Source code for pvlib.bifacial.pvfactors

The ``bifacial.pvfactors`` module contains functions for modeling back surface
plane-of-array irradiance using an external implementaton of the pvfactors
model (either ``solarfactors`` or the original ``pvfactors``).

import pandas as pd
import numpy as np

[docs]def pvfactors_timeseries( solar_azimuth, solar_zenith, surface_azimuth, surface_tilt, axis_azimuth, timestamps, dni, dhi, gcr, pvrow_height, pvrow_width, albedo, n_pvrows=3, index_observed_pvrow=1, rho_front_pvrow=0.03, rho_back_pvrow=0.05, horizon_band_angle=15.): """ Calculate front and back surface plane-of-array irradiance on a fixed tilt or single-axis tracker PV array configuration using the pvfactors model. The pvfactors bifacial irradiance model is described in [1]_. .. versionchanged:: 0.10.1 It is now recommended to install the ``solarfactors`` package (``pip install solarfactors``) instead of ``pvfactors`` for this function. For more information, see :ref:`bifacial`. Parameters ---------- solar_azimuth: numeric Sun's azimuth angles using pvlib's azimuth convention (deg) solar_zenith: numeric Sun's zenith angles (deg) surface_azimuth: numeric Azimuth angle of the front surface of the PV modules, using pvlib's convention (deg) surface_tilt: numeric Tilt angle of the PV modules, going from 0 to 180 (deg) axis_azimuth: float Azimuth angle of the rotation axis of the PV modules, using pvlib's convention (deg). This is supposed to be fixed for all timestamps. When modeling fixed-tilt arrays, set this value to be 90 degrees clockwise from ``surface_azimuth``. timestamps: datetime or DatetimeIndex List of simulation timestamps dni: numeric Direct normal irradiance (W/m2) dhi: numeric Diffuse horizontal irradiance (W/m2) gcr: float Ground coverage ratio of the pv array pvrow_height: float Height of the pv rows, measured at their center (m) pvrow_width: float Width of the pv rows in the considered 2D plane (m) albedo: float Ground albedo n_pvrows: int, default 3 Number of PV rows to consider in the PV array index_observed_pvrow: int, default 1 Index of the PV row whose incident irradiance will be returned. Indices of PV rows go from 0 to n_pvrows-1. rho_front_pvrow: float, default 0.03 Front surface reflectivity of PV rows rho_back_pvrow: float, default 0.05 Back surface reflectivity of PV rows horizon_band_angle: float, default 15 Elevation angle of the sky dome's diffuse horizon band (deg) Returns ------- poa_front: numeric Calculated incident irradiance on the front surface of the PV modules (W/m2) poa_back: numeric Calculated incident irradiance on the back surface of the PV modules (W/m2) poa_front_absorbed: numeric Calculated absorbed irradiance on the front surface of the PV modules (W/m2), after AOI losses poa_back_absorbed: numeric Calculated absorbed irradiance on the back surface of the PV modules (W/m2), after AOI losses References ---------- .. [1] Anoma, Marc Abou, et al. "View Factor Model and Validation for Bifacial PV and Diffuse Shade on Single-Axis Trackers." 44th IEEE Photovoltaic Specialist Conference. 2017. """ # Convert Series, list, float inputs to numpy arrays solar_azimuth = np.array(solar_azimuth) solar_zenith = np.array(solar_zenith) dni = np.array(dni) dhi = np.array(dhi) # GH 1127, GH 1332 surface_tilt = np.full_like(solar_zenith, surface_tilt) surface_azimuth = np.full_like(solar_zenith, surface_azimuth) # Import pvfactors functions for timeseries calculations. from import run_timeseries_engine # Build up pv array configuration parameters pvarray_parameters = { 'n_pvrows': n_pvrows, 'axis_azimuth': axis_azimuth, 'pvrow_height': pvrow_height, 'pvrow_width': pvrow_width, 'gcr': gcr } irradiance_model_params = { 'rho_front': rho_front_pvrow, 'rho_back': rho_back_pvrow, 'horizon_band_angle': horizon_band_angle } # Create report function def fn_build_report(pvarray): return {'total_inc_back': pvarray.ts_pvrows[index_observed_pvrow] .back.get_param_weighted('qinc'), 'total_inc_front': pvarray.ts_pvrows[index_observed_pvrow] .front.get_param_weighted('qinc'), 'total_abs_back': pvarray.ts_pvrows[index_observed_pvrow] .back.get_param_weighted('qabs'), 'total_abs_front': pvarray.ts_pvrows[index_observed_pvrow] .front.get_param_weighted('qabs')} # Run pvfactors calculations report = run_timeseries_engine( fn_build_report, pvarray_parameters, timestamps, dni, dhi, solar_zenith, solar_azimuth, surface_tilt, surface_azimuth, albedo, irradiance_model_params=irradiance_model_params) # Turn report into dataframe df_report = pd.DataFrame(report, index=timestamps) return (df_report.total_inc_front, df_report.total_inc_back, df_report.total_abs_front, df_report.total_abs_back)