mirror of
https://github.com/Akkudoktor-EOS/EOS.git
synced 2025-12-13 15:26:17 +00:00
Apply isort and ruff code style
This commit is contained in:
Michael Osthege
Andreas
@@ -1,31 +1,29 @@
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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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import os
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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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import matplotlib
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import numpy as np
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from modules.class_akku import *
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from modules.class_ems import *
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from modules.class_haushaltsgeraet import *
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from modules.class_heatpump import *
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from modules.class_inverter import *
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from modules.class_load import *
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from modules.class_load_container import *
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from modules.class_pv_forecast 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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matplotlib.use("Agg") # Setzt das Backend auf Agg
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import random
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import os
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from datetime import datetime
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from deap import algorithms, base, creator, tools
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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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@@ -33,19 +31,29 @@ def isfloat(num):
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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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def differential_evolution(
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population,
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toolbox,
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cxpb,
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mutpb,
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ngen,
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stats=None,
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halloffame=None,
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verbose=__debug__,
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):
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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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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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@@ -56,10 +64,10 @@ def differential_evolution(population, toolbox, cxpb, mutpb, ngen, stats=None, h
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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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@@ -73,345 +81,418 @@ def differential_evolution(population, toolbox, cxpb, mutpb, ngen, stats=None, h
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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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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.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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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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discharge_hours_bin = individual[
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: self.prediction_hours
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] # Erste 24 Werte sind Bool (Entladen)
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eautocharge_hours_float = individual[
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self.prediction_hours : self.prediction_hours * 2
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] # 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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spuelstart_int = individual[
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-1
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] # 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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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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del creator.FitnessMin
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if "Individual" in creator.__dict__:
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del creator.Individual
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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(
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"attr_float", random.uniform, 0, 1
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) # 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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# 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 = [
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self.toolbox.attr_bool() for _ in range(self.prediction_hours)
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] # 24 Bool-Werte für Entladen
