Optimal Renewable Energy Resource Based Distributed Generation Allocation in a Radial Distribution System

  • Kola Sampangi Sambaiah
  • T. JayabarathiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1048)


Distributed generation (DG) allocation is the most promising source for reducing network loss and enhancing bus voltage stability in a distribution system. Because of the vast availability and nonpolluting character of renewable energy resource, it is gaining more attention nowadays. The most widely used renewable-based DG (RDG) is wind turbine (WT) and solar photovoltaic (SPV). Power generation patterns of the WT and SPV modules are random and nonlinear because the power output of WT and SPV modules are dependent on wind speed and solar irradiation. These require a probabilistic model to represent the actual power generation. The present paper reflects the potency of WT and SPV modules for reducing system losses and enhancing voltage stability. A new hybrid gray wolf optimizer (HGWO) is proposed to solve the DG allocation problem. The proposed optimization method is tested on IEEE 12- and 15-bus radial distribution system (RDS) and it is found that the proposed HGWO has more potency in terms of loss reduction and voltage stability enhancement compared to the existing techniques.


Renewable energy source Optimal DG allocation Meta-heuristic optimization Hybrid gray wolf optimizer Wind and solar DG Renewable distributed generation 


  1. 1.
    Ackermann, T., Ran Andersson, G., Soder, L.: Distributed generation: a definition. Electr. Power Syst. Res. 57, 195–204 (2001)CrossRefGoogle Scholar
  2. 2.
    Kashem, M.A., Ledwich, G.: Multiple distributed generators for distribution feeder voltage support. IEEE Trans. Energy Convers. 20(3), 676–684 (2005)CrossRefGoogle Scholar
  3. 3.
    Hedayati, H., Nabaviniaki, S.A., Akbarimajd, A.: A method for placement of DG units in distribution networks. IEEE Trans. Power Deliv. 23(3), 1620–1628 (2008)CrossRefGoogle Scholar
  4. 4.
    Lee, S., Park, J.: Selection of optimal location and size of multiple distributed generations by using Kalman filter algorithm. IEEE Trans. Power Syst. 24(3), 1393–1400 (2009)CrossRefGoogle Scholar
  5. 5.
    Wang, C., Nehrir, M.H.: Analytical approaches for optimal placement of distributed generation sources in power systems. IEEE Trans. Power Syst. 19(4), 2068–2076 (2004)CrossRefGoogle Scholar
  6. 6.
    Sambaiah, K.S.: A review on optimal allocation and sizing techniques for DG in distribution systems. Int. J. Renew. Energy Res. (IJRER) 8(3), 1236–1256 (2018)Google Scholar
  7. 7.
    Kayal, P., Chanda, C.K.: Optimal mix of solar and wind distributed generations considering performance improvement of electrical distribution network. Renew. Energy 75, 173–186 (2015)CrossRefGoogle Scholar
  8. 8.
    Kayal, P., Chanda, C.K.: Placement of wind and solar based DGs in distribution system for power loss minimization and voltage stability improvement. Int. J. Electr. Power Energy Syst. 53, 795–809 (2013)CrossRefGoogle Scholar
  9. 9.
    Atwa, Y.M., El-Saadany, E.F., Salama, M.M.A., Seethapathy, R.: Optimal renewable resources mix for distribution system energy loss minimization. IEEE Trans. Power Syst. 25(1), 360–370 (2010)CrossRefGoogle Scholar
  10. 10.
    Mirjalili, S., Mohammad, S., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)CrossRefGoogle Scholar
  11. 11.
    Sanjay, R., Jayabarathi, T., Raghunathan, T., Ramesh, V., Mithulananthan, N.: Optimal allocation of distributed generation using hybrid grey wolf optimizer. IEEE Access 5, 14807–14818 (2017)CrossRefGoogle Scholar
  12. 12.
    Teng, J.: A direct approach for distribution system load flow solutions. IEEE Trans. Power Deliv. 18(3), 882–887 (2003)MathSciNetCrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Electrical EngineeringVellore Institute of TechnologyVelloreIndia

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