Application of modified enhanced differential evolution algorithms for reservoir operation during floods: a case study

Abstract

Abstract Operating a reservoir during flooding is a complex problem in which optimum decision-making is a difficult task. The present study demonstrates a solution for the operation of flooding problem in a multiple-purpose reservoir. A reservoir on River Narmada in central India is chosen as the case study. The multiple objective problems comprised maximization of hydropower releases, minimizing spills, and achieving stipulated target storage at the end of the operation period. The chosen optimization models are the Differential Evaluation Algorithm (DEA) and its variants: the Enhanced Differential Evolution Algorithm (EDEA) and the Modified Enhanced Differential Evaluation Algorithm (MEDEA). The EDEA model is modified in the present study to MEDEA. The results of all three models applied to the same case study are compared on convergence to an optimal solution. All three algorithms were tested on two of the popular benchmark functions that are Ackley and Sphere. The results of both applications demonstrated that MEDEA proved to be the best in terms of converging to the optimal solution, exhibiting better stability, and quality of final results. The outcomes of this study also provided an effective way to optimize large scale multi-purpose and multi-reservoir flood control operation problems.