Abstract

Network reconfiguration aims to minimize network real power loss through rearranging the status of open switches. The consumers of the distribution networks need a better voltage profile for efficient operation of various gadgets. This paper thus attempts to develop a new reconfiguration algorithm with an objective of improving the voltage profile of the distribution network without incurring any additional cost for installation of capacitors and tap-changing transformers. The algorithm uses a nature-inspired biogeography based optimization (BBO) that searches for optimal solution through the migration and mutation operators. Test results on a 33 and 69-node distribution networks reveal the superiority of the developed method.

References

• Merlin A and Back H. (1975). Search for a minimal loss operating spanning tree configuration for an urban power distribution system, Proc of the power systems computation conf (PSCC): 1–18.
• H. M. Khodr and J. Martinez-Crespo. (2009). Integral methodology for distribution systems reconfiguration based on optimal power flow using benders decomposition technique, IET Gen. Trans. & Dist., 3(6): 521–534.
• R. A. Jabr, R. Singh and B. C. Pal. (2012). Minimum loss network reconfiguration using mixed-integer convex programming, IEEE Trans on Power Systems, 27 (2): 1106–1115.
• J. A. Taylor and F. S. Hover. (2012). Convex models of distribution system reconfiguration, IEEE Trans. On Power Systems, 27(3): 1407–1413.
• Civanlar S, Grainger J and Yin H, Lee S .(1988). Distribution feeder reconfiguration for loss reduction, IEEE Trans Power Delivery, 3(3): 1217–1223.
• Goswami S and Basu S. (1992). A new algorithm for the reconfiguration of distribution feeders for loss minimization, IEEE Trans Power Delivery, 7(3):1482–1491.
• Shirmohammadi D and Hong H. (1989). Reconfiguration of electric distribution networks for resistive line loss reduction, IEEE Trans Power Delivery, 4(2):1492–1498.
• A.Y. Abdelaziz, R. A. Osama and S. M. El-Khodary. (2012). Reconfiguration of distribution systems for loss reduction using the hyper-cube ant colony optimization algorithm, IET Gen. Trans. & Dist., 6(2): 176–187.
• K. Sathish Kumar and T. Jayabarathi. (2012). Power system reconfiguration and loss minimization for distribution systems using Bacterial Foraging optimization algorithm, Int. J. Electr. Power Energy Syst, 36(1): 13–17.
• M. R. Andervazh, J. Olamaei and M. R. Haghifam. (2013). Adaptive multi-objective distribution network reconfiguration using multi-objective discrete Particles Swarm Optimisation algorithm and graph theory, IET Gen. Trans. & Dist., 7(12): 1367–1382.
• L. W. de Oliveira, E. J. de Oliveira, F. V. Gomes, I. C. Silva Jr, A. L. M. Marcato and P. V. C. Resende. (2014). Artificial Immune Systems applied to the reconfiguration of electrical power distribution networks for energy loss minimization, Int. J. Electr. Power Energy Syst., 56: 64–74.
• S. H. Mirhoseini, S. M. Hosseini, M. Ghanbari and M. Ahmadi. (2014). A new improved adaptive imperialist competitive algorithm to solve the reconfiguration problem of distribution systems for loss reduction and voltage profile improvement, Int. J. Electr. Power Energy Syst., 55: 128–143.
• N. Gupta, A. Swarnkar and K. R. Niazi. (2014). Distribution network reconfiguration for power quality and reliability improvement using Genetic Algorithms, Int. J. Electr. Power Energy Syst., 54: 664–671.
• D Simon. (2008). Biogeography-based optimization, IEEE Trans on Evolutionary Computation, 12(6): 702–713.
• R Rarick, D Simon, F Villaseca and B Vyakaranam. (2009). Biogeography-based optimization and the solution of the power flow problem, IEEE International Conference on Systems, Man, and Cybernetics, San Antonio, SMC: 1003–1008.
• A Bhattacharya and K.P Chattopadhyay. (2010). Solution of optimal reactive power flow using biogeography-based optimization, Int Journal of Electrical and Electronics Engineering, 4(8): 568-576.
• A.Bhattacharya and K.P Chattopadhyay. (2011). Application of biogeography-based optimization to solve different optimal power flow problems, IET Proc Gener., Transm & Distrib, 5(1): 70-80.
• S.Rajasomashekar and P Aravindhababu. (2012). Biogeography-based optimization technique for best compromise solution of economic emission dispatch, Swarm and Evolutionary Computations, dx.doi.org/10.1016/j.swevo.2012.06.001.
• Baran M and Wu F. (1989). Network reconfiguration in distribution systems for loss reduction and load balancing, IEEE Trans on Power Delivery, 4(2): 1401–1407.
• Kashem M, Ganapathy V and Jasmon G (2001) A geometrical approach for network reconfiguration based loss minimization in distribution systems, Int J Elect Power Energy Syst, 23(4): 295–304.