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Lastprognose entkoppelt, an den Optimierer wird jetzt nur das Array
übergeben. So sind unterschiedliche / eigenen Prognosen möglich
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@ -76,13 +76,40 @@ def flask_strompreis():
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return jsonify(specific_date_prices.tolist())
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@app.route('/gesamtlast', methods=['GET'])
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def flask_gesamtlast():
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if request.method == 'GET':
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year_energy = float(request.args.get("year_energy"))
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date_now,date = get_start_enddate(prediction_hours,startdate=datetime.now().date())
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###############
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# Load Forecast
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###############
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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] # Nur Erwartungswert!
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gesamtlast = Gesamtlast(prediction_hours=prediction_hours)
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gesamtlast.hinzufuegen("Haushalt", leistung_haushalt)
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# ###############
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# # WP
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# ##############
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# leistung_wp = wp.simulate_24h(temperature_forecast)
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# gesamtlast.hinzufuegen("Heatpump", leistung_wp)
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last = gesamtlast.gesamtlast_berechnen()
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print(last)
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#print(specific_date_prices)
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return jsonify(last.tolist())
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@app.route('/optimize', methods=['POST'])
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def flask_optimize():
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if request.method == 'POST':
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parameter = request.json
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# Erforderliche Parameter prüfen
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erforderliche_parameter = [ 'strompreis_euro_pro_wh','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","start_solution","pvpowernow","haushaltsgeraet_dauer","haushaltsgeraet_wh"]
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erforderliche_parameter = [ 'strompreis_euro_pro_wh', "gesamtlast",'pv_akku_cap', "einspeiseverguetung_euro_pro_wh", 'pv_forecast_url', 'eauto_min_soc', "eauto_cap","eauto_charge_efficiency","eauto_charge_power","eauto_soc","pv_soc","start_solution","pvpowernow","haushaltsgeraet_dauer","haushaltsgeraet_wh"]
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for p in erforderliche_parameter:
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if p not in parameter:
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return jsonify({"error": f"Fehlender Parameter: {p}"}), 400
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@ -51,7 +51,7 @@ class EnergieManagementSystem:
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eauto_soc_pro_stunde = []
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verluste_wh_pro_stunde = []
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haushaltsgeraet_wh_pro_stunde = []
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lastkurve_wh = self.gesamtlast.gesamtlast_berechnen()
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lastkurve_wh = self.gesamtlast
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assert len(lastkurve_wh) == len(self.pv_prognose_wh) == len(self.strompreis_euro_pro_wh), f"Arraygrößen stimmen nicht überein: Lastkurve = {len(lastkurve_wh)}, PV-Prognose = {len(self.pv_prognose_wh)}, Strompreis = {len(self.strompreis_euro_pro_wh)}"
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@ -4,6 +4,7 @@ from modules.class_load import *
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from modules.class_ems import *
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from modules.class_pv_forecast import *
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from modules.class_akku import *
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from modules.class_heatpump import *
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from modules.class_load_container import *
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from modules.class_inverter import *
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@ -173,13 +174,9 @@ class optimization_problem:
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#print("Start_date:",date_now)
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akku_size = parameter['pv_akku_cap'] # Wh
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year_energy = parameter['year_energy'] #2000*1000 #Wh
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einspeiseverguetung_euro_pro_wh = np.full(self.prediction_hours, parameter["einspeiseverguetung_euro_pro_wh"]) #= # € / Wh 7/(1000.0*100.0)
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max_heizleistung = parameter['max_heizleistung'] #1000 # 5 kW Heizleistung
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wp = Waermepumpe(max_heizleistung,self.prediction_hours)
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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"
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akku = PVAkku(kapazitaet_wh=akku_size,hours=self.prediction_hours,start_soc_prozent=parameter["pv_soc"], max_ladeleistung_w=5000)
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@ -191,7 +188,6 @@ class optimization_problem:
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eauto.set_charge_per_hour(laden_moeglich)
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min_soc_eauto = parameter['eauto_min_soc']
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start_params = parameter['start_solution']
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gesamtlast = Gesamtlast(prediction_hours=self.prediction_hours)
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###############
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# spuelmaschine
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@ -204,18 +200,12 @@ class optimization_problem:
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spuelmaschine = None
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###############
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# Load Forecast
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###############
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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] # Nur Erwartungswert!
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gesamtlast.hinzufuegen("Haushalt", leistung_haushalt)
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###############
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# PV Forecast
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@ -240,15 +230,10 @@ class optimization_problem:
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print(specific_date_prices)
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#print("https://api.akkudoktor.net/prices?start="+date_now+"&end="+date)
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###############
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# WP
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##############
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leistung_wp = wp.simulate_24h(temperature_forecast)
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gesamtlast.hinzufuegen("Heatpump", leistung_wp)
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wr = Wechselrichter(5000, akku)
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ems = EnergieManagementSystem(gesamtlast = gesamtlast, pv_prognose_wh=pv_forecast, strompreis_euro_pro_wh=specific_date_prices, einspeiseverguetung_euro_pro_wh=einspeiseverguetung_euro_pro_wh, eauto=eauto, haushaltsgeraet=spuelmaschine,wechselrichter=wr)
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ems = EnergieManagementSystem(gesamtlast = parameter["gesamtlast"], pv_prognose_wh=pv_forecast, strompreis_euro_pro_wh=specific_date_prices, einspeiseverguetung_euro_pro_wh=einspeiseverguetung_euro_pro_wh, eauto=eauto, haushaltsgeraet=spuelmaschine,wechselrichter=wr)
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o = ems.simuliere(start_hour)
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###############
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@ -281,7 +266,7 @@ class optimization_problem:
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print(parameter)
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print(best_solution)
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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, self.prediction_hours,einspeiseverguetung_euro_pro_wh,extra_data=extra_data)
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visualisiere_ergebnisse(parameter["gesamtlast"], pv_forecast, specific_date_prices, o,best_solution[0::2],best_solution[1::2] , temperature_forecast, start_hour, self.prediction_hours,einspeiseverguetung_euro_pro_wh,extra_data=extra_data)
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os.system("cp visualisierungsergebnisse.pdf ~/")
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# 'Eigenverbrauch_Wh_pro_Stunde': eigenverbrauch_wh_pro_stunde,
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@ -22,13 +22,13 @@ def visualisiere_ergebnisse(gesamtlast, pv_forecast, strompreise, ergebnisse, d
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plt.subplot(3, 3, 1)
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stunden = np.arange(0, prediction_hours)
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gesamtlast_array = np.array(gesamtlast)
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# Einzellasten plotten
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for name, last_array in gesamtlast.lasten.items():
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plt.plot(stunden, last_array, label=f'{name} (Wh)', marker='o')
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#for name, last_array in gesamtlast.lasten.items():
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plt.plot(stunden, gesamtlast_array, label=f'Last (Wh)', marker='o')
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# Gesamtlast berechnen und plotten
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gesamtlast_array = gesamtlast.gesamtlast_berechnen()
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gesamtlast_array = np.array(gesamtlast)
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plt.plot(stunden, gesamtlast_array, label='Gesamtlast (Wh)', marker='o', linewidth=2, linestyle='--')
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plt.xlabel('Stunde')
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plt.ylabel('Last (Wh)')
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