Abstract
Economic Load Dispatch (ELD) is one of the important optimization tasks, which provides an economic condition for the power systems. In this paper, Evolved Bat Algorithm (EBA) as an evolutionary based approach is presented to solve the constraint economic load dispatched problem of thermal plants. The output generating power for all the power-generation units can be determined by the optimal technique for transmission losses, power balance and generation capacity, so that the total constraint cost function is minimized. A piecewise quadratic function is used to show the fuel cost equation of each generation unit, and the B-coefficient matrix is used to represent transmission losses. The systems with six units and fifteen units of thermal plants are used to test the demonstration of the solution quality and computation efficiency of the feasibility of the application of the Evolved Bat Algorithm for ELD. The experimental results compared with the genetic algorithm (GA) method for ELD, and with the particle swarm optimization (PSO) method for ELD, show that the applied EBA method for ELD can provide the higher efficiency and accuracy.
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
Allen, B.F.W., Wood, J., Sheble, G.B.: Power Generation, Operation and Control, 3rd edn., p. 656. Wiley (2013)
Han, X.S., Gooi, H.B.: Effective economic dispatch model and algorithm. International Journal of Electrical Power & Energy Systems 29(2), 113–120 (2007)
Dillon, T.S., Edwin, K.W., Kochs, H.D., Taud, R.J.: Integer Programming Approach to the Problem of Optimal Unit Commitment with Probabilistic Reserve Determination. IEEE Transactions on Power Apparatus and Systems PAS-97(6), 2154–2166 (1978)
Liang, Z.X., Glover, J.D.: A zoom feature for a dynamic programming solution to economic dispatch including transmission losses. IEEE Transactions on Power Systems 7(2), 544–550 (1992)
Jong-Bae, P., Ki-Song, L., Joong-Rin, S., Lee, K.Y.: A particle swarm optimization for economic dispatch with nonsmooth cost functions. IEEE Transactions on Power Systems 20(1), 34–42 (2005)
Zwe-Lee, G.: Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Transactions on Power Systems 18(3), 1187–1195 (2003)
Po-Hung, C., Hong-Chan, C.: Large-scale economic dispatch by genetic algorithm. IEEE Transactions on Power Systems 10(4), 1919–1926 (1995)
Bakirtzis, A., Petridis, V., Kazarlis, S.: Genetic algorithm solution to the economic dispatch problem. IEE Proceedings Generation, Transmission and Distribution 141(4), 377–382 (1994)
Wong, K.P., Wong, Y.W.: Genetic and genetic/simulated-annealing approaches to economic dispatch. IEE Proceedings Generation, Transmission and Distribution 141(5), 507–513 (1994)
Simopoulos, D.N., Kavatza, S.D., Vournas, C.D.: Unit commitment by an enhanced simulated annealing algorithm. IEEE Transactions on Power Systems 21(1), 68–76 (2006)
Chowdhury, B.H., Rahman, S.: A review of recent advances in economic dispatch. IEEE Transactions on Power Systems 5(4), 1248–1259 (1990)
Pan, J.-S., Tsai, P.-W., Liao, B.-Y., Tsai, M.-J., Istanda, V.: Bat Algorithm Inspired Algorithm for Solving Numerical Optimization Problems. Applied Mechanics and Materials 148-149(2012), 134–137 (2011)
Yang, X.-S.: A New Metaheuristic Bat-Inspired Algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) NICSO 2010. SCI, vol. 284, pp. 65–74. Springer, Heidelberg (2010)
Rao, S.S., Rao, S.S.: Engineering Optimization: Theory and Practice. Wiley (2009)
Arora, J.S.: Introduction to Optimum Design. Academic Press (2011)
Michalewicz, Z., Schoenauer, M.: Evolutionary algorithms for constrained parameter optimization problems. Evol. Comput. 4(1), 1–32 (1996)
Zwe-Lee, G.: “Closure to “Discussion of ’Particle swarm optimization to solving the economic dispatch considering the generator constraints”. IEEE Transactions on Power Systems 19(4), 2122–2123 (2004)
Yuhui, S., Eberhart, R.C.: Empirical study of particle swarm optimization, vol. 3, p. 1950 (1999)
Srinivas, M., Patnaik, L.M.: Genetic algorithms: a survey. Computer 27(6), 17–26 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Dao, TK., Pan, TS., Nguyen, TT., Chu, SC. (2015). Evolved Bat Algorithm for Solving the Economic Load Dispatch Problem. In: Sun, H., Yang, CY., Lin, CW., Pan, JS., Snasel, V., Abraham, A. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol 329. Springer, Cham. https://doi.org/10.1007/978-3-319-12286-1_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-12286-1_12
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12285-4
Online ISBN: 978-3-319-12286-1
eBook Packages: EngineeringEngineering (R0)