Open AccessData‐driven cooperative load frequency control method for microgrids using effective exploration‐distributed multi‐agent deep reinforcement learning
Author(s) -
Li Jiawen,
Yang Shengchun,
Yu Tao
Publication year - 2021
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/rpg2.12323
Subject(s) - microgrid , reinforcement learning , computer science , controller (irrigation) , automatic frequency control , electric power system , wind power , control engineering , control (management) , control theory (sociology) , artificial intelligence , power (physics) , engineering , telecommunications , electrical engineering , physics , quantum mechanics , agronomy , biology
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.