考虑用户决策偏好的电动汽车充电引导策略

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考虑用户决策偏好的电动汽车充电引导策略

李恒杰^1*，张尊康¹，周云²，冯冬涵²，马喜平²

1．兰州理工大学电气工程与信息工程学院，甘肃省兰州市 730050；2.电力传输与功率变换控制教育部重点实验室(上海交通大学)，上海市闵行区 200240；3.国网甘肃省电力公司电力科学研究院，甘肃省兰州市 730070

基金资助:
国家自然科学基金项目(52167014)

Electric Vehicle Charging Guidance Strategy Considering user Decision Preference

LI Hengjie^1* ZHANG Zunkang¹ ZHOU Yun² FENG Donghan² MA Xiping²

1. School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, Gansu Province, China; 2. Key Laboratory of Power Transmission and Power Conversion Control, Ministry of Education (Shanghai Jiao Tong University), Minhang District, Shanghai 200240, China; 3. Electric Power Research Institute, State Grid Gansu Power Company, Lanzhou 730070, Gansu Province, China

摘要/Abstract

摘要： 【目的】在电动汽车充电引导过程中，由于用户在选择充电站中存在不同偏好，导致用户的充电站选择标准不同，影响用户参与优化调度。因此，针对用户选择充电站习惯以及用户与充电聚合商利益冲突，提出了一种考虑目标充电站选择习惯的电动汽车充电引导策略。【方法】采用自组织特征映射(self-organizing feature mapping，SOFM)来进行用户画像，再使用SHAP算法确定最佳画像结果。其次，使用熵权法来确定每类用户在选择充电站过程中时间成本和经济成本所占的权重；根据权重结果将用户的费用成本和时间成本归化为用户满意度。然后，根据用户满意度对用户进行充电引导。【结果】该策略相比于最短路径算法显著提升了用户选择充电站的满意度和充电桩的单位时间盈利。同时在不同的交通情况下该策略受影响较小。【结论】该策略有效地解决了用户充电决策偏好对充电引导的影响，对提高用户的充电利益和减少用户的充电拥堵有重要意义。

关键词:

"> 电动汽车, 充电站, 偏好, 用户画像, 引导, 满意度

Abstract: [Objectives]. In the process of EV charging guidance, users have different preferences in selecting charging stations, resulting in different selection criteria for charging stations, which affects users' participation in optimal scheduling. Therefore, in view of the user's habit of selecting charging stations and the interest conflict between users and charging aggregators, an EV charging guidance strategy considering the choice habit of target charging stations was proposed. [Methods]. self-organizing feature mapping (SOFM) was used for user profiling, and Shapley Additive ExPlanations (SHAP) were used to determine the best profiling results. Secondly, the entropy weight method is used to determine the weight of time cost and economic cost of each type of user in the process of selecting charging stations. According to the weight results, the user's expense cost and time cost are converted into user satisfaction. Then, the user is guided to charge according to the user satisfaction. [Results]. The example shows that compared with the shortest path algorithm, this strategy significantly improves the user's satisfaction in selecting charging stations and the profit per unit time of charging piles. Simultaneously, the strategy is less affected under different traffic conditions. [Conclusions]. This strategy effectively solves the influence of user's charging decision preference on charging guidance, which is of great significance for improving users' charging benefits and reducing users' charging congestion.

Key words: electric vehicle, charging station, preferences, user profiling, guidance, satisfaction

李恒杰, 张尊康, 周云, 冯冬涵, 马喜平. 考虑用户决策偏好的电动汽车充电引导策略[J]. 发电技术.

LI Hengjie, ZHANG Zunkang, ZHOU Yun, FENG Donghan, MA Xiping. Electric Vehicle Charging Guidance Strategy Considering user Decision Preference[J]. Power Generation Technology.