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Simultaneous Network Reconfiguration and DG Sizing Using Evolutionary Programming and Genetic Algorithm to Minimize Power Losses

  • Research Article - Electrical Engineering
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Abstract

Distribution network planning and operation require the identification of the best topological configuration that is able to fulfill the power demand with minimum power loss. This paper presents an effective method based on Evolutionary Programming (EP) and Genetic Algorithm (GA) to identify the switching operation plan for feeder reconfiguration and distributed generation size simultaneously. The main objectives of this paper are to gain the lowest reading of real power losses, upgrade the voltage profile in the system as well as satisfying other operating constraints. Their impacts on the network real power losses and voltage profiles are investigated. A comprehensive performance analysis is carried out on IEEE 33-bus radial distribution systems to prove the efficiency of the proposed methodology. The test result on the system showed the power loss reduction, and voltage profile improvement of the EP is superior to the GA method.

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References

  1. Abu-Mouti F.S., El-Hawary M.E.: Optimal distributed generation allocation and sizing in distribution systems via artificial bee colony algorithm. IEEE Trans. Power Deliv. 26(4), 2090–2101 (2011)

    Article  Google Scholar 

  2. Merlin, A.; Back, H.: Search for a minimal loss operating spanning tree configuration for an urban power distribution system. In: Proceedings of the 5th Power System Computation Conference (PSOC), Cambridge, 1–18 (1975)

  3. Shirmohammadi, D.; Hong, H.W.: Reconfiguration of electric distribution networks for resistive line loss reduction. IEEE Trans. Power Syst. 4(1), 1492–1498S (1989)

  4. Kashem M.A., Ganapathy V., Jasmon G.B.:: Network reconfiguration for load balancing in distribution networks. IEEE Proc. Gener. Transm. Distrib. 146(6), 563–567 (1999)

    Article  Google Scholar 

  5. Zhu J.Z.:: Optimal reconfiguration of electrical distribution networks using the refined genetic algorithm. Electr. Power Syst. Res. 62, 37–42 (2002)

    Article  Google Scholar 

  6. Hsiao, Y.T.: Multi-objective evolutionary programming method for feeder reconfiguration. IEEE Trans. Power Syst. 19(1), (2004)

  7. Hong, Y.Y.; Ho, S.Y.: Determination of network configuration considering multi-objective in distribution systems using genetic algorithms. IEEE Trans. Power Syst. 20(2) (2005)

  8. Prasad P.V., Sivanagaraju S., Sreenivasulu N.: Network reconfiguration for load balancing in radial distribution systems using genetic algorithm. Electr. Power Compon. Syst. 36(1), 3–72 (2007)

    Google Scholar 

  9. Gupta N., Swarnarand A., Nizai K.R.: Reconfiguration of distribution systems for real power loss minimization using adaptive particle swarm optimization. Electr. Power Compon. Syst. 39(4), 317–330 (2011)

    Article  Google Scholar 

  10. Wang, C.R.; Zhang, Y.E.: Distribution network reconfiguration based on modified particle swarm optimization algorithm. In Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian, 13–16 Aug 2006

  11. Sivanagaraju S., Viswanatha Rao J., Raju P.S.: Discrete particle swarm optimization to network reconfiguration for loss reduction and load balancing. Electr. Power Compon. Syst. 36(5), 513–524 (2008)

    Article  Google Scholar 

  12. Su C.-T., Chang C.-F., Chiou J.-P.: Distribution network reconfiguration for loss reduction by ant colony search algorithm. Electr. Power Syst. Res. 75(2–3), 190–199 (2005)

    Article  Google Scholar 

  13. Fang ZongWang, J.Y.: A Refined plant growth simulation algorithm for distribution network reconfiguration. IEEE Trans. Power Syst. 4244–4738 (2009)

  14. Sathish Kumar K., Jayabarathi T.: Power system reconfiguration and loss minimization for a distribution system using bacterial foraging optimization algorithm. Electr. Power Energy Syst. 36, 13–17 (2011)

    Article  Google Scholar 

  15. Cheng H.C., Chou C.C.: Network reconfiguration of distribution system using simulated annealing. Electr. Power Syst. Res. 29, 227–238 (1994)

    Article  Google Scholar 

  16. Olamie, J.; Niknam, T.; Gharehpetian, G.: Application of particle swarm optimization for distribution feeder reconfiguration considering distributed generators. Appl. Math. Comput. 575–586 (2008)

  17. Wu Y.K., Lee C.Y., Liu L.C., Tsai S.H.: Study of reconfiguration for the distribution system with distributed generators. IEEE Trans. Power Deliv. 25(3), 1678–1685 (2010)

    Article  Google Scholar 

  18. Rugithaicharoencheep N., Sirisumarannukul S.: Feeder reconfiguration for loss reduction in distribution system with distributed generators by Tabu Search. GMSARN Int. J. 3, 47–54 (2009)

    Google Scholar 

  19. Jamian, J.J.; Lim, Z.J.; Dahalan, W.M.; Mokhlis, H.; Mustafa, M.W.; Abdullah, M.N.: Reconfiguration distribution network with multiple distributed generation operation types using simplified artificial bees colony. Int. Rev. Electr. Eng. (IREE) 7(4), 5108–5118 Part b, July–August (2012. ISI-Cited Publication)

  20. Saadat H.: Power System Analysis. Mc Graw-Hill, Singapore (1999)

    Google Scholar 

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Correspondence to Wardiah Mohd Dahalan.

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Dahalan, W.M., Mokhlis, H., Ahmad, R. et al. Simultaneous Network Reconfiguration and DG Sizing Using Evolutionary Programming and Genetic Algorithm to Minimize Power Losses. Arab J Sci Eng 39, 6327–6338 (2014). https://doi.org/10.1007/s13369-014-1299-9

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  • DOI: https://doi.org/10.1007/s13369-014-1299-9

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