Voltage Control Method of Multienergy Distribution Grid Based on Deep Reinforcement Learning Considering Attention and Value Decomposition

Abstract

Multienergy distribution network (MEDN) with high penetration photovoltaics (PVs) may suffer from sharp voltage fluctuations and increased network losses. Existing methods struggle to achieve voltage control due to challenges such as high interarea communication latency and difficulties in power flow modeling caused by low coverage of measurement devices. To address these issues, this paper proposes a multiagent deep reinforcement learning (MADRL) method to realize the collaborative optimization of controllable devices, including hybrid energy storage system (HESS) and PV inverters. Furthermore, under the framework of decentralized partially observable Markov decision processes (Dec-POMDP), we integrate cross-agent attention (CAA) and factored value networks to enhance perception capabilities and improve value function fitting. The proposed method explicitly assigns credit to agents and dynamically captures electrical coupling relationships between agents and buses. The improved IEEE 33-bus and IEEE 141-bus distribution systems were used as case studies to compare with mainstream MADRL. Experimental results demonstrate that after offline deployment, the agents achieve global voltage control based solely on limited local observations within each zone, without relying on a complete power flow model or interarea communication. The comparative experiments verify the effectiveness, robustness, and scalability of this method.