Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach

TL;DR

This paper introduces a decentralized multi-agent reinforcement learning framework to optimize the dispatch of mobile power sources and repair crews in microgrids, especially under communication failures, improving resilience and load restoration.

Contribution

It proposes a hierarchical multi-agent reinforcement learning approach with a two-level decision framework for resilient microgrid management under communication disruptions.

Findings

Effective load restoration demonstrated on IEEE 33-bus and 69-bus networks.

Enhanced decision stability and scalability through embedded system dynamics.

Decentralized approach outperforms centralized methods in resilience scenarios.

Abstract

Mobile power sources (MPSs) have been gradually deployed in microgrids as critical resources to coordinate with repair crews (RCs) towards resilience enhancement owing to their flexibility and mobility in handling the complex coupled power-transport systems. However, previous work solves the coordinated dispatch problem of MPSs and RCs in a centralized manner with the assumption that the communication network is still fully functioning after the event. However, there is growing evidence that certain extreme events will damage or degrade communication infrastructure, which makes centralized decision making impractical. To fill this gap, this paper formulates the resilience-driven dispatch problem of MPSs and RCs in a decentralized framework. To solve this problem, a hierarchical multi-agent reinforcement learning method featuring a two-level framework is proposed, where the high-level action is used to switch decision-making between power and transport networks, and the low-level action constructed via a hybrid policy is used to compute continuous scheduling and discrete routing decisions in power and transport networks, respectively. The proposed method also uses an embedded function encapsulating system dynamics to enhance learning stability and scalability. Case studies based on IEEE 33-bus and 69-bus power networks are conducted to validate the effectiveness of the proposed method in load restoration.