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Received October 13, 2010
Accepted December 10, 2010
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Assessment of three forecasting methods for system marginal prices
Department of Civil Engineering, Hanyang University, Seoul 133-791, Korea 1Department of Chemical Engineering, Hanyang University, Seoul 133-791, Korea 2Department of Chemical Engineering, Seoul National University of Technology, Seoul 135-743, Korea
Korean Journal of Chemical Engineering, June 2011, 28(6), 1331-1339(9), 10.1007/s11814-010-0517-8
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Abstract
The electricity supply industry is being restructured worldwide into a competitive market structure in which electricity is produced by generators, transmitted by transmission companies, and distributed by suppliers according to new trading agreements. In this market, system marginal price (SMP) plays a very important role. Obviously, an accurate prediction would benefit all market participants involved. The SMP profile is a typical time series and, to some extent, similar to the load profile. In this study, an SMP forecasting model is developed based on load demand and supply as well as past SMP data. The proposed forecasting model is compared with NN method and wavelet combined with NN scheme. Due to the different life style during weekdays and weekend, we distinguish comparisons between weekdays and weekends in summer, autumn and winter. For weekend forecasting, the NN method exhibits better forecasting performance than other methods. During weekdays, the proposed SMP forecasting method shows the best forecasting performance among other methods.
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Bunn DW, Proc. IEEE., 88, 163 (2000)
Bastian J, Zhu J, Banunarayanan V, Mukerji R, IEEE Comput. Appl. Power., 12, 40 (1999)
Conejo AJ, Contreras J, Espnola R, Plazas MA, Int. J. Forecast., 21, 435 (2005)
Catalo JPS, Mariano SJPS, Mendes VMF, Ferreira LAFM, IEEE Trans. Power Syst., 24, 337 (2009)
Reis R, da Silva A, IEEE Trans. Power Syst., 20, 189 (2005)
Lee KY, Cha YT, Park JH, IEEE Trans. Power Syst., 7, 124 (1992)
Dillon TS, Niebur D, Neural Networks Applications in Power System, London, CRL Publishing (1996)
Conejo AJ, Plazas MA, Espinol R, IEEE Trans. Power Syst., 20, 1035 (2005)
Daubechies I, Ten Lectures On Wavelets, Philadelphia, Soc. Ind. Appl. Math., SIAM Press (1992)
Meyer Y, Wavelets Algorithms & Applications, Philadelphia, Soc. Ind. Appl. Math., SIAM Press (1993)
Chui CK, Wavelets: A Mathematical Tool For Signal Analysis, Philadelphia. Soc. Ind. Appl. Math., SIAM Press (1990)
Haykin S, Neural networks: A comprehensive foundation, New Jersey: Prentice-Hall (1999)
Principe JC, Euliano NR, Lefebvre WC, Neural and adaptive systems: Fundamentals through simulations, New York, Wiley (2000)
Szkuta BR, Sanabria LA, Dillon TS, IEEE Trans. Power Syst., 14, 851 (1999)
Ahn DH, Lee SJ, Load Forecasting of Power System, Proceedings of the Korean Institute Illuminating and Electrical Installation Engineers, Fall, 78-83 (2005)
