Comparative Study of Particle Swarm Optimization and Bee Swarm Optimization on a Dynamic Economic Load Dispatch Problem

Abstract

The economic operation of the generating systems has always occupied an important position in the electric power industry. The dynamic economic dispatch (DED) is one of the significant non-linear problems as optimization has to be carried out for the varying load demand. Mostly, 24-hr duration is accounted. The complexity further increases due to account of the valve-point loading effects and ramp-rate limits. The aim of the dynamic economic load dispatch problem is to find the optimal combination of generators in order to minimize the operating costs of the system. The load demand must be appropriately shared among the various generating units of the system. In this dissertation, a comparative study of two optimization techniques namely Particle swarm optimization (PSO) and Bee swarm optimization (BSO) is done on a dynamic economic dispatch problem considering both valve-point loading and ramp rate limits of the generator. The algorithms are implemented on a ten unit system neglecting transmission losses. The results obtained by both the technique are compared.