Cuckoo Search for Solving Economic Dispatch Load Problem
Author(s)
Adriane B. S. Serapião
ABSTRACT
Economic Load Dispatch (ELD) is a process of scheduling the required load demand among available generation units such that the fuel cost of operation is minimized. The ELD problem is formulated as a nonlinear constrained optimization problem with both equality and inequality constraints. In this paper, two test systems of the ELD problems are solved by adopting the Cuckoo Search (CS) Algorithm. A comparison of obtained simulation results by using the CS is carried out against six other swarm intelligence algorithms: Particle Swarm Optimization, Shuffled Frog Leaping Algorithm, Bacterial Foraging Optimization, Artificial Bee Colony, Harmony Search and Firefly Algorithm. The effectiveness of each swarm intelligence algorithm is demonstrated on a test system comprising three-generators and other containing six-generators. Results denote superiority of the Cuckoo Search Algorithm and confirm its potential to solve the ELD problem.
Cite this paper
A. Serapião, "Cuckoo Search for Solving Economic Dispatch Load Problem," Intelligent Control and Automation, Vol. 4 No. 4, 2013, pp. 385-390. doi: 10.4236/ica.2013.44046.
A. Serapião, "Cuckoo Search for Solving Economic Dispatch Load Problem," Intelligent Control and Automation, Vol. 4 No. 4, 2013, pp. 385-390. doi: 10.4236/ica.2013.44046.
References
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[1] L. Coelho and V. Mariani, “Combining of Chaotic Differential Evolution and Quadratic Programming for Economic Dispatch Optimization with Valve Point Effect,” IEEE Transaction on Power Systems, Vol. 21, No. 2, 2006, pp. 989-996.
http://dx.doi.org/10.1109/TPWRS.2006.873410 [2] M. A. Abido, “A Novel Multiobjective Evolutionary Algorithm for Environmental/Economic Power Dispatch,” Electric Power Systems Research, Vol. 65, No. 1, 2003, pp. 71-81.
http://dx.doi.org/10.1016/S0378-7796(02)00221-3 [3] Z. L. Gaing, “Particle Swarm Optimization to Solving the Economic Dispatch Considering the Generator Constraints,” IEEE Transactions on Power Systems, Vol. 18, No. 3, 2003, pp. 1187-1197.
http://dx.doi.org/10.1109/TPWRS.2003.814889 [4] J.-B. Park, K.-S. Lee, J.-R. Shin and K. Y. Lee, “A Particle Swarm Optimization for Economic Dispatch with Nonsmooth Cost Function,” IEEE Transactions on Power Systems, Vol. 20, No. 1, 2005, pp. 34-42.
http://dx.doi.org/10.1109/TPWRS.2004.831275 [5] N. Sinha, R. Chakrabarti and P. K. Chattopadhyay, “Evolutionary Programming Techniques for Economic Load Dispatch,” IEEE Transactions on Evolutionary Computation, Vol. 7, No. 1, 2003, pp. 83-94.
http://dx.doi.org/10.1109/TEVC.2002.806788 [6] B. K. Panigrahi, B. Ravikumar and V. Pandi, “Bacterial Foraging Optimisation: Nelder-Mead Hybrid Algorithm for Economic Load Dispatch,” IET Proceedings of Generation Transmission and Distribution, Vol. 2, 2008, pp. 556-565. [7] S. Pothiya, I. Ngamroo and W. Kongprawechnon, “Ant Colony Optimization for Economic Dispatch Problem with Non-Smooth Cost Functions,” International Journal of Electrical power and Energy System, Vol. 32, 2010, pp. 478-487. http://dx.doi.org/10.1016/j.ijepes.2009.09.016 [8] V. Ravikumar Pandi, B. K. Panigrahi, M. K. Mallick, A. Abraham and S. Das, “Improved Harmony Search for Economic Power Dispatch,” Proceedings of the Ninth International Conference on Hybrid Intelligent Systems (HIS’09), Vol. 3, 2009, pp. 403-408. [9] A. Bhattacharya and P. K. Chattopadhyay, “Biogeography-Based Optimization for Different Economic Load Dispatch Problems,” IEEE Transactions on Power Systems, Vol. 25, No. 2, 2010, pp. 1064-1077.
http://dx.doi.org/10.1109/TPWRS.2009.2034525 [10] B. Shaw, S. Ghoshal, V. Mukherjee and S. P. Goshal, “Solution of Economic Load Dispatch Problems by a Novel Seeker Optimization Algorithm,” International Journal on Electrical Engineering and Informatics, Vol. 3, No. 1, 2011, pp. 26-42. [11] A. B. S. Serapiao, “Fundamentos de Otimizacao por Inteligência de Enxames: uma Visao Geral,” Revista SBA Controle & Automacao, Vol. 20, No. 3, 2009, pp. 271-304. [12] X. S. Yang and S. Deb, “Cuckoo Search via Lévy Flights,” Proceedings of World Congress on Nature and Biologically Inspired Computing, Coimbatore, 9-11 December 2009, pp. 210-214. [13] J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” Proceedings of IEEE International Conference on Neural Networks, Perth, 27 November 1995, pp. 19421948.
http://dx.doi.org/10.1109/ICNN.1995.488968 [14] M. Eusuff and K. Lansey, “Optimizing of Water Distribution Network Design Using the Shuffled Frog-Leaping Algorithm,” Journal of Water Resources Planning and Manegement, Vol. 129, No. 3, 2003, pp. 210-225.
http://dx.doi.org/10.1061/(ASCE)0733-9496(2003)129:3(210) [15] K. M. Passino, “Biomimicry of Bacterial Foraging for Distributed Optimization and Control,” IEEE Control Systems Magazine, Vol. 22, No. 3, 2002, pp. 52-67.
http://dx.doi.org/10.1109/MCS.2002.1004010 [16] D. Karaboga and B. Basturk, “A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm,” Journal of Global Optimization, Vol. 39, No. 3, 2007, pp. 459-471.
http://dx.doi.org/10.1007/s10898-007-9149-x [17] Z. W. Geem, J. H. Kim and G. V. Loganathan, “A New Heuristic Optimization Algorithm: Harmony Search,” Simulation, Vol. 76, No. 2, 2001, pp. 60-68.
http://dx.doi.org/10.1177/003754970107600201 [18] X. S. Yang, “Firefly Algorithms for Multimodal Optimization”. In: O. Watanabe and T. Zeugmann, Eds., Stochastic Algorithms: Foundations and Applications. Lecture Notes in Computer Science, Springer-Verlag, Berlin, 2009, pp. 169-178. [19] M. Tuba, M. Subotic and N. Stanarevic, “Modified Cuckoo Search Algorithm for Unconstrained Optimization Problems,” Proceeding of the European Computing Conference, Paris, 2011, pp. 263-268.