Mathematical Programming

, Volume 156, Issue 1–2, pp 343–389 | Cite as

Combining sampling-based and scenario-based nested Benders decomposition methods: application to stochastic dual dynamic programming

Full Length Paper Series A

Abstract

Nested Benders decomposition is a widely used and accepted solution methodology for multi-stage stochastic linear programming problems. Motivated by large-scale applications in the context of hydro-thermal scheduling, in 1991, Pereira and Pinto introduced a sampling-based variant of the Benders decomposition method, known as stochastic dual dynamic programming (SDDP). In this paper, we embed the SDDP algorithm into the scenario tree framework, essentially combining the nested Benders decomposition method on trees with the sampling procedure of SDDP. This allows for the incorporation of different types of uncertainties in multi-stage stochastic optimization while still maintaining an efficient solution algorithm. We provide an illustration of the applicability of our method towards a least-cost hydro-thermal scheduling problem by examining an illustrative example combining both fuel cost with inflow uncertainty and by studying the Panama power system incorporating both electricity demand and inflow uncertainties.

Keywords

Stochastic dual dynamic programming Hydro-thermal power system  Nested Benders decomposition Sampling Scenario tree  Electricity demand and inflow uncertainty 

Mathematics Subject Classification

90C15 90C05 90C39 90C90 

References

  1. 1.
    Batlle, C., Barquín, J.: Fuel prices scenario generation based on a multivariate GARCH model for risk analysis in a wholesale electricity market. Int. J. Electr. Power. Energy Syst. 26(4), 273–280 (2004)CrossRefGoogle Scholar
  2. 2.
    Benders, J.F.: Partitioning procedures for solving mixed variables programming problems. Numer. Math. 4, 238–252 (1962)CrossRefMathSciNetMATHGoogle Scholar
  3. 3.
    Benders, J.F.: Partitioning procedures for solving mixed-variables programming problems. CMS 2, 3–19 (2005)CrossRefMathSciNetMATHGoogle Scholar
  4. 4.
    Bezerra, B., Kelman, R., Barroso, L.A., Flach, B., Latorre, M.L., Campodonico, N., Pereira, M.V.F.: Integrated electricity–gas operations planning in hydrothermal systems. In Proc. Symp. Specialists in Electric Operational and Expansion Planning (SEPOPE), Brazil (2006)Google Scholar
  5. 5.
    Birge, J.R., Louveaux, F.: Introduction to Stochastic Programming. Operations Research and Financial Engineering, 2nd edn. Springer, New York (2011)CrossRefGoogle Scholar
  6. 6.
    Casey, M.S., Sen, S.: The scenario generation algorithm for multistage stochastic linear programming. Math. Oper. Res. 30(3), 615–631 (2005)CrossRefMathSciNetMATHGoogle Scholar
  7. 7.
    Chabar, R.M., Pereira, M.V.F., Granville, S., Barroso, L.A., Iliadis, N.A.: Optimization of fuel contracts management and maintenance scheduling for thermal plants under price uncertainty. In: IEEE Power Systems Conference and Exposition, pp. 923–930 (2006)Google Scholar
  8. 8.
    Chabar, R.M., Granville, S., Pereira, M.V.F., Iliadis, N.A.: Energy, natural resources and environmental economics, chapter optimization of fuel contract management and maintenance scheduling for thermal plants in hydro-based power systems, pp. 201–219. Springer (2009)Google Scholar
  9. 9.
    Chen, Z.-L., Powell, W.B.: A convergent cutting-plane and partial-sampling algorithm for multistage stochastic linear programs with recourse. J. Optim. Theory Appl. 103, 497–524 (1999)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Costa, L.C.: Considering reliability constraints in the optimal power systems expansion planning problem. Master’s thesis, COPPE/UFRJ, May (2008)Google Scholar
