Real coded genetic algorithm optimization of long term reservoir operation

An optimization and simulation model holds promise as an efficient and robust method for long term reservoir operation, an increasingly important facet of managing water resources. Recently, genetic algorithms have been demonstrated to be highly effective optimization methods. According to previous studies, a real coded genetic algorithm (RGA) has many advantages over a binary coded genetic algorithm. Accordingly, this work applies an RGA to obtain the 10-day (the traditional period of reservoir operation in Taiwan) operating rule curves for the proposed reservoir system. The RGA is combined with an effective and flexible scheme for coding the reservoir rule curves and applied to an important reservoir in Taiwan, considering a water reservoir development scenario to the year 2021. Each rule curve is evaluated using a complex simulation model to determine a performance index for a given flow series. The process of generating and evaluating decision parameters is repeated until no further improvement in performance is obtained. Many experiments were performed to determine the suitable RGA components, including macro evolutionary (ME) selection and blend-a crossover. Macro evolution (ME) can be applied to prevent the premature problem of the conventional selection scheme of genetic algorithm. The purpose of adjusting alpha of a crossover scheme is to determine the exploratory or exploitative degree of various subpopulations. The appropriate rule curve searched by an RGA can minimize the water deficit and maintain the high water level of the reservoir. The results also show that the most promising RGA for this problem consists of these revised operators significantly improves the performance of a system. It is also very efficient for optimizing other highly nonlinear systems.