Application of improved particle swarm optimization in economic dispatch of power system

Abstract: This paper introduces an improved particle swarm optimization to solve economic dispatch problems involving numerous constraints. Depending on the type of generating units, there are optimization constraints and practical operating constraints of generators such as prohibited operating zones and ramp rate limits. The algorithm is a hybrid technique made up of particle swarm optimization and bat algorithm. Particle swarm optimization as the main algorithm integrates bat algorithm in order to boost its velocity and adjust the improved solution. The new technique is firstly tested on five different cases of economic dispatch problems comprising 6, 13, 15, 40 and 140 generating units. The simulation results show that it performs better than both particle swarm and bat technique

A hybrid GA–PS–SQP method to solve power system valve-point economic dispatch problems

This study presents a new approach based on a hybrid algorithm consisting of Genetic Algorithm (GA), Pattern Search (PS) and Sequential Quadratic Programming (SQP) techniques to solve the well-known power system Economic dispatch problem (ED). GA is the main optimizer of the algorithm, whereas PS and SQP are used to fine tune the results of GA to increase confidence in the solution. For illustrative purposes, the algorithm has been applied to various test systems to assess its effectiveness. Furthermore, convergence characteristics and robustness of the proposed method have been explored through comparison with results reported in literature. The outcome is very encouraging and suggests that the hybrid GA–PS–SQP algorithm is very efficient in solving power system economic dispatch problem