PV Lib Transposition Analysis¶
Using PV Lib from Sandia to calculate Plane of Array Irradiance for fixed tilt and tracking projects via the Perez Transposition Algorithm
In [19]:
#libraries and constants
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(rc={"figure.figsize": (12, 6)})
import numpy as np
import pandas as pd
import pvlib
import warnings
warnings.filterwarnings('ignore')
#nrel nsrdb API key
api_key='LhVBL5knO2kvXXwbrbgAtYUL5aGbwqLfuegppGzJ'
# User entered constants
location = pvlib.location.Location(32.2,-111, 'US/Arizona', 700, 'Tucson')
year = 2010
interval='60'
# atmospheric constants
albedo=.2
# fixed tilt inputs
surface_tilt=22.5
surface_azimuth=180
# tracking inputs
axis_tilt=0
axis_azimuth=180
max_angle=52,
backtrack=True
gcr=.5
# date to plot
plotDate='2010-03-11'
NSRDB Query¶
In [20]:
# nsrdb query
utc='false'
attributes = 'ghi,dhi,dni,wind_speed_10m_nwp,surface_air_temperature_nwp,solar_zenith_angle'
leap_year = 'false'
name = 'Stephen+Lightfoote'
reason_for_use = 'solar+resource'
affiliation = 'AWSTruepower'
email = 'slightfoote@awstruepower.com'
mailing_list = 'false'
url = 'http://developer.nrel.gov/api/solar/nsrdb_0512_download.csv?wkt=POINT({lon}%20{lat})&names={year}&leap_day={leap}&interval={interval}&utc={utc}&full_name={name}&email={email}&affiliation={affiliation}&mailing_list={mailing_list}&reason={reason}&api_key={api}&attributes={attr}'.format(year=year, lat=location.latitude, lon=location.longitude, leap=leap_year, interval=interval, utc=utc, name=name,email=email, mailing_list=mailing_list, affiliation=affiliation, reason=reason_for_use, api=api_key, attr=attributes)
# Return just the first 2 lines to get metadata:
info = pd.read_csv(url, nrows=1)
# See metadata for specified properties, e.g., timezone and elevation
timezone, elevation = info['Local Time Zone'], info['Elevation']
# get data
df = pd.read_csv('http://developer.nrel.gov/api'
'/solar/nsrdb_0512_download.csv?wkt=POINT({lon}%20{lat})&'
'names={year}&leap_day={leap}&interval={interval}&utc={utc}&'
'full_name={name}&email={email}&affiliation={affiliation}&'
'mailing_list={mailing_list}&reason={reason}&api_key={api}&attributes={attr}'
.format(year=year, lat=location.latitude, lon=location.longitude, leap=leap_year, interval=interval, utc=utc,
name=name, email=email, mailing_list=mailing_list, affiliation=affiliation,
reason=reason_for_use, api=api_key, attr=attributes), skiprows=2)
df = df.set_index(pd.DatetimeIndex(start='2010-01-01 00:30', end='2010-12-31 23:30', freq='1h',tz=location.tz))
df['times']= df.index
NSRDB Data¶
In [21]:
print(df.head(24))
In [22]:
df.plot()
Out[22]:
Solar Position¶
In [23]:
solpos = location.get_solarposition(df.times)
solpos[plotDate:plotDate].plot()
Out[23]:
Extraterrestrial Radiation¶
In [24]:
dni_extra = pvlib.irradiance.extraradiation(df.index)
dni_extra = pd.Series(dni_extra, index=df.index)
dni_extra.plot()
Out[24]:
Air Mass¶
In [25]:
airmass = pvlib.atmosphere.relativeairmass(solpos['apparent_zenith'])
airmass[plotDate:plotDate].plot()
Out[25]:
Angle of Indicence¶
In [26]:
# angle of indicence
aoi = pvlib.irradiance.aoi(surface_tilt,surface_azimuth,solpos['apparent_zenith'], solpos['azimuth'])
aoi[plotDate:plotDate].plot()
Out[26]:
Plane of Array¶
Using the Perez Transposition Model
Fixed Tilt¶
In [27]:
# plane of array irradiance fixed tilt
total_irrad = pvlib.irradiance.total_irrad(surface_tilt=surface_tilt,
surface_azimuth=surface_azimuth,
apparent_zenith=solpos['apparent_zenith'],
albedo=albedo,
azimuth=solpos['azimuth'],
dni=df['DNI'],
ghi=df['GHI'],
dhi=df['DHI'],
dni_extra=dni_extra,
model='perez',
solar_zenith=solpos['zenith'],
airmass=airmass)
In [28]:
# plot some sample data
df['poa_global_fixed']=total_irrad.poa_global
df[plotDate:plotDate][['GHI','poa_global_fixed','DNI','DHI']].plot()
df.plot(x='times',y=['poa_global_fixed','GHI','DHI'])
Out[28]:
Single Axis Tracking¶
In [29]:
tracker_data = pvlib.tracking.singleaxis(solpos['apparent_zenith'], solpos['azimuth'],
axis_tilt=0, axis_azimuth=180, max_angle=52,
backtrack=True, gcr=.5)
perez_diffuse = pvlib.irradiance.perez(tracker_data['surface_tilt'], tracker_data['surface_azimuth'],
df['DHI'], df['DNI'], dni_extra,
solpos['apparent_zenith'], solpos['azimuth'],airmass=airmass)
ground_irrad = pvlib.irradiance.grounddiffuse(tracker_data['surface_tilt'], df['GHI'], albedo=albedo)
poa_global_tracking = pvlib.tools.cosd(tracker_data['aoi'])*df['DNI'] + perez_diffuse + ground_irrad
poa_global_tracking[np.isnan(poa_global_tracking)]=0
Comparison¶
In [30]:
df['poa_global_tracking']=poa_global_tracking
df[plotDate:plotDate][['GHI','poa_global_fixed','poa_global_tracking','DNI','DHI']].plot()
df.plot(x='times',y=['poa_global_fixed','poa_global_tracking','GHI','DHI'])
Out[30]:
In [31]:
print(df[['GHI','poa_global_fixed','poa_global_tracking']].sum()*.001)