Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31093
Hybrid Optimization of Emission and Economic Dispatch by the Sigmoid Decreasing Inertia Weight Particle Swarm Optimization

Authors: Joko Pitono, Adi Soeprijanto, Takashi Hiyama


This paper present an efficient and reliable technique of optimization which combined fuel cost economic optimization and emission dispatch using the Sigmoid Decreasing Inertia Weight Particle Swarm Optimization algorithm (PSO) to reduce the cost of fuel and pollutants resulting from fuel combustion by keeping the output of generators, bus voltages, shunt capacitors and transformer tap settings within the security boundary. The performance of the proposed algorithm has been demonstrated on IEEE 30-bus system with six generating units. The results clearly show that the proposed algorithm gives better and faster speed convergence then linearly decreasing inertia weight.

Keywords: Particle Swarm Optimization, optimal power flow, Combined Economic Emission Dispatch, Sigmoid decreasing Inertia Weight

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1337


[1] Yong-Hua Song, Modern Optimisation Techniques in Power System. Kluwer Academic Publisher, Netherlands, 1999, ch. 1.
[2] E. Pablo, M.R. Juan, "Optimal Power Flow Subject to Security Constraints Solved With a Particle Swarm Optimizer," IEEE Transactions On Power Systems, Vol. 23, No. 1, pp. 33-40, February 2008.
[3] S.N. Singh, I. Erlich, "Particle Swarm Based Optimal estimation of Block Incremental Cost Curve," The 14th International Conference on Intelligent System Application to Power System, Kaohsiung Taiwan, pp. 257-263, November 2007.
[4] R. Thanushkodi, Vinodh, "An Efficient Particle Swarm Optimization for Economic Dispatch with Valve-Point Effect," Applied Computing Conference, Istanbul Turkey, pp. 182-187, May 2008.
[5] Jong-Bae Park, Yun-Won Jeong, "An Improved Particle Swarm Optimization for Economic Dispatch with Valve-Point Effect," International journal of Innovation in Energy Systems and Power, Vol. 1, No. 1, pp. 1-7, November 2006.
[6] Z. Al-Hamouz, S. Al-Sharif, "Application of Particle Swarm Optimization Algorithm for Optimal Reactive Power Planning, Control and Intelligent System, " Control and Intelligent Systems, Vol. 35, No. 2, pp. 66-72, 2007.
[7] R. Hassan, B. Cohanim, "A Comparison of Particle Swarm Optimization and The Genetic Algorithm," Massachusetts Institute of Technology Cambridge, pp. 1-13, 2004.
[8] F. Cus, U. Zuperl, "High speed end-milling optimisation using Particle Swarm Intelligence," Journal of Achievements in Materials and Manufacturing Engineering, Vol. 22, pp. 75-78, June 2007.
[9] Linda Slimani, T. Boukir, "Economic power dispatch of power system with pollution control using multiobjective Ant Colony Optimization," International Journal of Computation Intelligence Research, Vol.3, No.2, pp. 145-153, 2007.