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Adaptive PI Control Strategy for Optimal Microgrid Autonomous Operation

Author

Listed:
  • Ahmed M. Hussien

    (Electrical Engineering Department, Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt)

  • Jonghoon Kim

    (Energy Storage and Conversion Laboratory, Department of Electrical Engineering, Chungnam National University, Daejeon 34134, Korea)

  • Abdulaziz Alkuhayli

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

  • Mohammed Alharbi

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

  • 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)

  • Rania A. Turky

    (Electrical Engineering Department, Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt)

  • Francisco Jurado

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

Abstract

The present research produces a new technique for the optimum operation of an isolated microgrid (MGD) based on an enhanced block-sparse adaptive Bayesian algorithm (EBSABA). To update the proportional-integral (PI) controller gains online, the suggested approach considers the impact of the actuating error signal as well as its magnitude. To reach a compromise result between the various purposes, the Response Surface Methodology (RSMT) is combined with the sunflower optimization (SFO) and particle swarm optimization (PSO) algorithms. To demonstrate the success of the novel approach, a benchmark MGD is evaluated in three different Incidents: (1) removing the MGD from the utility (islanding mode); (2) load variations under islanding mode; and (3) a three-phase fault under islanding mode. Extensive simulations are run to test the new technique using the PSCAD/EMTDC program. The validity of the proposed optimizer is demonstrated by comparing its results with those obtained using the least mean and square root of exponential method (LMSRE) based adaptive control, SFO, and PSO methodologies. The study demonstrates the superiority of the proposed EBSABA over the LMSRE, SFO, and PSO approaches in the system’s transient reactions.

Suggested Citation

  • Ahmed M. Hussien & Jonghoon Kim & Abdulaziz Alkuhayli & Mohammed Alharbi & Hany M. Hasanien & Marcos Tostado-Véliz & Rania A. Turky & Francisco Jurado, 2022. "Adaptive PI Control Strategy for Optimal Microgrid Autonomous Operation," Sustainability, MDPI, vol. 14(22), pages 1-22, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:14928-:d:969879
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    References listed on IDEAS

    as
    1. Tabar, Vahid Sohrabi & Abbasi, Vahid, 2019. "Energy management in microgrid with considering high penetration of renewable resources and surplus power generation problem," Energy, Elsevier, vol. 189(C).
    2. Alghamdi, Baheej & Cañizares, Claudio, 2022. "Frequency and voltage coordinated control of a grid of AC/DC microgrids," Applied Energy, Elsevier, vol. 310(C).
    3. Uddin, Moslem & Romlie, M.F. & Abdullah, M.F. & Tan, ChiaKwang & Shafiullah, GM & Bakar, A.H.A., 2020. "A novel peak shaving algorithm for islanded microgrid using battery energy storage system," Energy, Elsevier, vol. 196(C).
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    Cited by:

    1. Mohammed Yousri Silaa & Oscar Barambones & José Antonio Cortajarena & Patxi Alkorta & Aissa Bencherif, 2023. "PEMFC Current Control Using a Novel Compound Controller Enhanced by the Black Widow Algorithm: A Comprehensive Simulation Study," Sustainability, MDPI, vol. 15(18), pages 1-23, September.
    2. Nour A. Mohamed & Hany M. Hasanien & Abdulaziz Alkuhayli & Tlenshiyeva Akmaral & Francisco Jurado & Ahmed O. Badr, 2023. "Hybrid Particle Swarm and Gravitational Search Algorithm-Based Optimal Fractional Order PID Control Scheme for Performance Enhancement of Offshore Wind Farms," Sustainability, MDPI, vol. 15(15), pages 1-25, August.
    3. Dina A. Zaki & Hany M. Hasanien & Mohammed Alharbi & Zia Ullah & Mariam A. Sameh, 2023. "Hybrid Driving Training and Particle Swarm Optimization Algorithm-Based Optimal Control for Performance Improvement of Microgrids," Energies, MDPI, vol. 16(11), pages 1-18, May.
    4. Ahmed Sulaiman Alsafran, 2023. "A Feasibility Study of Implementing IEEE 1547 and IEEE 2030 Standards for Microgrid in the Kingdom of Saudi Arabia," Energies, MDPI, vol. 16(4), pages 1-15, February.

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