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Reliability tracing of the integrated energy system using the improved shapley value

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  • Cao, Maosen
  • Shao, Changzheng
  • Hu, Bo
  • Xie, Kaigui
  • Zhou, Jiahao
  • Leng, Haimo
  • Zhang, Weixin

Abstract

The integrated energy system (IES) can significantly improve energy efficiency. However, it introduces multiple operational risks. To improve the reliability of IES, the critical components whose failures lead to high load shedding risks should be identified. Reliability tracing serves as an analysis tool for identifying critical components, providing an optimal plan for component selection and upgradation. This study proposes an IES reliability tracing framework that assigns reliability indices to components using the improved Shapley value. First, load shedding is allocated to each failed component with its basic fairness criterion, and the probability of the failure event as the weight is multiplied by the result. Subsequently, the sorting of critical components is modified to match the improvement in reliability. Thus, the weak parts of IES can be accurately identified by finding the components that contribute most to the reliability indices, thereby providing theoretical guidance for effective planning and maintenance of IES with limited resources. Furthermore, a reliability-oriented model considering the dynamic optimal energy flow of IES is established to improve the accuracy of reliability tracing, and case studies are performed using an integrated utility testing system to validate the effectiveness of the proposed framework.

Suggested Citation

  • Cao, Maosen & Shao, Changzheng & Hu, Bo & Xie, Kaigui & Zhou, Jiahao & Leng, Haimo & Zhang, Weixin, 2022. "Reliability tracing of the integrated energy system using the improved shapley value," Energy, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:energy:v:260:y:2022:i:c:s0360544222018941
    DOI: 10.1016/j.energy.2022.124997
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    References listed on IDEAS

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    Cited by:

    1. Yin, Linfei & Zheng, Da, 2024. "Decomposition prediction fractional-order PID reinforcement learning for short-term smart generation control of integrated energy systems," Applied Energy, Elsevier, vol. 355(C).

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