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

Sarah Keren, Chaimaa Essayeh, Stefano V. Albrecht, Thomas Morstyn

TL;DR

MARL is applied to energy networks to address decentralization and uncertainty by modeling interactions as SGs or Dec-POMDPs with a discount factor $\gamma$ to maximize discounted return. The survey organizes the domain into three challenge areas—grid-edge energy management (GEM), power system operation and control (PSOC), and electricity markets (EM)—and reviews CTDE and graph-based MARL approaches across these domains. It highlights progress in agent-based formulations and representative solution strategies while identifying open gaps in standardization, data availability, and realistic benchmarking. The work argues for stronger collaboration between power systems researchers and AI researchers, aiming to develop standardized benchmarks and data-driven simulators to unlock MARL's potential for more efficient, reliable, and decarbonized energy networks.

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.

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

TL;DR

MARL is applied to energy networks to address decentralization and uncertainty by modeling interactions as SGs or Dec-POMDPs with a discount factor to maximize discounted return. The survey organizes the domain into three challenge areas—grid-edge energy management (GEM), power system operation and control (PSOC), and electricity markets (EM)—and reviews CTDE and graph-based MARL approaches across these domains. It highlights progress in agent-based formulations and representative solution strategies while identifying open gaps in standardization, data availability, and realistic benchmarking. The work argues for stronger collaboration between power systems researchers and AI researchers, aiming to develop standardized benchmarks and data-driven simulators to unlock MARL's potential for more efficient, reliable, and decarbonized energy networks.

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.
Paper Structure (17 sections, 1 equation, 1 figure)

This paper contains 17 sections, 1 equation, 1 figure.

Table of Contents

  1. Introduction
  2. Key Computational Challenges in Energy Network Management
  3. gem
  4. psoc
  5. em
  6. MARL for Energy Network Management
  7. gem
  8. gem as marl:
  9. Example Solution Approaches:
  10. psoc
  11. psoc as a sg
  12. Solution Approaches
  13. Electricity Markets
  14. em as a sg
  15. Solution Approaches
  16. ...and 2 more sections

Figures (1)

  • Figure 1: Energy network challenges. From left to right: (a) an example energy network (b) optimizing the policy of a single prosumer: Section \ref{['sec:GEM']} (c) optimizing the operation of the electrical grid: Section \ref{['sec:psoc']} (d) creating new energy markets structures: Section \ref{['sec:EM']}.