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attrs += [
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self.toolbox.attr_float() for _ in range(self.prediction_hours)
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] # 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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def create_individual():
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attrs = [
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self.toolbox.attr_bool() for _ in range(self.prediction_hours)
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] # 24 Bool-Werte für Entladen
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attrs += [
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self.toolbox.attr_float() for _ in range(self.prediction_hours)
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] # 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(
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"individual", create_individual
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) # tools.initCycle, creator.Individual, (self.toolbox.attr_bool,self.toolbox.attr_bool), n=self.prediction_hours+1)
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self.toolbox.register(
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"population", tools.initRepeat, list, self.toolbox.individual
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)
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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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# 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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# print("Spuel:",self.opti_param)
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discharge_hours_bin, eautocharge_hours_float, spuelstart_int = (
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self.split_individual(individual)
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)
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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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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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# 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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# Setze die festen Werte für die letzten x Stunden
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for i in range(
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self.prediction_hours - self.fixed_eauto_hours, self.prediction_hours
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):
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eautocharge_hours_float[i] = (
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0.0 # Setze die letzten x Stunden auf einen festen Wert (oder vorgegebenen Wert)
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)
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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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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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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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gesamtbilanz = gesamtbilanz * -1.0
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discharge_hours_bin, eautocharge_hours_float, spuelstart_int = (
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self.split_individual(individual)
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)
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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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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 += (
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self.strafe * 10
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) # 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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if (
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discharge_hours_bin[i] == 0.0
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): # 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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# E-Auto nur die ersten self.fixed_eauto_hours
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for i in range(
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self.prediction_hours - self.fixed_eauto_hours, self.prediction_hours
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):
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if (
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eautocharge_hours_float[i] != 0.0
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): # 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 = (
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ems.eauto.ladezustand_in_prozent()
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) # 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 (
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eautocharge_hours_float[i] != 0.0
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): # 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 = (
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o["Gesamtbilanz_Euro"],
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o["Gesamt_Verluste"],
