Clustering of Power System Data and Its Use in Load Pocket Identification

When lines in a power system are constrained, the sensitivity of the power flows on these lines to generator output provides information about how the constraints divide the system and about the ability of sets of generators to increase revenue without increasing dispatch. Clustering is used to identify generators into groups with the potential for market advantage. In this paper, we discuss the implementation of several different clustering methods for identifying load pockets with potential market advantage.

[1]  T. D. Mount,et al.  An Engineering Approach to Monitoring Market Power in Restructured Markets for Electricity , 2001 .

[2]  Robert J. Thomas,et al.  Identifying the potential for market power in electric power systems in real-time , 2004, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the.

[3]  J. Gower,et al.  Minimum Spanning Trees and Single Linkage Cluster Analysis , 1969 .

[4]  Ovidiu Dan,et al.  Scalable Web Mining with Newistic , 2009, PAKDD.

[5]  D. Cheverez-Gonzalez,et al.  Admissible Locational Marginal Prices via Laplacian Structure in Network Constraints , 2009, IEEE Transactions on Power Systems.

[6]  Steven B. Leeb,et al.  Power signature analysis , 2003 .

[7]  Robert J. Thomas,et al.  Identification of load pockets and market power in electric power systems , 2005, Decis. Support Syst..

[8]  H. H. Yan,et al.  An improved Hopfield model for power system contingency classification , 1990, IEEE International Symposium on Circuits and Systems.

[9]  HyungSeon Oh,et al.  Estimation of a Sensitivity-Based Metric for Detecting Market Power , 2010 .

[10]  Bernard Lesieutre,et al.  A Sensitivity Approach to Detection of Market Power Potential , 2010 .

[11]  Jon Louis Bentley,et al.  An Algorithm for Finding Best Matches in Logarithmic Expected Time , 1977, TOMS.

[12]  T. J. Overbye,et al.  A Sensitivity Approach to Detection of Local Market Power Potential , 2011, IEEE Transactions on Power Systems.

[13]  Tian Zhang,et al.  BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.

[14]  Jon Louis Bentley,et al.  Multidimensional binary search trees used for associative searching , 1975, CACM.

[15]  Andrew Chi-Chih Yao,et al.  On Constructing Minimum Spanning Trees in k-Dimensional Spaces and Related Problems , 1977, SIAM J. Comput..

[16]  Gene H. Golub,et al.  Matrix computations (3rd ed.) , 1996 .

[17]  Robert J. Thomas,et al.  Identification of Market Power in Large-Scale Electric Energy Markets , 2006, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06).

[18]  A. K. David,et al.  Market Power in Electricity Supply , 2001, IEEE Power Engineering Review.

[19]  Jon Louis Bentley,et al.  Fast Algorithms for Constructing Minimal Spanning Trees in Coordinate Spaces , 1978, IEEE Transactions on Computers.

[20]  N.S.D. Brito,et al.  Fault detection and classification in transmission lines based on wavelet transform and ANN , 2006, IEEE Transactions on Power Delivery.

[21]  James Bushnell,et al.  Market Power in Electricity Markets: Beyond Concentration Measures , 1999 .

[22]  Thomas J. Overbye,et al.  Smart-Grid -enabled load and distributed generation as a reactive resource , 2010, 2010 Innovative Smart Grid Technologies (ISGT).

[23]  Fionn Murtagh,et al.  A Survey of Recent Advances in Hierarchical Clustering Algorithms , 1983, Comput. J..

[24]  M. Kezunovic,et al.  Fuzzy ART neural network algorithm for classifying the power system faults , 2005, IEEE Transactions on Power Delivery.

[25]  Sergei Vassilvitskii,et al.  On the Worst Case Complexity of the k-means Method , 2005 .

[26]  Vipin Kumar,et al.  Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.

[27]  Laurie J. Heyer,et al.  Exploring expression data: identification and analysis of coexpressed genes. , 1999, Genome research.

[28]  John C. Dalton,et al.  Assessing the competitiveness of restructured generation , 1997 .

[29]  Robin Podmore,et al.  Identification of Coherent Generators for Dynamic Equivalents , 1978, IEEE Transactions on Power Apparatus and Systems.

[30]  Luis Rouco,et al.  Representative Operating and Contingency Scenarios for the Design of UFLS Schemes , 2010, IEEE Transactions on Power Systems.