82 lines
3.7 KiB
Python
82 lines
3.7 KiB
Python
# 3 Möglichkeiten für Ertragsberechnung: Clear Sky Modell, TMY oder POA-Data
|
|
# Clear Sky: Strahlungsmodell, welches theoretische Strahlungsdaten an bestimmten Standpunkt enthält welche eine flache Oberfläche treffen - vewerndet g(i)-Strahlungsdaten
|
|
# TMY = Typical Meteorolical Year - reale Strahlungsdaten welche eine flache Oberfläche treffen - vewerndet g(i)-Strahlungsdaten
|
|
# POA = Plane of Array - was das PV-Modul (und dessen Ausrichtung) wirklich trifft - verwendet POA-Strahlungsdaten
|
|
|
|
# G(i), poa_global = Global irradiance on inclined plane (W/m^2)
|
|
# Gb(i), poa_direct = Beam (direct) irradiance on inclined plane (W/m^2)
|
|
# Gd(i), poa_sky_diffuse = Diffuse irradiance on inclined plane (W/m^2)
|
|
# Gr(i), poa_ground_diffuse = Reflected irradiance on inclined plane (W/m^2)
|
|
|
|
import pvlib
|
|
import pandas as pd
|
|
import matplotlib.pyplot as plt
|
|
from pvlib.modelchain import ModelChain
|
|
from pvlib.location import Location
|
|
from pvlib.pvsystem import PVSystem
|
|
from pvlib.temperature import TEMPERATURE_MODEL_PARAMETERS
|
|
|
|
# Daten Standort
|
|
latitude = 47.2675
|
|
longitude = 11.3910
|
|
tz = 'Europe/Vienna'
|
|
surface_tilt = 0
|
|
surface_azimuth = 180
|
|
year = 2019
|
|
|
|
# Daten für Datenbank -> Module + WR
|
|
database_module = pvlib.pvsystem.retrieve_sam('SandiaMod')
|
|
database_inverter = pvlib.pvsystem.retrieve_sam('CECInverter')
|
|
module = database_module['Canadian_Solar_CS5P_220M___2009_']
|
|
inverter = database_inverter['ABB__PVI_4_2_OUTD_US__208V_']
|
|
|
|
# PV-Anlage definieren
|
|
modules_per_string = 10
|
|
strings_per_inverter = 2
|
|
surface_tilt = 0
|
|
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)
|
|
|
|
modelchain = ModelChain(system, location)
|
|
|
|
# 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)
|
|
|
|
# clear_sky = location.get_clearsky(times)
|
|
# #clear_sky.plot(figsize=(16,9))
|
|
|
|
# modelchain.run_model(clear_sky)
|
|
|
|
# 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)
|
|
|
|
# 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')
|
|
|
|
# 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() |