Optimal Allocation and Sizing of Multiple DGs with Reactive Power Capabilities in a Three-Phase Unbalanced Distribution System

Abstract

Nowadays, Distributed Generators (DGs) are widely adopted in distribution networks to deliver fast, reliable, and clean power to the consumer maximize environmental preservation, and mitigate the impact of energy production on the environment. However, recurring issues like poor voltage profiling/stability and power loss arising from improper allocation and unsuitable sizing of the DGs have made it necessary for methods and approaches to be sought in order to mitigate these issues. This study proposes a method that can be used in optimizing the allocation and sizes of the DGs. The study employs the IEEE 37 node test system in OpenDSS to carry out power flow. The DG size, node, and power factor are the coordinated control variables presented in this study to minimize the power loss. Genetic Algorithm, Pattern Search, Particle Swarm Optimization, and Grey Wolf Optimizer algorithms have been exploited in the IEEE 37 node test feeder to find the optimal location, sizes, and power factors of the DGs. Notable variations resulting from four different cases considering power loss as an objective function are also presented. Results indicate that optimally sized and placed DGs operated with optimal power factors have reduced power losses by enhancing the voltage profile. In addition, the effect of the reactive power capability of DGs on the distribution system has been shown.

Keywords

• Unbalanced distributed network
• IEEE 37 node test feeder
• Distributed generation
• Genetic algorithm
• Power loss
• Optimization

References

1. Preeti Rani, Ved Parkash, Naveen Kumar Sharma,Technological aspects, utilization and impact on power system for distributed generation: A comprehensive survey,Renewable and Sustainable Energy Reviews,Volume 192,2024,114257
2. Bollen, Math HJ, and Fainan Hassan. Integration of distributed generation in the power system. John wiley & sons, 2011.
3. Borges, C. L., & Falcão, D. M. (2006, July). Optimal distributed generation allocation for reliability, losses, and voltage improvement. Int. Jour. of Elect.l Power & Ene. Sys., 28(6), 413–420. https://doi.org/10.1016/j.ijepes.2006.02.003.
4. Devabalaji, K., & Ravi, K. (2016, September). Optimal size and siting of multiple DG and DSTATCOM in radial distribution system using Bacterial Foraging Optimization Algorithm. Ain Shams Engineering Journal, 7(3), 959–971. https://doi.org/10.1016/j.asej.2015.07.002.
5. Y. Alinejad-Beromi, M. Sedighizadeh, M. R. Bayat and M. E. Khodayar, "Using genetic algorithm for distributed generation allocation to reduce losses and improve voltage profile," 2007 42nd International Universities Power Engineering Conference, Brighton, UK, 2007, pp. 954-959, doi: 10.1109/UPEC.2007.4469077.
6. Gandomkar, M., Vakilian, M., & Ehsan, M. A combination of genetic algorithm and simulated annealing for optimal DG allocation in distribution networks. Canadian Conference on Electrical and Computer Engineering, 2005. https://doi.org/10.1109/CCECE.2005.1557013.
7. Ntombela, M., Musasa, K., & Leoaneka, M. C. (2022, October 10). Power Loss Minimization and Voltage Profile Improvement by System Reconfiguration, DG Sizing, and Placement. Computation, 10(10), 180. https://doi.org/10.3390/computation10100180.
8. Selim, A., Kamel, S., & Jurado, F. (2020, January). Efficient optimization technique for multiple DG allocation in distribution networks. Applied Soft Computing, 86, 105938. https://doi.org/10.1016/j.asoc.2019.105938.

