基于蚁群算法的电动汽车快速充电站选址与容量确定优化方法  

Optimization Method of Electric Vehicle Fast Charging Station Site Selection and Capacity Determination Based on Ant Colony Algorithm

作　　者：王宏刚 陈常龙 于宙 WANG Honggang;CHEN Changlong;YU Zhou(Big Data Center of State Grid Corporation of China,Beijing 100052)

机构地区：[1]国家电网有限公司大数据中心,北京100052

出　　处：《工程数学学报》2025年第1期188-198,共11页Chinese Journal of Engineering Mathematics

摘　　要：随着人们对生态问题的日益关注和对化石燃料依赖性的减少,电动汽车作为可持续交通解决方案受到了广泛关注。电动汽车因其环保特性而备受青睐,然而其有限的能量存储能力限制了行驶距离,因此高效利用能源至关重要。为确保电动汽车能够及时获得补充能量,提出了一种基于蚁群算法的快速充电站选址与容量确定优化方法。研究考虑了实时定价、使用时间、关键峰值定价以及峰值时间回扣等因素,旨在制定最优充电定价策略。基于此策略,运用蚁群优化算法对电动汽车流量和充电需求进行了深入分析。通过综合考虑建设成本、设备购置成本、维护费用以及用户的行驶成本,构建了一个快速充电站选址与容量确定的优化模型,并采用蚁群算法对该模型进行求解,从而得出最优的选址与容量配置。实验结果表明,所提出的基于蚁群算法的优化方法具有更好的综合性能,适用于实际应用场景。With increasing awareness of ecological issues and diminishing reliance on fossil fuels,electric vehicles(EVs)have garnered widespread attention as a sustainable transportation solution.Electric vehicles are favored for their environmental friendliness,yet their limited energy storage capacity restricts driving range,making efficient energy utilization crucial.To ensure timely energy replenishment for electric vehicles,this paper presents an optimization method based on the ant colony algorithm for fast charging station location and capacity determination.This study takes into account factors such as real-time pricing,usage duration,critical peak pricing,and peak time rebates,aiming to devise an optimal charging pricing strategy.Based on this strategy,the ant colony optimization algorithm is applied to conduct an in-depth analysis of electric vehicle traffic and charging demand.By comprehensively considering construction costs,equipment procurement expenses,maintenance fees,and user driving costs,an optimization model for fast charging station location and capacity determination is constructed.The ant colony algorithm is then utilized to solve this model,yielding the optimal location and capacity configuration.Experimental results demonstrate that the proposed optimization method based on the ant colony algorithm exhibits superior comprehensive performance,making it suitable for practical application scenarios.

关 键 词：电动汽车 优化运行 蚁群算法 电动汽车充电站 

分 类 号：TM7[电气工程—电力系统及自动化]