Assessment of three forecasting methods for system marginal prices

Tae Hwan Lee; Kee Jun Lee; Byung Wan Jo; Lae Hyun Kim; Yeong Koo Yeo

Korean Journal of Chemical Engineering, Vol.28, No.6, 1331-1339, June, 2011

DOI10.1007/s11814-010-0517-8 Export Citation

Assessment of three forecasting methods for system marginal prices

Tae Hwan Lee, Kee Jun Lee, Byung Wan Jo¹, Lae Hyun Kim², and Yeong Koo Yeo^†

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

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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.

Keywords:Neural Network (NN);Wavelet Transform;System Marginal Prices;Price Forecasting

[References]

Yao SJ, Song YH, Zhang LZ, Cheng XY, Electric Machines and Power Systems., 28, 983 (2000)
Catalo JPS, Mariano SJPS, Mendes VMF, Ferreira LAFM, An artificial neural network approach for short-term electricity prices forecasting, in Proc. 14th Int. Conf. on Intelligent System Applications to Power Systems, Nov., 411-416 (2007)
Angelus A, Electricity J., 14, 32 (2001)
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)

[Referenced By]

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[1] Yao SJ, Song YH, Zhang LZ, Cheng XY, Electric Machines and Power Systems., 28, 983 (2000)

[2] Catalo JPS, Mariano SJPS, Mendes VMF, Ferreira LAFM, An artificial neural network approach for short-term electricity prices forecasting, in Proc. 14th Int. Conf. on Intelligent System Applications to Power Systems, Nov., 411-416 (2007)

[3] Angelus A, Electricity J., 14, 32 (2001)

[4] Bunn DW, Proc. IEEE., 88, 163 (2000)

[5] Bastian J, Zhu J, Banunarayanan V, Mukerji R, IEEE Comput. Appl. Power., 12, 40 (1999)

[6] Conejo AJ, Contreras J, Espnola R, Plazas MA, Int. J. Forecast., 21, 435 (2005)

[7] Catalo JPS, Mariano SJPS, Mendes VMF, Ferreira LAFM, IEEE Trans. Power Syst., 24, 337 (2009)

[8] Reis R, da Silva A, IEEE Trans. Power Syst., 20, 189 (2005)

[9] Lee KY, Cha YT, Park JH, IEEE Trans. Power Syst., 7, 124 (1992)

[10] Dillon TS, Niebur D, Neural Networks Applications in Power System, London, CRL Publishing (1996)

[11] Conejo AJ, Plazas MA, Espinol R, IEEE Trans. Power Syst., 20, 1035 (2005)

[12] Daubechies I, Ten Lectures On Wavelets, Philadelphia, Soc. Ind. Appl. Math., SIAM Press (1992)

[13] Meyer Y, Wavelets Algorithms & Applications, Philadelphia, Soc. Ind. Appl. Math., SIAM Press (1993)

[14] Chui CK, Wavelets: A Mathematical Tool For Signal Analysis, Philadelphia. Soc. Ind. Appl. Math., SIAM Press (1990)

[15] Haykin S, Neural networks: A comprehensive foundation, New Jersey: Prentice-Hall (1999)

[16] Principe JC, Euliano NR, Lefebvre WC, Neural and adaptive systems: Fundamentals through simulations, New York, Wiley (2000)

[17] Szkuta BR, Sanabria LA, Dillon TS, IEEE Trans. Power Syst., 14, 851 (1999)

[18] Ahn DH, Lee SJ, Load Forecasting of Power System, Proceedings of the Korean Institute Illuminating and Electrical Installation Engineers, Fall, 78-83 (2005)