Electric Vehicles Charging Scheduling Strategy Based on Time Cost of Users and Spatial Load Balancing in Multiple Microgrids
In a sustainable energy system, managing the charging demand of electric vehicles (EVs) becomes increasingly critical. Uncontrolled charging behaviors of large-scale EV fleets will exacerbate loads imbalanced in a multi-microgrid (MMG). At the same time, the time cost of users will increase signific...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/16/1/46 |
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| author | Jiaqi Zhang Yongxiang Xia Zhongyi Cheng Xi Chen |
| author_facet | Jiaqi Zhang Yongxiang Xia Zhongyi Cheng Xi Chen |
| author_sort | Jiaqi Zhang |
| collection | DOAJ |
| description | In a sustainable energy system, managing the charging demand of electric vehicles (EVs) becomes increasingly critical. Uncontrolled charging behaviors of large-scale EV fleets will exacerbate loads imbalanced in a multi-microgrid (MMG). At the same time, the time cost of users will increase significantly. To improve users’ charging experience and ensure stable operation of the MMG, we propose a new joint scheduling strategy that considers both time cost of users and spatial load balancing among MMGs. The time cost encompasses many factors, such as traveling time, queue waiting time, and charging time. Meanwhile, spatial load balancing seeks to mitigate the impact of large-scale EV charging on MMG loads, promoting a more equitable distribution of power resources across the MMG system. Compared to the Shortest Distance Matching Strategy (SDMS) and the Time Minimum Matching Strategy (TMMS) methods, our approach improves the average peak-to-valley ratio by 9.5% and 10.2%, respectively. Similarly, compared to the Load Balancing Matching Strategy (LBMS) and the Improved Load Balancing Matching Strategy (ILBMS) methods, our approach reduces the average time cost by 31.8% and 25% while maintaining satisfactory spatial load balancing. These results demonstrate that the proposed method achieves good results in handling electric vehicle scheduling problems. |
| format | Article |
| id | doaj-art-e9cda2a527a04a469f586f0d822ce138 |
| institution | Kabale University |
| issn | 2032-6653 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-e9cda2a527a04a469f586f0d822ce1382025-01-24T13:52:53ZengMDPI AGWorld Electric Vehicle Journal2032-66532025-01-011614610.3390/wevj16010046Electric Vehicles Charging Scheduling Strategy Based on Time Cost of Users and Spatial Load Balancing in Multiple MicrogridsJiaqi Zhang0Yongxiang Xia1Zhongyi Cheng2Xi Chen3School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaChina Electric Power Research Institute, Beijing 100192, ChinaIn a sustainable energy system, managing the charging demand of electric vehicles (EVs) becomes increasingly critical. Uncontrolled charging behaviors of large-scale EV fleets will exacerbate loads imbalanced in a multi-microgrid (MMG). At the same time, the time cost of users will increase significantly. To improve users’ charging experience and ensure stable operation of the MMG, we propose a new joint scheduling strategy that considers both time cost of users and spatial load balancing among MMGs. The time cost encompasses many factors, such as traveling time, queue waiting time, and charging time. Meanwhile, spatial load balancing seeks to mitigate the impact of large-scale EV charging on MMG loads, promoting a more equitable distribution of power resources across the MMG system. Compared to the Shortest Distance Matching Strategy (SDMS) and the Time Minimum Matching Strategy (TMMS) methods, our approach improves the average peak-to-valley ratio by 9.5% and 10.2%, respectively. Similarly, compared to the Load Balancing Matching Strategy (LBMS) and the Improved Load Balancing Matching Strategy (ILBMS) methods, our approach reduces the average time cost by 31.8% and 25% while maintaining satisfactory spatial load balancing. These results demonstrate that the proposed method achieves good results in handling electric vehicle scheduling problems.https://www.mdpi.com/2032-6653/16/1/46electric vehiclestime cost of usersspatial load balancingload distributionsafe operation |
| spellingShingle | Jiaqi Zhang Yongxiang Xia Zhongyi Cheng Xi Chen Electric Vehicles Charging Scheduling Strategy Based on Time Cost of Users and Spatial Load Balancing in Multiple Microgrids World Electric Vehicle Journal electric vehicles time cost of users spatial load balancing load distribution safe operation |
| title | Electric Vehicles Charging Scheduling Strategy Based on Time Cost of Users and Spatial Load Balancing in Multiple Microgrids |
| title_full | Electric Vehicles Charging Scheduling Strategy Based on Time Cost of Users and Spatial Load Balancing in Multiple Microgrids |
| title_fullStr | Electric Vehicles Charging Scheduling Strategy Based on Time Cost of Users and Spatial Load Balancing in Multiple Microgrids |
| title_full_unstemmed | Electric Vehicles Charging Scheduling Strategy Based on Time Cost of Users and Spatial Load Balancing in Multiple Microgrids |
| title_short | Electric Vehicles Charging Scheduling Strategy Based on Time Cost of Users and Spatial Load Balancing in Multiple Microgrids |
| title_sort | electric vehicles charging scheduling strategy based on time cost of users and spatial load balancing in multiple microgrids |
| topic | electric vehicles time cost of users spatial load balancing load distribution safe operation |
| url | https://www.mdpi.com/2032-6653/16/1/46 |
| work_keys_str_mv | AT jiaqizhang electricvehicleschargingschedulingstrategybasedontimecostofusersandspatialloadbalancinginmultiplemicrogrids AT yongxiangxia electricvehicleschargingschedulingstrategybasedontimecostofusersandspatialloadbalancinginmultiplemicrogrids AT zhongyicheng electricvehicleschargingschedulingstrategybasedontimecostofusersandspatialloadbalancinginmultiplemicrogrids AT xichen electricvehicleschargingschedulingstrategybasedontimecostofusersandspatialloadbalancinginmultiplemicrogrids |