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Bayesian updating of solar panel fragility curves and implications of higher panel strength for solar generation resilience

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  • Ceferino, Luis
  • Lin, Ning
  • Xi, Dazhi

Abstract

Solar generation can become a major and global source of clean energy by 2050. Nevertheless, few studies have assessed its resilience to extreme events, and none have used empirical data to characterize the fragility of solar panels. This paper develops fragility functions for rooftop and ground-mounted solar panels calibrated with solar panel structural performance data in the Caribbean for Hurricanes Irma and Maria in 2017 and Hurricane Dorian in 2019. After estimating the hurricane wind fields, we follow a Bayesian approach to estimate fragility functions for rooftop and ground-mounted panels based on the observations supplemented with existing numerical studies on solar panel vulnerability. Next, we apply the developed fragility functions to assess failure rates due to hurricane hazards in Miami-Dade, Florida, highlighting that the panels perform below the code requirements, especially rooftop panels. We also illustrate that strength increases can improve the panels' structural performance effectively. However, strength increases by a factor of two still cannot meet the reliability stated in the code.

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  • Ceferino, Luis & Lin, Ning & Xi, Dazhi, 2023. "Bayesian updating of solar panel fragility curves and implications of higher panel strength for solar generation resilience," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    • Handle: RePEc:eee:reensy:v:229:y:2023:i:c:s0951832022005117
    DOI: 10.1016/j.ress.2022.108896
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    2. Liu, Yushan & Li, Luyi & Chang, Zeming, 2023. "Efficient Bayesian model updating for dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 236(C).

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