Sung Tien-Wen, Li Wei, Liang Qiaoxin, Hong Chuanbo, Fang Qingjun
Fujian Provincial Key Laboratory of Big Data Mining and Applications, College of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, China.
Math Biosci Eng. 2023 Jul 28;20(9):15708-15736. doi: 10.3934/mbe.2023700.
This paper proposes a multi-objective queuing charging strategy for electric vehicles (EVs) based on metrics of public interest. It combines common charging modes, such as random charging mode, tariff-guided mode and stop-and-charge mode. It introduces the problem of queuing charging for EVs by considering the realistic imbalances of vehicle-pile ratios in these common modes. A travel model and a charging model were developed in this study. Experiments prove that the proposed strategy has the highest comprehensive evaluation index, achieves the aim of low charging cost and high travel rate and considers the queuing problem, which is unavoidable in reality. It improves the convenience of life and reduces the charging cost. The proposed strategy smoothens the EV charging load curve, largely reducing the burden of charging load fluctuations on the grid and achieving a win-win situation for both supply and demand.
本文基于公共利益指标提出了一种针对电动汽车的多目标排队充电策略。它结合了随机充电模式、电价引导模式和即停即充模式等常见充电模式。通过考虑这些常见模式下车辆与充电桩比例的现实不平衡情况，引入了电动汽车排队充电问题。本研究开发了一个出行模型和一个充电模型。实验证明，所提出的策略具有最高的综合评价指标，实现了低充电成本和高出行率的目标，并考虑了现实中不可避免的排队问题。它提高了生活便利性并降低了充电成本。所提出的策略使电动汽车充电负荷曲线更加平滑，大大减轻了充电负荷波动对电网的负担，实现了供需双赢。
