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Abstract
Reasonable capacity expansion optimization of determined charging stations is important for the popularization of electric vehicles (EVs). In this article, Gaussian two-step floating catchment area method and temporal clustering are adopted to study the unbalanced spatial and temporal distribution of charging station accessibility, as well as to determine candidate sites for capacity expansion optimization. Then, a two-level optimization model for capacity expansion of determined charging stations is proposed to reduce the computational complexity caused by the non-linearity and non-convexity of choice probability formulas when the discrete choice model is applied to statistically describe and analyze discrete behaviors such as users’ EV purchase preferences and charging station heterogeneity. In the proposed method, the lower-level model is described as a maximal coverage location problem by expressing the error term of the utility function as a linear combination of the random vectors from IID normal distribution and IID Gumbel distribution, which effectively simplifies the computing process. Finally, the two-level model can be transformed into an integer linear programming problem to optimize the capacity of determined charging stations. Experimental results show that the purchase rate of EVs is significantly improved, and the accessibility of charging stations during rush hours is more balanced than before.
Data Availability Statement
The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.
Additional information
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Notes on contributors
Xianfeng Xu
Xianfeng Xu received the B.E. degree from Harbin Engineering University, Harbin, China, in 2004 and the Ph.D. degree in signal processing from Xidian University, Xi’an, China, in 2010. He has been with the School of Electronic & Control Engineering, Chang’an University, Xi’an, China, as a Lecturer from 2010 to 2013, and as a vice professor from 2013 until now. He was a visiting scholar in University of California, Los Angeles (UCLA) in 2018. His research interests include signal processing, smart grid and intelligent transportation system.
Weifeng Zhao
Weifeng Zhao received the B.S. degree in electrical engineering and automation from Chang’an University, Xi’an, China, in 2020, and now continues to pursue the M.S. degree in electrical engineering in Chang’an University. His research interests include electric vehicle charging station planning.
Yong Lu
Yong Lu received the B.S., M.S., and Ph.D. degrees from the School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, China, in 2010, 2012, and 2016, respectively, all in electrical engineering. He is currently a Faculty Member of the School of Energy & Electrical Engineering, Chang’an University, Xi’an. His research areas include power quality, control of the power converters, and distributed generation.
Xinrong Huang
Xinrong Huang received the M.S. degree in electrical engineering from Northwestern Polytechnical University (NPU), Xi’an, China, in 2018, and received the Ph.D. degree in lifetime testing and modeling of lithium-ion batteries from the Department of Energy Technology, Aalborg University, Aalborg, Denmark, in 2021. She is supported by the China Scholarship Council from 2018 to 2021. Her research interests include Lithium-ion battery testing and modeling, battery charging strategies, and battery states estimation.
Zhuangzhuang Liu
Zhuangzhuang Liu received the B.S., M.S., and Ph.D. degrees from the School of Highway, Chang’an University, Xi’an, China, in 2010, 2013, and 2016, respectively, all in road engineering. He is currently a Faculty Member of the School of Highway, Chang’an University, Xi’an. His research areas include integration of transportation and energy.