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E-Auto/Wallbox wird jetzt mit diskreten Ladezuständen versehen, in der
config.py einstellbar Jeder DisCharge = 0 (Akkus nicht benutzen) wird mit 1Cent Strafe belegt, da die Lastverteilung fehlt. Also wenn es egal ist, soll er den Akku anschalten.
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@ -30,7 +30,7 @@ class Wechselrichter:
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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+verluste_laden_akku)
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# if hour == 12:
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# if hour == 1:
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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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@ -44,8 +44,8 @@ class Wechselrichter:
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verluste += rest_überschuss - netzeinspeisung
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else:
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netzeinspeisung = rest_überschuss
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#if hour ==10:
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# print(rest_überschuss," ",restleistung_nach_verbrauch, " Gela:",geladene_energie," Ver:",verluste_laden_akku)
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#if hour ==14:
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# print("Erz:",erzeugung," Last:",verbrauch, " RestPV:",rest_überschuss," ",restleistung_nach_verbrauch, " Gela:",geladene_energie," Ver:",verluste_laden_akku," Einsp:",netzeinspeisung)
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verluste += verluste_laden_akku
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@ -71,73 +71,4 @@ class Wechselrichter:
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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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@ -23,7 +23,8 @@ from deap import base, creator, tools, algorithms
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import numpy as np
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import random
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import os
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from config import *
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def isfloat(num):
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try:
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@ -38,7 +39,7 @@ class optimization_problem:
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self.strafe = strafe
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self.opti_param = None
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self.fixed_eauto_hours = prediction_hours-optimization_hours
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self.possible_charge_values = moegliche_ladestroeme_in_prozent
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def split_individual(self, individual):
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@ -74,7 +75,9 @@ class optimization_problem:
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# PARAMETER
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self.toolbox = base.Toolbox()
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self.toolbox.register("attr_bool", random.randint, 0, 1)
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self.toolbox.register("attr_float", random.uniform, 0, 1) # Für kontinuierliche Werte zwischen 0 und 1 (z.B. für E-Auto-Ladeleistung)
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#self.toolbox.register("attr_float", random.uniform, 0, 1) # Für kontinuierliche Werte zwischen 0 und 1 (z.B. für E-Auto-Ladeleistung)
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self.toolbox.register("attr_choice", random.choice, self.possible_charge_values) # Für diskrete Ladeströme
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self.toolbox.register("attr_int", random.randint, start_hour, 23)
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###################
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@ -83,14 +86,14 @@ class optimization_problem:
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if opti_param["haushaltsgeraete"]>0:
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def create_individual():
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attrs = [self.toolbox.attr_bool() for _ in range(self.prediction_hours)] # 24 Bool-Werte für Entladen
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attrs += [self.toolbox.attr_float() for _ in range(self.prediction_hours)] # 24 Float-Werte für Laden
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attrs += [self.toolbox.attr_choice() for _ in range(self.prediction_hours)] # 24 Float-Werte für Laden
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attrs.append(self.toolbox.attr_int()) # Haushaltsgerät-Startzeit
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return creator.Individual(attrs)
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else:
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def create_individual():
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attrs = [self.toolbox.attr_bool() for _ in range(self.prediction_hours)] # 24 Bool-Werte für Entladen
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attrs += [self.toolbox.attr_float() for _ in range(self.prediction_hours)] # 24 Float-Werte für Laden
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attrs += [self.toolbox.attr_choice() for _ in range(self.prediction_hours)] # 24 Float-Werte für Laden
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return creator.Individual(attrs)
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@ -113,14 +116,14 @@ class optimization_problem:
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#discharge_hours_bin = np.full(self.prediction_hours,0)
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ems.set_akku_discharge_hours(discharge_hours_bin)
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# Setze die festen Werte für die letzten x Stunden
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for i in range(self.prediction_hours - self.fixed_eauto_hours, self.prediction_hours):
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eautocharge_hours_float[i] = 0.0 # Setze die letzten x Stunden auf einen festen Wert (oder vorgegebenen Wert)
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#print(eautocharge_hours_float)
