Water Management in Tarbela Dam By using Bayesian Stochastic Dynamic Programming in Extreme Inflow Season

Abstract

Existing method of forecasting inflows at Tarbela have some limitations, also system needs an adequate operating policy model to deal with highly volatile inflow of summer months of June, July, August and September. In this paper, historical data of inflows from 1986 to 2014 have been used to forecast upcoming inflows at dam. Bayesian predictive distribution is used to predict future inflows. These forecasted inflows were further incorporated into operating policy model to determine the optimal release during the prescribed months. Weather volatility is a major factor causing unstable inflows. High temperature during summer period cause high inflows at dam. Considering weather volatility, this policy model is proposed for the flood season (15th June to 30th September), in which inflows and outflows are higher than rest of the year. This model maximizes the expected profit from hydro power production, minimizes the expected loss from flood damage and updates the proper estimate of current stage of reservoir storage.