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An Application of Grey Wolf Optimizer for Solving Combined Economic Emission Dispatch Problems

Hong Mee Song(1), Mohd Herwan Sulaiman(2*), Mohd Rusllim Mohamed(3)

(1) Universiti Malaysia Pahang, Malaysia
(2) Universiti Malaysia Pahang, Malaysia
(3) Universiti Malaysia Pahang, Malaysia
(*) Corresponding author


DOI: https://doi.org/10.15866/iremos.v7i5.2799

Abstract


Grey Wolf Optimizer (GWO) is a newly proposed algorithm that developed based on inspiration of grey wolves (Canis Lupus). This GWO algorithm mimics the nature of grey wolves in leadership hierarchy and hunting mechanism. This paper is in the purpose of introducing GWO to solve combined economic emission dispatch problem (CEED) in power system. CEED actually is a bi-objective problem where the objective of economic dispatch (ED) and emission dispatch (EMD) are combined into a single function by using price penalty factor. Hence, CEED is used to minimize total generation cost by minimized fuel cost and emission at the same time determines the optimum power generation. The proposed algorithm will be implemented in two different systems which are 6 and 11-generating unit test systems with different constraints on various power load demands. The results were compared with the optimization techniques reported in recent literature in order to observe the effectiveness of GWO.
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Keywords


Combined Economic Emission Dispatch; Economic Dispatch; Emission Dispatch; Grey Wolf Optimizer

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References


Harinder Pal Singh, Y.S.B., D.P.Kothari, Multiobjective Load Dispatch Using Particle Swarm Optimization, in 8th Conference on Industrial Electronics and Applications (ICIEA). 2013, IEEE. p. 272-277.
http://dx.doi.org/10.1109/iciea.2013.6566379

Sulaiman, M.H., An application of differential search algorithm in solving non-convex economic dispatch problems with valve-point effects, (2013) International Review on Modelling and Simulations (IREMOS), 6 (5), pp. 1593-1599.

Güvenç, U., Combined economic emission dispatch solution using genetic algorithm based on similarity crossover. Scientific Research and Essays, 2010. 5(17): p. 2451-2456.

S.Subramaniam, S.G., A Simple Approach for Emission Constrained Economic Dispatch Problems. International Journal of Computer Application, 2010. 8-No.11: p. 39-45.
http://dx.doi.org/10.5120/1340-1619

Hassan, M.Y., Power System Control. 3rd ed. 2010: Faculty of Electrical Engineering, UTM.

Abido, M.A., Multiobjective particle swarm optimization for environmental/economic dispatch problem. Electric Power Systems Research, 2009. 79(7): p. 1105-1113.
http://dx.doi.org/10.1016/j.epsr.2009.02.005

Bhuvnesh Khokhar, K.P.S.P., A Novel Weight-Improved Particle Swarm Optimization for Combined Economic and Emission Dispatch Problems International Journal of Engineering Science and Technology (IJEST), 2012. 4(05): p. 2015-2021.

R.Balamurugan, S.S., A Simplified Recursive Approach to Combined Economic Emission Dispatch. Electric Power Components and Systems, 2008(36): p. 17-27.
http://dx.doi.org/10.1080/15325000701473742

Vennila, H., Ruban Deva Prakash, T., Rajesh, R., A solution to the combined economic and emission dispatch using hybrid HB-SA algorithm on large scale power system, (2013) International Review on Modelling and Simulations (IREMOS), 6 (4), pp. 1256-1263.

Mohseni-Mansur, S.M., Pirayesh, A., Abdollahi-Mansoorkhani, H.R., Abedini-Duki, E., A multi objective framework for solving economic load dispatch problem including stochastic nature of wind power, (2013) International Review of Electrical Engineering (IREE), 8 (1), pp. 362-368.

Mirjalili, S., S.M. Mirjalili, and A. Lewis, Grey Wolf Optimizer. Advances in Engineering Software, 2014. 69(0): p. 46-61.
http://dx.doi.org/10.1016/j.advengsoft.2013.12.007

Venkatesh, P., R. Gnanadass, and N.P. Padhy, Comparison and application of evolutionary programming techniques to combined economic emission dispatch with line flow constraints. Power Systems, IEEE Transactions on, 2003. 18(2): p. 688-697.
http://dx.doi.org/10.1109/tpwrs.2003.811008

Mech, L.D., Alpha status, dominance, and division of labor in wolf packs. Can J Zool, 1999(77(1)): p. 196-203.
http://dx.doi.org/10.1139/z99-099

C. Muro, R.E., L. Spector, R. Coppinger, Wolf-pack (Canis Lupus) hunting strategies emerge from simple rules in computational simulations. Behav process, 2011(88): p. 192-197.
http://dx.doi.org/10.1016/j.beproc.2011.09.006

M.H. Sulaiman, M.R.M., Solving economic dispatch problems utilizing cuckoo search algorithm, in 8th International Power Engineering and Optimization Conference (PEOCO 2014). 2014, IEEE: Langkawi, Malaysia.
http://dx.doi.org/10.1109/peoco.2014.6814405


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