Improved GSA based on KHA and PSO algorithm for short-term hydrothermal scheduling

Abstract

The short-term hydrothermal scheduling(STHS) is a complex non-linear and time-varying optimization problem in power system, and it is non-convex when the effect of valve point is considered, which increases the difficulty of intelligent algorithm optimization. Many methods have been used to solve this problem but still have some spaces in improving the quality of the solutions. This paper proposes an improved gravitational search algorithm(GSA) based on krill herd algorithm(KHA) and particle swarm optimization(PSO) algorithm(KHA_PSO_GSA) to deal with the problem of STHS. Introducing the random parameters into the memory characteristics of speed update in PSO and according the times when the global optimal value remains unchanged to improve the mutation strategy of default individuals in KHA. Two standard test cases of four hydro power plants and three thermal power plants system are taken to verify the abilities of the proposed method. The results show that the proposed KHA_PSO_GSA is stronger than KHA, GSA and PSO_GSA in optimizing local and global capabilities. At the same time, KHA_PSO_GSA has a better solution in fuel cost and power transmission loss than other article methods.