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- Akku mit Verlusten + Bug in der test.py Gesamtlast kein update
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80
test.py
80
test.py
@@ -20,7 +20,7 @@ import os
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prediction_hours = 48
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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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@@ -32,12 +32,13 @@ max_heizleistung = 1000 # 5 kW Heizleistung
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wp = Waermepumpe(max_heizleistung,prediction_hours)
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akku = PVAkku(akku_size,prediction_hours)
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discharge_array = np.full(prediction_hours,1)
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laden_moeglich = np.full(prediction_hours,1)
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eauto = EAuto(soc=60, capacity = 60000, power_charge = 7000, load_allowed = laden_moeglich)
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discharge_array = np.full(prediction_hours,1) #np.array([1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0]) #
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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 = 20
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hohe_strafe = 10.0
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@@ -52,12 +53,14 @@ gesamtlast = Gesamtlast()
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lf = LoadForecast(filepath=r'load_profiles.npz', year_energy=year_energy)
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#leistung_haushalt = lf.get_daily_stats(date)[0,...] # Datum anpassen
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leistung_haushalt = lf.get_stats_for_date_range(date_now,date)[0,...].flatten()
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# print(date_now," ",date)
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# print(leistung_haushalt.shape)
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gesamtlast.hinzufuegen("Haushalt", leistung_haushalt)
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# PV Forecast
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###############
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#PVforecast = PVForecast(filepath=os.path.join(r'test_data', r'pvprognose.json'))
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PVforecast = PVForecast(url="https://api.akkudoktor.net/forecast?lat=50.8588&lon=7.3747&power=5400&azimuth=-10&tilt=7&powerInvertor=2500&horizont=20,40,30,30&power=4800&azimuth=-90&tilt=7&powerInvertor=2500&horizont=20,40,45,50&power=1480&azimuth=-90&tilt=70&powerInvertor=1120&horizont=60,45,30,70&power=1600&azimuth=5&tilt=60&powerInvertor=1200&horizont=60,45,30,70&past_days=5&cellCoEff=-0.36&inverterEfficiency=0.8&albedo=0.25&timezone=Europe%2FBerlin&hourly=relativehumidity_2m%2Cwindspeed_10m")
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PVforecast = PVForecast(prediction_hours = prediction_hours, url="https://api.akkudoktor.net/forecast?lat=50.8588&lon=7.3747&power=5400&azimuth=-10&tilt=7&powerInvertor=2500&horizont=20,40,30,30&power=4800&azimuth=-90&tilt=7&powerInvertor=2500&horizont=20,40,45,50&power=1480&azimuth=-90&tilt=70&powerInvertor=1120&horizont=60,45,30,70&power=1600&azimuth=5&tilt=60&powerInvertor=1200&horizont=60,45,30,70&past_days=5&cellCoEff=-0.36&inverterEfficiency=0.8&albedo=0.25&timezone=Europe%2FBerlin&hourly=relativehumidity_2m%2Cwindspeed_10m")
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pv_forecast = PVforecast.get_pv_forecast_for_date_range(date_now,date) #get_forecast_for_date(date)
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temperature_forecast = PVforecast.get_temperature_for_date_range(date_now,date)
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@@ -79,19 +82,20 @@ 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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print(gesamtlast.gesamtlast_berechnen())
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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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o = ems.simuliere(0)#ems.simuliere_ab_jetzt()
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pprint(o)
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pprint(o["Gesamtbilanz_Euro"])
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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)
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#visualisiere_ergebnisse(gesamtlast,leistung_haushalt,leistung_wp, pv_forecast, specific_date_prices, o, soc_eauto)
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#sys.exit()
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@@ -100,23 +104,40 @@ visualisiere_ergebnisse(gesamtlast,leistung_haushalt,leistung_wp, pv_forecast, s
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# Fitness-Funktion (muss Ihre EnergieManagementSystem-Logik integrieren)
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def evaluate(individual):
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# Hier müssen Sie Ihre Logik einbauen, um die Gesamtbilanz zu berechnen
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# basierend auf dem gegebenen `individual` (discharge_array)
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#akku.set_discharge_per_hour(individual)
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ems.reset()
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ems.set_akku_discharge_hours(individual)
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o = ems.simuliere_ab_jetzt()
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eauto.reset()
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ems.set_akku_discharge_hours(individual[:prediction_hours])
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eauto.set_laden_moeglich(individual[prediction_hours:])
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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_gesamtlast(gesamtlast.gesamtlast_berechnen())
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o = ems.simuliere(0)
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gesamtbilanz = o["Gesamtbilanz_Euro"]
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#print(individual, " ",gesamtbilanz)
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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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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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#if strafe > 0.0:
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# print(min_soc_eauto," - ",final_soc,"*10 = ",strafe)
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return (gesamtbilanz,)
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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("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, prediction_hours)
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toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, prediction_hours*2)
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toolbox.register("population", tools.initRepeat, list, toolbox.individual)
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toolbox.register("evaluate", evaluate)
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@@ -126,7 +147,7 @@ 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=100)
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population = toolbox.population(n=500)
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hof = tools.HallOfFame(1)
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stats = tools.Statistics(lambda ind: ind.fitness.values)
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@@ -134,20 +155,31 @@ def optimize():
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stats.register("min", np.min)
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stats.register("max", np.max)
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algorithms.eaSimple(population, toolbox, cxpb=0.5, mutpb=0.2, ngen=100,
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algorithms.eaSimple(population, toolbox, cxpb=0.4, mutpb=0.3, ngen=100,
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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 = ems.simuliere_ab_jetzt()
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pprint(o["Gesamtbilanz_Euro"])
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#ems.set_akku_discharge_hours(best_solution)
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ems.reset()
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eauto.reset()
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ems.set_akku_discharge_hours(best_solution[:prediction_hours])
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eauto.set_laden_moeglich(best_solution[prediction_hours:])
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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_gesamtlast(gesamtlast.gesamtlast_berechnen())
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o = ems.simuliere(0)
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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(load,leistung_haushalt,leistung_wp, pv_forecast, specific_date_prices, o)
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visualisiere_ergebnisse(gesamtlast,leistung_haushalt,leistung_wp, pv_forecast, specific_date_prices, o,soc_eauto,best_solution[:prediction_hours],best_solution[prediction_hours:] )
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# for data in forecast.get_forecast_data():
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