Data-driven cooperative load frequency control method for microgrids using effective exploration-distributed multi-agent deep reinforcement learning

Abstract

To reduce the total power generation cost and improve the frequency stability of an island microgrid integrating renewable energy generation sources, a data-driven cooperative load frequency control (DC-LFC) method is proposed for solving the coordination control problem occurring between the controller and power distributor of the system. A novel algorithm, termed the effective exploration-distributed multiagent twin-delayed deep deterministic policy gradient (EED-MATD3) algorithm, is further proposed, the design of which is structured based on the concepts of imitation learning, ensemble learning, and curriculum learning. The EED-MATD3 method employs various exploration strategies, and the controller and power distributor are treated as two agents. Through centralized training and decentralized execution, a robust cooperative control strategy is realized. The performance of the proposed algorithm is verified in an LFC model of Zhuhai Tandang Island, an island microgrid in the China Southern Power Grid.