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Startpunkt setzbar und Prognosezeitraum ebenfalls.
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@ -1,18 +1,19 @@
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import numpy as np
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class PVAkku:
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def __init__(self, kapazitaet_wh):
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def __init__(self, kapazitaet_wh, hours):
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# Kapazität des Akkus in Wh
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self.kapazitaet_wh = kapazitaet_wh
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# Initialer Ladezustand des Akkus in Wh
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self.soc_wh = 0
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self.discharge_array = np.full(24, 1)
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self.hours = hours
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self.discharge_array = np.full(self.hours, 1)
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def reset(self):
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self.soc_wh = 0
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self.discharge_array = np.full(24, 1)
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self.discharge_array = np.full(self.hours, 1)
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def set_discharge_per_hour(self, discharge_array):
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assert(len(discharge_array) == 24)
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assert(len(discharge_array) == self.hours)
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self.discharge_array = discharge_array
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def ladezustand_in_prozent(self):
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@ -1,35 +1,6 @@
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# class EnergieManagementSystem:
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# def __init__(self, akku, lastkurve_wh, pv_prognose_wh):
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# self.akku = akku
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# self.lastkurve_wh = lastkurve_wh
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# self.pv_prognose_wh = pv_prognose_wh
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from datetime import datetime
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from pprint import pprint
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# def simuliere(self):
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# eigenverbrauch_wh = 0
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# netzeinspeisung_wh = 0
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# netzbezug_wh = 0
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# for stunde in range(len(self.lastkurve_wh)):
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# verbrauch = self.lastkurve_wh[stunde]
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# erzeugung = self.pv_prognose_wh[stunde]
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# if erzeugung > verbrauch:
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# überschuss = erzeugung - verbrauch
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# eigenverbrauch_wh += verbrauch
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# geladene_energie = min(überschuss, self.akku.kapazitaet_wh - self.akku.soc_wh)
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# self.akku.energie_laden(geladene_energie)
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# netzeinspeisung_wh += überschuss - geladene_energie
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# else:
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# eigenverbrauch_wh += erzeugung
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# benötigte_energie = verbrauch - erzeugung
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# aus_akku = self.akku.energie_abgeben(benötigte_energie)
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# netzbezug_wh += benötigte_energie - aus_akku
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# return {
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# 'Eigenverbrauch_Wh': eigenverbrauch_wh,
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# 'Netzeinspeisung_Wh': netzeinspeisung_wh,
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# 'Netzbezug_Wh': netzbezug_wh
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# }
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class EnergieManagementSystem:
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@ -39,12 +10,30 @@ class EnergieManagementSystem:
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self.pv_prognose_wh = pv_prognose_wh
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self.strompreis_cent_pro_wh = strompreis_cent_pro_wh # Strompreis in Cent pro Wh
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self.einspeiseverguetung_cent_pro_wh = einspeiseverguetung_cent_pro_wh # Einspeisevergütung in Cent pro Wh
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# print("\n\nLastprognose:",self.lastkurve_wh.shape)
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# print("PV Prognose:",self.pv_prognose_wh.shape)
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# print("Preis Prognose:",self.strompreis_cent_pro_wh.shape)
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def set_akku_discharge_hours(self, ds):
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self.akku.set_discharge_per_hour(ds)
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def reset(self):
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self.akku.reset()
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def simuliere_ab_jetzt(self):
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jetzt = datetime.now()
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start_stunde = jetzt.hour
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# Berechne die Anzahl der Stunden bis zum gleichen Zeitpunkt am nächsten Tag
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stunden_bis_ende_tag = 24 - start_stunde
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# Füge diese Stunden zum nächsten Tag hinzu
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gesamt_stunden = stunden_bis_ende_tag + 24
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# Beginne die Simulation ab der aktuellen Stunde und führe sie für die berechnete Dauer aus
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return self.simuliere(start_stunde)
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def simuliere(self, start_stunde):
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eigenverbrauch_wh_pro_stunde = []
