SavedAdd your journal

Journals / Gazi Mühendislik Bilimleri Dergisi / 2026 / Volume: 11 Issue: 1

Optimal Location and Sizing of Distributed Generation Using Artificial Bee Colony and JAYA Algorithms

Salih Ermiş(KIRŞEHİR AHİ EVRAN ÜNİVERSİTESİ)0000-0002-1053-9160
Oğuz Taşdemir(KIRŞEHİR AHİ EVRAN ÜNİVERSİTESİ)0000-0003-1782-0024
Pages
—
Publication Date
2025-04-30
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results indicate that the JAYA algorithm achieves superior convergence and lower power losses compared to the ABC algorithm, while the ABC algorithm provides better voltage profile improvements.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The rapid increase in energy demand, environmental concerns, and the need for efficient energy resource utilization have highlighted the importance of distributed generation (DG) systems.
📊
Key Findings & Data:This study utilizes Artificial Bee Colony (ABC) and JAYA algorithms to solve the problem of optimal DG allocation and sizing in the IEEE 33 bus distribution system.
💡
Conclusion & Impact:Results indicate that the JAYA algorithm achieves superior convergence and lower power losses compared to the ABC algorithm, while the ABC algorithm provides better voltage profile improvements.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Distributed Generation (DG) · Optimal Placement · Artificial Bee Colony (ABC)

Abstract

The rapid increase in energy demand, environmental concerns, and the need for efficient energy resource utilization have highlighted the importance of distributed generation (DG) systems. The optimal placement and sizing of DG units are critical for reducing power losses, improving voltage profiles, and enhancing system reliability. This study utilizes Artificial Bee Colony (ABC) and JAYA algorithms to solve the problem of optimal DG allocation and sizing in the IEEE 33 bus distribution system. The ABC algorithm is inspired by the natural foraging behavior of honeybees and is recognized for its ability to efficiently explore complex solution spaces. The JAYA algorithm is a new optimization approach with its simple, parameter-free structure and fast convergence properties. The performance of both algorithms was evaluated based on convergence rate, power loss reduction, voltage improvement, and computational time. Results indicate that the JAYA algorithm achieves superior convergence and lower power losses compared to the ABC algorithm, while the ABC algorithm provides better voltage profile improvements. The findings of this study aim to contribute to the efficient planning and implementation of DG systems in modern power grids.

🔖

Cite This Article

Ermiş, S., Taşdemir, O. (2025). Optimal Location and Sizing of Distributed Generation Using Artificial Bee Colony and JAYA Algorithms. Gazi Mühendislik Bilimleri Dergisi, Volume: 11 Issue: 1. https://ajindex.com/articles/1397376

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (40)

