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Inverter Klasse hinzugefügt
Kleinere Bugs bei max_WR Leistung behoben
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@ -105,13 +105,16 @@ class PVAkku:
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# Aktualisierung des Ladezustands ohne die Kapazität zu überschreiten
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geladene_menge_ohne_verlust = min(self.kapazitaet_wh - self.soc_wh, effektive_lademenge)
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geladene_menge = geladene_menge_ohne_verlust * self.lade_effizienz
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self.soc_wh += geladene_menge
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verluste_wh = geladene_menge_ohne_verlust* (1.0-self.lade_effizienz)
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# Zusätzliche Verluste, wenn die Energiezufuhr die Kapazitätsgrenze überschreitet
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# zusatz_verluste_wh = 0
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# if effektive_lademenge > geladene_menge_ohne_verlust:
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140
modules/class_inverter.py
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140
modules/class_inverter.py
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@ -0,0 +1,140 @@
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class Wechselrichter:
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def __init__(self, max_leistung_wh, akku):
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self.max_leistung_wh = max_leistung_wh # Maximale Leistung, die der Wechselrichter verarbeiten kann
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self.akku = akku # Verbindung zu einem Akku-Objekt
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def energie_verarbeiten(self, erzeugung, verbrauch, hour):
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verluste = 0
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netzeinspeisung = 0
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netzbezug = 0.0
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eigenverbrauch = 0.0
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#eigenverbrauch = min(erzeugung, verbrauch) # Direkt verbrauchte Energie
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if erzeugung > verbrauch:
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if verbrauch > self.max_leistung_wh:
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verluste += erzeugung - self.max_leistung_wh
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restleistung_nach_verbrauch = self.max_leistung_wh - verbrauch
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netzbezug = -restleistung_nach_verbrauch
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eigenverbrauch = self.max_leistung_wh
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else:
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# if hour==10:
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# print("PV:",erzeugung)
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# print("Load:",verbrauch)
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# print("Max Leist:",self.max_leistung_wh)
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# PV > WR Leistung dann Verlust
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# Load
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restleistung_nach_verbrauch = erzeugung-verbrauch #min(self.max_leistung_wh - verbrauch, erzeugung-verbrauch)
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# Akku
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geladene_energie, verluste_laden_akku = self.akku.energie_laden(restleistung_nach_verbrauch, hour)
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rest_überschuss = restleistung_nach_verbrauch - geladene_energie
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# if hour == 12:
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# print("Erzeugung:",erzeugung)
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# print("Last:",verbrauch)
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# print("Akku:",geladene_energie)
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# print("Akku:",self.akku.ladezustand_in_prozent())
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# print("RestÜberschuss"," - ",rest_überschuss)
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# print("RestLesitung WR:",self.max_leistung_wh - verbrauch)
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# Einspeisung, restliche WR Kapazität
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if rest_überschuss > self.max_leistung_wh - verbrauch:
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netzeinspeisung = self.max_leistung_wh - verbrauch
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verluste += rest_überschuss - netzeinspeisung
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else:
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netzeinspeisung = rest_überschuss
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verluste += verluste_laden_akku
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eigenverbrauch = verbrauch
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else:
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benötigte_energie = verbrauch - erzeugung
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max_akku_leistung = self.akku.max_ladeleistung_w
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rest_ac_leistung = max(max_akku_leistung - erzeugung,0)
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if benötigte_energie < rest_ac_leistung:
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aus_akku, akku_entladeverluste = self.akku.energie_abgeben(benötigte_energie, hour)
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else:
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aus_akku, akku_entladeverluste = self.akku.energie_abgeben(rest_ac_leistung, hour)
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verluste += akku_entladeverluste
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netzbezug = benötigte_energie - aus_akku
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eigenverbrauch = erzeugung + aus_akku
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# if erzeugung > verbrauch:
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# if verbrauch > self.max_leistung_wh:
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# else:
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# überschuss = self.max_leistung_wh - verbrauch
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# geladene_energie, verluste_laden_akku = self.akku.energie_laden(überschuss, hour)
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# rest_überschuss = überschuss - geladene_energie
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# verluste += verluste_laden_akku
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# if (rest_überschuss > self.max_leistung_wh):
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# netzeinspeisung = self.max_leistung_wh
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# verluste += rest_überschuss - self.max_leistung_wh
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# else:
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# netzeinspeisung = rest_überschuss
