Solving the Economic Dispatch Problem using Novel Particle Swarm Optimization
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33122
Solving the Economic Dispatch Problem using Novel Particle Swarm Optimization

Authors: S. Khamsawang, S. Jiriwibhakorn

Abstract:

This paper proposes an improved approach based on conventional particle swarm optimization (PSO) for solving an economic dispatch(ED) problem with considering the generator constraints. The mutation operators of the differential evolution (DE) are used for improving diversity exploration of PSO, which called particle swarm optimization with mutation operators (PSOM). The mutation operators are activated if velocity values of PSO nearly to zero or violated from the boundaries. Four scenarios of mutation operators are implemented for PSOM. The simulation results of all scenarios of the PSOM outperform over the PSO and other existing approaches which appeared in literatures.

Keywords: Novel particle swarm optimization, Economic dispatch problem, Mutation operator, Prohibited operating zones, Differential Evolution.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1072507

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2322

References:


[1] A.J Wood and B.F. Wollenberg, Power generation operation and control, John Wiley and Sons, New York, 1984
[2] Hadi Sadaat. "Power system analysis". International editions. WCB/McGraw-Hill. 1999.
[3] Z.-L. Giang, "Particle swarm optimization to solving the economic dispatch considering the generator constraints", IEEE Trans. On Power system, August 2003, pp. 1187-2123.
[4] Jong-Bae Park, Ki Song Lee, Jong-Rin Shin, Kwang Y. Lee, " A particle swarm optimization for economic dispatch with nonsmooth cost functions" IEEE Trans. On Power System, vol. 20, no. 1, pp. 34-42, February. 2005.
[5] A. Immanuel Selvakumar, K. Thanushkodi, " Anti-predatory particle swarm optimization: Solution nonconvex economic dispatch problem," Electric Power System Research, online, 2007
[6] A. Immanuel Selvakumar, K. Thanushkodi, "A new particle swarm optimization solution to nonconvex economic dispatch problem," IEEE Trans. On Power system, vol. 22, no. 1, pp. 42-51, February. 2007.
[7] C. Jiejin, M. Xiaoqian, L. Lixiang and P.H. Peng, "Chaotic particle swarm optimization for economic dispatch considering the generator constraints", Energy Conversion & Management, 2007, pp 645-53.
[8] B.K. Panigrah, S.R. Yadav, S. Agrawal and M.K. Tiwari, "A clonal algorithm to solve economic load dispatch", Electric Power System Research, online, 2006.
[9] S. Pothiya, I. Ngamroo and W. Kongprawechnon,"Applica-tion of multiple tabu search algorithm to solve dynamic economic dispatch considering generator constraints", Energy Convers. Manage, 2007.
[10] Wong KP, Wong YW. "Genetic and Genetic/Simulated - Annealing approaches to economic dispatch," IEE Proc.Gener Transm. Distrib, vol. 141, no. 5, pp. 507-513, 1994.
[11] D.C. Walters and G. B. Sheble "Genetic algorithm solution of economic dispatch with valve point loading," IEEE Trans. Power Syst, vol. 8, no. 3, 1993.
[12] J. Kenedy and R. Eberhart, "particle swarm optimization", Proc. IEEE Int. Conf. Neural Networks, 1995, pp. 1942-48.
[13] M. Clerc and J. Kenedy, "The particle swarm: Explosion, stability and convergence in a multi-dimensional complex space", IEEE Trans. Evol. Comput., Jun.1998, pp. 91-96.
[14] Y. Shi and R. C. Eberhart, "Emparical study of particle swarm optimization", in Proc. Congr. Evol. Comput., NJ, 1999, pp. 1945-50.
[15] H. Yoshida, K. Kawata, Y. Fukuyama, S. Takayama, and Y. Nakanishi, " A particle swarm optimization for reactive power and voltage control considering voltage security assessment," IEEE Tran. Power Syst., vol. 15, pp. 1232-1239, Nov. 2000.
[16] S. Naka, T. Genti, T. Yura and Y. Fukuyama, " Practical distribution state estimation using hybrid particle swarm optimization," Proc. IEEE Power Eng. Winter Meeting, vol. 2, pp. 815-820, 2001.
[17] S. Naka, T. Genti, T. Yura and Y. Fukuyama, " Hybrid particle swarm optimization based distribution state estimation using constriction factor approach," Proc. Int. Conf. SCIS ISIS, vol. 2, pp. 1083-1088, 2002.
[18] D.T. Pham, A. Ghanbarzadeh, E. Koc, S. Otri, S. Rahim and M. Zaidi. "The bees algori-thm, a novel tool for complex optimisation problems", Proc 2nd Int Virtual Conf. Intelligent Prod. Mach. and Syst, 2006, pp.454-59.