Multireservoir Systems Optimization Using Genetic Algorithms: Case Study

Abstract

A genetic algorithm approach is presented for the optimization of multireservoir systems. The approach is demonstrated through application to a reservoir system in Indonesia by considering the existing development situation in the basin and two future water resource development scenarios. A generic genetic algorithm model for the optimization of reservoir systems has been developed that is easily transportable to any reservoir system. This generality is a distinct practical advantage of the genetic algorithm approach. A comparison of the genetic algorithm results with those produced by discrete differential dynamic programming is also presented. For each case considered in this study, the genetic algorithm results are very close to the optimum, and the technique appears to be robust. Contrary to methods based on dynamic programming, discretization of state variables is not required. Further, there is no requirement for trial state trajectories to initiate the search using a genetic algorithm. Model sensitivity and generalizations that can be drawn from this and earlier work by Wardlaw and Sharif are also considered.