Journal of South China University of Technology (Natural Science Edition) ›› 2021, Vol. 49 ›› Issue (10): 31-40.doi: 10.12141/j.issn.1000-565X.200612

Special Issue: 2021年交通运输工程

• Traffic & Transportation Engineering • Previous Articles     Next Articles

Distribution Path Optimization of Electric Vehicles Considering Charging and Discharging Strategy in Smart Grid

LI Jiale LIU Zhenbo WANG Xuefei   

  1. School of Civil and Transportation Engineering，Hebei University of Technology，Tianjin 300401，China∥Hebei Key Laboratory of Intelligent Construction，Management and Maintenance for Transportation Infrastructure，Tianjin 300401，China
  • Received:2020-10-12 Revised:2021-04-07 Online:2021-10-25 Published:2021-09-30
  • Contact: 王雪菲 ( 1989-) ，女，博士，副教授，主要从事土木工程研究。 E-mail:xuefei.wang@hebut.edu.cn
  • About author:李家乐 ( 1989-) ，男，博士，副教授，主要从事智慧交通研究。E-mail:jiale.li@hebut.edu.cn
  • Supported by:
    Supported by the Natural Science Foundation of Hebei Province ( E2019202072) ，and the State Key Laboratory of Ｒeliability and Intelligence of Electrical Equipment ( EEＲI_OY2020002)

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Abstract: In view of the phenomenon that more and more companies use electric vehicles ( EVs) to distribute cargo in urban areas，this paper presented an optimized EV distribution method with the time window by considering the intelligent charging strategy in the smart grid． The EVs can either charge or discharge when connecting to the smart grid through the vehicle to grid ( V2G) system． The V2G mode provides a more flexible way for EVs to operate． It can help EVs to increase efficiency and lower the cost at the same time． Based on the recharge and vehicle routing problem，this paper proposed a nonlinear integer programming model，considered the charging and discharging decisions of EVs，and proposed an improved genetic algorithm． Finally，25 cases were designed to verify the feasibility of the algorithm． The simulation result shows that the iterative efficiency of the improved genetic algorithm ( GA) is higher than other algorithms and the quality of the optimal solution is improved by 47% ．

Key words: smart grid, charging and discharging strategy, distribution path optimization, genetic algorithm ( GA)

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