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Hierarchical Model-Predictive Droop Control for Voltage and Frequency Restoration in AC Microgrids

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submitted on 2024-08-26, 06:07 and posted on 2024-08-26, 06:08 authored by Iresha Poonahela, Abdelbasset Krama, Sertac Bayhan, Ugur Fesli, Mohammad B. Shadmand, Haitham Abu-Rub, Miroslav M. Begovic

Abstract

The hierarchical control structure was introduced to allow the integration of power-electronics-based distributed generation into the microgrid in a smart and flexible manner. The main aim of the primary controller in such a structure is to achieve accurate active and reactive power sharing, whereas the secondary control aims to ensure voltage and frequency ( V/f ) stability. Generally, converter-level secondary controllers utilize classical nested loop control that suffer from a slow dynamic response and cumbersome parameter tuning. The existing-model-based and estimation-based secondary controllers are fast, but require complex design methodology, high communication bandwidth, and, consequently, higher data analysis and computational burden. This article presents a simple predictive-based secondary control for the ac microgrid that is fast and robust and has a low design complexity, low communication bandwidth, and no parameter tuning requirement in the secondary control layer. The proposed predictive control optimally restores voltage and frequency in the microgrid by predicting their trajectory deviations and leveraging the droop characteristic curves. Experimental tests performed with three parallel-connected grid-forming inverters in an islanded operation validate that the controller can accurately maintain V/f stability, while ensuring active and reactive power sharing.

Other Information

Published in: IEEE Open Journal of the Industrial Electronics Society
License: https://creativecommons.org/licenses/by/4.0
See article on publisher's website: https://dx.doi.org/10.1109/ojies.2023.3240070

Funding

Qatar National Research Fund (NPRP12C-33905-SP-213).

Qatar National Research Fund (NPRP12C-33905-SP-220).

History

Language

  • English

Publisher

IEEE

Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • Qatar Environment and Energy Research Institute - HBKU
  • Texas A&M University at Qatar