Virtual Power Plant Reactive Power Voltage Support Strategy Based on Deep Reinforcement Learning

Qihe Lou, Yanbin Li, Xi Chen, Dengzheng Wang, Yuntao Ju () and Liu Han
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Qihe Lou: School of Electrical and Control Engineering, North China Electric Power University, Beijing 102206, China
Yanbin Li: School of Electrical and Control Engineering, North China Electric Power University, Beijing 102206, China
Xi Chen: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Dengzheng Wang: State Grid Corporation of China, Beijing 100031, China
Yuntao Ju: School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
Liu Han: State Grid Corporation of China, Beijing 100031, China

Energies, 2024, vol. 17, issue 24, 1-19

Abstract: After the large-scale access of distributed power sources to the distribution network, significant high/low voltage problems have emerged. Using a virtual power plant to provide reactive power voltage regulation as an ancillary service effectively addresses voltage issues. However, since a third party manages the virtual power plant and contains both discrete and continuous regulation devices internally, there is a need to consider privacy protection. To address this, a training method that requires minimal boundary information and reward–penalty information for interaction between discrete and continuous action agents is proposed. This method uses distributed two-layer multi-agent deep reinforcement learning for the virtual power plant’s reactive power voltage support strategy. By utilizing actual engineering data and comparing it with the “centralized training” framework algorithm, this study proves the effectiveness of the deep reinforcement learning training method and reactive power voltage control strategy. It demonstrates advantages such as protecting the privacy of the virtual power plant and low training difficulty.

Keywords: virtual power plant; reactive power and voltage regulation; reinforcement learning; privacy protection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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