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a90a92b841
Choice für die E-Auto LAdeströme entfernt, scheint in DEAP buggy zu sein Initiale Lösung 3x eingefügt, damit diese bestehen bleibt
418 lines
18 KiB
Python
418 lines
18 KiB
Python
from flask import Flask, jsonify, request
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import numpy as np
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from modules.class_load import *
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from modules.class_ems import *
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from modules.class_pv_forecast import *
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from modules.class_akku import *
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from modules.class_heatpump import *
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from modules.class_load_container import *
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from modules.class_inverter import *
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from modules.class_sommerzeit import *
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from modules.visualize import *
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from modules.class_haushaltsgeraet import *
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import os
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from flask import Flask, send_from_directory
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from pprint import pprint
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import matplotlib
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matplotlib.use('Agg') # Setzt das Backend auf Agg
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import matplotlib.pyplot as plt
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import string
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from datetime import datetime
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from deap import base, creator, tools, algorithms
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import numpy as np
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import random
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import os
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from config import *
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def isfloat(num):
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try:
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float(num)
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return True
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except:
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return False
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def differential_evolution(population, toolbox, cxpb, mutpb, ngen, stats=None, halloffame=None, verbose=__debug__):
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"""Differential Evolution Algorithm"""
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# Evaluate the entire population
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fitnesses = list(map(toolbox.evaluate, population))
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for ind, fit in zip(population, fitnesses):
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ind.fitness.values = fit
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if halloffame is not None:
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halloffame.update(population)
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logbook = tools.Logbook()
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logbook.header = ['gen', 'nevals'] + (stats.fields if stats else [])
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for gen in range(ngen):
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# Generate the next generation by mutation and recombination
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for i, target in enumerate(population):
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a, b, c = random.sample([ind for ind in population if ind != target], 3)
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mutant = toolbox.clone(a)
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for k in range(len(mutant)):
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mutant[k] = c[k] + mutpb * (a[k] - b[k]) # Mutation step
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if random.random() < cxpb: # Recombination step
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mutant[k] = target[k]
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# Evaluate the mutant
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mutant.fitness.values = toolbox.evaluate(mutant)
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# Replace if mutant is better
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if mutant.fitness > target.fitness:
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population[i] = mutant
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# Update hall of fame
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if halloffame is not None:
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halloffame.update(population)
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# Gather all the fitnesses in one list and print the stats
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record = stats.compile(population) if stats else {}
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logbook.record(gen=gen, nevals=len(population), **record)
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if verbose:
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print(logbook.stream)
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return population, logbook
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class optimization_problem:
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def __init__(self, prediction_hours=24, strafe = 10, optimization_hours= 24):
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self.prediction_hours = prediction_hours#
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self.strafe = strafe
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self.opti_param = None
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self.fixed_eauto_hours = prediction_hours-optimization_hours
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self.possible_charge_values = moegliche_ladestroeme_in_prozent
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def split_individual(self, individual):
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"""
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Teilt das gegebene Individuum in die verschiedenen Parameter auf:
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- Entladeparameter (discharge_hours_bin)
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- Ladeparameter (eautocharge_hours_float)
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- Haushaltsgeräte (spuelstart_int, falls vorhanden)
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"""
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# Extrahiere die Entlade- und Ladeparameter direkt aus dem Individuum
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discharge_hours_bin = individual[:self.prediction_hours] # Erste 24 Werte sind Bool (Entladen)
