Uncertainty-Inflicted Event-Driven Resilient Recovery for Distribution Systems: A Semi-Markov Decision Process Approach
Chong Wang, Gengfeng Li, Can Wan, Zhaoyu Wang, Ping Ju, Shunbo Lei
Abstract
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. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -learning is employed to solve the proposed model, and the convergent estimations of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> 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.