9. Alhamali, M. E. Farrag, G. Bevan and D. M. Hepburn, "Determination of optimal site and capacity of DG systems in distribution network based on genetic algorithm," 2017 52nd UPEC, Heraklion, Greece, 2017, pp. 1-6, doi: 10.1109/UPEC.2017.8231996.
10. S. Biswas, S. K. Goswami and D. Bhattacharya, "Optimal Placement of Distributed Generation in an Unbalanced Radial Distribution System Considering Load Variation," 2019 IEEE Region 10 Symposium (TENSYMP), Kolkata, India, 2019, pp. 173-178, doi: 10.1109/TENSYMP46218.2019.8971226.
11. P. Alemi and G. B. Gharehpetian, "DG allocation using an analytical method to minimize losses and to improve voltage security," 2008 IEEE 2nd International Power and Energy Conference, Johor Bahru, Malaysia, 2008, pp. 1575-1580, doi: 10.1109/PECON.2008.4762731.
12. MATLAB, Natick, Massachusetts: The MathWorks Inc.; 2022.
13. RC, Kennedy J. Eberhart. "Particle swarm optimization." Proc IEEE Int Conf Neural Networks. Vol. 4. 1995.
14. Lakum, A., & Mahajan, V. (2021, August). A novel approach for optimal placement and sizing of active power filters in radial distribution system with nonlinear distributed generation using adaptive grey wolf optimizer. Engineering Science and Technology, an International Journal, 24(4), 911–924. https://doi.org/10.1016/j.jestch.2021.01.011.
15. KUMAR, T., & GANESH, D. (2015, January 15). Optimal allocation of DG unit for the radial distribution system using genetic algorithm. IJIREEICE, 87–90. https://doi.org/10.17148/ijireeice.2015.3118.
16. Sheren. (n.d.). Optimal Allocation of DG Units for Radial Distribution Systems using Genetic Algorithm. Int. Journal of Engin. and Advanced Tech., Volume-1(Issue-6, August 2012).
17. Madhusudhan, M., Kumar, N. & Pradeepa, H. Optimal location and capacity of DG systems in distribution network using genetic algorithm. Int. j. inf. tecnol. 13, 155–162 (2021). https://doi.org/10.1007/s41870-020-00545-2.
18. Gümüş, T. E., Emiroglu, S., & Yalcin, M. A. (2023, January). Optimal DG allocation and sizing in distribution systems with Thevenin based impedance stability index. International Journal of Electrical Power & Energy Systems, 144, 108555. https://doi.org/10.1016/j.ijepes.2022.108555
19. Das, G., Hazarika, D. Multi-objective Particle Swarm Optimization-Based Placement and Sizing of Distributed Generators Integrated to Unbalanced Low-Voltage Microgrids by Four-Leg Inverters. J. Inst. Eng. India Ser. B 104, 731–747 (2023).
20. Seyed Abbas Taher, Seyed Ahmadreza Afsari Optimal location and sizing of DSTATCOM in distribution systems by immune algorithm Electr. Power Energy Syst., 60 (2014), pp. 34-44.
21. Yuvaraj, T., & Ravi, K. (2018, December). Multi-objective simultaneous DG and DSTATCOM allocation in radial distribution networks using cuckoo searching algorithm. Alexandria Engineering Journal, 57(4), 2729–2742. https://doi.org/10.1016/j.aej.2018.01.001.
22. S. Biswas, S. K. Goswami and D. Bhattacharya, "Optimal Placement of Distributed Generation in an Unbalanced Radial Distribution System Considering Load Variation," 2019 IEEE Region 10 Symposium (TENSYMP), Kolkata, India, 2019, pp. 173-178, doi: 10.1109/TENSYMP46218.2019.8971226.
23. Maria Teresa Costa-Campi, Daniel Daví-Arderius, Elisa Trujillo-Baute,The economic impact of electricity losses,Energy Economics,Volume 75,2018,Pages 309-322,
24. Truong, K. H., Nallagownden, P., Elamvazuthi, I., & Vo, D. N. (2020, March). A Quasi-Oppositional-Chaotic Symbiotic Organisms Search algorithm for optimal allocation of DG in radial distribution networks. Applied Soft Computing, 88, 106067. https://doi.org/10.1016/j.asoc.2020.106067.
