Multi-Agent Reinforcement Learning for Distribution System Critical Load Restoration

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 multiagent 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 pages5
StatePublished - 2023

Bibliographical note

See NREL/CP-5D00-84636 for preprint

NREL Publication Number

  • NREL/CP-5D00-88291


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


Dive into the research topics of 'Multi-Agent Reinforcement Learning for Distribution System Critical Load Restoration'. Together they form a unique fingerprint.

Cite this