Data mining techniques contributions to support electrical vehicle demand response

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.

[1]  Z. Vale,et al.  Data Mining Contributions to Characterize MV Consumers and to Improve the Suppliers-Consumers Settlements , 2007, 2007 IEEE Power Engineering Society General Meeting.

[2]  Pedro Faria,et al.  An optimal scheduling problem in distribution networks considering V2G , 2011, 2011 IEEE Symposium on Computational Intelligence Applications In Smart Grid (CIASG).

[3]  H. Morais,et al.  Using data mining techniques to support DR programs definition in smart grids , 2011, 2011 IEEE Power and Energy Society General Meeting.

[4]  Zita Vale,et al.  Demand response programs definition supported by clustering and classification techniques , 2011, 2011 16th International Conference on Intelligent System Applications to Power Systems.

[5]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[6]  D. Thukaram,et al.  A robust three phase power flow algorithm for radial distribution systems , 1999 .

[7]  Robert C. Green,et al.  The impact of plug-in hybrid electric vehicles on distribution networks: a review and outlook , 2010, IEEE PES General Meeting.

[8]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[9]  Joshua Donald Gifford,et al.  Four economies of sustainable automotive transportation , 2011 .

[10]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[11]  Sergio Ramos,et al.  A Data-Mining-Based Methodology for Transmission Expansion Planning , 2011, IEEE Intelligent Systems.

[12]  Felix F. Wu,et al.  Network reconfiguration in distribution systems for loss reduction and load balancing , 1989 .

[13]  Pedro Faria,et al.  Demsi — A demand response simulator in the context of intensive use of distributed generation , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.