A Multi-Agent Reinforcement Learning Method for Cooperative Secondary Voltage Control of Microgrids

被引：3

作者：

Wang, Tianhao ^{[1
]}

Ma, Shiqian ^{[1
]}

Tang, Zhuo ^{[2
]}

Xiang, Tianchun ^{[3
]}

Mu, Chaoxu ^{[2
]}

Jin, Yao ^{[3
]}

机构：

[1] State Grid Tianjin Elect Power Co, Elect Power Res Inst, Huayuan Ind Zone, Binhai High Tech Zone, 8,Haitai Huake 4th Rd, Tianjin 300384, Peoples R China

[2] Tianjin Univ, Sch Elect & Informat Engn, 92 Weijin Rd, Tianjin 300072, Peoples R China

[3] State Grid Tianjin Elect Power Co, 39 Wujing,Guangfu St, Tianjin 300010, Peoples R China

来源：

ENERGIES | 2023年 / 16卷 / 15期

关键词：

multi-agent reinforcement learning; microgrid; voltage control; attention mechanism; FREQUENCY CONTROL; ISLANDED MICROGRIDS; FRAMEWORK; SYSTEM;

D O I：

10.3390/en16155653

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

This paper proposes a novel cooperative voltage control strategy for an isolated microgrid based on the multi-agent advantage actor-critic (MA2C) algorithm. The proposed method facilitates the collaborative operation of a distributed energy system (DES) by adopting an attention mechanism to adaptively boost information processing effectiveness through the assignment of importance scores. Additionally, the algorithm we propose, executed through a centralized training and decentralized execution framework, implements secondary control and effectively restores voltage deviation. The introduction of an attention mechanism alleviates the burden of information transmission. Finally, we illustrate the effectiveness of the proposed method through a DES consisting of six energy nodes.

引用

页数：18

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