mirror of
https://github.com/Akkudoktor-EOS/EOS.git
synced 2025-04-19 08:55:15 +00:00
298 lines
11 KiB
Python
298 lines
11 KiB
Python
from flask import Flask, jsonify, request
|
|
import numpy as np
|
|
from datetime import datetime
|
|
from modules.class_load import *
|
|
from modules.class_ems import *
|
|
from modules.class_pv_forecast import *
|
|
from modules.class_akku import *
|
|
from modules.class_strompreis import *
|
|
from modules.class_heatpump import *
|
|
from modules.class_load_container import *
|
|
from modules.class_eauto import *
|
|
|
|
from pprint import pprint
|
|
import matplotlib.pyplot as plt
|
|
from modules.visualize import *
|
|
from deap import base, creator, tools, algorithms
|
|
import numpy as np
|
|
import random
|
|
import os
|
|
|
|
|
|
start_hour = 11
|
|
prediction_hours = 24
|
|
date = (datetime.now().date() + timedelta(hours = prediction_hours)).strftime("%Y-%m-%d")
|
|
date_now = datetime.now().strftime("%Y-%m-%d")
|
|
|
|
akku_size = 30000 # Wh
|
|
year_energy = 2000*1000 #Wh
|
|
einspeiseverguetung_cent_pro_wh = np.full(prediction_hours, 7/(1000.0*100.0)) # € / Wh
|
|
|
|
max_heizleistung = 1000 # 5 kW Heizleistung
|
|
wp = Waermepumpe(max_heizleistung,prediction_hours)
|
|
|
|
akku = PVAkku(akku_size,prediction_hours)
|
|
discharge_array = np.full(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]) #
|
|
|
|
laden_moeglich = np.full(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])
|
|
#np.full(prediction_hours,1)
|
|
eauto = PVAkku(kapazitaet_wh=60000, hours=prediction_hours, lade_effizienz=0.95, entlade_effizienz=1.0, max_ladeleistung_w=10000 ,start_soc_prozent=10)
|
|
eauto.set_charge_per_hour(laden_moeglich)
|
|
min_soc_eauto = 80
|
|
hohe_strafe = 10.0
|
|
|
|
|
|
|
|
|
|
#Gesamtlast
|
|
#############
|
|
gesamtlast = Gesamtlast()
|
|
|
|
|
|
# Load Forecast
|
|
###############
|
|
lf = LoadForecast(filepath=r'load_profiles.npz', year_energy=year_energy)
|
|
#leistung_haushalt = lf.get_daily_stats(date)[0,...] # Datum anpassen
|
|
leistung_haushalt = lf.get_stats_for_date_range(date_now,date)[0,...].flatten()
|
|
# print(date_now," ",date)
|
|
# print(leistung_haushalt.shape)
|
|
gesamtlast.hinzufuegen("Haushalt", leistung_haushalt)
|
|
|
|
# PV Forecast
|
|
###############
|
|
#PVforecast = PVForecast(filepath=os.path.join(r'test_data', r'pvprognose.json'))
|
|
PVforecast = PVForecast(prediction_hours = prediction_hours, url="https://api.akkudoktor.net/forecast?lat=50.8588&lon=7.3747&power=5000&azimuth=-10&tilt=7&powerInvertor=10000&horizont=20,27,22,20&power=4800&azimuth=-90&tilt=7&powerInvertor=10000&horizont=30,30,30,50&power=1400&azimuth=-40&tilt=60&powerInvertor=2000&horizont=60,30,0,30&power=1600&azimuth=5&tilt=45&powerInvertor=1400&horizont=45,25,30,60&past_days=5&cellCoEff=-0.36&inverterEfficiency=0.8&albedo=0.25&timezone=Europe%2FBerlin&hourly=relativehumidity_2m%2Cwindspeed_10m")
|
|
pv_forecast = PVforecast.get_pv_forecast_for_date_range(date_now,date) #get_forecast_for_date(date)
|
|
|
|
temperature_forecast = PVforecast.get_temperature_for_date_range(date_now,date)
|
|
|
|
|
|
|
|
# Strompreise
|
|
###############
|
|
filepath = os.path.join (r'test_data', r'strompreise_akkudokAPI.json') # Pfad zur JSON-Datei anpassen
|
|
#price_forecast = HourlyElectricityPriceForecast(source=filepath)
|
|
price_forecast = HourlyElectricityPriceForecast(source="https://api.akkudoktor.net/prices?start="+date_now+"&end="+date+"")
|
|
specific_date_prices = price_forecast.get_price_for_daterange(date_now,date)
|
|
# print("13:",specific_date_prices[13])
|
|
# print("14:",specific_date_prices[14])
|
|
# print("15:",specific_date_prices[15])
|
|
# sys.exit()
|
|
# WP
|
|
##############
|
|
leistung_wp = wp.simulate_24h(temperature_forecast)
|
|
