Impact of Measured Spectrum Variation on Solar Photovoltaic Efficiencies Worldwide

Geoffrey Kinsey, Nicholas Riedel-Lynagskaer, Alonso-Abella Miguel, Matthew Boyd, Marilia Braga, Chunhui Shou, Raul Cordero, Benjamin Duck, Christopher Fell, Sarah Feron, George Georghiou, Nicholas Habryl, Jim John, Nipon Ketjoy, Gabriel Lopez, Atse Louwen, Elijah Maweza, Takashi Minemoto, Ankit Mittal, Cecile MoltoGuilherme Neves, Gustavo Garrido, Matthew Norton, Basant Paudyal, Enio Pereira, Yves Poissant, Lawrence Pratt, Qu Shen, Thomas Reindl, Marcus Rennhofer, Carlos Rodriguez-Gallegos, Ricardo Ruther, Wilfried van Sark, Miguel Sevillano-Bendezu, Hubert Seigneur, Jorge Tejero, Marios Theristis, Jan Tofflinger, Carolin Ulbrich, Waldeir Vilela, Xiangao Xia, Marcia Yamasoe

Research output: Contribution to journalArticlepeer-review

10 Scopus Citations

Abstract

In photovoltaic power ratings, a single solar spectrum, AM1.5, is the de facto standard for record laboratory efficiencies, commercial module specifications, and performance ratios of solar power plants. More detailed energy analysis that accounts for local spectral irradiance, along with temperature and broadband irradiance, reduces forecast errors to expand the grid utility of solar energy. Here, ground-level measurements of spectral irradiance collected worldwide have been pooled to provide a sampling of geographic, seasonal, and diurnal variation. Applied to nine solar cell types, the resulting divergence in solar cell efficiencies illustrates that a single spectrum is insufficient for comparisons of cells with different spectral responses. Cells with two or more junctions tend to have efficiencies below that under the standard spectrum. Silicon exhibits the least spectral sensitivity: relative weekly site variation ranges from 1% in Lima, Peru to 14% in Edmonton, Canada.
Original languageAmerican English
Pages (from-to)995-1016
Number of pages22
JournalRenewable Energy
Volume196
DOIs
StatePublished - 2022

NREL Publication Number

  • NREL/JA-5700-83771

Keywords

  • energy yield
  • forecasting
  • photovoltaics
  • spectral irradiance

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