Losses Analysis in TEP Considering Uncertainity in Demand by DPSO
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Losses Analysis in TEP Considering Uncertainity in Demand by DPSO

Authors: S. Jalilzadeh, A. Kimiyaghalam, A. Ashouri

Abstract:

This paper presents a mathematical model and a methodology to analyze the losses in transmission expansion planning (TEP) under uncertainty in demand. The methodology is based on discrete particle swarm optimization (DPSO). DPSO is a useful and powerful stochastic evolutionary algorithm to solve the large-scale, discrete and nonlinear optimization problems like TEP. The effectiveness of the proposed idea is tested on an actual transmission network of the Azerbaijan regional electric company, Iran. The simulation results show that considering the losses even for transmission expansion planning of a network with low load growth is caused that operational costs decreases considerably and the network satisfies the requirement of delivering electric power more reliable to load centers.

Keywords: DPSO, TEP, Uncertainty

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1060038

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References:


[1] A.R Abdelaziz, "Genetic algorithm-based power transmission expansion planning," The 7th IEEE Int. Conf. Electronics, Circuits and Systems, Jounieh, vol. 2, pp. 642-645, 2000.
[2] S. Binato, G.C. de Oliveira, and J.L. Araujo, "A greedy randomized adaptive search procedure for transmission expansion planning," IEEE Trans. Power Systems, vol. 16, No. 2, pp. 247-253, 2001.
[3] I. D. J. Silva, M. J. Rider, R. Romero, C. A. Murari, Transmission network expansion planning considering uncertainness in demand, Proc. 2005 IEEE Power Engineering Society General Meeting, Vol. 2, pp. 1424-1429.
[4] S. Binato, G.C. de Oliveira, and J.L. Araujo, "A greedy randomized adaptive search procedure for transmission expansion planning," IEEE Trans. Power Systems, vol. 16, No. 2, pp. 247-253, 2001.
[5] S. Binato, M. V. F. Periera, S. Granville, A new Benders decomposition approach to solve power transmission network design problems, IEEE Trans. Power Systems, Vol. 16, No. 2, 2001, pp. 235-240.
[6] R. Romero and A. Monticelli, "A hierarchical decomposition approach for transmission network expansion planning," IEEE Trans. Power Systems, vol. 9, pp. 373-380, 1994.
[7] M.V.F. Periera and L.M.V.G. Pinto, "Application of sensitivity analysis of load supplying capacity to interactive transmission expansion planning," IEEE Trans. Power App. System, Vol. PAS-104, pp. 381-389, 1985.
[8] R.A. Gallego, A. Monticelli, and R. Romero, "Transmission system expansion planning by an extended genetic algorithm," IEE Proc. Gener. Transm. Distribution, vol. 145, No. 3, pp. 329-335, 1998.
[9] E.L. da Silva, H.A. Gil, and J.M. Areiza, "Transmission network expansion planning under an improved genetic algorithm," IEEE Trans. Power Systems, vol. 15, No. 3, pp. 1168-1174, 2000.
[10] R. Romero, R.A. Gallego, and A. Monticelli, "Transmission system expansion planning by simulated annealing," IEEE Trans. Power Systems, vol. 11, No. 1, pp. 364-369, 1996.
[11] R.A. Gallego, A.B. Alves, A. Monticelli, and R. Romero, "Parallel simulated annealing applied to long term transmission network expansion planning," IEEE Trans. Power Systems, vol. 12, No. 1, pp. 181-188, 1997.
[12] R.A. Gallego, R. Romero, and A.J. Monticelli, "Tabu search algorithm for network synthesis," IEEE Trans. Power Systems, vol. 15, No. 2, pp. 490-495, 2000.
[13] S. Jalilzadeh, A. Kazemi, H. Shayeghi, and M. Mahdavi, "Technical and economic evaluation of voltage level in transmission network expansion planning using GA," Energy Conver. Management, vol. 49, No. 5, pp. 1119-1125, 2008.
[14] H. Shayeghi, S. Jalilzadeh, M. Mahdavi, and H. Haddadian, "Studying influence of two effective parameters on network losses in transmission expansion planning using DCGA," Energy Conver. Management, vol. 49, No. 11, pp. 3017-3024, 2008.
[15] H. Shayeghi, A. Jalili, and H.A. Shayanfar, "Multi-stage fuzzy load frequency control using PSO," Energy Conver. Management, vol. 49, No. 10, pp. 2570-2580, 2008.
[16] J. Kennedy, R. Eberhart, Y. Shi. Swarm intelligence, Morgan Kaufmann Publishers, San Francisco, 2001.
[17] J. Kennedy and R. Eberhart, "Particle swarm optimization," IEEE Int. Conf. Neural Networks, vol. 4, pp. 1942-1948, 1995.
[18] Z.S. Lu and Z.R. Hou, "Particle swarm optimization with adaptivemutation," Acta Electronica Sinica, vol. 32, No.3, pp. 416-420, 2004.
[19] A. Jalilvand, A. Kimiyaghalam, A. Ashouri, and M. Mahdavi, "Advanced particle swarm optimization-based PID controller parameters tuning," The 12th IEEE Int. Multitopic Conference, Pakistan, pp. 429- 435, 2008.
[20] M. Clerc and J. Kennedy, "The particle swarm-explosion, stability, and convergence in a multidimensional complex space," IEEE Trans. Evolut. Computation, vol. 6, No. 1, pp. 58-73, 2002.
[21] Y.X. Jin, H.Z. Cheng, J.Y. Yan, and L. Zhang, "New discrete method for particle swarm optimization and its application in transmission network expansion planning," Elect. Power Syst. Research, vol. 77, No. 3-4, pp. 227-233, 2007.
[22] M. Mahdavi, H. Shayeghi, A. Kazemi, "DCGA based evaluating role of bundle lines in TNEP considering expansion of substations from voltage level point of view," Energy Conver. Management, vol. 50, No. 8, pp. 2067-2073, 2009.