From aa29f3e528d593e4ef540d98b25ee4b02b2d27f8 Mon Sep 17 00:00:00 2001 From: Bla Bla Date: Thu, 28 Mar 2024 08:15:17 +0100 Subject: [PATCH] Flask Server --- flask_server.py | 219 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 219 insertions(+) create mode 100644 flask_server.py diff --git a/flask_server.py b/flask_server.py new file mode 100644 index 0000000..0b49a00 --- /dev/null +++ b/flask_server.py @@ -0,0 +1,219 @@ +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 = 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, 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] + + + + + +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=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" + + 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") + +