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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| Main Author: | |
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| Format: | Article |
| Language: | Chinese |
| Published: |
Editorial Office of Journal of Shanghai Jiao Tong University
2025-06-01
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| Series: | Shanghai Jiaotong Daxue xuebao |
| Subjects: | |
| Online Access: | https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-6-720.shtml |
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| Summary: | 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. |
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| ISSN: | 1006-2467 |