DocumentCode :
135243
Title :
Receding horizon power management for electrical vehicle charging
Author :
Xiaojun Geng ; Ramachandran, B. ; Khargonekar, Pramod
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of West Florida, Pensacola, FL, USA
fYear :
2014
fDate :
11-14 March 2014
Firstpage :
1
Lastpage :
7
Abstract :
This paper considers receding horizon optimization strategies of power management for a large scale network of EV charging loads. The problem formulation is unique in that charging requests are classified into a relative small number of load types. Two receding horizon schemes are proposed in the paper to deal with possible dynamic changes in the network: complete receding horizon optimization which produces better performance with more computation involved, and partial receding horizon optimization which trades performance with very light computation effort.
Keywords :
electric vehicles; optimisation; power consumption; EV charging loads; electrical vehicle charging; horizon power management; large scale network; receding horizon optimization strategies; Optimization; Vehicle dynamics; Vehicles; centralized strategy; electrical vehicle charging; power management; receding horizon optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power Systems Conference (PSC), 2014 Clemson University
Conference_Location :
Clemson, SC
Type :
conf
DOI :
10.1109/PSC.2014.6808130
Filename :
6808130
Link To Document :
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