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:

  1. Setup
  2. Load Forecast data
  3. Calculate modeling intermediates
  4. DC power using SAPM
  5. AC power using SAPM

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.

In [1]:
# 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
/Users/holmgren/git_repos/pvlib2/pvlib-python/pvlib/forecast.py:22: UserWarning: The forecast module algorithms and features are highly experimental. The API may change, the functionality may be consolidated into an io module, or the module may be separated into its own package.
  'module, or the module may be separated into its own package.')

Load Forecast data

pvlib forecast module only includes several models. To see the full list of forecast models visit the Unidata website:

http://www.unidata.ucar.edu/data/#tds

In [2]:
# Choose a location.
# Tucson, AZ
latitude = 32.2
longitude = -110.9
tz = 'US/Mountain'

Define some PV system parameters.

In [3]:
surface_tilt = 30
surface_azimuth = 180 # pvlib uses 0=North, 90=East, 180=South, 270=West convention
albedo = 0.2
In [4]:
start = pd.Timestamp(datetime.date.today(), tz=tz) # today's date
end = start + pd.Timedelta(days=7) # 7 days from today
In [5]:
# Define forecast model
fm = GFS()
#fm = NAM()
#fm = NDFD()
#fm = RAP()
#fm = HRRR()
In [6]:
# Retrieve data
forecast_data = fm.get_processed_data(latitude, longitude, start, end)

Let's look at the downloaded version of the forecast data.

In [7]:
forecast_data.head()
Out[7]:
temp_air wind_speed ghi dni dhi total_clouds low_clouds mid_clouds high_clouds
2018-08-09 06:00:00-06:00 26.598663 5.019923 0.000000 0.000000 0.000000 72.0 0.0 21.0 67.0
2018-08-09 09:00:00-06:00 26.888641 3.834616 145.558379 1.600285 144.823500 97.0 0.0 39.0 97.0
2018-08-09 12:00:00-06:00 25.488617 2.595709 322.775864 18.426892 306.226661 98.0 0.0 61.0 98.0
2018-08-09 15:00:00-06:00 32.419769 1.174503 315.885183 17.620191 300.108657 99.0 0.0 19.0 99.0
2018-08-09 18:00:00-06:00 47.617493 1.259972 135.185462 0.000000 135.185462 100.0 0.0 10.0 100.0

This is a pandas DataFrame object. It has a lot of great properties that are beyond the scope of our tutorials.

In [8]:
forecast_data['temp_air'].plot()
Out[8]:
<matplotlib.axes._subplots.AxesSubplot at 0x11b0ee7f0>

Plot the GHI data. Most pvlib forecast models derive this data from the weather models' cloud clover data.

In [9]:
ghi = forecast_data['ghi']
ghi.plot()
plt.ylabel('Irradiance ($W/m^{-2}$)')
Out[9]:
Text(0,0.5,'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.

In [10]:
# retrieve time and location parameters
time = forecast_data.index
a_point = fm.location
In [11]:
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.

In [12]:
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.

In [13]:
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.

In [14]:
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.

In [15]:
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

In [16]:
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

In [17]:
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')
Out[17]:
Text(0.5,1,'POA Irradiance')

Cell and module temperature

Calculate pv cell and module temperature

In [18]:
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)')
Out[18]:
Text(0,0.5,'Temperature (C)')

DC power using SAPM

Get module data from the web.

In [19]:
sandia_modules = pvsystem.retrieve_sam('SandiaMod')

Choose a particular module

In [20]:
sandia_module = sandia_modules.Canadian_Solar_CS5P_220M___2009_
sandia_module
Out[20]:
Vintage                                                          2009
Area                                                            1.701
Material                                                         c-Si
Cells_in_Series                                                    96
Parallel_Strings                                                    1
Isco                                                          5.09115
Voco                                                          59.2608
Impo                                                          4.54629
Vmpo                                                          48.3156
Aisc                                                         0.000397
Aimp                                                         0.000181
C0                                                            1.01284
C1                                                         -0.0128398
Bvoco                                                        -0.21696
Mbvoc                                                               0
Bvmpo                                                       -0.235488
Mbvmp                                                               0
N                                                              1.4032
C2                                                           0.279317
C3                                                           -7.24463
A0                                                           0.928385
A1                                                           0.068093
A2                                                         -0.0157738
A3                                                          0.0016606
A4                                                          -6.93e-05
B0                                                                  1
B1                                                          -0.002438
B2                                                          0.0003103
B3                                                         -1.246e-05
B4                                                           2.11e-07
B5                                                          -1.36e-09
DTC                                                                 3
FD                                                                  1
A                                                            -3.40641
B                                                          -0.0842075
C4                                                           0.996446
C5                                                           0.003554
IXO                                                           4.97599
IXXO                                                          3.18803
C6                                                            1.15535
C7                                                          -0.155353
Notes               Source: Sandia National Laboratories Updated 9...
Name: Canadian_Solar_CS5P_220M___2009_, dtype: object

Run the SAPM using the parameters we calculated above.

In [21]:
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)')
Out[21]:
Text(0,0.5,'DC Power (W)')

AC power using SAPM

Get the inverter database from the web

In [22]:
sapm_inverters = pvsystem.retrieve_sam('sandiainverter')

Choose a particular inverter

In [23]:
sapm_inverter = sapm_inverters['ABB__MICRO_0_25_I_OUTD_US_208_208V__CEC_2014_']
sapm_inverter
Out[23]:
Vac          208.000000
Paco         250.000000
Pdco         259.522050
Vdco          40.242603
Pso            1.771614
C0            -0.000025
C1            -0.000090
C2             0.000669
C3            -0.018900
Pnt            0.020000
Vdcmax        65.000000
Idcmax        10.000000
Mppt_low      20.000000
Mppt_high     50.000000
Name: ABB__MICRO_0_25_I_OUTD_US_208_208V__CEC_2014_, dtype: float64
In [24]:
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)
Out[24]:
(0, 171.32419690136939)

Plot just a few days.

In [25]:
p_ac[start:start+pd.Timedelta(days=2)].plot()
Out[25]:
<matplotlib.axes._subplots.AxesSubplot at 0x10d5135c0>

Some statistics on the AC power

In [26]:
p_ac.describe()
Out[26]:
count     56.000000
mean      31.469901
std       43.789764
min       -0.020000
25%       -0.020000
50%        9.526860
75%       54.687079
max      163.164949
dtype: float64
In [27]:
p_ac.index.freq
Out[27]:
<3 * Hours>
In [28]:
# integrate power to find energy yield over the forecast period
p_ac.sum() * 3
Out[28]:
5286.9434413292965
In [ ]: