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Optimal Scheduling of a Multiunit Hydro Power Station in a Short-Term Planning Horizon

  • Alberto BorghettiEmail author
  • Claudia D’Ambrosio
  • Andrea Lodi
  • Silvano Martello
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 212)

Abstract

This chapter deals with the problem of determining the commitment and the power generation of a single-reservoir pump storage hydro power plant. Two MILP models with different levels of complexity are computationally tested and compared with the natural MINLP formulation. In this specific optimization problem, the quality of the approximation provided by the piecewise linear approximation of nonlinear and nonconcave constraints is very effective in order to exploit the performance of MILP solvers.

Keywords

Water Volume Planning Horizon Electricity Market Piecewise Linear Approximation Linear Objective Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supplementary material

273578_1_En_8_MOESM1_ESM.pdf (105 kb)
(pdf 105 KB)

References

  1. 1.
    Baillo, A., Ventosa, M., Ramos, A., Rivier, M., & Canseco, A. (2002). Strategic unit commitment for generation companies in deregulated electricity markets. In B. F. Hobbs, M. Rothkopf, R. P. O’Neill & H. P. Chao (Eds.), The next generation of electric power unit commitment models. Boston: Kluwer Academic.Google Scholar
  2. 2.
    COIN-OR. (2014). Bonmin: https://projects.coin-or.org/Bonmin.
  3. 3.
    COIN-OR. (2014). Couenne. https://projects.coin-or.org/Couenne.
  4. 4.
    Borghetti, A., D’Ambrosio, C., Lodi, A., & Martello, S. (2008). An MILP approach for short-term hydro scheduling and unit commitment with head-dependent reservoir. IEEE Transactions on Power Systems, 23, 1115–1124.CrossRefGoogle Scholar
  5. 5.
    Carrión, M., & Arroyo, J. M. (2006). A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem. IEEE Transactions on Power Systems, 21, 1371–1378.CrossRefGoogle Scholar
  6. 6.
    Catalão, J., Mariano, S., Mendes, V., & Ferreira, L. (2006). Parameterisation effect on the behaviour of a head-dependent hydro chain using a nonlinear model. Electric Power Systems Research, 76, 404–412.CrossRefGoogle Scholar
  7. 7.
    Chang, C., & Waight, J. (1999). A mixed integer linear programming based hydro unit commitment. In Power Engineering Society Summer Meeting, July 18–22, 1999Google Scholar
  8. 8.
    Chang, G., Aganagic, M., Waight, J., Medina, J., Burton, T., Reeves, S., & Christoforidis, M. (2001). Experiences with mixed integer linear programming based approaches on short-term hydro scheduling. IEEE Transactions on Power Systems, 16, 743–749.CrossRefGoogle Scholar
  9. 9.
    Conejo, A., Arroyo, J., Contreras, J., & Villamor, F. (2002). Self-scheduling of a hydro producer in a pool-based electricity market. IEEE Transactions on Power Systems, 17, 1265–1272.CrossRefGoogle Scholar
  10. 10.
    Finardi, E., Da Silva, E., & Sagastizabal, C. (2005). Solving the unit commitment problem of hydropower plants via Lagrangian relaxation and sequential quadratic programming. Computational & Applied Mathematics, 24, 317–341.CrossRefGoogle Scholar
  11. 11.
    Garcia-Gonzalez, J., Parrilla, E., & Mateo, A. (2007). Risk-averse profit-based optimal scheduling of a hydro-chain in the day-ahead electricity market. European Journal of Operational Research, 182, 1354–1369.CrossRefGoogle Scholar
  12. 12.
  13. 13.
    Keha, A. B., de Farias, I. R., & Nemhauser, G. L. (2004). Models for representing piecewise linear cost functions. Operations Research Letters, 32, 44–48.CrossRefGoogle Scholar
  14. 14.
    Lu, N., Chow, J., & Desrochers, A. (2004). Pumped-storage hydro-turbine bidding strategies in a competitive electricity market. IEEE Transactions on Power Systems, 19, 834–841.CrossRefGoogle Scholar
  15. 15.
    Orero, S., & Irving, M. (1998). A genetic algorithm modelling framework and solution technique for short term optimal hydrothermal scheduling. IEEE Transactions on Power Systems, 13, 501–518.CrossRefGoogle Scholar
  16. 16.
    Padhy, N. (2004). Unit commitment—a bibliographical survey. IEEE Transactions on Power Systems, 19, 1196–1205.CrossRefGoogle Scholar
  17. 17.
    Piekutowki, M., Litwinowcz, T., & Frowd, R. (1994). Optimal short-term scheduling for a large-scale cascaded hydro system. IEEE Transactions on Power Systems, 9, 805–811.CrossRefGoogle Scholar
  18. 18.
    Sheblé, G. B. (1999). Computational auction mechanisms for restructured power industry operation. Berlin: Springer.CrossRefGoogle Scholar
  19. 19.
    Sinha, N., Chakrabarti, R., & Chattopadhyay, P. (2003). Fast evolutionary programming techniques for short-term hydrothermal scheduling. IEEE Transactions on Power Systems, 18, 214–220.CrossRefGoogle Scholar
  20. 20.
    Soft, S. (2002). Power system economics: Designing markets for electricity. New York: IEEE/Wiley.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Alberto Borghetti
    • 1
    Email author
  • Claudia D’Ambrosio
    • 2
  • Andrea Lodi
    • 1
  • Silvano Martello
    • 1
  1. 1.DEIUniversity of BolognaBolognaItaly
  2. 2.CNRS LIXÉcole PolytechniquePalaiseauFrance

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