gallery example for bifacial modeling with pvfactors

docs/examples/bifacial/README.rst

@@ -0,0 +1,3 @@
+Bifacial Modeling
+-----------------
+

docs/examples/bifacial/plot_bifi_model_mc.py

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+"""
+Bifacial Modeling - modelchain
+==============================
+
+Example of bifacial modeling using the modelchain
+"""
+
+# %%
+# This example shows how to complete a bifacial modeling example using the
+# :py:class:`pvlib.modelchain.ModelChain` with the
+# :py:func:`pvlib.bifacial.pvfactors_timeseries` function to transpose
+# GHI data to both front and rear Plane of Array (POA) irradiance.
+#
+# Unfortunately ``ModelChain`` does not yet support bifacial simulation
+# directly so we have to do the bifacial irradiance simulation ourselves.
+# Once the combined front + rear irradiance is known, we can pass that
+# to ``ModelChain`` and proceed as usual.
+#
+# Future versions of pvlib may make it easier to do bifacial modeling
+# with ``ModelChain``.
+
+import pandas as pd
+from pvlib import pvsystem
+from pvlib import location
+from pvlib import modelchain
+from pvlib.temperature import TEMPERATURE_MODEL_PARAMETERS as PARAMS
+from pvlib import bifacial
+
+# create site location and times characteristics
+lat, lon = 36.084, -79.817
+tz = 'Etc/GMT+5'
+times = pd.date_range('2021-06-21', '2021-6-22', freq='1T', tz=tz)
+
+# create site system characteristics
+axis_tilt = 0
+axis_azimuth = 180
+gcr = 0.35
+max_angle = 60
+pvrow_height = 3
+pvrow_width = 4
+albedo = 0.2
+bifaciality = 0.75
+
+# load temperature parameters and module/inverter specifications
+temp_model_parameters = PARAMS['sapm']['open_rack_glass_glass']
+cec_modules = pvsystem.retrieve_sam('CECMod')
+cec_module = cec_modules['Trina_Solar_TSM_300DEG5C_07_II_']
+cec_inverters = pvsystem.retrieve_sam('cecinverter')
+cec_inverter = cec_inverters['ABB__MICRO_0_25_I_OUTD_US_208__208V_']
+
+# create a location for site, and get solar position and clearsky data
+site_location = location.Location(lat, lon, tz=tz, name='Greensboro, NC')
+solar_position = site_location.get_solarposition(times)
+cs = site_location.get_clearsky(times)
+
+# load solar position and tracker orientation for use in pvsystem object
+sat_mount = pvsystem.SingleAxisTrackerMount(axis_tilt=axis_tilt,
+                                            axis_azimuth=axis_azimuth,
+                                            max_angle=max_angle,
+                                            backtrack=True,
+                                            gcr=gcr)
+
+# created for use in pvfactors timeseries
+orientation = sat_mount.get_orientation(solar_position['apparent_zenith'],
+                                        solar_position['azimuth'])
+
+# get rear and front side irradiance from pvfactors transposition engine
+irrad = bifacial.pvfactors_timeseries(solar_position['azimuth'],
+                                      solar_position['apparent_zenith'],
+                                      orientation['surface_azimuth'],
+                                      orientation['surface_tilt'],
+                                      axis_azimuth,
+                                      times,
+                                      cs['dni'],
+                                      cs['dhi'],
+                                      gcr,
+                                      pvrow_height,
+                                      pvrow_width,
+                                      albedo)
+
+# turn into pandas DataFrame
+irrad = pd.concat(irrad, axis=1)
+
+# create bifacial effective irradiance using aoi-corrected timeseries values
+irrad['effective_irradiance'] = (
+    irrad['total_abs_front'] + (irrad['total_abs_back'] * bifaciality)
+)
+
+# %%
+# With effective irradiance, we can pass data to ModelChain for
+# bifacial simulation.
+
+# dc arrays
+array = pvsystem.Array(mount=sat_mount,
+                       module_parameters=cec_module,
+                       temperature_model_parameters=temp_model_parameters)
+
+# create system object
+system = pvsystem.PVSystem(arrays=[array],
+                           inverter_parameters=cec_inverter)
+
+# create modelchain object for bifacial system and run bifacial simulation
+mc_bifi = modelchain.ModelChain(system, site_location, aoi_model='no_loss')
+mc_bifi.run_model_from_effective_irradiance(irrad)
+
+# plot results
+mc_bifi.results.ac.plot(title='Bifacial Simulation on June Solstice',
+                        ylabel='AC Power')

