"""
The ``bifacial`` module contains functions for modeling back surface
plane-of-array irradiance under various conditions.
"""
import pandas as pd
[docs]def pvfactors_timeseries(
solar_azimuth, solar_zenith, surface_azimuth, surface_tilt,
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.,
run_parallel_calculations=True, n_workers_for_parallel_calcs=None):
"""
Calculate front and back surface plane-of-array irradiance on
a fixed tilt or single-axis tracker PV array configuration, and using
the open-source "pvfactors" package.
Please refer to pvfactors online documentation for more details:
https://sunpower.github.io/pvfactors/
Inputs
------
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)
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)
run_parallel_calculations: bool, default True
pvfactors is capable of using multiprocessing. Use this flag to decide
to run calculations in parallel (recommended) or not.
n_workers_for_parallel_calcs: int, default None
Number of workers to use in the case of parallel calculations. The
default value of 'None' will lead to using a value equal to the number
of CPU's on the machine running the model.
Returns
-------
front_poa_irradiance: numeric
Calculated incident irradiance on the front surface of the PV modules
(W/m2)
back_poa_irradiance: numeric
Calculated incident irradiance on the back surface of the PV modules
(W/m2)
df_registries: pandas DataFrame
DataFrame containing detailed outputs of the simulation; for
instance the shapely geometries, the irradiance components incident on
all surfaces of the PV array (for all timestamps), etc.
In the pvfactors documentation, this is refered to as the "surface
registry".
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 pandas Series inputs to numpy arrays
if isinstance(solar_azimuth, pd.Series):
solar_azimuth = solar_azimuth.values
if isinstance(solar_zenith, pd.Series):
solar_zenith = solar_zenith.values
if isinstance(surface_azimuth, pd.Series):
surface_azimuth = surface_azimuth.values
if isinstance(surface_tilt, pd.Series):
surface_tilt = surface_tilt.values
if isinstance(dni, pd.Series):
dni = dni.values
if isinstance(dhi, pd.Series):
dhi = dhi.values
# Import pvfactors functions for timeseries calculations.
from pvfactors.timeseries import (calculate_radiosities_parallel_perez,
calculate_radiosities_serially_perez,
get_average_pvrow_outputs)
idx_slice = pd.IndexSlice
# Build up pv array configuration parameters
pvarray_parameters = {
'n_pvrows': n_pvrows,
'pvrow_height': pvrow_height,
'pvrow_width': pvrow_width,
'gcr': gcr,
'rho_ground': albedo,
'rho_front_pvrow': rho_front_pvrow,
'rho_back_pvrow': rho_back_pvrow,
'horizon_band_angle': horizon_band_angle
}
# Run pvfactors calculations: either in parallel or serially
if run_parallel_calculations:
df_registries, df_custom_perez = calculate_radiosities_parallel_perez(
pvarray_parameters, timestamps, solar_zenith, solar_azimuth,
surface_tilt, surface_azimuth, dni, dhi,
n_processes=n_workers_for_parallel_calcs)
else:
inputs = (pvarray_parameters, timestamps, solar_zenith, solar_azimuth,
surface_tilt, surface_azimuth, dni, dhi)
df_registries, df_custom_perez = calculate_radiosities_serially_perez(
inputs)
# Get the average surface outputs
df_outputs = get_average_pvrow_outputs(df_registries,
values=['qinc'],
include_shading=True)
# Select the calculated outputs from the pvrow to observe
ipoa_front = df_outputs.loc[:, idx_slice[index_observed_pvrow,
'front', 'qinc']]
ipoa_back = df_outputs.loc[:, idx_slice[index_observed_pvrow,
'back', 'qinc']]
# Set timestamps as index of df_registries for consistency of outputs
df_registries = df_registries.set_index('timestamps')
return ipoa_front, ipoa_back, df_registries