A Novel Approach to Optimized Frequency Load Shedding in Microgrids with Wind Power Integration using ANFIS Networks

Document Type : Research paper

Authors

1 Termiz University of Economics and Service, Farovon Street 4-b, Termez, Surxondaryo, Uzbekistan.

2 Scientific and Practical Center of Immunology, Allergology and Human Genomics, Samarkand State Medical University, Samarkand, Uzbekistan.

3 Kimyo International University in Tashkent, Tashkent, Uzbekistan.

4 Fergana State Technical University, Fergana, Uzbekistan.

5 Department of Use of Hydromelioration Systems, Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, Tashkent, Uzbekistan, and Western Caspian University, Scientific Researcher, Baku, Azerbaijan.

6 Tashkent State Transport University, 100167 Tashkent, Uzbekistan.

7 Department of “Architecture”, Urgench State University, Urgench, Uzbekistan.

8 Mamun University, 220912 Khiva, Khorezm, Uzbekistan.

Abstract

The increasing significance of renewable energy sources has led to a growing penetration of distributed generation units in distribution systems. This not only offers numerous economic benefits but also enables energy supply in islanded microgrid operation. In islanded mode, an effective load shedding scheme is crucial to maintain frequency balance and voltage stability within acceptable limits. This paper presents novel load shedding criteria, considering the impact of wind power integration and its inherent uncertainty in microgrids. Given the short electrical distances in microgrids, reactive power balance is of particular importance. Accordingly, the proposed load shedding method employs a combination of frequency and voltage criteria. The required amount of load shedding is determined through transient stability examination, and the load shedding process is implemented using an Adaptive Neuro-Fuzzy Inference System (ANFIS) in the microgrid. Simulation results demonstrate the effectiveness of the proposed method in load shedding and maintaining the stability of the microgrid. Specifically, by jointly exploiting frequency, voltage, and wind-speed information within an ANFIS framework trained from detailed transient stability studies, the proposed scheme is capable of preventing severe frequency drops and voltage instability under uncertain wind power generation. Furthermore, by quantifying the impact of including voltage as an ANFIS input, the study shows that the proposed microgrid-oriented design can reduce unnecessary load shedding and improve the economic performance of the system.

