Performance modeling and valuation of snow-covered PV systems: examination of a simplified approach to decrease forecasting error
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The advent of modern solar energy technologies can improve the costs of energy consumption on a global, national, and regional level, ultimately spanning stakeholders from governmental entities to utility companies, corporations, and residential homeowners. For those stakeholders experiencing the four seasons, accurately accounting for snow-related energy losses is important for effectively predicting photovoltaic performance energy generation and valuation. This paper provides an examination of a new, simplified approach to decrease snow-related forecasting error, in comparison to current solar energy performance models. A new method is proposed to allow model designers, and ultimately users, the opportunity to better understand the return on investment for solar energy systems located in snowy environments. The new method is validated using two different sets of solar energy systems located near Green Bay, WI, USA: a 3.0-kW micro inverter system and a 13.2-kW central inverter system. Both systems were unobstructed, facing south, and set at a tilt of 26.56°. Data were collected beginning in May 2014 (micro inverter system) and October 2014 (central inverter system), through January 2018. In comparison to reference industry standard solar energy prediction applications (PVWatts and PVsyst), the new method results in lower mean absolute percent errors per kilowatt hour of 0.039 and 0.055%, respectively, for the micro inverter system and central inverter system. The statistical analysis provides support for incorporating this new method into freely available, online, up-to-date prediction applications, such as PVWatts and PVsyst.
KeywordsSolar Photovoltaic Debris Snow Loss Derate
This work was performed, in part, at the Center for Nanoscale Materials, a U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences User Facility, under contract no. DE-AC02-06CH11357.
This research has been supported, in part, by the National Aeronautics and Space Administration under Grant No. NNX14AG57A issued through the Education Opportunities in NASA STEM (EONS) program for NICE-T, the National Science Foundation under Grant No. HRD-1417582 issued through the NSF Catalyzing Opportunities for Research and Education TCUP program, and the Environmental Protection Agency under Grant No. 83696601.
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