Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (11): 2508-2513.doi: 10.13229/j.cnki.jdxbgxb20211200

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Coordination scheduling of electric vehicle charge and discharge using adaptive genetic algorithm

Cui-yu LI1(),Ya-meng HU1,Ya-wei KANG2,De-liang ZHANG1   

  1. 1.School of Industrial Design Engineering，Hubei University of Technology，Wuhan 430068，China
    2.School of Mechanical Engineering，Hubei University of Technology，Wuhan 430068，China
  • Received:2021-11-15 Online:2022-11-01 Published:2022-11-16

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Abstract:

Aiming at the problems of long scheduling time， low scheduling accuracy， and large power consumption in the current method for electric vehicle charging and discharging coordinated scheduling， a method for electric vehicle charging and discharging coordinated deployment using adaptive genetic algorithm is proposed. Firstly, through the charging prediction of electric vehicles, the charging facilities of electric vehicles are reasonably planned, and on this basis, the relevant constraints are put forward to establish the charging and discharging cooperative scheduling model of electric vehicles. Then, the model was solved by adaptive genetic particle swarm optimization algorithm.Finally， through the calculation results， the coordinated charging and discharging scheduling of electric vehicles is completed. The experimental results show that the method proposed in this paper has short scheduling time， high accuracy and low power consumption when performing electric vehicle charging and discharging coordinated scheduling.

Key words: adaptive genetic algorithm, particle swarm algorithm, electric vehicle, charging and discharging, collaborative scheduling, scheduling model, charging method

CLC Number: 

  • TM721

Fig.1

Charging facility planning process"

Fig.2

Particle swarm optimization model solution flow chart"

Fig.3

Test results of collaborative scheduling time of three methods in different environments"

Fig.4

Circuit load rate test results of different cooperative scheduling methods"

Fig.5

Energy consumption test results of different collaborative scheduling methods"

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