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# eigenverbrauch = verbrauch
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# else:
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# benötigte_energie = verbrauch - erzeugung
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# max_akku_leistung = self.akku.max_ladeleistung_w
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# rest_ac_leistung = max(max_akku_leistung - erzeugung,0)
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# if benötigte_energie < rest_ac_leistung:
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# aus_akku, akku_entladeverluste = self.akku.energie_abgeben(benötigte_energie, hour)
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# else:
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# aus_akku, akku_entladeverluste = self.akku.energie_abgeben(rest_ac_leistung, hour)
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# verluste += akku_entladeverluste
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# netzbezug = benötigte_energie - aus_akku
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# eigenverbrauch = erzeugung + aus_akku
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# # Berechnung der gesamten verarbeiteten Energie
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# total_verarbeitet = eigenverbrauch
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# if total_verarbeitet > self.max_leistung_wh:
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# verluste += total_verarbeitet - self.max_leistung_wh
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return netzeinspeisung, netzbezug, verluste, eigenverbrauch
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# def energie_verarbeiten(self, erzeugung, verbrauch, hour):
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# verluste = 0
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# netzeinspeisung = 0
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# netzbezug = 0.0
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# eigenverbrauch = 0.0
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# if erzeugung > verbrauch:
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# überschuss = erzeugung - verbrauch
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# geladene_energie, verluste_laden_akku = self.akku.energie_laden(überschuss, hour)
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# verluste += verluste_laden_akku
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# netzeinspeisung = überschuss - geladene_energie-verluste_laden_akku
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# eigenverbrauch = verbrauch
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# netzbezug = 0.0
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# # Noch Netzbezug nötig
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# else:
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# netzeinspeisung = 0.0
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# benötigte_energie = verbrauch - erzeugung
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# aus_akku, akku_entladeverluste = self.akku.energie_abgeben(benötigte_energie, hour)
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# verluste += akku_entladeverluste
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# netzbezug = benötigte_energie - aus_akku
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# eigenverbrauch = erzeugung+aus_akku
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# return netzeinspeisung, netzbezug, verluste, eigenverbrauch # Keine Einspeisung, Netzbezug, aus Akku, Verluste, Eigenverbrauch
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@ -99,6 +99,7 @@ class optimization_problem:
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# Fitness-Funktion (muss Ihre EnergieManagementSystem-Logik integrieren)
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def evaluate(self,individual,ems,parameter,start_hour,worst_case):
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try:
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o = self.evaluate_inner(individual,ems,start_hour)
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except:
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@ -121,7 +122,7 @@ class optimization_problem:
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strafe = 0.0
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strafe = max(0,(parameter['eauto_min_soc']-ems.eauto.ladezustand_in_prozent()) * self.strafe )
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gesamtbilanz += strafe
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gesamtbilanz += o["Gesamt_Verluste"]/1000.0
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gesamtbilanz += o["Gesamt_Verluste"]/10000.0
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return (gesamtbilanz,)
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@ -131,7 +132,7 @@ class optimization_problem:
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# Genetischer Algorithmus
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def optimize(self,start_solution=None):
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population = self.toolbox.population(n=200)
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population = self.toolbox.population(n=1000)
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hof = tools.HallOfFame(1)
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stats = tools.Statistics(lambda ind: ind.fitness.values)
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@ -144,8 +145,8 @@ class optimization_problem:
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if start_solution is not None and start_solution != -1:
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population.insert(0, creator.Individual(start_solution))
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#algorithms.eaMuPlusLambda(population, self.toolbox, 100, 200, cxpb=0.4, mutpb=0.5, ngen=500, stats=stats, halloffame=hof, verbose=True)
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algorithms.eaSimple(population, self.toolbox, cxpb=0.4, mutpb=0.4, ngen=100, stats=stats, halloffame=hof, verbose=True)
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#algorithms.eaMuPlusLambda(population, self.toolbox, 100, 200, cxpb=0.2, mutpb=0.2, ngen=500, stats=stats, halloffame=hof, verbose=True)
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algorithms.eaSimple(population, self.toolbox, cxpb=0.2, mutpb=0.2, ngen=200, stats=stats, halloffame=hof, verbose=True)
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member = {"bilanz":[],"verluste":[],"nebenbedingung":[]}
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for ind in population:
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@ -161,6 +162,7 @@ class optimization_problem:
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def optimierung_ems(self,parameter=None, start_hour=None,worst_case=False):
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############
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# Parameter
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############
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@ -256,7 +258,7 @@ class optimization_problem:
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self.toolbox.register("evaluate", evaluate_wrapper)
