Optimal Distributed Generator Sizing and Placement by Analytical Method and PSO Algorithm Considering Optimal Reactive Power Dispatch
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Optimal Distributed Generator Sizing and Placement by Analytical Method and PSO Algorithm Considering Optimal Reactive Power Dispatch

Authors: Kyaw Myo Lin, Pyone Lai Swe, Khine Zin Oo

Abstract:

In this paper, an approach combining analytical method for the distributed generator (DG) sizing and meta-heuristic search for the optimal location of DG has been presented. The optimal size of DG on each bus is estimated by the loss sensitivity factor method while the optimal sites are determined by Particle Swarm Optimization (PSO) based optimal reactive power dispatch for minimizing active power loss. To confirm the proposed approach, it has been tested on IEEE-30 bus test system. The adjustments of operating constraints and voltage profile improvements have also been observed. The obtained results show that the allocation of DGs results in a significant loss reduction with good voltage profiles and the combined approach is competent in keeping the system voltages within the acceptable limits.

Keywords: Analytical approach, distributed generations, optimal size, optimal location, optimal reactive power dispatch, particle swarm optimization algorithm.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3607745

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References:


[1] B. Bakhsideh Zad, et al., “Optimal reactive power control of DGs for voltage regulation of MV distribution systems using sensitivity analysis method and PSO algorithm,” Electrical Power and Energy Systems, vol. 68, 2015, pp. 52-60.
[2] Ahmad Rezaee Jordehi, “Allocation of distributed generation units in electric power systems: A review,” Renewable and Sustainable Energy Reviews, vol. 56, 2016, pp. 893-905.
[3] Hung DQ, Mithulananthan N., Bansal RC, “Analytical expressions for DG allocation in primary distribution networks,” IEEE Trans. Energy Convers., vol.25, no. 3, 2010, pp.814-820.
[4] Satish Kansl, Vishal Kumar, Barjeev Tyagi, “Optimal placement of different type of DG sources in distribution networks,” Electrical Power and Energy Systems, vol. 53, 2013, pp. 752-760.
[5] B. Singh and J. Sharma, “A review on distributed generation planning,” Renewable and Sustainable Energy Reviews, vol. 76, 2017, pp. 529-544.
[6] Acharya N, Mahat P, Mithulanathan N, “An analytical approach for DG allocation in primary distribution network,” Electrical Power and Energy Systems, vol. 28, no. 10, 2006, pp. 669-678.
[7] K. Z. Oo, K. M. Lin and T. N. Aung, “Particle Swarm Optimization based optimal reactive power dispatch for power distribution network with distributed generation,” International Journal of Energy and Power Engineering, vol. 6, no. 4, 2017, pp. 53-60.
[8] A. Ghasemi and A. Tohidi, “Multi objective optimal reactive power dispatch using a new multi objective strategy,” Electrical Power and Energy Systems, vol.57, 2014, pp. 318-334.
[9] K. Naima et al., “ Use of Genetic Algorithm and Particle Swarm Optimization methods for the optimal control of the reactive power in Western Algerian power systems,” Energy Procedia, vol. 74, 2015, pp. 265-272.
[10] J. Zhu, R. D. Zimmerman and C. E. Murillo-Sanchez, MATPOWER 5.1 User’s Manual, March 20, 2015.
[11] M. R. AlRashidi and M. E. El-Hawary, “A survey of Particle Swarm Optimization applications in electric power systems,” IEEE Trans. On Evolutionary Computation, vol. 15, no. 4, 2009, pp. 913-918.
[12] Hadi Saadat, Power System Analysis, 2nd Edition, McGraw-Hill, Singapore, 2004.