Intelligent adjustment for power system operation mode based on deep reinforcement learning
Power flow adjustment is a sequential decision problem. The operator makes decisions to ensure that the power flow meets the system's operational constraints, thereby obtaining a typical operating mode power flow. However, this decision-making method relies heavily on human experience, which is...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-12-01
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| Series: | iEnergy |
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| Online Access: | https://www.sciopen.com/article/10.23919/IEN.2024.0028 |
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| _version_ | 1841550202169720832 |
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| author | Wei Hu Ning Mi Shuang Wu Huiling Zhang Zhewen Hu Lei Zhang |
| author_facet | Wei Hu Ning Mi Shuang Wu Huiling Zhang Zhewen Hu Lei Zhang |
| author_sort | Wei Hu |
| collection | DOAJ |
| description | Power flow adjustment is a sequential decision problem. The operator makes decisions to ensure that the power flow meets the system's operational constraints, thereby obtaining a typical operating mode power flow. However, this decision-making method relies heavily on human experience, which is inefficient when the system is complex. In addition, the results given by the current evaluation system are difficult to directly guide the intelligent power flow adjustment. In order to improve the efficiency and intelligence of power flow adjustment, this paper proposes a power flow adjustment method based on deep reinforcement learning. Combining deep reinforcement learning theory with traditional power system operation mode analysis, the concept of region mapping is proposed to describe the adjustment process, so as to analyze the process of power flow calculation and manual adjustment. Considering the characteristics of power flow adjustment, a Markov decision process model suitable for power flow adjustment is constructed. On this basis, a double Q network learning method suitable for power flow adjustment is proposed. This method can adjust the power flow according to the set adjustment route, thus improving the intelligent level of power flow adjustment. The method in this paper is tested on China Electric Power Research Institute (CEPRI) test system. |
| format | Article |
| id | doaj-art-8d4536dba5b040baa204cbcfd848b421 |
| institution | Kabale University |
| issn | 2771-9197 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | iEnergy |
| spelling | doaj-art-8d4536dba5b040baa204cbcfd848b4212025-01-10T06:52:43ZengTsinghua University PressiEnergy2771-91972024-12-013425226010.23919/IEN.2024.0028Intelligent adjustment for power system operation mode based on deep reinforcement learningWei Hu0Ning Mi1Shuang Wu2Huiling Zhang3Zhewen Hu4Lei Zhang5Department of Electrical Engineering, Tsinghua University, Beijing 100084, ChinaState Grid Ningxia Electric Power Co. Ltd., Ningxia 750001, ChinaNorth China Branch of State Grid Corporation of China, Beijing 100053, ChinaState Grid Ningxia Electric Power Co. Ltd., Ningxia 750001, ChinaCollege of Electrical Engineering and New Energy, China Three Gorges University, Hubei 443002, ChinaCollege of Electrical Engineering and New Energy, China Three Gorges University, Hubei 443002, ChinaPower flow adjustment is a sequential decision problem. The operator makes decisions to ensure that the power flow meets the system's operational constraints, thereby obtaining a typical operating mode power flow. However, this decision-making method relies heavily on human experience, which is inefficient when the system is complex. In addition, the results given by the current evaluation system are difficult to directly guide the intelligent power flow adjustment. In order to improve the efficiency and intelligence of power flow adjustment, this paper proposes a power flow adjustment method based on deep reinforcement learning. Combining deep reinforcement learning theory with traditional power system operation mode analysis, the concept of region mapping is proposed to describe the adjustment process, so as to analyze the process of power flow calculation and manual adjustment. Considering the characteristics of power flow adjustment, a Markov decision process model suitable for power flow adjustment is constructed. On this basis, a double Q network learning method suitable for power flow adjustment is proposed. This method can adjust the power flow according to the set adjustment route, thus improving the intelligent level of power flow adjustment. The method in this paper is tested on China Electric Power Research Institute (CEPRI) test system.https://www.sciopen.com/article/10.23919/IEN.2024.0028operation mode adjustmentdouble q network learningregion mappingdeep reinforcement learning |
| spellingShingle | Wei Hu Ning Mi Shuang Wu Huiling Zhang Zhewen Hu Lei Zhang Intelligent adjustment for power system operation mode based on deep reinforcement learning iEnergy operation mode adjustment double q network learning region mapping deep reinforcement learning |
| title | Intelligent adjustment for power system operation mode based on deep reinforcement learning |
| title_full | Intelligent adjustment for power system operation mode based on deep reinforcement learning |
| title_fullStr | Intelligent adjustment for power system operation mode based on deep reinforcement learning |
| title_full_unstemmed | Intelligent adjustment for power system operation mode based on deep reinforcement learning |
| title_short | Intelligent adjustment for power system operation mode based on deep reinforcement learning |
| title_sort | intelligent adjustment for power system operation mode based on deep reinforcement learning |
| topic | operation mode adjustment double q network learning region mapping deep reinforcement learning |
| url | https://www.sciopen.com/article/10.23919/IEN.2024.0028 |
| work_keys_str_mv | AT weihu intelligentadjustmentforpowersystemoperationmodebasedondeepreinforcementlearning AT ningmi intelligentadjustmentforpowersystemoperationmodebasedondeepreinforcementlearning AT shuangwu intelligentadjustmentforpowersystemoperationmodebasedondeepreinforcementlearning AT huilingzhang intelligentadjustmentforpowersystemoperationmodebasedondeepreinforcementlearning AT zhewenhu intelligentadjustmentforpowersystemoperationmodebasedondeepreinforcementlearning AT leizhang intelligentadjustmentforpowersystemoperationmodebasedondeepreinforcementlearning |