Mathematical Programming

, Volume 52, Issue 1–3, pp 359–375 | Cite as

Multi-stage stochastic optimization applied to energy planning

  • M. V. F. Pereira
  • L. M. V. G. Pinto


This paper presents a methodology for the solution of multistage stochastic optimization problems, based on the approximation of the expected-cost-to-go functions of stochastic dynamic programming by piecewise linear functions. No state discretization is necessary, and the combinatorial “explosion” with the number of states (the well known “curse of dimensionality” of dynamic programming) is avoided. The piecewise functions are obtained from the dual solutions of the optimization problem at each stage and correspond to Benders cuts in a stochastic, multistage decomposition framework. A case study of optimal stochastic scheduling for a 39-reservoir system is presented and discussed.


Linear Function Mathematical Method Dynamic Programming Stochastic Optimization Piecewise Linear Function 
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Copyright information

© The Mathematical Programming Society, Inc. 1991

Authors and Affiliations

  • M. V. F. Pereira
    • 1
  • L. M. V. G. Pinto
    • 1
  1. 1.Electric Engineering DepartmentCatholic University of Rio de JaneiroGavea, Rio de JaneiroBrazil

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