| 引用本文: | 任峰,向月,雷小林,罗超.基于GPS数据的电动出租车充电桩选址定容[J].电力自动化设备,2022,42(10): |
| REN Feng,XIANG Yue,LEI Xiaolin,LUO Chao.Location and capacity determination of electric taxi charging pile based on GPS data[J].Electric Power Automation Equipment,2022,42(10): |
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| 摘要: |
| 随着电动出租车规模化的增长，关于电动出租车集群充电桩的选址定容问题日益凸显。该类车辆的轨迹分布在城区，并且该类车辆对公共充电桩的依赖远高于私家电动汽车。为了能够满足这类车辆充电的需求，提出了一种基于全球卫星定位系统(GPS)考虑电动出租车运行轨迹、车辆进入停车场时序性和停车场现有负荷水平的电动出租车充电桩规划方法。采用k-means算法分析得到电动出租车的常驻点并将其作为精选地址的相关依据；计及相邻预选址之间的干扰，以最小花费成本为目标函数对充电桩预选址进行精选；考虑车辆进入停车场的时序性和停车场现有负荷水平计算充电桩的可安装数目。仿真结果表明，针对电动出租车这类特殊集群有常驻点，通过计算最小成本可进行进一步优化，避免出现2个预选址距离太近的情况；在精选地址下对车辆进场时序性和负荷进行分析，可为城区规划修建充电桩提供有力的理论支撑。 |
| 关键词: 电动出租车；充电桩；GPS k-means算法；轨迹挖掘；选址定容 |
| DOI：10.16081/j.epae.202208046 |
| 分类号:U469.72 |
| 基金项目:国家自然科学基金资助项目(52111530067) |
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| Location and capacity determination of electric taxi charging pile based on GPS data |
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REN Feng1,2, XIANG Yue1, LEI Xiaolin3, LUO Chao4
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1.College of Electrical Engineering, Sichuan University, Chengdu 610065, China;2.Sichuan Energy Investment Integrated Energy Co.,Ltd.,Chengdu 610065, China;3.Shaoguan Power Supply Bureau of Guangdong Power Grid Co.,Ltd.,Shaoguan 512028, China;4.Sichuan Xichang Electric Power Co.,Ltd.,Xichang 615000, China
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| Abstract: |
| With the growth of the scale of electric taxis, the problem of location and capacity determination of charging pile for electric taxi cluster has become increasingly prominent. The trajectories of such vehicles are distributed in urban areas, and the dependence of such vehicles on public charging piles is much higher than that of private electric vehicles. In order to meet the charging requirements of such vehicles, a charging pile planning method for electric taxis based on GPS(Global satellite Positioning System) is proposed, which considers the travelling trajectories of electric taxi, the time sequence of vehicles entering the parking lot and the existing load level of the parking lot. The k-means algorithm is used to analyze and obtain the resident points of electric taxis and taking them as the relevant basis for selecting sites. Then, considering the interference between adjacent pre-selected addresses, the minimum cost is taken as the objective function to select the pre-selected sites of charging pile. Furthermore, the number of charging piles that can be installed is calculated considering the time sequence of vehicles entering the parking lot and the existing load level of the parking lot. Simulative results show that for the special clusters such as electric taxis that have resident points, the further optimization is carried out through calculating the minimum cost to avoid the two pre-selected sites being too close. Analyzing the time sequence of vehicles entering the parking lot and the load under selection sites can provide the strong supporting theory for the construction of charging piles in urban planning. |
| Key words: electric taxis charging piles GPS k-means algorithm trajectory mining location and capacity determination |