25. Muthukumar Kandasamy, Renugadevi Thangavel, Thamaraiselvi Arumugam, Sureshkumar Kumaravel, Sakthivel Aruchamy, Wook-Won Kim, Zong Woo Geem,Strategic incorporation of DSTATCOM and distributed generations in balanced and unbalanced radial power distribution networks considering time varying loads,Energy Reports,Volume 9,2023,Pages 4345-4359
26. M. Naveen Babu, P.K. Dhal,Impact of load flow and network reconfiguration for unbalanced distribution systems,Measurement: Sensors,Volume 32,2024,101078
27. Sudipta Ghosh, S.P. Ghoshal, Saradindu Ghosh,Optimal sizing and placement of distributed generation in a network system, International Journal of Electrical Power & Energy Systems,Volume 32, Issue 8,2010,Pages 849-856,
28. I. Dumancic, C. Tranchita and J. Kluge, "Dynamic Simulation of Distribution Power Systems with Distributed Renewable Generation," 2021 IEEE PES/IAS PowerAfrica, Nairobi, Kenya, 2021, pp. 1-5, doi: 10.1109/PowerAfrica52236.2021.9543396.
29. K. P. Schneider, B. A. Mather, B. C. Pal, C. W. Ten, G. J. Shirek, H. Zhu, J. C. Fuller, J. L. R. Pereira, L. F. Ochoa, L. R. de Araujo, R. C. Dugan, S. Matthias, S. Paudyal, T. E. McDermott, and W Kersting, “Analytic Considerations and Design Basis for the IEEE Distribution Test Feeders,” IEEE Transactions on Power Systems, vol. PP, no. 99, pp. 1-1, 2017
30. W. H. Kersting, “Radial distribution test feeders,” IEEE Transactions on Power Systems, vol. 6, no. 3, pp. 975–985, Aug. 1991. doi: 10.1109/59.119237
31. Distribution System Analysis Subcommittee, IEEE 37 node test feeder, IEEE Power Engineering Society, The Institute of Electrical and Electronics Engineers, Inc. https://cmte.ieee.org/pes-testfeeders/wp-content/uploads/sites/167/2017/08/feeder37.zip
32. R. C. Dugan, T. E. McDermott, “An open source platform for collaborating on smart grid research,” in Proc. IEEE Power Energy Soc. Gen. Meet., Detroit, 2011, pp. 1–7. doi:10.1109/pes.2011.6039829
33. G. Nageswara Reddy, G. Pavan Kumar, 2013, Best Location of Distributed Generation on Distribution Networks Using GA, International Journal of Engineering Research & Technology, 2, Issue 11.
34. Al-Ammar, Essam. “Optimal Allocation and Sizing of Distributed Generation in Distribution Networks Using Genetic Algorithms.” 11th International Conference on Electrical Power Quality and Utilization (2011).
35. Holland, J.H., 1975. Adaptation in Natural and Artificial Systems, second ed. University of Michigan Press, Ann Arbor, MI, 1992.
36. P. Gopu, S. Naaz and K. Aiman, "Optimal Placement of Distributed Generation using Genetic Algorithm," 2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), Bhilai, India, 2021, pp. 1-6, doi: 10.1109/ICAECT49130.2021.9392496.
37. Majid Jaberipour, Esmaile Khorram, Behrooz Karimi,Particle swarm algorithm for solving systems of nonlinear equations,Computers & Mathematics with Applications,Volume 62, Issue 2,2011,Pages 566-576
38. Gardeux, V., H. Omran, M. G., Chelouah, R., Siarry, P., & Glover, F. (2017). Adaptive pattern search for large-scale optimization. Applied Intelligence, 47, 319-330.
39. Cihan Ersali, Baran Hekimoglu, Musa Yilmaz, Alfredo A. Martinez-Morales, Tahir Cetin Akinci,Disturbance rejecting PID-FF controller design of a non-ideal buck converter using an innovative snake optimizer with pattern search algorithm,Heliyon,Volume 10, Issue 14,2024
40. Mirjalili, Seyedali, Seyed Mohammad Mirjalili, and Andrew Lewis. "Grey wolf optimizer." Advances in engineering software 69 (2014): 46-61.
41. Fattahi, Hadi, Hossein Ghaedi, and Danial Jahed Armaghani. "Optimizing fracture toughness estimation for rock structures: A soft computing approach with GWO and IWO algorithms." Measurement (2024): 115306.
42. Moayedi, Hossein, Hoang Nguyen, and Loke Kok Foong. "Nonlinear evolutionary swarm intelligence of grasshopper optimization algorithm and gray wolf optimization for weight adjustment of neural network." Engineering with Computers 37.2 (2021): 1265-1275.