A Neuro-dynamic Programming Approach For Stochastic
Reservoir Management
Author(s)
A. Boukhtouta & B. F. Lamond
Abstract
A neuro-dynamic programming approach for
stochastic reservoir management
A. ~oukhtouta' & B. F. Lamond2
Defence Research and Development Canada Valcartiel; Canada
~e'partement Ope'rations et systkmes de de'cision,
Universite' Laval, Canada
Abstract
We propose an approach based on neural networks for optimizing a single hydroelectric
reservoir. A stochastic neuro-dynamic programming algorithm is used to
approximate the future value function by a neural function. The latter is used in
deriving the optimal policy. The approximation architecture, based on the feedforward
network, gives very smooth approximate functions even with a coarse
discretization of the state and action variables. The hydroelectric reservoir model
presented in our study assumes a piecewise linear reward of the electricity produced
and takes into account the turbine head effects and the stochastic inflows.
The method is illustrated with a numerical example.
1 Introduction
We consider a mathematical model for optimizing the e
Keywords
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