Solving Reservoir Management Problems
With Serially Correlated Inflows
Author(s)
A. Turgeon
Abstract
This paper addresses the problem of determining the optimal daily operating
policy of a small reservoir when the inflows are stochastic and multi-lag
autocorrelated. This optimization problem is difficult to solve when the number
of lags is large because each lag adds a state variable to the problem. The paper
presents two methods which solve the problem in a very short time, whatever the
number of lags. The first, which solves the optimization problem with stochastic
dynamic programming, represents the multi-lag autocorrelation by a single
hydrologic variable, whose value changes from day to day and is equal to the
conditional mean of the daily inflow. The second uses a large set of inflow
scenarios to determine the optimal warning curve for the reservoir. The optimal
daily operating policy is shown to consist in maintaining the reservoir level on,
or as close as possible to, that curve.
Keywords: daily reservoir operation, stochastic dynamic programming,
streamflow autoregressive model, spillage warning curve, hydrologic variable.
1 Introduction
This paper tackles the problem of determining the optimal daily operating policy
of a small reservoir over a period of one year. The problem is stochastic because
the reservoir inflows are random and cannot be predicted long in advance. The
difficulty in solving this problem comes from the persistence of the inflows, that
is, the fact that the inflows in day t can be correlated to those in days
1, 2,..., t t tp −−−. Persistence exists when there are long periods of high and
low inflows in the recorded data. Since floods and shortages usually occur in
Keywords
daily reservoir operation, stochastic dynamic programming,
streamflow autoregressive model, spillage warning curve, hydrologic variable.
Related Book
Other papers in this volume
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