Ant-Based Swarm Algorithm for Charging Coordination of Electric Vehicles
Uncontrolled charging of large-scale electric vehicles (EVs) can affect the safe and economic operation of power systems, especially at the distribution level. The centralized EVs charging optimization methods require complete information of physical appliances and using habits, which will cause pro...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-05-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2013/268942 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832553150127538176 |
|---|---|
| author | Shaolun Xu Donghan Feng Zheng Yan Liang Zhang Naihu Li Lei Jing Jianhui Wang |
| author_facet | Shaolun Xu Donghan Feng Zheng Yan Liang Zhang Naihu Li Lei Jing Jianhui Wang |
| author_sort | Shaolun Xu |
| collection | DOAJ |
| description | Uncontrolled charging of large-scale electric vehicles (EVs) can affect the safe and economic operation of power systems, especially at the distribution level. The centralized EVs charging optimization methods require complete information of physical appliances and using habits, which will cause problems of high dimensionality and communication block. Given this, an ant-based swarm algorithm (ASA) is proposed to realize the EVs charging coordination at the transformer level, which can overcome the drawbacks of centralized control method. First, the EV charging load model is developed, and the charging management structure based on swarm intelligence is presented. Second, basic data of the EV using habit is sampled by the Monte Carlo method, and the ASA is applied to realize the load valley filling. The load fluctuation and the transformer capacity are also considered in the algorithm. Finally, the charging coordination of 500 EVs under a 12.47 KV transformer is simulated to demonstrate the validity of the proposed method. |
| format | Article |
| id | doaj-art-c324c6ce797b49c581c919cbdc011780 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2013-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-c324c6ce797b49c581c919cbdc0117802025-02-03T05:55:24ZengWileyInternational Journal of Distributed Sensor Networks1550-14772013-05-01910.1155/2013/268942Ant-Based Swarm Algorithm for Charging Coordination of Electric VehiclesShaolun Xu0Donghan Feng1Zheng Yan2Liang Zhang3Naihu Li4Lei Jing5Jianhui Wang6 Key Laboratory of Control of Power Transmission and Conversion of Ministry of Education, Shanghai 200240, China Key Laboratory of Control of Power Transmission and Conversion of Ministry of Education, Shanghai 200240, China Key Laboratory of Control of Power Transmission and Conversion of Ministry of Education, Shanghai 200240, China Key Laboratory of Control of Power Transmission and Conversion of Ministry of Education, Shanghai 200240, China Alstom Grid Technology Center Co. Ltd., Shanghai 200240, China Alstom Grid Technology Center Co. Ltd., Shanghai 200240, China Argonne National Laboratory, Decision and Information Sciences Division, Argonne, IL 60439, USAUncontrolled charging of large-scale electric vehicles (EVs) can affect the safe and economic operation of power systems, especially at the distribution level. The centralized EVs charging optimization methods require complete information of physical appliances and using habits, which will cause problems of high dimensionality and communication block. Given this, an ant-based swarm algorithm (ASA) is proposed to realize the EVs charging coordination at the transformer level, which can overcome the drawbacks of centralized control method. First, the EV charging load model is developed, and the charging management structure based on swarm intelligence is presented. Second, basic data of the EV using habit is sampled by the Monte Carlo method, and the ASA is applied to realize the load valley filling. The load fluctuation and the transformer capacity are also considered in the algorithm. Finally, the charging coordination of 500 EVs under a 12.47 KV transformer is simulated to demonstrate the validity of the proposed method.https://doi.org/10.1155/2013/268942 |
| spellingShingle | Shaolun Xu Donghan Feng Zheng Yan Liang Zhang Naihu Li Lei Jing Jianhui Wang Ant-Based Swarm Algorithm for Charging Coordination of Electric Vehicles International Journal of Distributed Sensor Networks |
| title | Ant-Based Swarm Algorithm for Charging Coordination of Electric Vehicles |
| title_full | Ant-Based Swarm Algorithm for Charging Coordination of Electric Vehicles |
| title_fullStr | Ant-Based Swarm Algorithm for Charging Coordination of Electric Vehicles |
| title_full_unstemmed | Ant-Based Swarm Algorithm for Charging Coordination of Electric Vehicles |
| title_short | Ant-Based Swarm Algorithm for Charging Coordination of Electric Vehicles |
| title_sort | ant based swarm algorithm for charging coordination of electric vehicles |
| url | https://doi.org/10.1155/2013/268942 |
| work_keys_str_mv | AT shaolunxu antbasedswarmalgorithmforchargingcoordinationofelectricvehicles AT donghanfeng antbasedswarmalgorithmforchargingcoordinationofelectricvehicles AT zhengyan antbasedswarmalgorithmforchargingcoordinationofelectricvehicles AT liangzhang antbasedswarmalgorithmforchargingcoordinationofelectricvehicles AT naihuli antbasedswarmalgorithmforchargingcoordinationofelectricvehicles AT leijing antbasedswarmalgorithmforchargingcoordinationofelectricvehicles AT jianhuiwang antbasedswarmalgorithmforchargingcoordinationofelectricvehicles |