Optimization in Searching Daily Rule Curve at Mosul Regulating Reservoir, North Iraq using Genetic Algorithms

Abstract

To obtain optimal operating rules for storage reservoirs, large numbers of simulation and optimization models have been developed over the past several decades, which vary significantly in their mechanisms and applications. Rule curves are guidelines for long term reservoir operation. An efficient technique is required to find the optimal rule curves that can mitigate water shortage in long term operation. The investigation of developed Genetic Algorithm (GA) technique, which is an optimization approach base on the mechanics of natural selection, derived from the theory of natural evolution, was carried out to through the application to predict the daily rule curve of  Mosul regulating reservoir in Iraq.  Record daily inflows, outflow, water level in the reservoir for 19 year (1986-1990) and (1994-2007) were used in the developed model for assessing the optimal reservoir operation. The objective function is set to minimize the annual sum of squared deviation from the desired downstream release and desired storage volume in the reservoir. The decision variables are releases, storage volume, water level and outlet (demand) from the reservoir. The results of the GA model gave a good agreement during the comparison with the actual rule curve and the designed rating curve of the reservoir. The simulated result shows that GA-derived policies are promising and competitive and can be effectively used for daily reservoir operation in addition to the rational monthly operation and predicting also rating curve of reservoirs.