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eautocharge_hours_float = individual[self.prediction_hours:self.prediction_hours * 2] # Nächste 24 Werte sind Float (Laden)
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spuelstart_int = None
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if self.opti_param and self.opti_param.get("haushaltsgeraete", 0) > 0:
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spuelstart_int = individual[-1] # Letzter Wert ist Startzeit für Haushaltsgerät
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return discharge_hours_bin, eautocharge_hours_float, spuelstart_int
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def setup_deap_environment(self,opti_param, start_hour):
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self.opti_param = opti_param
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if "FitnessMin" in creator.__dict__:
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del creator.FitnessMin
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if "Individual" in creator.__dict__:
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del creator.Individual
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creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
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creator.create("Individual", list, fitness=creator.FitnessMin)
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# PARAMETER
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self.toolbox = base.Toolbox()
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self.toolbox.register("attr_bool", random.randint, 0, 1)
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self.toolbox.register("attr_float", random.uniform, 0, 1) # Für kontinuierliche Werte zwischen 0 und 1 (z.B. für E-Auto-Ladeleistung)
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#self.toolbox.register("attr_choice", random.choice, self.possible_charge_values) # Für diskrete Ladeströme
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self.toolbox.register("attr_int", random.randint, start_hour, 23)
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###################
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# Haushaltsgeraete
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#print("Haushalt:",opti_param["haushaltsgeraete"])
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if opti_param["haushaltsgeraete"]>0:
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def create_individual():
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attrs = [self.toolbox.attr_bool() for _ in range(self.prediction_hours)] # 24 Bool-Werte für Entladen
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attrs += [self.toolbox.attr_float() for _ in range(self.prediction_hours)] # 24 Float-Werte für Laden
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attrs.append(self.toolbox.attr_int()) # Haushaltsgerät-Startzeit
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return creator.Individual(attrs)
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else:
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def create_individual():
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attrs = [self.toolbox.attr_bool() for _ in range(self.prediction_hours)] # 24 Bool-Werte für Entladen
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attrs += [self.toolbox.attr_float() for _ in range(self.prediction_hours)] # 24 Float-Werte für Laden
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return creator.Individual(attrs)
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self.toolbox.register("individual", create_individual)#tools.initCycle, creator.Individual, (self.toolbox.attr_bool,self.toolbox.attr_bool), n=self.prediction_hours+1)
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self.toolbox.register("population", tools.initRepeat, list, self.toolbox.individual)
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self.toolbox.register("mate", tools.cxTwoPoint)
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self.toolbox.register("mutate", tools.mutFlipBit, indpb=0.1)
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#self.toolbox.register("mutate", mutate_choice, self.possible_charge_values, indpb=0.1)
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#self.toolbox.register("mutate", tools.mutUniformInt, low=0, up=len(self.possible_charge_values)-1, indpb=0.1)
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self.toolbox.register("select", tools.selTournament, tournsize=3)
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def evaluate_inner(self,individual, ems,start_hour):
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ems.reset()
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#print("Spuel:",self.opti_param)
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discharge_hours_bin, eautocharge_hours_float, spuelstart_int = self.split_individual(individual)
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# Haushaltsgeraete
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if self.opti_param["haushaltsgeraete"]>0:
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ems.set_haushaltsgeraet_start(spuelstart_int,global_start_hour=start_hour)
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#discharge_hours_bin = np.full(self.prediction_hours,0)
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ems.set_akku_discharge_hours(discharge_hours_bin)
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# Setze die festen Werte für die letzten x Stunden
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for i in range(self.prediction_hours - self.fixed_eauto_hours, self.prediction_hours):
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eautocharge_hours_float[i] = 0.0 # Setze die letzten x Stunden auf einen festen Wert (oder vorgegebenen Wert)
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#print(eautocharge_hours_float)
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ems.set_eauto_charge_hours(eautocharge_hours_float)
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o = ems.simuliere(start_hour)
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return o
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# Fitness-Funktion (muss Ihre EnergieManagementSystem-Logik integrieren)
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def evaluate(self,individual,ems,parameter,start_hour,worst_case):
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try:
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o = self.evaluate_inner(individual,ems,start_hour)
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except:
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return (100000.0,)
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gesamtbilanz = o["Gesamtbilanz_Euro"]
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if worst_case:
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gesamtbilanz = gesamtbilanz * -1.0
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discharge_hours_bin, eautocharge_hours_float, spuelstart_int = self.split_individual(individual)
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max_ladeleistung = np.max(moegliche_ladestroeme_in_prozent)
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strafe_überschreitung = 0.0
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# Ladeleistung überschritten?