gesamtlast.hinzufuegen("Heatpump", leistung_wp)
|
|
|
|
|
|
# EAuto
|
|
######################
|
|
# leistung_eauto = eauto.get_stuendliche_last()
|
|
# soc_eauto = eauto.get_stuendlicher_soc()
|
|
# gesamtlast.hinzufuegen("eauto", leistung_eauto)
|
|
|
|
# print(gesamtlast.gesamtlast_berechnen())
|
|
|
|
# EMS / Stromzähler Bilanz
|
|
#akku=None, pv_prognose_wh=None, strompreis_cent_pro_wh=None, einspeiseverguetung_cent_pro_wh=None, eauto=None, gesamtlast=None
|
|
|
|
ems = EnergieManagementSystem(akku=akku, gesamtlast = gesamtlast, pv_prognose_wh=pv_forecast, strompreis_cent_pro_wh=specific_date_prices, einspeiseverguetung_cent_pro_wh=einspeiseverguetung_cent_pro_wh, eauto=eauto)
|
|
|
|
|
|
o = ems.simuliere(start_hour)#ems.simuliere_ab_jetzt()
|
|
#pprint(o)
|
|
#pprint(o["Gesamtbilanz_Euro"])
|
|
|
|
#visualisiere_ergebnisse(gesamtlast, pv_forecast, specific_date_prices, o,discharge_array,laden_moeglich, temperature_forecast, start_hour, prediction_hours)
|
|
|
|
|
|
|
|
# Optimierung
|
|
|
|
def evaluate_inner(individual):
|
|
#print(individual)
|
|
discharge_hours_bin = individual[0::2]
|
|
eautocharge_hours_float = individual[1::2]
|
|
|
|
#print(discharge_hours_bin)
|
|
#print(len(eautocharge_hours_float))
|
|
ems.reset()
|
|
#eauto.reset()
|
|
ems.set_akku_discharge_hours(discharge_hours_bin)
|
|
ems.set_eauto_charge_hours(eautocharge_hours_float)
|
|
|
|
#eauto.set_laden_moeglich(eautocharge_hours_float)
|
|
#eauto.berechne_ladevorgang()
|
|
#leistung_eauto = eauto.get_stuendliche_last()
|
|
#gesamtlast.hinzufuegen("eauto", leistung_eauto)
|
|
|
|
#ems.set_gesamtlast(gesamtlast.gesamtlast_berechnen())
|
|
|
|
o = ems.simuliere(start_hour)
|
|
return o, eauto
|
|
|
|
# Fitness-Funktion (muss Ihre EnergieManagementSystem-Logik integrieren)
|
|
def evaluate(individual):
|
|
o,eauto = evaluate_inner(individual)
|
|
gesamtbilanz = o["Gesamtbilanz_Euro"]
|
|
|
|
# Überprüfung, ob der Mindest-SoC erreicht wird
|
|
final_soc = eauto.ladezustand_in_prozent() # Nimmt den SoC am Ende des Optimierungszeitraums
|
|
strafe = 0.0
|
|
#if final_soc < min_soc_eauto:
|
|
# Fügt eine Strafe hinzu, wenn der Mindest-SoC nicht erreicht wird
|
|
strafe = max(0,(min_soc_eauto - final_soc) * hohe_strafe ) # `hohe_strafe` ist ein vorher festgelegter Strafwert
|
|
gesamtbilanz += strafe
|
|
gesamtbilanz += o["Gesamt_Verluste"]/1000.0
|
|
return (gesamtbilanz,)
|
|
|
|
|
|
|
|
|
|
|
|
# Werkzeug-Setup
|
|
creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
|
|
creator.create("Individual", list, fitness=creator.FitnessMin)
|
|
|
|
toolbox = base.Toolbox()
|
|
|
|
toolbox.register("attr_bool", random.randint, 0, 1)
|
|
toolbox.register("attr_bool", random.randint, 0, 1)
|
|
toolbox.register("individual", tools.initCycle, creator.Individual, (toolbox.attr_bool,toolbox.attr_bool), n=prediction_hours)
|
|
|
|
|
|
|
|
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
|
|
|
|
toolbox.register("evaluate", evaluate)
|
|
toolbox.register("mate", tools.cxTwoPoint)
|
|
toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
|
|
toolbox.register("select", tools.selTournament, tournsize=3)
|
|
|
|
# Genetischer Algorithmus
|
|
def optimize():
|
|
population = toolbox.population(n=500)
|
|
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)
|
|
|
|
algorithms.eaMuPlusLambda(population, toolbox, 50, 100, cxpb=0.5, mutpb=0.5, ngen=500, stats=stats, halloffame=hof, verbose=True)
|
|
#algorithms.eaSimple(population, toolbox, cxpb=0.2, mutpb=0.2, ngen=1000, stats=stats, halloffame=hof, verbose=True)
|
|
return hof[0]
|
|
|
|
|
|
start_solution = optimize()
|
|
|
|
|
|
print("Start Lösung:", start_solution)
|
|
|
|
|
|
|
|
|
|
# # Werkzeug-Setup
|
|
# creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
|
|
