Distributed deep reinforcement learning for integrated generation-control and power-dispatch of interconnected power grid with various renewable units

Abstract

To mismatch various power disturbances of interconnected power grid, an integrated generation-control and power-dispatch (IGCPD) for performance-based frequency regulation market is proposed. Unlike the conventional automatic generation control with an independent controller and a power distributor, IGCPD can significantly improve the dynamic control performance via combining these two parts into an integrated decision agent. The IGCPD eliminates the waste of frequency regulation resources and repeated regulation of the units caused by the mismatch of the controller and power distributor. In addition, by setting the reward function reasonably, IGCPD can save regulatable capacity of fast units for regulation in the next large-scale disturbance. In order to enhance the decision ability, a swarm intelligence-based deep deterministic policy gradient (SI-DDPG) algorithm is proposed to acquire the control knowledge and implement a high-quality decision for this agent. Moreover, the swarm intelligence-based training sample can achieve a high learning robustness for SI-DDPG which solve the problem of low robustness in conventional deep reinforcement learning. The proposed technique is verified on an England two-area load frequency control model and the west China two-area LFC model. IGCPD improves the area control error control performance and reduce the frequency regulation mileage payment of the interconnected power grid.