The energy management strategy of a loop microgrid with wind energy prediction and energy storage system day-ahead optimization
Microgrid has been extensively applied in the modern power system as a supplementary mode for the distributed energy resources. The microgrid with wind energy is usually vulnerable to the intermittence and uncertainty of the wind energy. To increase the robustness of the microgrid, the energy storag...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-01-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1334588/full |
| _version_ | 1827376302451064832 |
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| author | Bin Xu Bin Xu Feng Zhang Rui Bai Hui Sun Shichuan Ding |
| author_facet | Bin Xu Bin Xu Feng Zhang Rui Bai Hui Sun Shichuan Ding |
| author_sort | Bin Xu |
| collection | DOAJ |
| description | Microgrid has been extensively applied in the modern power system as a supplementary mode for the distributed energy resources. The microgrid with wind energy is usually vulnerable to the intermittence and uncertainty of the wind energy. To increase the robustness of the microgrid, the energy storage system (ESS) is necessary to compensate the power imbalance between the power supply and the load. To further maximize the economic efficiency of the system, the system level control for the microgrid is desired to be optimized when it is integrated with the utility grid. Aiming at the aforementioned problem, this paper comprehensively analyzes the power flow of a typical loop microgrid. A transformer-based wind power prediction (WPP) algorithm is proposed and compared with recurrent neural networks algorithm. With the historical weather data, it can accurately predict the 24 h average wind energy. Based on the predicted wind energy and the time-of-use (TOU) electricity price, a day-ahead daily cycling profile of the ESS with particle swarm optimization algorithm is introduced. It comprehensively considers the system capacity constraints and the battery degree of health. The functionality of the proposed energy management strategy is validated from three levels. First, WPP is conducted with the proposed algorithm and the true historical weather data. It has validated the accuracy of the transformer algorithm in prediction of the hourly level wind energy. Second, with the predicted wind energy, a case study is given to validate the day-ahead daily cycling profile. A typical 1 MVA microgrid is utilized as the simulation model to validate performance of the daily cycling optimization algorithm. The case study results show that the ESS daily cycling can effectively reduce the daily energy expense and help to shave the peak power demand in the grid. |
| first_indexed | 2024-03-08T12:07:53Z |
| format | Article |
| id | doaj.art-9906cf15efde45d89f1bc4b0cefbbd70 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-03-08T12:07:53Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-9906cf15efde45d89f1bc4b0cefbbd702024-01-23T04:27:01ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2024-01-011110.3389/fenrg.2023.13345881334588The energy management strategy of a loop microgrid with wind energy prediction and energy storage system day-ahead optimizationBin Xu0Bin Xu1Feng Zhang2Rui Bai3Hui Sun4Shichuan Ding5Electric Power Research Institute, State Grid Anhui Electric Power Co., Ltd., Hefei, Anhui, ChinaSchool of Electrical Engineering and Automation, Anhui University, Hefei, Anhui, ChinaElectric Power Research Institute, State Grid Anhui Electric Power Co., Ltd., Hefei, Anhui, ChinaSchool of Electrical Engineering and Automation, Hefei University of Technology, Hefei, Anhui, ChinaElectric Power Research Institute, State Grid Anhui Electric Power Co., Ltd., Hefei, Anhui, ChinaSchool of Electrical Engineering and Automation, Anhui University, Hefei, Anhui, ChinaMicrogrid has been extensively applied in the modern power system as a supplementary mode for the distributed energy resources. The microgrid with wind energy is usually vulnerable to the intermittence and uncertainty of the wind energy. To increase the robustness of the microgrid, the energy storage system (ESS) is necessary to compensate the power imbalance between the power supply and the load. To further maximize the economic efficiency of the system, the system level control for the microgrid is desired to be optimized when it is integrated with the utility grid. Aiming at the aforementioned problem, this paper comprehensively analyzes the power flow of a typical loop microgrid. A transformer-based wind power prediction (WPP) algorithm is proposed and compared with recurrent neural networks algorithm. With the historical weather data, it can accurately predict the 24 h average wind energy. Based on the predicted wind energy and the time-of-use (TOU) electricity price, a day-ahead daily cycling profile of the ESS with particle swarm optimization algorithm is introduced. It comprehensively considers the system capacity constraints and the battery degree of health. The functionality of the proposed energy management strategy is validated from three levels. First, WPP is conducted with the proposed algorithm and the true historical weather data. It has validated the accuracy of the transformer algorithm in prediction of the hourly level wind energy. Second, with the predicted wind energy, a case study is given to validate the day-ahead daily cycling profile. A typical 1 MVA microgrid is utilized as the simulation model to validate performance of the daily cycling optimization algorithm. The case study results show that the ESS daily cycling can effectively reduce the daily energy expense and help to shave the peak power demand in the grid.https://www.frontiersin.org/articles/10.3389/fenrg.2023.1334588/fullwind power predictionoptimizationmicrogridenergy storage systemtime-of-use price |
| spellingShingle | Bin Xu Bin Xu Feng Zhang Rui Bai Hui Sun Shichuan Ding The energy management strategy of a loop microgrid with wind energy prediction and energy storage system day-ahead optimization Frontiers in Energy Research wind power prediction optimization microgrid energy storage system time-of-use price |
| title | The energy management strategy of a loop microgrid with wind energy prediction and energy storage system day-ahead optimization |
| title_full | The energy management strategy of a loop microgrid with wind energy prediction and energy storage system day-ahead optimization |
| title_fullStr | The energy management strategy of a loop microgrid with wind energy prediction and energy storage system day-ahead optimization |
| title_full_unstemmed | The energy management strategy of a loop microgrid with wind energy prediction and energy storage system day-ahead optimization |
| title_short | The energy management strategy of a loop microgrid with wind energy prediction and energy storage system day-ahead optimization |
| title_sort | energy management strategy of a loop microgrid with wind energy prediction and energy storage system day ahead optimization |
| topic | wind power prediction optimization microgrid energy storage system time-of-use price |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1334588/full |
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