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Inverter Klasse hinzugefügt

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Kleinere Bugs bei max_WR Leistung behoben
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Bla Bla
2024-05-02 10:27:33 +02:00
parent ae05608c6e
commit 1145d3c185
5 changed files with 425 additions and 260 deletions
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16
modules/class_optimize.py
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@@ -99,6 +99,7 @@ class optimization_problem:
# Fitness-Funktion (muss Ihre EnergieManagementSystem-Logik integrieren)
def evaluate(self,individual,ems,parameter,start_hour,worst_case):
try:
o = self.evaluate_inner(individual,ems,start_hour)
except:
@@ -121,7 +122,7 @@ class optimization_problem:
strafe = 0.0
strafe = max(0,(parameter['eauto_min_soc']-ems.eauto.ladezustand_in_prozent()) * self.strafe )
gesamtbilanz += strafe
gesamtbilanz += o["Gesamt_Verluste"]/1000.0
gesamtbilanz += o["Gesamt_Verluste"]/10000.0
return (gesamtbilanz,)
@@ -131,7 +132,7 @@ class optimization_problem:
# Genetischer Algorithmus
def optimize(self,start_solution=None):
population = self.toolbox.population(n=200)
population = self.toolbox.population(n=1000)
hof = tools.HallOfFame(1)
stats = tools.Statistics(lambda ind: ind.fitness.values)
@@ -144,8 +145,8 @@ 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=0.4, mutpb=0.5, ngen=500, stats=stats, halloffame=hof, verbose=True)
algorithms.eaSimple(population, self.toolbox, cxpb=0.4, mutpb=0.4, ngen=100, stats=stats, halloffame=hof, verbose=True)
#algorithms.eaMuPlusLambda(population, self.toolbox, 100, 200, cxpb=0.2, mutpb=0.2, ngen=500, stats=stats, halloffame=hof, verbose=True)
algorithms.eaSimple(population, self.toolbox, cxpb=0.2, mutpb=0.2, ngen=200, stats=stats, halloffame=hof, verbose=True)
member = {"bilanz":[],"verluste":[],"nebenbedingung":[]}
for ind in population:
@@ -161,6 +162,7 @@ class optimization_problem:
def optimierung_ems(self,parameter=None, start_hour=None,worst_case=False):
############
# Parameter
############
@@ -256,7 +258,7 @@ class optimization_problem:
self.toolbox.register("evaluate", evaluate_wrapper)
start_solution, extra_data = self.optimize(start_params)
best_solution = start_solution
best_solution = start_params
o = self.evaluate_inner(best_solution, ems,start_hour)
eauto = ems.eauto.to_dict()
spuelstart_int = None
@@ -269,8 +271,8 @@ class optimization_problem:
print(o)
print(parameter)
print(best_solution)
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("cp visualisierungsergebnisse.pdf ~/")