  1. T. Ackermann, G. Andersson, and L. Söder, "Distributed generation: a definition," Electric power systems research, vol. 57, no. 3, pp. 195-204, 2001. doi:10.1016/S0378-7796(01)00101-8
  2. T. Zhou and B. François, "Energy management and power control of a hybrid active wind generator for distributed power generation and grid integration," IEEE transactions on industrial electronics, vol. 58, no. 1, pp. 95-104, 2010. doi: 10.1109/TIE.2010.2046580
  3. Y. A.-R. I. Mohamed and E. F. El-Saadany, "A control scheme for PWM voltage-source distributed-generation inverters for fast load-voltage regulation and effective mitigation of unbalanced voltage disturbances," IEEE Transactions on Industrial Electronics, vol. 55, no. 5, pp. 2072-2084, 2008. doi: 10.1109/TIE.2008.917057
  4. F. Blaabjerg, R. Teodorescu, M. Liserre, and A. V. Timbus, "Overview of control and grid synchronization for distributed power generation systems," IEEE Transactions on industrial electronics, vol. 53, no. 5, pp. 1398-1409, 2006. doi: 10.1109/TIE.2006.881997
  5. N. Acharya, P. Mahat, and N. Mithulananthan, "An analytical approach for DG allocation in primary distribution network," International Journal of Electrical Power & Energy Systems, vol. 28, no. 10, pp. 669-678, 2006. doi: 10.1016/j.ijepes.2006.02.013
  6. D. Prakash and C. Lakshminarayana, "Multiple DG placements in radial distribution system for multi objectives using Whale Optimization Algorithm," Alexandria engineering journal, vol. 57, no. 4, pp. 2797-2806, 2018. doi: 10.1016/j.aej.2017.11.003
  7. De León-Aldaco, S. E., Calleja, H., & Alquicira, J. A. “Metaheuristic optimization methods applied to power converters: A review,” IEEE Transactions on Power Electronics, vol. 30, no. 12, pp. 6791-6803, 2015. doi: 10.1109/TPEL.2015.2397311
  8. O. S. Nduka and B. C. Pal, "Quantitative evaluation of actual loss reduction benefits of a renewable heavy DG distribution network," IEEE Transactions on Sustainable Energy, vol. 9, no. 3, pp. 1384-1396, 2017. doi: 10.1109/TSTE.2017.2776610
  9. M. P. HA, P. D. Huy, and V. K. Ramachandaramurthy, "A review of the optimal allocation of distributed generation: Objectives, constraints, methods, and algorithms," Renewable and Sustainable Energy Reviews, vol. 75, pp. 293-312, 2017. doi: 10.1016/j.rser.2016.10.071
  10. V. H. Méndez et al., "Impact of distributed generation on distribution investment deferral," International Journal of Electrical Power & Energy Systems, vol. 28, no. 4, pp. 244-252, 2006. doi: 10.1016/j.ijepes.2005.11.016
  11. Ayodele, T. R., Ogunjuyigbe, A. S. O., & Akinola, O. O. “Optimal location, sizing, and appropriate technology selection of distributed generators for minimizing power loss using genetic algorithm,” Journal of Renewable Energy, vol. 2015 no. 1, 832917, pp. 1-9, 2015. doi.10.1155/2015/832917
  12. F. S. Abu-Mouti and M. El-Hawary, "Optimal distributed generation allocation and sizing in distribution systems via artificial bee colony algorithm," IEEE transactions on power delivery, vol. 26, no. 4, pp. 2090-2101, 2011. doi: 10.1109/TPWRD.2011.2158246
  13. M. AlRashidi and M. AlHajri, "Optimal planning of multiple distributed generation sources in distribution networks: A new approach," Energy conversion and management, vol. 52, no. 11, pp. 3301-3308, 2011. doi: 10.1016/j.enconman.2011.06.001
  14. A. Arief, M. B. Nappu, S. M. Rachman, and M. Darusman, "Optimal photovoltaic placement at the southern sulawesi power system for stability improvement," in 2017 4th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), 18-19 October 2017, Semarang, Indonesia: IEEE, pp. 87-92. doi: 10.1109/ICITACEE.2017.8257681
  15. M. K. Abd, S. J. Cheng, and H. S. Sun, "Optimal placement and optimal mix of RDGs for improving voltage stability and maximum loadability using PSAT," in 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA), 05-07 June 2016, Hefei, China: IEEE, pp. 1574-1578. doi: 10.1109/ICIEA.2016.7603836
  16. I. Khenissi, R. Sellami, M. A. Fakhfakh, and R. Neji, "Power loss minimization using optimal placement and sizing of photovoltaic distributed generation under daily load consumption profile with PSO and GA algorithms," Journal of Control, Automation and Electrical Systems, vol. 32, no. 5, pp. 1317-1331, 2021. doi: 10.1007/s40313-021-00744-7
  17. M. Haque, "Use of local information to determine the distance to voltage collapse," International Journal of Emerging Electric Power Systems, vol. 9, no. 2, 2008. doi: 10.2202/1553-779X.1911
  18. A. Öner and A. Abur, "Voltage stability based placement of distributed generation against extreme events," Electric Power Systems Research, vol. 189, p. 106713, 2020. doi: 10.1016/j.epsr.2020.106713
  19. R. Prakash and B. Sujatha, "Optimal placement and sizing of DG for power loss minimization and VSI improvement using bat algorithm," in 2016 National Power Systems Conference (NPSC), 19-21 December 2016, Bhubaneswar, India: IEEE, pp. 1-6. doi: 10.1109/NPSC.2016.7858964
  20. B. M'hamdi, M. Teguar, and B. Tahar, "Optimal DG unit placement and sizing in radial distribution network for power loss minimization and voltage stability enhancement," Periodica polytechnica Electrical engineering and computer science, vol. 64, no. 2, pp. 157-169, 2020. doi: 10.3311/PPee.15057