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eauto_roi,
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)
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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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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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strafe = max(
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0,
|
||||
(parameter["eauto_min_soc"] - ems.eauto.ladezustand_in_prozent())
|
||||
* self.strafe,
|
||||
)
|
||||
gesamtbilanz += strafe - restwert_akku + strafe_überschreitung
|
||||
# gesamtbilanz += o["Gesamt_Verluste"]/10000.0
|
||||
|
||||
return (gesamtbilanz,)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# Genetischer Algorithmus
|
||||
def optimize(self,start_solution=None):
|
||||
|
||||
|
||||
def optimize(self, start_solution=None):
|
||||
population = self.toolbox.population(n=300)
|
||||
hof = tools.HallOfFame(1)
|
||||
|
||||
|
||||
stats = tools.Statistics(lambda ind: ind.fitness.values)
|
||||
stats.register("avg", np.mean)
|
||||
stats.register("min", np.min)
|
||||
stats.register("max", np.max)
|
||||
|
||||
print("Start:",start_solution)
|
||||
|
||||
|
||||
print("Start:", start_solution)
|
||||
|
||||
if start_solution is not None and start_solution != -1:
|
||||
population.insert(0, creator.Individual(start_solution))
|
||||
population.insert(1, creator.Individual(start_solution))
|
||||
population.insert(2, creator.Individual(start_solution))
|
||||
|
||||
algorithms.eaMuPlusLambda(population, self.toolbox, mu=100, lambda_=200, cxpb=0.5, mutpb=0.3, ngen=400, stats=stats, halloffame=hof, verbose=True)
|
||||
#algorithms.eaSimple(population, self.toolbox, cxpb=0.3, mutpb=0.3, ngen=200, stats=stats, halloffame=hof, verbose=True)
|
||||
#algorithms.eaMuCommaLambda(population, self.toolbox, mu=100, lambda_=200, cxpb=0.2, mutpb=0.4, ngen=300, stats=stats, halloffame=hof, verbose=True)
|
||||
#population, log = differential_evolution(population, self.toolbox, cxpb=0.2, mutpb=0.5, ngen=200, stats=stats, halloffame=hof, verbose=True)
|
||||
|
||||
population.insert(0, creator.Individual(start_solution))
|
||||
population.insert(1, creator.Individual(start_solution))
|
||||
population.insert(2, creator.Individual(start_solution))
|
||||
|
||||
|
||||
algorithms.eaMuPlusLambda(
|
||||
population,
|
||||
self.toolbox,
|
||||
mu=100,
|
||||
lambda_=200,
|
||||
cxpb=0.5,
|
||||
mutpb=0.3,
|
||||
ngen=400,
|
||||
stats=stats,
|
||||
halloffame=hof,
|
||||
verbose=True,
|
||||
)
|
||||
# algorithms.eaSimple(population, self.toolbox, cxpb=0.3, mutpb=0.3, ngen=200, stats=stats, halloffame=hof, verbose=True)
|
||||
# algorithms.eaMuCommaLambda(population, self.toolbox, mu=100, lambda_=200, cxpb=0.2, mutpb=0.4, ngen=300, stats=stats, halloffame=hof, verbose=True)
|
||||
# population, log = differential_evolution(population, self.toolbox, cxpb=0.2, mutpb=0.5, ngen=200, stats=stats, halloffame=hof, verbose=True)
|
||||
|
||||
|
||||
member = {"bilanz":[],"verluste":[],"nebenbedingung":[]}
|
||||
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)
|
||||
|
||||
|
||||
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, startdate=None):
|
||||
|
||||
|
||||
def optimierung_ems(
|
||||
self, parameter=None, start_hour=None, worst_case=False, startdate=None
|
||||
):
|
||||
############
|
||||
# Parameter
|
||||
# Parameter
|
||||
############
|
||||
if startdate == None:
|
||||
date = (datetime.now().date() + timedelta(hours = self.prediction_hours)).strftime("%Y-%m-%d")
|
||||
date_now = datetime.now().strftime("%Y-%m-%d")
|
||||
date = (
|
||||
datetime.now().date() + timedelta(hours=self.prediction_hours)
|
||||
).strftime("%Y-%m-%d")
|
||||
date_now = datetime.now().strftime("%Y-%m-%d")
|
||||
else:
|
||||
date = (startdate + timedelta(hours = self.prediction_hours)).strftime("%Y-%m-%d")
|
||||
date_now = startdate.strftime("%Y-%m-%d")
|
||||
#print("Start_date:",date_now)
|
||||
|
||||
akku_size = parameter['pv_akku_cap'] # Wh
|
||||
|
||||
einspeiseverguetung_euro_pro_wh = np.full(self.prediction_hours, parameter["einspeiseverguetung_euro_pro_wh"]) #= # € / Wh 7/(1000.0*100.0)
|
||||
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]) #
|
||||
akku = PVAkku(kapazitaet_wh=akku_size,hours=self.prediction_hours,start_soc_prozent=parameter["pv_soc"], max_ladeleistung_w=5000)
|
||||
date = (startdate + timedelta(hours=self.prediction_hours)).strftime(
|
||||
"%Y-%m-%d"
|
||||
)
|
||||
date_now = startdate.strftime("%Y-%m-%d")
|
||||
# print("Start_date:",date_now)
|
||||
|
||||
akku_size = parameter["pv_akku_cap"] # Wh
|
||||
|
||||
einspeiseverguetung_euro_pro_wh = np.full(
|
||||
self.prediction_hours, parameter["einspeiseverguetung_euro_pro_wh"]
|
||||
) # = # € / Wh 7/(1000.0*100.0)
|
||||
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]) #
|
||||
akku = PVAkku(
|
||||
kapazitaet_wh=akku_size,
|
||||
hours=self.prediction_hours,
|
||||
start_soc_prozent=parameter["pv_soc"],
|
||||
max_ladeleistung_w=5000,
|
||||
)
|
||||
akku.set_charge_per_hour(discharge_array)
|
||||
|
||||
|
||||
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])
|
||||
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"])
|
||||
|
||||
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])
|
||||
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']
|
||||
|
||||
min_soc_eauto = parameter["eauto_min_soc"]
|
||||
start_params = parameter["start_solution"]
|
