Optimal Generation Scheduling of Wind-CSP Systems in Day-Ahead Electricity Markets

  • H. M. I. PousinhoEmail author
  • P. Freire
  • J. Esteves
  • V. M. F. Mendes
  • C. Pereira Cabrita
  • M. Collares-Pereira
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 450)


This paper presents a coordination approach to maximize the total profit of wind power systems coordinated with concentrated solar power systems, having molten-salt thermal energy storage. Both systems are effectively handled by mixed-integer linear programming in the approach, allowing enhancement on the operational during non-insolation periods. Transmission grid constraints and technical operating constraints on both systems are modeled to enable a true management support for the integration of renewable energy sources in day-ahead electricity markets. A representative case study based on real systems is considered to demonstrate the effectiveness of the proposed approach.


Concentrated solar power Wind power Mixed-integer linear programming Transmission constraints 


  1. 1.
    Energy 2020 – A strategy for competitive, sustainable and secure energy (2011).
  2. 2.
    Taylor, J.A., Callaway, D.S., Poolla, K.: Competitive energy storage in the presence of renewables. IEEE Trans. Power Syst. 28, 985–996 (2013)CrossRefGoogle Scholar
  3. 3.
    Lannoye, E., Flynn, D., O’Malley, M.: Evaluation of power system flexibility. IEEE Trans. Power Syst. 27, 922–931 (2012)CrossRefGoogle Scholar
  4. 4.
    Sioshansi, R., Denholm, P.: The value of concentrating solar power and thermal energy storage. IEEE Trans. Sust. Energy 1, 173–183 (2010)CrossRefGoogle Scholar
  5. 5.
    Dominguez, R., Baringo, L., Conejo, A.J.: Optimal offering strategy for a concentrating solar power plant. Applied Energy 98, 316–325 (2012)CrossRefGoogle Scholar
  6. 6.
    Madaeni, S.H., Sioshansi, R., Denholm, P.: How thermal energy storage enhances the economic viability of concentrating solar power. Proc. IEEE 100, 335–347 (2012)CrossRefGoogle Scholar
  7. 7.
    García-González, J., de la Muela, R.M.R., Santos, L.M., González, A.M.: Stochastic joint optimization of wind generation and pumped-storage units in an electricity market. IEEE Trans. Power Syst. 23, 460–468 (2008)CrossRefGoogle Scholar
  8. 8.
    Chen, C.-L.: Simulated annealing-based optimal wind-thermal coordination scheduling. IET Gener. Transm. Distrib. 1, 447–455 (2007)CrossRefGoogle Scholar
  9. 9.
    Kamalinia, S., Shahidehpour, M.: Generation expansion planning in wind-thermal power systems. IET Gener. Transm. Distrib. 4, 940–951 (2010)CrossRefGoogle Scholar
  10. 10.
    Daneshi, H., Srivastava, A.K.: Security-constrained unit commitment with wind generation and compressed air energy storage. IET Gener. Transm. Distrib. 6, 167–175 (2012)CrossRefGoogle Scholar
  11. 11.
    de la Nieta, A.A.S., Contreras, J., Munoz, J.I.: Optimal coordinated wind-hydro bidding strategies in day-ahead markets. IEEE Trans. Power Syst. 28, 798–809 (2013)CrossRefGoogle Scholar
  12. 12.
    Khodayar, M.E., Shahidehpour, M., Wu, L.: Enhancing the dispatchability of variable wind generation by coordination with pumped-storage hydro units in stochastic power systems. IEEE Trans. Power Syst. 28, 2808–2818 (2013)CrossRefGoogle Scholar
  13. 13.
    Li, Z., Guo, Q., Sun, H., Wang, Y., Xin, S.: Emission-concerned wind-EV coordination on the transmission grid side with network constraints: Concept and case study. IEEE Trans. Smart Grid 4, 1692–1704 (2013)CrossRefGoogle Scholar
  14. 14.
    Usaola, J.: Operation of concentrating solar power plants with storage in spot electricity markets. IET Renew. Power Gener. 6, 59–66 (2012)CrossRefGoogle Scholar
  15. 15.
    Cplex, Gams, Solver manuals. Gams/Cplex (2014).
  16. 16.
    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. Intell. Syst. Electr. Eng. Commun. 17, 5–11 (2009)Google Scholar
  17. 17.
    National Renewable Energy Laboratory, USA, Solar Advisor Model User Guide for Version 2.0 (2008)
  18. 18.
    Market operator of the electricity market of the Iberian Peninsula, OMEL (2014).

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • H. M. I. Pousinho
    • 1
    • 2
    Email author
  • P. Freire
    • 3
  • J. Esteves
    • 3
  • V. M. F. Mendes
    • 1
    • 3
  • C. Pereira Cabrita
    • 4
  • 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
  4. 4.CISEUniversity of Beira InteriorCovilhãPortugal

Personalised recommendations