Keywords

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References

  1. L. Tightiz and J. Yoo, “A review on a data-driven microgrid management system integrating an active distribution network: challenges, issues, and new trends,” Energies, vol. 15, no. 22, p. 8739, 2022.
  2. R. S. Pinto, C. Unsihuay-Vila, and F. H. Tabarro, “Coordinated operation and expansion planning for multiple microgrids and active distribution networks under uncertainties,” Appl. Energy, vol. 297, p. 117108, 2021.
  3. R. Parsibenehkohal, M. Jamil, and A. A. Khan, “A multistage framework for coordinated scheduling of networked microgrids in active distribution systems with hydrogen refueling and charging stations,” Int. J. Hydrogen Energy, vol. 71, pp. 1442–1455, 2024.
  4. M. Aghahadi, A. Bosisio, M. Merlo, A. Berizzi, A. Pegoiani, and S. Forciniti, “Digitalization processes in distribution grids: a comprehensive review of strategies and challenges,” Appl. Sci., vol. 14, no. 11, p. 4528, 2024.
  5. V. Nikam and V. Kalkhambkar, “A review on control strategies for microgrids with distributed energy resources, energy storage systems, and electric vehicles,” Int. Trans. Electr. Energy Syst., vol. 31, no. 1, p. e12607, 2021.
  6. S. M. Mortezaie, “New DC–DC converters design techniques: a comprehensive approach to recent advances,” Procedia Environ. Sci., Eng. Manag., vol. 12, no. 1, pp. 55–62, 2025.
  7. F. Davoodabadi, I. Ramezani, A. A. Mahmoodi-k, and P. Ahmadizadeh, “Identification of tire forces using dual unscented Kalman filter algorithm,” Nonlinear Dyn., vol. 78, no. 3, pp. 1907–1919, 2014.
  8. M. Rezaee and V. A. Maleki, “On the complex mode shapes and natural frequencies of clamped–clamped fluid-conveying pipe,” Appl. Ocean Res., vol. 150, p. 104113, 2024.
  9. H. Liang and S. Pirouzi, “Energy management system based on economic flexi-reliable operation for smart distribution networks with hydrogen storage and renewable sources,” Energy, vol. 293, p. 130745, 2024.
  10. Z. Luo, H. Liu, N. Wang, T. Zhao, and J. Tian, “Optimal adaptive decentralized under-frequency load shedding for islanded smart distribution networks considering wind power uncertainty,” Appl. Energy, vol. 365, p. 123162, 2024.
  11. M. Ghotbi-Maleki, R. M. Chabanloo, and H. Javadi, “Load shedding strategy using online voltage estimation for mitigating fault-induced delayed voltage recovery in smart networks,” Electr. Power Syst. Res., vol. 214, p. 108899, 2023.
  12. S. Zhang et al., “Optimization of emergency frequency control strategy for power systems considering both source and load uncertainties,” Front. Energy Res., vol. 12, p. 1465301, 2024.
  13. M. Yadipour, F. Hashemzadeh, and M. Baradarannia, “A novel strategy to enlarge the domain of attraction of affine nonlinear systems,” Itogi Nauki Tekh. Sovrem. Mat. Prilozh., vol. 178, pp. 91–101, 2020.
  14. N. Tietze, K. Wulff, and J. Reger, “Local stabilisation of nonlinear systems with time- and state-dependent perturbations using sliding-mode model-following control,” in Proc. IEEE Conf. Decis. Control, pp. 6620–6627, 2024.
  15. S. Sourani Yancheshmeh, A. Ebrahimpour, and T. Deemyad, “Optimizing chassis design for autonomous vehicles in challenging environments based on finite element analysis and genetic algorithm,” in Proc. ASME Int. Mech. Eng. Congr. Expo., vol. 88681, p. V010T12A020, 2024.
  16. C. Ye et al., “Emergency control strategy for high-proportion renewable power systems considering frequency aggregation response of multi-type power generations,” IEEE Access, vol. 12, pp. 45–55, 2024.
  17. S. Younus, A. A. Al-Taei, O. Al-Yozbaky, and I. Bashir, “An overview of various strategies for dealing with the under-frequency load shedding problem in power systems,” Al-Rafidain Eng. J., vol. 29, no. 1, pp. 46–67, 2024.
  18. F.-C. Baiceanu et al., “A load shedding approach for islanded operation in industrial electrical systems,” in Proc. Int. Conf. Electron., Comput. Artif. Intell., pp. 1–6, 2022.
  19. Z. Boumous, S. Boumous, M. Sedraoui, and M. Bechouat, “Adaptive fuzzy-GPC control for robust energy management in microgrids under fault conditions and renewable energy uncertainty,” Iran. J. Sci. Technol., Trans. Electr. Eng., pp. 1–17, 2025.
  20. N. Khosravi, D. Çelik, H. Bevrani, and S. Echalih, “Microgrid stability: A comprehensive review of challenges, trends, and emerging solutions,” Int. J. Electr. Power Energy Syst., vol. 170, p. 110829, 2025.
  21. C. H. Inga Espinoza and M. T. Palma, “A coordinated neurofuzzy control system for hybrid energy storage integration: Virtual inertia and frequency support in low-inertia power systems,” Energies, vol. 18, no. 17, p. 4728, 2025.
  22. C. Ghenai et al., “Short-term building electrical load forecasting using adaptive neuro-fuzzy inference system,” J. Build. Eng., vol. 52, p. 104323, 2022.
  23. S. Tabassum, A. R. V. Babu, and D. K. Dheer, “Real-time power quality enhancement in smart grids through IoT and adaptive neuro-fuzzy systems,” Sci. Technol. Energy Transition, vol. 79, p. 89, 2024.
  24. N. Elboughdiri et al., “Intelligent demand-side energy management via optimized anfis–gene expression programming in hybrid renewable–grid systems,” Sci. Rep., vol. 15, no. 1, p. 43065, 2025.
  25. H. Alsharif, M. Jalili, and K. N. Hasan, “Fast frequency response services in low inertia power systems—a review,” Energy Rep., vol. 9, pp. 228–237, 2023.
  26. Z. Zhao et al., “Decentralized grid-forming control strategy and dynamic characteristics analysis of high-penetration wind power microgrids,” IEEE Trans. Sustain. Energy, vol. 13, no. 4, pp. 2211–2225, 2022.
  27. A. Chebabhi, I. Tegani, and A. D. Benhamadouche, “Modeling and forecasting uncertainties in renewable energy systems: A stochastic approach for microgrid planning,” Comput. Electr. Eng., vol. 127, p. 110588, 2025.
  28. M. N. H. Shazon, S. R. Deeba, and S. R. Modak, “A frequency and voltage stability-based load shedding technique for low inertia power systems,” IEEE Access, vol. 9, pp. 78947–78961, 2021.
  29. H. H. A.-W. Al-Sadooni and R. H. Al-Rubayi, “Combinational load shedding using load frequency control and voltage stability indicator,” Int. J. Electr. Comput. Eng., vol. 12, no. 5, pp. 4661–4671, 2022.
  30. F. d. P. García-López et al., “Experimental assessment of a centralized controller for high-resolution active distribution networks,” Energies, vol. 11, no. 12, p. 3364, 2018.
  31. A. Kumar et al., “Optimal clean energy resource allocation in balanced and unbalanced operation of sustainable electrical energy distribution networks,” Energies, vol. 17, no. 18, p. 4572, 2024.
  32. Y. Wang, Z. Wang, and H. Sheng, “Optimizing wind turbine integration in microgrids through enhanced multi-control of energy storage and micro-resources for improved stability,” J. Clean. Prod., vol. 444, p. 140965, 2024.
Journal of Operation and Automation in Power Engineering
Volume 13, Special Issue
Intelligent and Sustainable Power Systems (ISPS): AI-Driven Innovations for Renewable Integration and Smart Grid Resilience
2025
Pages 89-100

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History

  • Receive Date: 27 November 2025
  • Revise Date: 23 December 2025
  • Accept Date: 25 December 2025
  • First Publish Date: 25 December 2025

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