  21. A. S. Hassan, Y. Sun, and Z. Wang, "Multi-objective for optimal placement and sizing DG units in reducing loss of power and enhancing voltage profile using BPSO-SLFA," Energy Reports, vol. 6, pp. 1581-1589, 2020. doi: 10.1016/j.egyr.2020.06.013
  22. A. Pal, A. K. Chakraborty, and A. R. Bhowmik, "Optimal placement and sizing of DG considering power and energy loss minimization in distribution system," International Journal on Electrical Engineering and Informatics, vol. 12, no. 3, pp. 624-653, 2020. doi: 10.15676/ijeei.2020.12.3.12
  23. S. Essallah and A. Khedher, "Optimization of distribution system operation by network reconfiguration and DG integration using MPSO algorithm," Renewable Energy Focus, vol. 34, pp. 37-46, 2020. doi: 10.1016/j.ref.2020.04.002
  24. A. P. Sirat, H. Mehdipourpicha, N. Zendehdel, and H. Mozafari, "Sizing and allocation of distributed energy resources for loss reduction using heuristic algorithms," in 2020 IEEE power and energy conference at Illinois (PECI), 27-28 February 2020, Champaign, IL, USA, IEEE, pp. 1-6. doi: 10.1109/PECI48348.2020.9064636
  25. R. Vempalle and P. Dhal, "Loss minimization by reconfiguration along with distributed generator placement at radial distribution system with hybrid optimization techniques," Technology and Economics of Smart Grids and Sustainable Energy, vol. 5, no. 1, p. 18, 2020. doi: 10.1007/s40866-020-00088-2
  26. R. A. Lone, S. Javed Iqbal, and A. S. Anees, "Optimal location and sizing of distributed generation for distribution systems: an improved analytical technique," International Journal of Green Energy, vol. 21, no. 3, pp. 682-700, 2024. doi: 10.1080/15435075.2023.2207638
  27. R. K. Sharma and B. K. Naick, "A Novel Artificial Rabbits Optimization Algorithm for Optimal Location and Sizing of Multiple Distributed Generation in Radial Distribution Systems," Arabian Journal for Science and Engineering, vol. 49, no. 5, pp. 6981-7012, 2024. doi: 10.1007/s13369-023-08559-1
  28. R. K. Chillab, A. S. Jaber, M. B. Smida, and A. Sakly, "Optimal DG location and sizing to minimize losses and improve voltage profile using Garra Rufa optimization," Sustainability, vol. 15, no. 2, p. 1156, 2023. doi: 10.3390/su15021156
  29. E. Karunarathne, J. Pasupuleti, J. Ekanayake, and D. Almeida, "Network loss reduction and voltage improvement by optimal placement and sizing of distributed generators with active and reactive power injection using fine-tuned PSO," Indonesian Journal of Electrical Engineering and Computer Science, vol. 21, no. 2, pp. 647-656, 2021. doi: 10.11591/ijeecs.v21.i2.pp647-656
  30. D. Karaboga and B. Basturk, "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm," Journal of global optimization, vol. 39, pp. 459-471, 2007. doi: 10.1007/s10898-007-9149-x
  31. K. Taheri, M. Hasanipanah, S. B. Golzar, and M. Z. A. Majid, "A hybrid artificial bee colony algorithm-artificial neural network for forecasting the blast-produced ground vibration," Engineering with Computers, vol. 33, pp. 689-700, 2017. doi: 10.1007/s00366-016-0497-3
  32. Rao, R. V., & Saroj, A. “A self-adaptive multi-population based Jaya algorithm for engineering optimization,” Swarm and Evolutionary computation, vol. 37, pp. 1-26, 2017. doi: 10.1016/j.swevo.2017.04.008
  33. R. Buddala and S. S. Mahapatra, "Improved teaching–learning-based and JAYA optimization algorithms for solving flexible flow shop scheduling problems," Journal of Industrial Engineering International, vol. 14, pp. 555-570, 2018. doi: 10.1007/s40092-017-0244-4
  34. Rao, R. “Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems,” International Journal of Industrial Engineering Computations, vol. 7, no. 1, pp. 19-34, 2016. doi: 10.5267/j.ijiec.2015.8.004
  35. E. Irmak, M. Yeşilbudak, and O. Taşdemir, "Enhanced PV Power Prediction Considering PM10 Parameter by Hybrid JAYA-ANN Model," Electric Power Components and Systems, vol. 52, no. 11, pp. 1998-2007, 2024. doi: 10.1080/15325008.2024.2322668
  36. J. Mendoza, M. Lopez, C. Coello Coello, and E. Lopez, "Microgenetic multiobjective reconfiguration algorithm considering power losses and reliability indices for medium voltage distribution network," IET Generation, Transmission & Distribution, vol. 3, no. 9, pp. 825-840, 2009. doi: 10.1049/iet-gtd.2009.0009
  37. M. P. Lalitha, N. S. Reddy, and V. V. Reddy, "Optimal DG placement for maximum loss reduction in radial distribution system using ABC algorithm," International journal of reviews in computing, vol. 3, no. 1, pp. 44-52, 2010.
  38. K. D. Mistry and R. Roy, "Enhancement of loading capacity of distribution system through distributed generator placement considering techno-economic benefits with load growth," International Journal of Electrical Power & Energy Systems, vol. 54, pp. 505-515, 2014. doi: 10.1016/j.ijepes.2013.07.032
  39. M. Kefayat, A. L. Ara, and S. N. Niaki, "A hybrid of ant colony optimization and artificial bee colony algorithm for probabilistic optimal placement and sizing of distributed energy resources," Energy Conversion and Management, vol. 92, pp. 149-161, 2015. doi: 10.1016/j.enconman.2014.12.037
  40. W. Haider, S. J. U. Hassan, A. Mehdi, A. Hussain, G. O. M. Adjayeng, and C.-H. Kim, "Voltage profile enhancement and loss minimization using optimal placement and sizing of distributed generation in reconfigured network," Machines, vol. 9, no. 1, p. 20, 2021. doi: 10.3390/machines9010020