Day-Ahead Optimal Dispatch Strategy for Active Distribution Network Based on Improved Deep Reinforcement Learning
To effectively deal with the challenge of optimal dispatch caused by uncertainties such as renewable energy in active distribution network, a day-ahead optimal dispatch strategy for active distribution network based on improved deep reinforcement learning is proposed. First, the day-ahead optimal di...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9676620/ |
| _version_ | 1818715658750263296 |
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| author | Xiaoyu Li Xueshan Han Ming Yang |
| author_facet | Xiaoyu Li Xueshan Han Ming Yang |
| author_sort | Xiaoyu Li |
| collection | DOAJ |
| description | To effectively deal with the challenge of optimal dispatch caused by uncertainties such as renewable energy in active distribution network, a day-ahead optimal dispatch strategy for active distribution network based on improved deep reinforcement learning is proposed. First, the day-ahead optimal dispatch problem for the active distribution network is modeled as a multistage stochastic programming model. Multistage models attempt to capture the dynamics of unfolding uncertainties over time, so that the interaction between decision making and uncertainty unfolding is represented more accurately and realistically, and the decision can be adjusted dynamically and adaptively. Second, with the help of deep neural network to approximate the power flow model of the active distribution network in a data-driven manner, an improved deep reinforcement learning algorithm is proposed to solve the corresponding multistage stochastic programming problem. The proposed improved deep reinforcement learning algorithm effectively solves the problem that trial-and-error information acquisition methods are difficult to apply to high-cost power systems. Finally, the effectiveness of the proposed day-ahead optimization dispatch strategy for active distribution network based on improved deep reinforcement learning is verified by a modified IEEE33 case. |
| first_indexed | 2024-12-17T19:06:52Z |
| format | Article |
| id | doaj.art-1c1e4af7129444c7bd939f54341dedea |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T19:06:52Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1c1e4af7129444c7bd939f54341dedea2022-12-21T21:35:58ZengIEEEIEEE Access2169-35362022-01-01109357937010.1109/ACCESS.2022.31418249676620Day-Ahead Optimal Dispatch Strategy for Active Distribution Network Based on Improved Deep Reinforcement LearningXiaoyu Li0https://orcid.org/0000-0003-0735-5560Xueshan Han1https://orcid.org/0000-0002-5869-0898Ming Yang2https://orcid.org/0000-0002-0020-8683Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, Jinan, ChinaKey Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, Jinan, ChinaKey Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, Jinan, ChinaTo effectively deal with the challenge of optimal dispatch caused by uncertainties such as renewable energy in active distribution network, a day-ahead optimal dispatch strategy for active distribution network based on improved deep reinforcement learning is proposed. First, the day-ahead optimal dispatch problem for the active distribution network is modeled as a multistage stochastic programming model. Multistage models attempt to capture the dynamics of unfolding uncertainties over time, so that the interaction between decision making and uncertainty unfolding is represented more accurately and realistically, and the decision can be adjusted dynamically and adaptively. Second, with the help of deep neural network to approximate the power flow model of the active distribution network in a data-driven manner, an improved deep reinforcement learning algorithm is proposed to solve the corresponding multistage stochastic programming problem. The proposed improved deep reinforcement learning algorithm effectively solves the problem that trial-and-error information acquisition methods are difficult to apply to high-cost power systems. Finally, the effectiveness of the proposed day-ahead optimization dispatch strategy for active distribution network based on improved deep reinforcement learning is verified by a modified IEEE33 case.https://ieeexplore.ieee.org/document/9676620/Active distribution networkmultistage stochastic programmingoptimal dispatchdata drivendeep learningdeep reinforcement learning |
| spellingShingle | Xiaoyu Li Xueshan Han Ming Yang Day-Ahead Optimal Dispatch Strategy for Active Distribution Network Based on Improved Deep Reinforcement Learning IEEE Access Active distribution network multistage stochastic programming optimal dispatch data driven deep learning deep reinforcement learning |
| title | Day-Ahead Optimal Dispatch Strategy for Active Distribution Network Based on Improved Deep Reinforcement Learning |
| title_full | Day-Ahead Optimal Dispatch Strategy for Active Distribution Network Based on Improved Deep Reinforcement Learning |
| title_fullStr | Day-Ahead Optimal Dispatch Strategy for Active Distribution Network Based on Improved Deep Reinforcement Learning |
| title_full_unstemmed | Day-Ahead Optimal Dispatch Strategy for Active Distribution Network Based on Improved Deep Reinforcement Learning |
| title_short | Day-Ahead Optimal Dispatch Strategy for Active Distribution Network Based on Improved Deep Reinforcement Learning |
| title_sort | day ahead optimal dispatch strategy for active distribution network based on improved deep reinforcement learning |
| topic | Active distribution network multistage stochastic programming optimal dispatch data driven deep learning deep reinforcement learning |
| url | https://ieeexplore.ieee.org/document/9676620/ |
| work_keys_str_mv | AT xiaoyuli dayaheadoptimaldispatchstrategyforactivedistributionnetworkbasedonimproveddeepreinforcementlearning AT xueshanhan dayaheadoptimaldispatchstrategyforactivedistributionnetworkbasedonimproveddeepreinforcementlearning AT mingyang dayaheadoptimaldispatchstrategyforactivedistributionnetworkbasedonimproveddeepreinforcementlearning |