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.pyplot as plt from modules.visualize import * from deap import base, creator, tools, algorithms import numpy as np import random import os start_hour = 11 prediction_hours = 24 date = (datetime.now().date() + timedelta(hours = prediction_hours)).strftime("%Y-%m-%d") date_now = datetime.now().strftime("%Y-%m-%d") akku_size = 30000 # Wh year_energy = 2000*1000 #Wh einspeiseverguetung_cent_pro_wh = np.full(prediction_hours, 7/(1000.0*100.0)) # € / Wh max_heizleistung = 1000 # 5 kW Heizleistung wp = Waermepumpe(max_heizleistung,prediction_hours) akku = PVAkku(akku_size,prediction_hours) 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]) #np.full(prediction_hours,1) eauto = PVAkku(kapazitaet_wh=60000, hours=prediction_hours, lade_effizienz=0.95, entlade_effizienz=1.0, max_ladeleistung_w=10000 ,start_soc_prozent=10) eauto.set_charge_per_hour(laden_moeglich) min_soc_eauto = 80 hohe_strafe = 10.0 #Gesamtlast ############# 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() # print(date_now," ",date) # print(leistung_haushalt.shape) 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="https://api.akkudoktor.net/forecast?lat=50.8588&lon=7.3747&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") 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) # print("13:",specific_date_prices[13]) # print("14:",specific_date_prices[14]) # print("15:",specific_date_prices[15]) # sys.exit() # WP ############## leistung_wp = wp.simulate_24h(temperature_forecast) gesamtlast.hinzufuegen("Heatpump", leistung_wp) # EAuto ###################### # leistung_eauto = eauto.get_stuendliche_last() # soc_eauto = eauto.get_stuendlicher_soc() # gesamtlast.hinzufuegen("eauto", leistung_eauto) # print(gesamtlast.gesamtlast_berechnen()) # EMS / Stromzähler Bilanz #akku=None, pv_prognose_wh=None, strompreis_cent_pro_wh=None, einspeiseverguetung_cent_pro_wh=None, eauto=None, gesamtlast=None 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)#ems.simuliere_ab_jetzt() #pprint(o) #pprint(o["Gesamtbilanz_Euro"]) #visualisiere_ergebnisse(gesamtlast, pv_forecast, specific_date_prices, o,discharge_array,laden_moeglich, temperature_forecast, start_hour, prediction_hours) # Optimierung def evaluate_inner(individual): #print(individual) discharge_hours_bin = individual[0::2] eautocharge_hours_float = individual[1::2] #print(discharge_hours_bin) #print(len(eautocharge_hours_float)) ems.reset() #eauto.reset() ems.set_akku_discharge_hours(discharge_hours_bin) ems.set_eauto_charge_hours(eautocharge_hours_float) #eauto.set_laden_moeglich(eautocharge_hours_float) #eauto.berechne_ladevorgang() #leistung_eauto = eauto.get_stuendliche_last() #gesamtlast.hinzufuegen("eauto", leistung_eauto) #ems.set_gesamtlast(gesamtlast.gesamtlast_berechnen()) o = ems.simuliere(start_hour) return o, eauto # Fitness-Funktion (muss Ihre EnergieManagementSystem-Logik integrieren) def evaluate(individual): o,eauto = evaluate_inner(individual) gesamtbilanz = o["Gesamtbilanz_Euro"] # Überprüfung, ob der Mindest-SoC erreicht wird final_soc = eauto.ladezustand_in_prozent() # Nimmt den SoC am Ende des Optimierungszeitraums strafe = 0.0 #if final_soc < min_soc_eauto: # Fügt eine Strafe hinzu, wenn der Mindest-SoC nicht erreicht wird strafe = max(0,(min_soc_eauto - final_soc) * hohe_strafe ) # `hohe_strafe` ist ein vorher festgelegter Strafwert 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("evaluate", evaluate) 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, 50, 100, cxpb=0.5, mutpb=0.5, 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] start_solution = optimize() print("Start Lösung:", start_solution) # # 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_float", random.uniform, 0.0, 1.0) # toolbox.register("individual", tools.initCycle, creator.Individual, (toolbox.attr_bool,toolbox.attr_float), n=prediction_hours) # start_individual = toolbox.individual() # start_individual[:] = start_solution # toolbox.register("population", tools.initRepeat, list, toolbox.individual) # toolbox.register("evaluate", evaluate) # 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=1000) # population[0] = start_individual # 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=0.5, mutpb=0.2, ngen=1000, 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] # best_solution = optimize() best_solution = start_solution print("Beste Lösung:", best_solution) #ems.set_akku_discharge_hours(best_solution) o,eauto = evaluate_inner(best_solution) # soc_eauto = eauto.get_stuendlicher_soc() # print(soc_eauto) # pprint(o) # pprint(eauto.get_stuendlicher_soc()) #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) visualisiere_ergebnisse(gesamtlast, pv_forecast, specific_date_prices, o,best_solution[0::2],best_solution[1::2] , temperature_forecast, start_hour, prediction_hours) # for data in forecast.get_forecast_data(): # print(data.get_date_time(), data.get_dc_power(), data.get_ac_power(), data.get_windspeed_10m(), data.get_temperature())for data in forecast.get_forecast_data(): # app = Flask(__name__) # @app.route('/getdata', methods=['GET']) # def get_data(): # # Hole das Datum aus den Query-Parametern # date_str = request.args.get('date') # year_energy = request.args.get('year_energy') # try: # # Konvertiere das Datum in ein datetime-Objekt # date_obj = datetime.strptime(date_str, '%Y-%m-%d') # filepath = r'.\load_profiles.npz' # Pfad zur JSON-Datei anpassen # lf = cl.LoadForecast(filepath=filepath, year_energy=float(year_energy)) # specific_date_prices = lf.get_daily_stats('2024-02-16') # # Berechne den Tag des Jahres # #day_of_year = date_obj.timetuple().tm_yday # # Konvertiere den Tag des Jahres in einen String, falls die Schlüssel als Strings gespeichert sind # #day_key = int(day_of_year) # #print(day_key) # # Überprüfe, ob der Tag im Jahr in den Daten vorhanden ist # array_list = lf.get_daily_stats(date_str) # pprint(array_list) # pprint(array_list.shape) # if array_list.shape == (2,24): # #if day_key < len(load_profiles_exp): # # Konvertiere das Array in eine Liste für die JSON-Antwort # #((load_profiles_exp_l[day_key]).tolist(),(load_profiles_std_l)[day_key].tolist()) # return jsonify({date_str: array_list.tolist()}) # else: # return jsonify({"error": "Datum nicht gefunden"}), 404 # except ValueError: # # Wenn das Datum nicht im richtigen Format ist oder ungültig ist # return jsonify({"error": "Ungültiges Datum"}), 400 # if __name__ == '__main__': # app.run(debug=True)