A deep-learning based method for accelerating dynamic reconfiguration of distribution networks
Distribution network dynamic reconfiguration (DNDR), formulated as a mixed-integer quadratic programming (MIQP) problem, is computationally intractable for large-scale systems due to combinatorial complexity and temporal coupling. To address this issue, this paper presents a deep learning-based fram...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525003552 |
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| author | Yuxuan Wu Tao Qian Jingwen Ye Qinran Hu Qiangsheng Bu Zhigang Ye |
| author_facet | Yuxuan Wu Tao Qian Jingwen Ye Qinran Hu Qiangsheng Bu Zhigang Ye |
| author_sort | Yuxuan Wu |
| collection | DOAJ |
| description | Distribution network dynamic reconfiguration (DNDR), formulated as a mixed-integer quadratic programming (MIQP) problem, is computationally intractable for large-scale systems due to combinatorial complexity and temporal coupling. To address this issue, this paper presents a deep learning-based framework that converts the MIQP problem into a solvable quadratic program (QP), enabling accelerated DNR. The framework uses the Informer model with ProbSparse self-attention to identify temporal dependencies in time series data and predict the status of line switches. It then uses a threshold decision mechanism to determine which predicted switch states to fix, thereby reducing the number of binary variables. Case studies in a modified IEEE 33-node system demonstrate the effectiveness of the framework, achieving a 99% acceleration in model solution speed without compromising feasibility or optimality. This methodology combines data-driven prediction and optimization to offer a scalable solution for real-time grid reconfiguration in dynamic environments. |
| format | Article |
| id | doaj-art-cbf4972585de467c9248bfca85da5f96 |
| institution | DOAJ |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-cbf4972585de467c9248bfca85da5f962025-08-20T03:03:55ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-09-0117011080710.1016/j.ijepes.2025.110807A deep-learning based method for accelerating dynamic reconfiguration of distribution networksYuxuan Wu0Tao Qian1Jingwen Ye2Qinran Hu3Qiangsheng Bu4Zhigang Ye5School of Electrical Engineering, Southeast University, Nanjing, 210000, ChinaSchool of Electrical Engineering, Southeast University, Nanjing, 210000, China; Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Nanjing, 210094, China; Corresponding author at: School of Electrical Engineering, Southeast University, Nanjing, 210000, China.School of Electrical Engineering, Southeast University, Nanjing, 210000, ChinaSchool of Electrical Engineering, Southeast University, Nanjing, 210000, China; Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Nanjing, 210094, ChinaState Grid Jiangsu Electric Power CO., LTD., Nanjing, 210096, ChinaState Grid Jiangsu Electric Power CO., LTD., Nanjing, 210096, ChinaDistribution network dynamic reconfiguration (DNDR), formulated as a mixed-integer quadratic programming (MIQP) problem, is computationally intractable for large-scale systems due to combinatorial complexity and temporal coupling. To address this issue, this paper presents a deep learning-based framework that converts the MIQP problem into a solvable quadratic program (QP), enabling accelerated DNR. The framework uses the Informer model with ProbSparse self-attention to identify temporal dependencies in time series data and predict the status of line switches. It then uses a threshold decision mechanism to determine which predicted switch states to fix, thereby reducing the number of binary variables. Case studies in a modified IEEE 33-node system demonstrate the effectiveness of the framework, achieving a 99% acceleration in model solution speed without compromising feasibility or optimality. This methodology combines data-driven prediction and optimization to offer a scalable solution for real-time grid reconfiguration in dynamic environments.http://www.sciencedirect.com/science/article/pii/S0142061525003552Dynamic reconfigurationDistribution networksDeep learningInformer |
| spellingShingle | Yuxuan Wu Tao Qian Jingwen Ye Qinran Hu Qiangsheng Bu Zhigang Ye A deep-learning based method for accelerating dynamic reconfiguration of distribution networks International Journal of Electrical Power & Energy Systems Dynamic reconfiguration Distribution networks Deep learning Informer |
| title | A deep-learning based method for accelerating dynamic reconfiguration of distribution networks |
| title_full | A deep-learning based method for accelerating dynamic reconfiguration of distribution networks |
| title_fullStr | A deep-learning based method for accelerating dynamic reconfiguration of distribution networks |
| title_full_unstemmed | A deep-learning based method for accelerating dynamic reconfiguration of distribution networks |
| title_short | A deep-learning based method for accelerating dynamic reconfiguration of distribution networks |
| title_sort | deep learning based method for accelerating dynamic reconfiguration of distribution networks |
| topic | Dynamic reconfiguration Distribution networks Deep learning Informer |
| url | http://www.sciencedirect.com/science/article/pii/S0142061525003552 |
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