Title :
Using data mining techniques to support DR programs definition in smart grids
Author :
Vale, Z. ; Morais, H. ; Ramos, S. ; Soares, J. ; Faria, P.
Author_Institution :
GECAD - Knowledge Eng. & Decision-Support Res. Group, Polytech. Inst. of Porto, Porto, Portugal
Abstract :
In recent years, Power Systems (PS) have experimented many changes in their operation. The introduction of new players managing Distributed Generation (DG) units, and the existence of new Demand Response (DR) programs make the control of the system a more complex problem and allow a more flexible management. An intelligent resource management in the context of smart grids is of huge important so that smart grids functions are assured. This paper proposes a new methodology to support system operators and/or Virtual Power Players (VPPs) to determine effective and efficient DR programs that can be put into practice. This method is based on the use of data mining techniques applied to a database which is obtained for a large set of operation scenarios. The paper includes a case study based on 27, 000 scenarios considering a diversity of distributed resources in a 32 bus distribution network.
Keywords :
data mining; distributed power generation; distribution networks; power engineering computing; smart power grids; 32 bus distribution network; DG units; DR programs; VPP; data mining techniques; demand response programs; distributed generation units; intelligent resource management; power systems; smart grids; virtual power players; Classification algorithms; Clustering algorithms; Data mining; Databases; Electricity; Energy resources; Load management; Clustering; Data Mining; Demand Response (DR); Energy Resources Management; Intelligent Power Systems; Locational Marginal Prices (LMP); Mixed Integer No-Linear Programming (MINLP);
Conference_Titel :
Power and Energy Society General Meeting, 2011 IEEE
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4577-1000-1
Electronic_ISBN :
1944-9925
DOI :
10.1109/PES.2011.6039081
