Abstract
This paper presents a novel stochastic optimization approach to solve constrained economic load dispatch (ELD) problem using Hybrid Bacterial Foraging-Differential Evolution optimization algorithm. In this hybrid approach computational chemotaxis of BFOA, which may also be viewed as a stochastic gradient search, has been coupled with DE type mutation and crossover of the optimization agents. The proposed methodology easily takes care of solving non-convex economic load dispatch problems along with different constraints like transmission losses, dynamic operation constraints (ramp rate limits) and prohibited operating zones. Simulations were performed over various standard test systems with different number of generating units and comparisons are performed with other existing relevant approaches. The findings affirmed the robustness and proficiency of the proposed methodology over other existing techniques.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Choudhary, B.H., Rahman, S.: A Review of recent advances in economic dispatch. IEEE Trans. on Power System 5(4), 1248–1259 (1990)
Happ, H.H.: Optimal power dispatch-a comprehensive survey. IEEE Trans on Power Apparatus and Systems PAS-96, 841–854 (1971)
Wood, A.J., Wollenberg, B.F.: Power Generation, Operation and Control. John Wiley & Sons, New York (1984)
Chen, C.L., Wang, S.C.: Branch and bound scheduling for thermal generating units. IEEE Trans. on Energy Conversion 8(2), 184–189 (1993)
Lee, K.Y., et al.: Fuel cost minimization for both real and reactive power dispatches. IEE Proc. C. Gener. Trsns. & distr. 131(3), 85–93 (1984)
Lin, C.E., Viviani, G.L.: Hierarchical economic dispatch for piecewise quadratic cost functions. IEEE Trans. on Power Apparatus Systems PAS103, 1170–1175 (1984)
Bakirtzis, A., Petridis, V., Kazarlis, S.: Genetic algorithm solution to the economic dispatch problem. Proc. Inst. Elect. Eng. Gen., Transm. Distrib. 141(4), 377–382 (1994)
Lee, F.N., Breipohl, A.M.: Reserve constrained economic dispatch with prohibited operating zones. IEEE Trans. Power Syst. 8, 246–254 (1993)
Sheble, G.B., Brittig, K.: Refined genetic algorithm-economic dispatch example. In: IEEE Paper 94 WM 199-0 PWRS, presented at the IEEE/PES, Winter Meeting (1994)
Walters, D.C., Sheble, G.B.: Genetic algorithm solution of economic dispatch with valve point loading. IEEE Trans. on Power Systems 8(3), 1325–1332 (1993)
Ma, A.A., El-Keib, Smith, R.E.: A genetic algorithm-based approach to economic dispatch of power systems. In: IEEE conference (1994)
Gaing, Z.-L.: Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Trans. on Power Systems 18(3), 1187–1195 (2003)
Park, J.B., Lee, K.S., Shin, J.R., Lee, K.Y.: A Particle Swarm optimization for economic dispatch with non-smooth cost functions. IEEE Transactions on Power Systems 20(1), 34–42 (2005)
Chen, P.H., Chang, H.C.: Large scale economic dispatch by genetic algorithm. IEEE Trans. Power Syst. 10(4), 1919–1926 (1995)
Yalcionoz, T., Altun, H., Uzam, M.: Economic dispatch solution using a genetic algorithm based on arithmetic crossover. In: Proc. IEEE Proto Power Tech. Conf., Proto, Portugal (September 2001)
Fung, C.C., Chow, S.Y., Wong, K.P.: Solving the economic dispatch problem with an integrated parallel genetic algorithm. In: Proc. PowerCon Int. Conf., vol. 3, pp. 1257–1262 (2000)
Fogel, D.B.: Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, 2nd edn. IEEE Press, Piscataway (2000)
Eberhart, R.C., Shi, Y.: Comparison between genetic algorithms and particle swarm optimization. In: Proc. IEEE Int. Conf. Evol. Comput., May 1998, pp. 611–616 (1998)
Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE. Control Syst. Mag., 52–67 (June 2002)
Price, K., Storn, R., Lampinen, J.: Differential Evolution - A Practical Approach to Global Optimization. Springer, Heidelberg (2005)
Biswas, A., Dasgupta, S., Das, S., Abraham, A.: A Synergy of Differential Evolution and Bacterial Foraging Algorithm for Global Optimization. International Journal on Neural and Mass-Parallel Computing and Information Systems, Neural Network World 17(6), 607–626 (2007)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Vesterstrøm, J., Thomson, R.: A Comparative Study of Differential Evolution, Particle Swarm Optimization, and Evolutionary Algorithms on Numerical Benchmark Problems. In: Proc. Sixth Congress on Evolutionary Computation (CEC 2004). IEEE Press, Los Alamitos (2004)
Lampinen, J., Zelinka, I.: On Stagnation of the Differential Evolution Algorithm. In: Ošmera, P. (ed.) Proceedings of MENDEL 2000, 6th International Mendel Conference on Soft Computing, Brno, Czech Republic, June 7–9 (2000)
Jiejin, C., Xiaoqian, M., Lixiang, L., Haipeng, P.: Chaotic particle swarm optimization for economic dispatch considering the generator constraints. Energy conver. and Management 48, 645–653 (2007)
Immanuel Selvakumar, A., Thanushkodi, K.: A new particle swarm optimization Solution to nonconvex economic dispatch problems. IEEE Trans. on power systems 22(1), 42–51 (2007)
Coelho, L.D.S., Mariani, V.C.: Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve point effect. IEEE Trans. on Power systems 21(2), 989–996 (2006)
Coelho, L.D.S., Mariani, V.C.: Erratum Correlation to - Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve point effect. IEEE Trans. on Power systems 21(3), 1465 (2006)
Sinha, N., Chakrabarti, R., Chattopadhyay, P.K.: Evolutionary programming techniques for economic load dispatch. IEEE Trans. Evol. Comput. 7(1), 83–94 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Biswas, A. et al. (2009). Economic Load Dispatch Using a Chemotactic Differential Evolution Algorithm. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds) Hybrid Artificial Intelligence Systems. HAIS 2009. Lecture Notes in Computer Science(), vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-02319-4_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02318-7
Online ISBN: 978-3-642-02319-4
eBook Packages: Computer ScienceComputer Science (R0)