A method for improving the accuracy of numerical simulations of a photovoltaic panel. (October 2021)

• Record Type: Journal Article
• Authors: Sohani, Ali; Sayyaadi, Hoseyn; Doranehgard, Mohammad Hossein; Nizetic, Sandro; Li, Larry K.B.
• Is Part Of: Sustainable energy technologies and assessments. Volume 47(2021)
• Publication Date: 2021-10
• Subjects: Numerical simulation -- Photovoltaic (PV) technology -- Solar power generation -- Temperature effects; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2021.101433
• Languages: English
• ISSNs: 2213-1388

Abstract

Highlights: Considering temperature dependency of properties of layers leads to more accuracy. Annual error of power prediction decreases from 7.29 to 3.44% for mono type. The power prediction error for poly type is improved from 7.64 to 4.48%. Error goes up by increasing standard deviation of module surface temperature (SD). The higher SD is, the much more difference new and conventional approaches have. Abstract: Numerical simulations of photovoltaic solar panels are performed using temperature-dependent layer properties. The results are compared with experimental data recorded from a 50 W mono-crystalline panel and a 50 W poly-crystalline panel. The comparison shows that, for both panels, introducing temperature dependencies in the layer properties can significantly improve the accuracy of numerical simulations. On a sample day in August 2019, the mean absolute error in power prediction is found to decrease from 9.13 to 4.32% for the mono-crystalline panel and from 9.49 to 5.55% for the poly-crystalline panel, representing accuracy improvements of 52.7% and 41.5%, respectively. On an annual basis, the accuracy of estimating the power generated by the mono- and poly-crystalline panels improves by 52.8% and 41.4%, respectively. Finally, it is found that as the standard deviation of the temperature distribution on the panel increases, so does the effect of the temperature-dependent layer properties. This study highlights the need to account for the temperature dependencies of the different layer properties when numerically simulating photovoltaic panels.