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Assessment of three forecasting methods for system marginal prices

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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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References

  1. S. J. Yao, Y. H. Song, L. Z. Zhang and X. Y. Cheng, Electric Machines and Power Systems, 28, 983 (2000).

    Article  Google Scholar 

  2. J. P. S. Catalo, S. J. P. S. Mariano, V. M. F. Mendes and L. A. F. M. Ferreira, An artificial neural network approach for short-term electricity prices forecasting, in Proc. 14 th Int. Conf. on Intelligent System Applications to Power Systems, Nov., 411–416 (2007).

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

    Article  Google Scholar 

  4. D.W. Bunn, Proc. IEEE, 88, 163 (2000).

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. A. J. Conejo, J. Contreras, R. Espnola and M. A. Plazas, Int. J. Forecast, 21, 435 (2005).

    Article  Google Scholar 

  7. J. P. S. Catalo, S. J. P. S. Mariano, V.M. F. Mendes and L. A. F. M. Ferreira, IEEE Trans. Power Syst., 24, 337 (2009).

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. K.Y. Lee, Y. T. Cha and J. H. Park, IEEE Trans. Power Syst., 7, 124 (1992).

    Article  Google Scholar 

  10. T. S. Dillon and D. Niebur, Neural Networks Applications in Power System, London, CRL Publishing (1996).

    Google Scholar 

  11. A. J. Conejo, M.A. Plazas and R. Espinol, IEEE Trans. Power Syst., 20, 1035 (2005).

    Article  Google Scholar 

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

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

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

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

    Google Scholar 

  16. J. C. Principe, N. R. Euliano and W. C. Lefebvre, Neural and adaptive systems: Fundamentals through simulations, New York, Wiley (2000).

    Google Scholar 

  17. B. R. Szkuta, L. A. Sanabria and T. S. Dillon, IEEE Trans. Power Syst., 14, 851 (1999).

    Article  Google Scholar 

  18. D. H. Ahn and S. J. Lee, Load Forecasting of Power System, Proceedings of the Korean Institute Illuminating and Electrical Installation Engineers, Fall, 78–83 (2005).

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Correspondence to Yeong Koo Yeo.

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Lee, T.H., Lee, K.J., Jo, B.W. et al. Assessment of three forecasting methods for system marginal prices. Korean J. Chem. Eng. 28, 1331–1339 (2011). https://doi.org/10.1007/s11814-010-0517-8

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  • DOI: https://doi.org/10.1007/s11814-010-0517-8

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