Forecast to Power Tutorial¶
This tutorial will walk through the process of going from Unidata forecast model data to AC power using the SAPM.
Table of contents:
This tutorial requires pvlib >= 0.6.0.
Authors:
- Derek Groenendyk (@moonraker), University of Arizona, November 2015
- Will Holmgren (@wholmgren), University of Arizona, November 2015, January 2016, April 2016, July 2016, August 2018
Setup¶
These are just your standard interactive scientific python imports that you'll get very used to using.
# built-in python modules
import datetime
import inspect
import os
# scientific python add-ons
import numpy as np
import pandas as pd
# plotting stuff
# first line makes the plots appear in the notebook
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib as mpl
# finally, we import the pvlib library
from pvlib import solarposition,irradiance,atmosphere,pvsystem
from pvlib.forecast import GFS, NAM, NDFD, RAP, HRRR
Load Forecast data¶
pvlib forecast module only includes several models. To see the full list of forecast models visit the Unidata website:
# Choose a location.
# Tucson, AZ
latitude = 32.2
longitude = -110.9
tz = 'US/Mountain'
Define some PV system parameters.
surface_tilt = 30
surface_azimuth = 180 # pvlib uses 0=North, 90=East, 180=South, 270=West convention
albedo = 0.2
start = pd.Timestamp(datetime.date.today(), tz=tz) # today's date
end = start + pd.Timedelta(days=7) # 7 days from today
# Define forecast model
fm = GFS()
#fm = NAM()
#fm = NDFD()
#fm = RAP()
#fm = HRRR()
# Retrieve data
forecast_data = fm.get_processed_data(latitude, longitude, start, end)
Let's look at the downloaded version of the forecast data.
forecast_data.head()
This is a pandas DataFrame object. It has a lot of great properties that are beyond the scope of our tutorials.
forecast_data['temp_air'].plot()
Plot the GHI data. Most pvlib forecast models derive this data from the weather models' cloud clover data.
ghi = forecast_data['ghi']
ghi.plot()
plt.ylabel('Irradiance ($W/m^{-2}$)')
Calculate modeling intermediates¶
Before we can calculate power for all the forecast times, we will need to calculate:
- solar position
- extra terrestrial radiation
- airmass
- angle of incidence
- POA sky and ground diffuse radiation
- cell and module temperatures
The approach here follows that of the pvlib tmy_to_power notebook. You will find more details regarding this approach and the values being calculated in that notebook.
Solar position¶
Calculate the solar position for all times in the forecast data.
The default solar position algorithm is based on Reda and Andreas (2004). Our implementation is pretty fast, but you can make it even faster if you install numba and use add method='nrel_numba' to the function call below.
# retrieve time and location parameters
time = forecast_data.index
a_point = fm.location
solpos = a_point.get_solarposition(time)
#solpos.plot()
The funny looking jump in the azimuth is just due to the coarse time sampling in the TMY file.
DNI ET¶
Calculate extra terrestrial radiation. This is needed for many plane of array diffuse irradiance models.
dni_extra = irradiance.get_extra_radiation(fm.time)
#dni_extra.plot()
#plt.ylabel('Extra terrestrial radiation ($W/m^{-2}$)')
Airmass¶
Calculate airmass. Lots of model options here, see the atmosphere module tutorial for more details.
airmass = atmosphere.get_relative_airmass(solpos['apparent_zenith'])
#airmass.plot()
#plt.ylabel('Airmass')
The funny appearance is due to aliasing and setting invalid numbers equal to NaN. Replot just a day or two and you'll see that the numbers are right.
POA sky diffuse¶
Use the Hay Davies model to calculate the plane of array diffuse sky radiation. See the irradiance module tutorial for comparisons of different models.
poa_sky_diffuse = irradiance.haydavies(surface_tilt, surface_azimuth,
forecast_data['dhi'], forecast_data['dni'], dni_extra,
solpos['apparent_zenith'], solpos['azimuth'])
#poa_sky_diffuse.plot()
#plt.ylabel('Irradiance ($W/m^{-2}$)')
POA ground diffuse¶
Calculate ground diffuse. We specified the albedo above. You could have also provided a string to the surface_type keyword argument.
poa_ground_diffuse = irradiance.get_ground_diffuse(surface_tilt, ghi, albedo=albedo)
#poa_ground_diffuse.plot()
#plt.ylabel('Irradiance ($W/m^{-2}$)')
AOI¶
Calculate AOI
aoi = irradiance.aoi(surface_tilt, surface_azimuth, solpos['apparent_zenith'], solpos['azimuth'])
#aoi.plot()
#plt.ylabel('Angle of incidence (deg)')
Note that AOI has values greater than 90 deg. This is ok.
POA total¶
Calculate POA irradiance
poa_irrad = irradiance.poa_components(aoi, forecast_data['dni'], poa_sky_diffuse, poa_ground_diffuse)
poa_irrad.plot()
plt.ylabel('Irradiance ($W/m^{-2}$)')
plt.title('POA Irradiance')
Cell and module temperature¶
Calculate pv cell and module temperature
temperature = forecast_data['temp_air']
wnd_spd = forecast_data['wind_speed']
pvtemps = pvsystem.sapm_celltemp(poa_irrad['poa_global'], wnd_spd, temperature)
pvtemps.plot()
plt.ylabel('Temperature (C)')
DC power using SAPM¶
Get module data from the web.
sandia_modules = pvsystem.retrieve_sam('SandiaMod')
Choose a particular module
sandia_module = sandia_modules.Canadian_Solar_CS5P_220M___2009_
sandia_module
Run the SAPM using the parameters we calculated above.
effective_irradiance = pvsystem.sapm_effective_irradiance(poa_irrad.poa_direct, poa_irrad.poa_diffuse,
airmass, aoi, sandia_module)
sapm_out = pvsystem.sapm(effective_irradiance, pvtemps['temp_cell'], sandia_module)
#print(sapm_out.head())
sapm_out[['p_mp']].plot()
plt.ylabel('DC Power (W)')
AC power using SAPM¶
Get the inverter database from the web
sapm_inverters = pvsystem.retrieve_sam('sandiainverter')
Choose a particular inverter
sapm_inverter = sapm_inverters['ABB__MICRO_0_25_I_OUTD_US_208_208V__CEC_2014_']
sapm_inverter
p_ac = pvsystem.snlinverter(sapm_out.v_mp, sapm_out.p_mp, sapm_inverter)
p_ac.plot()
plt.ylabel('AC Power (W)')
plt.ylim(0, None)
Plot just a few days.
p_ac[start:start+pd.Timedelta(days=2)].plot()
Some statistics on the AC power
p_ac.describe()
p_ac.index.freq
# integrate power to find energy yield over the forecast period
p_ac.sum() * 3