Offering Strategies of Wind Power Producers in a Day-Ahead Electricity Market

  • R. Laia
  • H. M. I. PousinhoEmail author
  • R. Melício
  • V. M. F. Mendes
  • M. Collares-Pereira
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 450)


This paper presents a stochastic optimization-based approach applied to offer strategies of a wind power producer in a day-ahead electricity market. Further from facing the uncertainty on the wind power the market forces wind power producers to face the uncertainty of the market-clearing electricity price. Also, the producer faces penalties in case of being unable to fulfill the offer. An efficient mixed-integer linear program is presented to develop the offering strategies, having as a goal the maximization of profit. A case study with data from the Iberian Electricity Market is presented and results are discussed to show the effectiveness of the proposed approach.


Mixed-integer linear programming Stochastic optimization Wind power Offering strategies 


  1. 1.
    Pousinho, H.M.I., Catalão, J.P.S., Mendes, V.M.F.: Offering strategies for a wind power producer considering uncertainty through a stochastic model. In: Proceedings of PMAPS 2012 (2012)Google Scholar
  2. 2.
    Kongnam, C., Nuchprayoon, S.: Feed-in tariff scheme for promoting wind energy generation. In: IEEE Bucharest Power Tech Conference (2009)Google Scholar
  3. 3.
    Bitar, E.Y., Poolla, K.: Selling wind power in electricity markets: the status today, the opportunities tomorrow. In: 2012 American Control Conference, June 27-June 29, Canada (2012)Google Scholar
  4. 4.
    Barros, J., Leite, H.: Feed-in tariffs for wind energy in Portugal: current status and prospective future. Electrical Power Quality and Utilisation (EPQU) (2011)Google Scholar
  5. 5.
    Barros, J., Leite, H.: Feed-in tariffs for wind energy in Portugal: current status and prospective future. Electrical Power Quality and Utilisation (EPQU) (2011)Google Scholar
  6. 6.
    Al-Awami, A.T., El-Sharkawi, M.A.: Coordinated trading of wind and thermal energy. IEEE Transactions on Sustainable Energy 2(3) (2011)Google Scholar
  7. 7.
    Cena, A.: The impact of wind energy on the electricity price and on the balancing power costs: the Spanish case. In: The European Wind Energy Conference (EWEC), Marseille, France, March 2009Google Scholar
  8. 8.
    El-Fouly, T.H.M., Zeineldin, H.H., El-Saadany, E.F., Salama, M.M.A.: Impact of wind generation control strategies, penetration level and installation location on electricity market prices. IET Renewable Power Generation 2(3), 162–169 (2008)CrossRefGoogle Scholar
  9. 9.
    Angarita, J.L., Usaola, J., Martinez-Crespo, J.: Combined hydro wind generation bids in a pool-based electricity market. Energy Policy 79(7), 1038–1046 (2009)Google Scholar
  10. 10.
    Bathurst, G.N., Strbac, G.: Value of combining energy storage and wind in short-term energy and balancing markets. Electric Power Systems Research 67(1), 1–8 (2003)CrossRefGoogle Scholar
  11. 11.
    Gonzalez, J.G., Muela, R.M.R., Santos, L.M., Gonzalez, A.M.: Stochastic joint optimization of wind generation and pumped-storage units in an electricity market. IEEE Transactions on Power Systems 23(2), 460–468 (2008)CrossRefGoogle Scholar
  12. 12.
    Hedman, K., Sheble, G.: Comparing hedging methods for wind power: using pumped storage hydro units vs options purchasing. In: International Conference on Probabilistic Methods Applied to Power Systems PMAPS, pp. 61–66, Stockholm, Sweden (2006)Google Scholar
  13. 13.
    Bathurst, G.N., Weatherill, J., Strbac, G.: Trading wind generation in short term energy markets. IEEE Transactions on Power Systems 17(3), 782–789 (2002)CrossRefGoogle Scholar
  14. 14.
    Matevosyan, J.: Soder, L: Minimization of imbalance cost trading wind power on the short-term power market. IEEE Transactions on Power Systems 21(3), 1396–1404 (2006)CrossRefGoogle Scholar
  15. 15.
    Pinson, P., Chevallier, C., Kariniotakis, G.N.: Trading wind generation from short-term probabilistic forecasts of wind power. IEEE Transactions on Power Systems 22(3), 1148–1156 (2007)CrossRefGoogle Scholar
  16. 16.
    Ruiz, P.A., Philbrick, C.R.,Sauer, P.W.: Wind power day-ahead uncertainty management through stochastic unit commitment policies. In: Proc. IEEE/PES 2009 Power Syst. Conf. Expo. (PSCE 2009), pp.1–9 (2009)Google Scholar
  17. 17.
    Fan, S., Liao, J.R.: Yokoyama, R, Chen, L.N., Lee, W. J.: Forecasting the wind generation using a two-stage network based on meteorological information. IEEE Trans. Energy Convers. 24, 474–482 (2009)CrossRefGoogle Scholar
  18. 18.
    Kusiak, A., Zheng, H.Y., Song, Z.: Wind farm power prediction: A data-mining approach. Wind Energy 12, 275–293 (2009)CrossRefGoogle Scholar
  19. 19.
    Catalão, J.P.S., Pousinho, H.M.I., Mendes, V.M.F.: An artificial neural network approach for short-term wind power forecasting in Portugal. Eng. Int. Syst. 17, 5–11 (2009)Google Scholar
  20. 20.
    Catalão, J.P.S., Mariano, S.J.P.S., Mendes, V. M. F., Ferreira, L. A. F. M.: Short-term electricity prices forecasting in a competitive market: a neural network approach. Electr. Power Syst. Res. 77, 1297–1304 (2007)Google Scholar
  21. 21.
    Coelho, L.D., Santos, A.A.P.: A RBF neural network model with GARCH errors: Application to electricity price forecasting. Electr. Power Syst. Res. 81, 74–83 (2011)CrossRefGoogle Scholar
  22. 22.
    Amjady, N., Daraeepour, A.: Mixed price and load forecasting of electricity markets by a new iterative prediction method. Electr. Power Syst. Res. 79, 1329–1336 (2009)CrossRefGoogle Scholar
  23. 23.
    Floudas, C.A., Xiaoxia, L.: Mixed integer linear programming in process scheduling: modeling, algorithms, and applications. Annals of Operations Research 139, 131–162 (2005)CrossRefzbMATHMathSciNetGoogle Scholar
  24. 24. Scholar

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • R. Laia
    • 1
    • 2
  • H. M. I. Pousinho
    • 1
    • 2
    Email author
  • R. Melício
    • 1
    • 2
  • V. M. F. Mendes
    • 1
    • 3
  • M. Collares-Pereira
    • 1
  1. 1.University of ÉvoraÉvoraPortugal
  2. 2.IDMEC/LAETA, Instituto Superior TécnicoUniversidade de LisboaLisbonPortugal
  3. 3.Instituto Superior of Engenharia de LisboaLisbonPortugal

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