Development of a methodology for clustering electricity-price series to improve customer response initiatives

The aim of this study is to propose a methodology in order to obtain a better support management decisions in terms of planning of bids and energy offers in real-time energy markets. Specifically, the authors use self-organising maps and statistical Ward's linkage to classify electricity market prices into different clusters (high homogeneity inside each cluster). In the second stage, the authors use non-parametric estimation to extract some price patterns in the above mentioned clusters. The knowledge contained within these patterns supplies customers with market-based information on which to focus its energy use decisions. The methodology proposed has been applied to New England (USA) market.

[1]  J. Contreras,et al.  ARIMA Models to Predict Next-Day Electricity Prices , 2002, IEEE Power Engineering Review.

[2]  C. Senabre,et al.  Classification, Filtering, and Identification of Electrical Customer Load Patterns Through the Use of Self-Organizing Maps , 2006, IEEE Transactions on Power Systems.

[3]  D. W. Scott,et al.  Nonparametric Estimation of Probability Densities and Regression Curves , 1988 .

[4]  J. Contreras,et al.  Simulating oligopolistic pool-based electricity markets: a multiperiod approach , 2003 .

[5]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory, Third Edition , 1989, Springer Series in Information Sciences.

[6]  D.W. Bunn,et al.  Forecasting loads and prices in competitive power markets , 2000, Proceedings of the IEEE.

[7]  A.J. Conejo,et al.  Day-ahead electricity price forecasting using the wavelet transform and ARIMA models , 2005, IEEE Transactions on Power Systems.

[8]  Subhash Sharma Applied multivariate techniques , 1995 .

[9]  J. Contreras,et al.  Forecasting electricity prices for a day-ahead pool-based electric energy market , 2005 .

[10]  Marco van Akkeren,et al.  A GARCH forecasting model to predict day-ahead electricity prices , 2005, IEEE Transactions on Power Systems.

[11]  M. Wand,et al.  An Effective Bandwidth Selector for Local Least Squares Regression , 1995 .

[12]  J. Dunn Well-Separated Clusters and Optimal Fuzzy Partitions , 1974 .

[13]  G. Chicco,et al.  Comparisons among clustering techniques for electricity customer classification , 2006, IEEE Transactions on Power Systems.

[14]  J. H. Ward Hierarchical Grouping to Optimize an Objective Function , 1963 .