3 research outputs found
Inflow forecasting using Artificial Neural Networks for reservoir operation
In this study, multi-layer perceptron (MLP) artificial neural networks have
been applied to forecast one-month-ahead inflow for the Ubonratana reservoir,
Thailand. To assess how well the forecast inflows have performed in the
operation of the reservoir, simulations were carried out guided by the
systems rule curves. As basis of comparison, four inflow situations were
considered: (1) inflow known and assumed to be the historic (Type A);
(2) inflow known and assumed to be the forecast (Type F); (3) inflow known
and assumed to be the historic mean for month (Type M); and (4) inflow is
unknown with release decision only conditioned on the starting reservoir
storage (Type N). Reservoir performance was summarised in terms of
reliability, resilience, vulnerability and sustainability. It was found that
Type F inflow situation produced the best performance while Type N was the
worst performing. This clearly demonstrates the importance of good inflow
information for effective reservoir operation
Genetic algorithms optimization of hedging rules for operation of the multi-purpose Ubonratana Reservoir in Thailand
This study has developed optimal hedging policies for the multi-purpose Ubonratana Reservoir in northeastern Thailand based on its existing rule curves. The hedging policy was applied whenever the reservoir storage falls below a critical level for each month of the year. The decision variables, i.e. the set of monthly storages defining the critical rule curve that triggers rationing and the rationing ratio, were optimized by genetic algorithm (GA). Both single stage (i.e. with one critical rule curve and one rationing ratio) and two-stage (with two critical rule curves and ratios) of the hedging policy were considered in the optimization. To test the effect of the optimized hedging policies on reservoir performance, simulations were carried out, forced alternatively with the existing rule curves (i.e. without hedging) and the two optimized hedging policies. Performance was summarized in terms of reliability (time- and volume-based) and vulnerability. The results showed that the vulnerability was significantly reduced by using the optimized hedging rules. However, the number of water shortages increased with the optimized rules, causing the time-based reliability to worsen significantly. This should not be of concern since, although the number of shortages increased, the associated shortage quantities on most of these additional occasions were small, leaving the volumetric reliability largely unchanged