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Optimized Distributed Cooperative Control for Islanded Microgrid Based on Dragonfly Algorithm

Author

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  • Falah Noori Saeed Al-dulaimi

    (School of Engineering and Natural Sciences, Altinbas University, Istanbul 34218, Turkey
    Electrical Engineering Technical College, Middle Technical University, Baghdad 10001, Iraq)

  • Sefer Kurnaz

    (School of Engineering and Natural Sciences, Altinbas University, Istanbul 34218, Turkey)

Abstract

This study introduces novel stochastic distributed cooperative control (SDCC) in the context of island microgrids (MGs). A proportional resonant (PR) controller and virtual impedance droop control in stationary reference frames are employed in cooperation with distributed averaging secondary control optimized by the dragonfly algorithm (DA). The suggested approach demonstrates the capability to achieve mean-square synchronization for the voltage and frequency restoration of distributed generators (DGs) to ensure efficient active power sharing. Therefore, a sparse communication network has been used to avoid data congestion and reduce the need for extensive communication and information exchange. The proposed system offers an instinctive compromise between voltage regulation and reactive power sharing. A conventional centralized secondary control with PR droop control is simulated for performance evaluation and comparison purposes. In this study, empirical evidence is demonstrated to support the MG’s ability to confront communication failure and its ability to work reliably during plug-and-play operations.

Suggested Citation

  • Falah Noori Saeed Al-dulaimi & Sefer Kurnaz, 2023. "Optimized Distributed Cooperative Control for Islanded Microgrid Based on Dragonfly Algorithm," Energies, MDPI, vol. 16(22), pages 1-24, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:22:p:7675-:d:1284062
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    References listed on IDEAS

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    1. Yinghao Shan & Liqian Ma & Xiangkai Yu, 2023. "Hierarchical Control and Economic Optimization of Microgrids Considering the Randomness of Power Generation and Load Demand," Energies, MDPI, vol. 16(14), pages 1-23, July.
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