from flask import Flask, jsonify, request import numpy as np from datetime import datetime from modules.class_load import * from modules.class_ems import * from modules.class_pv_forecast import * from modules.class_akku import * from modules.class_strompreis import * from modules.class_heatpump import * from modules.class_load_container import * from modules.class_eauto import * from pprint import pprint import matplotlib matplotlib.use('Agg') # Setzt das Backend auf Agg import matplotlib.pyplot as plt from modules.visualize import * from deap import base, creator, tools, algorithms import numpy as np import random import os start_hour = datetime.now().hour prediction_hours = 24 hohe_strafe = 10.0 def evaluate_inner(individual, ems): discharge_hours_bin = individual[0::2] eautocharge_hours_float = individual[1::2] #print(discharge_hours_bin) #print(len(eautocharge_hours_float)) ems.reset() ems.set_akku_discharge_hours(discharge_hours_bin) ems.set_eauto_charge_hours(eautocharge_hours_float) o = ems.simuliere(start_hour) return o # Fitness-Funktion (muss Ihre EnergieManagementSystem-Logik integrieren) def evaluate(individual,ems,parameter): o = evaluate_inner(individual,ems) gesamtbilanz = o["Gesamtbilanz_Euro"] # Überprüfung, ob der Mindest-SoC erreicht wird final_soc = ems.eauto.ladezustand_in_prozent() # Nimmt den SoC am Ende des Optimierungszeitraums strafe = 0.0 strafe = max(0,(parameter['eauto_min_soc']-ems.eauto.ladezustand_in_prozent()) * hohe_strafe ) # print(ems.eauto.charge_array) # print(ems.eauto.ladezustand_in_prozent()) # print(strafe) gesamtbilanz += strafe gesamtbilanz += o["Gesamt_Verluste"]/1000.0 return (gesamtbilanz,) # Werkzeug-Setup creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", list, fitness=creator.FitnessMin) toolbox = base.Toolbox() toolbox.register("attr_bool", random.randint, 0, 1) toolbox.register("attr_bool", random.randint, 0, 1) toolbox.register("individual", tools.initCycle, creator.Individual, (toolbox.attr_bool,toolbox.attr_bool), n=prediction_hours) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("mate", tools.cxTwoPoint) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selTournament, tournsize=3) # Genetischer Algorithmus def optimize(): population = toolbox.population(n=500) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", np.mean) stats.register("min", np.min) stats.register("max", np.max) algorithms.eaMuPlusLambda(population, toolbox, 100, 200, cxpb=0.3, mutpb=0.3, ngen=500, stats=stats, halloffame=hof, verbose=True) #algorithms.eaSimple(population, toolbox, cxpb=0.2, mutpb=0.2, ngen=1000, stats=stats, halloffame=hof, verbose=True) return hof[0] app = Flask(__name__) # Dummy-Funktion für die Durchführung der Simulation/Optimierung # Ersetzen Sie diese Logik durch Ihren eigentlichen Optimierungscode def durchfuehre_simulation(parameter): ############ # Parameter ############ date = (datetime.now().date() + timedelta(hours = prediction_hours)).strftime("%Y-%m-%d") date_now = datetime.now().strftime("%Y-%m-%d") akku_size = parameter['pv_akku_cap'] # Wh year_energy = parameter['year_energy'] #2000*1000 #Wh einspeiseverguetung_cent_pro_wh = np.full(prediction_hours, parameter["einspeiseverguetung_euro_pro_wh"]) #= # € / Wh 7/(1000.0*100.0) max_heizleistung = parameter['max_heizleistung'] #1000 # 5 kW Heizleistung wp = Waermepumpe(max_heizleistung,prediction_hours) pv_forecast_url = parameter['pv_forecast_url'] #"https://api.akkudoktor.net/forecast?lat=52.52&lon=13.405&power=5000&azimuth=-10&tilt=7&powerInvertor=10000&horizont=20,27,22,20&power=4800&azimuth=-90&tilt=7&powerInvertor=10000&horizont=30,30,30,50&power=1400&azimuth=-40&tilt=60&powerInvertor=2000&horizont=60,30,0,30&power=1600&azimuth=5&tilt=45&powerInvertor=1400&horizont=45,25,30,60&past_days=5&cellCoEff=-0.36&inverterEfficiency=0.8&albedo=0.25&timezone=Europe%2FBerlin&hourly=relativehumidity_2m%2Cwindspeed_10m" akku = PVAkku(kapazitaet_wh=akku_size,hours=prediction_hours,start_soc_prozent=parameter["pv_soc"]) 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]) # 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]) eauto = PVAkku(kapazitaet_wh=parameter["eauto_cap"], hours=prediction_hours, lade_effizienz=parameter["eauto_charge_efficiency"], entlade_effizienz=1.0, max_ladeleistung_w=parameter["eauto_charge_power"] ,start_soc_prozent=parameter["eauto_soc"]) eauto.set_charge_per_hour(laden_moeglich) min_soc_eauto = parameter['eauto_min_soc'] gesamtlast = Gesamtlast() ############### # Load Forecast ############### lf = LoadForecast(filepath=r'load_profiles.npz', year_energy=year_energy) #leistung_haushalt = lf.get_daily_stats(date)[0,...] # Datum anpassen leistung_haushalt = lf.get_stats_for_date_range(date_now,date)[0,...].flatten() gesamtlast.hinzufuegen("Haushalt", leistung_haushalt) ############### # PV Forecast ############### #PVforecast = PVForecast(filepath=os.path.join(r'test_data', r'pvprognose.json')) PVforecast = PVForecast(prediction_hours = prediction_hours, url=pv_forecast_url) pv_forecast = PVforecast.get_pv_forecast_for_date_range(date_now,date) #get_forecast_for_date(date) temperature_forecast = PVforecast.get_temperature_for_date_range(date_now,date) ############### # Strompreise ############### filepath = os.path.join (r'test_data', r'strompreise_akkudokAPI.json') # Pfad zur JSON-Datei anpassen #price_forecast = HourlyElectricityPriceForecast(source=filepath) price_forecast = HourlyElectricityPriceForecast(source="https://api.akkudoktor.net/prices?start="+date_now+"&end="+date+"") specific_date_prices = price_forecast.get_price_for_daterange(date_now,date) ############### # WP ############## leistung_wp = wp.simulate_24h(temperature_forecast) gesamtlast.hinzufuegen("Heatpump", leistung_wp) 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) o = ems.simuliere(start_hour) def evaluate_wrapper(individual): return evaluate(individual, ems, parameter) toolbox.register("evaluate", evaluate_wrapper) start_solution = optimize() best_solution = start_solution o = evaluate_inner(best_solution, ems) eauto = ems.eauto.to_dict() discharge_hours_bin = best_solution[0::2] eautocharge_hours_float = best_solution[1::2] #print(o) visualisiere_ergebnisse(gesamtlast, pv_forecast, specific_date_prices, o,best_solution[0::2],best_solution[1::2] , temperature_forecast, start_hour, prediction_hours) #print(eauto) return {"discharge_hours_bin":discharge_hours_bin, "eautocharge_hours_float":eautocharge_hours_float ,"result":o ,"eauto_obj":eauto} @app.route('/simulation', methods=['POST']) def simulation(): if request.method == 'POST': parameter = request.json # Erforderliche Parameter prüfen erforderliche_parameter = [ 'pv_akku_cap', 'year_energy',"einspeiseverguetung_euro_pro_wh", 'max_heizleistung', 'pv_forecast_url', 'eauto_min_soc', "eauto_cap","eauto_charge_efficiency","eauto_charge_power","eauto_soc","pv_soc"] for p in erforderliche_parameter: if p not in parameter: return jsonify({"error": f"Fehlender Parameter: {p}"}), 400 # Optional Typen der Parameter prüfen und sicherstellen, dass sie den Erwartungen entsprechen # if not isinstance(parameter['start_hour'], int): # return jsonify({"error": "start_hour muss vom Typ int sein"}), 400 # Simulation durchführen ergebnis = durchfuehre_simulation(parameter) return jsonify(ergebnis) if __name__ == '__main__': app.run(debug=True, host="0.0.0.0")