Deep-Reinforcement-Learning-Based Two-Timescale Voltage Control for Distribution Systems

Jing Zhang, Yiqi Li, Zhi Wu, Chunyan Rong, Tao Wang, Zhang Zhang and Suyang Zhou
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Jing Zhang: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Yiqi Li: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Zhi Wu: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Chunyan Rong: Institute of State Grid Hebei Electric Power Company Economic and Technological Research, Shijiazhuang 050000, China
Tao Wang: Institute of State Grid Hebei Electric Power Company Economic and Technological Research, Shijiazhuang 050000, China
Zhang Zhang: Institute of State Grid Hebei Electric Power Company Economic and Technological Research, Shijiazhuang 050000, China
Suyang Zhou: School of Electrical Engineering, Southeast University, Nanjing 210096, China

Energies, 2021, vol. 14, issue 12, 1-15

Abstract: Because of the high penetration of renewable energies and the installation of new control devices, modern distribution networks are faced with voltage regulation challenges. Recently, the rapid development of artificial intelligence technology has introduced new solutions for optimal control problems with high dimensions and dynamics. In this paper, a deep reinforcement learning method is proposed to solve the two-timescale optimal voltage control problem. All control variables are assigned to different agents, and discrete variables are solved by a deep Q network (DQN) agent while the continuous variables are solved by a deep deterministic policy gradient (DDPG) agent. All agents are trained simultaneously with specially designed reward aiming at minimizing long-term average voltage deviation. Case study is executed on a modified IEEE-123 bus system, and the results demonstrate that the proposed algorithm has similar or even better performance than the model-based optimal control scheme and has high computational efficiency and competitive potential for online application.

Keywords: deep reinforcement learning; two timescales; voltage control; distribution network (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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