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Optimal Ultra-Local Model Control Integrated with Load Frequency Control of Renewable Energy Sources Based Microgrids

Author

Listed:
  • Abualkasim Bakeer

    (Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Gaber Magdy

    (Department of Electrical Engineering, Faculty of Energy Engineering, Aswan University, Aswan 81528, Egypt
    Department of Electrical Engineering, University of Jaén, 23700 Linares, Spain)

  • Andrii Chub

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Francisco Jurado

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

  • Mahmoud Rihan

    (Electrical Engineering Department, Faculty of Engineering, South Valley University, Qena 83521, Egypt)

Abstract

Since renewable energy sources (RESs) have an intermittent nature, conventional secondary frequency control, i.e., load frequency control (LFC), cannot mitigate the effects of variations in system frequency. Thus, this paper proposes incorporating ultralocal model (ULM) control into LFC to enhance microgrid (µG) frequency stability. ULM controllers are regarded as model-free controllers that yield high rejection rates for disturbances caused by load/RES uncertainties. Typically, ULM parameters are set using trial-and-error methods, which makes it difficult to determine the optimal values that will provide the best system performance and stability. To address this issue, the African vultures optimization algorithm (AVOA) was applied to fine-tune the ULM parameters, thereby stabilizing the system frequency despite different disturbances. The proposed LFC controller was compared with the traditional secondary controller based on an integral controller to prove its superior performance. For several contingencies, the simulation results demonstrated that the proposed controller based on the optimal ULM coupled with LFC could significantly promote RESs into the µG.

Suggested Citation

  • Abualkasim Bakeer & Gaber Magdy & Andrii Chub & Francisco Jurado & Mahmoud Rihan, 2022. "Optimal Ultra-Local Model Control Integrated with Load Frequency Control of Renewable Energy Sources Based Microgrids," Energies, MDPI, vol. 15(23), pages 1-20, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9177-:d:992594
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    References listed on IDEAS

    as
    1. Gaber Magdy & Abualkasim Bakeer & Morsy Nour & Eduard Petlenkov, 2020. "A New Virtual Synchronous Generator Design Based on the SMES System for Frequency Stability of Low-Inertia Power Grids," Energies, MDPI, vol. 13(21), pages 1-17, October.
    2. Ghazi A. Ghazi & Hany M. Hasanien & Essam A. Al-Ammar & Rania A. Turky & Wonsuk Ko & Sisam Park & Hyeong-Jin Choi, 2022. "African Vulture Optimization Algorithm-Based PI Controllers for Performance Enhancement of Hybrid Renewable-Energy Systems," Sustainability, MDPI, vol. 14(13), pages 1-26, July.
    3. Mahmoud Rihan & Mahmoud Nasrallah & Barkat Hasanin & Adel El-Shahat, 2022. "A Proposed Controllable Crowbar for a Brushless Doubly-Fed Reluctance Generator, a Grid-Integrated Wind Turbine," Energies, MDPI, vol. 15(11), pages 1-29, May.
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    Cited by:

    1. Mokhtar Aly & Emad A. Mohamed & Abdullah M. Noman & Emad M. Ahmed & Fayez F. M. El-Sousy & Masayuki Watanabe, 2023. "Optimized Non-Integer Load Frequency Control Scheme for Interconnected Microgrids in Remote Areas with High Renewable Energy and Electric Vehicle Penetrations," Mathematics, MDPI, vol. 11(9), pages 1-31, April.
    2. Rekha Guchhait & Biswajit Sarkar, 2023. "Increasing Growth of Renewable Energy: A State of Art," Energies, MDPI, vol. 16(6), pages 1-29, March.
    3. Sameer Al-Dahidi & Piero Baraldi & Miriam Fresc & Enrico Zio & Lorenzo Montelatici, 2024. "Feature Selection by Binary Differential Evolution for Predicting the Energy Production of a Wind Plant," Energies, MDPI, vol. 17(10), pages 1-19, May.

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