  11. 11.
    de Matos, V.L., Finardi, E.C.: A computational study of a stochastic optimization model for long term hydrothermal scheduling. Int. J. Electr. Power Energy Syst. 43(1), 1443–1452 (2012)CrossRefGoogle Scholar
  12. 12.
    de Queiroza, A.R., Morton, D.P.: Sharing cuts under aggregated forecasts when decomposing multi-stage stochastic programs. Oper. Res. Lett. 41(3), 311–316 (2013)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Diniz, A.L., dos Santos, T.N.: Multi-period stage definition for the multi stage Benders decomposition approach applied to hydrothermal scheduling. In: EngOpt 2008—International Conference on Engineering Optimization, Rio de Janeiro, Brazil (2008)Google Scholar
  14. 14.
    Donohue, C.J.: Stochastic network programming and the dynamic vehicle allocation problem. PhD thesis, University of Michigan (1996)Google Scholar
  15. 15.
    Donohue, C.J., Birge, J.R.: The abridged nested decomposition method for multistage stochastic linear programs with relatively complete recourse. Algorithm. Oper. Res. 1(1), 20–30 (2006)MathSciNetMATHGoogle Scholar
  16. 16.
    dos Santos, T.N., Diniz, A.L.: A new multiperiod stage definition for the multistage Benders decomposition approach applied to hydrothermal scheduling. IEEE Trans. Power Syst. 24(3), 1383–1392 (2009)CrossRefGoogle Scholar
  17. 17.
    Dupačová, J., Gröwe-Kuska, N., Römisch, W.: Scenario reduction in stochastic programming: an approach using probability metrics. Math. Program. 95, 493–511 (2003)CrossRefMathSciNetMATHGoogle Scholar
  18. 18.
    Gassmann, H.I.: MSLiP: a computer code for the multistage stochastic linear programming problem. Math. Program. 47, 407–423 (1990)CrossRefMathSciNetMATHGoogle Scholar
  19. 19.
    Gjelsvik, A., Wallace, S.W.: Methods for stochastic medium-term scheduling in hydro-dominated power systems. Technical report, Norwegian Electric Power Research Institute, Trondheim. EFI TR A4438 (1996)Google Scholar
  20. 20.
    Gjelsvik, A., Belsnes, M.M., Håland, M.: A case of hydro scheduling with a stochastic price model. In: Broch, E., Lysne, D.K., Flatabø, N., Helland-Hansen, E. (eds.) Procedings of the 3rd International Conference on Hydropower, pp. 211–218. Trondheim/Norway/30 June 2 July 1997. A.A. Balkema, Rotterdam (1997)Google Scholar
  21. 21.
    Gjelsvik, A., Mo, B., Haugstad, A.: Long- and medium-term operations planning and stochastic modelling in hydro-dominated power systems based on stochastic dual dynamic programming. In: Rebennack, S., Pardalos, P.M., Pereira, M.V.F., Iliadis, N.A. (eds.) Handbook of Power Systems. Energy Systems. Springer, Berlin (2010)Google Scholar
  22. 22.
    Gorenstin, B., Costa, J.P., Pereira, M.V.F., Campodónico, N.M.: Power system expansion planning under uncertainty. IEEE Trans. Power Syst. 8(1), 129–136 (1993)CrossRefGoogle Scholar
  23. 23.
    Granville, S., Oliveira, G.C., Thome, L.M., Campodonico, N., Latorre, M.L., Pereira, M.V.F., Barroso, L.A.: Stochastic optimization of transmission constrained and large scale hydrothermal systems in a competitive framework. In: IEEE Power Engineering Society General Meeting, vol. 2. Toronto (2003)Google Scholar
  24. 24.
    Gröwe-Kuska, N., Heitsch, H., Römisch, W.: Scenario reduction and scenario tree construction for power management problems. In: IEEE Power Tech Conference. Bologna, Italy (2003)Google Scholar
  25. 25.
    Heitsch, H., Römisch, W.: Scenario reduction algorithms in stochastic programming. Comput. Optim. Appl. 24(2–3), 187–206 (2003)CrossRefMathSciNetMATHGoogle Scholar
  26. 26.
    Heitsch, H., Römisch, W.: Scenario tree modeling for multistage stochastic programs. Math. Program. 118, 371–406 (2009)CrossRefMathSciNetMATHGoogle Scholar
