Skip to main content
Log in

Decomposition approaches for block-structured chance-constrained programs with application to hydro-thermal unit commitment

  • Original Article
  • Published:
Mathematical Methods of Operations Research Aims and scope Submit manuscript

Abstract

The unit commitment problem, aims at computing the production schedule that satisfies the offer-demand equilibrium at minimal cost. Often such problems are considered in a deterministic framework. However uncertainty is present and non-negligible. Robustness of the production schedule is therefore a key question. In this paper, we will investigate this robustness when hydro valleys are made robust against uncertainty on inflows and the global schedule is robust against uncertainty on customer load. Both robustness requirements will be modelled by using bilateral joint chance constraints. Since this is a fairly large model, we will investigate several decomposition procedures and compare these on several typical numerical instances. The latter decomposition procedures are clearly a prerequisite if robust unit commitment is ever to be used in practice. We will show that an efficient decomposition procedure exists and can be used to derive a robust production schedule. The obtained results are illustrated on a convex simplification of a unit commitment problem in order to avoid the use of heuristics. The investigated decomposition approaches can be applied trivially to a non-convex setting, but will need to be followed by appropriate heuristics. How this may work in practice is also illustrated.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Babonneau F, Vial J, Apparigliato R (2010) Robust optimization for environmental and energy planning (Chapter 3 in [21]), International series in operations research & management science. Springer, vol 138

  • Batut J, Renaud A (1992) Daily scheduling with transmission constraints: a new class of algorithms. IEEE Trans Power Syst 7(3):982–989

    Article  Google Scholar 

  • Beltran C, Heredia F (2002) Unit commitment by augmented lagrangian relaxation: testing two decomposition approaches. J Optim Theory Appl 112(2):295–314

    Article  MATH  MathSciNet  Google Scholar 

  • Bertsimas D, Litvinov E, Sun XA, Zhao J, Zheng T (2013) Adaptive robust optimization for the security constrained unit commitment problem. IEEE Trans Power Syst 28(1):52–63

    Article  Google Scholar 

  • Bonnans J, Gilbert J, Lemaréchal C, Sagastizábal C (2006) Numerical optimization: theoretical and practical aspects, 2nd edn. Springer

  • Borghetti A, Frangioni A, Lacalandra F, Nucci C (2003) Lagrangian heuristics based on disaggregated bundle methods for hydrothermal unit commitment. IEEE Trans Power Syst 18:313–323

    Article  Google Scholar 

  • Briant O, Lemaréchal C, Meurdesoif P, Michel S, Perrot N, Vanderbeck F (2008) Comparison of bundle and classical column generation. Math Program 113(2):299–344

    Article  MATH  MathSciNet  Google Scholar 

  • Bruhns A, Deurveilher G, Roy J (2005) A non-linear regression model for mid-term load forecasting and improvements in seasonality. PSCC 2005 Luik

  • Cohen G (1980) Auxiliairy problem principle and decomposition of optimization problems. J Optim Theory Appl 32(3):277–305

    Article  MATH  MathSciNet  Google Scholar 

  • Cohen G, Zhu D (1983) Decomposition-coordination methods in large-scale optimization problems. the non-differentiable case and the use of augmented Lagrangians. Large scale systems, theory and applications 1

  • Dentcheva D (2009) Optimisation models with probabilistic constraints. Chapter 4 in [43]. MPS-SIAM series on optimization. SIAM and MPS, Philadelphia

  • de Oliveira W, Sagastizábal C (2014) Level bundle methods for oracles with on demand accuracy. Optim Methods Softw, pp 1–31

  • Ding X, Lee WJ, Jianxue W, Liu L (2010) Studies on stochastic unit commitment formulation with flexible generating units. Electric Power Syst Res 80:130141

  • Doukopoulos-Hechmé G, Charousset-Brignol S, Malick J, Lemaréchal C (2010) The short-term electricity production management problem at EDF. OPTIMA 84:2–6

    Google Scholar 

  • Dubost L, Gonzalez R, Lemaréchal C (2005) A primal-proximal heuristic applied to french unitcommitment problem. Math Program 104(1):129–151

    Article  MATH  MathSciNet  Google Scholar 

  • Duranyildiz I, Önöz B, Bayazit M (1999) A chance-constrained LP model for short term reservoir operation optimization. Turkish J Eng 23:181–186

    Google Scholar 

  • Edirisinghe N, Patterson E, Saadouli N (2000) Capacity planning model for a multipurpose water reservoir with target-priority operation. Ann Oper Res 100:273–303

    Article  MATH  MathSciNet  Google Scholar 

  • Filar J, Haurie A (2010) Uncertainty and environmental decision making: a handbook of research and best practice, international series in operations research & management science, vol 138. Springer

  • Frangioni A, Gentile C (2006) Solving non-linear single-unit commitment problems with ramping constraints. Oper Res 54(4):767–775

    Article  MATH  MathSciNet  Google Scholar 

  • Frangioni A, Gentile C, Lacalandra F (2008) Solving unit commitment problems with general ramp contraints. Int J Electric Power Energy Syst 30:316–326

    Article  Google Scholar 

  • Frangioni A, Gentile C, Lacalandra F (2011) Sequential lagrangian-MILP approaches for unit commitment problems. Int J Electric Power Energy Syst 33:585–593

    Article  Google Scholar 

  • Guignard M (2003) Lagrangean relaxation. TOP 11(2):151–228

    Article  MATH  MathSciNet  Google Scholar 

  • Hiriart-Urruty J, Lemaréchal C (1996) Convex analysis and minimization algorithms II, 2nd edn. No. 306 in Grundlehren der mathematischen Wissenschaften. Springer

  • Jünger M, Naddef D (eds) (2001) Computational combinatorial optimization: optimal or provably near-optimal solutions. Lecture Notes in Computer Science. Springer

  • Langrene N, van Ackooij W, Bréant F (2011) Dynamic constraints for aggregated units: formulation and application. IEEE Trans Power Syst 26(3):1349–1356

    Article  Google Scholar 

  • Lemaréchal C (2001) Lagrangian relaxation. In [27] (Chapter 4). Springer

  • Lemaréchal C, Sagastizábal C (1994) An approach to variable metric bundle methods. Lect Notes Control Inform Sci 197:144–162

    Article  Google Scholar 

  • Lemaréchal C, Sagastizábal C (1997) Variable metric bundle methods: from conceptual to implementable forms. Math Program 76(3):393–410

    Article  MATH  Google Scholar 

  • Loucks DP, Stedinger JR, Haith DA (1981) Water resource systems planning and analysis. Prentice-Halls, Inc.

