Electric power has remained a basic essential for the advancement of any nation's economy. Increasing human exercises because of innovative advancement combined with populace development has made the interest for power dramatically increasing continuously, subsequently widening the gap between power generated and the demand by the consumers. This work provides sensitivity based method for the allocation of a distributed generation in a distribution network aimed at improving the voltage profile and reduce power loss in the quest to narrow the gap between power generated and that demanded by the consumers. Using the Loss sensitivity method, a DG of 153kW was allocated to bus 5 that sees a power reduction of 46% with voltage profile improved within constraints. Voltage sensitivity index was calculated at all nodes. Bus 17 was found to have the minimum VSI. In this case DG sizes were taken in step size of 17.5kW starting from 30 kW till 170 kW at different power factors of 1.0, 0.9, 0.85, and 0.8. The DG sizes were tested at the selected power for various DG sizes. 135kW DG at unity power factor was installed. After comparing the two methods it can be concluded that loss reduction in loss sensitivity method is more and it is better in terms of selecting the optimal location for the placement of DG. For the purpose of sizing the voltage sensitivity analysis index method is a better option.
E. C. Ashigwuike, and S. A Benson, ‘Optimal Location and Sizing of Distributed Generation in Distribution Network Using Adaptive Neuro-Fuzzy Logic Technique’, Vol. 4, No. 4, PP.83-89 April 2019
P.S Georgilakis, N.D. Hatziargyriou, ‘Optimal Distributed Generation Placement in Power Distribution Networks: Models, Methods, and Future Research’, IEEE Transactions on Power Systems, Vol. 28, No. 3, pp. 3420-3428, 2013.
W. El-Khattam, M.M.A. Salama, “Distributed generation technologies, definitions and benefits”, Electric Power System Research, Vol 71, pp. 119-128, 2004.
P.V. Babua, S.P. Singhb, “Optimal Placement of DG in Distribution network for Power loss minimization using NLP & PLS Technique”, 5th International Conference on Advances in Energy Research, ICAER 2015December 2015, Mumbai, India, pp. 15-17.
M. H. Moradi and M. Abedini, “A combination of GA and PSO for optimal location and sizing of DG”, Electrical Power and Energy Systems- Elsevier, Vol. 34, No. 1, pp. 66-74, 2012.
D. K. Khatod, V. Pant, J. D. Sharma “Evolutionary Programming Based optimal Placement of renewable distributed generators” IEEE Transactions on Power Systems, Vol.28, No. 4, pp. 683-695, 2013.
J. A. M. Garcia and A. J. G. Mena “Optimal distributed generation location and size using a modified teaching–learning based optimization algorithm” Electric Power and Energy System, Elsevier, Vol.50, No.1, pp. 65-75, 2013.
P. S. Georgilakis and N. D. Hatziargyriou, “Optimal DG placement in power distribution networks: Models, Methods and Future research”, IEEE Transactions on Power Systems, Vol. 28, No. 3, pp. 3420-3428, 2013.
T. N. Shukla, S. P. Singh and K. B. Naik, “Allocation of optimal DG using GA for minimum power losses in distribution systems”, International Journal of Engineering Sciences and Technology, Vol. 2, No. 3, pp. 94-106, 2010.
S. Ghosh, S. P. Ghoshal and S. Ghosh, “Optimal sizing and placement of distributed generation in a network system”, Electrical Power and Energy Systems- Elsevier, Vol. 32, No. 1, pp. 849-856, 2010.
Diagram of an Electric Power System, available at: http://en.wikipedia.org/wiki/Electric_power_transmission
This work is licensed under a Creative Commons Attribution 4.0 International License.
The names and email addresses entered in this journal site will be used exclusively for the stated purposes of this journal and will not be made available for any other purpose or to any other party.
Submission of the manuscript represents that the manuscript has not been published previously and is not considered for publication elsewhere.