Multi-Agent Deep Reinforcement Learning for Sectional AGC Dispatch
Jiawen Li, Tao Yu, Hanxin Zhu, Fusheng Li, Dan Lin, Zhuohuan Li
Abstract
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.