  • Nowak M, Römisch W (2000) Stochastic lagrangian relaxation applied to power scheduling in a hydro-thermal system under uncertainty. Ann Oper Res 100(1–4):251–272

    Article  MATH  MathSciNet  Google Scholar 

  • Ozturk U, Mazumdar M, Norman B (2004) A solution to the stochastic unit commitment problem using chance constrained programming. IEEE Trans Power Syst 19(3):1589–1598

    Article  Google Scholar 

  • Philpott A, Craddock M, Waterer H (2000) Hydro-electric unit commitment subject to uncertain demand. Eur J Oper Res 125:410–424

    Article  MATH  Google Scholar 

  • Prékopa A (1995) Stochastic programming. Kluwer, Dordrecht

    Book  Google Scholar 

  • Prékopa A (2003) Probabilistic programming. In [41] (Chapter 5). Elsevier, Amsterdam

  • Prékopa A, Szántai T (1978) Flood control reservoir system design using stochastic programming. Math Program Study 9:138–151

    Article  Google Scholar 

  • Ruszczyński A (1995) On convergence of an augmented lagrangian decomposition method for sparse convex optimization. Math Oper Res 20(3):634–656

    Article  MATH  MathSciNet  Google Scholar 

  • Ruszczyński A, Shapiro A (2003) Stochastic programming, handbooks in operations research and management science, vol 10. Elsevier, Amsterdam

    Google Scholar 

  • Sagastizábal C (2012) Divide to conquer: decomposition methods for energy optimization. Math Program 134(1):187–222

    Article  MATH  MathSciNet  Google Scholar 

  • Shapiro A, Dentcheva D, Ruszczyński A (2009) Lectures on stochastic programming. Modeling and theory, MPS-SIAM series on optimization, vol. 9. SIAM and MPS, Philadelphia

  • Soenen R (1977) Contribution à l’étude des systèmes de conduite en temps réel en vue de la commande d’unités de fabrication. Ph.D. thesis, University of Lille

  • Szántai T (1988) A computer code for solution of probabilistic-constrained stochastic programming problems. In: Ermoliev Y, Wets RJ-B (eds) Numerical techniques for stochastic optimization, pp 229–235

  • Tahanan M, van Ackooij W, Frangioni A, Lacalandra F (2014) Large-scale unit commitment under uncertainty: a literature survey. Preprint available http://compass2.di.unipi.it/TR/default.aspx

  • Takriti S, Birge J, Long E (1996) A stochastic model for the unit commitment problem. IEEE Trans Power Syst 11:1497–1508

    Article  Google Scholar 

  • Takriti S, Krasenbrink B, Wu L (2000) Incorporating fuel constraints and electricity spot prices into the stochastic unit commitment problem. Oper Res 48(2):268–280

    Article  Google Scholar 

  • van Ackooij W, de Oliveira W (2014) Level bundle methods for constrained convex optimization with various oracles. Comput Optim Appl 57(3):555–597

    Article  MATH  MathSciNet  Google Scholar 

  • van Ackooij W, Sagastizábal C (2014) Constrained bundle methods for upper inexact oracles with application to joint chance constrained energy problems. Siam J Optim 24(2):733–765

    Article  MATH  MathSciNet  Google Scholar 

  • van Ackooij W, Henrion R, Möller A, Zorgati R (2010) On probabilistic constraints induced by rectangular sets and multivariate normal distributions. Math Methods Oper Res 71(3):535–549

    Article  MATH  MathSciNet  Google Scholar 

  • van Ackooij W, Henrion R, Möller A, Zorgati R (2014) Joint chance constrained programming for hydro reservoir management. Optim Eng 15:509–531

    MathSciNet  Google Scholar 

  • Veinott A (1967) The supporting hyperplane method for unimodal programming. Oper Res 15:147–152

    Article  MATH  MathSciNet  Google Scholar 

  • Wang S, Shahidehpour S, Kirschen D, Mokhtari S, Irisarri G (1995) Short-term generation scheduling with transmission and environmental constraints using an augmented lagrangian relaxation. IEEE Trans Power Syst 10(3):1294–1301

    Article  Google Scholar 

  • Wu L, Shahidehpour M, Li T (2007) Stochastic security-constrained unit commitment. IEEE Trans Power Syst 22(2)

  • Zhao L, Zeng B (2012) Robust unit commitment problem with demand response and wind energy. In: Proceedings of IEEE power and energy society general meeting

Download references

Acknowledgments

The author would like to thank René Henrion, Claudia Sagastizábal and Nadia Oudjane for the ongoing collaboration and their comments on this paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wim van Ackooij.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

van Ackooij, W. Decomposition approaches for block-structured chance-constrained programs with application to hydro-thermal unit commitment. Math Meth Oper Res 80, 227–253 (2014). https://doi.org/10.1007/s00186-014-0478-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00186-014-0478-5

Keywords

Mathematics Subject Classification

Navigation