Optimierung Entladezustand Akku

test.py

@@ -9,8 +9,9 @@ from modules.class_strompreis 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
 
 
 date = "2024-02-16"
@@ -18,7 +19,17 @@ akku_size = 1000 # Wh
 year_energy = 2000*1000 #Wh
 einspeiseverguetung_cent_pro_wh = np.full(24, 7/1000.0)
 
+
 akku = PVAkku(akku_size)
+discharge_array = np.full(24,1)
+# discharge_array[12] = 0
+# discharge_array[13] = 0
+# discharge_array[14] = 0
+# discharge_array[15] = 0
+# discharge_array[16] = 0
+# discharge_array[17] = 0
+# discharge_array[18] = 1
+# akku.set_discharge_per_hour(discharge_array)
 
 # Load Forecast
 lf = LoadForecast(filepath=r'load_profiles.npz', year_energy=year_energy)
@@ -40,7 +51,58 @@ specific_date_prices = price_forecast.get_prices_for_date(date)
 # EMS / Stromzähler Bilanz
 ems = EnergieManagementSystem(akku, specific_date_load, pv_forecast, specific_date_prices, einspeiseverguetung_cent_pro_wh)
 o = ems.simuliere()
-pprint(o)
+pprint(o["Gesamtbilanz_Euro"])
+
+
+# Optimierung
+
+# Fitness-Funktion (muss Ihre EnergieManagementSystem-Logik integrieren)
+def evaluate(individual):
+    # Hier müssen Sie Ihre Logik einbauen, um die Gesamtbilanz zu berechnen
+    # basierend auf dem gegebenen `individual` (discharge_array)
+    #akku.set_discharge_per_hour(individual)
+    ems.reset()
+    ems.set_akku_discharge_hours(individual)
+    o = ems.simuliere()
+    gesamtbilanz = o["Gesamtbilanz_Euro"]
+    #print(individual, " ",gesamtbilanz)
+    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("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 24)
+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=100)
+    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.eaSimple(population, toolbox, cxpb=0.5, mutpb=0.2, ngen=100, 
+                        stats=stats, halloffame=hof, verbose=True)
+    return hof[0]
+
+best_solution = optimize()
+print("Beste Lösung:", best_solution)
+
+ems.set_akku_discharge_hours(best_solution)
+o = ems.simuliere()
+pprint(o["Gesamtbilanz_Euro"])
+
 
 
 visualisiere_ergebnisse(specific_date_load, pv_forecast, specific_date_prices, o)