A Novel Grey Wolf Optimization Algorithm for Optimal DG Units Capacity and Location in Microgrids

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Published Apr 1, 2016
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Volume 12, Issue 2, June 2016

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P. Dinakara Prasad Reddy
Research Scholar(JNTUK), Department of EEE, S V University College of Engineering, S V University, Tirupati-517502
V. C. Veera Reddy
Professor, EEE Department, AITS, Tirupati-517502
T. Gowri Manohar
Professor, S V University College of Engineering, S V University, Tirupati-517502
B. Chandra Sekhar
Senior Research Fellow, Energy Efficiency & Renewable Engineering Division, Central Power Research Institute, Bangalore-560080

Abstract

Distributed Generator (DG) resources are small electric generating plants that can provide power to homes, businesses or industrial facilities in distribution feeders. By optimal placement of DG we can reduce power loss and improve the voltage profile. However, the values of DGs are largely dependent on their types, sizes and locations as they were installed in distribution feeders. The main contribution of the paper is to find the optimal locations of DG units and sizes. Index vector method is used for optimal DG locations. In this paper new optimization algorithm i.e. Grey wolf optimization algorithm is proposed to determine the optimal DG size. This paper uses three different types of DG units for compensation. The proposed methods have been tested on 15-bus, 34-bus, and 69-bus radial distribution systems. MATLAB>sup/sup<, Version 8.3 software is used for simulation.

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How to Cite
Dinakara Prasad Reddy, P., Veera Reddy, V. C., Gowri Manohar, T., & Chandra Sekhar, B. (2016). A Novel Grey Wolf Optimization Algorithm for Optimal DG Units Capacity and Location in Microgrids. Power Research - A Journal of CPRI, 12(2), 219–226. Retrieved from https://cprijournal.in/index.php/pr/article/view/204

References

  1. D Q Hung, N Mithulananthan, K Y Lee,Optimal placement of dispatchable and non dispatchable renewable DG units in distribution networks for minimizing energy loss, International Journal of Electrical Power & Energy Systems, Vol. 55, pp.179– 186,2014.
  2. M Jalali, K Zare, M T Hagh, A multi-stage MINLP-based model for sub-transmission system expansion planning considering the placement of DG units, International Journal of Electrical Power & Energy Systems.Vol. 63, pp. 8–16, 2013.
  3. S Gopiya Naik, D K Khatod, M P Sharma, Optimal allocation of combined DG and capacitor for real power loss minimization in distribution networks, International Journal of Electrical Power & Energy Systems.Vol. 53, pp. 967–973, 2013
  4. S K Injeti, N Prema Kumar, A novel approach to identify optimal access point and capacity of multiple DGs in a small, medium and large scale radial distribution systems, International Journal of Electrical Power & Energy Systems. Vol. 45, pp.142–151, 2013. doi:10.1016/j.ijepes.2012.08.043.
  5. S Kansal, V Kumar, B Tyagi, Optimal placement of different type of DG sources in distribution networks, International Journal of Electrical Power & Energy Systems.Vol. 53, pp. 752–760, 2013.
  6. Partha Kayal, C K Chanda, Placement of wind and solar based DGs in distribution system for power loss minimization and voltage stability improvement, International Journal of Electrical Power & Energy Systems, Vol. 53, pp. 795-809, ISSN 01420615, December 2013.
  7. Monica Alonso, Hortensia Amaris, Carlos Alvarez-Ortega, Integration of renewable energy sources in smart grids by means of evolutionary optimization algorithms, Expert Systems with Applications, Vol. 39, No. 5, April 2012, pp. 5513-5522, ISSN 0957-4174.
  8. D D Wu, M Junjie, W Yulong, L Yang, Information Engineering and Complexity Science - Part IISize and Location of Distributed Generation in Distribution System Based on Immune Algorithm, Systems Engineering Procedia, Vol. 4, pp.124-132, ISSN 2211-3819, 2012.
  9. M M Aman, G B Jasmon, H Mokhlis, A H Bakar, Optimal placement and sizing of a DG based on a new power stability index and line losses, International Journal of Electrical Power & Energy Systems. Vol.
  10. , pp. 1296-1304, 2012.
  11. M H Moradi, M Abedini, A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems, International Journal of Electrical Power & Energy Systems, Vol. 34, pp. 66-74, 2012.
  12. Q Kang, T Lan, Y Yasn, L Wang, Q Wu, Group search optimizer based optimal location and capacity of distributed generations, Neuro computing, Vol. 78, pp.55–63, 2012.
  13. R K Singh, S K Goswami, Optimum allocation of distributed generations based on nodal pricing for profit, loss reduction, and voltage improvement including voltage rise issue, International Journal of Electrical Power & Energy Systems, Vol. 32, pp. 637– 644, 2010.
  14. J H Teng, Y H Liu, C Y Chen, C F Chen, Value-based distributed generator placements for service quality improvements, International Journal of Electrical Power & Energy Systems, Vol. 29, pp. 268–274, 2007.
  15. N Acharya, P Mahat, N Mithulananthan, An analytical approach for DG allocation in primary distribution network, International Journal of Electrical Power & Energy Systems, Vol. 28, pp. 669–678, 2008.
  16. V V S N Murthy, A Kumar, Comparison of optimal DG allocation methods in radial distribution systems based on sensitivity approaches, International Journal of Electrical Power & Energy Systems, Vol. 53, pp. 450–467, 2013.
  17. S Mirjalili, S M Mirjalili, A Lewis, G W Optimizer, S Mirjalili, S M Mirjalili, et al., Grey Wolf Optimizer, Vol. 1, issue 1, pp. 46–61, 2014.
  18. S Devi, M Geethanjali, Application of Modified Bacterial Foraging Optimization algorithm for optimal placement and sizing of Distributed Generation, Expert Systems with Applications. Vol. 41, pp. 2772–2781, 2014.
  19. D P R P, C H Prasad, M C V Suresh, Capacitor Placement Using Bat Algorithm for Maximum Annual Savings in Radial Distribution systems, Vol. 4, pp. 105–109, 2014.