Resilience-Improving Based Optimization of Post-Disaster Emergency Maintenance Strategy for Transmission Networks

doi:10.11930/j.issn.1004-9649.202009052

Abstract

Abstract: Extreme natural disasters and other events are likely to cause large-scale failures of the transmission networks. In order to minimize the economic losses and improve the post-disaster resilience of transmission networks, firstly, according to the connotation of transmission network resilience, the expected system load recovery efficiency is proposed as the resilience evaluation index; secondly, the post-disaster emergency maintenance process of transmission networks is analyzed, and a collaborative optimization model of time-uncertainty based post-disaster emergency maintenance strategy is established for transmission networks, which not only considers the resilience-improving economy of transmission networks, but also the constraints such as resources and path planning in the process of post-disaster maintenance. An improved particle swarm optimization (PSO) algorithm is proposed for the optimization model, which uses such methods as the multi-dimensional indefinite length coding, sub-group collaborative optimization, and Monte-Carlo-simulation-based fitness evaluation to improve the standard PSO algorithm. The simulation results of an IEEE RTS-79 case show that the improved particle swarm optimization algorithm can effectively solve the proposed optimization model, and the results of the collaborative optimization model can effectively improve the transmission network resilience index and the economy of post-disaster emergency maintenance.

Key words: transmission network resilience, post-disaster transmission network emergency maintenance, transmission network resilience evaluation index, improved particle swarm optimization algorithm, path planning

LIANG Haiping, SHI Haoyan, WANG Yan, LIU Yingpei, WANG Xinming. Resilience-Improving Based Optimization of Post-Disaster Emergency Maintenance Strategy for Transmission Networks[J]. Electric Power, 2022, 55(3): 142-151.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202009052

https://www.electricpower.com.cn/EN/Y2022/V55/I3/142

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