On Solving Multistage Stochastic Programs with Coherent Risk Measures

Andy Philpott (), Vitor de Matos () and Erlon Finardi ()
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Andy Philpott: Electric Power Optimization Centre, Department of Engineering Science, University of Auckland, Auckland, 1142, New Zealand
Vitor de Matos: Plan4 Engenharia SS, Florianópolis-SC, 88034-160, Brazil
Erlon Finardi: Laboratório de Planejamento em Sistemas de Energia Elétrica, Universidade Federal de Santa Catarina, Florianópolis-SC, 88040-900, Brazil

Operations Research, 2013, vol. 61, issue 4, 957-970

Abstract: We consider a class of multistage stochastic linear programs in which at each stage a coherent risk measure of future costs is to be minimized. A general computational approach based on dynamic programming is derived that can be shown to converge to an optimal policy. By computing an inner approximation to future cost functions, we can evaluate an upper bound on the cost of an optimal policy, and an outer approximation delivers a lower bound. The approach we describe is particularly useful in sampling-based algorithms, and a numerical example is provided to show the efficacy of the methodology when used in conjunction with stochastic dual dynamic programming.

Keywords: dynamic programming; stochastic programming; energy; coherent risk measure (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (39)

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