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for ladeleistung in eautocharge_hours_float:
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if ladeleistung > max_ladeleistung:
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# Berechne die Überschreitung
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überschreitung = ladeleistung - max_ladeleistung
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# Füge eine Strafe hinzu (z.B. 10 Einheiten Strafe pro Prozentpunkt Überschreitung)
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strafe_überschreitung += self.strafe * 10 # Hier ist die Strafe proportional zur Überschreitung
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# Für jeden Discharge 0, eine kleine Strafe von 1 Cent, da die Lastvertelung noch fehlt. Also wenn es egal ist, soll er den Akku entladen lassen
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for i in range(0, self.prediction_hours):
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if discharge_hours_bin[i] == 0.0: # Wenn die letzten x Stunden von einem festen Wert abweichen
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gesamtbilanz += 0.01 # Bestrafe den Optimierer
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# E-Auto nur die ersten self.fixed_eauto_hours
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for i in range(self.prediction_hours - self.fixed_eauto_hours, self.prediction_hours):
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if eautocharge_hours_float[i] != 0.0: # Wenn die letzten x Stunden von einem festen Wert abweichen
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gesamtbilanz += self.strafe # Bestrafe den Optimierer
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# Überprüfung, ob der Mindest-SoC erreicht wird
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final_soc = ems.eauto.ladezustand_in_prozent() # Nimmt den SoC am Ende des Optimierungszeitraums
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if (parameter['eauto_min_soc']-ems.eauto.ladezustand_in_prozent()) <= 0.0:
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#print (parameter['eauto_min_soc']," " ,ems.eauto.ladezustand_in_prozent()," ",(parameter['eauto_min_soc']-ems.eauto.ladezustand_in_prozent()))
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for i in range(0, self.prediction_hours):
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if eautocharge_hours_float[i] != 0.0: # Wenn die letzten x Stunden von einem festen Wert abweichen
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gesamtbilanz += self.strafe # Bestrafe den Optimierer
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eauto_roi = (parameter['eauto_min_soc']-ems.eauto.ladezustand_in_prozent())
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individual.extra_data = (o["Gesamtbilanz_Euro"],o["Gesamt_Verluste"], eauto_roi )
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restenergie_akku = ems.akku.aktueller_energieinhalt()
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restwert_akku = restenergie_akku*parameter["preis_euro_pro_wh_akku"]
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# print(restenergie_akku)
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# print(parameter["preis_euro_pro_wh_akku"])
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# print(restwert_akku)
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# print()
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strafe = 0.0
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strafe = max(0,(parameter['eauto_min_soc']-ems.eauto.ladezustand_in_prozent()) * self.strafe )
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gesamtbilanz += strafe - restwert_akku + strafe_überschreitung
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#gesamtbilanz += o["Gesamt_Verluste"]/10000.0
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return (gesamtbilanz,)
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# Genetischer Algorithmus
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def optimize(self,start_solution=None):
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population = self.toolbox.population(n=300)
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hof = tools.HallOfFame(1)
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stats = tools.Statistics(lambda ind: ind.fitness.values)
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stats.register("avg", np.mean)
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stats.register("min", np.min)
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stats.register("max", np.max)
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print("Start:",start_solution)
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if start_solution is not None and start_solution != -1:
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population.insert(0, creator.Individual(start_solution))
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population.insert(1, creator.Individual(start_solution))
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population.insert(2, creator.Individual(start_solution))
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algorithms.eaMuPlusLambda(population, self.toolbox, mu=100, lambda_=200, cxpb=0.5, mutpb=0.3, ngen=400, stats=stats, halloffame=hof, verbose=True)
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#algorithms.eaSimple(population, self.toolbox, cxpb=0.3, mutpb=0.3, ngen=200, stats=stats, halloffame=hof, verbose=True)
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#algorithms.eaMuCommaLambda(population, self.toolbox, mu=100, lambda_=200, cxpb=0.2, mutpb=0.4, ngen=300, stats=stats, halloffame=hof, verbose=True)
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#population, log = differential_evolution(population, self.toolbox, cxpb=0.2, mutpb=0.5, ngen=200, stats=stats, halloffame=hof, verbose=True)
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member = {"bilanz":[],"verluste":[],"nebenbedingung":[]}
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for ind in population:
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if hasattr(ind, 'extra_data'):
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extra_value1, extra_value2,extra_value3 = ind.extra_data
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member["bilanz"].append(extra_value1)
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member["verluste"].append(extra_value2)
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member["nebenbedingung"].append(extra_value3)
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return hof[0], member
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def optimierung_ems(self,parameter=None, start_hour=None,worst_case=False, startdate=None):
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############
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# Parameter
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############
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if startdate == None:
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date = (datetime.now().date() + timedelta(hours = self.prediction_hours)).strftime("%Y-%m-%d")
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date_now = datetime.now().strftime("%Y-%m-%d")
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else:
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date = (startdate + timedelta(hours = self.prediction_hours)).strftime("%Y-%m-%d")
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date_now = startdate.strftime("%Y-%m-%d")
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#print("Start_date:",date_now)
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akku_size = parameter['pv_akku_cap'] # Wh
