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Decentralized Goal-Function-Based Microgrid Primary Control with Voltage Harmonics Compensation

Author

Listed:
  • Marko Vekić

    (Department of Power, Electronic and Telecommunication Engineering, University of Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia)

  • Milan Rapaić

    (Department of Computing and Control Engineering, University of Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia)

  • Ivana Todorović

    (Department of Power, Electronic and Telecommunication Engineering, University of Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia)

  • Stevan Grabić

    (Department of Power, Electronic and Telecommunication Engineering, University of Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia)

Abstract

This paper proposes goal-function-based decentralized control of microgrids. In addition to being an instrument for maintaining the grid voltage and frequency stability, each grid-tie inverter generates a current component with the aim of compensating for voltage distortion in the node where it is connected. The designed goal-function does not need to rely on the assumption that a microgrid is dominantly inductive or resistive to derive its control law, as is mostly the case with the droop-based approach. The priorities of the proposed scheme can be adjusted according to user preferences. In addition, the control algorithm is independent of network topology, can be applied in both islanded and non-islanded microgrids, and secure system scalability. The proposed method is verified by detailed simulations.

Suggested Citation

  • Marko Vekić & Milan Rapaić & Ivana Todorović & Stevan Grabić, 2024. "Decentralized Goal-Function-Based Microgrid Primary Control with Voltage Harmonics Compensation," Energies, MDPI, vol. 17(19), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:19:p:4961-:d:1492048
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    References listed on IDEAS

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    1. Muhammad Awais & Laiq Khan & Said Ghani Khan & Qasim Awais & Mohsin Jamil, 2023. "Adaptive Neural Network Q-Learning-Based Full Recurrent Adaptive NeuroFuzzy Nonlinear Control Paradigms for Bidirectional-Interlinking Converter in a Grid-Connected Hybrid AC-DC Microgrid," Energies, MDPI, vol. 16(4), pages 1-40, February.
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