Journal of Applied Science and Engineering

Published by Tamkang University Press

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Analysis of the microgrid’s frequency resilience using a lightweight selfish sheep optimisation algorithm

 2026

 Volume 34

Anjiang Liu, Youzhuo Zheng, Di Weng, Hengrong Zhang, and Xinhao Li

Electric Power Research Institute, Guizhou Power Grid Co., Ltd, Guiyang, 550002, China

Received: March 19, 2026
Accepted: April 23, 2026
Publication Date: August 09, 2026

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MG model for analysis of frequency stability

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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The lightweighting problem in microgrid (MG) simulation models is first officially described in this paper as an optimisation assignment with the goal of minimising the disturbances to the microgrid frequency robustness. This paper formally presents the lightweighting problem in microgrid (MG) simulations as an optimization task aimed at minimizing frequency disturbances. We propose a Selfish Herd Optimization (SHO)-based technique and its FA-SHO enhancement, using group and predator parameters to model prey–predator interactions. The algorithms optimize gain values and controllers to improve system robustness. Experimental results show that FA-SHO outperforms SHO, with faster convergence, lower error metrics, and better frequency response, while the SMC ensures strong dynamic stability and resilience.

Keywords: microgrid, lightweight algorithm, selfish flock optimization, voltage stability control, SHO algorithm, FA-SHO algorithm

