Data-Based Orderly Charging Strategy Considering Users’ Charging Choices
This work proposes a centralized data-based orderly charging strategy that considers the user’s charging choices. Three charging choices for different types of users are described. Then, a scheduling model of electric vehicles based on the time dimension is established. In this strategy, the optimiz...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/19/6923 |
| _version_ | 1797575912771813376 |
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| author | Ye Tao Yupu Chen Miaohua Huang Lan Yang |
| author_facet | Ye Tao Yupu Chen Miaohua Huang Lan Yang |
| author_sort | Ye Tao |
| collection | DOAJ |
| description | This work proposes a centralized data-based orderly charging strategy that considers the user’s charging choices. Three charging choices for different types of users are described. Then, a scheduling model of electric vehicles based on the time dimension is established. In this strategy, the optimization model not only considers the demand of the grid side and the user side, but also takes the driving data of electric vehicles as the driver. The grid-side optimization involves minimizing the equivalent load fluctuation, and the user-side is optimized to minimize the charging cost and maximize the charging electric quantity. The scheduling capabilities of the three charging strategies are analyzed based on a series of driving data of electric vehicles. The results show that the peak-valley difference and equivalent load fluctuation of the power grid in the data-based orderly charging strategy reduced by 22.2% and 22.7%, respectively, and the charging cost of users also reduced much more than the other two charging strategies. Additionally, the effect of users’ charging choices on the charging strategy is analyzed, and the results show that the orderly charging strategy that considers users’ charging choices can effectively decrease the scheduling deviation caused by users’ charging choices. It greatly improves the security and economy of the grid. |
| first_indexed | 2024-03-10T21:46:01Z |
| format | Article |
| id | doaj.art-bebe92655cdb4b2fae961fc403d991e7 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T21:46:01Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-bebe92655cdb4b2fae961fc403d991e72023-11-19T14:20:46ZengMDPI AGEnergies1996-10732023-10-011619692310.3390/en16196923Data-Based Orderly Charging Strategy Considering Users’ Charging ChoicesYe Tao0Yupu Chen1Miaohua Huang2Lan Yang3School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, ChinaThis work proposes a centralized data-based orderly charging strategy that considers the user’s charging choices. Three charging choices for different types of users are described. Then, a scheduling model of electric vehicles based on the time dimension is established. In this strategy, the optimization model not only considers the demand of the grid side and the user side, but also takes the driving data of electric vehicles as the driver. The grid-side optimization involves minimizing the equivalent load fluctuation, and the user-side is optimized to minimize the charging cost and maximize the charging electric quantity. The scheduling capabilities of the three charging strategies are analyzed based on a series of driving data of electric vehicles. The results show that the peak-valley difference and equivalent load fluctuation of the power grid in the data-based orderly charging strategy reduced by 22.2% and 22.7%, respectively, and the charging cost of users also reduced much more than the other two charging strategies. Additionally, the effect of users’ charging choices on the charging strategy is analyzed, and the results show that the orderly charging strategy that considers users’ charging choices can effectively decrease the scheduling deviation caused by users’ charging choices. It greatly improves the security and economy of the grid.https://www.mdpi.com/1996-1073/16/19/6923electric vehicledriving datausers’ charging choices |
| spellingShingle | Ye Tao Yupu Chen Miaohua Huang Lan Yang Data-Based Orderly Charging Strategy Considering Users’ Charging Choices Energies electric vehicle driving data users’ charging choices |
| title | Data-Based Orderly Charging Strategy Considering Users’ Charging Choices |
| title_full | Data-Based Orderly Charging Strategy Considering Users’ Charging Choices |
| title_fullStr | Data-Based Orderly Charging Strategy Considering Users’ Charging Choices |
| title_full_unstemmed | Data-Based Orderly Charging Strategy Considering Users’ Charging Choices |
| title_short | Data-Based Orderly Charging Strategy Considering Users’ Charging Choices |
| title_sort | data based orderly charging strategy considering users charging choices |
| topic | electric vehicle driving data users’ charging choices |
| url | https://www.mdpi.com/1996-1073/16/19/6923 |
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