform, 0.0, 1.0)
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# toolbox.register("individual", tools.initCycle, creator.Individual, (toolbox.attr_bool,toolbox.attr_float), n=prediction_hours)
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# start_individual = toolbox.individual()
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# start_individual[:] = start_solution
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# toolbox.register("population", tools.initRepeat, list, toolbox.individual)
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# toolbox.register("evaluate", evaluate)
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# toolbox.register("mate", tools.cxTwoPoint)
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# toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
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# toolbox.register("select", tools.selTournament, tournsize=3)
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# # Genetischer Algorithmus
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# def optimize():
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# population = toolbox.population(n=1000)
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# population[0] = start_individual
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# hof = tools.HallOfFame(1)
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# stats = tools.Statistics(lambda ind: ind.fitness.values)
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# stats.register("avg", np.mean)
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# stats.register("min", np.min)
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# stats.register("max", np.max)
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# algorithms.eaMuPlusLambda(population, toolbox, 100, 200, cxpb=0.5, mutpb=0.2, ngen=1000, stats=stats, halloffame=hof, verbose=True)
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# #algorithms.eaSimple(population, toolbox, cxpb=0.2, mutpb=0.2, ngen=1000, stats=stats, halloffame=hof, verbose=True)
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# return hof[0]
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# best_solution = optimize()
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# print("Beste Lösung:", best_solution)
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# #ems.set_akku_discharge_hours(best_solution)
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# o,eauto = evaluate_inner(best_solution)
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# soc_eauto = eauto.get_stuendlicher_soc()
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# print(soc_eauto)
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# pprint(o)
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# pprint(eauto.get_stuendlicher_soc())
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visualisiere_ergebnisse(gesamtlast,leistung_haushalt,leistung_wp, pv_forecast, specific_date_prices, o,soc_eauto,best_solution[0::2],best_solution[1::2] , temperature_forecast)
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#visualisiere_ergebnisse(gesamtlast,leistung_haushalt,leistung_wp, pv_forecast, specific_date_prices, o,soc_eauto,best_solution[0::2],best_solution[1::2] , temperature_forecast)
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visualisiere_ergebnisse(gesamtlast, pv_forecast, specific_date_prices, o,best_solution[0::2],best_solution[1::2] , temperature_forecast, start_hour, prediction_hours)
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# for data in forecast.get_forecast_data():
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