A Strategy for Smoothing Power Fluctuations of New Energy Based on Improved Power Prediction Accuracy and Market Transaction
The uncertainty of new energy results in power prediction errors, causing new energy producers to bear high wind curtailment losses and deviation penalties due to bidding deviations. To address these issues, this paper proposes a feature-constrained multi-layer perception (MLP) power prediction algo...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Shanghai Jiao Tong University
2025-02-01
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| Series: | Shanghai Jiaotong Daxue xuebao |
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| Online Access: | https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-2-221.shtml |
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| author | LIANG Yiheng, YANG Dongmei, LIU Gang, YE Wenjie, YANG Yize, QIAN Tao, HU Qinran |
| author_facet | LIANG Yiheng, YANG Dongmei, LIU Gang, YE Wenjie, YANG Yize, QIAN Tao, HU Qinran |
| author_sort | LIANG Yiheng, YANG Dongmei, LIU Gang, YE Wenjie, YANG Yize, QIAN Tao, HU Qinran |
| collection | DOAJ |
| description | The uncertainty of new energy results in power prediction errors, causing new energy producers to bear high wind curtailment losses and deviation penalties due to bidding deviations. To address these issues, this paper proposes a feature-constrained multi-layer perception (MLP) power prediction algorithm, combined with storage-based bilateral transactions, to provide power support and reduce bidding deviations. First, the MLP model is enhanced by improving the relevancy of the hidden layers through adaptive learning, which strengthens its ability to capture nonlinear rules in input data and improves power prediction accuracy. Then, the algorithm allows for bilateral transactions between new energy producers and storage enterprise before entering the day-ahead market, helping mitigate the penalties associated with prediction errors including deviation and curtailment costs. Finally, the case study demonstrates that the feature-constrained MLP effectively improves the power prediction accuracy. Additionally, engaging in bilateral transactions with storage enterprise significantly reduces the costs incurred by new energy producers due to bid deviations. |
| format | Article |
| id | doaj-art-efa110a06d3f42efab4eb24e52053690 |
| institution | DOAJ |
| issn | 1006-2467 |
| language | zho |
| publishDate | 2025-02-01 |
| publisher | Editorial Office of Journal of Shanghai Jiao Tong University |
| record_format | Article |
| series | Shanghai Jiaotong Daxue xuebao |
| spelling | doaj-art-efa110a06d3f42efab4eb24e520536902025-08-20T02:47:49ZzhoEditorial Office of Journal of Shanghai Jiao Tong UniversityShanghai Jiaotong Daxue xuebao1006-24672025-02-0159222122910.16183/j.cnki.jsjtu.2023.312A Strategy for Smoothing Power Fluctuations of New Energy Based on Improved Power Prediction Accuracy and Market TransactionLIANG Yiheng, YANG Dongmei, LIU Gang, YE Wenjie, YANG Yize, QIAN Tao, HU Qinran01. NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211100, China;2. NARI Technology Co., Ltd., Nanjing 211100, China;3. School of Electrical Engineering, Southeast University, Nanjing 210096, ChinaThe uncertainty of new energy results in power prediction errors, causing new energy producers to bear high wind curtailment losses and deviation penalties due to bidding deviations. To address these issues, this paper proposes a feature-constrained multi-layer perception (MLP) power prediction algorithm, combined with storage-based bilateral transactions, to provide power support and reduce bidding deviations. First, the MLP model is enhanced by improving the relevancy of the hidden layers through adaptive learning, which strengthens its ability to capture nonlinear rules in input data and improves power prediction accuracy. Then, the algorithm allows for bilateral transactions between new energy producers and storage enterprise before entering the day-ahead market, helping mitigate the penalties associated with prediction errors including deviation and curtailment costs. Finally, the case study demonstrates that the feature-constrained MLP effectively improves the power prediction accuracy. Additionally, engaging in bilateral transactions with storage enterprise significantly reduces the costs incurred by new energy producers due to bid deviations.https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-2-221.shtmlnew energy power predictionfeature-constrained multi-layer perception (mlp)bilateral transaction |
| spellingShingle | LIANG Yiheng, YANG Dongmei, LIU Gang, YE Wenjie, YANG Yize, QIAN Tao, HU Qinran A Strategy for Smoothing Power Fluctuations of New Energy Based on Improved Power Prediction Accuracy and Market Transaction Shanghai Jiaotong Daxue xuebao new energy power prediction feature-constrained multi-layer perception (mlp) bilateral transaction |
| title | A Strategy for Smoothing Power Fluctuations of New Energy Based on Improved Power Prediction Accuracy and Market Transaction |
| title_full | A Strategy for Smoothing Power Fluctuations of New Energy Based on Improved Power Prediction Accuracy and Market Transaction |
| title_fullStr | A Strategy for Smoothing Power Fluctuations of New Energy Based on Improved Power Prediction Accuracy and Market Transaction |
| title_full_unstemmed | A Strategy for Smoothing Power Fluctuations of New Energy Based on Improved Power Prediction Accuracy and Market Transaction |
| title_short | A Strategy for Smoothing Power Fluctuations of New Energy Based on Improved Power Prediction Accuracy and Market Transaction |
| title_sort | strategy for smoothing power fluctuations of new energy based on improved power prediction accuracy and market transaction |
| topic | new energy power prediction feature-constrained multi-layer perception (mlp) bilateral transaction |
| url | https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-2-221.shtml |
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