Multi-timescale optimal control strategy for energy storage using LSTM prediction–correction in the active distribution network
The daily output of wind power is inversely proportional to the load demand in most situations, which will lead to an increase in peak-to-valley difference and fluctuation. To solve this problem, this study proposes a long short-term memory prediction–correction-based multi-timescale optimal control...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-09-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1240764/full |
| _version_ | 1797678080822607872 |
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| author | Junjian Wu Yiwei Chen Jinhui Zhou Chengtao Jiang Wei Liu |
| author_facet | Junjian Wu Yiwei Chen Jinhui Zhou Chengtao Jiang Wei Liu |
| author_sort | Junjian Wu |
| collection | DOAJ |
| description | The daily output of wind power is inversely proportional to the load demand in most situations, which will lead to an increase in peak-to-valley difference and fluctuation. To solve this problem, this study proposes a long short-term memory prediction–correction-based multi-timescale optimal control strategy for energy storage. First, the proposed strategy performs a long short-term memory (LSTM) prediction on the power of wind power and load. Then, it establishes a predictive planning model to improve the effect of peak shaving and the operating income of energy storage. Finally, it uses the method of online correction of power lines for peak shaving to further optimize the energy storage power according to the error between the residual energy of energy storage and the planned residual energy in the actual peak shaving process. By comparing with traditional strategies, the proposed strategy is found to be significantly better than the constant power strategy and the power difference strategy in the peak shaving effect and operating income. |
| first_indexed | 2024-03-11T22:54:29Z |
| format | Article |
| id | doaj.art-40d2d2ba86574a54842365c37a988417 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-03-11T22:54:29Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-40d2d2ba86574a54842365c37a9884172023-09-21T17:43:22ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-09-011110.3389/fenrg.2023.12407641240764Multi-timescale optimal control strategy for energy storage using LSTM prediction–correction in the active distribution networkJunjian Wu0Yiwei Chen1Jinhui Zhou2Chengtao Jiang3Wei Liu4State Grid Wenzhou Electric Power Supply Company, Wenzhou, ChinaState Grid Rui’an Electric Power Supply Company, Rui’an, ChinaState Grid Zhejiang Electric Power Co., Ltd. Research Institute, Hangzhou, Zhejiang, ChinaSchool of Automation, Nanjing University of Science and Technology, Nanjing, ChinaSchool of Automation, Nanjing University of Science and Technology, Nanjing, ChinaThe daily output of wind power is inversely proportional to the load demand in most situations, which will lead to an increase in peak-to-valley difference and fluctuation. To solve this problem, this study proposes a long short-term memory prediction–correction-based multi-timescale optimal control strategy for energy storage. First, the proposed strategy performs a long short-term memory (LSTM) prediction on the power of wind power and load. Then, it establishes a predictive planning model to improve the effect of peak shaving and the operating income of energy storage. Finally, it uses the method of online correction of power lines for peak shaving to further optimize the energy storage power according to the error between the residual energy of energy storage and the planned residual energy in the actual peak shaving process. By comparing with traditional strategies, the proposed strategy is found to be significantly better than the constant power strategy and the power difference strategy in the peak shaving effect and operating income.https://www.frontiersin.org/articles/10.3389/fenrg.2023.1240764/fullpeak shavingenergy storageLSTMprediction-correctionmulti-time-scale |
| spellingShingle | Junjian Wu Yiwei Chen Jinhui Zhou Chengtao Jiang Wei Liu Multi-timescale optimal control strategy for energy storage using LSTM prediction–correction in the active distribution network Frontiers in Energy Research peak shaving energy storage LSTM prediction-correction multi-time-scale |
| title | Multi-timescale optimal control strategy for energy storage using LSTM prediction–correction in the active distribution network |
| title_full | Multi-timescale optimal control strategy for energy storage using LSTM prediction–correction in the active distribution network |
| title_fullStr | Multi-timescale optimal control strategy for energy storage using LSTM prediction–correction in the active distribution network |
| title_full_unstemmed | Multi-timescale optimal control strategy for energy storage using LSTM prediction–correction in the active distribution network |
| title_short | Multi-timescale optimal control strategy for energy storage using LSTM prediction–correction in the active distribution network |
| title_sort | multi timescale optimal control strategy for energy storage using lstm prediction correction in the active distribution network |
| topic | peak shaving energy storage LSTM prediction-correction multi-time-scale |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1240764/full |
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