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Snow-induced PV loss modeling using production-data inferred PV system models
RISE Research Institutes of Sweden, Built Environment, Energy and Resources.ORCID iD: 0000-0003-0245-7082
SMHI, Sweden.
SMHI, Sweden.
2021 (English)In: Energies, E-ISSN 1996-1073, Vol. 14, no 6, article id 1574Article in journal (Refereed) Published
Abstract [en]

Snow-induced photovoltaic (PV)-energy losses (snow losses) in snowy and cold locations vary up to 100% monthly and 34% annually, according to literature. Levels that illustrate the need for snow loss estimation using validated models. However, to our knowledge, all these models build on limited numbers of sites and winter seasons, and with limited climate diversity. To overcome this limitation in underlying statistics, we investigate the estimation of snow losses using a PV system’s yield data together with freely available gridded weather datasets. To develop and illustrate this approach, 263 sites in northern Sweden are studied over multiple winters. Firstly, snow-free production is approximated by identifying snow-free days and using corresponding data to infer tilt and azimuth angles and a snow-free performance model incorporating shading effects, etc. This performance model approximates snow-free monthly yields with an average hourly standard deviation of 6.9%, indicating decent agreement. Secondly, snow losses are calculated as the difference between measured and modeled yield, showing annual snow losses up to 20% and means of 1.5-6.2% for winters with data for at least 89 sites. Thirdly, two existing snow loss estimation models are compared to our calculated snow losses, with the best match showing a correlation of 0.73 and less than 1% bias for annual snow losses. Based on these results, we argue that our approach enables studying snow losses for high numbers of PV systems and winter seasons using existing datasets. © 2021 by the authors.

Place, publisher, year, edition, pages
MDPI AG , 2021. Vol. 14, no 6, article id 1574
Keywords [en]
Photovoltaics, PV system modeling, PV system performance, Reanalysis data, Remote sensing, Shading, Snow, Snow losses, Soiling, Climate models, Energy dissipation, Loss estimation, Northern sweden, Performance Model, Photovoltaic energy, Production data, Shading effect, Standard deviation, Winter seasons
National Category
Energy Systems
Identifiers
URN: urn:nbn:se:ri:diva-54703DOI: 10.3390/en14061574Scopus ID: 2-s2.0-85107952823OAI: oai:DiVA.org:ri-54703DiVA, id: diva2:1575655
Note

 Funding details: Energimyndigheten, 38180-2; Funding text 1: Funding: This research was funded by Energimyndigheten (Swedish Energy Agency), project number 38180-2.

Available from: 2021-06-30 Created: 2021-06-30 Last updated: 2023-08-28Bibliographically approved

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van Noord, Michiel

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