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ems.set_eauto_charge_hours(eautocharge_hours_float)
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@ -141,9 +144,15 @@ class optimization_problem:
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if worst_case:
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gesamtbilanz = gesamtbilanz * -1.0
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discharge_hours_bin, eautocharge_hours_float, spuelstart_int = self.split_individual(individual)
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# Für jeden Discharge 0, eine kleine Strafe von 1 Cent, da die Lastvertelung noch fehlt. Also wenn es egal ist, soll er den Akku entladen lassen
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for i in range(0, self.prediction_hours):
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if discharge_hours_bin[i] == 0.0: # Wenn die letzten x Stunden von einem festen Wert abweichen
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gesamtbilanz += 0.01 # Bestrafe den Optimierer
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# E-Auto nur die ersten self.fixed_eauto_hours
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eautocharge_hours_float = individual[self.prediction_hours:self.prediction_hours * 2]
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for i in range(self.prediction_hours - self.fixed_eauto_hours, self.prediction_hours):
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if eautocharge_hours_float[i] != 0.0: # Wenn die letzten x Stunden von einem festen Wert abweichen
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gesamtbilanz += self.strafe # Bestrafe den Optimierer
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@ -186,7 +195,7 @@ 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=400)
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population = self.toolbox.population(n=600)
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hof = tools.HallOfFame(1)
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stats = tools.Statistics(lambda ind: ind.fitness.values)
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@ -199,7 +208,7 @@ 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, mu=200, lambda_=300, cxpb=0.3, mutpb=0.4, ngen=500, stats=stats, halloffame=hof, verbose=True)
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algorithms.eaMuPlusLambda(population, self.toolbox, mu=200, lambda_=300, cxpb=0.3, mutpb=0.4, ngen=300, stats=stats, halloffame=hof, verbose=True)
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#algorithms.eaSimple(population, self.toolbox, cxpb=0.2, mutpb=0.2, ngen=1000, stats=stats, halloffame=hof, verbose=True)
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member = {"bilanz":[],"verluste":[],"nebenbedingung":[]}
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@ -231,12 +240,12 @@ class optimization_problem:
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akku_size = parameter['pv_akku_cap'] # 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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discharge_array = np.full(self.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(kapazitaet_wh=akku_size,hours=self.prediction_hours,start_soc_prozent=parameter["pv_soc"], max_ladeleistung_w=5000)
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discharge_array = np.full(self.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.set_charge_per_hour(discharge_array)
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laden_moeglich = np.full(self.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])
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eauto = PVAkku(kapazitaet_wh=parameter["eauto_cap"], hours=self.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"])
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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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170
test.py
170
test.py
@ -23,7 +23,7 @@ import os
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start_hour = 13
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start_hour = 10
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pv_forecast= [
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0,
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@ -33,45 +33,45 @@ pv_forecast= [
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0,
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0,
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0,
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35.4104640357043,
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436.191574979506,
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734.585613834398,
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914.346108603927,
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1019.5228214119,
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1766.84136350058,
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5980.60975052259,
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6236.00681862336,
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5893.38154543782,
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4309.88538120413,
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3338.29004915145,
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2177.55660706753,
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1091.00542545193,
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437.819525591319,
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44.2226537829726,
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8.05499380056326,
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352.906710152794,
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728.510230116837,
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930.282113186742,
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1043.25445504815,
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1106.74498341506,
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1161.69140358941,
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6018.82237954771,
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5519.06508185542,
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3969.87633262384,
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3017.96293205546,
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1943.06957539177,
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1007.17065928121,
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319.672404988219,