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netzeinspeisung_wh_pro_stunde = []
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@ -53,19 +42,17 @@ class EnergieManagementSystem:
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einnahmen_euro_pro_stunde = []
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akku_soc_pro_stunde = []
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ende = len(self.lastkurve_wh) # Berechnet das Ende basierend auf der Länge der Lastkurve
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ende = min( len(self.lastkurve_wh),len(self.pv_prognose_wh), len(self.strompreis_cent_pro_wh))
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#print(ende)
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# Berechnet das Ende basierend auf der Länge der Lastkurve
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for stunde in range(start_stunde, ende):
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# Anpassung, um sicherzustellen, dass Indizes korrekt sind
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verbrauch = self.lastkurve_wh[stunde]
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erzeugung = self.pv_prognose_wh[stunde]
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strompreis = self.strompreis_cent_pro_wh[stunde] if stunde < len(self.strompreis_cent_pro_wh) else self.strompreis_cent_pro_wh[-1]
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# for stunde in range(len(self.lastkurve_wh)):
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# verbrauch = self.lastkurve_wh[stunde]
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# erzeugung = self.pv_prognose_wh[stunde]
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# strompreis = self.strompreis_cent_pro_wh[stunde]
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#print(verbrauch," ",erzeugung," ",strompreis)
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stündlicher_netzbezug_wh = 0
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stündliche_kosten_euro = 0
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stündliche_einnahmen_euro = 0
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@ -92,8 +79,16 @@ class EnergieManagementSystem:
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# Berechnung der Gesamtbilanzen
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gesamtkosten_euro = sum(kosten_euro_pro_stunde) - sum(einnahmen_euro_pro_stunde)
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expected_length = ende - start_stunde
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array_names = ['Eigenverbrauch_Wh_pro_Stunde', 'Netzeinspeisung_Wh_pro_Stunde', 'Netzbezug_Wh_pro_Stunde', 'Kosten_Euro_pro_Stunde', 'akku_soc_pro_stunde', 'Einnahmen_Euro_pro_Stunde']
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all_arrays = [eigenverbrauch_wh_pro_stunde, netzeinspeisung_wh_pro_stunde, netzbezug_wh_pro_stunde, kosten_euro_pro_stunde, akku_soc_pro_stunde, einnahmen_euro_pro_stunde]
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return {
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inconsistent_arrays = [name for name, arr in zip(array_names, all_arrays) if len(arr) != expected_length]
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if inconsistent_arrays:
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raise ValueError(f"Inkonsistente Längen bei den Arrays: {', '.join(inconsistent_arrays)}. Erwartete Länge: {expected_length}, gefunden: {[len(all_arrays[array_names.index(name)]) for name in inconsistent_arrays]}")
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out = {
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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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@ -105,42 +100,5 @@ class EnergieManagementSystem:
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'Gesamtkosten_Euro': sum(kosten_euro_pro_stunde)
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}
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# def simuliere(self):
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# eigenverbrauch_wh = 0
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# netzeinspeisung_wh = 0
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# netzbezug_wh = 0
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# kosten_euro = 0
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# einnahmen_euro = 0
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# for stunde in range(len(self.lastkurve_wh)):
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# verbrauch = self.lastkurve_wh[stunde]
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# erzeugung = self.pv_prognose_wh[stunde]
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# strompreis = self.strompreis_cent_pro_wh[stunde]
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# if erzeugung > verbrauch:
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# überschuss = erzeugung - verbrauch
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# eigenverbrauch_wh += verbrauch
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# geladene_energie = min(überschuss, self.akku.kapazitaet_wh - self.akku.soc_wh)
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# self.akku.energie_laden(geladene_energie)
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# netzeinspeisung_wh += überschuss - geladene_energie
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# einnahmen_euro += (überschuss - geladene_energie) * self.einspeiseverguetung_cent_pro_wh[stunde] / 100
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# else:
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# eigenverbrauch_wh += erzeugung
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# benötigte_energie = verbrauch - erzeugung
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# aus_akku = self.akku.energie_abgeben(benötigte_energie)
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# netzbezug_wh += benötigte_energie - aus_akku
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# print(strompreis)
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# kosten_euro += (benötigte_energie - aus_akku) * strompreis / 100
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# gesamtkosten_euro = kosten_euro - einnahmen_euro
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# return {
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# 'Eigenverbrauch_Wh': eigenverbrauch_wh,
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# 'Netzeinspeisung_Wh': netzeinspeisung_wh,
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# 'Netzbezug_Wh': netzbezug_wh,
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# 'Kosten_Euro': kosten_euro,
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# 'Einnahmen_Euro': einnahmen_euro,
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# 'Gesamtkosten_Euro': gesamtkosten_euro
