Multi-Agent Reinforcement Learning for Distribution System Critical Load Restoration: Preprint

Yiyun Yao, Xiangyu Zhang, Jiyu Wang, Fei Ding

Research output: Contribution to conferencePaper


Grid resilience has become a critical topic recently because of the increasing occurrence of extreme events and the growing integration of intermittent renewable energy sources. To build a resilient distribution system, this paper develops a multi-agent reinforcement learning-based (MARL) method to coordinate distribution energy resources (DERs) dispatch, load pickup, and network reconfiguration for load restoration after a system outage. With the help of two types of control agents, namely critical load restoration (CLR) and coordination (COR) agents, system loads can be restored efficiently, given available resources. The effectiveness and superiority of the proposed algorithm are demonstrated through simulations and comparative studies on a real distribution feeder in Western Colorado.
Original languageAmerican English
Number of pages8
StatePublished - 2023

Bibliographical note

See NREL/CP-5D00-88291 for paper as published in proceedings

NREL Publication Number

  • NREL/CP-5D00-84636


  • distribution system
  • grid resilience
  • load restoration
  • multi-agent reinforcement learning


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