Optimal Placement of Distributed Generation Using Bacterial Foraging Optimization Algorithm

Article Preview

Abstract:

This paper presents the determination of the optimal distributed generation (DG) placement using bacterial foraging optimization algorithm (BFOA). The BFO mimics the seeking-nutrient behavior of the E. coli bacteria. It is utilized here to find the location and size of the DG installation in radial distribution system in order to obtain minimum system losses. The operation constraints include bus voltage limits, distribution line thermal limits, system power balance and generation power limits. The algorithm is tested on the IEEE 33 bus system. The result shows that the algorithm could be used as an alternative to the other techniques and improvement of the algorithm is required for acceleration and better accuracy of the calculation.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

329-332

Citation:

Online since:

August 2015

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Information on www. eppo. go. th.

Google Scholar

[2] S.R.A. Rahim, T.K.A. Rahman, I. Musirin, M.H. Hussain, M.H. Sulaiman, O. Aliman, Z.M. Isa, Implementation of DG for loss minimization and voltage profile in distribution system, The 4 th Int. Power Eng. and Opt. Conf. (PEOCO2010). (2010) 490-494.

DOI: 10.1109/peoco.2010.5559251

Google Scholar

[3] K. Nara, Y. Hayashi, K. Ikeda, T. Ashizawa, Application of tabu search to optimal placement of distributed generators, IEEE Power Eng. Soc. Winter Meet. 2 (2001) 918-923.

DOI: 10.1109/pesw.2001.916995

Google Scholar

[4] F.S. Abu-Mouti, M.E. El-Hawar, Optimal distributed generation allocation and sizing in distribution systems via artificial bee colony algorithm, IEEE Trans. on Power Del. 26 (2011) 2090-2101.

DOI: 10.1109/tpwrd.2011.2158246

Google Scholar

[5] D.Q. Hung, N. Mithulananthan, R.C. Bansal, Analytical expressions for DG allocation in primary distribution networks, IEEE Trans. on energy conv. 25 (2010) 814-820.

DOI: 10.1109/tec.2010.2044414

Google Scholar

[6] K.M. Passino, Biomimicry of bacterial foraging for distributed optimization and control, IEEE Control Syst. Mag. 22 (2002) 52–67.

DOI: 10.1109/mcs.2002.1004010

Google Scholar

[7] M.E. Baran, F.F. Wu, Network reconfiguration in distribution systems for loss reduction and load balancing, IEEE Trans. on Power Del. 4 (1989) 1401-1407.

DOI: 10.1109/61.25627

Google Scholar

[8] N. Acharya, P. Mahat, N. Mithulananthan, An analytical approach for DG allocation in primary distribution network, Electr. Power Energy Syst. 28 (2006) 669-678.

DOI: 10.1016/j.ijepes.2006.02.013

Google Scholar

[9] W. Krueasuk, W. Ongsakul, Optimal placement of distributed generation using particle swarm optimization, In proc. of the 2006 Australian Universities Power Eng. Conf. (AUPEC). (2006).

Google Scholar

[10] T.N. Shukla, S.P. Singh, K.B. Naik, Allocation of optimal distributed generation using GA for minimum system losses in radial distribution networks, Int. J. Eng., Sci. Tech. 2 (2010) 94-106.

DOI: 10.4314/ijest.v2i3.59178

Google Scholar