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Bat Algorithm Approaches for Solving the Combined Economic and Emission Dispatch Problem
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 124 - Number 1 |
| Year of Publication: 2015 |
| Authors: Dimitrios Gonidakis, Aristidis Vlachos |
10.5120/ijca2015905288
|
Dimitrios Gonidakis, Aristidis Vlachos . Bat Algorithm Approaches for Solving the Combined Economic and Emission Dispatch Problem. International Journal of Computer Applications. 124, 1 ( August 2015), 1-7. DOI=10.5120/ijca2015905288
Abstract
Combined economic and emission dispatch (CEED) is a multi-objective optimization problem aim of which is the simultaneous minimization of operating cost and pollutant emission by allocating generation among thermal units of an electric power system. Objective functions are conflicted and several equality and inequality constraints must be satisfied. This paper uses two bat algorithm based approaches for solving CEED. One of them hybridizes bat algorithm with differential evolution strategies while the other one inserts a mutation operator into the original bat algorithm. Both methods are applied to a 10-generator sample power system. Numerical results from the proposed algorithms are compared to those obtained by other techniques in recent literature.
References
- Abido, M. A., 2009. “Multiobjective particle swarm optimization for environmental/economic dispatch problem”, Electric Power Systems Research, Vol. 79, No. 7, pp. 1105-1113.
- Afzalan, E. and Joorabian, M., 2013. “Emission, reserve and economic load dispatch problem with non-smooth and non-convex cost functions using epsilon-multi-objective genetic algorithm variable”, International Journal of Electrical Power & Energy Systems, Vol. 52, pp. 55-67.
- Azizipanah-Abarghooee., R and Niknam, T., 2012. “A new improved Bat Algorithm for fuzzy interactive multiobjective economic / emission dispatch with load and wind power uncertainty”, Uncertainty Modeling in Knowledge Engineering and Decision Making, pp. 388-393.
- Chatterjee, A., Ghoshal, S. P. and Mukherjee, V., 2012. “Solution of combined economic and emission dispatch problems of power systems by an opposition-based harmony search algorithm”, International Journal of Electrical Power & Energy Systems, Vol. 39, No. 1, pp. 9-20.
- Danaraj, R. M. S. and Gajendran, F., 2005. “Quadratic Programming Solution to Emission and Economic Dispatch Problems”, Journal of Institution of Engineers (India), Vol. 86, pp. 129-132.
- Farag, A., Al-Baiyat, S. and Cheng, T., 1995. “Economic load dispatch multiobjective optimization procedures using linear programming techniques”, IEEE Transactions on Power Systems, Vol. 10, No. 2, pp. 731-738.
- Fister, I., Fister, D. and Yang, X. S., 2013. “A Hybrid Bat Algorithm”, Elektrotehniški vestnik, Vol. 80, No. 1-2, pp. 1-7.
- Karakonstantis, I. and Vlachos, A., 2015. “Ant Colony Optimization for Continuous Domains applied to Emission and Economic Dispatch Problems”, Journal of Information and Optimization Sciences, Vol. 36, No. 1-2, pp. 23-42.
- King, R. T. F. A. and Rughooputh, H.C.S., 2003. “Elitist Multiobjective Evolutionary Algorithm for Environmental/Economic Dispatch”, Evolutionary Computation, 2003. CEC '03. The 2003 Congress on, Vol. 2, pp. 1108-1114.
- Kothari, D. P. and Dhillon, J. S., 2011. Power system optimization (2nd ed.), Prentice-Hall of India.
- Manteaw, E. D. and Odero, N. A., 2012. “Combined Economic and Emission Dispatch Solution Using ABC_PSO Hybrid Algorithm with Valve Point Loading Effect”, International Journal of Scientific and Research Publications, Vol. 2, No. 12, 9 pages.
- Sasikala, J. and Ramaswamy, M., 2010. “Optimal λ based Economic Emission Dispatch using Simulated Annealing”, International Journal of Computer Applications, Vol. 1, No. 10, pp. 55-63.
- Sonmez, Y., 2011. “Multi-objective environmental / economic dispatch solution with penalty factor using artificial bee colony algorithm”, Scientific Research and Essays, Vol. 6, No. 13, pp. 2824-2831.
- Storn, R. and Price K., 1997. “Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces”, Journal of Global Optimization, Vol. 11, No. 4, pp. 341–359.
- Yang, X. S., 2010. “A New Metaheuristic Bat-Inspired Algorithm”, Nature Inspired Cooperative Strategies for Optimization (NISCO 2010): Studies in Computational Intelligence, Vol. 284, pp. 65-74.
- Yokoyama, R., Bae, S., Morita, T. and Sasaki, H., 1988. “Multiobjective optimal generation dispatch based on probability security criteria”, IEEE Transactions on Power Systems, Vol. 3, No.1, pp. 317-324.
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