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Robust Electric Vehicle Aggregation for Ancillary Service Provision Considering Battery Aging

descriptionPublicationkeyboard_double_arrow_right Article , Journal 01 May 2018 Singapore Publisher:Institute of Electrical and Electronics Engineers (IEEE)Journal:IEEE Transactions on Smart Grid, volume 9, pages 1,728-1,738 (issn: 1949-3053, eissn: 1949-3061,

Authors: Dante F. Recalde Melo; Annette Trippe; Hoay Beng Gooi; Tobias Massier;

doi: 10.1109/tsg.2016.2598851

handle: 10356/139797

Robust Electric Vehicle Aggregation for Ancillary Service Provision Considering Battery Aging

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Abstract

Introduction of demand response (DR) programs could help improve the overall power system stability, even out energy valleys and also push the prices lower due to the increased competitiveness. Liberalization of electricity markets provides possibilities for load aggregators to schedule consumption and obtain revenue by direct participation in demand response programs. This paper proposes a robust algorithm for aggregation of flexible loads within the same distribution network. Participation in DR programs is investigated considering electric vehicle (EV) located at the same carpark. Battery aging is considered and a utilization compensation scheme is proposed for EV drivers. A robust algorithm based on a receding horizon linear problem is designed for the load aggregator considering EV constraints, price uncertainties, and battery aging.

Country

Singapore

Related Organizations

Nanyang Technological University
Singapore
Technical University of Munich
Germany
TUM CREATE
Singapore

Keywords

629, 330, Engineering::Electrical and electronic engineering, :Electrical and electronic engineering [Engineering], Demand Response, Electric Vehicles

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	57
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 1%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 1%