New EMS to incorporate smart parking lots into demand response

Elham Akhavan-Rezai; Mostafa F. Shaaban; Ehab F. El-Saadany; Fakhri Karray

doi:10.1109/TSG.2016.2587901

New EMS to incorporate smart parking lots into demand response

Elham Akhavan-Rezai, Mostafa F. Shaaban, Ehab F. El-Saadany, Fakhri Karray

Electrical Engineering

Research output: Contribution to journal › Article › peer-review

34 Scopus citations

Abstract

Demand response (DR) seeks to involve end-use customers in modifying their electricity usage and to offer incentive payments to encourage lower electricity use at times of high prices. This paper provides an approach that realizes DR by developing an energy management system for incorporating aggregated plug-in electric vehicles (PEVs) in parking lots. This approach includes real-time interaction between the aggregator and PEV owners, whereby the aggregator proposes a number of offers and the owner responds based on his/her preference. The optimization problem is defined as mixed integer nonlinear programming. An extensive performance evaluation using MATLAB/GAMS simulation of the 38-bus test system verifies the success and effectiveness of the proposed method.

Original language	British English
Pages (from-to)	1376-1386
Number of pages	11
Journal	IEEE Transactions on Smart Grid
Volume	9
Issue number	2
DOIs	https://doi.org/10.1109/TSG.2016.2587901
State	Published - 2018

Keywords

And smart parking lots
Artificial neural network
Decision-making
Demand response
Distribution systems
Electric vehicles
Energy management system
Markov chain

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10.1109/TSG.2016.2587901

Cite this

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title = "New EMS to incorporate smart parking lots into demand response",

abstract = "Demand response (DR) seeks to involve end-use customers in modifying their electricity usage and to offer incentive payments to encourage lower electricity use at times of high prices. This paper provides an approach that realizes DR by developing an energy management system for incorporating aggregated plug-in electric vehicles (PEVs) in parking lots. This approach includes real-time interaction between the aggregator and PEV owners, whereby the aggregator proposes a number of offers and the owner responds based on his/her preference. The optimization problem is defined as mixed integer nonlinear programming. An extensive performance evaluation using MATLAB/GAMS simulation of the 38-bus test system verifies the success and effectiveness of the proposed method.",

keywords = "And smart parking lots, Artificial neural network, Decision-making, Demand response, Distribution systems, Electric vehicles, Energy management system, Markov chain",

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AU - Akhavan-Rezai, Elham

AU - Shaaban, Mostafa F.

AU - El-Saadany, Ehab F.

AU - Karray, Fakhri

N1 - Publisher Copyright: c 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

PY - 2018

Y1 - 2018

N2 - Demand response (DR) seeks to involve end-use customers in modifying their electricity usage and to offer incentive payments to encourage lower electricity use at times of high prices. This paper provides an approach that realizes DR by developing an energy management system for incorporating aggregated plug-in electric vehicles (PEVs) in parking lots. This approach includes real-time interaction between the aggregator and PEV owners, whereby the aggregator proposes a number of offers and the owner responds based on his/her preference. The optimization problem is defined as mixed integer nonlinear programming. An extensive performance evaluation using MATLAB/GAMS simulation of the 38-bus test system verifies the success and effectiveness of the proposed method.

AB - Demand response (DR) seeks to involve end-use customers in modifying their electricity usage and to offer incentive payments to encourage lower electricity use at times of high prices. This paper provides an approach that realizes DR by developing an energy management system for incorporating aggregated plug-in electric vehicles (PEVs) in parking lots. This approach includes real-time interaction between the aggregator and PEV owners, whereby the aggregator proposes a number of offers and the owner responds based on his/her preference. The optimization problem is defined as mixed integer nonlinear programming. An extensive performance evaluation using MATLAB/GAMS simulation of the 38-bus test system verifies the success and effectiveness of the proposed method.

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KW - Decision-making

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KW - Distribution systems

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New EMS to incorporate smart parking lots into demand response

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Keywords

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