Economic Load Dispatch Using Bacterial Foraging Optimization Algorithm Based on Evolution Strategies

Abstract:

This paper presents a novel modified bacterial foraging optimization(BFO) to solve economic loaddispatch (ELD) problems. BFO isalready successfully employed to solve variousoptimization problems. However original BFOfor small problems with moderate dimensionand searching space is satisfactory. As searchspace and complexity growexponentially in scalable ELD problems, it shows poorconvergence properties. To tackle this complex problem considering itshigh-dimensioned search space, the Evolution Strategies is introduced to thebasic BFO. The chemotactic step is adjusted to have a dynamic non-linearbehavior in order to improve balancing the global and local search. Theproposed algorithm is validated using several thermal generation test systems.The results are compared with those obtained by other algorithms previouslyapplied to solve the problem considering valve-point effects and power losses.

• Periodical: Advanced Materials Research (Volumes 860-863)
• Pages: 2040-2045
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.860-863.2040
• Online since: December 2013
• Authors: Xiao Hua Feng*, Yu Yao He, Juan Yu
• Keywords: Bacterial Foraging Optimization, Economic Load Dispatch, Evolution Strategies

References

[1] WOOD A.J., WOLLENBERG B.F.: Power generation operation and control (Wiley, New York, USA, 1996, 2nd edn. ).

[2] P. Aravindhababu and K.R. Nayar, Economic dispatch based on optimal lambda using radial basis function network, International Journal of Electrical Power & Energy Systems, vol. 24, pp.551-556, 10. (2002).

DOI: 10.1016/s0142-0615(01)00063-1

[3] VICTOIRE T.A.A., JEYAKUMAR A.E.: Reserve constrained dynamic dispatch of units with valve-point effects, IEEE Trans. Power Syst., 2005, 20, (3), p.1273–1282 on, vol. 8, pp.1325-1332, (1993).

DOI: 10.1109/tpwrs.2005.851958

[4] N. Sinha, R. Chakrabarti and P.K. Chattopadhyay, Evolutionary programming techniques for economic load dispatch, Evolutionary Computation, IEEE Transactions on, vol. 7, pp.83-94, (2003).

DOI: 10.1109/tevc.2002.806788

[5] D.C. Walters and G.B. Sheble, Genetic algorithm solution of economic dispatch with valve point loading, Power Systems, IEEE Transactions on, vol. 8, pp.1325-1332, (1993).

DOI: 10.1109/59.260861

[6] A.I. El-Gallad, M. El-Hawary, A.A. Sallam and A. Kalas, Swarm intelligence for hybrid cost dispatch problem, Electrical and Computer Engineering, 2001. Canadian Conference on, vol. 2, pp.753-757 vol. 2, (2001).

DOI: 10.1109/ccece.2001.933536

[7] A.I. Selvakumar and K. Thanushkodi, A New Particle Swarm Optimization Solution to Nonconvex Economic Dispatch Problems, Power Systems, IEEE Transactions on, vol. 22, pp.42-51, (2007).

DOI: 10.1109/tpwrs.2006.889132

[8] K.M. Passino, Biomimicry of bacterial foraging for distributed optimization and control, Control Systems Magazine, IEEE, vol. 22, pp.52-67, (2002).

DOI: 10.1109/mcs.2002.1004010

[9] PANIGRAHI B.K., PANDI V.R.: Bacterial foraging optimisation: Nelder-Mead hybrid algorithm for economic load dispatch, IET Gener. Transm. Distrib., 2008, 2, (4), p.556–565.

DOI: 10.1049/iet-gtd:20070422

[10] I.A. Farhat ,M.E. El-Hawary: Dynamic adaptive bacterial foraging algorithm for optimum economic dispatch with valve-point effects and wind power. IET Gener. Transm. Distrib., 2010, Vol. 4, Iss. 9, p.989–999.

DOI: 10.1049/iet-gtd.2010.0109

[11] H. Beyer, The Theory of Evolution Strategies, G. Rozenberg, T. B¨ack,A. Eiben, J. Kok, and H. Spanik, Eds. Springer, (2001).

[12] ALHAJRI M.F., ALRASHIDI M.R., EL-HAWARY M.E.: Hybrid particle swarm optimization approach for optimal distribution generation sizing and allocation in distribution systems,. Canadian Conf. on Electr. Comp. Eng. (CCECE 2007), 2007, p.1290–1293.

DOI: 10.1109/ccece.2007.328

[13] JOINES J.A., HOUCK C.R.: On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's,. Proc. First IEEE Conf. on Evolutionary Computation, 1994 (IEEE World Congress on Computational Intelligence), 1994, vol. 2, p.579.

DOI: 10.1109/icec.1994.349995

[14] MICHALEWICZ Z., SCHOENAUER M.: Evolutionary algorithms for constrained parameter optimization problems, Evol. Comput., 1996, 4, (1), p.1–32.

DOI: 10.1162/evco.1996.4.1.1

[15] KOZIEL S., MICHALEWICZ Z.: Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization, Evol. Comput., 1999, 7, (1), p.19–44.

DOI: 10.1162/evco.1999.7.1.19

[16] SU C., LIN C.: New approach with a Hopfield modeling framework to economic dispatch, IEEE Trans. Power Syst., 2000, 15, (2), p.541–545.

DOI: 10.1109/59.867138