Ant Lion Optimization and Grey Wolf Optimizer to Determine the Optimal location of single and Multiple Distribution Generation and Capacitor Shunt on Power Distribution Networks
DOI:
https://doi.org/10.25729/esr.2025.03.0005Keywords:
Capacitor shunt, Distributed generation, Grey Wolf Optimizer, Ant Lion Optimization, IEEE 33-bus systemAbstract
Power supply challenges continue to be a significant issue, necessitating ongoing efforts to enhance power generation, network infrastructure, and system configurations. Notably, the integration of distributed generation (DG) units and capacitor shunts (CSs) has emerged as a key advancement. With the escalating complexity and scale of power systems, optimizing these components to maximize benefits and mitigate drawbacks has become essential. Consequently, optimization has become a crucial element in the development of algorithms. This study proposes the utilization of the Grey Wolf Optimizer (GWO) and Ant Lion Optimization (ALO) methods for determining the optimal sizing of capacitor shunts and distributed generation units within distribution systems. The Reconfiguration Method (RM) is employed to identify the optimal location for single-generation units and capacitor shunts, while the Loss Sensitivity Factor (LSF) is utilized to determine the optimal placement for multiple DG units and CSs. The primary objective is to minimize real power losses across the entire system by selecting the optimal sizes with GWO and ALO, while also optimizing their placement using LSF and RM. The performance of the proposed algorithms, GWO and ALO, was evaluated using the IEEE 33-bus power system. Four distinct scenarios were employed to demonstrate the superiority and effectiveness of these optimization methods
References
M. G. Hemeida, A. Ahmed, A. A. Mohamed, S. Alkhalaf, A. M. B. El-dine, “Optimal allocation of distributed generators DG based Manta Ray Foraging Optimization algorithm (MRFO),” Ain Shams Eng. J., vol. 12, no. 1, pp. 609–619, 2021. DOI: 10.1016/j.asej.2020.07.009.
K. S. Sambaiah, “A Review on Optimal Allocation and Sizing Techniques for DG in Distribution Systems,” International Journal of Renewable Energy Research, vol. 8, no. 3, pp. 1236–1256, 2018.
M. M. Sayed, M. Y. Mahdy, S. H. E. A. Aleem, H. K. M. Youssef, T. A. Boghdady, “Simultaneous Distribution Network Reconfiguration and Optimal Allocation of Renewable-Based Distributed Generators and Shunt Capacitors under Uncertain Conditions,” Energies, vol. 15, no. 6, Art. no. 2299, 2022. DOI: 10.3390/en15062299.
A. Selim, S. Kamel, A. A. Mohamed, E. E. Elattar, “Optimal Allocation of Multiple Types of Distributed Generations in Radial Distribution Systems Using a Hybrid Technique,” Sustainability, vol. 13, no. 12, Art. no. 6644, 2021. DOI: 10.3390/su13126644 2021.
A. R. Ali, A. Abdul, R. Altahir, S. Alwash, M. Al-Kaabi, “Investigation Study of Injecting Numerous DGs in IEEE 69 – bus Radial Networks Using Enhanced PSO and Ant Lion Optimization Algorithms,” in 2023 3rd International Conference on Electrical Machines and Drives (ICEMD), Tehran, Iran, Islamic Republic of, 2023, pp. 1–7. DOI: 10.1109/ICEMD60816.2023.10429267.
B. Hussein, A. Igeb, M. Al-Kaabi, P. P. Oshchepkov, M. Muhssin, “Grey Wolf Optimizer Algorithm to Solve Optimal Placement and Sizing of the Distributed Generation Sources in Distribution Radial Networks,” in 2023 International Symposium on Fundamentals of Electrical Engineering (ISFEE), Bucharest, Romania, 2023, pp. 1–6. DOI: 10.1109/ISFEE60884.2023.10637122.
J. Al Hasheme, M. Al-Kaabi, L. Toma, “Optimal allocation of distribution generator for radial distribution network using grey wolf optimizer,” U.P.B. Sci. Bull., ser. C, vol. 86, no. 3, pp. 371–390, 2024.
A. Naeem, A. Saheb, D. Nazarpour, M. Al-kaabi, “Economic Dispatch Using Harris Hawks Optimization with Renewable Energy Source and Capacitor Shunt,” in 2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Iasi, Romania, 2024, pp. 1–8. DOI: 10.1109/ECAI61503.2024.10607530.
Almoataz Y. Abdelaziz, Mirna Fouad Ali, Eman Beshr, “Hybrid Siting and Sizing of Distributed Generators and Shunt Capacitors with System Reconfiguration using Wild Horse Optimizer,” J. Adv. Res. Applied Sciences and Eng. Technol., vol. 38, no. 2, pp. 196–213, 2024.
