Abstract
In this paper, a multiobjective teaching-learning-based optimization algorithm with non-domination based sorting is applied to solve the environmental/economic dispatch (EED) problem containing the incommensurable objectives of best economic dispatch and least emission dispatch. The address of the environmental concerns that arise in the present day due to the operation of fossil fuel fired electric generators and global warming requires the transformation of the classical single objective economic load dispatch problem into multiobjective environmental/economic dispatch problem. In the work presented a test system of forty units is taken with fuel cost and emission as two conflicting objectives to be optimized simultaneously. The mathematical model used considers practical upper and lower bounds applicable to the generators. The valve point loading of the generator is mimicked in the modeling to accommodate a more realistic system. The simulation result reveals that the proposed approach is a competitive one to the current existing methods for finding the best optimal pareto front of two conflicting objectives and has the better robustness.
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References
IEEE Current Operating Problems Working Group. Potential impacts of clean air regulations on system operations 10(2), 647–656 (1995)
Zahavi, J., Eisenberg, L.: An application of the Economic-environmental power dispatch. IEEE Trans. Syst., Man, Cybernet. SMC-7(7), 523–530 (1977)
Brodesky, S.F., Hahn, R.W.: Assessing the influence of power pools on emission constrained economic dispatch. IEEE Trans. Power Systems 1(1), 57–62 (1986)
Yokoyama, R., Bae, S.H., Morita, T., Sasaki, H.: Multiobjective optimal generation dispatch based on probability security criteria. IEEE Trans. Power Systems 3(1), 317–324 (1988)
Deb, K.: Multiobjective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)
Abido, M.A.: A Novel Multiobjective Evolutionary Algorithm for Environmental / Economic Power Dispatch. Electric Power Systems Research 65(1), 71–81 (2003)
Abido, M.A.: A Niched Pareto Genetic Algorithm for Environmental/ Economic Power Dispatch. Electric Power Systems Research 25(2), 97–105 (2003)
Abido, M.A.: Environmental / Economic Power Dispatch using Multiobjective Evolutionary Algorithms. IEEE Trans. Power Systems 18(4), 1529–1537 (2003)
Ah King, R.T.F, Rughooputh, H.C.S., Deb, K.: Evolutionary Multi-Objective Environmental/Economic Dispatch: Stochastic vs. Deterministic Approaches. KanGAL Report Number-2004019, 1–15 (2004)
Ah King, R.T.F, Rughooputh, H.C.S., Deb, K.: Stochastic Evolutionary Multiobjective Environmental/Economic Dispatch. In: Proceedings of IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, pp. 946–953 (2006)
Basu, M.: Economic environmental dispatch using multi-objective differential evolution. Applied Soft Computing 11(2), 2845–2853 (2011)
Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design 43(3), 303–315 (2011)
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Krishnanand, K.R., Panigrahi, B.K., Rout, P.K., Mohapatra, A. (2011). Application of Multi-Objective Teaching-Learning-Based Algorithm to an Economic Load Dispatch Problem with Incommensurable Objectives. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27172-4_82
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DOI: https://doi.org/10.1007/978-3-642-27172-4_82
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