Multi-Agent Reinforcement Learning for Energy Networks: Computational Challenges, Progress and Open Problems

TL;DR

This paper surveys how multi-agent reinforcement learning can address decentralization, decarbonization, and computational challenges in modern energy networks, highlighting recent progress and open problems.

Contribution

It provides a comprehensive review of MARL applications in energy networks, identifying key challenges, recent advancements, and future open research directions.

Findings

MARL supports decentralized energy management

Recent research shows MARL improves network efficiency

Open problems include scalability and real-world deployment

Abstract

The rapidly changing architecture and functionality of electrical networks and the increasing penetration of renewable and distributed energy resources have resulted in various technological and managerial challenges. These have rendered traditional centralized energy-market paradigms insufficient due to their inability to support the dynamic and evolving nature of the network. This survey explores how multi-agent reinforcement learning (MARL) can support the decentralization and decarbonization of energy networks and mitigate the associated challenges. This is achieved by specifying key computational challenges in managing energy networks, reviewing recent research progress on addressing them, and highlighting open challenges that may be addressed using MARL.