Demand-driven operation of reservoirs using uncertainty-based optimal operating policies

In this study a demand driven stochastic dynamic programming (DDSP) model is developed that allows the use of actual variable monthly demand in generating the operating policies. In DDSP, the uncertainties of the streamflow process, and the forecasts are captured using Bayesian decision theory. (BDT)-probabilities are continuously updated for each month. Furthermore, monthly demand along with inflow, storage, and flow forecast are included as hydrologic state variables in the algorithm. In this model the associated penalty for each operating policy is a function of the release and the expected storage. The operating policies are compared and tested in a hydrologic real-time simulation model and in a real-life operation model. The objectives of this paper are: to evaluate the usefulness and the hydrologic reliability of the generated operating policies by DDSP model; and to demonstrate how the assumption of fixed demand in optimization is deficient when the demand is actually variable or uncertain. The reliability of the operating policies is measured in terms of meeting the required demand when the operating policies are applied in simulation/operation models.