Parallel deep reinforcement learning‐based power flow state adjustment considering static stability constraint
To solve the problem of manpower and time consumption caused by power flow state adjustment in a large‐scale power grid, a power system operation state adjustment method considering the static stability constraint based on parallel deep reinforcement learning is proposed. By introducing the process...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-12-01
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| Series: | IET Generation, Transmission & Distribution |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-gtd.2020.1377 |
| _version_ | 1818083070543134720 |
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| author | Wang Tianjing Tang Yong |
| author_facet | Wang Tianjing Tang Yong |
| author_sort | Wang Tianjing |
| collection | DOAJ |
| description | To solve the problem of manpower and time consumption caused by power flow state adjustment in a large‐scale power grid, a power system operation state adjustment method considering the static stability constraint based on parallel deep reinforcement learning is proposed. By introducing the process of adjusting the power flow state that satisfies static stability, the Markov decision‐making process of adjusting power flow is constructed. Then, based on the positioning of the adjustment target, the selection of actionable devices and the calculation of the amount of action, a power flow state adjustment strategy is developed. The adjustment process is accelerated through sensitivity, transfer ratio and load margin. Then, a parallel deep reinforcement learning model is established, and it maps actions to power flow adjustment to form a pair of generator actions and realises parallel adjustment of multi‐sectional objectives. In addition, the reinforcement learning strategy and the deep learning network are improved to promote learning efficiency. Finally, the New England 39‐bus standard system and actual power grid are used to verify the effectiveness of the method. |
| first_indexed | 2024-12-10T19:32:09Z |
| format | Article |
| id | doaj.art-eabb5e9f542a42c8a44605e81e8033cb |
| institution | Directory Open Access Journal |
| issn | 1751-8687 1751-8695 |
| language | English |
| last_indexed | 2024-12-10T19:32:09Z |
| publishDate | 2020-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj.art-eabb5e9f542a42c8a44605e81e8033cb2022-12-22T01:36:14ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952020-12-0114256276628410.1049/iet-gtd.2020.1377Parallel deep reinforcement learning‐based power flow state adjustment considering static stability constraintWang Tianjing0Tang Yong1Laboratory of Power Grid Safety and Energy ConservationChina Electric Power Research InstituteNo. 15 Xiaoying East Road, Haidian DistrictBeijingPeople's Republic of ChinaLaboratory of Power Grid Safety and Energy ConservationChina Electric Power Research InstituteNo. 15 Xiaoying East Road, Haidian DistrictBeijingPeople's Republic of ChinaTo solve the problem of manpower and time consumption caused by power flow state adjustment in a large‐scale power grid, a power system operation state adjustment method considering the static stability constraint based on parallel deep reinforcement learning is proposed. By introducing the process of adjusting the power flow state that satisfies static stability, the Markov decision‐making process of adjusting power flow is constructed. Then, based on the positioning of the adjustment target, the selection of actionable devices and the calculation of the amount of action, a power flow state adjustment strategy is developed. The adjustment process is accelerated through sensitivity, transfer ratio and load margin. Then, a parallel deep reinforcement learning model is established, and it maps actions to power flow adjustment to form a pair of generator actions and realises parallel adjustment of multi‐sectional objectives. In addition, the reinforcement learning strategy and the deep learning network are improved to promote learning efficiency. Finally, the New England 39‐bus standard system and actual power grid are used to verify the effectiveness of the method.https://doi.org/10.1049/iet-gtd.2020.1377parallel deep reinforcement learning‐based power flow state adjustmentstatic stability constraintlarge‐scale power gridpower system operation state adjustment methodMarkov decision‐making processadjustment target |
| spellingShingle | Wang Tianjing Tang Yong Parallel deep reinforcement learning‐based power flow state adjustment considering static stability constraint IET Generation, Transmission & Distribution parallel deep reinforcement learning‐based power flow state adjustment static stability constraint large‐scale power grid power system operation state adjustment method Markov decision‐making process adjustment target |
| title | Parallel deep reinforcement learning‐based power flow state adjustment considering static stability constraint |
| title_full | Parallel deep reinforcement learning‐based power flow state adjustment considering static stability constraint |
| title_fullStr | Parallel deep reinforcement learning‐based power flow state adjustment considering static stability constraint |
| title_full_unstemmed | Parallel deep reinforcement learning‐based power flow state adjustment considering static stability constraint |
| title_short | Parallel deep reinforcement learning‐based power flow state adjustment considering static stability constraint |
| title_sort | parallel deep reinforcement learning based power flow state adjustment considering static stability constraint |
| topic | parallel deep reinforcement learning‐based power flow state adjustment static stability constraint large‐scale power grid power system operation state adjustment method Markov decision‐making process adjustment target |
| url | https://doi.org/10.1049/iet-gtd.2020.1377 |
| work_keys_str_mv | AT wangtianjing paralleldeepreinforcementlearningbasedpowerflowstateadjustmentconsideringstaticstabilityconstraint AT tangyong paralleldeepreinforcementlearningbasedpowerflowstateadjustmentconsideringstaticstabilityconstraint |