Voltage Control Based on Multi-Agent Safe Deep Reinforcement Learning
To address issues of voltage limit violations and fluctuations caused by the high penetration of distributed photovoltaic (PV) systems in the distribution network, a voltage control method based on multi-agent safe deep reinforcement learning is proposed. The voltage control with PV is modeled as a...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2025-02-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202404047 |
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| author | Yi ZENG Yi ZHOU Jixiang LU Liangcai ZHOU Ningkai TANG Hong LI |
| author_facet | Yi ZENG Yi ZHOU Jixiang LU Liangcai ZHOU Ningkai TANG Hong LI |
| author_sort | Yi ZENG |
| collection | DOAJ |
| description | To address issues of voltage limit violations and fluctuations caused by the high penetration of distributed photovoltaic (PV) systems in the distribution network, a voltage control method based on multi-agent safe deep reinforcement learning is proposed. The voltage control with PV is modeled as a decentralized partially observable Markov decision process. A safety layer is introduced in the deep policy network for agent design, while the voltage barrier function based on traditional optimization model voltage constraints is used in defining the agent reward function. Testing results on the IEEE 33-bus system demonstrate that the proposed method can generate voltage control strategies that meet safety constraints under high photovoltaic penetration scenarios, and it can be used to assist dispatchers in making real-time decisions online. |
| format | Article |
| id | doaj-art-9f46da15344c40daaab68a45a373fbea |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2025-02-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-9f46da15344c40daaab68a45a373fbea2025-08-20T02:47:33ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492025-02-0158211111710.11930/j.issn.1004-9649.202404047zgdl-58-02-zengyiVoltage Control Based on Multi-Agent Safe Deep Reinforcement LearningYi ZENG0Yi ZHOU1Jixiang LU2Liangcai ZHOU3Ningkai TANG4Hong LI5State Grid Electric Power Research Institute (NARI Group Corporation), Nanjing 211106, ChinaEast China Branch of State Grid Corporation of China, Shanghai 200120, ChinaState Grid Electric Power Research Institute (NARI Group Corporation), Nanjing 211106, ChinaEast China Branch of State Grid Corporation of China, Shanghai 200120, ChinaState Grid Electric Power Research Institute (NARI Group Corporation), Nanjing 211106, ChinaState Grid Electric Power Research Institute (NARI Group Corporation), Nanjing 211106, ChinaTo address issues of voltage limit violations and fluctuations caused by the high penetration of distributed photovoltaic (PV) systems in the distribution network, a voltage control method based on multi-agent safe deep reinforcement learning is proposed. The voltage control with PV is modeled as a decentralized partially observable Markov decision process. A safety layer is introduced in the deep policy network for agent design, while the voltage barrier function based on traditional optimization model voltage constraints is used in defining the agent reward function. Testing results on the IEEE 33-bus system demonstrate that the proposed method can generate voltage control strategies that meet safety constraints under high photovoltaic penetration scenarios, and it can be used to assist dispatchers in making real-time decisions online.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202404047volt-var controlsafe deep reinforcement learningmulti-agent |
| spellingShingle | Yi ZENG Yi ZHOU Jixiang LU Liangcai ZHOU Ningkai TANG Hong LI Voltage Control Based on Multi-Agent Safe Deep Reinforcement Learning Zhongguo dianli volt-var control safe deep reinforcement learning multi-agent |
| title | Voltage Control Based on Multi-Agent Safe Deep Reinforcement Learning |
| title_full | Voltage Control Based on Multi-Agent Safe Deep Reinforcement Learning |
| title_fullStr | Voltage Control Based on Multi-Agent Safe Deep Reinforcement Learning |
| title_full_unstemmed | Voltage Control Based on Multi-Agent Safe Deep Reinforcement Learning |
| title_short | Voltage Control Based on Multi-Agent Safe Deep Reinforcement Learning |
| title_sort | voltage control based on multi agent safe deep reinforcement learning |
| topic | volt-var control safe deep reinforcement learning multi-agent |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202404047 |
| work_keys_str_mv | AT yizeng voltagecontrolbasedonmultiagentsafedeepreinforcementlearning AT yizhou voltagecontrolbasedonmultiagentsafedeepreinforcementlearning AT jixianglu voltagecontrolbasedonmultiagentsafedeepreinforcementlearning AT liangcaizhou voltagecontrolbasedonmultiagentsafedeepreinforcementlearning AT ningkaitang voltagecontrolbasedonmultiagentsafedeepreinforcementlearning AT hongli voltagecontrolbasedonmultiagentsafedeepreinforcementlearning |