梁伟, 刘晓楠, 张智达, 杨肇辉, 崔文庆, 金尚婷. 考虑用户个体异质性和态度潜变量的电动私家车随机充电决策方法[J]. 现代电力. DOI: 10.19725/j.cnki.1007-2322.2023.0344
引用本文: 梁伟, 刘晓楠, 张智达, 杨肇辉, 崔文庆, 金尚婷. 考虑用户个体异质性和态度潜变量的电动私家车随机充电决策方法[J]. 现代电力. DOI: 10.19725/j.cnki.1007-2322.2023.0344
LIANG Wei, LIU Xiaonan, ZHANG Zhida, YANG Zhaohui, CUI Wenqing, JIN Shangting. A Random Charging Decision Method for Electric Private Cars Considering Individual Heterogeneity of Users and Latent Attitudinal Variables[J]. Modern Electric Power. DOI: 10.19725/j.cnki.1007-2322.2023.0344
Citation: LIANG Wei, LIU Xiaonan, ZHANG Zhida, YANG Zhaohui, CUI Wenqing, JIN Shangting. A Random Charging Decision Method for Electric Private Cars Considering Individual Heterogeneity of Users and Latent Attitudinal Variables[J]. Modern Electric Power. DOI: 10.19725/j.cnki.1007-2322.2023.0344

考虑用户个体异质性和态度潜变量的电动私家车随机充电决策方法

A Random Charging Decision Method for Electric Private Cars Considering Individual Heterogeneity of Users and Latent Attitudinal Variables

  • 摘要: 针对电动私家车(electric private car, EPC)充电决策行为,介绍一种考虑用户个体异质性和态度潜变量的EPC随机充电决策方法。该方法首先通过分析充电决策的影响因素,并针对不同因素设计调查问卷。其次,针对态度潜变量设计问题,得到测量指标以间接量化态度潜变量,并通过问卷信度和效度分析调查数据的质量;然后,考虑影响用户个体充电决策的充电场景变量、态度潜变量以及个体社会经济属性,利用混合选择模型(hybrid choice model, HCM)建立充电效用,确定充电概率。通过调查数据估计模型参数,最后确定充电效用与3类影响因素的具体表达式。与基于混合Logit模型的预测结果相比,所提方法的预测结果精度更高,采用所提方法后误差缩小了将近14%。

     

    Abstract: According to the charging decision behavior of electric private cars (EPC), a random charging decision method for EPCs is proposed with individual heterogeneity and latent attitude variables taken into account. Firstly, an analysis on the influencing factors of charging decision is conducted, and questionnaires for the different factors are designed. Secondly, to address the design problem of latent variables of attitude, a measurement index is derived to indirectly quantify latent variables of attitude. Additionally, the quality of the survey data is analyzed through an examination of questionnaire data reliability and validity. Then, considering the charging scene variables, attitude latent variables and individual socioeconomic attributes that affect individual charging decisions, the hybrid choice model (HCM) is employed to establish the charging utility, and the charging probability is subsequently determined. Through the investigation data, the model parameters are estimated, leading to the determination of specific expressions of charging utility and three types of influencing factors. Compared to the prediction results based on the mixed Logit model, the proposed method achieves higher prediction accuracy, and the error has been reduced by nearly 14% when adopting the proposed method.

     

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