Site-Specific Solar Power Assessment using In Situ Atmospheric Measurements

Soft shadows from clouds (and aerosols, such as smoke) are a first order inhibitor of solar radiation, which is crucial for photovoltaic (PV) panel power generation. Integrating PV generated power into hybrid microgrids, to minimize non-renewable fuel consumption while providing an uninterrupted power resource, requires skilled knowledge of atmospheric conditions and distributed power grid management. If the network communication link becomes severely limited (e.g., due to a disaster), more emphasis on the local atmospheric input will be required to balance the resources. Such a restriction is what we’ve imposed in our research, which focuses on the use of local in-situ measurements to estimate the current and future surface solar radiation as input to a hybrid (solar/traditional) power distribution.

In this presentation, a first order solar radiation model is used to estimate local surface solar radiation fluxes. We also discuss the Atmospheric Renewable Energy Field Study #2 (ARE2), which acquired a comprehensive dataset for calibrating and advancing the solar radiation model. The ARE2 data also support the future implementation of machine learning (ML) for atmospheric assessment, as the initial step toward an automated sky/cloud assessment process needed to complete the in-situ measurement/model goals.