Evaluating planning and operational policies for urban public electric vehicle charging infrastructure

被引:3
作者
An, Yichan [1 ]
Papakonstantinou, Ilia [2 ]
Son, Sanghoon [3 ]
Lee, Jinwoo [1 ]
机构
[1] Korea Adv Inst Sci & Technol, Dept Civil & Environm Engn, Daejeon, South Korea
[2] New York Univ Abu Dhabi, Div Engn, Abu Dhabi, U Arab Emirates
[3] Jeju Res Inst, Reg Planning & Environm Div, Jeju Si, Jeju Do, South Korea
基金
新加坡国家研究基金会;
关键词
Electric vehicle; level of service; policies; public charging infrastructure; weighted delay; PLUG-IN HYBRID; CONSUMER PREFERENCES; INCENTIVES; STATIONS; ADOPTION; DEPLOYMENT; RESPONSES; IMPACTS; DEMAND;
D O I
10.1080/15568318.2024.2390450
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
It is expected that public electric vehicle charging demands will gradually increase in Korea. In this study, we propose a macroscopic indicator of the level of service (LOS) of urban public fast charging infrastructure to quantitively evaluate the joint impacts of possible planning and operational policies on it. The indicator is defined as the reliability-based capacity, the number of electric vehicles that can be charged within the maximum targeted weighted delay between wait and detour. We consider three combinable policies: changing the distribution of chargers in the planning stage; assigning the optimal charging station to each charging event in real-time and evenly distributing daily charging demands in the operational phase. For multiple policy combinations and various demand cases, we compare their LOS levels, calculated by Monte Carlo simulations that can efficiently address the complex joint impact of stochastic demands. The simulation experiments are designed to explicitly discover the impacts of policies along with varying charging demands. The results show that certain combinations of proposed policies can effectively and efficiently improve LOS.
引用
收藏
页码:730 / 748
页数:19
相关论文
共 85 条
[11]   A methodology to evaluate the competitiveness of electric delivery trucks [J].
Davis, Brian A. ;
Figliozzi, Miguel A. .
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2013, 49 (01) :8-23
[12]   Intention-Aware Routing of Electric Vehicles [J].
de Weerdt, Mathijs M. ;
Stein, Sebastian ;
Gerding, Enrico H. ;
Robu, Valentin ;
Jennings, Nicholas R. .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2016, 17 (05) :1472-1482
[13]  
Deloitte, 2021, ELECT VEHICLE CHARGI
[14]   Charging infrastructure planning for promoting battery electric vehicles: An activity-based approach using multiday travel data [J].
Dong, Jing ;
Liu, Changzheng ;
Lin, Zhenhong .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2014, 38 :44-55
[15]  
Electric Vehicle Database, 2021, SPECIFICATION TESLA
[16]  
EVAdoption, 2021, EV CHARGING STATIONS
[17]   Pricing strategies of domestic and imported electric vehicle manufacturers and the design of government subsidy and tariff policies [J].
Fan, Zhi-Ping ;
Cao, Yue ;
Huang, Chun-Yong ;
Li, Yongli .
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2020, 143
[18]  
Farrell J., 2007, Handbook of Industrial Organization, V3, P1967, DOI [10.1016/S1573-448X(06)03031-7, DOI 10.1016/S1573-448X(06)03031-7]
[19]   Understanding charging behaviour of electric vehicle users [J].
Franke, Thomas ;
Krems, Josef F. .
TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR, 2013, 21 :75-89
[20]  
FuelEconomy.gov, 2021, EPA FUEL EC TESLA MO