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start_solution, extra_data = self.optimize(start_params)
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best_solution = start_solution
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best_solution = start_params
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o = self.evaluate_inner(best_solution, ems,start_hour)
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eauto = ems.eauto.to_dict()
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spuelstart_int = None
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@ -269,8 +271,8 @@ class optimization_problem:
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print(o)
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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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os.system("cp visualisierungsergebnisse.pdf ~/")
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@ -170,52 +170,52 @@ def visualisiere_ergebnisse(gesamtlast, pv_forecast, strompreise, ergebnisse, d
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if extra_data != None:
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plt.figure(figsize=(14, 10))
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plt.subplot(1, 2, 1)
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f1 = np.array(extra_data["verluste"])
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f2 = np.array(extra_data["bilanz"])
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n1 = np.array(extra_data["nebenbedingung"])
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scatter = plt.scatter(f1, f2, c=n1, cmap='viridis')
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plt.figure(figsize=(14, 10))
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plt.subplot(1, 2, 1)
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f1 = np.array(extra_data["verluste"])
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f2 = np.array(extra_data["bilanz"])
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n1 = np.array(extra_data["nebenbedingung"])
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scatter = plt.scatter(f1, f2, c=n1, cmap='viridis')
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# Farblegende hinzufügen
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plt.colorbar(scatter, label='Nebenbedingung')
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# Farblegende hinzufügen
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plt.colorbar(scatter, label='Nebenbedingung')
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pdf.savefig() # Speichert die komplette Figure im PDF
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plt.close() # Schließt die Figure
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pdf.savefig() # Speichert die komplette Figure im PDF
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plt.close() # Schließt die Figure
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plt.figure(figsize=(14, 10))
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filtered_verluste = np.array([v for v, n in zip(extra_data["verluste"], extra_data["nebenbedingung"]) if n < 0.01])
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filtered_bilanz = np.array([b for b, n in zip(extra_data["bilanz"], extra_data["nebenbedingung"]) if n< 0.01])
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beste_verluste = min(filtered_verluste)
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schlechteste_verluste = max(filtered_verluste)
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beste_bilanz = min(filtered_bilanz)
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schlechteste_bilanz = max(filtered_bilanz)
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plt.figure(figsize=(14, 10))
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filtered_verluste = np.array([v for v, n in zip(extra_data["verluste"], extra_data["nebenbedingung"]) if n < 0.01])
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filtered_bilanz = np.array([b for b, n in zip(extra_data["bilanz"], extra_data["nebenbedingung"]) if n< 0.01])
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beste_verluste = min(filtered_verluste)
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schlechteste_verluste = max(filtered_verluste)
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beste_bilanz = min(filtered_bilanz)
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schlechteste_bilanz = max(filtered_bilanz)
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data = [filtered_verluste, filtered_bilanz]
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labels = ['Verluste', 'Bilanz']
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# Plot-Erstellung
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fig, axs = plt.subplots(1, 2, figsize=(10, 6), sharey=False) # Zwei Subplots, getrennte y-Achsen
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data = [filtered_verluste, filtered_bilanz]
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labels = ['Verluste', 'Bilanz']
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# Plot-Erstellung
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fig, axs = plt.subplots(1, 2, figsize=(10, 6), sharey=False) # Zwei Subplots, getrennte y-Achsen
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# Erster Boxplot für Verluste
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#axs[0].boxplot(data[0])
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axs[0].violinplot(data[0],
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showmeans=True,
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showmedians=True)
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axs[0].set_title('Verluste')
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axs[0].set_xticklabels(['Verluste'])
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# Erster Boxplot für Verluste
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#axs[0].boxplot(data[0])
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axs[0].violinplot(data[0],
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showmeans=True,
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showmedians=True)
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axs[0].set_title('Verluste')
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axs[0].set_xticklabels(['Verluste'])
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# Zweiter Boxplot für Bilanz
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axs[1].violinplot(data[1],
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showmeans=True,
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showmedians=True)
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axs[1].set_title('Bilanz')
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axs[1].set_xticklabels(['Bilanz'])
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# Zweiter Boxplot für Bilanz
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axs[1].violinplot(data[1],
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showmeans=True,
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showmedians=True)
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axs[1].set_title('Bilanz')
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axs[1].set_xticklabels(['Bilanz'])
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# Feinabstimmung
