Unit Commitment and Economic Dispatch via Graph Attention Neural Network–Based Parallel Distributed Coordination Algorithm
The joint optimisation of unit commitment and economic dispatch (ED) is one of the key issues in smart grid scheduling and control. Integrating the discrete on/off statuses of units in the unit commitment problem with the continuous active power outputs in ED significantly increases the overall comp...
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| Vydáno v: | IET control theory & applications Ročník 19; číslo 1 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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01.01.2025
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| ISSN: | 1751-8644, 1751-8652 |
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| Abstract | The joint optimisation of unit commitment and economic dispatch (ED) is one of the key issues in smart grid scheduling and control. Integrating the discrete on/off statuses of units in the unit commitment problem with the continuous active power outputs in ED significantly increases the overall complexity of the combined optimisation problem. We propose an innovative distributed algorithm based on a graph attention network to address this challenge. The graph neural network is used to extract the inter‐unit relational features and predict the future power dispatch schedule of each unit, while the parallel distributed coordination algorithm (PDCA), acting as the power dispatch algorithm, schedules and controls the output power of the units, including their start‐up and shut‐down states. Experimental results show that our algorithm performs well on both the IEEE 30‐bus and IEEE 118‐bus test systems, achieving a 1559 times speed boost compared to advanced solvers, and reaching economic optimality while satisfying all critical constraints to obtain an industrial acceptable solution. |
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| AbstractList | The joint optimisation of unit commitment and economic dispatch (ED) is one of the key issues in smart grid scheduling and control. Integrating the discrete on/off statuses of units in the unit commitment problem with the continuous active power outputs in ED significantly increases the overall complexity of the combined optimisation problem. We propose an innovative distributed algorithm based on a graph attention network to address this challenge. The graph neural network is used to extract the inter‐unit relational features and predict the future power dispatch schedule of each unit, while the parallel distributed coordination algorithm (PDCA), acting as the power dispatch algorithm, schedules and controls the output power of the units, including their start‐up and shut‐down states. Experimental results show that our algorithm performs well on both the IEEE 30‐bus and IEEE 118‐bus test systems, achieving a 1559 times speed boost compared to advanced solvers, and reaching economic optimality while satisfying all critical constraints to obtain an industrial acceptable solution. |
| Author | Shi, Liang Xia, Min Geng, Jian Liu, Jun Zhou, Siyi Yu, Fang |
| Author_xml | – sequence: 1 givenname: Siyi orcidid: 0009-0003-2059-2226 surname: Zhou fullname: Zhou, Siyi organization: Nanjing University of Information Science and Technology Nanjing China – sequence: 2 givenname: Liang orcidid: 0000-0002-6587-0635 surname: Shi fullname: Shi, Liang organization: Nanjing University of Information Science and Technology Nanjing China – sequence: 3 givenname: Min surname: Xia fullname: Xia, Min organization: Nanjing University of Information Science and Technology Nanjing China – sequence: 4 givenname: Jian surname: Geng fullname: Geng, Jian organization: China Electric Power Research Institute Nanjing Jiangsu China – sequence: 5 givenname: Jun surname: Liu fullname: Liu, Jun organization: China Electric Power Research Institute Nanjing Jiangsu China – sequence: 6 givenname: Fang surname: Yu fullname: Yu, Fang organization: China Electric Power Research Institute Nanjing Jiangsu China |
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