Coordinated Volt/VAR Control in Distribution Networks Considering Demand Response via Safe Deep Reinforcement Learning

Dong Hua, Fei Peng, Suisheng Liu, Qinglin Lin, Jiahui Fan () and Qian Li
Additional contact information
Dong Hua: School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China
Fei Peng: School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China
Suisheng Liu: Guangdong KingWa Energy Technology Co., Ltd., Guangzhou 510000, China
Qinglin Lin: Guangdong KingWa Energy Technology Co., Ltd., Guangzhou 510000, China
Jiahui Fan: Guangdong KingWa Energy Technology Co., Ltd., Guangzhou 510000, China
Qian Li: Consultation and Evaluation Center, Energy Development Research Institute, CSG, Guangzhou 510000, China

Energies, 2025, vol. 18, issue 2, 1-12

Abstract: Volt–VAR control (VVC) is essential in maintaining voltage stability and operational efficiency in distribution networks, particularly with the increasing integration of distributed energy resources. Traditional methods often struggle to manage real-time fluctuations in demand and generation. First, various resources such as static VAR compensators, photovoltaic systems, and demand response strategies are incorporated into the VVC scheme to enhance voltage regulation. Then, the VVC scheme is formulated as a constrained Markov decision process. Next, a safe deep reinforcement learning (SDRL) algorithm is proposed, incorporating a novel Lagrange multiplier update mechanism to ensure that the control policies adhere to safety constraints during the learning process. Finally, extensive simulations with the IEEE-33 test feeder demonstrate that the proposed SDRL-based VVC approach effectively improves voltage regulation and reduces power losses.

Keywords: voltage/VAR control; demand response; safe deep reinforcement learning; distribution networks; renewable energy sources (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: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/2/333/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/2/333/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:2:p:333-:d:1566377

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().