Performance analysis of crystalline silicon and amorphous silicon photovoltaic systems in Iowa: 2011 to 2014

Asgharzadeh, Amir; Foresman, Eric C.; Toor, Fatima

doi:10.1109/pvsc.2016.7750124

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…Powers et al [4] Power; Empirical 3; 1 location 1 Townsend and Powers [6] Power; Empirical 4; 2 locations 2 Andrews et al [9] Power; Empirical 1 2 Hong et al [15] Power; Empirical 70 Up to 4 Zamo et al [17] Power; Empirical 28 2 Shishavan et al [18] Power; Empirical 2 3 Van Noord et al [7] Power; Empirical 6; 3 locations 2 Ross [11] Cover; Principal n/a n/a Lorenz et al [19] Cover; Empirical 14 (77) 1 Marion et al; Ryberg and Freeman [10,16] Cover; Empirical 6 (3); 2 locations 2 (1) Bosman and Darling [20] Cover; Empirical (1) 2 Rahmatmand [21] Cover; Principal n/a n/a Table 1 shows that most studies have a clearly limited statistical base, with limitations in one or more of the following three parameters: number of PV plants, geographical distribution, i.e., (micro-)climates, and number of winter seasons. This statistical limitation creates uncertainty regarding the general applicability and a need for broad validation efforts, extending all of the three mentioned parameters.…”

Section: Author (S) Model Type Number Of Pv Systems 1 Number Of Wintersmentioning

confidence: 99%

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

2021

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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.

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