Abstract

Electricity industries around the world have significantly restructured in order to improve their economic efficiency, reliability of power systems and accountability. Accurate prediction of day-ahead electricity nodal price has now become an important activity to address the system operations and price volatility in the restructured electricity market. This will facilitate the market participants to estimate the risk and have better market oriented decision making.

References

• Ray, D. and Alvarado, F. Use of an engineering model for economic analysis in the electric utility industry. Advanced Workshop on Regulation and Public Utility Economics, 1998, May 25-27.
• S. B. Warkad S. B., Khedkar M. K., Dhole G. M. Economics of AC-DC OPF Based Nodal Prices for Restructured Electric Power System. International Journal of Electrical and Power Engineering.2009, 3 (6), 276-288
• Hong, Y.Y and Hsiao, C.Y. Locational marginal price forecasting in deregulated electricity markets using artificial intelligence. IEE Proc.-Generation, Transmission, Distribution, 149(5), 2002, 621-626.
• Aggarwal, S., Saini, L., and Ashwani Kumar. Electricity Price Forecasting in Ontario Electricity Market Using Wavelet Transform in Artificial Neural Network Based Model. International Journal of Control, Automation, and Systems, 6 (5), 2008, 639-650.
• Catal˜ao, J.P.S., Mariano, S.J.P.S., Mendesb, V.M.F. and Ferreira, L.A.F.M. Short-term electricity prices forecasting in a competitive market: A neural network approach. Electric Power Systems Research,39, 2007, 1297–1304.
• Contreras, J., Espínola, R., Nogales, F. J., and Conejo, A. J. ARIMA models to predict next-day electricity prices. IEEE Transactions. on Power System, 18 (3), 2003, 1014–1020.
• Conejo, A. J., Plazas, M. A., Espinola, R. and Molina, A. B. Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Transactions on Power System, 20 (2), 2005, 1035-1042.
• Garcia, R. Contreras, J., Akkeren, M. and Garcia, J. B. C. A GARCH forecasting model to predict day-ahead electricity prices. IEEE Transactions on Power System, 20 (2), 2005, 867–874.
• Fan, S., Mao, C. and Chen L. Next-day electricity-price forecasting using a hybrid network. IET Generation Transmission Distribution, 1 (1), 2007, 176-182.
• Li, Guang, Liu, Ching, Mattson, Chris and Lawarrée, J. Day-Ahead Electricity Price Forecasting in a Grid Environment. IEEE Transactions on Power System, 22 (1), 2007, 266–274.
• Pavlos S., Georgilakis Artificial Intelligence solution to electricity price forecasting problem. Journal of Applied Artificial Intelligence, 21 (8), 2007, 707-727.
• Rodriguez, C. P. and Anders, G. J. Energy price forecasting in the Ontario competitive power system market. IEEE Transactions. on Power System, 19 (1), 2004, 366–374.
• Zhang, Li, Luh, Peter, B., and Kasiviswanathan, K. Energy Clearing Price Prediction and Confidence Interval Estimation With Cascaded Neural Networks. IEEE Transactions on Power System, 18 (1), 2003, 99–105.
• Zhang, L. and Luh, P. B. Neural network-based market clearing price prediction and confidence interval estimation with an improved extended Kalman filter method. IEEE Transactions on Power System, 20 (1), 2005, 59–66.
• Mandal, P., Srivastava, A.K., and Park, Jung-Wook. An Effort to Optimize Similar Days Parameters for ANN-Based Electricity Price Forecasting. IEEE Transactions on Industry Applications, 45 (5), 2009, 1888–1896.
• Lu, C. N., Chen, S. S., Ong, C. M. The Incorporation of HVDC Equations in Optimal Power Flow Methods using Sequential Quadratic Programming Techniques. IEEE Transactions on Power System, 3 (3), 1988, 1005-1011.
• Chen, L., Suzuki, H., Wachi, T. and Shimura, Y. Components of Nodal Prices for Electric Power Systems. IEEE Transactions on Power System, 17 (1), 2002, 41-49.