With the rapid growth of solar PV around the world, project developers, owners and operators need efficient and robust solutions to help them develop, operate and maintain PV systems at scale. Programmatic access to solar energy simulations helps stakeholders make informed decisions across a wide range of applications, such as residential and commercial PV system asset management, and utility-scale PV plant operations. Clean Power Research offers energy simulations for solar performance assessment via SolarAnywhere® SystemCheck® and Forecast, and is pleased to now support pvlib models.

SolarAnywhere users now can leverage the SolarAnywhere API to run the latest, open-source pvlib solar simulation models with high-fidelity SolarAnywhere® Data. On-demand access to specific, data-driven intelligence, such as estimated PV generation and site-specific snow loss estimates, enables solar developers and owners to reduce the financial and operational risks of their solar assets.

With the integration of pvlib, the SolarAnywhere API now supports the following features:

  • Full time-series simulations of plane-of-array irradiance
  • Modeling of fixed tilt and single-axis tracking PV systems with backtracking for single PV systems and fleets
  • Modeling of reflection losses to quantify the impact of optical losses on PV energy estimates
  • Snow loss estimates based on location-specific, time-series weather data available with SolarAnywhere Sites

SolarAnywhere’s flexible architecture also enables more frequent addition of features in the future.

Advantages of using open-source pvlib models in the SolarAnywhere API

Clean Power Research chose the open-source pvlib modeling platform because of its ease-of-use, transparency and popularity among the solar modeling community.

“Pvlib python has evolved to provide a robust and comprehensive foundation for custom solar energy modeling applications,” said Mark Mikofski, co-maintainer of pvlib python.

Initially developed at Sandia National Laboratories, pvlib python is a peer-reviewed, community-maintained, open-source library developed on GitHub. It includes several PV system models that are developed, validated and frequently updated by the solar community. As evidenced by its growing impact, pvlib-python was recently named a NumFOCUS affiliated project. This recognition places the software in company with SciPy and Statsmodels, both “go-to” libraries for scientific and statistical computation.

With pvlib capabilities, SolarAnywhere offers key advantages when modeling your solar PV projects, including:

  • PV modeling that is straightforward and reliable — SolarAnywhere API pvlib models support NREL PVWatts as the core engine for simulating module and inverter performance. The PVWatts model has been used extensively by solar project developers and financiers for PV performance assessment and is also supported by energy simulation tools such as the System Advisor Model (SAM). PVWatts enables SolarAnywhere API users to reliably and quickly model any PV system with just a few basic inputs about the system type and configuration.
  • Loss estimates that are specific and data-driven — Snow and soiling losses can vary widely between project sites due to environmental variations, exhibiting high seasonal and inter-annual variability. Using generic assumptions for these losses can increase the uncertainty of yield calculations and O&M costs. SolarAnywhere uses locationally-precise time-series irradiance and weather data as the input to the snow loss model in pvlib. This data-driven approach results in a more site-specific PV energy estimate that takes into account when snow and dust were actually impacting PV output.

To learn more about pvlib solar simulation models integrated into SolarAnywhere, visit the Energy Modeling Resource page and SolarAnywhere API Documentation.

Why we’re joining the open-source PV modeling community

At Clean Power Research, our mission is to power the energy transformation with solutions that inform, streamline and quantify energy-related decisions and processes. Recognizing the need to accelerate the pace of development and support for solar project finance, deployment and asset management, Clean Power Research has now joined the pvlib open-source community. This active community includes contributors from academia, national laboratories and private industry.

We’re making our PV performance modeling research advancements—developed in collaboration with Dr. Richard Perez and his lab at the University at Albany (SUNY-Albany)—easily accessible to the broader solar community by contributing to the pvlib open-source project. Our first contribution to the pvlib project was a bug fix that addressed a minor issue with the existing implementation of the Perez diffuse radiation model. Through similar future contributions, Clean Power Research will continue to foster innovation and research in PV modeling, promoting greater transparency and collaboration across the solar PV community.

With the addition of pvlib models to SolarAnywhere, solar project developers, owners and operators can easily and reliably operate PV systems at scale while reducing financial and operational risk to their solar projects.

To learn more about our trusted solar data and energy intelligence services, please contact us.

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