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Multi-stage stochastic optimization applied to energy planning

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Abstract

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.

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Authors and Affiliations

  1. Electric Engineering Department, Catholic University of Rio de Janeiro, P.O. Box 38063, 22452, Gavea, Rio de Janeiro, RJ, Brazil

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

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  1. M. V. F. Pereira
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  2. L. M. V. G. Pinto
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Pereira, M.V.F., Pinto, L.M.V.G. Multi-stage stochastic optimization applied to energy planning. Mathematical Programming 52, 359–375 (1991). https://doi.org/10.1007/BF01582895

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  • DOI: https://doi.org/10.1007/BF01582895

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