Intelligent and Efficient Electrical Systems pp 143-152 | Cite as

# Effectual Particle Swarm Optimization Algorithm for the Solution of Non-convex Economic Load Dispatch Problem

## Abstract

The economic load dispatch (ELD) problem is a significant problem in the operation of thermal generating station. It is considered as an optimization problem and is defined for minimized total generation cost, subject to various constraints such as linear constraints and nonlinear constraints in order to meet the power demand. To solve the ELD problem, a new effectual particle swarm optimization (EPSO) algorithm is developed. In this algorithm, the PSO is initialized and the diversity of the PSO is improved by applying the mutation operation of Differential evolution (DE). Enrichment of the population is done by applying the migration operation of Biogeography-based optimization (BBO). Hence, the EPSO algorithm is applied to a non-convex ELD problem to get a better optimal solution. As a result, the total generation cost is much reduced with minimum transmission loss compared to other methods. In order to prove its ability, the EPSO algorithm is applied to a six-unit system.

## Keywords

Economic dispatch Particle swam optimization Biogeography-based optimization Differential evolution## References

- 1.Balamurugan R, Subramanian S (2008) Differential evolution-based dynamic economic dispatch of generating units with valve-point effects. Electr Power Compon Syst 36(8):828–843CrossRefGoogle Scholar
- 2.Bhattacharya A, Chattopadhyay PK (2010) Biogeography-based optimization for different economic load dispatch problems. IEEE Trans Power Syst 25(2):1064–1077CrossRefGoogle Scholar
- 3.Deb K (2000) Optimization for engineering design: algorithms and examples. Prentice Hall, India Fourth PrintingGoogle Scholar
- 4.Selvakumar AI, Thanushkodi K (2007) A new particle swarm optimization solution to nonconvex economic dispatch problems. IEEE Trans Power Syst 22(1):42–51CrossRefGoogle Scholar
- 5.Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713CrossRefGoogle Scholar
- 6.Bhattacharya A, Chattopadhyay PK (2010) Hybrid differential evolution with biogeography based optimization for solution of economic load dispatch. IEEE Trans Power Syst 25(4):1955–1964CrossRefGoogle Scholar
- 7.Das S, Abraham A, Konar A (2008) Particle swarm optimization and differential evolution algorithms: technical analysis, applications and hybridization perspectives. J Comput Inf Sci 38:1–38Google Scholar
- 8.Wood AJ, Wollenberg BF (2007) Power generation, operation and control, 2nd edn. Wiley, New YorkGoogle Scholar
- 9.Neto AP, Unsihuay C, Saavedra OR (2005) Efficient evolutionary strategy optimization procedure to solve the nonconvex economic dispatch problem with generator constraints. IEE Proc Gener Transm Distrib 152(5):653–660CrossRefGoogle Scholar
- 10.Park JB, Jeong YW, Kim HH, Shin JR (2006) An improved particle swarm optimization for economic dispatch with valve-point effect. Int J Innov Energy Syst Power 1(1):1–7Google Scholar
- 11.Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol 4, pp 1942–1948 (1995)Google Scholar
- 12.Gaing ZL (2003) Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Trans Power Syst 18(3):1187–1195CrossRefGoogle Scholar
- 13.Khamsawang S, Jiriwibhakorn S (2009) Solving the economic dispatch problem using novel particle swarm optimization. Int J Electr Electron Eng 3(1):41–46Google Scholar
- 14.Dewangan SK, Jain A, Huddar AP (2015) A traditional approach to solve economic load dispatch problem considering the generator constraints. IOSR J Electr Electron Eng (IOSR-JEEE) 10(2) Ver. III, 27–32Google Scholar