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A constraint generation scheme to probabilistic linear problems with an application to power system expansion planning

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Abstract

In this paper, we first describe a constraint generation scheme for probabilistic mixed integer programming problems. Next, we present a decomposition approach to the peak capacity expansion planning of interconnected hydrothermal generating systems, with bounds on the transmission capacity between the regions. The objective is to minimize investments in generating units and interconnection links, subject to constraints on supply reliability. The problem is formulated as a stochastic integer program. The constraint generation scheme, which is similar to Benders decomposition, is applied in the solution of the peak capacity expansion problem. The master problem in this decomposition scheme is an integer program, solved by implicit enumeration. The operating subproblem corresponds to a stochastic network flow problem, and is solved by a maximum flow algorithm and Monte Carlo simulation. The approach is illustrated through a case study involving the expansion of the system of the Brazilian Southeastern region.

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References

  1. E. Balas, An additive algorithm for solving linear programs with zero-one variables, Oper. Res. 13(1965)517–546.

    Google Scholar 

  2. F. Beglari and M.A. Laughton, The combined costs method for optimal economic planning of an electrical power system, IEEE Trans. Power Apparatus and Systems 94(1975)1935–1942.

    Google Scholar 

  3. J.F. Benders, Partitioning procedures for solving mixed variables programming problems, Numer. Math. 4(1962)238–252.

    Google Scholar 

  4. J.R. Birge and F.V. Louveaux, A multicut algorithm for two stage linear programs, Euro. J. Oper. Res. 34(1988)384–393.

    Google Scholar 

  5. J. Bloom, Solving an electricity generating capacity expansion planning problem by generalized Benders decomposition, Oper. Res. 31(1983)84–100.

    Google Scholar 

  6. J. Bloom, M. Caramanis and L. Charny, Long-range generation planning using generalized Benders decomposition: Implementation and experience, Oper. Res. 32(1984)290–313.

    Google Scholar 

  7. R.R. Booth, Power system simulation model based on probability analysis, IEEE Trans. Power Apparatus and Systems 91(1972)62–69.

    Google Scholar 

  8. G.H. Bradley, G.G. Brown and G.W. Graves, Design and implementation of large scale primal transshipment algorithms, Manag. Sci. 24(1977)1–34.

    Google Scholar 

  9. G. Coté, Reliability aspects of optimal generation planning models for power systems, D.Sc. Dissertation, University of London (1975).

  10. G.B. Dantzig, S. Granville, M.V.F. Pereira, B. Avi-Ithzak, M. Avriel, A. Monticelli and L.M.V. Pinto,Mathematical Decomposition Techniques for Power System Expansion Planning, Vols. 1–5, EPRI Report EL-5299, Electric Power Research Institute (1988).

  11. G.B. Dantzig and G. Infanger, Large scale stochastic linear programs — Benders decomposition and importance sampling, Technical Report SOL 91-04, Department of Operations Research, Stanford University (1991).

  12. F. Glover, A multiphase dual algorithm for the zero-one integer programming problem, Oper. Res. 13(1965)879–919.

    Google Scholar 

  13. J.L. Higle and S. Sen, Stochastic decomposition: An algorithm for two-stage linear programs with recourse, Math. Oper. Res. 16(1991)650–669.

    Google Scholar 

  14. INSIGHT, Inc.,User Manual for GNET — A System for Solution of Capacitated Network Flow Problems, Alexandria, USA (1981).

  15. J.E. Kelley, The cutting plane method for solving convex problems, J. Soc. Ind. Appl. Math. 8(1960)703–712.

    Google Scholar 

  16. J. Kleijnen,Statistical Techniques in Simulation — Part I (Marcel Dekker, New York, 1974).

    Google Scholar 

  17. G. Laporte and F.V. Louveaux, The integer L-shaped method for stochastic integer problems, Publication CRT-788, Centre de Recherche sur les Transports, Université de Montréal (1991).

  18. L.S. Lasdon,Optimization Theory for Large Systems (Macmillan, New York, 1970).

    Google Scholar 

  19. M.E.P. Maceira, M.V.F. Pereira, G.C. Oliveira and L.M.V. Pinto, Combining analytical models and Monte Carlo techniques in probabilistic power system analysis, IEEE Trans. Power Syst. PES-7(1992)265–272.

    Google Scholar 

  20. M. Minoux,Mathematical Programming: Theory and Algorithms (Wiley, New York, 1986).

    Google Scholar 

  21. F. Noonan and R.J. Giglio, Planning electric power generation: A nonlinear mixed integer model employing Benders decomposition, Manag. Sci. 23(1977)946–956.

    Google Scholar 

  22. G.C. Oliveira, M.V.F. Pereira, S.H.F. Cunha and S. Granville, Multi-area capacity expansion with reliability constraints,Proc. 9th Power System Computation Conf., Cascais, Portugal (1987) pp. 161–168.

  23. Y.M. Park, K.Y. Lee and L.T.P. Youn, A new analytical approach for long-term generation expansion planning based on maximum principle and Gaussian distribution function, IEEE Trans. Power Apparatus and Systems 104(1985)390–397.

    Google Scholar 

  24. M.V.F. Pereira, Hydroelectric system planning, in:Expansion for Electrical Generating Systems, (IAEA, Vienna, 1984) chapter 3.

    Google Scholar 

  25. M.V.F. Pereira, L.M.V. Pinto, M. Mazumdar, S.H.F. Cunha and G.C. Oliveira, Development of a composite system reliability evaluation program, EPRI Technical Report EL-6926, Electric Power Research Institute (Palo Alto, 1990).

  26. R.Y. Rubinstein,Simulation and the Monte Carlo Method (Wiley, New York, 1981).

    Google Scholar 

  27. H.M. Salkin,Integer Programming (Addison-Wesley, Reading, 1975).

    Google Scholar 

  28. S.M. Selby,Standard Mathematical Tables (The Chemical Rubber Co., 1965).

  29. H.A. Taha,Integer Programming: Theory, Applications, and Computations (Academic Press, Orlando, 1975).

    Google Scholar 

  30. M.J. Teixeira, H.J.C. Pinto, M.V.F. Pereira and M.F. McCoy, Developing concurrent processing applications for power system planning and operations, IEEE Trans. Power Systems PES-5(1992) 659–664.

    Google Scholar 

  31. L.A. Terry, M.V.F. Pereira, T.A. Araripe Neto, L.F.C.A. Silva and P.R.H. Salles, Coordinating the energy generation of the Brazilian national hydrothermal electrical generating system, Interfaces 16(1986)16–38.

    Google Scholar 

  32. R. Van Slyke and R.J.-B. Wets, L-shaped linear programs with application to optimal control and stochastic optimization, SIAM J. Appl. Math. 17(1969)638–663.

    Google Scholar 

  33. R.J.-B. Wets, Large scale linear programming techniques, in:Numerical Methods in Stochastic Optimization, ed. Y. Ermoliev and R.J.-B. Wets (Springer, Berlin, 1988) chapter 3.

    Google Scholar 

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Leopoldino, C.M.A., Pereira, M.V.F., Pinto, L.M.V. et al. A constraint generation scheme to probabilistic linear problems with an application to power system expansion planning. Ann Oper Res 50, 367–385 (1994). https://doi.org/10.1007/BF02085648

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