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

• Published in: Energies (Basel) Vol. 14; no. 12; p. 3540
• Main Authors: Zhang, Jing, Li, Yiqi, Wu, Zhi, Rong, Chunyan, Wang, Tao, Zhang, Zhang, Zhou, Suyang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2021
• Subjects: Agents (artificial intelligence); Artificial intelligence; Batteries; Computer applications; Control algorithms; Control equipment; Deep learning; deep reinforcement learning; distribution network; Electric potential; Energy storage; Market planning; Mathematical programming; Methods; Optimal control; Optimization; Reinforcement; two timescales; Variables; Voltage; voltage control
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en14123540

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.