pvlib python: a python package for modeling solar energy systems

Abstract

pvlib python is a community-supported open source tool that provides a set of functions and classes for simulating the performance of photovoltaic energy systems. pvlib python aims to provide reference implementations of models relevant to solar energy, including for example algorithms for solar position, clear sky irradiance, irradiance transposition, DC power, and DC-to-AC power conversion. pvlib python is an important component of a growing ecosystem of open source tools for solar energy (William F. Holmgren, Hansen, Stein, & Mikofski, 2018). pvlib python is developed on GitHub by contributors from academia, national laboratories , and private industry. pvlib python is released with a BSD 3-clause license allowing permissive use with attribution. pvlib python is extensively tested for functional and algorithm consistency. Continuous integration services check each pull request on multiple platforms and Python versions. The pvlib python API is thoroughly documented and detailed tutorials are provided for many features. The documentation includes help for installation and guidelines for contributions. The documentation is hosted at readthe-docs.org as of this writing. A Google group and StackOverflow tag provide venues for user discussion and help. The pvlib python API was designed to serve the various needs of the many subfields of solar power research and engineering. It is implemented in three layers: core functions, the Location and PVSystem classes, and the ModelChain class. The core API consists of a collection of functions that implement algorithms. These algorithms are typically implementations of models described in peer-reviewed publications. The functions provide maximum user flexibility, however many of the function arguments require an unwieldy number of parameters. The next API level contains the Location and PVSystem classes. These abstractions provide simple methods that wrap the core function API layer. The method API simplification is achieved by separating the data that represents the object (object attributes) from the data that the object methods operate on (method arguments). For example, a Location is represented by a latitude, longitude, elevation, timezone, and name, which are Location object attributes. Then a Location object method operates on a datetime to get the corresponding solar position. The methods combine these data sources when calling the function layer, then return the results to the user. The final level of API is the ModelChain class, designed to simplify and standardize the process of stitching together the many modeling steps necessary to convert a time series of weather data to AC solar power generation, given a PV system and a location.