Distributionally robust resilience optimization of post-disaster power system considering multiple uncertainties

Chen Zhang, Yan-Fu Li, Hanxiao Zhang, Yujin Wang, Yuelong Huang and Jianyu Xu

Reliability Engineering and System Safety, 2024, vol. 251, issue C

Abstract: In an era where extreme weather events are becoming more frequent and severe, the resilience of power systems against such disruptions is vital for societal stability. This study introduces a comprehensive framework for reducing the resilience loss of power systems after such disruptive events, incorporating a detailed analysis of the inherent uncertainties that challenge post-disaster restoration efforts. We categorize these uncertainties into time and demand-related factors and establish a tailored resilience measure to evaluate the efficacy of power system restoration schedules. We develop a two-stage stochastic programming model that minimizes expected resilience loss, integrating the routing of restoration crews—a crucial aspect that directly influences restoration timeliness and efficiency. Furthermore, we pioneer a distributionally robust optimization model utilizing an ambiguity set based on Wasserstein distance to navigate demand uncertainties. The applicability and effectiveness of the proposed models are demonstrated through a case study of Guangxi Province’s power grid, illustrating their potential to improve post-disaster recovery strategies.

Keywords: Resilience optimization; Power system; Vehicle routing; Distributionally robust optimization (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:251:y:2024:i:c:s0951832024004393

DOI: 10.1016/j.ress.2024.110367

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