Application of multi-objective genetic algorithms to multireservoir system optimization in the Han River basin

Abstract

The objective of multireservoir system optimization is to achieve an optimal reservoir operating plan by the effective use of water resources. Many optimization techniques have been applied for the last decades, and researchers have recently interested in the heuristic approaches like evolutionary computation. This study proposes a methodology for applying multi-objective genetic algorithms (MOGAs) to a multireservoir system optimization in the Han River basin. The second generation evolutionary multiobjective technique, NSGA-II, is used. The simulation model is applied to the Han River basin and the performance of the model is compared with the historical reservoir operation records. Two different cases are performed to evaluate the applicability of NSGA-II. Case 1 shows the basic performance of NSGA-II as applied to multireservoir system optimization, and Case 2 presents the methodology to discriminate the critical decision variables. In addition, the alternative releases and storages by NSGA-II are compared with the historical releases and storages. Cases 1 and 2 show that NSGA-II can be applied to multireservoir system optimization, and the alternative releases and storages computed using the results from NSGA-II can be used as the possible resrvoir operating plans that supply more water resources to downstream than the historical releases.