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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.

Suggested Citation

  • 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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    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.
    3. Yang, Dazhi & Wang, Wenting & Gueymard, Christian A. & Hong, Tao & Kleissl, Jan & Huang, Jing & Perez, Marc J. & Perez, Richard & Bright, Jamie M. & Xia, Xiang’ao & van der Meer, Dennis & Peters, Ian , 2022. "A review of solar forecasting, its dependence on atmospheric sciences and implications for grid integration: Towards carbon neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).

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