Multiagent Reinforcement Learning in Enhancing Resilience of Microgrids under Extreme Weather Events

TL;DR

This paper introduces a multi-agent deep reinforcement learning framework for energy management in microgrids, improving resilience and reducing costs during extreme weather events by coordinating distributed energy resources efficiently.

Contribution

The paper proposes a novel MADRL-based EMS with gated recurrent units and action masking, enhancing microgrid resilience and scalability compared to existing methods.

Findings

Reduced operational costs during outages

Enhanced microgrid resilience in simulations

Effective coordination of DERs using MADRL

Abstract

Grid resilience is crucial in light of power interruptions caused by increasingly frequent extreme weather events. Well-designed energy management systems (EMS) have made progress in improving microgrid resilience through the coordination of distributed energy resources (DERs), but still face significant challenges in addressing the uncertainty of load demand caused by extreme weather. The integration of deep reinforcement learning (DRL) into EMS design enables optimized microgrid control strategies for coordinating DERs. Building on this, we proposed a cooperative multi-agent deep reinforcement learning (MADRL)-based EMS framework to provide flexible scalability for microgrids, enhance resilience and reduce operational costs during power outages. Specifically, the gated recurrent unit with a gating mechanism was introduced to extract features from temporal data, which enables the EMS to coordinate DERs more efficiently. Next, the proposed MADRL method incorporating action masking techniques was evaluated in the IEEE 33-Bus system using real-world data on renewable generation and power load. Finally, the numerical results demonstrated the superiority of the proposed method in reducing operating costs as well as the effectiveness in enhancing microgrid resilience during power interruptions.