Uncertainty-Inflicted Event-Driven Resilient Recovery for Distribution Systems: A Semi-Markov Decision Process Approach

被引：0

作者：

Wang, Chong ^{[1
]}

Li, Gengfeng ^{[2
]}

Wan, Can ^{[3
]}

Wang, Zhaoyu ^{[4
]}

Ju, Ping ^{[1
]}

Lei, Shunbo ^{[5
,6
]}

机构：

[1] Hohai Univ, Sch Eletr & Power Engn, Nanjing 211100, Peoples R China

[2] Xi An Jiao Tong Univ, Dept Elect Engn, State Key Lab Elect Insulat & Power Equipment, Smart Grid Key Lab Shaanxi Prov, Xian 710049, Peoples R China

[3] Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China

[4] Iowa State Univ, Dept Elect & Comp Engn, Ames, IA 50011 USA

[5] Chinese Univ Hong Kong, Sch Sci & Engn, Shenzhen 518172, Peoples R China

[6] Shenzhen Inst Artificial Intelligence & Robot Soc, Shenzhen 518129, Peoples R China

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 2025年 / 40卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Maintenance engineering; Load modeling; Disasters; Power system reliability; Decision making; Microgrids; Optimization; Repair; resilient recovery; semi-Markov decision process; uncertain decision-making; RADIALITY CONSTRAINTS; RECONFIGURATION; ENHANCEMENT; RESTORATION; FORMULATION; STRATEGY; REPAIR;

D O I：

10.1109/TPWRS.2024.3386851

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：

Repair and reconfiguration are vital for power recovery after outages caused by natural disasters in distribution systems, but sequential and uncertainty-inflicted decision points due to uncertain repair periods make power recovery complicated. This paper proposes semi-Markov decision process (SMDP)-based resilient recovery with sequentially event-driven repair and reconfiguration in consideration of uncertainty-inflicted decision-making points. The sequential repair/reconfiguration actions in consideration of uncertain repair periods are considered as uncertainty-inflicted event-driven processes. The sequential repair states with different repair crews are established as semi-Markov states. The whole sequential and uncertain decision-making process is modeled as a semi-Markov decision process-based optimization model, which is an event-driven recursive model. Q-learning is employed to solve the proposed model, and the convergent estimations of Q values for semi-Markov states map the original model into an event-driven deterministic optimization based on the sequential repairs that actually occurred over the time horizon. IEEE 123-bus system is used to validate the proposed model.

引用

页码：368 / 380

页数：13