Emergency resources scheduling in distribution system: From cyber-physical-social system perspective

被引:6
|
作者
Chen, Liudong [1 ]
Li, Ying [2 ]
Chen, Yubing [1 ]
Liu, Nian [1 ]
Li, Chenchen [1 ]
Zhang, Hongyu [2 ]
机构
[1] North China Elect Power Univ, State Key Lab Alternate Elect Power Syst Renewabl, Beijing 102206, Peoples R China
[2] State Grid Beijing Elect Power Co, Beijing 100051, Peoples R China
基金
国家重点研发计划;
关键词
Outage management; Emergency scheduling; Cyber-physical-social system; Multi-objective; Co-optimization; OUTAGE MANAGEMENT; REPAIR; STRATEGY;
D O I
10.1016/j.epsr.2022.108114
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
With the frequently happened aging electrical grid and extreme weather events, outage management and service restoration face the challenges of flexible scheduling multiple resources for different kinds of objectives. Previous research mainly focuses on the single restoration objective or single emergency resource management. This paper proposes a new emergency scheduling framework under the cyber-physical-social system (CPSS) perspective, which includes the scheduling of multiple components from CPSS, i.e., crew, mobile distributed generators, and resources. Under the framework, a novel co-scheduling model with the goal of minimizing time-consuming, cost, and shedding load are also built as a mixed-integer non-linear optimization model. Then we formulate this problem as advanced multiple salesmen traveling problem with a routing stage and a scheduling stage, and design a solution algorithm based on the genetic algorithm to solve this problem. In the solving algorithm, the optimal routing solutions are got at first, then the emergency scheduling solutions are optimized in the second stage based on the routing results. Finally, a numerical case study on the IEEE 123-bus network shows the effectiveness of the co-optimization process, which can provide a routing solution for each depot, and the corresponding optimization objective results will increase 25.75% and 73.92% when compared with time-consuming optimization and load-shedding optimization.
引用
收藏
页数:11
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