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A Neural Approximation for the Optimal Control of a Hydroplant with Random Inflows and Concave Revenues

Bernard LamondAbdeslem BoukhtoutaUniversité Laval. Faculté des sciences de l'administration. Direction de la recherche

出版	Faculté des sciences de l'administration de l'Université Laval, Direction de la recherche, 2003
ISBN	28952417919782895241799
URL	http://books.google.com.hk/books?id=CwEVAQAACAAJ&hl=&source=gbs_api

註釋

We present a method to compute an approximately optimal policy for the control of a hydroelectric reservoir with random inflows and concave, piecewise linear revenues from electricity sales. Our approach uses neuro-dynamic programming to approximate the future value function by a neural network. Our model takes into account the head variations on the turbine efficiency and assumes the water flows at a steady rate during each period of the planning horizon, while related models in the literature have made less realistic assumptions of constant head or that the natural inflows were unusable until the next period. Moreover, we extend previous results on the concavity of the expected future rewards as a function of the potential energy in the reservoir and on the structure of optimal decision rules. Our approximation architecture, based on the feedforward network, then gives very smooth approximate functions allowing the use of a coarse discretization of the state and inflow variables in the training step of the neural functions.