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Violinenplot zur Optimierung / Zielfunktionalen

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Worst / Best Case im Vergleich (bei Erfüllung der Nebenbedingungen)
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Bla Bla
2024-04-17 15:34:03 +02:00
parent be5a99f86a
commit b037b6dcfc
2 changed files with 81 additions and 22 deletions
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48
modules/class_optimize.py
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@@ -110,16 +110,17 @@ class optimization_problem:
final_soc = ems.eauto.ladezustand_in_prozent() # Nimmt den SoC am Ende des Optimierungszeitraums
eauto_roi = max(0,(parameter['eauto_min_soc']-ems.eauto.ladezustand_in_prozent()) )
individual.extra_data = (o["Gesamtbilanz_Euro"],o["Gesamt_Verluste"], eauto_roi )
strafe = 0.0
if worst_case:
strafe = abs(parameter['eauto_min_soc']-ems.eauto.ladezustand_in_prozent()) * self.strafe
gesamtbilanz += strafe
gesamtbilanz -= o["Gesamt_Verluste"]/1000.0
else:
strafe = max(0,(parameter['eauto_min_soc']-ems.eauto.ladezustand_in_prozent()) * self.strafe )
gesamtbilanz += strafe
gesamtbilanz += o["Gesamt_Verluste"]/1000.0
strafe = max(0,(parameter['eauto_min_soc']-ems.eauto.ladezustand_in_prozent()) * self.strafe )
gesamtbilanz += strafe
gesamtbilanz += o["Gesamt_Verluste"]/1000.0
return (gesamtbilanz,)
@@ -128,7 +129,7 @@ class optimization_problem:
# Genetischer Algorithmus
def optimize(self,start_solution=None):
population = self.toolbox.population(n=100)
population = self.toolbox.population(n=300)
hof = tools.HallOfFame(1)
stats = tools.Statistics(lambda ind: ind.fitness.values)
@@ -141,9 +142,19 @@ class optimization_problem:
if start_solution is not None and start_solution != -1:
population.insert(0, creator.Individual(start_solution))
#algorithms.eaMuPlusLambda(population, self.toolbox, 100, 200, cxpb=0.3, mutpb=0.3, ngen=500, stats=stats, halloffame=hof, verbose=True)
algorithms.eaSimple(population, self.toolbox, cxpb=0.8, mutpb=0.8, ngen=400, stats=stats, halloffame=hof, verbose=True)
return hof[0]
#algorithms.eaMuPlusLambda(population, self.toolbox, 100, 200, cxpb=0.4, mutpb=0.5, ngen=500, stats=stats, halloffame=hof, verbose=True)
algorithms.eaSimple(population, self.toolbox, cxpb=0.4, mutpb=0.4, ngen=400, stats=stats, halloffame=hof, verbose=True)
member = {"bilanz":[],"verluste":[],"nebenbedingung":[]}
for ind in population:
if hasattr(ind, 'extra_data'):
extra_value1, extra_value2,extra_value3 = ind.extra_data
member["bilanz"].append(extra_value1)
member["verluste"].append(extra_value2)
member["nebenbedingung"].append(extra_value3)
return hof[0], member
def optimierung_ems(self,parameter=None, start_hour=None,worst_case=False):
@@ -240,7 +251,7 @@ class optimization_problem:
return self.evaluate(individual, ems, parameter,start_hour,worst_case)
self.toolbox.register("evaluate", evaluate_wrapper)
start_solution = self.optimize(start_params)
start_solution, extra_data = self.optimize(start_params)
best_solution = start_solution
o = self.evaluate_inner(best_solution, ems,start_hour)
eauto = ems.eauto.to_dict()
@@ -256,13 +267,8 @@ class optimization_problem:
print(o)
if worst_case:
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,filename="visualisierungsergebnisse_worst.pdf")
os.system("scp visualisierungsergebnisse_worst.pdf andreas@192.168.1.135:")
else:
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)
os.system("scp visualisierungsergebnisse.pdf andreas@192.168.1.135:")
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)
os.system("scp visualisierungsergebnisse.pdf andreas@192.168.1.135:")
#print(eauto)
return {"discharge_hours_bin":discharge_hours_bin, "eautocharge_hours_float":eautocharge_hours_float ,"result":o ,"eauto_obj":eauto,"start_solution":best_solution,"spuelstart":spuelstart_int}