• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

ELECTRIC POWER CONSTRUCTION ›› 2022, Vol. 43 ›› Issue (4): 91-99.doi: 10.12204/j.issn.1000-7229.2022.04.010

• Smart Grid • Previous Articles     Next Articles

Load Frequency Control Strategy Based on Deep Q Learning for Island Microgrid with Electric Vehicles

FAN Peixiao1(), YANG Jun1(), XIAO Jinxing2, XU Bingyan2, YE Ying2, LI Yonghui1, LI Rui1()   

  1. 1. School of Electrical and Automation, Wuhan University, Wuhan 430072, China
    2. State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China
  • Received:2021-05-31 Online:2022-04-01 Published:2022-03-24
  • Contact: LI Rui E-mail:whufpx0408@163.com;JYang@whu.edu.cn;lirui@whu.edu.cn
  • Supported by:
    State Grid Corporation of China Research Program(52093220000H)


The load frequency control is of vital importance to maintain the stable operation of the island microgrid. Aiming at the frequency control problem when the microgrid is subjected to strong random disturbances and network topology parameters change, this paper proposes a load frequency control strategy based on deep Q-learning (DQN) for island microgrid with electric vehicles. Firstly, a frequency control model of electric vehicle considering the randomness of user charging behavior is established, and a load frequency control (LFC) model for microgrid including photovoltaic power, wind power, micro gas turbines, electric vehicles and their random power increment constraints is built. Secondly, the structure of frequency controller based on DQN is designed, and the definitions of state space, action space and reward function are completed in turn, and the optimal hyperparameters is obtained through adjustment. Finally, the simulation results show that, compared with PI control and FUZZY control, the DQN controller proposed in this paper has the ability of online learning and experience playback, which can deal with the LFC problem of microgrid with strong randomness more effectively, and it can better adapt to the complex operating conditions of system parameters and structure changes.

Key words: island microgrid, electric vehicles, frequency control, deep Q-learning

CLC Number: