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A Genetic Algorithm Method for Optimal Distribution Reconfiguration Considering Photovoltaic Based DG Source in Smart Grid

  • Mustafa MosbahEmail author
  • Salem Arif
  • Ridha Djamel Mohammedi
  • Samir Hamid Oudjana
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 62)

Abstract

The distribution network have a very weakly meshed reconfiguration, with loops between different source stations, but the operation is carried out via a tree-based reconfiguration. This reconfiguration is determined by the opening and closing of switches in order to minimize the total power losses taking account the technical, security and topological distribution network constraints. In this paper, a Genetic Algorithm (GA) method based on graphs theory is proposed to design an optimal reconfiguration in presence of a photovoltaic based Distributed Generation source. The proposed method is tested on IEEE distribution network (69 bus) and validated on Algerian distribution network (116 bus). The proposed method was developed under MATLAB software. Certain results are better then others papers viewpoint active losses.

Keywords

Distribution network Optimal configuration Photovoltaic source 

References

  1. 1.
    Civanlar, S., et al.: Distribution feeder reconfiguration for loss reduction. IEEE Trans. Power Deliv. 3, 1217–1223 (1988)CrossRefGoogle Scholar
  2. 2.
    Shirmohammadi, D., Hong, H.W.: Reconfiguration of electric distribution networks for resistive line loss reduction. IEEE Trans. Power Deliv. 4, 1492–1498 (1989)CrossRefGoogle Scholar
  3. 3.
    Baran, M.E., Wu, F.F.: Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Trans. Power Deliv. 4, 1401–1407 (1989)CrossRefGoogle Scholar
  4. 4.
    Solo, A.M.G., et al.: A knowledge-based approach for network radiality in distribution system reconfiguration. IEEE Trans. Power Eng. Soc. Gener. Meet. (2006)Google Scholar
  5. 5.
    Aoki, K., et al.: New approximate optimization method for distribution system planning. IEEE Trans. Power Syst. 5, 126–132 (1990)CrossRefGoogle Scholar
  6. 6.
    Abnndams, R.N., Laughton, M.A.: Optimal planning of networks using mixed-integer programming. IEEE Proc. 121, 139–148 (1974)Google Scholar
  7. 7.
    Mosbah, M., et al.: Optimum dynamic distribution network reconfiguration using minimum spanning tree algorithm. In: IEEE 5th International Conference on Electrical Engineering, Boumerdes (2017)Google Scholar
  8. 8.
    Jabr, R.A., et al.: Minimum loss network reconfiguration using mixed-integer convex programming. IEEE Trans. Power Syst. 27, 1106–1115 (2012)CrossRefGoogle Scholar
  9. 9.
    Tomoiaga, B., et al.: Optimal reconfiguration of power distribution systems using a genetics algorithm based on NSGA-II. Energies 6, 1439–1455 (2013)CrossRefGoogle Scholar
  10. 10.
    Mosbah, M., et al.: Optimal Algerian distribution network reconfiguration using ant lion algorithm for active power losses. In: IEEE 3rd International Conference on PAIS 2018, Tebessa, Algeria (2018)Google Scholar
  11. 11.
    Th Nguyen, T., et al.: Multi-objective electric distribution network reconfiguration solution using runner-root algorithm. Appl. Soft Comput. 52, 93–108 (2017)CrossRefGoogle Scholar
  12. 12.
    Chicco, G., Mazza, A.: Assessment of optimal distribution network reconfiguration results using stochastic dominance concepts. Susta Energy Grids Netw. 9, 75–79 (2017)CrossRefGoogle Scholar
  13. 13.
    Mosbah, M., et al.: Optimal reconfiguration of an Algerian distribution network in presence of a wind turbine using genetic algorithm. In: 1st International Conference on Artificial Intelligence in Renewable Energetic System, IC-AIRES 2017. Springer, Cham (2018)CrossRefGoogle Scholar
  14. 14.
    Badran, O., et al.: Optimal reconfiguration of distribution system connected with distributed generations: a review of different methodologies. Renew. Sustain. Energy Rev. 73, 854–867 (2017)CrossRefGoogle Scholar
  15. 15.
    Mosbah, M., et al.: Optimal of shunt capacitor placement and size in Algerian distribution network using particle swarm optimization. In: IEEE Proceeding of ICMIC 2016, Algiers, Algeria (2016)Google Scholar
  16. 16.
    Mosbah, M., et al.: Genetic algorithms based optimal load shedding with transient stability constraints. In: Proceedings of the 2014 IEEE International Conference on Electrical Sciences and Technologies in Maghreb (2014)Google Scholar
  17. 17.
    Mosbah, M., et al.: Optimal sizing and placement of distributed generation in transmission systems. In: ICREGA 2016, Belfort, France, 8–10 February 2016Google Scholar
  18. 18.
    Holland, J.H.: Adaptation in Nature and Artificial Systems. The University of Michigan Press (1975)Google Scholar
  19. 19.
    Kashem, M.A., et al.: Loss reduction in distribution networks using network reconfiguration algorithm. Electr. Mach. Power Syst. 26, 815–829 (1998)CrossRefGoogle Scholar
  20. 20.
    Rao, R.S., et al.: Power loss minimization in distribution system using network reconfiguration in the presence of distributed generation. IEEE Trans. Power Syst. 28, 317–325 (2013)CrossRefGoogle Scholar
  21. 21.
    Ding, F., Laparo, K.A.: Hierarchical decentralized network reconfiguration for smart distribution systems Part II: applications to test systems. IEEE Trans. Power Syst. 30, 744–752 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mustafa Mosbah
    • 1
    Email author
  • Salem Arif
    • 1
  • Ridha Djamel Mohammedi
    • 2
  • Samir Hamid Oudjana
    • 3
  1. 1.LACoSERE Laboratory, Department of Electrical EngineeringAmar Telidji University of LaghouatLaghouatAlgeria
  2. 2.Department of Electrical EngineeringUniversity of DjelfaDjelfaAlgeria
  3. 3.Unité de Recherche Appliquée en Energies Renouvelables (URAER)GhardaiaAlgeria

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