Short-range fixed head hydrothermal scheduling using Fast genetic algorithm

Abstract

This paper presents a Fast genetic algorithm for solving Hydrothermal scheduling (HTS) problem. Genetic Algorithms (GAs) perform powerful global searches, but their long computation times, put a limitation when solving large scale optimization problems. The present paper describes a Fast GA (FGA) to overcome this limitation, by starting with random solutions within the search space and narrowing down the search space by considering the minimum and maximum errors of the population members. Since the search space is restricted to a small region within the available search space the algorithm works very fast. This algorithm reduces the computational burden and number of generations to converge. The proposed algorithm has been demonstrated for HTS of various combinations of Hydro thermal systems. In all the cases Fast GA shows reliable convergence. The final results obtained using Fast GA are compared with simple (conventional) GA and found to be encouraging.