docs/examples/bifacial/plot_bifi_model_pvwatts.py

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+"""
+Bifacial Modeling - procedural
+==============================
+
+Example of bifacial modeling using procedural method
+"""
+
+# %%
+# This example shows how to complete a bifacial modeling example using the
+# :py:func:`pvlib.pvsystem.pvwatts_dc` with the
+# :py:func:`pvlib.bifacial.pvfactors_timeseries` function to transpose
+# GHI data to both front and rear Plane of Array (POA) irradiance.
+
+import pandas as pd
+from pvlib import location
+from pvlib import tracking
+from pvlib import bifacial
+from pvlib import temperature
+from pvlib import pvsystem
+import matplotlib.pyplot as plt
+
+# using Greensboro, NC for this example
+lat, lon = 36.084, -79.817
+tz = 'Etc/GMT+5'
+times = pd.date_range('2021-06-21', '2021-06-22', freq='1T', tz=tz)
+
+# create location object and get clearsky data
+site_location = location.Location(lat, lon, tz=tz, name='Greensboro, NC')
+cs = site_location.get_clearsky(times)
+
+# get solar position data
+solar_position = site_location.get_solarposition(times)
+
+# set ground coverage ratio and max_angle to
+# pull orientation data for a single-axis tracker
+gcr = 0.35
+max_phi = 60
+orientation = tracking.singleaxis(solar_position['apparent_zenith'],
+                                  solar_position['azimuth'],
+                                  max_angle=max_phi,
+                                  backtrack=True,
+                                  gcr=gcr
+                                  )
+
+# set axis_azimuth, albedo, pvrow width and height, and use
+# the pvfactors  engine for both front and rear-side absorbed irradiance
+axis_azimuth = 180
+pvrow_height = 3
+pvrow_width = 4
+albedo = 0.2
+irrad = bifacial.pvfactors_timeseries(solar_position['azimuth'],
+                                      solar_position['apparent_zenith'],
+                                      orientation['surface_azimuth'],
+                                      orientation['surface_tilt'],
+                                      axis_azimuth,
+                                      cs.index,
+                                      cs['dni'],
+                                      cs['dhi'],
+                                      gcr,
+                                      pvrow_height,
+                                      pvrow_width,
+                                      albedo
+                                      )
+
+# turn into pandas DataFrame
+irrad = pd.concat(irrad, axis=1)
+
+# using bifaciality factor and pvfactors results, create effective
+# irradiance data
+bifaciality = 0.75
+effective_irrad_bifi = irrad['total_abs_front'] + (irrad['total_abs_back']
+                                                   * bifaciality)
+
+
+# get cell temperature using the Faiman model
+# it is worth noting that this example uses total frontside incident
+# irradiance, however, for bifacial modeling, total irradiance (inclusive of
+# front and rear side) may be needed.
+temp_cell = temperature.faiman(irrad['total_inc_front'], temp_air=25,
+                               wind_speed=1)
+
+# using the pvwatts_dc model and parameters detailed above,
+# set pdc0 and return DC power for both bifacial and monofacial
+pdc0 = 1
+gamma_pdc = -0.0043
+pdc_bifi = pvsystem.pvwatts_dc(effective_irrad_bifi,
+                               temp_cell,
+                               pdc0,
+                               gamma_pdc=gamma_pdc
+                               ).fillna(0)
+pdc_bifi.plot(title='Bifacial Simulation on June Solstice', ylabel='DC Power')
+
+# %%
+# For illustration, perform monofacial simulation using pvfactors front-side
+# irradiance (AOI-corrected), and plot along with bifacial results.
+
+effective_irrad_mono = irrad['total_abs_front']
+pdc_mono = pvsystem.pvwatts_dc(effective_irrad_mono,
+                               temp_cell,
+                               pdc0,
+                               gamma_pdc=gamma_pdc
+                               ).fillna(0)
+
+# plot monofacial results
+plt.figure()
+plt.title('Bifacial vs Monofacial Simulation - June Solstice')
+pdc_bifi.plot(label='Bifacial')
+pdc_mono.plot(label='Monofacial')
+plt.ylabel('DC Power')
+plt.legend()
+# sphinx_gallery_thumbnail_number = 2

docs/sphinx/source/whatsnew/v0.9.1.rst

@@ -32,7 +32,10 @@ Documentation
 ~~~~~~~~~~~~~
 * Fix documentation return error in :py:meth:`pvlib.forecast.ForecastModel.cloud_cover_to_transmittance_linear`
   (:issue:`1367`, :pull:`1370`)
-
+* Add gallery example illustrating bifacial simulation using :py:class:`pvlib.modelchain.ModelChain`
+  and the :py:func:`pvlib.bifacial.pvfactors_timeseries` function (:pull:`1394`)
+* Add gallery example illustrating bifacial simulation using procedural functions
+  and the :py:func:`pvlib.bifacial.pvfactors_timeseries` function (:pull:`1394`)
 
 Requirements
 ~~~~~~~~~~~~
@@ -46,3 +49,4 @@ Contributors
 * Kevin Anderson (:ghuser:`kanderso-nrel`)
 * Adam R. Jensen (:ghuser:`AdamRJensen`)
 * Johann Loux (:ghuser:`JoLo90`)
+* Saurabh Aneja (:ghuser:`spaneja`)

setup.py

@@ -59,7 +59,7 @@
     'doc': ['ipython', 'matplotlib', 'sphinx == 3.1.2',
             'pydata-sphinx-theme', 'sphinx-gallery', 'docutils == 0.15.2',
             'pillow', 'netcdf4', 'siphon',
-            'sphinx-toggleprompt >= 0.0.5'],
+            'sphinx-toggleprompt >= 0.0.5', 'pvfactors'],
     'test': TESTS_REQUIRE
 }
 EXTRAS_REQUIRE['all'] = sorted(set(sum(EXTRAS_REQUIRE.values(), [])))