Resilience-Oriented Coordination of Networked Microgrids: a Shapley Q-Value Learning Approach

Dawei Qiu, Yi Wang, Jianhong Wang, Ning Zhang, Goran Strbac, Chongqing Kang

Research output: Contribution to journalArticlepeer-review


High-impact and low-probability extreme events have occurred more frequently than before because of rapid climate change, which can seriously damage distribution systems. However, conventional distribution management can be dysfunctional after an event, destroying its centralized supervision towards resilience enhancement. In this context, networked microgrids (NMGs) with distributed energy resources provide a viable solution for the resilience enhancement of distribution systems. Existing literature tends to employ model-based optimization approaches for resilient operations of NMGs, which require complete system models and can be time-consuming. To address these challenges, this article suggests a decentralized framework for resilience-oriented coordination of NMGs and proposes a novel multi-agent reinforcement learning (MARL) method to solve it. Specifically, the proposed MARL method develops an efficient credit assignment scheme for NMGs to learn their contributions to the distribution system resilience via the Shapley Q-value technique with more efficient resilience enhancement. Case studies based on two modified IEEE 15- and 69-bus distribution networks are conducted to validate the effectiveness of the proposed MARL method in enabling effective coordination among NMGs and providing a high resilience level.

Original languageEnglish
Pages (from-to)3401-3416
Number of pages16
JournalIEEE Transactions on Power Systems
Issue number2
Early online date16 May 2023
Publication statusPublished - 1 Mar 2024


  • Multi-agent reinforcement learning
  • Shapley Q-value
  • networked microgrids
  • resilience


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