Coordinated Charging Schedule Optimization for Electric Vehicles
Considering Travel Characteristics

doi:10.16097/j.cnki.1009-6744.2024.01.024

Abstract

Abstract: Considering the large-scale imbalance between charging supply and demand and low resource utilization caused by the disorderly charging of electric vehicles (EV), this paper proposes a scheduling optimization strategy for cooperative charging of electric vehicles based on analyzing user travel characteristics. The study uses the economic incentives to change the charging choice of EV users, and coordinates the output power of charging stations at different periods according to the time-of-use electricity price strategy of the power grid. The optimization model of EV cooperative charging scheduling is developed with the goal of maximizing the revenue of charging stations. To reduce the dimension of the solution space and improve the speed of finding the solution, the model is decomposed into the main problem of charging scheduling and the sub-problem of coordinated power allocation of the station. The improved genetic algorithm is used to encode and solve the main problem of the model, and the Gurobi solver is used to solve the sub-problem. The simulation experiments are carried out on both the classic road network and the real road network. The results show that the EV cooperative charging scheduling can improve the utilization rate of charging resources and the benefit of the station. With the increase of scheduling compensation, the effect of station revenue enhancement gradually decreases. Higher peak-valley price difference can motivate charging stations to actively implement charging scheduling and coordinated distribution of charging power in time periods, improve station service rate, and alleviate load fluctuation of the grid.

Key words: urban traffic, charging scheduling, genetic algorithm, electric vehicles (EV), travel characteristics

摘要： 针对大规模电动汽车无序充电导致的充电供需不平衡和资源利用率低等问题，在分析用户出行特征的基础上，提出电动汽车协同充电的调度优化策略。通过经济激励改变电动汽车用户的充电选择，并结合电网的分时电价策略协调充电站内各时段输出功率，以充电站收益最大化为目标建立电动汽车协同充电调度优化模型。为降低解空间的维度，加快求解速度，将模型分解为充电调度主问题和站点功率协调分配子问题，利用改进遗传算法编码求解模型主问题，并通过调用Gurobi求解器求解子问题。最后，分别在经典路网和现实路网中进行仿真实验。结果表明：电动汽车协同充电调度能够提高充电资源利用率和站点收益；随着调度补偿力度增大，站点收益提升效果逐渐减弱；较高的电力峰谷价差可以激励充电站主动实施充电调度和时段充电功率的协调分配，提高站点服务率并缓解电网负载波动。

关键词: 城市交通, 充电调度, 遗传算法, 电动汽车, 出行特征

CLC Number:

U121

GE Xianlong, WANG Bo, YANG Yushu, YANG Tanyue, YIN Zuofa. Coordinated Charging Schedule Optimization for Electric Vehicles Considering Travel Characteristics[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(1): 240-252.

葛显龙, 王博, 杨育树, 杨昙月, 尹作发. 考虑出行特征的电动汽车协同充电调度优化研究[J]. 交通运输系统工程与信息, 2024, 24(1): 240-252.

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Coordinated Charging Schedule Optimization for Electric Vehicles Considering Travel Characteristics

考虑出行特征的电动汽车协同充电调度优化研究

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