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Haushaltsgeräte wie z.B. Spülmaschine

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flask / Optimierung besser getrennt
Optimierung Bug, bei mehrfahcen neustart
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Bla Bla
2024-04-02 16:46:16 +02:00
parent f817504528
commit 7feb52d854
6 changed files with 199 additions and 299 deletions
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122
modules/class_optimize.py
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@@ -9,6 +9,7 @@ from modules.class_heatpump import *
from modules.class_load_container import *
from modules.class_sommerzeit import *
from modules.visualize import *
from modules.class_haushaltsgeraet import *
import os
from flask import Flask, send_from_directory
from pprint import pprint
@@ -32,52 +33,93 @@ def isfloat(num):
class optimization_problem:
def __init__(self, prediction_hours=24, strafe = 10):
# Werkzeug-Setup
creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
creator.create("Individual", list, fitness=creator.FitnessMin)
self.toolbox = base.Toolbox()
self.prediction_hours = prediction_hours#
self.strafe = strafe
self.opti_param = None
def setup_deap_environment(self,opti_param):
self.opti_param = opti_param
if "FitnessMin" in creator.__dict__:
del creator.FitnessMin
if "Individual" in creator.__dict__:
del creator.Individual
creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
creator.create("Individual", list, fitness=creator.FitnessMin)
# PARAMETER
self.toolbox = base.Toolbox()
self.toolbox.register("attr_bool", random.randint, 0, 1)
self.toolbox.register("attr_bool", random.randint, 0, 1)
self.toolbox.register("individual", tools.initCycle, creator.Individual, (self.toolbox.attr_bool,self.toolbox.attr_bool), n=self.prediction_hours)
self.toolbox.register("attr_int", random.randint, 0, 23)
###################
# Haushaltsgeraete
if opti_param["haushaltsgeraete"]>0:
def create_individual():
attrs = [self.toolbox.attr_bool() for _ in range(2*self.prediction_hours)] + [self.toolbox.attr_int()]
return creator.Individual(attrs)
else:
def create_individual():
attrs = [self.toolbox.attr_bool() for _ in range(2*self.prediction_hours)]
return creator.Individual(attrs)
self.toolbox.register("individual", create_individual)#tools.initCycle, creator.Individual, (self.toolbox.attr_bool,self.toolbox.attr_bool), n=self.prediction_hours+1)
self.toolbox.register("population", tools.initRepeat, list, self.toolbox.individual)
self.toolbox.register("mate", tools.cxTwoPoint)
self.toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
self.toolbox.register("select", tools.selTournament, tournsize=3)
return
def evaluate_inner(self,individual, ems,start_hour):
ems.reset()
# Haushaltsgeraete
if self.opti_param["haushaltsgeraete"]>0:
spuelstart_int = individual[-1]
individual = individual[:-1]
ems.set_haushaltsgeraet_start(spuelstart_int,global_start_hour=start_hour)
discharge_hours_bin = individual[0::2]
eautocharge_hours_float = individual[1::2]
ems.reset()
ems.set_akku_discharge_hours(discharge_hours_bin)
ems.set_eauto_charge_hours(eautocharge_hours_float)
o = ems.simuliere(start_hour)
return o
# Fitness-Funktion (muss Ihre EnergieManagementSystem-Logik integrieren)
def evaluate(self,individual,ems,parameter,start_hour):
o = self.evaluate_inner(individual,ems,start_hour)
def evaluate(self,individual,ems,parameter,start_hour,worst_case):
try:
o = self.evaluate_inner(individual,ems,start_hour)
except:
return (100000.0,)
gesamtbilanz = o["Gesamtbilanz_Euro"]
if worst_case:
gesamtbilanz = gesamtbilanz * -1.0
# Überprüfung, ob der Mindest-SoC erreicht wird
final_soc = ems.eauto.ladezustand_in_prozent() # Nimmt den SoC am Ende des Optimierungszeitraums
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
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
return (gesamtbilanz,)
@@ -104,7 +146,7 @@ class optimization_problem:
return hof[0]
def optimierung_ems(self,parameter=None, start_hour=None):
def optimierung_ems(self,parameter=None, start_hour=None,worst_case=False):
############
# Parameter
@@ -130,10 +172,18 @@ class optimization_problem:
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"])
eauto.set_charge_per_hour(laden_moeglich)
min_soc_eauto = parameter['eauto_min_soc']
start_params = parameter['start_solution']
gesamtlast = Gesamtlast()
###############
# spuelmaschine
##############
if parameter["haushaltsgeraet_dauer"] >0:
spuelmaschine = Haushaltsgeraet(hours=self.prediction_hours, verbrauch_kwh=parameter["haushaltsgeraet_wh"], dauer_h=parameter["haushaltsgeraet_dauer"])
spuelmaschine.set_startzeitpunkt(start_hour) # Startet jetzt
else:
spuelmaschine = None
###############
# Load Forecast
@@ -173,29 +223,49 @@ class optimization_problem:
leistung_wp = wp.simulate_24h(temperature_forecast)
gesamtlast.hinzufuegen("Heatpump", leistung_wp)
ems = EnergieManagementSystem(akku=akku, gesamtlast = gesamtlast, pv_prognose_wh=pv_forecast, strompreis_euro_pro_wh=specific_date_prices, einspeiseverguetung_euro_pro_wh=einspeiseverguetung_euro_pro_wh, eauto=eauto)
ems = EnergieManagementSystem(akku=akku, gesamtlast = gesamtlast, pv_prognose_wh=pv_forecast, strompreis_euro_pro_wh=specific_date_prices, einspeiseverguetung_euro_pro_wh=einspeiseverguetung_euro_pro_wh, eauto=eauto, haushaltsgeraet=spuelmaschine)
o = ems.simuliere(start_hour)
###############
# Optimizer Init
##############
opti_param = {}
opti_param["haushaltsgeraete"] = 0
if spuelmaschine != None:
opti_param["haushaltsgeraete"] = 1
self.setup_deap_environment(opti_param)
def evaluate_wrapper(individual):
return self.evaluate(individual, ems, parameter,start_hour)
return self.evaluate(individual, ems, parameter,start_hour,worst_case)
self.toolbox.register("evaluate", evaluate_wrapper)
start_solution = self.optimize(start_params)
best_solution = start_solution
o = self.evaluate_inner(best_solution, ems,start_hour)
eauto = ems.eauto.to_dict()
spuelstart_int = None
# Haushaltsgeraete
if self.opti_param["haushaltsgeraete"]>0:
spuelstart_int = best_solution[-1]
best_solution = best_solution[:-1]
discharge_hours_bin = best_solution[0::2]
eautocharge_hours_float = best_solution[1::2]
#print(o)
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)
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:")
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}
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}