  27. 27.
    Heitsch, H., Römisch, W., Strugarek, C.: Stability of multistage stochastic programs. SIAM J. Optim. 17, 511–525 (2006)CrossRefMathSciNetMATHGoogle Scholar
  28. 28.
    Homem-de-Mello, T., de Matos, V.L., Finardi, E.C.: Sampling strategies and stopping criteria for stochastic dual dynamic programming: a case study in long-term hydrothermal scheduling. Energy Syst. 2(1), 1–31 (2011)CrossRefGoogle Scholar
  29. 29.
    Høyland, K., Wallace, S.W.: Generating scenario trees for multistage decision problems. Manag. Sci. 47(2), 295–307 (2001)CrossRefGoogle Scholar
  30. 30.
    Iliadis, N.A.: Financial risk modelling in electricity portfolio optimisation. PhD thesis, Doctoral School of EPFL, August (2006)Google Scholar
  31. 31.
    Iliadis, N.A., Perira, M.V.F., Granville, S., Finger, M., Haldi, P.-A., Barroso, L.-A.: Bechmarking of hydroelectric stochastic risk management models using financial indicators. In: Power Engineering Society General Meeting, pp. 1–8 (2006)Google Scholar
  32. 32.
    Infanger, G., Morton, D.P.: Cut sharing for multistage stochastic linear programs with interstage dependency. Math. Program. 75, 241–256 (1996)MathSciNetMATHGoogle Scholar
  33. 33.
    Kuhn, D.: Aggregation and discretization in multistage stochastic programming. Math. Program. 113, 61–94 (2008)CrossRefMathSciNetMATHGoogle Scholar
  34. 34.
    Maceira, M.E.P., Damázio, J.M.: The use of PAR(p) model in the stochastic dual dynamic programming optimization scheme used in the operation planning of the Brazilian hydropower system. In: 8th International Conference on Probabilistic Methods Applied to Power Systems, Iowa State University, Ames, Iowa, Sept 12–16 (2004)Google Scholar
  35. 35.
    Maceira, M.E.P., Duarte, V.S., Penna, D.D.J., Moraes, L.A.M., Melo, A.C.G.: Ten years of application of stochastic dual dynamic programming in official and agent studies in Brazil—description of the NEWAVE program. In: 16th Power Systems Computation Conference—PSCC, Glasgow, SCO, July (2008)Google Scholar
  36. 36.
    Mirkov, R., Pflug, GCh.: Tree approximations of dynamic stochastic programs. SIAM J. Optim. 18(3), 1082–1105 (2007)CrossRefMathSciNetMATHGoogle Scholar
  37. 37.
    Mo, B., Gjelsvik, A., Grundt, A.: Integrated risk management of hydro power scheduling and contract management. IEEE Trans. Power Syst. 16(2), 216–221 (2001)CrossRefGoogle Scholar
  38. 38.
    Morton, D.P.: An enhanced decomposition algorithm for multistage stochastic hydroelectric scheduling. Ann. Oper. Res. 64, 211–235 (1996)CrossRefMathSciNetMATHGoogle Scholar
  39. 39.
    Nowak, M.P., Römisch, W.: Stochastic Lagrangian relaxation applied to power scheduling in a hydro-thermal system under uncertainty. Ann. Oper. Res. 100(1–4), 251–272 (2000)CrossRefMathSciNetMATHGoogle Scholar
  40. 40.
    Olsen, P.: Discretization of multistage stochastic programming problems. Math. Program. Stud. 6, 111–124 (1976)CrossRefMathSciNetGoogle Scholar
  41. 41.
    Pennanen, T.: Epi-convergent discretization of multistage stochastic programs. Math. Oper. Res. 30(1), 245–256 (2005)CrossRefMathSciNetMATHGoogle Scholar
  42. 42.
    Pennanen, T.: Epi-convergent discretizations of multistage stochastic programs via integration quadratures. Math. Program. 116, 461–479 (2009)CrossRefMathSciNetMATHGoogle Scholar
  43. 43.
    Pereira, M.V.F., Pinto, L.M.V.G.: Stochastic optimization of a multireservoir hydroelectric system: a decomposition approach. Water Resour. Res. 21(6), 779–792 (1985)CrossRefGoogle Scholar
  44. 44.
    Pereira, M.V.F., Pinto, L.M.V.G.: Multi-stage stochastic optimization applied to energy planning. Math. Program. 52, 359–375 (1991)CrossRefMathSciNetMATHGoogle Scholar
