Distribution Network Expansion Planning Based on Multi-objective PSO Algorithm

Abstract

This paper presents a novel approach for electrical distribution network expansion planning using multi-objective particle swarm optimization (PSO). The optimization objectives are: investment and operation cost, energy losses cost, and power congestion cost. A two-phase multi-objective PSO algorithm is employed to solve this optimization problem, which can accelerate the convergence and guarantee the diversity of Pareto-optimal front set as well. The feasibility and effectiveness of both the proposed multi-objective planning approach and the improved multi-objective PSO have been verified by the 18-node typical system.

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C. Zhang, Y. Ding, Q. Wu, Q. Wang and J. Østergaard, "Distribution Network Expansion Planning Based on Multi-objective PSO Algorithm," Energy and Power Engineering, Vol. 5 No. 4B, 2013, pp. 975-979. doi: 10.4236/epe.2013.54B187.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] A. Barin, L. F. Pozzatti, L. N. Canha, et al., “Multi-objective Analysis of Impacts of Distributed Generation Placement on the Operational Characteristics of Networks for Distribution System Planning,” International Journal of Electrical Power & Energy Systems, Vol. 32, 2010, pp. 1157-1164. doi:10.1016/j.ijepes.2010.06.015
[2] Z. Kai, A. P. Agalgaonkar, K. M. Muttaqi and S. Perera, “Multi-objective Optimization for Distribution System Planning with Renewable Energy Resources,” in Proc. 2010 IEEE International Energy Conference, pp. 670-675.
[3] A. H. Mantway and M. M. Al-Muhaini, “Multi-objective BPSO Algorithm for Distribution System Expansion Planning Including Distributed Generation,” in Proc. 2008 IEEE International Transmission & Distribution, pp. 134-141.
[4] R. Rosado and D. Navarro, “Possibilistic Model Based on Fuzzy Sets for the Multiobjective Optimal Planning of Electric Power Distribution Networks,” IEEE Trans. Power Systems, Vol. 19, 2004, pp. 1801-1810. doi:10.1109/TPWRS.2004.835678
[5] A. Soroudi and M. Ehsan, “A Distribution Network Expansion Planning Model Considering Distributed Generation Options and Techo-Economical Issues,” International Journal of Energy, Vol. 35, 2010, pp. 3364-3374.
[6] E. G. Carrano, F. G. Guimaraes, R. H. C. Takahashi, et al., “Electric Distribution Network Expansion Under Load-Evolution Uncertainty Using an Immune System Inspired Algorithm,” IEEE Trans. Power Systems, Vol. 22, 2007, pp. 851-861.
[7] T. S. Chung, K. K. Lee, G. J. Chen, J. D. Xie and G. Q. Tang, “Multi-Objective Transmission Network Planning by a Hybrid GA Approach with Fuzzy Decision Analysis,” International Journal of Electrical Power & Energy Systems, Vol. 25, 2003, pp. 187-192.
[8] R. Poli, J. Kennedy and T. Blackwell, Particle swarm optimization an overview, Vol. I. MIT: Wiley, 2010, p. 2.
[9] A. M. R. Sierra and C. A. Coello, “Multi-objective Particle Swarm Optimizers: A Survey of the State-of-the-art,” International Journal of Computational Intelligence Research, Vol. 3, 2006, pp. 287-308.
[10] S. Mostaghim and J. Teich, “Strategies for finding good local guides in multi-objective particle swarm optimization (MOP-SO),” in Proc. 2003 IEEE Swarm Intelligence Symposium, pp. 26-33.
[11] M. S. Lechuga, and C. A. Coello, “Handling Multiple Objectives with Particle Swarm Optimization,” IEEE Trans. Evolutionary Computation, Vol. 3, 2006, pp. 256-279.
[12] N. C. Sahoo, S. Ganguly and D. Das, “Fuzzy-Pareto-dominance Driven Possibilistic Model Based Planning of Electrical Distribution Systems Using Multi-objective Particle Swarm Optimization,” Expert Systems with Applications, Vol. 39, 2012, pp. 881-893. doi:10.1016/j.eswa.2011.07.086
[13] X. Wang and J. R. McDonald, Modern Power System Planning, McGraw-Hill Book Company, London, 1993.

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