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# }
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return out
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@ -12,22 +12,7 @@ class LoadForecast:
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self.data_year_energy = None
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self.year_energy = year_energy
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self.load_data()
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# def get_prices_for_date(self, query_date):
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# query_date = datetime.strptime(query_date, '%Y-%m-%d').date()
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# prices_for_date = [price for price in self.price_data if price.starts_at.date() == query_date]
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# return prices_for_date
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# def get_price_for_datetime(self, query_datetime):
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# query_datetime = datetime.strptime(query_datetime, '%Y-%m-%d %H').replace(minute=0, second=0, microsecond=0)
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# query_datetime = query_datetime.replace(tzinfo=timezone(timedelta(hours=1)))
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# for price in self.price_data:
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# #print(price.starts_at.replace(minute=0, second=0, microsecond=0) , " ", query_datetime, " == ",price.starts_at.replace(minute=0, second=0, microsecond=0) == query_datetime)
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# if price.starts_at.replace(minute=0, second=0, microsecond=0) == query_datetime:
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# return price
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# return None
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def get_daily_stats(self, date_str):
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"""
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Gibt den 24-Stunden-Verlauf mit Erwartungswert und Standardabweichung für ein gegebenes Datum zurück.
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@ -66,6 +51,27 @@ class LoadForecast:
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return hourly_stats
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def get_stats_for_date_range(self, start_date_str, end_date_str):
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"""
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Gibt die Erwartungswerte und Standardabweichungen für einen Zeitraum zurück.
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:param start_date_str: Startdatum als String im Format "YYYY-MM-DD"
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:param end_date_str: Enddatum als String im Format "YYYY-MM-DD"
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:return: Ein Array mit den aggregierten Daten für den Zeitraum
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"""
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start_date = datetime.strptime(start_date_str, "%Y-%m-%d")
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end_date = datetime.strptime(end_date_str, "%Y-%m-%d")
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start_day_of_year = start_date.timetuple().tm_yday
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end_day_of_year = end_date.timetuple().tm_yday
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# Beachten, dass bei Schaltjahren der Tag des Jahres angepasst werden muss
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stats_for_range = self.data_year_energy[start_day_of_year-1:end_day_of_year] # -1 da die Indizierung bei 0 beginnt
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# Hier kannst du entscheiden, wie du die Daten über den Zeitraum aggregieren möchtest
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# Zum Beispiel könntest du Mittelwerte, Summen oder andere Statistiken über diesen Zeitraum berechnen
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return stats_for_range
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def load_data(self):
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@ -117,6 +117,35 @@ class PVForecast:
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return np.array(daily_forecast)
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def get_pv_forecast_for_date_range(self, start_date_str, end_date_str):
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start_date = datetime.strptime(start_date_str, "%Y-%m-%d").date()
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end_date = datetime.strptime(end_date_str, "%Y-%m-%d").date()
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date_range_forecast = []
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for data in self.forecast_data:
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data_date = datetime.strptime(data.get_date_time(), "%Y-%m-%dT%H:%M:%S.%f%z").date()
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if start_date <= data_date <= end_date:
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date_range_forecast.append(data)
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ac_power_forecast = np.array([data.get_ac_power() for data in date_range_forecast])
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return ac_power_forecast
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def get_temperature_for_date_range(self, start_date_str, end_date_str):
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start_date = datetime.strptime(start_date_str, "%Y-%m-%d").date()
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end_date = datetime.strptime(end_date_str, "%Y-%m-%d").date()
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date_range_forecast = []
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for data in self.forecast_data:
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data_date = datetime.strptime(data.get_date_time(), "%Y-%m-%dT%H:%M:%S.%f%z").date()
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if start_date <= data_date <= end_date:
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date_range_forecast.append(data)
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forecast_data = date_range_forecast
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temperature_forecast = [data.get_temperature() for data in forecast_data]
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return np.array(temperature_forecast)
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@ -43,7 +43,23 @@ class HourlyElectricityPriceForecast:
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def get_price_for_date(self, date_str):
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"""Gibt alle Preise für das spezifizierte Datum zurück."""
