Combined Economic And Emission Dispatch (CEED) Considering Losses Using Artificial Bee Colony And Particle Swarm Optimization Hybrid With Cardinal Priority Ranking

Abstract

The problem of power system optimization has become a deciding factor in current power system engineering practice with emphasis on cost and emission reduction. The economic and emission dispatch problem has been addressed in this thesis using two efficient optimization methods, Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). A hybrid produced from these two algorithms is implemented on a 3-generator test system, 30-bus 6 generator IEEE test system and a 10 generato.r test system. The results are compared with PSO, Genetic Algorithm (GA), with respect to the 3-generator test system, ABC, Fuzzy Controlled Genetic Algorithm (FCGA) and Non Sorting Genetic Algorithm (NSGA-II), with respect to the 6-generator test system and differential evolution, Non sorting genetic algorithm II and Strength Pareto Evolutionary Algorithm, with respect to the 10-generator test system. This proposed optimization method is found to be effective on the combined economic and emission dispatch problem.