Determination of Optimal Allocation and Penetration Level of Distributed Energy Resources Considering Short Circuit Currents

Document Type : Original Article


1 School of Engineering, Damghan University, Damghan, Iran

2 Semnan Electric Power Distribution Company, Semnan, Iran

3 School of Engineering, Islamic Azad University, Semnan Branch, Semnan, Iran


The integration of Distributed Energy Resources (DER) in the distribution network has plenty of advantages if their allocation and Penetration Level (PL) are done appropriately. Hence, the challenge of finding the best allocation and PL of DERs in large distribution networks is an important but intricate problem. This paper proposes a novel methodology to simultaneously determine the optimal location/capacity and PL of DERs based on both power losses and voltage deviation minimization, while constraints of voltage profile of feeders under light loading and short circuit capability of the CBs are met. Moreover, a Multi-Objective Mutation based PSO (MOMPSO) is presented that by introducing two modifications of dynamic inertia weight and utilizing a mutation operator improves exploration and exploitation searchability as well as convergence capability of the PSO algorithm. The proposed methodology is tested on a practical distribution network to evaluate its effectiveness in finding optimal location and capacity of DERs along with the feeders.


1. Quijano, D.A. and Padilha-Feltrin, A., "Optimal integration of
distributed generation and conservation voltage reduction in
active distribution networks", International Journal of
Electrical Power & Energy Systems,  Vol. 113, (2019), 197207.
2. Abdelati, R., Boussaada, M. and YAHIA, H., "Emulation, model
identification and new-approach characterization of a PV panel
(technical note)", International Journal of Engineering,  Vol.
31, No. 8, (2018), 1222-1227. 
3. Pesaran H.A, M., Huy, P.D. and Ramachandaramurthy, V.K., "A
review of the optimal allocation of distributed generation:
Objectives, constraints, methods, and algorithms", Renewable
and Sustainable Energy Reviews,  Vol. 75, (2017), 293-312. 
4. Zhan, H., Wang, C., Wang, Y., Yang, X., Zhang, X., Wu, C. and
Chen, Y., "Relay protection coordination integrated optimal
placement and sizing of distributed generation sources in
distribution networks", IEEE Transactions on Smart Grid, 
Vol. 7, No. 1, (2016), 55-65. 
5. HassanzadehFard, H. and Jalilian, A., "Optimal sizing and
location of renewable energy-based dg units in distribution
systems considering load growth", International Journal of 
Electrical Power & Energy Systems,  Vol. 101, (2018), 356370.
6. El-Ela, A.A.A., El-Sehiemy, R.A. and Abbas, A.S., "Optimal
placement and sizing of distributed generation and capacitor
banks in distribution systems using water cycle algorithm",
IEEE Systems Journal,  Vol. 12, No. 4, (2018), 3629-3636. 
7. Murty, V.V.S.N. and Kumar, A., "Optimal placement of dg in
radial distribution systems based on new voltage stability index
under load growth", International Journal of Electrical Power
& Energy Systems,  Vol. 69, (2015), 246-256. 
8. Khatod, D.K., Pant, V. and Sharma, J., "Evolutionary
programming based optimal placement of renewable distributed
generators", IEEE Transactions on Power Systems,  Vol. 28,
No. 2, (2013), 683-695. 
9. Hung, D.Q., Mithulananthan, N. and Lee, K.Y., "Optimal
placement of dispatchable and nondispatchable renewable dg
units in distribution networks for minimizing energy loss",
International Journal of Electrical Power & Energy Systems, 
Vol. 55, (2014), 179-186. 
10. Celli, G., Ghiani, E., Mocci, S. and Pilo, F., "A multiobjective
evolutionary algorithm for the sizing and siting of distributed
generation", IEEE Transactions on Power Systems,  Vol. 20,
No. 2, (2005), 750-757. 
11. Wang, Z., Chen, B., Wang, J. and Begovic, M.M., "Stochastic
