The distributed economic dispatch of smart grid based on deep reinforcement learning
Abstract In order to solve the problems of inefficient, inflexible and insecure for traditional centralized algorithm in the process of optimization dispatch, and with the application of artificial intelligence technology to smart grids, the novel distributed solution is proposed by using the deep r...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-09-01
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| Series: | IET Generation, Transmission & Distribution |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/gtd2.12206 |
| _version_ | 1798002902699081728 |
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| author | Yang Fu Xiaoyan Guo Yang Mi Minghan Yuan Xiaolin Ge Xiangjing Su Zhenkun Li |
| author_facet | Yang Fu Xiaoyan Guo Yang Mi Minghan Yuan Xiaolin Ge Xiangjing Su Zhenkun Li |
| author_sort | Yang Fu |
| collection | DOAJ |
| description | Abstract In order to solve the problems of inefficient, inflexible and insecure for traditional centralized algorithm in the process of optimization dispatch, and with the application of artificial intelligence technology to smart grids, the novel distributed solution is proposed by using the deep reinforcement learning and the consensus theory to optimize the economic dispatch. Firstly, the optimal commitment sequence of massive units is realized through constructing deep reinforcement learning model. Secondly, the optimal unit output and efficient economic dispatch can be obtained by utilizing the improved consensus algorithm together with Adam's algorithm. Finally, simulation results of IEEE‐14 and IEEE‐162 node systems may demonstrate the effectiveness of the proposed solution for the smart grids with complex network structures, which can not only solve the problem of massive data processing, but also it may reduce the dependence on the exact objective function when dealing with extremely complex load distribution scenes and distributed powers. |
| first_indexed | 2024-04-11T11:59:32Z |
| format | Article |
| id | doaj.art-4d329b6b2d574d2eb2c6dff2b2fbc3f9 |
| institution | Directory Open Access Journal |
| issn | 1751-8687 1751-8695 |
| language | English |
| last_indexed | 2024-04-11T11:59:32Z |
| publishDate | 2021-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj.art-4d329b6b2d574d2eb2c6dff2b2fbc3f92022-12-22T04:24:54ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952021-09-0115182645265810.1049/gtd2.12206The distributed economic dispatch of smart grid based on deep reinforcement learningYang Fu0Xiaoyan Guo1Yang Mi2Minghan Yuan3Xiaolin Ge4Xiangjing Su5Zhenkun Li6College of Electrical Engineering Shanghai University of Electric Power Shanghai People's Republic of ChinaCollege of Electrical Engineering Shanghai University of Electric Power Shanghai People's Republic of ChinaCollege of Electrical Engineering Shanghai University of Electric Power Shanghai People's Republic of ChinaCollege of Electrical Engineering Shanghai University Shanghai People's Republic of ChinaCollege of Electrical Engineering Shanghai University of Electric Power Shanghai People's Republic of ChinaCollege of Electrical Engineering Shanghai University of Electric Power Shanghai People's Republic of ChinaCollege of Electrical Engineering Shanghai University of Electric Power Shanghai People's Republic of ChinaAbstract In order to solve the problems of inefficient, inflexible and insecure for traditional centralized algorithm in the process of optimization dispatch, and with the application of artificial intelligence technology to smart grids, the novel distributed solution is proposed by using the deep reinforcement learning and the consensus theory to optimize the economic dispatch. Firstly, the optimal commitment sequence of massive units is realized through constructing deep reinforcement learning model. Secondly, the optimal unit output and efficient economic dispatch can be obtained by utilizing the improved consensus algorithm together with Adam's algorithm. Finally, simulation results of IEEE‐14 and IEEE‐162 node systems may demonstrate the effectiveness of the proposed solution for the smart grids with complex network structures, which can not only solve the problem of massive data processing, but also it may reduce the dependence on the exact objective function when dealing with extremely complex load distribution scenes and distributed powers.https://doi.org/10.1049/gtd2.12206Optimisation techniquesPower system management, operation and economicsPower engineering computingReinforcement learning |
| spellingShingle | Yang Fu Xiaoyan Guo Yang Mi Minghan Yuan Xiaolin Ge Xiangjing Su Zhenkun Li The distributed economic dispatch of smart grid based on deep reinforcement learning IET Generation, Transmission & Distribution Optimisation techniques Power system management, operation and economics Power engineering computing Reinforcement learning |
| title | The distributed economic dispatch of smart grid based on deep reinforcement learning |
| title_full | The distributed economic dispatch of smart grid based on deep reinforcement learning |
| title_fullStr | The distributed economic dispatch of smart grid based on deep reinforcement learning |
| title_full_unstemmed | The distributed economic dispatch of smart grid based on deep reinforcement learning |
| title_short | The distributed economic dispatch of smart grid based on deep reinforcement learning |
| title_sort | distributed economic dispatch of smart grid based on deep reinforcement learning |
| topic | Optimisation techniques Power system management, operation and economics Power engineering computing Reinforcement learning |
| url | https://doi.org/10.1049/gtd2.12206 |
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