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7.87634136648885,
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0,
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0,
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0,
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0,
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-0.0269415125679914,
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0,
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0,
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0,
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0,
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25.5745140893473,
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494.188146846569,
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943.821134036728,
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1458.66413119635,
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1819.46147983229,
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2127.45430524539,
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2267.78128099068,
|
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5944.86706099518,
|
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5337.1322153025,
|
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4376.56125932204,
|
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3020.00212091936,
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2414.53994231359,
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1373.626161377,
|
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517.764497317134,
|
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35.619750070296,
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0,
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5.04340865393592,
|
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335.585179721385,
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705.32093965119,
|
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1121.11845108965,
|
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1604.78796905453,
|
||||
2157.38417470292,
|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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383.299960578605,
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0,
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0,
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0
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]
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@ -177,60 +177,60 @@ strompreis_euro_pro_wh = [
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0.00027800228
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]
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gesamtlast= [
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546.16318964697,
|
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893.072526185525,
|
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448.7325491406,
|
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460.696954446666,
|
||||
497.688171532182,
|
||||
468.186120420737,
|
||||
424.440426628658,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
460.284228901714,
|
||||
498.457870099476,
|
||||
469.01826331988,
|
||||
424.293997897019,
|
||||
454.660942633609,
|
||||
1070.71990586461,
|
||||
1096.44557410693,
|
||||
1199.72027861112,
|
||||
1294.75410706442,
|
||||
1459.61174223338,
|
||||
1295.77661554687,
|
||||
1304.95591385395,
|
||||
1188.62778631227,
|
||||
1310.43099742786,
|
||||
1108.58589249073,
|
||||
1101.73849714744,
|
||||
2114.05576978017,
|
||||
1143.68031998738,
|
||||
858.607350786608,
|
||||
786.574111043611,
|
||||
693.463415886943
|
||||
676.712691350422,
|
||||
876.187995931743,
|
||||
527.13496018672,
|
||||
468.8832716908,
|
||||
531.379343927472,
|
||||
517.948592590007,
|
||||
483.146247717859,
|
||||
472.284832630916,
|
||||
1011.67951144825,
|
||||
995.004317471209,
|
||||
1053.06955100748,
|
||||
1063.9080395892,
|
||||
1320.56143113193,
|
||||
1132.02504127723,
|
||||
1163.67246837107,
|
||||
1176.81613875329,
|
||||
1216.21914051274,
|
||||
1103.77675478374,
|
||||
1129.12158352941,
|
||||
1178.70748410006,
|
||||
1050.97894301995,
|
||||
988.55813665172,
|
||||
912.383030600675,
|
||||
704.613809064162,
|
||||
516.371536532904,
|
||||
868.049462163551,
|
||||
694.342395302237,
|
||||
608.791374542592,
|
||||
556.310160150771,
|
||||
488.88509383088,
|
||||
506.910948217211,
|
||||
804.891484351704,
|
||||
1141.97850300923,
|
||||
1056.97012155463,
|
||||
992.46421110044,
|
||||
1155.98941936038,
|
||||
827.012550864246,
|
||||
1257.97979633947,
|
||||
1232.66876472966,
|
||||
871.261677859026,
|
||||
860.884647456424,
|
||||
1158.02879027548,
|
||||
1222.71811626233,
|
||||
1221.03860924522,
|
||||
949.989048056282,
|
||||
987.007654562746,
|
||||
733.993140774617,
|
||||
592.972573276025
|
||||
]
|
||||
|
||||
start_solution= [
|
||||
0,
|
||||
1,
|
||||
0,
|
||||
1,
|
||||
1,
|
||||
1,
|
||||
0,
|
||||
1,
|
||||
@ -325,11 +325,11 @@ start_solution= [
|
||||
1,
|
||||
1
|
||||
]
|
||||
parameter= {"preis_euro_pro_wh_akku": 30e-05,'pv_soc': 95.00, 'pv_akku_cap': 30000, 'year_energy': 4100000, 'einspeiseverguetung_euro_pro_wh': 7e-05, 'max_heizleistung': 1000,"gesamtlast":gesamtlast, 'pv_forecast': pv_forecast, "temperature_forecast":temperature_forecast, "strompreis_euro_pro_wh":strompreis_euro_pro_wh, 'eauto_min_soc': 0, 'eauto_cap': 60000, 'eauto_charge_efficiency': 0.95, 'eauto_charge_power': 7590, 'eauto_soc': 53, 'pvpowernow': 211.137503624, 'start_solution': start_solution, 'haushaltsgeraet_wh': 937, 'haushaltsgeraet_dauer': 0}
|
||||
parameter= {"preis_euro_pro_wh_akku": 10e-05,'pv_soc': 80, 'pv_akku_cap': 26400, 'year_energy': 4100000, 'einspeiseverguetung_euro_pro_wh': 7e-05, 'max_heizleistung': 1000,"gesamtlast":gesamtlast, 'pv_forecast': pv_forecast, "temperature_forecast":temperature_forecast, "strompreis_euro_pro_wh":strompreis_euro_pro_wh, 'eauto_min_soc': 0, 'eauto_cap': 60000, 'eauto_charge_efficiency': 0.95, 'eauto_charge_power': 11040, 'eauto_soc': 54, 'pvpowernow': 211.137503624, 'start_solution': start_solution, 'haushaltsgeraet_wh': 937, 'haushaltsgeraet_dauer': 0}
|
||||
|
||||
|
||||
|
||||
opt_class = optimization_problem(prediction_hours=48, strafe=10,optimization_hours=24)
|
||||
ergebnis = opt_class.optimierung_ems(parameter=parameter, start_hour=start_hour)
|
||||
print(ergebnis)
|
||||
print(jsonify(ergebnis))
|
||||
#print(ergebnis)
|
||||
#print(jsonify(ergebnis))
|
||||
Loading…
x
Reference in New Issue
Block a user