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

## Project description

# 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:

## 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.

## Project details

## Release history Release notifications

## Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help | File type | Python version | Upload date |
---|---|---|---|

pvfactors-0.1.5.tar.gz (50.7 kB) Copy SHA256 hash SHA256 | Source | None |