Parameter uncertainty analysis of reservoir operating rules based on implicit stochastic optimization

Abstract

Reservoir operating rules are often derived using either a fitting or a simulation-based optimization method in the context of implicit stochastic optimization. Analysis of the parameter uncertainty in reservoir operating rules and their impact is necessary for robust solutions. In the present study, parameter uncertainty for reservoir operating rules is analyzed using two statistical methods, linear regression (LR) and Bayesian simulation (BS). LR estimates the confidence interval based on fitting the operating rules to the optimal deterministic solution. BS deals with the operating rule parameters as stochastic variables and treats the goodness-of-fit to the optimal deterministic solution or the operation profits as the likelihood measure. Two alternative techniques, the generalized likelihood uncertainty estimation (GLUE) and Markov Chain Monte Carlo method (MCMC), are implemented for the BS uncertainty analysis. These methods were applied to the operating rules of China’s Three Gorges Reservoir. The LR performed less than the BS, and the MCMC outperformed the GLUE. Even for the BS methods, the operation profits criterion was better than the goodness-of-fit criterion for deriving the reservoir operating rules.