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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/22667

Title: A coordinated framework for optimized charging of EV fleet in smart grid
Authors: Usman, Muhammad
Knapen, Luk
Yasar, Ansar
Bellemans, Tom
Janssens, Davy
Wets, Geert
Issue Date: 2016
Publisher: Elsevier B.V
Citation: Shakshuki, Elhadi (Ed.). The 11th International Conference on Future Networks and Communications (FNC 2016) / The 13th International Conference on Mobile Systems and Pervasive Computing (MobiSPC 2016) / Affiliated Workshops, Elsevier B.V,p. 332-339 (Art N° 94)
Series/Report: Procedia Computer Science
Abstract: Electric vehicles can be sustainable alternative in contrast to conventional fossil fuel powered vehicles only if the green energy is used to power them. Without coordination among electric vehicles and grid operator, it can imbalance the power production and demand. This paper presents an automated coordinated mechanism among EV fleet and the grid operator that plans a charging strategy for electric vehicles while sustaining the grid capacity constraints. The intelligent planner plans the charging strategy at the cheaper moments and keep the vehicle charged enough to complete its scheduled trips It suggests a charging pattern for the electric vehicle by using the time dependent electric prices and available power at the given time slots. It also ensures the cheapest charging cost and fulfills the constraints of battery state of the charge. A central power tracker is also introduced which keeps track of the available and required power at each time slot. According to the current market share of the electric vehicles, a fraction of the daily agendas, created by an operational activity-based model, is used to test the framework. Moreover, an experiment has been set up, it makes use of wind and solar renewable energy to power the vehicles.
Notes: Usman, M (reprint author), Univ Hasselt, Transportat Res Inst IMOB, Diepenbeek, Belgium. muhammad.usman@uhasselt.be
URI: http://hdl.handle.net/1942/22667
DOI: 10.1016/j.procs.2016.08.049
ISI #: 000387293800042
ISSN: 1877-0509
Category: C1
Type: Proceedings Paper
Validation: ecoom, 2017
Appears in Collections: Research publications

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