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 NO X , SO X and CO x 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.
KeywordsParticle Swarm Optimization Particle Swarm Optimization Algorithm Fuel Cost Optimal Power Flow Genetic Algorithm Operator
Unable to display preview. Download preview PDF.
- 10.Yuryevich, J., Wang, K.P.: Evolutionary Programming based optimal power flow algorithms. IEEE Transactions on Power Systems 14(4), 1245–1250Google Scholar
- 11.Deb, K.: Multi-objective evolutionary algorithm. John Wiley & Sons publicationsGoogle Scholar
- 14.Devaraj, D., Preetha Roselyn, J.: Improved genetic algorithm for voltage security constrained optimal power flow problem. Int. Journal Energy Technology and Policy 5(4), 475–488Google Scholar
- 16.Bouktir, T., Labdani, R., Slimani, L.: Economic Power Dispatch of Power System With Pollution Control Using Multi objective Particle Swarm Optimization. Journal of Pure and Applied Sciences 4, 57–77 (2007)Google Scholar
- 17.Kennedy, J., Eberhart, R.: Swarm intelligence. Morgan Kaufmann publishers (2001)Google Scholar