Multi-Agent Deep Reinforcement Learning for Sectional AGC Dispatch

被引：17

作者：

Li, Jiawen ^{[1
]}

Yu, Tao ^{[1
]}

Zhu, Hanxin ^{[1
]}

Li, Fusheng ^{[1
]}

Lin, Dan ^{[1
]}

Li, Zhuohuan ^{[1
]}

机构：

[1] South China Univ Technol, Coll Elect Power, Guangzhou 510640, Peoples R China

来源：

IEEE ACCESS | 2020年 / 8卷

基金：

中国国家自然科学基金;

关键词：

Automatic generation control; Security; Power grids; Training; Phasor measurement units; Optimization; Voltage measurement; hierarchical multi-agent deep deterministic policy gradient; sectional AGC dispatch; reinforcement learning; AUTOMATIC-GENERATION CONTROL;

D O I：

10.1109/ACCESS.2020.3019929

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Aiming at the problem of coordinating system economy, security and control performance in secondary frequency regulation of the power grid, a sectional automatic generation control (AGC) dispatch framework is proposed. The dispatch of AGC is classified as three sections with the sectional dispatch method. Besides, a hierarchical multi-agent deep deterministic policy gradient (HMA-DDPG) algorithm is proposed for the framework in this paper. This algorithm, considering economy and security of the system in AGC dispatch, can ensure the control performance of AGC. Furthermore, through simulation, the control effect of the sectional dispatch method and several AGC dispatch methods on the Guangdong province power grid system and the IEEE 39 bus system is compared. The result shows that the best effect can be achieved with the sectional dispatch method.

引用

页码：158067 / 158081

页数：15

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