Optimal fast charging station placing and sizing

被引:374
作者
Sadeghi-Barzani, Payam [1 ]
Rajabi-Ghahnavieh, Abbas [2 ]
Kazemi-Karegar, Hosein [3 ]
机构
[1] Islamic Azad Univ, Sci & Res Branch, Dept Energy Engn, Tehran, Iran
[2] Sharif Univ Technol, Dept Energy Engn, Tehran, Iran
[3] Shahid Beheshti Univ, Fac Elect & Comp Engn, Tehran, Iran
关键词
Electric vehicle; Charging; Station; Location; Reliability; Loss of charging; ELECTRIC VEHICLES;
D O I
10.1016/j.apenergy.2014.03.077
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Fast charging stations are vital components for public acceptance of electric vehicle (EV). The stations are connected to the electric grid and can recharge an electric vehicle in less than 20 min. Charging station development is highly influenced by the government policy in allocating station development costs. This paper presents a Mixed-Integer Non-Linear (MINLP) optimization approach for optimal placing and sizing of the fast charging stations. The station development cost, EV energy loss, electric gird loss as well as the location of electric substations and urban roads are among the factors included in the proposed approach. Geographic information has been used to determine EV energy loss and station electrification cost. The optimization problem is solved using genetic algorithm technique. Application of the proposed approach to analyze the impact of different station development policies has been discussed. The impact of electric grid reliability on charging station place and size has been evaluated using a proposed index to evaluate loss of charging cost. Results showed the robustness and efficacy of the proposed method to determine optimal place and size of the charging stations. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:289 / 299
页数:11
相关论文
共 30 条
[21]   A Spatial-Temporal model for grid impact analysis of plug-in electric vehicles [J].
Mu, Yunfei ;
Wu, Jianzhong ;
Jenkins, Nick ;
Jia, Hongjie ;
Wang, Chengshan .
APPLIED ENERGY, 2014, 114 :456-465
[22]   Life-cycle analysis of charging infrastructure for electric vehicles [J].
Nansai, K ;
Tohno, S ;
Kono, M ;
Kasahara, M ;
Moriguchi, Y .
APPLIED ENERGY, 2001, 70 (03) :251-265
[23]   Geodemographic analysis and estimation of early plug-in hybrid electric vehicle adoption [J].
Saarenpaa, Jukka ;
Kolehmainen, Mikko ;
Niska, Harri .
APPLIED ENERGY, 2013, 107 :456-464
[24]  
Saxena S, 2013, APPL ENERGY
[25]  
Schey S, 2012, 1 LOOK IMPACT ELECT, P1
[26]   The economics of fast charging infrastructure for electric vehicles [J].
Schroeder, Andreas ;
Traber, Thure .
ENERGY POLICY, 2012, 43 :136-144
[27]   Strategic charging method for plugged in hybrid electric vehicles in smart grids; a game theoretic approach [J].
Sheikhi, A. ;
Bahrami, Sh. ;
Ranjbar, A. M. ;
Oraee, H. .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2013, 53 :499-506
[28]   Targeting plug-in hybrid electric vehicle policies to increase social benefits [J].
Skerlos, Steven J. ;
Winebrake, James J. .
ENERGY POLICY, 2010, 38 (02) :705-708
[29]   Traffic-Constrained Multiobjective Planning of Electric-Vehicle Charging Stations [J].
Wang, Guibin ;
Xu, Zhao ;
Wen, Fushuan ;
Wong, Kit Po .
IEEE TRANSACTIONS ON POWER DELIVERY, 2013, 28 (04) :2363-2372
[30]   Estimating the potential of controlled plug-in hybrid electric vehicle charging to reduce operational and capacity expansion costs for electric power systems with high wind penetration [J].
Weis, Allison ;
Jaramillo, Paulina ;
Michalek, Jeremy .
APPLIED ENERGY, 2014, 115 :190-204