  45. 45.
    Pereira, M.V.F., Campodnico, N., Kelman, R.: Application of stochastic dual DP and extensions to hydrothermal scheduling. Technical report 2.0, PSRI, April 1999. PSRI Technical Report 012/99Google Scholar
  46. 46.
    Philpott, A.B., de Matos, V.L.: Dynamic sampling algorithms for multi-stage stochastic programs with risk aversion. Eur. J. Oper. Res. 218(2), 470–483 (2012)CrossRefMATHGoogle Scholar
  47. 47.
    Philpott, A.B., Guan, Z.: On the convergence of stochastic dual dynamic programming and related methods. Oper. Res. Lett. 36(4), 450–455 (2008)CrossRefMathSciNetMATHGoogle Scholar
  48. 48.
    Powell, W.B.: Approximate Dynamic Programming: Solving the Curses of Dimensionality, 2nd edn. Wiley, New York (2011)CrossRefGoogle Scholar
  49. 49.
    Read, E.G.: A dual approach to stochastic dynamic programming for reservoir release scheduling. In: Esogbue, A.O. (ed.) Dynamic Programming for Optimal Water Resources System Management, pp. 361–372. Prentice Hall, NY (1989)Google Scholar
  50. 50.
    Read, E.G., Hindsberger, M.: Constructive dual DP for reservoir optimization. In: Rebennack, S., Pardalos, P.M., Pereira, M.V.F., Iliadis, N.A. (eds.) Handbook of Power Systems. Energy Systems. Springer, Berlin (2010)Google Scholar
  51. 51.
    Read, E.G., Culy, J.G., Halliburton, T.S., Winter, N.L.: A simulation model for long-term planning of the New Zealand power system. In: Rand, G.K. (ed.) Operational Research, pp. 493–507. North Holland, New York (1987)Google Scholar
  52. 52.
    Rebennack, S., Flach, B., Pereira, M.V.F., Pardalos, P.M.: Stochastic hydro-thermal scheduling under CO\(_2\) emission constraints. IEEE Trans. Power Syst. 27(1), 58–68 (2012)CrossRefGoogle Scholar
  53. 53.
    Rockafellar, R.T., Uryasev, S.: Optimization of conditional value-at-risk. J. Risk 2(3), 21–42 (2000)Google Scholar
  54. 54.
    Shapiro, A.: Analysis of stochastic dual dynamic programming method. Eur. J. Oper. Res. 209, 63–72 (2011)CrossRefMATHGoogle Scholar
  55. 55.
    Shapiro, A., Tekaya, W., da Costa, J.P., Soares, M.P.: Risk neutral and risk averse stochastic dual dynamic programming method. Eur. J. Oper. Res. 224, 375–391 (2013)CrossRefMATHGoogle Scholar
  56. 56.
    Shrestha, G.B., Pokharel, B.K., Lie, T.T., Fleten, S.-E.: Medium term power planning with bilateral contracts. IEEE Trans. Power Syst. 20(5), 627–633 (2005)CrossRefGoogle Scholar
  57. 57.
    Velásquez, J.: GDDP: generalized dual dynamic programming theory. Ann. Oper. Res. 117, 21–31 (2002)CrossRefMathSciNetMATHGoogle Scholar
  58. 58.
    Wallace, S.W., Fleten, S.-E.: Stochastic programming, volume 10 of Handbooks in Operations Research and Management Science, chapter Stochastic programming models in energy, pp. 637–677. North-Holland (2003)Google Scholar
  59. 59.
    Wets, R.J.-B.: Stochastic programs with fixed recourse: the equivalent deterministic program. SIAM Rev. 16(3), 309–339 (1974)CrossRefMathSciNetMATHGoogle Scholar
  60. 60.
    Yakowitz, S.: Dynamic programming applications in water resources. Water Resour. Res. 18(4), 673–696 (1982)CrossRefGoogle Scholar
  61. 61.
    Zhou, Q., Tesfatsion, L., Liu, C.-C.: Scenario generation for price forecasting in restructured wholesale power markets. In: Power Systems Conference and Exposition (2009)Google Scholar
  62. 62.
    Zimmermann, H.-J.: An application-oriented view of modeling uncertainty. Eur. J. Oper. Res. 122(2), 190–198 (2000)CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society 2015

Authors and Affiliations

  1. 1.Division of Economics and BusinessColorado School of MinesGoldenUSA

Personalised recommendations