DocumentCode
82770
Title
An Optimal and Distributed Demand Response Strategy With Electric Vehicles in the Smart Grid
Author
Zhao Tan ; Peng Yang ; Nehorai, Arye
Author_Institution
Preston M. Green Dept. of Electr. & Syst. Eng., Washington Univ. in St. Louis, St. Louis, MO, USA
Volume
5
Issue
2
fYear
2014
fDate
Mar-14
Firstpage
861
Lastpage
869
Abstract
In this paper, we propose a new model of demand response management for the future smart grid that integrates plug-in electric vehicles and renewable distributed generators. A price scheme considering fluctuation cost is developed. We consider a market where users have the flexibility to sell back the energy generated from their distributed generators or the energy stored in their plug-in electric vehicles. A distributed optimization algorithm based on the alternating direction method of multipliers is developed to solve the optimization problem, in which consumers need to report their aggregated loads only to the utility company, thus ensuring their privacy. Consumers can update their loads scheduling simultaneously and locally to speed up the optimization computing. Using numerical examples, we show that the demand curve is flattened after the optimization, even though there are uncertainties in the model, thus reducing the cost paid by the utility company. The distributed algorithms are also shown to reduce the users´ daily bills.
Keywords
demand side management; distributed power generation; electric vehicles; power system management; renewable energy sources; smart power grids; demand response management; distributed demand response strategy; distributed optimization algorithm; plug-in electric vehicles; renewable distributed generators; smart grid; Companies; Electric vehicles; Electricity; Generators; Load management; Load modeling; Optimization; Alternating direction method of multipliers; demand response; distributed optimization; electric vehicle; fluctuation cost; smart grid;
fLanguage
English
Journal_Title
Smart Grid, IEEE Transactions on
Publisher
ieee
ISSN
1949-3053
Type
jour
DOI
10.1109/TSG.2013.2291330
Filename
6728731
Link To Document