Multi-Agent Reinforcement Learning for Energy Networks: Computational Challenges, Progress and Open Problems
arxiv(2024)
摘要
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.
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