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Manta Ray Foraging Optimization for the Virtual Inertia Control of Islanded Microgrids Including Renewable Energy Sources

Author

Listed:
  • Amr Saleh

    (Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt)

  • Walid A. Omran

    (Faculty of Engineering and Materials Science, German University in Cairo, Cairo 12613, Egypt)

  • Hany M. Hasanien

    (Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt)

  • Marcos Tostado-Véliz

    (Department of Electrical Engineering, Superior Polytechnic School of Linares, University of Jaén, 23700 Linares, Spain)

  • Abdulaziz Alkuhayli

    (Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Francisco Jurado

    (Department of Electrical Engineering, Superior Polytechnic School of Linares, University of Jaén, 23700 Linares, Spain)

Abstract

Nowadays, the penetration level of renewable energy sources (RESs) has increased dramatically in electrical networks, especially in microgrids. Due to the replacement of conventional synchronous generators by RESs, the inertia of the microgrid is significantly reduced. This has a negative impact on the dynamics and performance of the microgrid in the face of uncertainties, resulting in a weakening of microgrid stability, especially in an islanded operation. Hence, this paper focuses on enhancing the dynamic security of an islanded microgrid using a frequency control concept based on virtual inertia control. The control in the virtual inertia control loop was based on a proportional-integral (PI) controller optimally designed by the Manta Ray Foraging Optimization (MRFO) algorithm. The performance of the MRFO-based PI controller was investigated considering various operating conditions and compared with that of other evolutionary optimization algorithm-based PI controllers. To achieve realistic simulations conditions, actual wind data and solar power data were used, and random load fluctuations were implemented. The results show that the MRFO-based PI controller has a superior performance in frequency disturbance alleviation and reference frequency tracking compared with the other considered optimization techniques.

Suggested Citation

  • Amr Saleh & Walid A. Omran & Hany M. Hasanien & Marcos Tostado-Véliz & Abdulaziz Alkuhayli & Francisco Jurado, 2022. "Manta Ray Foraging Optimization for the Virtual Inertia Control of Islanded Microgrids Including Renewable Energy Sources," Sustainability, MDPI, vol. 14(7), pages 1-19, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:4189-:d:784895
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    References listed on IDEAS

    as
    1. Thongchart Kerdphol & Masayuki Watanabe & Yasunori Mitani & Veena Phunpeng, 2019. "Applying Virtual Inertia Control Topology to SMES System for Frequency Stability Improvement of Low-Inertia Microgrids Driven by High Renewables," Energies, MDPI, vol. 12(20), pages 1-16, October.
    2. Hyung-Joon Kim & Mun-Kyeom Kim, 2019. "Multi-Objective Based Optimal Energy Management of Grid-Connected Microgrid Considering Advanced Demand Response," Energies, MDPI, vol. 12(21), pages 1-28, October.
    3. Iris, Çağatay & Lam, Jasmine Siu Lee, 2021. "Optimal energy management and operations planning in seaports with smart grid while harnessing renewable energy under uncertainty," Omega, Elsevier, vol. 103(C).
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    5. Ali Dokht Shakibjoo & Mohammad Moradzadeh & Seyed Zeinolabedin Moussavi & Lieven Vandevelde, 2020. "A Novel Technique for Load Frequency Control of Multi-Area Power Systems," Energies, MDPI, vol. 13(9), pages 1-19, April.
    6. Vjatseslav Skiparev & Ram Machlev & Nilanjan Roy Chowdhury & Yoash Levron & Eduard Petlenkov & Juri Belikov, 2021. "Virtual Inertia Control Methods in Islanded Microgrids," Energies, MDPI, vol. 14(6), pages 1-20, March.
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    Cited by:

    1. 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.

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