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einspeiseverguetung_euro_pro_wh = np.full(self.prediction_hours, parameter["einspeiseverguetung_euro_pro_wh"]) #= # € / Wh 7/(1000.0*100.0)
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discharge_array = np.full(self.prediction_hours,1) #np.array([1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0]) #
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akku = PVAkku(kapazitaet_wh=akku_size,hours=self.prediction_hours,start_soc_prozent=parameter["pv_soc"], max_ladeleistung_w=5000)
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akku.set_charge_per_hour(discharge_array)
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laden_moeglich = np.full(self.prediction_hours,1) # np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0])
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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"])
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eauto.set_charge_per_hour(laden_moeglich)
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min_soc_eauto = parameter['eauto_min_soc']
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start_params = parameter['start_solution']
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###############
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# spuelmaschine
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##############
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print(parameter)
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if parameter["haushaltsgeraet_dauer"] >0:
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spuelmaschine = Haushaltsgeraet(hours=self.prediction_hours, verbrauch_kwh=parameter["haushaltsgeraet_wh"], dauer_h=parameter["haushaltsgeraet_dauer"])
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spuelmaschine.set_startzeitpunkt(start_hour) # Startet jetzt
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else:
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spuelmaschine = None
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###############
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# PV Forecast
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###############
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#PVforecast = PVForecast(filepath=os.path.join(r'test_data', r'pvprognose.json'))
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# PVforecast = PVForecast(prediction_hours = self.prediction_hours, url=pv_forecast_url)
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# #print("PVPOWER",parameter['pvpowernow'])
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# if isfloat(parameter['pvpowernow']):
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# PVforecast.update_ac_power_measurement(date_time=datetime.now(), ac_power_measurement=float(parameter['pvpowernow']))
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# #PVforecast.print_ac_power_and_measurement()
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pv_forecast = parameter['pv_forecast'] #PVforecast.get_pv_forecast_for_date_range(date_now,date) #get_forecast_for_date(date)
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temperature_forecast = parameter['temperature_forecast'] #PVforecast.get_temperature_for_date_range(date_now,date)
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###############
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# Strompreise
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###############
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specific_date_prices = parameter["strompreis_euro_pro_wh"]
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print(specific_date_prices)
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#print("https://api.akkudoktor.net/prices?start="+date_now+"&end="+date)
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wr = Wechselrichter(10000, akku)
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ems = EnergieManagementSystem(gesamtlast = parameter["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,wechselrichter=wr)
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o = ems.simuliere(start_hour)
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###############
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# Optimizer Init
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##############
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opti_param = {}
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opti_param["haushaltsgeraete"] = 0
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if spuelmaschine != None:
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opti_param["haushaltsgeraete"] = 1
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self.setup_deap_environment(opti_param, start_hour)
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def evaluate_wrapper(individual):
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return self.evaluate(individual, ems, parameter,start_hour,worst_case)
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self.toolbox.register("evaluate", evaluate_wrapper)
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start_solution, extra_data = self.optimize(start_params)
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best_solution = start_solution
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o = self.evaluate_inner(best_solution, ems,start_hour)
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eauto = ems.eauto.to_dict()
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spuelstart_int = None
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discharge_hours_bin, eautocharge_hours_float, spuelstart_int = self.split_individual(best_solution)
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print(parameter)
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print(best_solution)
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visualisiere_ergebnisse(parameter["gesamtlast"], pv_forecast, specific_date_prices, o,discharge_hours_bin,eautocharge_hours_float , temperature_forecast, start_hour, self.prediction_hours,einspeiseverguetung_euro_pro_wh,extra_data=extra_data)
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os.system("cp visualisierungsergebnisse.pdf ~/")
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# 'Eigenverbrauch_Wh_pro_Stunde': eigenverbrauch_wh_pro_stunde,
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# 'Netzeinspeisung_Wh_pro_Stunde': netzeinspeisung_wh_pro_stunde,
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# 'Netzbezug_Wh_pro_Stunde': netzbezug_wh_pro_stunde,
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# 'Kosten_Euro_pro_Stunde': kosten_euro_pro_stunde,
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# 'akku_soc_pro_stunde': akku_soc_pro_stunde,
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# 'Einnahmen_Euro_pro_Stunde': einnahmen_euro_pro_stunde,
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# 'Gesamtbilanz_Euro': gesamtkosten_euro,
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# 'E-Auto_SoC_pro_Stunde':eauto_soc_pro_stunde,
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# 'Gesamteinnahmen_Euro': sum(einnahmen_euro_pro_stunde),
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# 'Gesamtkosten_Euro': sum(kosten_euro_pro_stunde),
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# "Verluste_Pro_Stunde":verluste_wh_pro_stunde,
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# "Gesamt_Verluste":sum(verluste_wh_pro_stunde),
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# "Haushaltsgeraet_wh_pro_stunde":haushaltsgeraet_wh_pro_stunde
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#print(eauto)
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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,"simulation_data":o}
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