Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4959514 | European Journal of Operational Research | 2017 | 39 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Luckny Zéphyr, Pascal Lang, Bernard F. Lamond, Pascal Côté,