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plt.tight_layout()
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# Feinabstimmung
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plt.tight_layout()
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pdf.savefig() # Speichert den aktuellen Figure-State im PDF
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446
test.py
446
test.py
@ -1,14 +1,16 @@
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from flask import Flask, jsonify, request
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import numpy as np
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from datetime import datetime
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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_strompreis 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_eauto import *
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from modules.class_optimize import *
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# 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_strompreis 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_eauto import *
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from modules.class_optimize import *
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from pprint import pprint
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import matplotlib.pyplot as plt
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@ -19,193 +21,165 @@ import random
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import os
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start_hour = 11
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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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start_hour = 8
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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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akku_size = 30000 # Wh
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year_energy = 2000*1000 #Wh
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einspeiseverguetung_cent_pro_wh = np.full(prediction_hours, 7/(1000.0*100.0)) # € / Wh
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# akku_size = 30000 # Wh
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# year_energy = 2000*1000 #Wh
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# einspeiseverguetung_cent_pro_wh = np.full(prediction_hours, 7/(1000.0*100.0)) # € / Wh
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max_heizleistung = 1000 # 5 kW Heizleistung
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wp = Waermepumpe(max_heizleistung,prediction_hours)
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# 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) #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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# akku = PVAkku(akku_size,prediction_hours)
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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])
|
||||
#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
|
||||
# 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
|
||||
#[1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]
|
||||
individual = [1, 1, # 0
|
||||
0, 1, # 1
|
||||
0, 0, # 2
|
||||
0, 1, # 3
|
||||
0, 0, # 4
|
||||
1, 0, # 5
|
||||
0, 1, # 6
|
||||
0, 0, # 7
|
||||
0, 0, # 8
|
||||
1, 0,
|
||||
0, 0,
|
||||
1, 0,
|
||||
0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]
|
||||
parameter= {'pv_soc': 92.4052, 'pv_akku_cap': 30000, 'year_energy': 4100000, 'einspeiseverguetung_euro_pro_wh': 7e-05, 'max_heizleistung': 1000, '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', 'eauto_min_soc': 100, 'eauto_cap': 60000, 'eauto_charge_efficiency': 0.95, 'eauto_charge_power': 5500, 'eauto_soc': 77, 'pvpowernow': 211.137503624, 'start_solution': individual, 'haushaltsgeraet_wh': 937, 'haushaltsgeraet_dauer': 0}
|
||||
|
||||
|
||||
|
||||
|
||||
#Gesamtlast
|
||||
#############
|
||||
gesamtlast = Gesamtlast()
|
||||
opt_class = optimization_problem(prediction_hours=24, strafe=10)
|
||||
ergebnis = opt_class.optimierung_ems(parameter=parameter, start_hour=start_hour)
|
||||
|
||||
|
||||
# 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)
|
||||
# #Gesamtlast
|
||||
# #############
|
||||
# gesamtlast = Gesamtlast()
|
||||
|
||||
# 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)
|
||||
# # 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)
|
||||
# # 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)
|
||||
# # EAuto
|
||||
# ######################
|
||||
# # leistung_eauto = eauto.get_stuendliche_last()
|
||||
# # soc_eauto = eauto.get_stuendlicher_soc()
|
||||
# # gesamtlast.hinzufuegen("eauto", leistung_eauto)
|
||||
|
||||
# print(gesamtlast.gesamtlast_berechnen())
|
||||
# # 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 / 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)
|
||||
# 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"])
|
||||
# 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)
|
||||
# #visualisiere_ergebnisse(gesamtlast, pv_forecast, specific_date_prices, o,discharge_array,laden_moeglich, temperature_forecast, start_hour, prediction_hours)
|
||||
|
||||
|
||||
|
||||
# Optimierung
|
||||
# # Optimierung
|
||||
|
||||
def evaluate_inner(individual):
|
||||
#print(individual)
|
||||
discharge_hours_bin = individual[0::2]
|
||||
eautocharge_hours_float = individual[1::2]
|
||||
# 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)
|
||||
# #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)
|
||||
# #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())
|
||||
# #ems.set_gesamtlast(gesamtlast.gesamtlast_berechnen())
|
||||
|
||||
o = ems.simuliere(start_hour)
|
||||
return o, eauto
|
||||
# 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"]
|
||||
# # 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,)
|
||||
# # 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)
|
||||
# toolbox.register("attr_bool", random.randint, 0, 1)
|
||||
# toolbox.register("individual", tools.initCycle, creator.Individual, (toolbox.attr_bool,toolbox.attr_bool), n=prediction_hours)
|
||||
|
||||
|
||||
# start_individual = toolbox.individual()
|
||||
# start_individual[:] = start_solution
|
||||
|
||||
# toolbox.register("population", tools.initRepeat, list, toolbox.individual)
|
||||
|
||||
@ -216,8 +190,7 @@ print("Start Lösung:", start_solution)
|
||||
|
||||
# # Genetischer Algorithmus
|
||||
# def optimize():
|
||||
# population = toolbox.population(n=1000)
|
||||
# population[0] = start_individual
|
||||
# population = toolbox.population(n=500)
|
||||
# hof = tools.HallOfFame(1)
|
||||
|
||||
# stats = tools.Statistics(lambda ind: ind.fitness.values)
|
||||
@ -225,73 +198,120 @@ print("Start Lösung:", start_solution)
|
||||
# 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.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]
|
||||
|
||||
# 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')
|
||||
|
||||
# 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
|
||||
# # # 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())
|
||||
# # # 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
|
||||
# # 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)
|
||||
# # if __name__ == '__main__':
|
||||
# # app.run(debug=True)
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user