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 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.
Abstract (translated)
由于电力网络 rapidly变化的建筑和功能，以及可再生能源和分布式能源资源的日益普及，产生了各种技术和管理挑战。这使得传统集中式能源市场范式由于无法支持网络的动态和演变性质而变得不足。本调查探讨了多智能体强化学习（MARL）如何支持能源网络的分散化和脱碳，并减轻与12个相关挑战相关的负担。这是通过指定管理能源网络的关键计算挑战，回顾针对这些挑战的最近研究进展，并强调可以使用MARL解决的开放挑战来实现的。
URL
https://arxiv.org/abs/2404.15583
