Multi-objective optimization for improving EV users’ adhesion with hybrid demand response strategy
With the popularization of electric vehicles (EVs), the charging behavior of users will inevitably affect the power grid in the process of vehicle-to-grid (V2G). It is particularly important to consider the behavior characteristics and charging habits of EV users to guide their charging behavior. Th...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484723006753 |
| _version_ | 1797688415046598656 |
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| author | Ziyin He Hui Hou Tingting Hou Rengcun Fang Jinrui Tang Changjun Xie |
| author_facet | Ziyin He Hui Hou Tingting Hou Rengcun Fang Jinrui Tang Changjun Xie |
| author_sort | Ziyin He |
| collection | DOAJ |
| description | With the popularization of electric vehicles (EVs), the charging behavior of users will inevitably affect the power grid in the process of vehicle-to-grid (V2G). It is particularly important to consider the behavior characteristics and charging habits of EV users to guide their charging behavior. This paper presents a hybrid demand response (HDR) strategy based on price-sensitive demand response (PSDR), as well as incentive-based demand response (IBDR) strategy. PSDR strategy based on time-of-use (TOU) price is to guide EV users charging behavior by implementing dynamic TOU price. IBDR strategy is to put forward a charging point accumulation mechanism based on incentive subsidies and set charging reward and punishment points for different types of users according to the type of users. Then converting the TOU price into the form of points and setting a limited value for incentive points in combination with peak and valley periods to obtain the HDR strategy. Considering the uncertainty of users’ response, the model of users’ participation response is established. The multi-objective optimization model of reward and punishment coefficient and unit integral value is established by comprehensively considering various benefits. Finally, simulation results show that this strategy can effectively improve the adhesion of EV users and reduce the impact of their charging on the power grid. |
| first_indexed | 2024-03-12T01:30:39Z |
| format | Article |
| id | doaj.art-c49b3c8c625141fcb6b452170e4bd716 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-12T01:30:39Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-c49b3c8c625141fcb6b452170e4bd7162023-09-12T04:15:55ZengElsevierEnergy Reports2352-48472023-09-019316322Multi-objective optimization for improving EV users’ adhesion with hybrid demand response strategyZiyin He0Hui Hou1Tingting Hou2Rengcun Fang3Jinrui Tang4Changjun Xie5School of Automation, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Automation, Wuhan University of Technology, Wuhan 430070, China; Shenzhen Research Institute, Wuhan University of Technology, Shenzhen 518000, China; Corresponding author.Economics and Technology Research Institute, State Grid Hubei Electric Power Company, Wuhan 430030, ChinaEconomics and Technology Research Institute, State Grid Hubei Electric Power Company, Wuhan 430030, ChinaSchool of Automation, Wuhan University of Technology, Wuhan 430070, China; Shenzhen Research Institute, Wuhan University of Technology, Shenzhen 518000, ChinaSchool of Automation, Wuhan University of Technology, Wuhan 430070, China; Shenzhen Research Institute, Wuhan University of Technology, Shenzhen 518000, ChinaWith the popularization of electric vehicles (EVs), the charging behavior of users will inevitably affect the power grid in the process of vehicle-to-grid (V2G). It is particularly important to consider the behavior characteristics and charging habits of EV users to guide their charging behavior. This paper presents a hybrid demand response (HDR) strategy based on price-sensitive demand response (PSDR), as well as incentive-based demand response (IBDR) strategy. PSDR strategy based on time-of-use (TOU) price is to guide EV users charging behavior by implementing dynamic TOU price. IBDR strategy is to put forward a charging point accumulation mechanism based on incentive subsidies and set charging reward and punishment points for different types of users according to the type of users. Then converting the TOU price into the form of points and setting a limited value for incentive points in combination with peak and valley periods to obtain the HDR strategy. Considering the uncertainty of users’ response, the model of users’ participation response is established. The multi-objective optimization model of reward and punishment coefficient and unit integral value is established by comprehensively considering various benefits. Finally, simulation results show that this strategy can effectively improve the adhesion of EV users and reduce the impact of their charging on the power grid.http://www.sciencedirect.com/science/article/pii/S2352484723006753TOU priceIncentive systemEV charging optimizationUsers’ responseAdhesive degree of users |
| spellingShingle | Ziyin He Hui Hou Tingting Hou Rengcun Fang Jinrui Tang Changjun Xie Multi-objective optimization for improving EV users’ adhesion with hybrid demand response strategy Energy Reports TOU price Incentive system EV charging optimization Users’ response Adhesive degree of users |
| title | Multi-objective optimization for improving EV users’ adhesion with hybrid demand response strategy |
| title_full | Multi-objective optimization for improving EV users’ adhesion with hybrid demand response strategy |
| title_fullStr | Multi-objective optimization for improving EV users’ adhesion with hybrid demand response strategy |
| title_full_unstemmed | Multi-objective optimization for improving EV users’ adhesion with hybrid demand response strategy |
| title_short | Multi-objective optimization for improving EV users’ adhesion with hybrid demand response strategy |
| title_sort | multi objective optimization for improving ev users adhesion with hybrid demand response strategy |
| topic | TOU price Incentive system EV charging optimization Users’ response Adhesive degree of users |
| url | http://www.sciencedirect.com/science/article/pii/S2352484723006753 |
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