This paper is about the joint operation of wind power with thermal power for bidding in day-ahead electricity market. Start-up and variable costs of operation, start-up/shut-down ramp rate limits, and ramp-up limit are modeled for the thermal units. Uncertainty not only due to the electricity market price, but also due to wind power is handled in the context of stochastic mix integer linear programming. The influence of the ratio between the wind power and the thermal power installed capacities on the expected profit is investigated. Comparison between joint and disjoint operations is discussed as a case study.


Stochastic Mixed integer linear programming Wind-thermal 



This work is funded by Portuguese Funds through the Foundation for Science and Technology-FCT under the project LAETA 2015‐2020, reference UID/EMS/50022/2013; FCT Research Unit nº 151 C‐MAST Center for Mechanical and Aerospace Sciences and Technology.


  1. 1.
    Laia, R., Pousinho, H.M.I., Melício, R., Mendes, V.M.F.: Self-scheduling and bidding strategies of thermal units with stochastic emission constraints. Energy Convers. Manage. 89, 975–984 (2015)CrossRefGoogle Scholar
  2. 2.
    Kongnam, C., Nuchprayoon, S.: Feed-in tariff scheme for promoting wind energy generation. In: IEEE Bucharest Power Technical Conference, Bucharest, Rumania, pp. 1–6 (2009)Google Scholar
  3. 3.
    Bitar, E.Y., Poolla, K.: Selling wind power in electricity markets: the status today, the opportunities tomorrow. In: American Control Conference, Montreal, Canada, pp. 3144–3147 (2012)Google Scholar
  4. 4.
    Barros, J., Leite, H.: Feed-in tariffs for wind energy in Portugal: current status and prospective future. In: 11th International Conference on Electrical Power Quality and Utilization, Lisbon, Portugal¸pp. 1–5 (2011)Google Scholar
  5. 5.
    Al-Awami, A.T., El-Sharkawi, M.A.: Coordinated trading of wind and thermal energy. IEEE Trans. Sustain. Energy 2(3), 277–287 (2011)CrossRefGoogle Scholar
  6. 6.
    Cena, A.: The impact of wind energy on the electricity price and on the balancing power costs: the Spanish case. In: European Wind Energy Conference, Marceille, France, pp. 1–6 (2009)Google Scholar
  7. 7.
    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 Renew. Power Gener. 2, 162–169 (2008)CrossRefGoogle Scholar
  8. 8.
    Bathurst, G.N., Weatherill, J., Strbac, G.: Trading wind generation in short term energy markets. IEEE Trans. Power Syst. 17, 782–789 (2002)CrossRefGoogle Scholar
  9. 9.
    Matevosyan, J., Solder, L.: Minimization of imbalance cost trading wind power on the short-term power market. IEEE Trans. Power Syst. 21, 1396–1404 (2006)CrossRefGoogle Scholar
  10. 10.
    Pinson, P., Chevallier, C., Kariniotakis, G.N.: Trading wind generation from short-term probabilistic forecasts of wind power. IEEE Trans. Power Syst. 22, 1148–1156 (2007)CrossRefGoogle Scholar
  11. 11.
    Ruiz, P.A., Philbrick, C.R., Sauer, P.W.: Wind power day-ahead uncertainty management through stochastic unit commitment policies. In: IEEE/PES Power System Conference and Exposition, Seattle, USA, pp. 1–9 (2009)Google Scholar
  12. 12.
    Laia, R., Pousinho, H.M.I., Melício, R., Mendes, V.M.F.: Optimal bidding strategies of wind-thermal power producers. In: Camarinha-Matos, L.M., J. Falcão, A., Vafaei, N., Najdi, S. (eds.) DoCEIS 2016. IFIP AICT, vol. 470, pp. 494–503. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-31165-4_46CrossRefGoogle Scholar
  13. 13.
    Fan, S., Liao, J.R., Yokoyama, R., Chen, L.N., Lee, W.J.: Trading wind generation from short-term probabilistic forecasts of wind power. IEEE Trans. Power Syst. 24, 474–482 (2009)Google Scholar
  14. 14.
    Kusiak, A., Zheng, H., Song, Z.: Wind farm power prediction: a data-mining approach. Wind Energy 12, 275–293 (2009)CrossRefGoogle Scholar
  15. 15.
    Laia, R., Pousinho, H.M.I., Melício, R., Mendes, V.M.F., Reis, A.H.: Schedule of thermal units with emissions in a spot electricity market. In: Tomic, S., Graça, P., Camarinha-Matos, L.M. (eds.) DoCEIS 2013. IFIP AICT, vol. 394, pp. 361–370. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  16. 16.
    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)CrossRefGoogle Scholar
  17. 17.
    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)MathSciNetCrossRefGoogle Scholar
  18. 18.
    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
  19. 19.
    Morales, J.M., Conejo, A.J., Ruiz, J.P.: Short-term trading for a wind power producer. IEEE Trans. Power Syst. 25(1), 554–564 (2010)CrossRefGoogle Scholar
  20. 20.
    Laia, R., Pousinho, H.M.I., Melício, R., Mendes, V.M.F., Collares-Pereira, M.: Spinning reserve and emission unit commitment through stochastic optimization. In: IEEE SPEEDAM, Ischia, Italy, pp. 444–448 (2014)Google Scholar

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Authors and Affiliations

  • Rui Laia
    • 1
    • 2
  • Hugo M. I. Pousinho
    • 1
  • Rui Melício
    • 1
    • 2
    Email author
  • Victor M. F. Mendes
    • 2
    • 3
    • 4
  1. 1.IDMEC, Instituto Superior TécnicoUniversidade de LisboaLisbonPortugal
  2. 2.Departamento de Física, Escola de Ciências e TecnologiaUniversidade de ÉvoraÉvoraPortugal
  3. 3.Instituto Superior de Engenharia de LisboaLisbonPortugal
  4. 4.C‐MAST Center for Mechanical and Aerospace Sciences and TechnologyLisbonPortugal

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