dg placement for conservation voltage reduction based on
multiple replications procedure", IEEE Transactions on Power
Delivery,  Vol. 30, No. 3, (2015), 1039-1047. 
12. Georgilakis, P.S. and Hatziargyriou, N.D., "Optimal distributed
generation placement in power distribution networks: Models,
methods, and future research", IEEE Transactions on Power
Systems,  Vol. 28, No. 3, (2013), 3420-3428. 
13. Sanjay, R., Jayabarathi, T., Raghunathan, T., Ramesh, V. and
Mithulananthan, N., "Optimal allocation of distributed
generation using hybrid grey wolf optimizer", IEEE Access, 
Vol. 5, (2017), 14807-14818. 
14. Hung, D.Q. and Mithulananthan, N., "Multiple distributed
generator placement in primary distribution networks for loss
reduction", IEEE Transactions on Industrial Electronics,  Vol.
60, No. 4, (2013), 1700-1708. 
15. Ameli, A., Bahrami, S., Khazaeli, F. and Haghifam, M., "A
multiobjective particle swarm optimization for sizing and
placement of DGS from DG owner's and distribution company's
viewpoints", IEEE Transactions on Power Delivery,  Vol. 29,
No. 4, (2014), 1831-1840. 
16. Zhao, Q., Wang, S., Wang, K. and Huang, B., "Multi-objective
optimal allocation of distributed generations under uncertainty
based on d-s evidence theory and affine arithmetic",
International Journal of Electrical Power & Energy Systems, 
Vol. 112, (2019), 70-82. 
17. Ganguly, S. and Samajpati, D., "Distributed generation
allocation on radial distribution networks under uncertainties of
load and generation using genetic algorithm", IEEE
Transactions on Sustainable Energy,  Vol. 6, No. 3, (2015),
18. Sheng, W., Liu, K., Liu, Y., Meng, X. and Li, Y., "Optimal
placement and sizing of distributed generation via an improved
nondominated sorting genetic algorithm ii", IEEE Transactions
on Power Delivery,  Vol. 30, No. 2, (2015), 569-578. 
19. Jazaeri, M., Farzinfar, M. and Razavi, F., "Evaluation of the
impacts of relay coordination on power system reliability",
International Transactions on Electrical Energy Systems,  Vol.
25, No. 12, (2015), 3408-3421. 
20. Vatani, M., Alkaran, D.S., Sanjari, M.J. and Gharehpetian, G.B.,
"Multiple distributed generation units allocation in distribution
network for loss reduction based on a combination of analytical
and genetic algorithm methods", IET Generation, Transmission
& Distribution,  Vol. 10, No. 1, (2016), 66-72. 
21. NAZARPOUR, d. and Sattarpour, T., "Assessing the impact of
size and site of dgs and sms in active distribution networks for
energy losses cost", International Journal of Engineering, 
Vol. 28, No. 7, (2015), 1002-1010. 
22. Kennedy, J. and Eberhart, R., "Particle swarm optimization", in
Proceedings of ICNN'95 - International Conference on Neural
Networks. Vol. 4, (1995), 1942-1948 vol.1944. 
23. Coello, C.A.C., Lamont, G.B. and Van Veldhuizen, D.A.,
"Evolutionary algorithms for solving multi-objective problems,
Springer,  Vol. 5,  (2007). 
24. Coello, C.A.C., Pulido, G.T. and Lechuga, M.S., "Handling
multiple objectives with particle swarm optimization", IEEE
Transactions on Evolutionary Computation,  Vol. 8, No. 3,
(2004), 256-279. 
25. Farzinfar, M., Jazaeri, M. and Razavi, F., "A new approach for
optimal coordination of distance and directional over-current
relays using multiple embedded crossover pso", International
Journal of Electrical Power & Energy Systems,  Vol. 61,
(2014), 620-628. 
26. Amjady, N. and Soleymanpour, H.R., "Daily hydrothermal
generation scheduling by a new modified adaptive particle
swarm optimization technique", Electric Power Systems
Research,  Vol. 80, No. 6, (2010), 723-732. 
27. Mishra, A., Farzinfar, M., Bahadornejad, M. and Nair, N.-K.C.,
"Evaluating the impact of different PV control strategies on
distribution network operation", in 2014 Australasian
Universities Power Engineering Conference (AUPEC), IEEE.,
(2014), 1-6.