Modified differential evolution algorithm: A novel approach to optimize the operation of hydrothermal power systems while considering the different constraints and valve point loading effects

Abstract

This paper proposes an efficient and new modified differential evolution algorithm (ENMDE) for solving two short-term hydrothermal scheduling (STHTS) problems. The first is to take the available water constraint into account, and the second is to consider the reservoir volume constraints. The proposed method in this paper is a new, improved version of the conventional differential evolution (CDE) method to enhance solution quality and shorten the maximum number of iterations based on two new modifications. The first focuses on a self-tuned mutation operation to open the local search zone based on the evaluation of the quality of the solution, while the second focuses on a leading group selection technique to keep a set of dominant solutions. The contribution of each modification to the superiority of the proposed method over CDE is also investigated by implementing CDE with the self-tuned mutation (STMDE), CDE with the leading group selection technique (LGSDE), and CDE with the two modifications. In addition, particle swarm optimization (PSO), the bat algorithm (BA), and the flower pollination algorithm (FPA) methods are also implemented through four study cases for the first problem, and two study cases for the second problem. Through extensive numerical study cases, the effectiveness of the proposed approach is confirmed.