B. C. Sujatha, A. Usha, R. S. Geetha, “Optimal Planning of PV Sources and D-STATCOM Devices with Network Reconfiguration Employing Modified Ant Lion Optimizer,” Energies, vol. 17, no. 10, Art. no. 2238, 2024. DOI: 10.3390/en17102238.
B. P. Nanda, D. P. Mishra, S. R. Salkuti, “A modified ant lion optimization algorithm for efficient distributed generation allocation in power distribution networks,” Electric Power Systems Research, vol. 246, Art. no. 111705, 2025. DOI: 10.1016/j.epsr.2025.111705.
A. R. A. Wafa, “Ant‐lion optimizer‐based multi‐objective optimal simultaneous allocation of distributed generations and synchronous condensers in distribution networks,” Int. Trans. Electr. Energy Syst., vol. 29, pp. 1–14, 2019. DOI: 10.1002/etep.2755.
R. A. Lone, S. J. Iqbal, A. S. Anees, “Optimal location and sizing of distributed generation for distribution systems: An improved analytical technique,” Int. J. Green Energy, vol. 21, no. 3, pp. 682–700, 2024. DOI: 10.1080/15435075.2023.2207638.
M. Alqahtani, P. Marimuthu, V. Moorthy, B. Pangedaiah, C. R. Reddy, M. K. Kumar, M. Khalid, “Investigation and Minimization of Power Loss in Radial Distribution Network Using Gray Wolf Optimization,” Energies, vol. 16, no. 12, Art. no. 4571, 2023. DOI: 10.3390/en16124571.
M. K. Das, “Optimal Placement of Distributed Generation in Distribution Networks Using Grey Wolf Optimization,” Master's Thesis, Dept. of Electrical Engineering, Tribhuvan University, Pulchowk Campus, Lalitpur, Nepal, Jun. 2023.
M. Varan, A. Erduman, F. Menevşeoğlu, “A Grey Wolf Optimization Algorithm-Based Optimal Reactive Power Dispatch with Wind-Integrated Power Systems,” Energies, vol. 16, no. 13, Art. no. 5021, 2023. DOI: 10.3390/en16135021.
N. C. Sahoo, K. Prasad, “A fuzzy genetic approach for network reconfiguration to enhance voltage stability in radial distribution systems,” vol. 47, pp. 3288–3306, 2006. DOI: 10.1016/j.enconman.2006.01.004.
M. Kefayat, A. L. Ara, S. A. 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.
S. Mirjalili, S. Mohammad, A. Lewis, “Advances in Engineering Software Grey Wolf Optimizer,” Adv. Eng. Softw., vol. 69, pp. 46–61, 2014. DOI: 10.1016/j.advengsoft.2013.12.007.
A. S. Assiri, A. G. Hussien, M. Amin, “Ant Lion Optimization: Variants, Hybrids, and Applications,” IEEE Access, vol. 8, pp. 77746–77764, 2020. DOI: 10.1109/ACCESS.2020.2990338.
D. R. Prabha, T. Jayabarathi, R. Umamageswari, S. Saranya, “Optimal location and sizing of distributed generation unit using intelligent water drop algorithm,” Sustainable Energy Technologies and Assessments, vol. 11, pp. 106–113, 2015.
M. M. Othman, W. El-Khattam, Y. G. Hegazy, A. Y. Abdelaziz, “Optimal Placement and Sizing of Distributed Generators in Unbalanced Distribution Systems Using Supervised Big Bang-Big Crunch Method,” IEEE Transactions on Power Systems, vol. 30, no. 2, pp. 911–919, 2015.
S. Kaur, G. Kumbhar, J. Sharma, “A MINLP technique for optimal placement of multiple DG units in distribution systems,” Electrical Power and Energy Systems, vol. 63, pp. 609–617, 2014.
N. Acharya, P. Mahat, N. Mithulananthan, “An analytical approach for DG allocation in primary distribution network,” Electrical Power and Energy Systems, vol. 28, no. 10, pp. 669–678, 2006.
S. Kansal, V. Kumar, B. Tyagi, “Optimal placement of different type of DG sources in distribution networks,” Electrical Power and Energy Systems, vol. 53, pp. 752–760, 2013.
M. P. Lalitha, V. C. V. Reddy, V. Usha, “Optimal DG placement for minimum real power loss in radial distribution systems using PSO,” Journal of Theoretical and Applied Information Technology, vol. 13, no. 2, pp. 107–114, 2010.
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