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A Multi-Stakeholder Actuarial Risk Management Framework for Transmission Systems Against Extreme Weather Events

  • Yuhong Zhao
  • , Yibo Ding
  • , Xianzhuo Sun
  • , Wenzhuo Shi
  • , Zhengyang Hu
  • , Zhao Xu
  • , Chen Chen
  • , Zhaohong Bie
  • Xi'an Jiaotong University
  • Hong Kong Polytechnic University
  • Shandong University
  • Northwestern Polytechnical University Xian
  • The H ong Kong Polytechnic University

科研成果: 期刊稿件文章同行评审

摘要

Increasingly frequent extreme weather events pose significant risks of large-scale blackouts and substantial economic losses to power systems and electricity customers. In recent years, resilience insurance has emerged as an effective risk management tool to address this challenge. However, despite sufficient premium contributions from electricity customers, current insurance schemes may still expose insurance companies to high financial risks due to extreme tail events, thus demotivating them from entering the market. To this end, this paper develops a novel actuarial risk management framework consisting of risk assessment and insurance design to hedge against weather-induced catastrophe risks in transmission systems (TSs). Specifically, in the risk assessment phase, cascading failures and the aging effects of transmission lines, factors that exacerbate the heavy-tailed characteristics of weather-induced catastrophe risks, are comprehensively considered. A multi-stakeholder resilience insurance scheme is further designed, introducing reinsurance policies for the first time to alleviate insurers' financial burden, with the optimal retention derived from a risk-return Nash bargaining game. Numerical experiments on the modified IEEE 30-bus test system verify that the proposed method enables more accurate assessments for weather-induced catastrophe risks in TSs. Additionally, it helps mitigate insurers' insolvency risks while maintaining affordable premiums. Furthermore, the Nash bargaining game proves effective in helping insurers balance risk and return in decision-making. Finally, the proposed risk management framework exhibits excellent scalability when applied to large-scale real-world systems.

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