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Combined optimization of system reliability improvement and resilience with mixed cascading failures in dependent network systems

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  • Zhou, Jian
  • Coit, David W.
  • Felder, Frank A.
  • Tsianikas, Stamatis

Abstract

Efficient operation of modern society is largely dependent upon the reliable functioning of critical infrastructure with networked structures. The resilience of these network systems becomes a matter of great concern since the increasingly complex network dependency contributes to, although uncommon, devastating cascading failures in real-world network systems. Traditionally, system hardening and restoration are employed sequentially to reduce system failure influence while it may not be effective for mitigating cascading failures in dependent network systems. A new cascading failure model that considers the mix of multiple failures resulting from local and global network load redistributions associated with network dependency is established. A framework which jointly optimizes network system hardening and restoration for improving resilience against mixed cascading failures is proposed. It is designed to minimize total cost, including system hardening cost, restoration cost and economic loss caused by cascading failures. In the case studies, the impact of cascading failures in power generation and transmission systems on distribution systems is explored regarding the comprehensive power network resilience. Mixed-integer genetic algorithms are developed to solve this stochastic problem. The results show that this research can provide insights on how to mitigate and restore real-life dependent network systems cost-effectively from future unforeseen cascading failures.

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  • Zhou, Jian & Coit, David W. & Felder, Frank A. & Tsianikas, Stamatis, 2023. "Combined optimization of system reliability improvement and resilience with mixed cascading failures in dependent network systems," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
  • Handle: RePEc:eee:reensy:v:237:y:2023:i:c:s0951832023002909
    DOI: 10.1016/j.ress.2023.109376
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