Economics at your fingertips  

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
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Series data maintained by Dana Niculescu ().

Page updated 2018-02-24
Handle: RePEc:eee:ejores:v:262:y:2017:i:2:p:586-601