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 12 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.

翻译：电力网络架构与功能的快速演变，以及可再生与分布式能源渗透率的持续提升，引发了诸多技术与管理层面的挑战。这使得传统集中式能源市场范式因无法适应网络动态演变的特性而显得力不从心。本综述探讨了多智能体强化学习如何支撑能源网络的去中心化与脱碳进程，并缓解12项相关挑战。为此，我们明确了能源网络管理中的核心计算挑战，梳理了应对这些挑战的最新研究进展，并指出了可借助该技术解决的未解问题。