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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/16304

Title: Electric Vehicles in the Smart Grid
Authors: De Ridder, F.
D'Hulst, R.
Issue Date: 2014
Publisher: Information Science Reference (an imprint of IGI Global)
Citation: Janssens, Davy ; Yasar, Ansar-Ul-Haque ; Knapen, Luk (Ed.). Data Science and Simulation in Transportation Research, p. 340-363
Series/Report: Advances in Data Mining and Database Management (ADMDM) Book Series
Abstract: This chapter presents a coordination algorithm for charging electric vehicles that can be used for avoiding capacity problems in the power distribution grid and for decreasing imbalance costs for retailers. Since it is expected that the fraction of electric vehicles will exceed 50% in the next decades, charging these vehicles will roughly double the domestic power consumption. Not all parts of the grid are expected to be able to provide the required power. Good estimates of the vehicles’ use (routes driven, trip duration and length, when and where cars are parked) is crucial information to test the grid. The authors have chosen to use FEATHERS, an agent-based behavioral model, to provide this information. In a first case study, charging is coordinated to prevent grid capacity problems. In a second case study, charging and discharging of electric vehicles is employed by retailers to lower imbalance costs and by vehicle owners to lower charging costs. The coordination scheme can halve the imbalance cost if only charging is considered. If, on the other hand, electric vehicles can both charge and discharge, imbalance costs can completely be avoided and some revenues can be generated. The proposed coordination algorithm is a distributed algorithm, where all sensitive information that is privately owned, such as parking times, trip information, battery management, etc. is only used by the EVs. The functioning of the proposed algorithm is illustrated by simulations. It is shown that the charging can be rescheduled so that grid capacity violations are avoided. The novelty of this work is that both spatial and temporal information is used.
URI: http://hdl.handle.net/1942/16304
Link to publication: http://www.igi-global.com/book/data-science-simulation-transportation-research/78944#table-of-contents
DOI: 10.4018/978-1-4666-4920-0.ch016
ISI #: 000364530200018
ISBN: 9781466649200
ISSN: 2327-1981
Category: B2
Type: Book Section
Validation: vabb, 2017
Appears in Collections: Research publications

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