Optimal charging scheduling for large-scale EV (electric vehicle) deployment based on the interaction of the smart-grid and intelligent-transport systems

被引：130
作者：
Luo, Yugong [1 ]
Zhu, Tao [1 ]
Wan, Shuang [1 ]
Zhang, Shuwei [1 ]
Li, Keqiang [1 ]
机构：
[1] Tsinghua Univ, Dept Automot Engn, State Key Lab Automot Safety & Energy, Beijing 100084, Peoples R China
来源：
ENERGY | 2016年 / 97卷
关键词：
Battery-switch EV; Electric vehicles; Fast-charging EV; Intelligent-transport system; Optimal scheduling strategy; Smart-grid system; 100-PERCENT RENEWABLE ENERGY; INTEGRATION; MANAGEMENT; OPERATION; POWER; MODEL;
D O I：
10.1016/j.energy.2015.12.140
中图分类号：
O414.1 [热力学];
学科分类号：
摘要：
The widespread use of electric vehicles (EVs) is becoming an imminent trend. Research has been done on the scheduling of EVs from the perspective of the charging characteristic, improvement in the safety and economy of the power grid, or the traffic jams in the transport system caused by a large number of EVs driven to charging stations. There is a lack of systematic studies considering EVs, the power grid, and the transport system all together. In this paper, a novel optimal charging scheduling strategy for different types of EVs is proposed based on not only transport system information, such as road length, vehicle velocity and waiting time, but also grid system information, such as load deviation and node voltage. In addition, a charging scheduling simulation platform suitable for large-scale EV deployment is developed based on actual charging scenarios. The simulation results show that the improvements in both the transport system efficiency and the grid system operation can be obtained by using the optimal strategy, such as the node voltage drop is decreased, the power loss is reduced, and the load curve is optimized. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码：359 / 368
页数：10
相关论文
共 31 条
[1]   A new model based on optimal scheduling of combined energy exchange modes for aggregation of electric vehicles in a residential complex [J].
Amirioun, Mohammad Hassan ;
Kazemi, Ahad .
ENERGY, 2014, 69 :186-198
[2]  
[Anonymous], 2011, P 8 ACM INT WORKSHOP, DOI [10.1145/2030698.2030706, DOI 10.1145/2030698.2030706]
[3]  
[Anonymous], P 2011 INT C EL MACH
[4]   Optimal recharging strategy for battery-switch stations for electric vehicles in France [J].
Armstrong, M. ;
Moussa, C. El Hajj ;
Adnot, J. ;
Galli, A. ;
Riviere, P. .
ENERGY POLICY, 2013, 60 :569-582
[5]   Spatial and Temporal Model of Electric Vehicle Charging Demand [J].
Bae, Sungwoo ;
Kwasinski, Alexis .
IEEE TRANSACTIONS ON SMART GRID, 2012, 3 (01) :394-403
[6]   A class of multi-period semi-variance portfolio for petroleum exploration and development [J].
Guo, Qiulin ;
Li, Jianzhong ;
Zou, Caineng ;
Guo, Yujuan ;
Yan, Wei .
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 2012, 43 (10) :1883-1890
[7]   How accurate are drivers' predictions of their own mobility? Accounting for psychological factors in the development of intelligent charging technology for electric vehicles [J].
Hahnel, Ulf J. J. ;
Goelz, Sebastian ;
Spada, Hans .
TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE, 2013, 48 :123-131
[8]   Optimal Scheduling for Charging and Discharging of Electric Vehicles [J].
He, Yifeng ;
Venkatesh, Bala ;
Guan, Ling .
IEEE TRANSACTIONS ON SMART GRID, 2012, 3 (03) :1095-1105
[9]   Optimal scheduling of electric vehicles in an intelligent parking lot considering vehicle-to-grid concept and battery condition [J].
Honarmand, Masoud ;
Zakariazadeh, Alireza ;
Jadid, Shahram .
ENERGY, 2014, 65 :572-579
[10]   Utilizing real-time information transferring potentials to vehicles to improve the fast-charging process in electric vehicles [J].
Johnson, Jennifer ;
Chowdhury, Mashrur ;
He, Yiming ;
Taiber, Joachim .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2013, 26 :352-366
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