Coordinated Volt/VAR Control in Distribution Networks Considering Demand Response via Safe Deep Reinforcement Learning
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,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/2/333 |
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| author | Dong Hua Fei Peng Suisheng Liu Qinglin Lin Jiahui Fan Qian Li |
| author_facet | Dong Hua Fei Peng Suisheng Liu Qinglin Lin Jiahui Fan Qian Li |
| author_sort | Dong Hua |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-47bbebcacf794da29cddf814cb92f8b0 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-47bbebcacf794da29cddf814cb92f8b02025-01-24T13:31:05ZengMDPI AGEnergies1996-10732025-01-0118233310.3390/en18020333Coordinated Volt/VAR Control in Distribution Networks Considering Demand Response via Safe Deep Reinforcement LearningDong Hua0Fei Peng1Suisheng Liu2Qinglin Lin3Jiahui Fan4Qian Li5School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, ChinaSchool of Electric Power Engineering, South China University of Technology, Guangzhou 510641, ChinaGuangdong KingWa Energy Technology Co., Ltd., Guangzhou 510000, ChinaGuangdong KingWa Energy Technology Co., Ltd., Guangzhou 510000, ChinaGuangdong KingWa Energy Technology Co., Ltd., Guangzhou 510000, ChinaConsultation and Evaluation Center, Energy Development Research Institute, CSG, Guangzhou 510000, ChinaVolt–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.https://www.mdpi.com/1996-1073/18/2/333voltage/VAR controldemand responsesafe deep reinforcement learningdistribution networksrenewable energy sources |
| spellingShingle | Dong Hua Fei Peng Suisheng Liu Qinglin Lin Jiahui Fan Qian Li Coordinated Volt/VAR Control in Distribution Networks Considering Demand Response via Safe Deep Reinforcement Learning Energies voltage/VAR control demand response safe deep reinforcement learning distribution networks renewable energy sources |
| title | Coordinated Volt/VAR Control in Distribution Networks Considering Demand Response via Safe Deep Reinforcement Learning |
| title_full | Coordinated Volt/VAR Control in Distribution Networks Considering Demand Response via Safe Deep Reinforcement Learning |
| title_fullStr | Coordinated Volt/VAR Control in Distribution Networks Considering Demand Response via Safe Deep Reinforcement Learning |
| title_full_unstemmed | Coordinated Volt/VAR Control in Distribution Networks Considering Demand Response via Safe Deep Reinforcement Learning |
| title_short | Coordinated Volt/VAR Control in Distribution Networks Considering Demand Response via Safe Deep Reinforcement Learning |
| title_sort | coordinated volt var control in distribution networks considering demand response via safe deep reinforcement learning |
| topic | voltage/VAR control demand response safe deep reinforcement learning distribution networks renewable energy sources |
| url | https://www.mdpi.com/1996-1073/18/2/333 |
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