DocumentCode
3101639
Title
Data mining techniques contributions to support electrical vehicle demand response
Author
Soares, João ; Ramos, Sérgio ; Vale, Zita ; Morais, Hugo ; Faria, Pedro
Author_Institution
Knowledge Eng. & Decision-Support Res. Group, Polytech. Inst. of Porto, Porto, Portugal
fYear
2012
fDate
7-10 May 2012
Firstpage
1
Lastpage
8
Abstract
The introduction of Electric Vehicles (EVs) together with the implementation of smart grids will raise new challenges to power system operators. This paper proposes a demand response program for electric vehicle users which provides the network operator with another useful resource that consists in reducing vehicles charging necessities. This demand response program enables vehicle users to get some profit by agreeing to reduce their travel necessities and minimum battery level requirements on a given period. To support network operator actions, the amount of demand response usage can be estimated using data mining techniques applied to a database containing a large set of operation scenarios. The paper includes a case study based on simulated operation scenarios that consider different operation conditions, e.g. available renewable generation, and considering a diversity of distributed resources and electric vehicles with vehicle-to-grid capacity and demand response capacity in a 33 bus distribution network.
Keywords
battery powered vehicles; data mining; distribution networks; integer programming; nonlinear programming; power engineering computing; smart power grids; bus distribution network; data mining techniques; distributed resource diversity; electrical vehicle demand response; minimum battery level requirements; mixed integer nonlinear programming; power system operators; renewable generation; smart grids; vehicle-to-grid capacity; Batteries; Clustering algorithms; Data mining; Discharges (electric); Load management; Partial discharges; Vehicles; Classification; Clustering; Data Mining; Demand Response; Electric Vehicle; Mixed Integer Non-Linear Programming (MINLP);
fLanguage
English
Publisher
ieee
Conference_Titel
Transmission and Distribution Conference and Exposition (T&D), 2012 IEEE PES
Conference_Location
Orlando, FL
ISSN
2160-8555
Print_ISBN
978-1-4673-1934-8
Electronic_ISBN
2160-8555
Type
conf
DOI
10.1109/TDC.2012.6281444
Filename
6281444
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