Application of Particle Swarm Optimization for Economic Load Dispatch and Loss Reduction

Authors: N. Phanthuna, J. Jaturacherdchaiskul, S. Lerdvanittip, S. Auchariyamet

Abstract:

This paper proposes a particle swarm optimization (PSO) technique to solve the economic load dispatch (ELD) problems. For the ELD problem in this work, the objective function is to minimize the total fuel cost of all generator units for a given daily load pattern while the main constraints are power balance and generation output of each units. Case study in the test system of 40-generation units with 6 load patterns is presented to demonstrate the performance of PSO in solving the ELD problem. It can be seen that the optimal solution given by PSO provides the minimum total cost of generation while satisfying all the constraints and benefiting greatly from saving in power loss reduction.

Keywords: Particle Swarm Optimization, Economic Load Dispatch, Loss Reduction.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1088320

References:

[1] Y. Ting-Fang; P. Chun-Hua, "Application of an improved Particle Swarm Optimization to economic load dispatch in power plant," in Proc IEEE Int. Conf. Advanced Computer Theory and Engineering, 2010.
[2] A. J. Wood, and B. F. Wollenberg, Power Generation, Operation, and Control, New York, John Wiley & Sons, 1996.
[3] K. P. Wang and C. C. Fung, "Simulate annealing base economic dispatch algorithm," IEE Proc C vol.140, no.6, pp. 509-515, November 1993.
[4] J. Y. Fan, and L. Zhang, "Real-time economic dispatch with line flow and emission constrains using quadratic programming," IEEE Trans. Power Systems, vol. 13, pp. 320-325, May 1998.
[5] D. C. Walters and G.B. Sheble, "Genetic algorithm solution of economic dispatch with valve point loading," IEEE Trans. Power Systems, vol. 8, no. 3, pp. 1325-1332, August 1993.
[6] M. Lin, F. S. Cheng and M. T. Tsay, "An improved tabu search for economic dispatch with multiple minima," IEEE Trans. on Power Systems, vol. 17, no. 1, pp. 108-112, February 2002.
[7] J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proc IEEE Int. Conf. Neural Networks, vol. 4, pp. 1942-1948,1995.
[8] A. Mahor, V. Prasad, S. Rangnekar, "Economic dispatch using particle swarm optimization: A review," Renewable and Sustainable Energy Reviews, vol. 13, no 8, pp. 2134-2141, October 2009.
[9] Z. L. Ging. "Particle swarm optimization to solving the economic dispatch considering the generation constraints," IEEE Trans. power system, vol. 18, no.3 pp. 1187-95, August 2003.
[10] J. B. Park, K. S. Lee, J. R. Shin, and K. Y. Lee, "A particle swarm optimization for economic dispatch with nonsmooth cost functions," IEEE Trans. on Power Systems, vol. 20, no. 1, pp.34-42, February 2005.
[11] Y. Fukuyama, "Fundamentals of particle swarm optimization technique," in Modern heuristic optimization techniques: theory and applications to power system, K. W. and M. A. El-sharkawi Eds. New Jersey: John Wiely & Sons, 2008, pp. 74-75.
[12] A. P. Engelbrecht, Computational Intelligence: An Introduction 2nd ed. West Sussex: John Wiley & Sons Ltd. 2007.
[13] H. Yoshida, K. Kawata, Y. Fukuyama, S. Takayama and Y. Nakanishi, "A particle swarm optimization for reactive power and voltage control considering voltage security assessment," IEEE Trans. Power Systems, vol. 15, pp. 1232-1239, November 2000.
[14] H. Saadat, "Power System Analysis," McGraw-hill companies, Inc, 1999.
[15] R. Poli, J. Kennedy, and T. Blackwell, "Particle swarm optimization: An overview," Swarm Intell., vol. 1, no. 1, pp. 33-58,2007.
[16] W. Jiekang, Z. Jianquan, C. Guotong, and Z. Hongliang, "A Hybrid Method for Optimal Scheduling of Short-Term Electric Power Generation of Cascaded Hydroelectric Plants Based on Particle Swarm Optimization and Chance-Constrained Programming," IEEE Transactions on Power Systems, vol. 23, no. 4, November 2008.
[17] N. Sinha, R. Chakrabarti, P. K. Chattopadhyay, "Evolutionary programming techniques foreconomic load dispatch," IEEE Trans. Evol. Comput., vol. 7, pp. 83-94, Feb. 2003