Electric Power ›› 2022, Vol. 55 ›› Issue (12): 43-50.DOI: 10.11930/j.issn.1004-9649.202207086

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Frequency-Voltage Digital Intelligent Control Strategy of Microgrid with User-Side Energy Storage Based on Deep Q-Learning

LIN Rihui, CHEN Youli   

  1. State Grid Fujian Electric Power Company, Fuzhou 350003, China
  • Received:2022-07-29 Revised:2022-09-23 Published:2022-12-28
  • Supported by:
    This work is supported by Science and Technology Project of SGCC (No.5213402100NT)

Abstract: Frequency and voltage are important criteria for measuring energy indicators. Considering the problem of frequency/voltage regulation caused by load fluctuations in microgrids, a smart monitoring-control strategy for microgrids with user-side energy storage based on deep Q-learning (DQN) is proposed in this paper. Firstly, in view of the randomness of user behavior, the random constraints on the output of energy storage on the user side are added, and a four-quadrant charging and discharging model is introduced to build a charging and discharging model for user-side energy storage. On this basis, a cooperative control model of frequency and voltage in the microgrid is built. Secondly, the frequency/voltage controller structure and digital intelligent control platform based on DQN are designed. With the real-time frequency deviation and voltage deviation of the system and the upper and lower limit constraints of the output power of user-side energy storage as the state space and the output of each unit in the system as the action space, a global reward function including two local rewards is designed on the basis of the two control objectives of frequency and voltage. The results of the calculation examples indicate that compared with the traditional PID controller, the DQN controller proposed in this paper can simultaneously meet the control requirements of frequency and voltage and can more effectively deal with the power quality problems caused by load fluctuations.

Key words: island microgrid, user-side energy storage, coordinated frequency/voltage control, deep Q-learning, digital platform