Urban public charging station locating method for electric vehicles based on land use approach

被引:100
作者
Csiszar, Csaba [1 ]
Csonka, Balint [1 ]
Foldes, David [1 ]
Wirth, Ervin [2 ]
Lovas, Tamas [2 ]
机构
[1] Budapest Univ Technol & Econ BME, Fac Transportat Engn & Vehicle Engn KJK, Dept Transport Technol & Econ KUKG, Muegyet Rkp 3, H-1111 Budapest, Hungary
[2] Budapest Univ Technol & Econ BME, Fac Civil Engn EMK, Dept Photogrammetry & Geoinformat FMT, Muegyet Rkp 3, H-1111 Budapest, Hungary
基金
欧盟地平线“2020”;
关键词
Electric vehicle; Urban charging infrastructure; Multicriteria method; Charging demand; Hexagon-based; Land use; Heterogeneous data; SYSTEM; MODEL;
D O I
10.1016/j.jtrangeo.2018.11.016
中图分类号
F [经济];
学科分类号
02 ;
摘要
Since the use of electric vehicles decreases the local pollution and noise emission electromobility gains a huge potential in sustainable transport. The currently insufficient charging network, namely the low number of stations and ill-chosen locations are a significant barrier of the widespread of electric vehicles in many countries. Accordingly, localization of new charging stations is of utmost importance. In this study, we develop a two-level charging station locating method. Weighted multicriteria methods were introduced to evaluate territory segments and allocate charging stations within a segment applying a hexagon-based approach and using a greedy algorithm. The novelty of the method in comparison with previous studies is that it assesses the potential of electric vehicle use on macro-level and the possible locations of charging stations on micro-level with a focus on the land-use. We apply the method to Hungary (macro evaluation) and to a district of the capital city, Budapest (micro evaluation). It is found that charging stations at P + R facilities, close to concentrated services and high density areas are better suited to serve urban public charging demand than gas stations.
引用
收藏
页码:173 / 180
页数:8
相关论文
共 35 条
[1]   Modelling of electric and parallel-hybrid electric vehicle using Matlab/Simulink environment and planning of charging stations through a geographic information system and genetic algorithms [J].
Alegre, Susana ;
Miguez, Juan V. ;
Carpio, Jose .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2017, 74 :1020-1027
[2]   Optimal allocation for electric vehicle charging stations using Trip Success Ratio [J].
Alhazmi, Yassir A. ;
Mostafa, Haytham A. ;
Salama, Magdy M. A. .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2017, 91 :101-116
[3]   A demand-side approach to the optimal deployment of electric vehicle charging stations in metropolitan areas [J].
Andrenacci, N. ;
Ragona, R. ;
Valenti, G. .
APPLIED ENERGY, 2016, 182 :39-46
[4]  
[Anonymous], COMMUNICATIONS
[5]  
[Anonymous], 2014 IEEE PES GEN M
[6]  
[Anonymous], 2017, INT J TRANSP DEV INT
[7]  
[Anonymous], COMMUNICATIONS
[8]   Electric vehicle charging demand forecasting model based on big data technologies [J].
Arias, Mariz B. ;
Bae, Sungwoo .
APPLIED ENERGY, 2016, 183 :327-339
[9]   Optimal planning of electric vehicle charging station at the distribution system using hybrid optimization algorithm [J].
Awasthi, Abhishek ;
Venkitusamy, Karthikeyan ;
Padmanaban, Sanjeevikumar ;
Selvamuthukumaran, Rajasekar ;
Blaabjerg, Frede ;
Singh, Asheesh K. .
ENERGY, 2017, 133 :70-78
[10]   Siting public electric vehicle charging stations in Beijing using big-data informed travel patterns of the taxi fleet [J].
Cai, Hua ;
Jia, Xiaoping ;
Chiu, Anthony S. F. ;
Hu, Xiaojun ;
Xu, Ming .
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT, 2014, 33 :39-46