Relieving Transmission Congestion by Optimal Rescheduling of Generators Using PSO

Abstract:

Article Preview

Congestion management is one of the most important issues for secure and reliable system operations. One of the most practiced techniques for congestion management is rescheduling the real power output of generators in the system. In this paper Particle Swarm Optimization (PSO) is used to determine the optimal generation levels to alleviate transmission congestion. Numerical results on IEEE 30 Bus test system is presented and the experimental outcomes demonstrate that PSO is one among the demanding optimization methods which are certainly capable of obtaining higher quality solutions for the proposed problem.

Info:

Periodical:

Edited by:

R. Edwin Raj, M. Marsaline Beno and M. Carolin Mabel

Pages:

213-218

Citation:

K. Muthulakshmi and C.K. Babulal, "Relieving Transmission Congestion by Optimal Rescheduling of Generators Using PSO", Applied Mechanics and Materials, Vol. 626, pp. 213-218, 2014

Online since:

August 2014

Export:

Price:

$38.00

* - Corresponding Author

[1] M. A. Abido, Optimal power flow using particle swarm optimization, Int. J. Elect. Power Energy Syst., Vol. 24, No. 7, p.563–571. (2002).

DOI: https://doi.org/10.1016/s0142-0615(01)00067-9

[2] Capitanescu F. and Custem T.V. A unified management of congestions due to voltage instability and thermal overload, Electric Power Systems Research, Vol. 77, No. 10, pp.1274-1283, (2007).

DOI: https://doi.org/10.1016/j.epsr.2006.09.015

[3] J. Conejo, F. Milano, and R.G. Bertrand, Congestion management ensuring voltage stability, , IEEE Trans. Power Syst., vol. 21, no. 1, p.357–364, Feb. (2006).

DOI: https://doi.org/10.1109/tpwrs.2005.860910

[4] C. -N. Chien-Ning Yu and M. Ilic, Congestion clusters-based markets for transmission management, in Proceedings of IEEE Power Eng. Soc. Winter Meeting , Vol. 2, p.1–11, (1999).

DOI: https://doi.org/10.1109/pesw.1999.747270

[5] R. D. Christie, B. Wollenberg, and I. Wangensteen, Transmission management in the deregulated environment, Proceedings of IEEE , Vol. 88, No. 2, p.170–195, (2000).

DOI: https://doi.org/10.1109/5.823997

[6] Z. -L. Gaing, Particle swarm optimization to solving the economic dispatch considering the generator constraints, IEEE Trans. Power Syst., Vol. 18, No. 3, p.1187–1195, (2003).

DOI: https://doi.org/10.1109/tpwrs.2003.814889

[7] J. Kennedy and R. Eberhart, Particle swarm optimization, in Proceedings of IEEE Int. Conf. Neural Networks, Perth, Australia, p.1942–1948, (1995).

[8] Kumar, S. C. Srivastava and S.N. Singh, Congestion management in competitive power market: A bibliographical survey, Electrical Power Systems Research, Vol. 76, pp.153-164, (2005).

DOI: https://doi.org/10.1016/j.epsr.2005.05.001

[9] Kumar, S. C. Srivastava and S.N. Singh, A zonal congestion management approach using ac transmission congestion distribution factors, Electric Power Systems Research, Vol. 72, pp.85-93, (2004).

DOI: https://doi.org/10.1016/j.epsr.2004.03.011

[10] R. Storn, K. Price, Differential Evolution, a simple and efficient heuristic strategy for global optimization over continuous spaces, Journal of Global Optimization, pp.341-359, (1997).

[11] Sudipta Dutta and S. P. Singh, Optimal Rescheduling of Generators for Congestion Management Based on Particle Swarm Optimization, IEEE transactions on Power Systems, Vol. 23, no. 4, November (2008).

DOI: https://doi.org/10.1109/tpwrs.2008.922647

[12] J. vesterstrom, R. Thomsen, A comparative study of differential evolution, particle swarm optimization and evolutionary algorithms on numerical benchmark problems, IEEE Congress on Evolutionary Computation, pp.980-987, (2004).

DOI: https://doi.org/10.1109/cec.2004.1331139

[13] Wood A.J. and Wollenberg, B.F. Power Generation Operation and Control, New York: John Wiley & sons, 1996, pp.416-436.

[14] R.D. Zimmerman Carlos, E. Murillo-Sanchez, MATPOWER, a MATLAB power system simulation package. Version 4. 1. 0. http: /www. pserc. cornell. edu/matpower.