IJSTR

International Journal of Scientific & Technology Research

IJSTR@Facebook IJSTR@Twitter IJSTR@Linkedin
Home About Us Scope Editorial Board Blog/Latest News Contact Us
CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT
QR CODE
IJSTR-QR Code

IJSTR >> Volume 4 - Issue 11, November 2015 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Multi-Objective Method For Electrical Distribution Network Using Modified Firefly Algorithm

[Full Text]

 

AUTHOR(S)

Ali Mahmudi, Hamid Keyvani

 

KEYWORDS

Index Terms: Multi-objective optimization, load flow, reconfiguration, wind turbine, distribution system, uncertainty.

 

ABSTRACT

Abstract: Due to the increasing consumption of electrical energy, appropriate design of future network and reconfiguration of the current network is of considerable importance. In this paper, the proposed method based on stochastic load flow in the presence of a wind turbine as well as the modified firefly optimization algorithm has been reviewed for optimal management of reconfiguration strategy and the IEEE 32-bus standard network has been used to observe its performance. The objective functions evaluated include: 1) minimization of the total cost of active power losses in the network, 2) reducing the total network operating costs and 3) reducing total emissions produced by the network. The appropriate solution of reconfiguration problem is also considered regarding the uncertainty caused by the wind turbines.

 

REFERENCES

[1] Shin-An Y., Chan-Nan L. (2009), “Distribution Feeder Scheduling Considering Variable Load Profile and Outage Costs”, IEEE Transactions on Power Systems, Vol. 24, No. 2.

[2] Vanderson Gomes. F., Carneiro.S., Pereira. J. L. R., Garcia Mpvpan, and RamosAraujo, L. (2005), “A new heuristic reconfiguration algorithm for large distribution systems”, IEEE Transactions on Power Systems, vol. 20 no.3, pp.1373–1378.

[3] Zheu Q., Shirmohammadi D., and Liu W.H., “Distribution Feeder Reconfiguration for Service Restoration and Load Balancing”, IEEE Trans. On Power System, vol. 12, no.2, pp. 724-729.

[4] Merlin A., Back H. (1975), “Search for a minimal-loss operating spanning tree configuration in an urban power distribution system” Proceedings of 5th Power System Computation Conference (PSCC), Cambridge, UK, pp. 1-18.

[5] Shirmohammadi D., Wayne Hong H. (1989), “Reconfiguration of Electric Distribution Network for Resistive Losses Reduction”, IEEE Trans. on PWRD, pp. 1402-1408.

[6] Baran M.E., Wu F.F. (1989), “ Network reconfiguration in distribution systems for loss reduction and load balancing”, IEEE Trans. Power Delivery 4 (2) 1401–1407.

[7] Schmidt H.P. and Kagan N. (2005), “ Fast reconfiguration of distribution systems considering loss minimization”, IEEE Trans. Power Syst. vol. 20 no.3, pp.1311–1319.

[8] Jeon Y.J. , Kim J.C. , Kim J.O. , Shin J.R., and Lee K.Y. (2002), “ An Efficient Simulated Annealing Algorithm for Network Reconfiguration in Large-Scale Distribution Systems”, IEEE Transaction on Power Delivery. vol.17 no.4, pp.1070–1078.

[9] Augugliaro A., Dusonchet L., Ippolito M., and Sanseverino E.R. (2003), “Minimum Losses Reconfiguration of MV Distribution Networks Through Local Control of Tie-Switches”, IEEE Transaction on Power Delivery, vol.18, no.3, pp. 762–771.

[10] Das D. (2006), “A Fuzzy Multi-Objective Approach for Network Reconfiguration of Distribution Systems”, IEEE Transaction on Power Delivery. vol. 21, no.1, pp.202–209.

[11] Rao R.S., Narasimham S.V.L., Raju M.R., and Rao A.S. (2011), “Optimal Network Reconfiguration of Large-Scale Distribution System Using Harmony Search Algorithm”, IEEE Transactions on Power Systems. vol. 26 no.3, pp.1080-1088.

[12] Olamaie J., Niknam T., Gharehpetion G. (2012), “Application of particle swarm optimization for distribution feeder reconfiguration considering distributed generators,” Appl Math Comput; 20(1):575–86.

[13] Niknam T. (2011), “Application of honey-bee mating optimization on state estimation of a power distribution system including distributed generators,” J Zhejiang Univ Sci;9(12):1753–64.

[14] Malekpour A.R. , Niknam T. , Pahwa A. , and Kavousi Fard A. (2012), “ Multi-objective Stochastic Distribution Feeder Reconfiguration in Systems with Wind Power Generators and Fuel Cells Using Point Estimate Method,” IEEE Trans. Power Systems, 1483 – 1492.

[15] Rosenblueth E. (2003), “Point estimation for probability moments,” Proc. Nat. Acad. Sci., vol. 72, no. 10, pp. 3812–3814.

[16] T. Apostolopoulos, A. Vlachos, “Application of the Firefly Algorithm for Solving the Economic Emissions Load Dispatch Problem,”International Journal of Combinatorics, ID: 523806 (2011) 1-23

[17] X. S. Yang, “ Nature-Inspired Metaheuristic Algorithms,” Frome: Luniver Press. (2009), ISBN 1905986106.

[18] Horn J., Nafpliotis N., and Goldberg D.E. (2007), “A Niched Pareto Genetic Algorithm for Multiobjective Optimization,” IEEE World Congress on Computational Intelligence, Piscataway, NJ. Vol. 1, pp. 82-87.

[19] Niknam T. (2010), “An efficient hybrid evolutionary based on PSO and ACO algorithms for distribution feeder reconfiguration,” European Trans on Elect Power, vol. 20, pp. 575 – 590.

[20] Niknam T., Taheri S.I. , Aghaei J., Tabatabaei S., Nayeripour M. (2011), “ A modified honey bee mating optimization algorithm for multiobjective placement of renewable energy resources,” Applied Energy , vol. 88 , pp. 4817-4830.

[21] McDermott T.E., Drezga I., Broadwater R.P. (1999), “A heuristic nonlinear constructive method for distribution system reconfiguration,” IEEE Trans on Power Sys, vol. 14, pp. 478 – 483, 1999.

[22] Carneiro S.J, Pereira J.L., Vinagre M.P., Garcia P.A., Araujo L.R. (2005), “A New Heuristic Reconfiguration Algorithm for Large Distribution Systems,” IEEE Tran on Power sys, vol. 20 , pp. 1373 – 1378.

[23] Niknam T., Kavousifard A., Seifi A. (2011), “Distribution feeder reconfiguration considering fuel cell/wind/photovoltaic power Plants,” Journal of Renewable Energy, vol. 37, pp. 213-225.