||||
|
||||
###############
|
||||
# spuelmaschine
|
||||
##############
|
||||
print(parameter)
|
||||
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
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
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
|
||||
|
||||
###############
|
||||
# PV Forecast
|
||||
###############
|
||||
#PVforecast = PVForecast(filepath=os.path.join(r'test_data', r'pvprognose.json'))
|
||||
# PVforecast = PVForecast(filepath=os.path.join(r'test_data', r'pvprognose.json'))
|
||||
# PVforecast = PVForecast(prediction_hours = self.prediction_hours, url=pv_forecast_url)
|
||||
# #print("PVPOWER",parameter['pvpowernow'])
|
||||
# if isfloat(parameter['pvpowernow']):
|
||||
# PVforecast.update_ac_power_measurement(date_time=datetime.now(), ac_power_measurement=float(parameter['pvpowernow']))
|
||||
# #PVforecast.print_ac_power_and_measurement()
|
||||
pv_forecast = parameter['pv_forecast'] #PVforecast.get_pv_forecast_for_date_range(date_now,date) #get_forecast_for_date(date)
|
||||
temperature_forecast = parameter['temperature_forecast'] #PVforecast.get_temperature_for_date_range(date_now,date)
|
||||
|
||||
# PVforecast.update_ac_power_measurement(date_time=datetime.now(), ac_power_measurement=float(parameter['pvpowernow']))
|
||||
# #PVforecast.print_ac_power_and_measurement()
|
||||
pv_forecast = parameter[
|
||||
"pv_forecast"
|
||||
] # PVforecast.get_pv_forecast_for_date_range(date_now,date) #get_forecast_for_date(date)
|
||||
temperature_forecast = parameter[
|
||||
"temperature_forecast"
|
||||
] # PVforecast.get_temperature_for_date_range(date_now,date)
|
||||
|
||||
###############
|
||||
# Strompreise
|
||||
# Strompreise
|
||||
###############
|
||||
specific_date_prices = parameter["strompreis_euro_pro_wh"]
|
||||
print(specific_date_prices)
|
||||
#print("https://api.akkudoktor.net/prices?start="+date_now+"&end="+date)
|
||||
|
||||
# print("https://api.akkudoktor.net/prices?start="+date_now+"&end="+date)
|
||||
|
||||
wr = Wechselrichter(10000, akku)
|
||||
|
||||
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)
|
||||
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,
|
||||
)
|
||||
o = ems.simuliere(start_hour)
|
||||
|
||||
|
||||
###############
|
||||
# Optimizer Init
|
||||
##############
|
||||
opti_param = {}
|
||||
opti_param["haushaltsgeraete"] = 0
|
||||
if spuelmaschine != None:
|
||||
opti_param["haushaltsgeraete"] = 1
|
||||
|
||||
opti_param["haushaltsgeraete"] = 1
|
||||
|
||||
self.setup_deap_environment(opti_param, start_hour)
|
||||
|
||||
def evaluate_wrapper(individual):
|
||||
return self.evaluate(individual, ems, parameter,start_hour,worst_case)
|
||||
|
||||
return self.evaluate(individual, ems, parameter, start_hour, worst_case)
|
||||
|
||||
self.toolbox.register("evaluate", evaluate_wrapper)
|
||||
start_solution, extra_data = self.optimize(start_params)
|
||||
best_solution = start_solution
|
||||
o = self.evaluate_inner(best_solution, ems,start_hour)
|
||||
o = self.evaluate_inner(best_solution, ems, start_hour)
|
||||
eauto = ems.eauto.to_dict()
|
||||
spuelstart_int = None
|
||||
discharge_hours_bin, eautocharge_hours_float, spuelstart_int = self.split_individual(best_solution)
|
||||
|
||||
|
||||
discharge_hours_bin, eautocharge_hours_float, spuelstart_int = (
|
||||
self.split_individual(best_solution)
|
||||
)
|
||||
|
||||
print(parameter)
|
||||
print(best_solution)
|
||||
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)
|
||||
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,
|
||||
)
|
||||
os.system("cp visualisierungsergebnisse.pdf ~/")
|
||||
|
||||
# 'Eigenverbrauch_Wh_pro_Stunde': eigenverbrauch_wh_pro_stunde,
|
||||
# 'Netzeinspeisung_Wh_pro_Stunde': netzeinspeisung_wh_pro_stunde,
|
||||
# 'Netzbezug_Wh_pro_Stunde': netzbezug_wh_pro_stunde,
|
||||
# 'Kosten_Euro_pro_Stunde': kosten_euro_pro_stunde,
|
||||
# 'akku_soc_pro_stunde': akku_soc_pro_stunde,
|
||||
# 'Einnahmen_Euro_pro_Stunde': einnahmen_euro_pro_stunde,
|
||||
# 'Gesamtbilanz_Euro': gesamtkosten_euro,
|
||||
# 'E-Auto_SoC_pro_Stunde':eauto_soc_pro_stunde,
|
||||
# 'Gesamteinnahmen_Euro': sum(einnahmen_euro_pro_stunde),
|
||||
# 'Gesamtkosten_Euro': sum(kosten_euro_pro_stunde),
|
||||
# "Verluste_Pro_Stunde":verluste_wh_pro_stunde,
|
||||
# "Gesamt_Verluste":sum(verluste_wh_pro_stunde),
|
||||
# "Haushaltsgeraet_wh_pro_stunde":haushaltsgeraet_wh_pro_stunde
|
||||
|
||||
#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,"simulation_data":o}
|
||||
|
||||
|
||||
|
||||
|
||||
# 'Eigenverbrauch_Wh_pro_Stunde': eigenverbrauch_wh_pro_stunde,
|
||||
# 'Netzeinspeisung_Wh_pro_Stunde': netzeinspeisung_wh_pro_stunde,
|
||||
# 'Netzbezug_Wh_pro_Stunde': netzbezug_wh_pro_stunde,
|
||||
# 'Kosten_Euro_pro_Stunde': kosten_euro_pro_stunde,
|
||||
# 'akku_soc_pro_stunde': akku_soc_pro_stunde,
|
||||
# 'Einnahmen_Euro_pro_Stunde': einnahmen_euro_pro_stunde,
|
||||
# 'Gesamtbilanz_Euro': gesamtkosten_euro,
|
||||
# 'E-Auto_SoC_pro_Stunde':eauto_soc_pro_stunde,
|
||||
# 'Gesamteinnahmen_Euro': sum(einnahmen_euro_pro_stunde),
|
||||
# 'Gesamtkosten_Euro': sum(kosten_euro_pro_stunde),
|
||||
# "Verluste_Pro_Stunde":verluste_wh_pro_stunde,
|
||||
# "Gesamt_Verluste":sum(verluste_wh_pro_stunde),
|
||||
# "Haushaltsgeraet_wh_pro_stunde":haushaltsgeraet_wh_pro_stunde
|
||||
|
||||
# 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,
|
||||
"simulation_data": o,
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user