Snow-Induced PV Loss Modeling Using Production-Data Inferred PV System Models

Abstract

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.

Field‐Induced Transparent Electrode‐Integrated Transparent Solar Cells and Heater for Active Energy Windows: Broadband Energy Harvester

“…In countries such as Sweden, Norway, Canada, and Russia, which are in subtropical and northern regions, there are more mean snow-cover days, that is, ≈100, and there are even habitats with 300-365 of mean snow-cover days. [22,[42][43][44][45][46][47][48] Photovoltaics (PVs) have been deployed significantly in these regions due to their reduced cost; however, due to ice precipitations, using PV panels has produced annual power losses from 15% to 34%. [42,44,49] Snow-related energy losses account for power losses of 35% (annual) and 70% (snow season) for PV modules with a tilt angle of 0°(Calumet, MI, USA); these losses can slightly be reduced to 30% (annual) and 60% (snow season), respectively, with a tilt angle of 35°.…”

“…[22,[42][43][44][45][46][47][48] Photovoltaics (PVs) have been deployed significantly in these regions due to their reduced cost; however, due to ice precipitations, using PV panels has produced annual power losses from 15% to 34%. [42,44,49] Snow-related energy losses account for power losses of 35% (annual) and 70% (snow season) for PV modules with a tilt angle of 0°(Calumet, MI, USA); these losses can slightly be reduced to 30% (annual) and 60% (snow season), respectively, with a tilt angle of 35°. [45] Further, precipitated snow creates an albedo effect, reflecting a significant amount of solar radiation into the atmosphere.…”

State‐of‐Play and Emerging Challenges in Photovoltaic Energy Yield Simulations: A Multi‐Case Multi‐Model Benchmarking Study

“…Their validation, through comparison with actual PV yield data, showed that gradient‐boosted trees obtained the minimum prediction error and thus the best‐performing simulation of the PV energy yields under the impact of snow. In a rather different approach, to overcome the limitation in underlying statistics for snow losses estimations in PV, van Noord et al [ 21 ] investigated the estimation of snow losses using a PV system's yield data together with freely available gridded weather datasets, enabling snow loss modeling for high numbers of PV systems and winter seasons using existing large datasets. Finally, with regard to the modeling of PV degradation mechanisms and their impact on energy yield losses estimations, a comprehensive review study from Lindig et al [ 22 ] shed light on several analytical models for degradation mechanisms, notably for corrosion and PID, in three different climatic zones/stress profiles.…”

Approximating Shading Ratio Using the Total-Sky Imaging System: An Application for Photovoltaic Systems

“…The problem with these models is that they can predict the amount of loss in the PV output power; however, they cannot indicate the shading factor resulting from the clouds in the sky. In contrast, the output power losses in PV systems can also result from cumulative dust [17], snow [18], hotspots [19][20][21], or cracks [22][23][24][25]. Therefore, assuming that all PV output power losses are caused by shading is substantially incorrect.…”