High efficient valley-filling strategy for centralized coordinated charging of large-scale electric vehicles

被引:140
作者
Jian, Linni [1 ,2 ]
Zheng, Yanchong [1 ,2 ]
Shao, Ziyun [3 ]
机构
[1] Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen 518055, Peoples R China
[2] Shenzhen Key Lab Elect Direct Drive Technol, Shenzhen, Peoples R China
[3] Guangzhou Univ, Sch Mech & Elect Engn, Guangzhou, Guangdong, Peoples R China
关键词
Electric vehicle; Coordinated charging; Valley-filling; Charging scheduling; Computational complexity; DEMAND; MANAGEMENT;
D O I
10.1016/j.apenergy.2016.10.117
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
080707 [能源环境工程]; 082001 [油气井工程];
摘要
With the rapid development of electric vehicles (EVs), the impacts on power grids arising from EV charging have drawn increasing attention worldwide. Uncoordinated charging of large-scale EVs will inevitably elevate load peaks at rush hours, therefore, poses serious challenges to the stability and security of power grid. Coordinated charging is expected to alleviate these negative impacts by utilizing the surplus power in the lower-demand hours with the help of the so-called valley filling algorithm. Nevertheless, when the amount of EVs involved reaches 1 million or above, the complexity of the scheduling method becomes a critical issue. In this paper, a very high efficient valley-filling strategy is proposed. Two key indexes, viz., capacity margin index and charging priority index are defined, the former one is used to select the target time slots on which the power grid has abundant surplus power for EV charging, and the latter one is used to determine the charging priority of EVs on each time slot. The simulation results demonstrate that the coordinated charging scheme with the proposed valley-filling strategy significantly outperforms the uncoordinated charging in the aspect of suppressing the elevated peak loads of power grid. Moreover, the complexity analysis shows that the proposed algorithm is much high-efficient than. its existing counterparts. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:46 / 55
页数:10
相关论文
共 43 条
[1]
2014 Nissan North America Inc, 2014, 2014 NISS LEAF BROCH
[2]
Smart charging and appliance scheduling approaches to demand side management [J].
Adika, Christopher O. ;
Wang, Lingfeng .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2014, 57 :232-240
[3]
Real-Time Distributed Control for Smart Electric Vehicle Chargers: From a Static to a Dynamic Study [J].
Ardakanian, Omid ;
Keshav, Srinivasan ;
Rosenberg, Catherine .
IEEE TRANSACTIONS ON SMART GRID, 2014, 5 (05) :2295-2305
[4]
Bai X, 2015, INT J ELEC POWER, V68, P369
[5]
Bayerische Motoren Werke, 2013, NEW BMW I3 LAUNCH
[6]
Global against divided optimization for the participation of an EV aggregator in the day-ahead electricity market. Part I: Theory [J].
Bessa, R. J. ;
Matos, M. A. .
ELECTRIC POWER SYSTEMS RESEARCH, 2013, 95 :309-318
[7]
Chen NJ, 2012, INT CONF SMART GRID, P13, DOI 10.1109/SmartGridComm.2012.6485952
[8]
Coordinated In-home Charging of Plug-in Electric Vehicles from a Household Smart Microgrid [J].
Davydova, Anastasia ;
Chakirov, Roustiam ;
Vagapov, Yuriy ;
Komenda, Taras ;
Lupin, Sergey .
AFRICON, 2013, 2013, :1049-1052
[9]
Sustainable transportation based on electric vehicle concepts: a brief overview [J].
Eberle, Ulrich ;
von Helmolt, Rittmar .
ENERGY & ENVIRONMENTAL SCIENCE, 2010, 3 (06) :689-699
[10]
Electricity costs for an electric vehicle fueling station with Level 3 charging [J].
Flores, Robert J. ;
Shaffer, Brendan P. ;
Brouwer, Jacob .
APPLIED ENERGY, 2016, 169 :813-830