End-to-End Collaborative Optimization Method for Microgrid Power Prediction and Optimal Scheduling
Microgrids, as one of the effective methods for integrating new energy sources, play a crucial role in the new-type power systems. In microgrids with high renewable energy penetration, the objectives of renewable energy power forecasting and microgrid optimal scheduling may be misaligned. To address...
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Editorial Office of Journal of Shanghai Jiao Tong University
2025-06-01
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| Series: | Shanghai Jiaotong Daxue xuebao |
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| Online Access: | https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-6-720.shtml |
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| author | ZHANG Li, WANG Bao, JIA Jianxiong, SONG Zhumeng, YE Yutong, YU Yue, LIN Jiaqing, XU Xiaoyuan |
| author_facet | ZHANG Li, WANG Bao, JIA Jianxiong, SONG Zhumeng, YE Yutong, YU Yue, LIN Jiaqing, XU Xiaoyuan |
| author_sort | ZHANG Li, WANG Bao, JIA Jianxiong, SONG Zhumeng, YE Yutong, YU Yue, LIN Jiaqing, XU Xiaoyuan |
| collection | DOAJ |
| description | Microgrids, as one of the effective methods for integrating new energy sources, play a crucial role in the new-type power systems. In microgrids with high renewable energy penetration, the objectives of renewable energy power forecasting and microgrid optimal scheduling may be misaligned. To address this issue, this study proposes an end-to-end optimization model which combines power forecasting with day-ahead and intraday scheduling to maximize the operational benefits of the microgrid. It also provides a corresponding solution method. Initially, a bi-level optimization framework is established. The upper level focuses on training the power forecasting model, formulated as a combined forecasting problem, while the lower level aims to minimize microgrid operational costs. The result of the lower-level optimization is used as the loss function to optimize the forecasting weights in the upper level. Subsequently, a heuristic algorithm iteratively is employed to solve the upper and lower level problems, thereby obtaining forecasting results and scheduling schemes which minimize the operational costs. Finally, the effectiveness of the proposed method in enhancing microgrid operational benefits is validated by integrating real renewable energy data into a typical microgrid extended from the IEEE 33-node and IEEE 123-node systems. |
| format | Article |
| id | doaj-art-3c8ff1c2b7894c2aa034cfd6dbf08c29 |
| institution | Kabale University |
| issn | 1006-2467 |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | Editorial Office of Journal of Shanghai Jiao Tong University |
| record_format | Article |
| series | Shanghai Jiaotong Daxue xuebao |
| spelling | doaj-art-3c8ff1c2b7894c2aa034cfd6dbf08c292025-08-20T03:31:30ZzhoEditorial Office of Journal of Shanghai Jiao Tong UniversityShanghai Jiaotong Daxue xuebao1006-24672025-06-0159672073110.16183/j.cnki.jsjtu.2024.224End-to-End Collaborative Optimization Method for Microgrid Power Prediction and Optimal SchedulingZHANG Li, WANG Bao, JIA Jianxiong, SONG Zhumeng, YE Yutong, YU Yue, LIN Jiaqing, XU Xiaoyuan01. Economic and Technological Research Institute, State Grid Anhui Electric Power Co., Ltd., Hefei 230000, China;2. Key Laboratory of Control of Power Transmission and Conversion of the Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, ChinaMicrogrids, as one of the effective methods for integrating new energy sources, play a crucial role in the new-type power systems. In microgrids with high renewable energy penetration, the objectives of renewable energy power forecasting and microgrid optimal scheduling may be misaligned. To address this issue, this study proposes an end-to-end optimization model which combines power forecasting with day-ahead and intraday scheduling to maximize the operational benefits of the microgrid. It also provides a corresponding solution method. Initially, a bi-level optimization framework is established. The upper level focuses on training the power forecasting model, formulated as a combined forecasting problem, while the lower level aims to minimize microgrid operational costs. The result of the lower-level optimization is used as the loss function to optimize the forecasting weights in the upper level. Subsequently, a heuristic algorithm iteratively is employed to solve the upper and lower level problems, thereby obtaining forecasting results and scheduling schemes which minimize the operational costs. Finally, the effectiveness of the proposed method in enhancing microgrid operational benefits is validated by integrating real renewable energy data into a typical microgrid extended from the IEEE 33-node and IEEE 123-node systems.https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-6-720.shtmlmicrogrid schedulingnew energy predictioncomposite forecastingend-to-end optimization |
| spellingShingle | ZHANG Li, WANG Bao, JIA Jianxiong, SONG Zhumeng, YE Yutong, YU Yue, LIN Jiaqing, XU Xiaoyuan End-to-End Collaborative Optimization Method for Microgrid Power Prediction and Optimal Scheduling Shanghai Jiaotong Daxue xuebao microgrid scheduling new energy prediction composite forecasting end-to-end optimization |
| title | End-to-End Collaborative Optimization Method for Microgrid Power Prediction and Optimal Scheduling |
| title_full | End-to-End Collaborative Optimization Method for Microgrid Power Prediction and Optimal Scheduling |
| title_fullStr | End-to-End Collaborative Optimization Method for Microgrid Power Prediction and Optimal Scheduling |
| title_full_unstemmed | End-to-End Collaborative Optimization Method for Microgrid Power Prediction and Optimal Scheduling |
| title_short | End-to-End Collaborative Optimization Method for Microgrid Power Prediction and Optimal Scheduling |
| title_sort | end to end collaborative optimization method for microgrid power prediction and optimal scheduling |
| topic | microgrid scheduling new energy prediction composite forecasting end-to-end optimization |
| url | https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-6-720.shtml |
| work_keys_str_mv | AT zhangliwangbaojiajianxiongsongzhumengyeyutongyuyuelinjiaqingxuxiaoyuan endtoendcollaborativeoptimizationmethodformicrogridpowerpredictionandoptimalscheduling |