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Resilience-based restoration strategy optimization for interdependent gas and power networks

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  • Sang, Maosheng
  • Ding, Yi
  • Bao, Minglei
  • Li, Siying
  • Ye, Chengjin
  • Fang, Youtong

Abstract

The ever-increasing interdependencies of gas and power transmission networks have made it possible for disruptions in one subnetwork to permeate to the connected ones and trigger widespread blackouts. One of the most critical issues is to restore the interdependent gas and power networks (IGPNs) to normal operation as soon as possible after complete blackouts. The interdependencies of gas and power networks, together with different operating characteristics bring about additional complexities to the formulation of restoration schemes. As a necessary and challenging task, restoration strategy optimization for IGPNs is tackled in this paper. Firstly, system functionality metrics are defined to characterize the real-time performance level, then resilience metrics are developed by capturing recovery features of system functionality. Secondly, the restoration sequence optimization model is developed to determine the restoration sequence of failed components to maximize resilience, in which restoration characteristics in terms of repair modes, repair time, and recovery costs are considered. To relieve the computational burden, the skeleton-network reconfiguration model is proposed to determine critical components to restore within limited resources. Moreover, linearization methods are utilized to transform models into mixed-integer linear programming problems. The results in test cases not only illustrate the effectiveness of the proposed approach to enhance the system resilience, but also illustrate the impacts of resources, crews, and repair modes on resilience, which help system operators to constitute restoration strategies quickly and develop resilience enhancement measures.

Suggested Citation

  • Sang, Maosheng & Ding, Yi & Bao, Minglei & Li, Siying & Ye, Chengjin & Fang, Youtong, 2021. "Resilience-based restoration strategy optimization for interdependent gas and power networks," Applied Energy, Elsevier, vol. 302(C).
    • Handle: RePEc:eee:appene:v:302:y:2021:i:c:s0306261921009387
    DOI: 10.1016/j.apenergy.2021.117560
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    7. Lv, Chaoxian & Liang, Rui & Jin, Wei & Chai, Yuanyuan & Yang, Tiankai, 2022. "Multi-stage resilience scheduling of electricity-gas integrated energy system with multi-level decentralized reserve," Applied Energy, Elsevier, vol. 317(C).
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    9. Li, Xue & Du, Xiaoxue & Jiang, Tao & Zhang, Rufeng & Chen, Houhe, 2022. "Coordinating multi-energy to improve urban integrated energy system resilience against extreme weather events," Applied Energy, Elsevier, vol. 309(C).

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