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A novel metric for quantifying solar irradiance stability: Mapping solar irradiance variability to photovoltaic power generation

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  • Tian, Qun
  • Li, Jinxiao
  • Xie, Zhiang
  • Li, Puxi
  • Wang, Ya
  • Chen, Dongwei
  • Zheng, Yue

Abstract

The daily stability of solar irradiance significantly influences photovoltaic (PV) power generation; however, existing metrics for assessing it normally fail to robustly correlate with daily PV output. To address this gap, we introduce a new metric, the solar instability index (SII), formulated by applying the Wasserstein distance to assess the deviation of intra-day solar irradiance pattern from the anticipated diurnal cycle. In our case station, SII closely correlates with atmospheric moisture and available solar energy, suggesting its strong association with synoptic weather events that lead to solar resource loss. We further scrutinize the efficacy of SII alongside two existing metrics through two case studies. The results demonstrate that SII excels in capturing low-frequency variations in solar irradiance without relying on arbitrarily assigned parameters, thereby outperforming the other two metrics in establishing a robust correlation with PV power output. As such, in scenarios involving site selection for PV power plant, SII stands as a valuable metric for assessing the potential stability of daily PV power generation.

Suggested Citation

  • Tian, Qun & Li, Jinxiao & Xie, Zhiang & Li, Puxi & Wang, Ya & Chen, Dongwei & Zheng, Yue, 2025. "A novel metric for quantifying solar irradiance stability: Mapping solar irradiance variability to photovoltaic power generation," Renewable Energy, Elsevier, vol. 239(C).
    • Handle: RePEc:eee:renene:v:239:y:2025:i:c:s0960148124021037
    DOI: 10.1016/j.renene.2024.122035
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    References listed on IDEAS

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    1. 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).
    2. da Rocha, Vinicius Roggério & Costa, Rodrigo Santos & Martins, Fernando Ramos & Gonçalves, André Rodrigues & Pereira, Enio Bueno, 2022. "Variability index of solar resource based on data from surface and satellite," Renewable Energy, Elsevier, vol. 201(P1), pages 354-378.
    3. Zhang, Chunxiao & Shen, Chao & Yang, Qianru & Wei, Shen & Lv, Guoquan & Sun, Cheng, 2020. "An investigation on the attenuation effect of air pollution on regional solar radiation," Renewable Energy, Elsevier, vol. 161(C), pages 570-578.
    4. Peled, A. & Appelbaum, J., 2013. "Evaluation of solar radiation properties by statistical tools and wavelet analysis," Renewable Energy, Elsevier, vol. 59(C), pages 30-38.
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    1. Shi, Chaojun & Xie, Xiongbin & Zhang, Ke & Zhang, Xiaoyun & Su, Zibo & Xiao, Junchi, 2026. "CloudPVNet: A fine-grained ground-based cloud image segmentation method for photovoltaic power forecasting," Renewable Energy, Elsevier, vol. 258(C).
    2. Angulo, José R. & Conde, Luis A. & Pleshcheva, Vlada & García, Michael A. & Gosgot, Wildor & Barrena, Miguel & Muñoz-Cerón, Emilio & de la Casa, Juan & Töfflinger, Jan A., 2026. "Effective DC power rating of PV arrays under challenging operating conditions in desert and tropical regions," Renewable Energy, Elsevier, vol. 258(C).

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