*(0086) Optimal Transmission Expansion Planning considering Distributed Generations by using Non- dominated sorting genetic algorithm-II (NSGA-II)
Abstract
Reconstructing power systems has changed the traditional planning of power systems and has raised new challenges in Transmission Expansion Planning (TEP). Because of these reason, new methods and criteria have been formed for planning transmission in reconstructed environments. Thus, a dynamic programming was used for transmission efficiency based on multi-objective optimization in this research. In this model, investment cost, cost of density and dependability have been considered three objectives of optimization. In this paper, NSGAII multi-objective genetic algorithm was used to solve this non-convex and mixed integer problem. A fuzzy decision method has been used to choose the final optimal answer from the Pareto solutions obtained from NSGAII. Moreover, to confirm the efficiency of NSGAII multi-objective genetic algorithm in solving TEP problem, this algorithm was implemented in an IEEE 24 bus system and the gained results were compared with previous works in this field.References
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[2] R. Fang and D. J. Hill, “A new strategy for transmission expansion in competitive electricity markets,” IEEE Trans. PWRS, Vol.18, No.1, pp.374-380, Feb.2003.
[3] M. O. Buygi, H. M. Shanechi and M. Shahidehpour, “Transmission planning in deregulated environments,” IJE, Vol. 15, pp. 245-255, Sep. 2002.
[4] Z. A. Styczynski, “Power network planning using Game theory, ” Proc. Int. Conf. on Power System. Trondheim, pp.607-613, 1999.
[5] O. B. Guven, M. Shahidehpour, “Congestion-driven transmission planning considering the impact of generator expansion,” IEEE Trans Power Syst, pp781–239, 2008.
[6] D. Silva, I. Romero and R. Murari CA, “Transmission network expansion planning with security constraints,” IET Gener Transm Distrib, pp.828–36, 2005.
[7] N. Alguacil, A. L. Motto and A. J. Conejo, “Transmission expansion planning: a mixed integer LP approach,” IEEE Trans Power Syst, pp.18:1070–7, 2003.
[8] D. Kalyanmoy. Multi-objective optimization using evolutionary algorithms. New York: Wiley; 2003.
[9] K. J. Kim, Y. M. Park and K. Y. Lee, “Optimal long term transmission expansion planning based on maximum principle,” IEEE Trans. PWRS, Vol.3, pp. 1494-1501, November 1988.
[10] M. Xie, J. Zhong and F. Wu, “Multiyear transmission expansion planning using ordinal optimization,” IEEE Trans Power Syst, pp.22:1420–8, 2007.
[11] R. Romero, R. A. Callego and A. Monticelli, “Transmission system expansion planning by simulated annealing,” IEEE Trans PWRS, Vol. 11, pp. 364-369, February 1996.
[12] R. C. G. Teive, E. L. Silva and L. G. S. Fonseca, “A cooperative expert system for TEP of electrical power system,” IEEE Trans, PWRS, Vol.13, pp.636-642, May. 1998.
[13] H. Kim and E. T. All, “Transmission system expansion planning of KEPCO system using fuzzy set theory,” IEEE Power Eng, Vol.1, pp.535-540, July 2002.
[14] A. A. Foroud, A. A. Abdoos, R. Keypour and Meisam Amirahmadi, “A multi-objective framework for dynamic transmission expansion planning in competitive electricity market,” Electrical Power and Energy Systems, pp 861–872, 2010.
[15] D. l. Torre, A. J. Conejo, J. Contreras, “Felexible Transmission expansion planning considering distributed generation impact,” IEEE Trans Power Syst, Vol.26, no. 3, Augest 2011.
[16] IEEE Reliability Test System Task Force of the Applications of Probability Methods Subcommittee, "IEEE reliability test system," IEEE Transactions on Power Apparatus and Systems, Vol. 98, No. 6, Nov./Dec. 1979, pp. 2047-2054.
Published
2021-03-19
How to Cite
AHMADI KAMARPOSHTI, Mehrdad; KABALCI, Ersan.
*(0086) Optimal Transmission Expansion Planning considering Distributed Generations by using Non- dominated sorting genetic algorithm-II (NSGA-II).
Journal of Power Technologies, [S.l.], v. 101, n. 1, p. 70-77, mar. 2021.
ISSN 2083-4195.
Available at: <https://papers.itc.pw.edu.pl/index.php/JPT/article/view/1615>. Date accessed: 22 dec. 2024.
Issue
Section
Electrical Engineering
Keywords
dynamic programming for transmission efficiency network, multi-objective optimization, multi-objective genetic algorithm, fuzzy decision method.
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