Economic Emission OPF Using Hybrid GA-Particle Swarm Optimization

This paper presents a Hybrid Genetic Algorithm (HGA) Particle Swarm Optimization (PSO) approach to solve Economic Emission Optimal Power Flow problem. The proposed approach optimizes two conflicting objective functions namely, fuel cost minimization and emission level minimization of polluted gases namely NOX, SOX and COx simultaneously while satisfying operational constraints. An improved PSO which permits the control variables to be represented in their natural form is proposed to solve this combinatorial optimization problem. In addition, the incorporation of genetic algorithm operators in PSO improves the effectiveness of the proposed algorithm. The validity and effectiveness have been tested with IEEE 30 bus system and the results show that the proposed algorithm is competent in solving Economic Emission OPF problem in comparison with other existing methods.