# creator.create("Individual", list, fitness=creator.FitnessMin)
|
|
|
|
# toolbox = base.Toolbox()
|
|
|
|
|
|
|
|
|
|
# toolbox.register("attr_bool", random.randint, 0, 1)
|
|
# toolbox.register("attr_float", random.uniform, 0.0, 1.0)
|
|
# toolbox.register("individual", tools.initCycle, creator.Individual, (toolbox.attr_bool,toolbox.attr_float), n=prediction_hours)
|
|
|
|
# start_individual = toolbox.individual()
|
|
# start_individual[:] = start_solution
|
|
|
|
# toolbox.register("population", tools.initRepeat, list, toolbox.individual)
|
|
|
|
# toolbox.register("evaluate", evaluate)
|
|
# toolbox.register("mate", tools.cxTwoPoint)
|
|
# toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
|
|
# toolbox.register("select", tools.selTournament, tournsize=3)
|
|
|
|
# # Genetischer Algorithmus
|
|
# def optimize():
|
|
# population = toolbox.population(n=1000)
|
|
# population[0] = start_individual
|
|
# 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)
|
|
|
|
# algorithms.eaMuPlusLambda(population, toolbox, 100, 200, cxpb=0.5, mutpb=0.2, ngen=1000, stats=stats, halloffame=hof, verbose=True)
|
|
# #algorithms.eaSimple(population, toolbox, cxpb=0.2, mutpb=0.2, ngen=1000, stats=stats, halloffame=hof, verbose=True)
|
|
# return hof[0]
|
|
|
|
# best_solution = optimize()
|
|
best_solution = start_solution
|
|
print("Beste Lösung:", best_solution)
|
|
|
|
#ems.set_akku_discharge_hours(best_solution)
|
|
o,eauto = evaluate_inner(best_solution)
|
|
|
|
# soc_eauto = eauto.get_stuendlicher_soc()
|
|
# print(soc_eauto)
|
|
# pprint(o)
|
|
# pprint(eauto.get_stuendlicher_soc())
|
|
|
|
|
|
#visualisiere_ergebnisse(gesamtlast,leistung_haushalt,leistung_wp, pv_forecast, specific_date_prices, o,soc_eauto,best_solution[0::2],best_solution[1::2] , temperature_forecast)
|
|
visualisiere_ergebnisse(gesamtlast, pv_forecast, specific_date_prices, o,best_solution[0::2],best_solution[1::2] , temperature_forecast, start_hour, prediction_hours)
|
|
|
|
|
|
# for data in forecast.get_forecast_data():
|
|
# print(data.get_date_time(), data.get_dc_power(), data.get_ac_power(), data.get_windspeed_10m(), data.get_temperature())for data in forecast.get_forecast_data():
|
|
|
|
|
|
|
|
|
|
# app = Flask(__name__)
|
|
|
|
|
|
|
|
# @app.route('/getdata', methods=['GET'])
|
|
# def get_data():
|
|
# # Hole das Datum aus den Query-Parametern
|
|
# date_str = request.args.get('date')
|
|
# year_energy = request.args.get('year_energy')
|
|
|
|
# try:
|
|
# # Konvertiere das Datum in ein datetime-Objekt
|
|
# date_obj = datetime.strptime(date_str, '%Y-%m-%d')
|
|
# filepath = r'.\load_profiles.npz' # Pfad zur JSON-Datei anpassen
|
|
# lf = cl.LoadForecast(filepath=filepath, year_energy=float(year_energy))
|
|
# specific_date_prices = lf.get_daily_stats('2024-02-16')
|
|
|
|
|
|
# # Berechne den Tag des Jahres
|
|
# #day_of_year = date_obj.timetuple().tm_yday
|
|
|
|
# # Konvertiere den Tag des Jahres in einen String, falls die Schlüssel als Strings gespeichert sind
|
|
# #day_key = int(day_of_year)
|
|
# #print(day_key)
|
|
# # Überprüfe, ob der Tag im Jahr in den Daten vorhanden ist
|
|
# array_list = lf.get_daily_stats(date_str)
|
|
# pprint(array_list)
|
|
# pprint(array_list.shape)
|
|
# if array_list.shape == (2,24):
|
|
# #if day_key < len(load_profiles_exp):
|
|
# # Konvertiere das Array in eine Liste für die JSON-Antwort
|
|
# #((load_profiles_exp_l[day_key]).tolist(),(load_profiles_std_l)[day_key].tolist())
|
|
|
|
# return jsonify({date_str: array_list.tolist()})
|
|
# else:
|
|
# return jsonify({"error": "Datum nicht gefunden"}), 404
|
|
# except ValueError:
|
|
# # Wenn das Datum nicht im richtigen Format ist oder ungültig ist
|
|
# return jsonify({"error": "Ungültiges Datum"}), 400
|
|
|
|
# if __name__ == '__main__':
|
|
# app.run(debug=True)
|
|
|