Deep reinforcement learning-based adaptive voltage control of active distribution networks with multi-terminal soft open point

Li, Peng, Wei, Mingjiang, Ji, Haoran, Xi, Wei, Yu, Hao, Wu, Jianzhong ORCID: https://orcid.org/0000-0001-7928-3602, Yao, Hao and Chen, Junjian 2022. Deep reinforcement learning-based adaptive voltage control of active distribution networks with multi-terminal soft open point. International Journal of Electrical Power & Energy Systems 141 , 108138. 10.1016/j.ijepes.2022.108138

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Abstract

The integration of highly penetrated distributed generators (DGs) aggravates the rise of voltage violations in distribution networks. Connected by multi-terminal soft open points (M−SOPs), distribution networks gradually evolve into an interconnected flexible architecture with high controllability. Distribution networks with M−SOPs can exchange active power flexibly, and M−SOPs can provide local reactive power support to alleviate voltage violations. However, conventional model-based M−SOP optimization methods cannot regulate voltage profiles adaptively owing to the rapid fluctuations of DGs. In this paper, a data-driven voltage control method is proposed for M−SOPs using a deep deterministic policy gradient network (DDPG). First, the data-driven voltage control framework is proposed for M−SOPs based on DDPG. The M−SOP−based voltage control problem is reformatted as a Markov decision process (MDP) to construct the DDPG agent. Based on real-time measurement, the DDPG agent can adaptively regulate the M−SOP operation to address the frequent DG fluctuations. Then, a multi-dimensional and dynamic boundary action masking approach is proposed to address the complex coupling in the action space of M−SOPs. Finally, the effectiveness of the proposed method was verified using the IEEE 33-node system. The results show that the proposed method can adaptively alleviate the voltage fluctuations caused by rapid DG power variations.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0142-0615
Date of Acceptance:	15 March 2022
Last Modified:	10 Nov 2022 11:02
URI:	https://orca.cardiff.ac.uk/id/eprint/149066

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