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

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). For approaching to real conditions of economic load dispatch problem generation cost function of power system is considered non-smooth. On the other hand, the reduction of pollution from power plants as part of the problem purposes is considered and, therefore, at the same time economic load dispatch, environmental friendly dispatch has been done. However, for optimum economic load dispatch limitations of generation units, including generation and consumption balance in the system, generation limit and the rate of increase and decrease as well as network losses in the optimization process considered that the restrictions in the proposed algorithm consideredA hybrid produced from these two algorithms is implemented on a 3-generator test system, 30-bus 6 generator IEEE test system and a 10 generator 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