Python/Seminar_Smartgrids
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Modell funktioniert, Jahr 2019 eingestellt

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patha
2025-01-01 12:08:07 +01:00
parent abe1c9d83d
commit eb130b6be2
5 changed files with 8863 additions and 8854 deletions
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pv_input.py
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@@ -39,44 +39,48 @@ surface_azimuth = 180
# Temperaturparameter definieren
temperature_parameters = TEMPERATURE_MODEL_PARAMETERS['sapm']['open_rack_glass_glass']
# Location + PVSystem-Objekte anlegen und Modelchain damit füttern
location = Location(latitude, longitude, tz)
system = PVSystem(surface_tilt=surface_tilt, surface_azimuth=surface_azimuth, module_parameters=module,
inverter_parameters=inverter, temperature_model_parameters=temperature_parameters,
modules_per_string=modules_per_string, strings_per_inverter=strings_per_inverter)
def create_production_profile():
# Location + PVSystem-Objekte anlegen und Modelchain damit füttern
location = Location(latitude, longitude, tz)
system = PVSystem(surface_tilt=surface_tilt, surface_azimuth=surface_azimuth, module_parameters=module,
inverter_parameters=inverter, temperature_model_parameters=temperature_parameters,
modules_per_string=modules_per_string, strings_per_inverter=strings_per_inverter)
modelchain = ModelChain(system, location)
modelchain = ModelChain(system, location)
# Ertragssimulation mit Clear-Sky Modell
# Ertragssimulation mit Clear-Sky Modell
# Index-Spalte mit Zeiten für clear-sky Dataset anlegen
# times = pd.date_range(start='2020-01-01', end ='2020-12-31', freq='1h', tz=location.tz)
# Index-Spalte mit Zeiten für clear-sky Dataset anlegen
# times = pd.date_range(start='2020-01-01', end ='2020-12-31', freq='1h', tz=location.tz)
# clear_sky = location.get_clearsky(times)
# #clear_sky.plot(figsize=(16,9))
# clear_sky = location.get_clearsky(times)
# #clear_sky.plot(figsize=(16,9))
# modelchain.run_model(clear_sky)
# modelchain.run_model(clear_sky)
# Ertragssimulation mit realen Strahlungsdaten aus Wetterjahr
# Ertragssimulation mit realen Strahlungsdaten aus Wetterjahr
# Hier ist Süden Azimuth = 0°, bei PVLib ist es 180°
poa_data, meta, inputs = pvlib.iotools.get_pvgis_hourly(latitude=latitude, longitude=longitude, start=year, end=year, raddatabase='PVGIS-SARAH3', components=True, surface_tilt=surface_tilt,
surface_azimuth=surface_azimuth-180, outputformat='json', usehorizon=True, userhorizon=None, pvcalculation=False, peakpower=None,
pvtechchoice='crystSi', mountingplace='free', loss=0, trackingtype=0, optimal_surface_tilt=False, optimalangles=False,
url='https://re.jrc.ec.europa.eu/api/', map_variables=True, timeout=30)
# Hier ist Süden Azimuth = 0°, bei PVLib ist es 180°
poa_data, meta, inputs = pvlib.iotools.get_pvgis_hourly(latitude=latitude, longitude=longitude, start=year, end=year, raddatabase='PVGIS-SARAH3', components=True, surface_tilt=surface_tilt,
surface_azimuth=surface_azimuth-180, outputformat='json', usehorizon=True, userhorizon=None, pvcalculation=False, peakpower=None,
pvtechchoice='crystSi', mountingplace='free', loss=0, trackingtype=0, optimal_surface_tilt=False, optimalangles=False,
url='https://re.jrc.ec.europa.eu/api/', map_variables=True, timeout=30)
# Notwendige Spalten ausrechnen und hinzufügen, sodass Modelchain sie verwenden kann
poa_data['poa_diffuse'] = poa_data['poa_sky_diffuse'] + poa_data['poa_ground_diffuse']
poa_data['poa_global'] = poa_data['poa_diffuse'] + poa_data['poa_direct']
# Notwendige Spalten ausrechnen und hinzufügen, sodass Modelchain sie verwenden kann
poa_data['poa_diffuse'] = poa_data['poa_sky_diffuse'] + poa_data['poa_ground_diffuse']
poa_data['poa_global'] = poa_data['poa_diffuse'] + poa_data['poa_direct']
# Daten in csv exportieren
#poa_data.to_csv('poa_data.csv')
# Daten in csv exportieren
#poa_data.to_csv('poa_data.csv')
# Index des Dataframe mit datetime-Index von Pandas überschreiben
poa_data.index = pd.to_datetime((poa_data.index))
# Diese Funktion benötigt POA-Daten anstatt g(i)-Daten -> DOKU
modelchain.run_model_from_poa(poa_data)
# Index des Dataframe mit datetime-Index von Pandas überschreiben
poa_data.index = pd.to_datetime((poa_data.index))
# Diese Funktion benötigt POA-Daten anstatt g(i)-Daten -> DOKU
modelchain.run_model_from_poa(poa_data)
# Ergebnis plotten
modelchain.results.ac.plot(figsize=(16,9))
plt.show()
# Ergebnis plotten
#modelchain.results.ac.plot(figsize=(16,9))
#plt.show()
# Rückgabewert
return modelchain.results.ac