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Long-term spatial and temporal solar resource variability over America using the NSRDB version 3 (1998–2017)

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  • Habte, Aron
  • Sengupta, Manajit
  • Gueymard, Christian
  • Golnas, Anastasios
  • Xie, Yu

Abstract

The study assesses the long-term spatial and temporal solar resource variability in America using the 20-year National Renewable Energy Laboratory's (NREL's) National Solar Radiation Database (NSRDB). The coefficient of variation (COV) is used to analyze the spatial and temporal (interannual and seasonal) variability. Further, both spatial and temporal long-term variability are analyzed using the Köppen-Geiger climate classification. The temporal variability is found that, on average, the continental United States (CONUS) COV reaches up to 5% for global horizontal irradiance (GHI) and 10% for direct normal irradiance (DNI), and that the NSRDB domain's COV is roughly twice that of CONUS. For the seasonal variability analysis, the winter months are found to exhibit higher COV than the other seasons. In particular, December exhibits the highest variability, reaching on average 30% for DNI and 20% for GHI over various areas. On the other hand, the summer months demonstrate significantly lower variability, reaching only less than 20% for DNI and 10% for GHI, on average. Similarly, the spatial variability is analyzed by comparing each pixel to its neighbors. The long-term spatial variability is found to increase with the number of neighboring pixels being considered, which is equivalent to an increase in distance (within a 100-km x 100-km square grid). As expected, the DNI spatial variability is higher than that of GHI. Moreover, the annual solar irradiance anomalies are found to reach ±25% for both GHI and DNI (and even exceed those value in some instances) during each year of the 20-year period.

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  • Habte, Aron & Sengupta, Manajit & Gueymard, Christian & Golnas, Anastasios & Xie, Yu, 2020. "Long-term spatial and temporal solar resource variability over America using the NSRDB version 3 (1998–2017)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    • Handle: RePEc:eee:rensus:v:134:y:2020:i:c:s1364032120305736
    DOI: 10.1016/j.rser.2020.110285
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    References listed on IDEAS

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    1. Calif, Rudy & Soubdhan, Ted, 2016. "On the use of the coefficient of variation to measure spatial and temporal correlation of global solar radiation," Renewable Energy, Elsevier, vol. 88(C), pages 192-199.
    2. Leloux, Jonathan & Lorenzo, Eduardo & García-Domingo, Beatriz & Aguilera, Jorge & Gueymard, Christian A., 2014. "A bankable method of assessing the performance of a CPV plant," Applied Energy, Elsevier, vol. 118(C), pages 1-11.
    3. Mohammadi, Kasra & Goudarzi, Navid, 2018. "Study of inter-correlations of solar radiation, wind speed and precipitation under the influence of El Niño Southern Oscillation (ENSO) in California," Renewable Energy, Elsevier, vol. 120(C), pages 190-200.
    4. Abreu, Edgar F.M. & Canhoto, Paulo & Prior, Victor & Melicio, R., 2018. "Solar resource assessment through long-term statistical analysis and typical data generation with different time resolutions using GHI measurements," Renewable Energy, Elsevier, vol. 127(C), pages 398-411.
    5. Labed, S. & Lorenzo, E., 2004. "The impact of solar radiation variability and data discrepancies on the design of PV systems," Renewable Energy, Elsevier, vol. 29(7), pages 1007-1022.
    6. Kariuki, Boniface Wainaina & Sato, Tomonori, 2018. "Interannual and spatial variability of solar radiation energy potential in Kenya using Meteosat satellite," Renewable Energy, Elsevier, vol. 116(PA), pages 88-96.
    7. Sun, Xixi & Bright, Jamie M. & Gueymard, Christian A. & Acord, Brendan & Wang, Peng & Engerer, Nicholas A., 2019. "Worldwide performance assessment of 75 global clear-sky irradiance models using Principal Component Analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 111(C), pages 550-570.
    8. Sengupta, Manajit & Xie, Yu & Lopez, Anthony & Habte, Aron & Maclaurin, Galen & Shelby, James, 2018. "The National Solar Radiation Data Base (NSRDB)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 51-60.
    9. Monger, Samuel H. & Morgan, Eric R. & Dyreson, Ana R. & Acker, Thomas L., 2016. "Applying the kriging method to predicting irradiance variability at a potential PV power plant," Renewable Energy, Elsevier, vol. 86(C), pages 602-610.
    10. Polo, J. & Téllez, F.M. & Tapia, C., 2016. "Comparative analysis of long-term solar resource and CSP production for bankability," Renewable Energy, Elsevier, vol. 90(C), pages 38-45.
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    2. Maldonado-Salguero, Patricia & Bueso-Sánchez, María Carmen & Molina-García, Ángel & Sánchez-Lozano, Juan Miguel, 2022. "Spatio-temporal dynamic clustering modeling for solar irradiance resource assessment," Renewable Energy, Elsevier, vol. 200(C), pages 344-359.
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