Cluster Computing

, Volume 22, Supplement 3, pp 6849–6854 | Cite as

Optimal sizing and distribution system reconfiguration of hybrid FC/WT/PV system using cluster computing based on harmony search algorithm

  • M. Deva Brinda
  • A. SureshEmail author
  • M. R. Rashmi


Reconfigurable hybrid energy systems are vividly becoming very popular. Many algorithms were developed to optimize the best configuration of the distributed system in order to maximize the voltage stability index (VSI), minimize the power loss, minimize the cost of energy generated by distributed generating sources (DGS) and minimize the total emission produced by DGS and the grid. This paper Presented application of harmony search algorithm to for optimal sizing and distribution system reconfiguration of hybrid fuel cell (FC), wind turbine (WT) and photovoltaic (PV) system for maximizing VSI, minimizing the cost, power loss and emission and also proposal a cluster computing based novel method to identify the bus to which the renewable energy sources can be connected for optimal utilization.


Voltage stability index Hybrid energy system Distributed generating sources Harmony search algorithm Fuel cost 


  1. 1.
    Acharya, N., Mahat, P., Mithulananthan, N.: An analytical approach for DG allocation in primary distribution network. Int. J. Electr. Power Energy Syst. 28, 669–678 (2006)CrossRefGoogle Scholar
  2. 2.
    Robb, D.: Standing up to transmission reliability standards. Power Eng. Int. 12, 20–22 (2004)Google Scholar
  3. 3.
    Billinton, R.: Evaluation of different operation strategies in small stand-alone power systems. IEEE Trans. Energy Convers. 20, 654–660 (2005)CrossRefGoogle Scholar
  4. 4.
    Wilk, J., Gjerde, J.O., Gjengedal, T., Gustafsson, M.: Steady state power system issues when planning large wind farms. In: Proceedings of the IEEE Power Engineering Society Winter Meeting, vol. 1 pp. 199–204 (2002)Google Scholar
  5. 5.
    Hocaoglu, F.O., Kurban, M.: A preliminary detailed study on constructed hybrid (wind-photovoltaic) system under climatically conditions of Eskisehir region in Turkey. In: Proceedings of the IEEE International Power and Energy Conference PECon’06, pp. 40–43 (2006)Google Scholar
  6. 6.
    Nehrir, M.H., Lameres, B.J., Venkataramanan, G., Gerez, V., Alvarado, L.A.: An approach to evaluate the general performance of standalone wind/photovoltaic generating systems. IEEE Trans. Energy Convers. 15(4), 433–439 (2000)CrossRefGoogle Scholar
  7. 7.
    Luna-Rubio, R., Trejo-Perea, M., Vargas-Va’zquez, D., R’ıos-Moreno, G.J.: Optimal sizing of renewable hybrids energy systems: a review of methodologies. Sol. Energy 86(4), 1077–1088 (2012)CrossRefGoogle Scholar
  8. 8.
    Koutroulis, E., Kolokotsa, D., Potirakis, A., Kalaitzakis, K.: Methodology for optimal sizing of stand-alone photovoltaic/wind generator systems using genetic algorithms. Sol. Energy 80, 1072–1088 (2006)CrossRefGoogle Scholar
  9. 9.
    Dehghan, S., Kiani, B., Kazemi, A., Parizad, A.: Optimal sizing of a hybrid wind/PV plant considering reliability indices. World Acad. Sci. Eng. Technol. 3(8), 481–489 (2009)Google Scholar
  10. 10.
    Das, D.: A fuzzy multi-objective approach for network reconfiguration of distribution systems. IEEE Trans. Power Deliv. 21, 202–209 (2006)CrossRefGoogle Scholar
  11. 11.
    Mendoza, J.E., Lopez, M.E., Coello, C.A.C., Lopez, E.A.: Micro genetic multi-objective reconfiguration algorithm considering power losses and reliability indices for medium voltage distribution network. IET Gener. Transm. Distrib. 3(9), 825–840 (2009)CrossRefGoogle Scholar
  12. 12.
    Abdelaziz, A.Y., Mohammed, F.M., Mekhamer, S.F., Badr, M.A.L.: Distribution systems reconfiguration using a modified particleswarm optimization algorithm. Electr. Power Syst. Res. 79, 1521–1530 (2009)CrossRefGoogle Scholar
  13. 13.
    Abdelaziz, A.Y., Mohammed, F.M., Mekhamer, S.F., Badr, M.A.L.: Distribution system reconfiguration using a modified Tabu Search algorithm. Electr. Power Syst. Res. 80, 943–953 (2010)CrossRefGoogle Scholar
  14. 14.
    Rao, R.S., Narasimham, S.V.L., Raju, M.R., Rao, A.S.: Optimal network reconfiguration of large-scale distribution system using harmony search algorithm. IEEE Trans. Power Syst. 26, 1080–1088 (2011)CrossRefGoogle Scholar
  15. 15.
    Nasiraghdam, H., Jadid, S.: Optimal hybrid PV/WT/FC sizing and distribution system reconfiguration using multi-objective artificial bee colony algorithm. Elsevier Solar Energy 86, 3057–3071 (2012)CrossRefGoogle Scholar
  16. 16.
    Sedighizadeh, M., Esmaili, M., Esmaei, M.: Application of the hybrid Big Bang-Big Crunch algorithm to optimal reconfiguration and distributed generation power allocation in distribution systems. Sci. Direct Energy 76(1), 920–930 (2014)Google Scholar
  17. 17.
    Borges, C.L.T., Falcao, D.M., Taranto, G.N.: Cluster based power system analysis applications. In: Proceedings of the IEEE International Conference on Cluster Computing, (2000)
  18. 18.
    Green R.C., Wang, L., Alam, M.: High performance computing for electric power systems: Applications and trends. In: Proceedings of the Power and Energy Society General Meeting, IEEE, (2000)
  19. 19.
    Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001). CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Anna UniversityChennaiIndia
  2. 2.Department of Electrical and Electronics EngineeringS.A. Engineering CollegeChennaiIndia
  3. 3.Department of Electrical and Electronics EngineeringAmrita UniversityBengaluruIndia

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