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Approximate stochastic dynamic programming for hydroelectric production planning

Luckny Zéphyr, Pascal Lang, Bernard F. Lamond and Pascal Côté

European Journal of Operational Research, 2017, vol. 262, issue 2, 586-601

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.

Keywords: Dynamic programming; Curse of dimensionality; Dynamic decision process; Value function; Simplicial state space partitioning (search for similar items in EconPapers)
Date: 2017
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