Approximate stochastic dynamic programming for hydroelectric production planning

Authors: Zéphyr, LucknyLang, PascalLamond, BernardCôté, Pascal
Abstract: This paper presents a novel approach for approximate stochastic dynamic programming (ASDP) over a continuous state space when the optimization phase has a near-convex structure. The approach entails a simplicial partitioning of the state space. Bounds on the true value function are used to refine the partition. We also provide analytic formulae for the computation of the expectation of the value function in the “uni-basin” case where natural inflows are strongly correlated. The approach is experimented on several configurations of hydro-energy systems. It is also tested against actual industrial data.
Document Type: Article de recherche
Issue Date: 23 March 2017
Open Access Date: 23 March 2019
Document version: AM
Permalink: http://hdl.handle.net/20.500.11794/13667
This document was published in: European journal of operational research, (2017)
https://doi.org/10.1016/j.ejor.2017.03.050
Elsevier
Alternative version: 10.1016/j.ejor.2017.03.050
Collection:Articles publiés dans des revues avec comité de lecture

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