Mirrors/EOS
1
0
Fork 0
mirror of https://github.com/Akkudoktor-EOS/EOS.git synced 2025-12-13 15:26:17 +00:00
Code Issues Actions 1 Packages Projects Releases Wiki Activity

Lastprognose entkoppelt, an den Optimierer wird jetzt nur das Array

Browse Source 
übergeben. So sind unterschiedliche / eigenen Prognosen möglich
This commit is contained in:
Bla Bla
2024-07-30 10:51:26 +02:00
parent 5e842d5ee4
commit b5ff38fb03
4 changed files with 39 additions and 27 deletions
Download Patch File Download Diff File Expand all files Collapse all files

27
modules/class_optimize.py
View File

@@ -4,6 +4,7 @@ from modules.class_load import *
from modules.class_ems import *
from modules.class_pv_forecast import *
from modules.class_akku import *
from modules.class_heatpump import *
from modules.class_load_container import *
from modules.class_inverter import *
@@ -173,13 +174,9 @@ class optimization_problem:
#print("Start_date:",date_now)
akku_size = parameter['pv_akku_cap'] # Wh
year_energy = parameter['year_energy'] #2000*1000 #Wh
einspeiseverguetung_euro_pro_wh = np.full(self.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,self.prediction_hours)
pv_forecast_url = parameter['pv_forecast_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"
akku = PVAkku(kapazitaet_wh=akku_size,hours=self.prediction_hours,start_soc_prozent=parameter["pv_soc"], max_ladeleistung_w=5000)
@@ -191,7 +188,6 @@ class optimization_problem:
eauto.set_charge_per_hour(laden_moeglich)
min_soc_eauto = parameter['eauto_min_soc']
start_params = parameter['start_solution']
gesamtlast = Gesamtlast(prediction_hours=self.prediction_hours)
###############
# spuelmaschine
@@ -204,18 +200,12 @@ class optimization_problem:
spuelmaschine = None
###############
# 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] # Nur Erwartungswert!
gesamtlast.hinzufuegen("Haushalt", leistung_haushalt)
###############
# PV Forecast
@@ -240,15 +230,10 @@ class optimization_problem:
print(specific_date_prices)
#print("https://api.akkudoktor.net/prices?start="+date_now+"&end="+date)
###############
# WP
##############
leistung_wp = wp.simulate_24h(temperature_forecast)
gesamtlast.hinzufuegen("Heatpump", leistung_wp)
wr = Wechselrichter(5000, akku)
ems = EnergieManagementSystem(gesamtlast = gesamtlast, pv_prognose_wh=pv_forecast, strompreis_euro_pro_wh=specific_date_prices, einspeiseverguetung_euro_pro_wh=einspeiseverguetung_euro_pro_wh, eauto=eauto, haushaltsgeraet=spuelmaschine,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)
###############
@@ -281,7 +266,7 @@ class optimization_problem:
print(parameter)
print(best_solution)
visualisiere_ergebnisse(gesamtlast, pv_forecast, specific_date_prices, o,best_solution[0::2],best_solution[1::2] , temperature_forecast, start_hour, self.prediction_hours,einspeiseverguetung_euro_pro_wh,extra_data=extra_data)
visualisiere_ergebnisse(parameter["gesamtlast"], pv_forecast, specific_date_prices, o,best_solution[0::2],best_solution[1::2] , temperature_forecast, start_hour, self.prediction_hours,einspeiseverguetung_euro_pro_wh,extra_data=extra_data)
os.system("cp visualisierungsergebnisse.pdf ~/")
# 'Eigenverbrauch_Wh_pro_Stunde': eigenverbrauch_wh_pro_stunde,