Hybrid Approach for Placement of Multiple DGs in Primary Distribution Networks

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Hybrid Approach for Placement of Multiple DGs in Primary Distribution Networks

Author : Divesh Kumar and Satish Kansal
Volume 7 No.2 July-December 2018 pp 90-95

Abstract

This paper proposes the hybrid approach for multiple distributed generators placement to achieve a high loss reduction in a primary distribution networks. The analytical method has been extended for practical power injections. This approach is based on improved analytical expressions to calculate the optimum size of DGs and heuristic technique to identify the best locations for DG allocations. The optimal power factors of the DGs have also been evaluated in this work. To validate the proposed hybrid approach, results have been compared with particle swarm optimization (PSO) technique and existing fast improved analytical (IA) approach results. The proposed technique has been tested on 33-bus test system.

Keywords

Analytical Expressions, Distributed Generation, Particle Swarm Optimization (PSO), Optimal Size, Optimal Location, Power Loss

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References

[1] C. Wang and M.H. Nehrir, “Analytical approaches for optimal placement of distributed generation sources in power systems”, IEEE Trans. Power Syst., Vol. 19, No. 4, pp. 2068-2076, 2004.
[2] N. Acharya, P. Mahat, and N. Mithulananthan, “An analytical approach for DG allocation in primary distribution network”, Int. J. Electrical Power & Energy Systems, Vol. 28, No. 10, pp. 669-678, 2006.
[3] N. Jain, S. N. Singh, and S. C. Srivastava, “Particle Swarm Optimization Based Method for Optimal Siting and Sizing of Multiple Distributed Generators”, 16th National Power System Conference, Hyderabad, December 2010.
[4] V. Kumar, Rohit Kumar, I. Gupta, and H.O. Gupta, “DG integrated approach for service restoration under cold load pickup”, IEEE Trans. Power Del., Vol. 25, No.1, pp. 398-406, 2010.
[5] A. Keane and M. O. Malley, “Optimal allocation of embedded generation on distribution networks”, IEEE Trans. Power Syst., Vol. 20, No. 3, pp. 1640-1646, 2005.
[6] M.F. AlHajri, M.R. AlRashidi, and M.E. El-Hawary, “Hybrid Particle Swarm Optimization Approach for Optimal Distribution Generation Sizing and Allocation in Distribution Systems”, Canadian Conference on Electrical and Computer Engineering Canadian Conference, (CCECE) Vancouver, pp.1290-1293, 2007.
[7] P.R. Lopez, F. Jurado, N.R. Reyes, S.G. Galan, and M. Gomez, “ Particle swarm optimization for biomass-fuelled systems with technical constraints” Engineering Applications of Artificial Intelligence, Vol. 21, pp.1389-1396, 2008.
[8] L.Y. Wong, S.R. Abdul Rahim,M.H. Sulaiman, and O. Aliman, “Distributed generation installation using particle swarm optimization”, Proc. of Inter. conf Power Engineering and Optimizatio,. Shah Alam Selangor Malaysia, pp. 159-163, 2010.
[9] W. El-Khattam, Y.G. Hegazy, and M.M.A. Salama, “An integrated distributed generation optimization model for distribution system planning”, IEEE Trans on Power Syst, Vol. 20, pp.1158-1165, 2005.
[10] D. Singh, D. Singh, and K. S. Verma, “Multiobjective Optimization for DG Planning With Load Models”, IEEE Trans. Power Syst., Vol. 24, No.1, pp. 427-436, 2009.
[11] T. Gozel, and M. H. Hocaoglu, “An analytical method for the sizing and sitting of distributed generators in radial systems”, Electr. Power Syst. Res., Vol. 79, pp. 912-918, 2009.
[12] D. Q. Hung, N. Mithulananthan, and R. C. Bansal, “Analytical expressions for DG allocation in primary distribution networks”, IEEE Trans. Energy Conversion, Vol. 25, No. 3, pp. 814-820, 2010.
[13] R.K. Singh, and S.K. Goswami, “Optimum allocation of distributed generations based on nodal pricing for profit, loss reduction and voltage improvement including voltage rise issue”, Inter Journal of Elect Power and Energy Syst, Vol. 32, pp. 637-644, 2010.
[14] H. Hedayati, S.A. Nabaviniaki, and A. Akbarimajd, “A method for placement of DG units in distribution networks”, IEEE Trans on Power Deliv, Vol. 23, pp.1620-1628, 2010.
[15] M.H. Moradi, and M. Abedini, “Optimal multi-distributed generation location and capacity by genetic algorithms. Proc. of Inter. Conf on Power Engineering and Optimization, Shah Alam Selangor Malaysia, pp.440–444, 2010.
[16] G. Celli, E. Ghiani, S. Mocci, and F. Pilo, “A multiobjective evolutionary algorithm for the sizing and siting of distributed generation”, IEEE Trans on Power Syst, Vol. 20, pp. 750-757, 2005.
[17] W. Prommee, and W. Ongsakul, “Optimal multi-distributed generation placement by adaptive weight particle swarm optimization”, Proc. of Inter. Conf on Control, Automation and Systems, Seoul Korea, pp.1663–1668, 2008.
[18] Y.M. Atwa, E.F. El-Saadany, M.M.A. Salama, and R. Seethapathy, “Optimal renewable resources mix for distribution system energy loss minimization”, IEEE Trans Power Syst, Vol. 25, pp. 360-370, 2010.
[19] D. Q. Hung, and N. Mithulananthan, “Multiple distributed generators placement in Primary Distribution Networks for loss reduction”, IEEE Trans. Industrial Electronics, Vol. 99, 2011
[20] I.O. Elgerd. Electric energy system theory: an introduction. McGraw-Hill 1971.
[21] H.L. Wills, Power Distribution Planning Reference Book. New York: Marcel Deckker, 2004.
[22] M. E. Baran and F. F. Wu, “Optimum sizing of capacitor placed on radial distribution systems”, IEEE Trans. Power Delivery, Vol. 4, No. 1, pp. 735-743, 1989.
[23] J.Kennedy, and R. Eberhart, “Particle Swarm Optimizer”, IEEE International Conference on Neural Networks, Perth(Australia), IEEE Service Centre Piscataway, NJ, IV, pp. 1942-1948, 1995.
[24] R.C. Eberhart and Y. Shi , “Comparing inertial weights and constriction factor in particle swarm optimization”, proc. of the 2000 Inter. congress on Evaluating computation, San Diego, Calfornia, Piscataway, NJ, pp. 84-88, 2000.
[25] M. A. Kashem, V. Ganapathy, G. B. Jasmon and M. I. Buhari, “A novel method for loss minimization in distribution networks”, IEEE Int. Conf. Electric Utility Deregulation and Restructuring and Power Technologies, Proc., pp. 251-256, 2000.

Asian Journal of Electrical Sciences (AJES) is a quarterly international peer-reviewed journal of Electrical Sciences. One of the objectives of this journal is to disseminate knowledge on various research issues connected with the topics include, but are not limited to:

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Editorial Note

Editorial Dr. Koray Ulgen