Optimization of Non-Convex Economic Dispatch Problem Using Hybrid Approach Based on Bacterial Foraging and Genetic Algorithm

Abstract

Fulfillment of consumer demand is a foremost challenge for all electrical power utilities. An electrical power system consists of several generating units and each of these units owns a distinct operating set of operating parameters. A fundamental challenges lies in a fact that there may not be a correlation between operating costs of these machines and their generated output. Major reason for lack of the correlation includes the ramp-rate limits, transmission losses and manipulation due to valve-points in the generation units cost function, and thus, it becomes a non-linear optimization problem. In view of this fact, it creates a rigorous need to devise a robust solution to cater for such a non-linear optimization scenario. In this paper, bacterial foraging and genetic algorithms based hybrid technique is used to effectively tackle the economic dispatch problem. The presented technique incorporates two modifications in the original bacterial foraging algorithm including differential evolution inspired bacterial movement and genetic algorithm based bacterial reproduction. Where inspired bacterial movement involves modification in the directional movement for each bacterium in such a manner that every bacterium tries to improve its direction and position based on differential evolution. The proposed technique is applied to non-convex dynamic economic dispatch (DED) problem in order to obtain optimal solution within feasible functional limits while satisfying the load demand at the same time. The obtained results demonstrate that the proposed hybrid approach outperform the other techniques in term of optimal solution and significant reduction of computational time in the given scenarios.