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gallery example for bifacial modeling with pvfactors (#1394)
* adding initial gallery examples * updating bifacial examples added pvfactors into setup.py, cleaned up some white spacing, and hoping for the best... * gallery_updates * whatsnew * updates from comments * comment update * updated comments, warnings, and variables * whitespace * small edits * Apply suggestions from code review Co-authored-by: Cliff Hansen <[email protected]> * Update docs/sphinx/source/whatsnew/v0.9.1.rst Co-authored-by: Cliff Hansen <[email protected]> Co-authored-by: Kevin Anderson <[email protected]>
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‎docs/examples/bifacial/README.rst

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Bifacial Modeling
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-----------------
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‎docs/examples/bifacial/plot_bifi_model_mc.py

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

‎docs/examples/bifacial/plot_bifi_model_pvwatts.py

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

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

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~~~~~~~~~~~~~
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* Fix documentation return error in :py:meth:`pvlib.forecast.ForecastModel.cloud_cover_to_transmittance_linear`
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(:issue:`1367`, :pull:`1370`)
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* Add gallery example illustrating bifacial simulation using the
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:py:func:`pvlib.bifacial.pvfactors_timeseries` function and either
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:py:class:`pvlib.modelchain.ModelChain` or procedural functions.
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(:pull:`1394`)
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* Fix some typos (:pull:`1414`)
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Requirements
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* Kevin Anderson (:ghuser:`kanderso-nrel`)
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* Adam R. Jensen (:ghuser:`AdamRJensen`)
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* Johann Loux (:ghuser:`JoLo90`)
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* Saurabh Aneja (:ghuser:`spaneja`)
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* Jack Kelly (:ghuser:`JackKelly`)

‎setup.py

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'doc': ['ipython', 'matplotlib', 'sphinx == 3.1.2',
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'pydata-sphinx-theme == 0.8.0', 'sphinx-gallery',
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'docutils == 0.15.2', 'pillow', 'netcdf4', 'siphon',
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'sphinx-toggleprompt >= 0.0.5'],
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'sphinx-toggleprompt >= 0.0.5', 'pvfactors'],
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'test': TESTS_REQUIRE
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}
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EXTRAS_REQUIRE['all'] = sorted(set(sum(EXTRAS_REQUIRE.values(), [])))

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