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Resteinspseisung durch Verluste des Akkus
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Bla Bla
2024-03-29 19:49:47 +01:00
parent 1f0161aca9
commit dad1745db2
3 changed files with 20 additions and 158 deletions
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27
flask_server.py
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@@ -76,8 +76,8 @@ toolbox.register("select", tools.selTournament, tournsize=3)
# Genetischer Algorithmus
def optimize():
population = toolbox.population(n=500)
def optimize(start_solution=None):
population = toolbox.population(n=100)
hof = tools.HallOfFame(1)
stats = tools.Statistics(lambda ind: ind.fitness.values)
@@ -85,8 +85,13 @@ def optimize():
stats.register("min", np.min)
stats.register("max", np.max)
algorithms.eaMuPlusLambda(population, toolbox, 100, 200, cxpb=0.3, mutpb=0.3, 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)
print("Start:",start_solution)
if start_solution is not None and start_solution != -1:
population.insert(0, creator.Individual(start_solution))
#algorithms.eaMuPlusLambda(population, toolbox, 100, 200, cxpb=0.3, mutpb=0.3, ngen=500, stats=stats, halloffame=hof, verbose=True)
algorithms.eaSimple(population, toolbox, cxpb=0.5, mutpb=0.8, ngen=200, stats=stats, halloffame=hof, verbose=True)
return hof[0]
@@ -104,7 +109,7 @@ def durchfuehre_simulation(parameter):
############
date = (datetime.now().date() + timedelta(hours = prediction_hours)).strftime("%Y-%m-%d")
date_now = datetime.now().strftime("%Y-%m-%d")
akku_size = parameter['pv_akku_cap'] # Wh
year_energy = parameter['year_energy'] #2000*1000 #Wh
@@ -124,6 +129,7 @@ def durchfuehre_simulation(parameter):
eauto.set_charge_per_hour(laden_moeglich)
min_soc_eauto = parameter['eauto_min_soc']
start_params = parameter['start_solution']
gesamtlast = Gesamtlast()
@@ -171,9 +177,10 @@ def durchfuehre_simulation(parameter):
toolbox.register("evaluate", evaluate_wrapper)
start_solution = optimize()
print()
print("START:",start_params)
print()
start_solution = optimize(start_params)
best_solution = start_solution
o = evaluate_inner(best_solution, ems)
eauto = ems.eauto.to_dict()
@@ -185,7 +192,7 @@ def durchfuehre_simulation(parameter):
visualisiere_ergebnisse(gesamtlast, pv_forecast, specific_date_prices, o,best_solution[0::2],best_solution[1::2] , temperature_forecast, start_hour, prediction_hours)
#print(eauto)
return {"discharge_hours_bin":discharge_hours_bin, "eautocharge_hours_float":eautocharge_hours_float ,"result":o ,"eauto_obj":eauto}
return {"discharge_hours_bin":discharge_hours_bin, "eautocharge_hours_float":eautocharge_hours_float ,"result":o ,"eauto_obj":eauto,"start_solution":best_solution}
@@ -196,7 +203,7 @@ def simulation():
parameter = request.json
# Erforderliche Parameter prüfen
erforderliche_parameter = [ 'pv_akku_cap', 'year_energy',"einspeiseverguetung_euro_pro_wh", 'max_heizleistung', 'pv_forecast_url', 'eauto_min_soc', "eauto_cap","eauto_charge_efficiency","eauto_charge_power","eauto_soc","pv_soc"]
erforderliche_parameter = [ 'pv_akku_cap', 'year_energy',"einspeiseverguetung_euro_pro_wh", 'max_heizleistung', 'pv_forecast_url', 'eauto_min_soc', "eauto_cap","eauto_charge_efficiency","eauto_charge_power","eauto_soc","pv_soc","start_solution"]
for p in erforderliche_parameter:
if p not in parameter:
return jsonify({"error": f"Fehlender Parameter: {p}"}), 400