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Virtual Inertia Control in Autonomous Microgrids via a Cascaded Controller for Battery Energy Storage Optimized by Firefly Algorithm and a Comparison Study with GA, PSO, ABC, and GWO

Author

Listed:
  • Farhad Amiri

    (Electrical Engineering Department, Faculty of Engineering, Bu-Ali Sina University, Hamedan 6516738695, Iran)

  • Mohsen Eskandari

    (The School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia)

  • Mohammad Hassan Moradi

    (Electrical Engineering Department, Faculty of Engineering, Bu-Ali Sina University, Hamedan 6516738695, Iran)

Abstract

Modern (micro) grids host inverter-based generation units for utilizing renewable and sustainable energy resources. Due to the lack of physical inertia and, thus, the low inertia level of inverter-interfaced energy resources, the frequency dynamic is adversely affected, which critically impacts the stability of autonomous microgrids. The idea of virtual inertia control (VIC), assisted by battery energy storage systems (BESSs), has been presented to improve the frequency dynamic in islanded microgrids. This study presents the PD-FOPID cascaded controller for the BESS, a unique method for enhancing the performance of VIC in islanded microgrids. Using the firefly algorithm (FA), the settings of this controller are optimally tuned. This approach is robust to disruptions due to uncertainties in islanded microgrids. In several scenarios, the performance of the suggested approach is compared with those of other control techniques, such as VIC based on an MPC controller, VIC based on a robust H-infinite controller, adaptive VIC, and VIC based on an optimized PI controller. The simulation results in MATLAB show that the suggested methodology in the area of VIC is better than previous methods.

Suggested Citation

  • Farhad Amiri & Mohsen Eskandari & Mohammad Hassan Moradi, 2023. "Virtual Inertia Control in Autonomous Microgrids via a Cascaded Controller for Battery Energy Storage Optimized by Firefly Algorithm and a Comparison Study with GA, PSO, ABC, and GWO," Energies, MDPI, vol. 16(18), pages 1-22, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6611-:d:1239494
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    References listed on IDEAS

    as
    1. Ruiming Liu & Shengtie Wang & Guangchen Liu & Sufang Wen & Jianwei Zhang & Yuechao Ma, 2022. "An Improved Virtual Inertia Control Strategy for Low Voltage AC Microgrids with Hybrid Energy Storage Systems," Energies, MDPI, vol. 15(2), pages 1-21, January.
    2. Shafaat Ullah & Laiq Khan & Irfan Sami & Ghulam Hafeez & Fahad R. Albogamy, 2021. "A Distributed Hierarchical Control Framework for Economic Dispatch and Frequency Regulation of Autonomous AC Microgrids," Energies, MDPI, vol. 14(24), pages 1-23, December.
    3. Amr Saleh & Hany M. Hasanien & Rania A. Turky & Balgynbek Turdybek & Mohammed Alharbi & Francisco Jurado & Walid A. Omran, 2023. "Optimal Model Predictive Control for Virtual Inertia Control of Autonomous Microgrids," Sustainability, MDPI, vol. 15(6), pages 1-25, March.
    4. Yousef Asadi & Mohsen Eskandari & Milad Mansouri & Andrey V. Savkin & Erum Pathan, 2022. "Frequency and Voltage Control Techniques through Inverter-Interfaced Distributed Energy Resources in Microgrids: A Review," Energies, MDPI, vol. 15(22), pages 1-29, November.
    5. Mahmoud Elshenawy & Ashraf Fahmy & Adel Elsamahy & Shaimaa A. Kandil & Helmy M. El Zoghby, 2022. "Optimal Power Management of Interconnected Microgrids Using Virtual Inertia Control Technique," Energies, MDPI, vol. 15(19), pages 1-30, September.
    6. Thongchart Kerdphol & Fathin S. Rahman & Yasunori Mitani & Komsan Hongesombut & Sinan Küfeoğlu, 2017. "Virtual Inertia Control-Based Model Predictive Control for Microgrid Frequency Stabilization Considering High Renewable Energy Integration," Sustainability, MDPI, vol. 9(5), pages 1-21, May.
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