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Sommerzeit Probleme (bei Zeitumstellung gibt es eine Stunde weniger)

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Für die PVForecast kann jetzt ein aktuelle Messwert der PV Leistung
eingegeben werden!
This commit is contained in:
Bla Bla
2024-03-31 13:00:01 +02:00
parent 97f407073a
commit c265885286
6 changed files with 161 additions and 50 deletions
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49
flask_server.py
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@@ -1,6 +1,5 @@
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 *
@@ -8,23 +7,37 @@ 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 modules.class_sommerzeit import *
from modules.visualize import *
import os
from flask import Flask, send_from_directory
from pprint import pprint
import matplotlib
matplotlib.use('Agg') # Setzt das Backend auf Agg
import matplotlib.pyplot as plt
from modules.visualize import *
import string
from datetime import datetime
from deap import base, creator, tools, algorithms
import numpy as np
import random
import os
start_hour = datetime.now().hour
# if ist_dst_wechsel(datetime.now()):
# prediction_hours = 23 # Anpassung auf 23 Stunden für DST-Wechseltage
# else:
# prediction_hours = 24 # Standardwert für Tage ohne DST-Wechsel
prediction_hours = 24
start_hour = datetime.now().hour
hohe_strafe = 10.0
# print(prediction_hours)
# sys.exit()
def isfloat(num):
try:
float(num)
return True
except ValueError:
return False
def evaluate_inner(individual, ems):
@@ -91,7 +104,7 @@ def optimize(start_solution=None):
population.insert(0, creator.Individual(start_solution))
#algorithms.eaMuPlusLambda(population, toolbox, 100, 200, cxpb=0.3, mutpb=0.3, ngen=500, stats=stats, halloffame=hof, verbose=True)
algorithms.eaSimple(population, toolbox, cxpb=0.5, mutpb=0.8, ngen=200, stats=stats, halloffame=hof, verbose=True)
algorithms.eaSimple(population, toolbox, cxpb=0.8, mutpb=0.8, ngen=400, stats=stats, halloffame=hof, verbose=True)
return hof[0]
@@ -104,6 +117,8 @@ app = Flask(__name__)
# Ersetzen Sie diese Logik durch Ihren eigentlichen Optimierungscode
def durchfuehre_simulation(parameter):
start_hour = datetime.now().hour
############
# Parameter
############
@@ -113,7 +128,7 @@ def durchfuehre_simulation(parameter):
akku_size = parameter['pv_akku_cap'] # Wh
year_energy = parameter['year_energy'] #2000*1000 #Wh
einspeiseverguetung_cent_pro_wh = np.full(prediction_hours, parameter["einspeiseverguetung_euro_pro_wh"]) #= # € / Wh 7/(1000.0*100.0)
einspeiseverguetung_euro_pro_wh = np.full(prediction_hours, parameter["einspeiseverguetung_euro_pro_wh"]) #= # € / Wh 7/(1000.0*100.0)
max_heizleistung = parameter['max_heizleistung'] #1000 # 5 kW Heizleistung
wp = Waermepumpe(max_heizleistung,prediction_hours)
@@ -146,7 +161,13 @@ def durchfuehre_simulation(parameter):
###############
#PVforecast = PVForecast(filepath=os.path.join(r'test_data', r'pvprognose.json'))
PVforecast = PVForecast(prediction_hours = 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 = 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)
@@ -165,7 +186,7 @@ def durchfuehre_simulation(parameter):
leistung_wp = wp.simulate_24h(temperature_forecast)
gesamtlast.hinzufuegen("Heatpump", leistung_wp)
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)
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)
o = ems.simuliere(start_hour)
@@ -189,7 +210,9 @@ def durchfuehre_simulation(parameter):
#print(o)
visualisiere_ergebnisse(gesamtlast, pv_forecast, specific_date_prices, o,best_solution[0::2],best_solution[1::2] , temperature_forecast, start_hour, prediction_hours)
visualisiere_ergebnisse(gesamtlast, pv_forecast, specific_date_prices, o,best_solution[0::2],best_solution[1::2] , temperature_forecast, start_hour, prediction_hours,einspeiseverguetung_euro_pro_wh)
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}
@@ -203,7 +226,7 @@ def simulation():
parameter = request.json
# Erforderliche Parameter prüfen
erforderliche_parameter = [ 'pv_akku_cap', 'year_energy',"einspeiseverguetung_euro_pro_wh", 'max_heizleistung', 'pv_forecast_url', 'eauto_min_soc', "eauto_cap","eauto_charge_efficiency","eauto_charge_power","eauto_soc","pv_soc","start_solution"]
erforderliche_parameter = [ 'pv_akku_cap', 'year_energy',"einspeiseverguetung_euro_pro_wh", 'max_heizleistung', 'pv_forecast_url', 'eauto_min_soc', "eauto_cap","eauto_charge_efficiency","eauto_charge_power","eauto_soc","pv_soc","start_solution","pvpowernow"]
for p in erforderliche_parameter:
if p not in parameter:
return jsonify({"error": f"Fehlender Parameter: {p}"}), 400
@@ -217,6 +240,10 @@ def simulation():
return jsonify(ergebnis)
@app.route('/visualisierungsergebnisse.pdf')
def get_pdf():
return send_from_directory('', 'visualisierungsergebnisse.pdf')