SolarAnywhere Energy Modeling – SolarAnywhere

Introduction

Clean Power Research offers solar energy performance assessment via the SolarAnywhere® API, which is available in SolarAnywhere SystemCheck® and Forecast. SolarAnywhere energy performance simulations have been used to inform decisions and develop scalable, robust solutions for a wide range of solar applications, such as:

• Residential and commercial PV system asset management
• Utility-scale PV system forecasting for market scheduling and operations
• Electric distribution grid planning and interconnection screening
• Commercial PV system forecasting for demand charge reduction

Energy Simulation Options

SolarAnywhere energy modeling services have been designed with a modular architecture to provide flexibility in selecting from available weather data and PV energy simulation models. The API supports the use of SolarAnywhere typical year (TGY) and time-series (Sites) data, as well as NREL TMY3 weather data. Figure 1 below depicts the modular architecture of SolarAnywhere’s energy modeling services.

SolarAnywhere Energy Simulation Models

SolarAnywhere customers can choose from two available simulation models: pvlib and CprPVForm.

Pvlib

Initially developed at Sandia National Laboratories, pvlib python is a peer-reviewed, community maintained, open-source library developed on GitHub. Pvlib provides a set of functions and classes for simulating the performance of PV energy systems. The broader solar community develops, validates and frequently updates pvlib models. The pvlib models integrated into SolarAnywhere support NREL PVWatts as the core engine for simulating module and inverter performance. PVWatts makes internal assumptions about module and inverter characteristics, enabling SolarAnywhere users to quickly model any PV system type and configuration with just a few key inputs.

To enable SolarAnywhere customers to take advantage of the latest research in PV modeling, SolarAnywhere offers the most recent and stable version of pvlib through its API services. To learn more about the various pvlib models, see SolarAnywhere API documentation.

SolarAnywhere Energy Modeling API Documentation

CprPVForm

The PVForm model was originally developed by Sandia National Laboratories in 1985. In partnership with the University at Albany (SUNY) team, Clean Power Research developed an in-house implementation of PVForm called ‘CprPVForm’. The CprPVForm model implements many modeling algorithms found in the PVForm and PVWatts v1 models, and has been used in assessing the energy performance of PV fleets.

Comparison of pvlib and CprPVForm Model Capabilities

Selecting which SolarAnywhere simulation model to use is dependent on the needs of a specific project. The pvlib models are ideal for simulating the performance of commercial and utility-scale systems due to the ability to model a variety of configurations such as single axis systems with backtracking. When using pvlib with SolarAnywhere’s high-fidelity weather data, SolarAnywhere customers get access to site-specific intelligence such as estimated PV generation and snow loss estimates. This enables solar developers and owners to reduce the financial and operational risks to their solar assets.

The CprPVForm model is capable of simulating near-object shading effects based on a user-provided obstruction profile. Near-object shading can be especially relevant for residential rooftop PV systems. The CprPVForm model is ideal when it’s important to model shade effects in detail, such as for individual or fleets of residential PV systems.

SolarAnywhere plans to improve pvlib modeling capabilities for residential systems by developing and contributing our research advancements to the pvlib community in the future.

Figure 2 shows a comparison of the two simulation models supported by SolarAnywhere.

Figure 2: Comparison of pvlib and CprPVForm Models

Model Validation and Sources of Uncertainty in PV Energy Estimates

Pvlib is composed of several PV performance models that are developed, updated and validated by the broader solar community. Figure 3 shows the various models included in SolarAnywhere’s implementation of pvlib, and their relevant publications with model description and validation.

Figure 3: Pvlib Models Implemented in SolarAnywhere

PVWatts Model

The PVWatts model forms the core of pvlib, and is used for module (DC) and inverter (AC) modeling. An NREL study comparing the available module models found that the annual energy output of the PVWatts model was in close agreement with other models, such as the Sandia and the California Energy Commission (CEC) Module models (study referenced a hypothetical 200kW PV system with an 86 percent derate factor). Figure 4 summarizes the findings of the study.

Figure 4: Comparison of PV Module Models

Typical Sources of Uncertainty in PV Production Estimates

The overall uncertainty in energy estimates is driven by a number of factors, including uncertainty in the weather data, loss assumptions and PV performance models. Figure 5 depicts the various sources of uncertainty as reported in the publication: “On-site performance verification to reduce yield prediction uncertainties.”17

Figure 5: Typical Sources of Uncertainty in Energy Estimates and Associated Ranges

SolarAnywhere Energy Modeling API Documentation

References

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11. Marion B. Schaefer R, Caine H, Sanchez G. 2013. Measured and modeled photovoltaic system energy losses from snow for Colorado and Wisconsin locations. Solar Energy. 97:112-121. Link.
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13. Coello M, Boyle L. 2019. Simple Model for Predicting Time Series Soiling of Photovoltaic Panels. IEEE Journal of Photovoltaics. Volume 9(5). DOI: 10.1109/JPHOTOV.2019.2919628. Link.
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15. Anoma MA, Jacob D, Bourne, BC, Scholl JA, Riley DM, Hansen CW. 2017. View Factor Model and Validation for Bifacial PV and Diffuse Shade on Single-Axis Trackers. IEEE 44th Photovoltaic Specialist Conference (PVSC). DOI: 10.1109/PVSC.2017.8366704. Link.
16. Dobos AP. 2013. PV Modeling in SAM. 2013 Sandia PV Performance Modeling Workshop. Link.
17. Reich N, Zenke J, Muller B, Kiefer K, Farnung B. 2015. On-site performance verification to reduce yield prediction uncertainties. IEEE 42nd Photovoltaic Specialist Conference (PVSC). DOI: 10.1109/PVSC.2015.7355614. Link.

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