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date_prices = [entry["marketpriceEurocentPerKWh"] for entry in self.prices if date_str in entry['start']]
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return date_prices
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return np.array(date_prices)/(1000.0*100.0)
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def get_price_for_daterange(self, start_date_str, end_date_str):
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"""Gibt alle Preise zwischen dem Start- und Enddatum zurück."""
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start_date = datetime.strptime(start_date_str, "%Y-%m-%d")
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end_date = datetime.strptime(end_date_str, "%Y-%m-%d")
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price_list = []
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while start_date <= end_date:
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date_str = start_date.strftime("%Y-%m-%d")
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daily_prices = self.get_price_for_date(date_str)
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if daily_prices.size > 0:
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price_list.extend(daily_prices)
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start_date += timedelta(days=1)
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return np.array(price_list)
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@ -3,7 +3,7 @@ import matplotlib.pyplot as plt
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def visualisiere_ergebnisse(last,leistung_haushalt,leistung_wp, pv_forecast, strompreise, ergebnisse):
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stunden = np.arange(1, 25) # 1 bis 24 Stunden
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stunden = np.arange(1, len(last)+1) # 1 bis 24 Stunden
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# Last und PV-Erzeugung
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plt.figure(figsize=(14, 10))
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@ -20,8 +20,9 @@ def visualisiere_ergebnisse(last,leistung_haushalt,leistung_wp, pv_forecast, str
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plt.grid(True)
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# Strompreise
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stundenp = np.arange(1, len(strompreise)+1)
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plt.subplot(3, 1, 2)
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plt.plot(stunden, strompreise, label='Strompreis (€/Wh)', color='purple', marker='s')
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plt.plot(stundenp, strompreise, label='Strompreis (€/Wh)', color='purple', marker='s')
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plt.title('Strompreise')
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plt.xlabel('Stunde des Tages')
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plt.ylabel('Preis (€/Wh)')
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@ -30,15 +31,15 @@ def visualisiere_ergebnisse(last,leistung_haushalt,leistung_wp, pv_forecast, str
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plt.figure(figsize=(18, 12))
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stunden = np.arange(1, len(ergebnisse['Eigenverbrauch_Wh_pro_Stunde'])+1)
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# Eigenverbrauch, Netzeinspeisung und Netzbezug
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plt.subplot(3, 2, 1)
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plt.plot(stunden, ergebnisse['Eigenverbrauch_Wh_pro_Stunde'], label='Eigenverbrauch (Wh)', marker='o')
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plt.plot(stunden, ergebnisse['Netzeinspeisung_Wh_pro_Stunde'], label='Netzeinspeisung (Wh)', marker='x')
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plt.plot(stunden, ergebnisse['akku_soc_pro_stunde'], label='Akku (%)', marker='x')
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plt.plot(stunden, ergebnisse['Netzbezug_Wh_pro_Stunde'], label='Netzbezug (Wh)', marker='^')
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plt.plot(stunden, pv_forecast, label='PV-Erzeugung (Wh)', marker='x')
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plt.plot(stunden, last, label='Last (Wh)', marker='o')
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#plt.plot(stunden, pv_forecast, label='PV-Erzeugung (Wh)', marker='x')
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#plt.plot(stunden, last, label='Last (Wh)', marker='o')
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plt.title('Energiefluss pro Stunde')
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plt.xlabel('Stunde')
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49
test.py
49
test.py
@ -16,44 +16,42 @@ import random
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import os
