Charging Behavior Portrait of Electric Vehicle Users Based on Fuzzy C-Means Clustering Algorithm

Aixin Yang, Guiqing Zhang (), Chenlu Tian, Wei Peng and Yechun Liu
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Aixin Yang: Shandong Key Laboratory of Intelligent Building Technology, School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China
Guiqing Zhang: Shandong Key Laboratory of Intelligent Building Technology, School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China
Chenlu Tian: Shandong Key Laboratory of Intelligent Building Technology, School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China
Wei Peng: Shandong Key Laboratory of Intelligent Building Technology, School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China
Yechun Liu: Shandong Key Laboratory of Intelligent Building Technology, School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China

Energies, 2024, vol. 17, issue 7, 1-26

Abstract: The rapid increase in electric vehicles (EVs) has led to a continuous expansion of electric vehicle (EV) charging stations, imposing significant load pressures on the power grid. Implementing orderly charging scheduling for EVs can mitigate the impact of large-scale charging on the power grid. However, the charging behavior of EVs significantly impacts the efficiency of orderly charging plans. By integrating user portrait technology and conducting research on optimized scheduling for EV charging, EV users can be accurately classified to meet the diverse needs of various user groups. This study establishes a user portrait model suitable for park areas, providing user group classification based on the user response potential for scheduling optimization. First, the FCM and feature aggregation methods are utilized to classify the quantities of features of EV users, obtaining user portrait classes. Second, based on these classes, a user portrait inventory for each EV is derived. Third, based on the priority of user response potential, this study presents a method for calculating the feature data of different user groups. The individual data information and priorities from the user portrait model are inputted into the EV-optimized scheduling model. The optimization focuses on the user charging cost and load fluctuation, with the non-dominated sorting genetic algorithm II utilized to obtain the solutions. The results demonstrate that the proposed strategy effectively addresses the matching issue between the EV user response potential and optimal scheduling modes without compromising the normal use of EVs by users. This classification approach facilitates the easier acceptance of scheduling tasks by participating users, leading to optimized outcomes that better meet practical requirements.

Keywords: EVs; charging behavior portrait; fuzzy c-mean; feature quantity (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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