pvfactors 0.1.5a1

2D View Factor Model to calculate the irradiance incident on various surfaces of PV arrays

pvfactors (open-source fork of vf_model)

pvfactors is a tool designed for PV professionals to calculate the irradiance incident on surfaces of a photovoltaic array. It relies on the use of 2D geometries and view factors integrated mathematically into a linear system of equations.

This package is the open-source fork of the original 'vf_model' package developed by SunPower, and which had over 300 commits. The package was used for all the material presented at IEEE PVSC 44 2017 (see [1] and link to paper).

Documentation

The documentation can be found here. Please refer to the TL;DR section below to find a quick start tutorial.

Installation

pvfactors is currently compatible and tested with Python versions 2.7 and 3.6, and is available in PyPI.

The easiest way to install pvfactors is to use pip as follows:

$ pip install pvfactors

The package wheel files are also available in the release section of the Github repository.

Requirements

Requirements are included in the setup.py file of the package. Here is a list of important dependencies:

• shapely
• numpy
• scipy
• pandas
• pvlib-python

Notebook demo

The following Jupyter notebook is a good way to get a quick overview: notebook

TL;DR - Quick Start

Given some timeseries inputs:

import numpy as np
from datetime import datetime
timestamps = np.array([datetime(2017, 8, 31, 11), datetime(2017, 8, 31, 12)])
solar_zenith = np.array([20., 10.])
solar_azimuth = np.array([110., 140.])
surface_tilt = np.array([10., 0.])
surface_azimuth = np.array([90., 90.])
dni = np.array([1000., 300.])
dhi = np.array([50., 500.])

And some PV array parameters:

pvarray_parameters = {
    'n_pvrows': 3,         # number of pv rows
    'pvrow_height': 1.75,  # height of pvrows (measured at center / torque tube)
    'pvrow_width': 2.44,   # width of pvrows
    'gcr': 0.4,            # ground coverage ratio
    'rho_ground': 0.2,     # albedo
}

The user can quickly run a timeseries simulation using pvfactors as shown below:

from pvfactors.timeseries import calculate_radiosities_serially_perez

df_registries, _ = calculate_radiosities_serially_perez((
    pvarray_parameters, timestamps,
    solar_zenith, solar_azimuth,
    surface_tilt, surface_azimuth, dni, dhi))

Progress: |██████████████████████████████████████████████████| 100.0% Complete

If the raw outputs are too detailed for the user, they can be formatted quickly thanks to helper functions:

from pvfactors.timeseries import get_average_pvrow_outputs

df_avg_outputs = get_average_pvrow_outputs(df_registries,
                                           values=['qinc', 'isotropic_term', 'reflection_term', 'horizon_term'])
df_avg_outputs

pvrow_index	0	...	2
surface_side	back	front	...	back	front
term	horizon_term	isotropic_term	qinc	reflection_term	shaded	horizon_term	isotropic_term	qinc	reflection_term	shaded	...	horizon_term	isotropic_term	qinc	reflection_term	shaded	horizon_term	isotropic_term	qinc	reflection_term	shaded
timestamps
2017-08-31 11:00:00	1.221181	0.117972	93.339589	92.000435	False	1.640681	20.982303	1035.231572	1.217963e+00	False	...	1.640681	0.199001	97.261729	95.422048	False	1.640681	20.849825	1034.889434	1.008302e+00	False
2017-08-31 12:00:00	0.000000	0.863797	83.197488	82.333691	False	0.000000	206.332401	795.442326	-5.684342e-14	False	...	0.000000	382.033336	170.382030	-211.651306	False	0.000000	206.332401	795.442326	-5.684342e-14	False

2 rows × 30 columns

The user can also plot the pv array at a given time of the simulation:

from pvfactors.plot import plot_array_from_registry

f, ax = plt.subplots(figsize=(10, 4))
plot_array_from_registry(ax, df_registries.set_index('timestamps').loc["2017-8-31 11:00:00", :])
ax.set_xlim(-2, 15)
plt.show()

Contributing

Contributions are much needed in order to improve this package. If you wish to contribute, you can start by forking the repository and installing pvfactors using pip in the root folder of the package:

$ pip install .

To install the package in editable mode, you can use:

$ pip install -e .

References

[1] Anoma, M., Jacob, D., Bourne, B. C., Scholl, J. A., Riley, D. M., & Hansen, C. W. (2017). View Factor Model and Validation for Bifacial PV and Diffuse Shade on Single-Axis Trackers. In 44th IEEE Photovoltaic Specialist Conference.