Coordinated Optimization of Active and Reactive Power of Active Distribution Network Based on Safety Reinforcement Learning
A safe reinforcement learning method based on offline strategies is proposed. Through offline training of a large amount of historical operating data of the distribution network, it gets rid of the traditional optimization method. Dependence on complete and accurate models. First, combined with the...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2024-03-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202311065 |
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| author | Hao JIAO Yanyan YIN Chen WU Jian LIU Chunlei XU Xian XU Guoqiang SUN |
| author_facet | Hao JIAO Yanyan YIN Chen WU Jian LIU Chunlei XU Xian XU Guoqiang SUN |
| author_sort | Hao JIAO |
| collection | DOAJ |
| description | A safe reinforcement learning method based on offline strategies is proposed. Through offline training of a large amount of historical operating data of the distribution network, it gets rid of the traditional optimization method. Dependence on complete and accurate models. First, combined with the distribution network parameter information, an active and reactive power optimization model based on the constrained Markov decision process (CMDP) was established; then, a new safety reinforcement learning method was designed based on the original dual optimization method. The cost function is minimized while maximizing future discount rewards; finally, simulations are performed on power distribution system. The simulation results show that the proposed method can online generate a dispatching strategy that satisfies complex constraints and has economic benefits based on real-time observation information of the distribution network. |
| format | Article |
| id | doaj-art-4f3615c660c3418b87c2dcd03ce72d61 |
| institution | OA Journals |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2024-03-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-4f3615c660c3418b87c2dcd03ce72d612025-08-20T01:58:24ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492024-03-01573435010.11930/j.issn.1004-9649.202311065zgdl-57-03-jiaohaoCoordinated Optimization of Active and Reactive Power of Active Distribution Network Based on Safety Reinforcement LearningHao JIAO0Yanyan YIN1Chen WU2Jian LIU3Chunlei XU4Xian XU5Guoqiang SUN6State Grid Jiangsu Electric Power Science Research Institute, Nanjing 211103, ChinaCollege of Electrical and Power Engineering, Hohai University, Nanjing 211100, ChinaState Grid Jiangsu Electric Power Co., Ltd., Nanjing 210024, ChinaState Grid Jiangsu Electric Power Science Research Institute, Nanjing 211103, ChinaState Grid Jiangsu Electric Power Co., Ltd., Nanjing 210024, ChinaState Grid Jiangsu Electric Power Co., Ltd., Nanjing 210024, ChinaCollege of Electrical and Power Engineering, Hohai University, Nanjing 211100, ChinaA safe reinforcement learning method based on offline strategies is proposed. Through offline training of a large amount of historical operating data of the distribution network, it gets rid of the traditional optimization method. Dependence on complete and accurate models. First, combined with the distribution network parameter information, an active and reactive power optimization model based on the constrained Markov decision process (CMDP) was established; then, a new safety reinforcement learning method was designed based on the original dual optimization method. The cost function is minimized while maximizing future discount rewards; finally, simulations are performed on power distribution system. The simulation results show that the proposed method can online generate a dispatching strategy that satisfies complex constraints and has economic benefits based on real-time observation information of the distribution network.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202311065active distribution networkactive and reactive power coordination optimizationsafety reinforcement learning |
| spellingShingle | Hao JIAO Yanyan YIN Chen WU Jian LIU Chunlei XU Xian XU Guoqiang SUN Coordinated Optimization of Active and Reactive Power of Active Distribution Network Based on Safety Reinforcement Learning Zhongguo dianli active distribution network active and reactive power coordination optimization safety reinforcement learning |
| title | Coordinated Optimization of Active and Reactive Power of Active Distribution Network Based on Safety Reinforcement Learning |
| title_full | Coordinated Optimization of Active and Reactive Power of Active Distribution Network Based on Safety Reinforcement Learning |
| title_fullStr | Coordinated Optimization of Active and Reactive Power of Active Distribution Network Based on Safety Reinforcement Learning |
| title_full_unstemmed | Coordinated Optimization of Active and Reactive Power of Active Distribution Network Based on Safety Reinforcement Learning |
| title_short | Coordinated Optimization of Active and Reactive Power of Active Distribution Network Based on Safety Reinforcement Learning |
| title_sort | coordinated optimization of active and reactive power of active distribution network based on safety reinforcement learning |
| topic | active distribution network active and reactive power coordination optimization safety reinforcement learning |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202311065 |
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