基于电动汽车大数据的多等级充电站选址与服务能力研究

Location and Service Capability of Multilevel Charging Stations Based on Electric Vehicle Big Data

电动汽车充电站规划对设施建设成本、后期盈利以及用户满意度具有重要影响。提出一个多等级充电站双目标优化选址模型,包含2个不可相互转换的优化目标——最小化充电站建设总成本和车辆行驶成本,考虑不同等级充电站的建设成本和服务能力。首先,通过分析已有车辆运行轨迹大数据,提出车辆停驻状态分析方法,基于改进K-means聚类算法获取电动汽车的潜在充电需求,进行充电站初次选址;然后,构建电动汽车多等级充电站选址模型,分析充电需求,并基于禁忌搜索(Tabu Search, TS)算法进行求解和充电站二次选址;最后,基于排队论建立充电设备定容优化模型,并利用成都市电动出租车GPS轨迹大数据进行实证分析,分别基于K-means聚类和多等级选址模型2种方案对充电站进行选址,从服务能力及经济性方便性等方面对比选址结果。研究结果表明:2种选址方案均具有较好可行性,基于多等级选址模型方案的2.5 km服务半径覆盖率为91.2%,虽较K-means聚类选址方案的93.6%略差,但均超过90%覆盖率。经济效益方面,基于多等级选址模型方案明显优于K-means聚类选址;该方案在建设成本降低了11.7%的前提下,总盈利提高14.4%,利润率提升23.4%;表明提出的电动汽车充电设施选址定容方法可有效降低充电设施建设成本,提升设施盈利水平以及用户充电体验,进而获得良好的社会效益。

The planning of electric vehicle(EV) charging station has significant impacts on facility construction costs, long-term profitability, and user satisfaction. In this paper, an optimization location model of multilevel charging stations is proposed, which contains two noninterchangeable optimization objectives, minimizing the total cost of charging station construction and vehicle driving cost, to consider the construction cost and service capacity of different levels of charging stations. By analyzing the big data of the existing vehicle trajectories, various vehicle parking states and durations were retrieved, and the potential charging demand of EVs was obtained based on an improved K-means clustering algorithm for the initial location of charging stations. A multilevel EV charging station location model was constructed to analyze the charging demand, which was solved based on the Tabu Search algorithm to evaluate the secondary locations of charging stations. A charging equipment capacity optimization model was established based on the queueing theory. Global Positioning System trajectory data of electric taxis in Chengdu, China were used for an empirical analysis. The charging station locations were determined based on K-means clustering and multilevel location model. The two schemes were compared based on service capacity, economic performance, as well as convenience issues. The location schemes obtained by the two methods are feasible. The coverage of the 2.5 km service radius of the multilevel locations is 91.2%, slightly worse than that of the K-means clustering scheme(93.6%), while both are above 90% coverage rate. In terms of economic benefits, the scheme based on the location model is significantly better than the K-means clustering scheme. The total profit is increased by 14.4%, while the profit rate is increased by 23.4% based on the multilevel modeling scenario with a relative reduction of 11.7% in construction cost. The proposed location and capacity scheme of EV charging facilities can effectively reduce the construction cost of charging facilities, and improve the profitability and user charging convenience, and thus has social benefits.