  1. [1] K.Han,K.Yang,andL.Yin,(2022)“Lightweight Actor Critic Generative Adversarial Networks for Real-Time Smart Generation Control of Microgrids” Applied Energy 317: 119163. DOI: 10.1016/j.apenergy.2022.119163.
  2. [2] S. Lu and X. Li, (2021) “Quantum-Resistant Lightweight Authentication and Key Agreement Protocol for Fog-Based Microgrids” IEEE Access 9: 27588–27600. DOI: 10.1109/ACCESS.2021.3058180.
  3. [3] S. Leonori, A. Martino, F. M. F. Mascioli, and A. Rizzi, (2020) “Microgrid Energy Management Systems Design by Computational Intelligence Techniques” Applied Energy 277: 115524. DOI: 10.1016/j.apenergy.2020.115524.
  4. [4] C. Xia, W. Li, X. Chang, T. Yang, and A. Y. Zomaya, (2023) “A Rank-Based Multiple-Choice Secretary Algorithm for Minimising Microgrid Operating Cost under Uncertainties” Frontiers in Energy 17(2): 198–210. DOI: 10.1007/s11708-023-0874-8.
  5. [5] R. Kumar and N. Sinha, (2021) “Voltage Stability of Solar Dish-Stirling Based Autonomous DC Microgrid Using Grey Wolf Optimised FOPID-Controller” International Journal of Sustainable Energy 40(5): 412–429. DOI: 10.1080/14786451.2020.1806843.
  6. [6] M. Bhuyan, A. K. Barik, and D. C. Das, (2020) “GOA Optimised Frequency Control of Solar-Thermal/Sea-Wave/Biodiesel Generator Based Interconnected Hybrid Microgrids with DC Link” International Journal of Sustainable Energy 39(7): 615–633. DOI: 10.1080/14786451.2020.1741589.
  7. [7] M. Shehab, L. Abualigah, H. Al Hamad, H. Alabool, M. Alshinwan, and A. M. Khasawneh, (2020) “Moth–Flame Optimization Algorithm: Variants and Applications” Neural Computing and Applications 32(14): 9859–9884. DOI: 10.1007/s00521-019-04570-6.
  8. [8] N. V. Rajeesh Kumar, N. Jaya Lakshmi, B. Mallala, and V. Jadhav, (2023) “Secure Trust Aware Multi-Objective Routing Protocol Based on Battle Competitive Swarm Optimization in IoT” Artificial Intelligence Review 56(Suppl 2): 1685–1709. DOI: 10.1007/s10462-023-10560-x.
  9. [9] J. Heidary, M. Gheisarnejad, H. Rastegar, and M. H. Khooban, (2022) “Survey on Microgrids Frequency Regulation: Modeling and Control Systems” Electric Power Systems Research 213: 108719. DOI: 10.1016/j.epsr.2022.108719.
  10. [10] C. Zhang, G. Shan, B. H. Roh, F. Zhu, and J. Jiang, (2026) “FEI-Hi: Federated Edge Intelligence for Healthcare Informatics” IEEE Journal of Biomedical and Health Informatics 30(3): 1853–1866. DOI: 10.1109/JBHI.2025.3588551.
  11. [11] C. Zhang, B. H. Roh, and G. Shan, (2024) “Federated Anomaly Detection” Proceedings of the 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks – Supplemental Volume (DSN-S): 148–149.
  12. [12] N. K. Jena, S. Sahoo, B. K. Sahu, and K. B. Mohanty, (2021) “Frequency Stability Analysis with Fuzzy Adaptive Selfish Herd Optimization Based Optimal Sliding Mode Controller for Microgrids” International Journal of Emerging Electric Power Systems 22(5): 547–568. DOI: 10.1515/ijeeps-2021-0105.
  13. [13] A. Saha, P. Dash, N. R. Babu, T. Chiranjeevi, M. Dhananjaya, and Ł. Knypiński, (2022) “Dynamic Stability Evaluation of an Integrated Biodiesel-Geothermal Power Plant-Based Power System with Spotted Hyena Optimized Cascade Controller” Sustainability 14(22): 14842. DOI: 10.3390/su142214842.
  14. [14] A. Yimit, K. Iigura, and Y. Hagihara, (2020) “Refined Selfish Herd Optimizer for Global Optimization Problems” Expert Systems with Applications 139: 112838. DOI: 10.1016/j.eswa.2019.112838.
  15. [15] D. W. Sankey, R. F. Storms, R. J. Musters, T. W. Russell, C. K. Hemelrijk, and S. J. Portugal, (2021) “Absence of ‘Selfish Herd’ Dynamics in Bird Flocks under Threat” Current Biology 31(14): 3192–3198. DOI: 10.1016/j.cub.2021.05.009.
  16. [16] R. Zhao, Y. Wang, G. Xiao, C. Liu, P. Hu, and H. Li, (2022) “A Method of Path Planning for Unmanned Aerial Vehicle Based on the Hybrid of Selfish Herd Optimizer and Particle Swarm Optimizer” Applied Intelligence 52(14): 16775–16798. DOI: 10.1007/s10489-021-02353-y.
  17. [17] G. G. Wang, C. L. Wei, Y. Wang, and W. Pedrycz, (2021) “Improving Distributed Anti-Flocking Algorithm for Dynamic Coverage of Mobile Wireless Networks with Obstacle Avoidance” Knowledge-Based Systems 225: 107133. DOI: 10.1016/j.knosys.2021.107133.
  18. [18] A. Villalón, M. Rivera, Y. Salgueiro, J. Muñoz, T. Dragicević, and F. Blaabjerg, (2020) “Predictive Control for Microgrid Applications: A Review Study” Energies 13(10): 2454. DOI: 10.3390/en13102454.
  19. [19] R. Trivedi and S. Khadem, (2022) “Implementation of Artificial Intelligence Techniques in Microgrid Control Environment: Current Progress and Future Scopes” Energy and AI 8: 100147. DOI: 10.1016/j.egyai.2022.100147.
  20. [20] S. P. Bihari, P. K. Sadhu, K. Sarita, B. Khan, L. D. Arya, R. K. Saket, and D. P. Kothari, (2021) “A Comprehensive Review of Microgrid Control Mechanism and Impact Assessment for Hybrid Renewable Energy Integration” IEEE Access 9: 88942–88958. DOI: 10.1109/ACCESS.2021.3090266.
  21. [21] J. Ali, R. H. Jhaveri, M. Alswailim, and B. H. Roh, (2023) “ESCALB: An Effective Slave Controller Allocation-Based Load Balancing Scheme for Multi-Domain SDN-Enabled IoT Networks” Journal of King Saud University–Computer and Information Sciences 35(6): 101566. DOI: 10.1016/j.jksuci.2023.101566.
  22. [22] A. Dinesh and J. Rangaraj, (2025) “An Energy-Efficient Routing Protocol for Wireless Body Area Networks Using Hybrid Artificial Bee Colony Optimization and Chicken Swarm Optimization Algorithm” Journal of Engineering and Applied Science 72(1): 45. DOI: 10.1186/s44147-024-00533-4.