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date = "2024-02-26"
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akku_size = 1000 # Wh
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year_energy = 2000*1000 #Wh
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einspeiseverguetung_cent_pro_wh = np.full(24, 7/1000.0)
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prediction_hours = 48
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date = (datetime.now().date() + timedelta(days=1)).strftime("%Y-%m-%d")
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date_now = datetime.now().strftime("%Y-%m-%d")
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max_heizleistung = 5000 # 5 kW Heizleistung
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akku_size = 30000 # Wh
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year_energy = 2000*1000 #Wh
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einspeiseverguetung_cent_pro_wh = np.full(prediction_hours, 7/(1000.0*100.0)) # € / Wh
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max_heizleistung = 1000 # 5 kW Heizleistung
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wp = Waermepumpe(max_heizleistung)
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akku = PVAkku(akku_size)
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discharge_array = np.full(24,1)
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akku = PVAkku(akku_size,prediction_hours)
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discharge_array = np.full(prediction_hours,1)
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# Load Forecast
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###############
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lf = LoadForecast(filepath=r'load_profiles.npz', year_energy=year_energy)
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leistung_haushalt = lf.get_daily_stats(date)[0,...] # Datum anpassen
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pprint(leistung_haushalt.shape)
|
||||
#leistung_haushalt = lf.get_daily_stats(date)[0,...] # Datum anpassen
|
||||
leistung_haushalt = lf.get_stats_for_date_range(date_now,date)[0,...].flatten()
|
||||
|
||||
|
||||
# PV Forecast
|
||||
###############
|
||||
#PVforecast = PVForecast(filepath=os.path.join(r'test_data', r'pvprognose.json'))
|
||||
PVforecast = PVForecast(url="https://api.akkudoktor.net/forecast?lat=52.52&lon=13.405&power=5400&azimuth=-10&tilt=7&powerInvertor=2500&horizont=20,40,30,30&power=4800&azimuth=-90&tilt=7&powerInvertor=2500&horizont=20,40,45,50&power=1480&azimuth=-90&tilt=70&powerInvertor=1120&horizont=60,45,30,70&power=1600&azimuth=5&tilt=60&powerInvertor=1200&horizont=60,45,30,70&past_days=5&cellCoEff=-0.36&inverterEfficiency=0.8&albedo=0.25&timezone=Europe%2FBerlin&hourly=relativehumidity_2m%2Cwindspeed_10m")
|
||||
pv_forecast = PVforecast.get_forecast_for_date(date)
|
||||
temperature_forecast = PVforecast.get_temperature_forecast_for_date(date)
|
||||
pprint(pv_forecast.shape)
|
||||
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+"&end="+date+"")
|
||||
|
||||
|
||||
|
||||
|
||||
specific_date_prices = price_forecast.get_price_for_date(date)
|
||||
pprint(f"Preise für {date}: {specific_date_prices}")
|
||||
|
||||
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)
|
||||
|
||||
# WP
|
||||
leistung_wp = wp.simulate_24h(temperature_forecast)
|
||||
@ -64,9 +62,10 @@ load = leistung_haushalt + leistung_wp
|
||||
|
||||
# EMS / Stromzähler Bilanz
|
||||
ems = EnergieManagementSystem(akku, load, pv_forecast, specific_date_prices, einspeiseverguetung_cent_pro_wh)
|
||||
o = ems.simuliere()
|
||||
o = ems.simuliere_ab_jetzt()
|
||||
pprint(o)
|
||||
pprint(o["Gesamtbilanz_Euro"])
|
||||
|
||||
#sys.exit()
|
||||
|
||||
# Optimierung
|
||||
|
||||
@ -77,7 +76,7 @@ def evaluate(individual):
|
||||
#akku.set_discharge_per_hour(individual)
|
||||
ems.reset()
|
||||
ems.set_akku_discharge_hours(individual)
|
||||
o = ems.simuliere()
|
||||
o = ems.simuliere_ab_jetzt()
|
||||
gesamtbilanz = o["Gesamtbilanz_Euro"]
|
||||
#print(individual, " ",gesamtbilanz)
|
||||
return (gesamtbilanz,)
|
||||
@ -88,7 +87,7 @@ creator.create("Individual", list, fitness=creator.FitnessMin)
|
||||
|
||||
toolbox = base.Toolbox()
|
||||
toolbox.register("attr_bool", random.randint, 0, 1)
|
||||
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 24)
|
||||
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, prediction_hours)
|
||||
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
|
||||
|
||||
toolbox.register("evaluate", evaluate)
|
||||
@ -114,7 +113,7 @@ best_solution = optimize()
|
||||
print("Beste Lösung:", best_solution)
|
||||
|
||||
ems.set_akku_discharge_hours(best_solution)
|
||||
o = ems.simuliere()
|
||||
o = ems.simuliere_ab_jetzt()
|
||||
pprint(o["Gesamtbilanz_Euro"])
|
||||
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user