A novel dung beetle optimization algorithm of distribution network reconfiguration for power loss reduction and reliability improvement
DOI:
https://doi.org/10.4108/ew.11841Keywords:
distribution network reconfiguration, artificial intelligence algorithm, IDBO algorithm, distributed generationAbstract
In the evolution of energy systems, investigations into distribution networks have concentrated on enhancing reliability and optimizing performance. Distribution networks with distributed energy resources are expected to significantly enhance power accommodation capacity, but load flow distribution and network optimization remain key challenges. This study aims to develop an improved dung beetle optimization (IDBO) algorithm to minimize active power losses and node voltage deviations in distribution networks, while validating its superiority over existing algorithms across different network configurations. The IDBO algorithm is enhanced through three key strategies: (i) A variable spiral search strategy is employed to improve search efficiency and global exploration. (ii) A levy flight strategy is introduced to prevent algorithm premature convergence. (iii) A t-distribution adjustment strategy based on iteration count is adopted to strengthen local search capability. To verify whether the IDBO algorithm can achieve the optimal load flow distribution that meets the reliability requirements of distribution network operation, relevant validations have been conducted. Experimental results demonstrate that IDBO achieves faster convergence and superior performance on classical test functions. In practical applications to IEEE 33-bus and 69-bus distribution systems, it significantly reduces power losses and improves voltage profiles compared to other algorithms. The proposed IDBO algorithm provides an effective solution for distribution network reconfiguration, demonstrating enhanced optimization capability, improved convergence characteristics, and reliable performance across various operating conditions.
Downloads
References
[1] Xiangli Chen, Yanhong Cheng, Yao Zhang, Taiyu Gu, Ye Tian. Standard demand analysis of new distribution system. Procedia Computer Science. 2024; 247: 1409-1415.
[2] BNational Energy Administration. Policy interpretation of "Guiding Opinions on High-quality Development of Distribution Network under New Situation". https://www.nea.gov.cn/. (Accessed March 1, 2024).
[3] Santos, S. F., Gough, M., Fitiwi, D. Z., Pogeira, J., Shafie-khah, M., Catalão, J. P. S.. Dynamic Distribution System Reconfiguration Considering Distributed Renewable Energy Sources and Energy Storage Systems. IEEE Systems Journal. 2022; 16 (3): 3723-3733.
[4] Badran, O., Mekhilef, S., Mokhlis, H., Dahalan, W.. Optimal reconfiguration of distribution system connected with distributed generations: A review of different methodologies. Renewable and Sustainable Energy Reviews. 2017; 73: 854-867.
[5] Wang, H.-J., Pan, J.-S., Nguyen, T.-T., Weng, S.. Distribution network reconfiguration with distributed generation based on parallel slime mould algorithm. Energy. 2022; 244: 123011.
[6] Azad-Farsani, E., Sardou, I. G., Abedini, S., Distribution Network Reconfiguration based on LMP at DG connected busses using game theory and self-adaptive FWA. Energy. 2021; 215: 119146.
[7] Rahmati, K., Taherinasab, S.. The importance of reconfiguration of the distribution network to achieve minimization of energy losses using the dragonfly algorithm. e-Prime - Advances in Electrical Engineering. Electronics and Energy. 2023; 5: 100270.
[8] Ji, X., Yin, Z., Zhang, Y., Xu, B., liu, Q.. Real-time autonomous dynamic reconfiguration based on deep learning algorithm for distribution network. Electric Power Systems Research. 2021; 195: 107132.
[9] Raut, U., Mishra, S.. An improved sine–cosine algorithm for simultaneous network reconfiguration and DG allocation in power distribution systems. Applied Soft Computing. 2020; 92: 106293.
[10] Diaaeldin, I. M., Aleem, S. H. E. A., El-Rafei, A., Abdelaziz, A. Y., Ćalasan, M.. In Optimal Network Reconfiguration and Distributed Generation Allocation using Harris Hawks Optimization. 2020 24th International Conference on Information Technology (IT), 18-22 Feb. 2020. p. 1-6.
[11] Cikan, M., Kekezoglu, B.. Comparison of metaheuristic optimization techniques including Equilibrium optimizer algorithm in power distribution network reconfiguration. Alexandria Engineering Journal. 2022; 61 (2): 991-1031.
[12] Jangdoost, A., Keypour, R., Golmohamadi, H.. Optimization of distribution network reconfiguration by a novel RCA integrated with genetic algorithm. Energy Systems. 2020; (3): 1053.
[13] Noruzi Azghandi, M., Shojaei, A. A., Toosi, S., Lotfi, H.. Optimal reconfiguration of distribution network feeders considering electrical vehicles and distributed generators. Evolutionary Intelligence. 2023; 16 (1): 49-66.
[14] Anteneh, D., Khan, B., Mahela, O. P., Alhelou, H. H., Guerrero, J. M.. Distribution network reliability enhancement and power loss reduction by optimal network reconfiguration. Computers & Electrical Engineering. 2021; 96: 107518.
[15] Shaheen, A. M.; Elsayed, A. M.; Ginidi, A. R.; El-Sehiemy, R. A.; Elattar, E., A heap-based algorithm with deeper exploitative feature for optimal allocations of distributed generations with feeder reconfiguration in power distribution networks. Knowledge-Based Systems. 2022; 241: 108269.
[16] Jafar-Nowdeh, A.; Babanezhad, M.; Arabi-Nowdeh, S.; Naderipour, A.; Kamyab, H.; Abdul-Malek, Z.; Ramachandaramurthy, V. K., Meta-heuristic matrix moth–flame algorithm for optimal reconfiguration of distribution networks and placement of solar and wind renewable sources considering reliability. Environmental Technology & Innovation. 2020; 20: 101118.
[17] Wu, Y.; Liu, J.; Wang, L.; An, Y.; Zhang, X., Distribution Network Reconfiguration Using Chaotic Particle Swarm Chicken Swarm Fusion Optimization Algorithm. Energies. 2023; 16 (20): 7185.
[18] Alqahtani, M., Marimuthu, P., Moorthy, V., Pangedaiah, B., Reddy, C. R., Kiran Kumar, M., Khalid, M.. Investigation and Minimization of Power Loss in Radial Distribution Network Using Gray Wolf Optimization. Energies. 2023; 16 (12): 4571.
[19] Xue J, Shen B.. Dung beetle optimizer: a new meta-heuristic algorithm for global optimization. The Journal of Supercomputing. 2023; 79: 7305-36.
[20] Wu Y, Wang L, Wan Z, Liu J, Fu D, An Y, Zhang X. Dynamic reconfiguration of multiobjective distribution networks considering the variation of load and DG using a novel LDEDBO algorithm. Sci Rep. 2024; 14(1): 31524.
[21] Mirjalili S, Lewis A. The whale optimization algorithm. Advances in Engineering Software. 2016; 95(5): 51-67.
[22] Mirjalili S, Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge Based Systems. 2015; 228- 249.
[23] Jiang M, Ma F, Zhang Y, Lv S, Pei Z, Wu G. Collaborative Scheduling Optimization of Container Port Berths and Cranes under Low-Carbon Environment. Sustainability. 2024; 16(7):2985.
[24] Zhong C, Li G, Meng Z. Beluga whale optimization: A novel nature-inspired metaheuristic algorithm. Knowledge-based systems. 2022; 251: 109215.
[25] Dhiman G, Kumar V. Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-based systems. 2019; 165: 169-196.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Yanmin Wu, Ruiliang Guo, Lu Wang, Xiangqian Zhu, Zhenjie Wan
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
