Dynamic Control of Microgrids with Battery Energy Storage Systems: Grid-Forming and Grid-Following Modes with Adaptive Disconnection Criteria

The increasing integration of renewable energy sources (RESs), such as photovoltaic (PV) systems, into modern power grids has accelerated the adoption of microgrid technology, enhancing grid flexibility and resilience. However, the intermittent nature of RESs presents significant challenges to microgrid stability, especially during grid disturbances. To address these challenges, battery energy storage systems (BESS) have become essential for microgrid operations, enabling dynamic control of voltage and frequency. This paper proposes a novel control strategy for microgrids that leverages a BESS capable of seamlessly transitioning between grid-following (GFL) and grid-forming (GFM) modes. These transitions are governed by real-time monitoring of key parameters: voltage, frequency, phase difference, and active power. It incorporates an adaptive disconnection mechanism that dynamically adjusts threshold values for parameter deviations, ensuring a coordinated and robust response. Notably, the microgrid transitions to islanded mode and disconnects from the main grid with the BESS shifts to grid-forming mode only when the rate of change of all monitored parameters simultaneously exceeds their respective thresholds. This coordinated approach ensures reliable decision-making while minimizing unnecessary disconnections. Simulation results validate the proposed strategy, demonstrating its effectiveness in enhancing microgrid resilience, reducing false positives, and ensuring smooth transitions between grid-connected and islanded modes. The case study demonstrate the proposed model's superior fault detection and recovery capabilities over traditional fixed threshold strategies. This research provides a robust solution to improve microgrid stability and reliability, addressing the complexities associated with the increasing penetration of RESs.

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Publication Details

Published
2025-03-07
DOI
https://doi.org/10.1109/icgea64602.2025.11009642
Citations
1
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
1.21

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article

Dynamic Control of Microgrids with Battery Energy Storage Systems: Grid-Forming and Grid-Following Modes with Adaptive Disconnection Criteria

Xu Zhang, Hongjian Sun, Jinjie Liu
1 citations
Microgrid Control and Optimization
1.21
article

Dynamic Control of Microgrids with Battery Energy Storage Systems: Grid-Forming and Grid-Following Modes with Adaptive Disconnection Criteria

Xu Zhang, Hongjian Sun, Jinjie Liu
article en
1 citations

Abstract

The increasing integration of renewable energy sources (RESs), such as photovoltaic (PV) systems, into modern power grids has accelerated the adoption of microgrid technology, enhancing grid flexibility and resilience. However, the intermittent nature of RESs presents significant challenges to microgrid stability, especially during grid disturbances. To address these challenges, battery energy storage systems (BESS) have become essential for microgrid operations, enabling dynamic control of voltage and frequency. This paper proposes a novel control strategy for microgrids that leverages a BESS capable of seamlessly transitioning between grid-following (GFL) and grid-forming (GFM) modes. These transitions are governed by real-time monitoring of key parameters: voltage, frequency, phase difference, and active power. It incorporates an adaptive disconnection mechanism that dynamically adjusts threshold values for parameter deviations, ensuring a coordinated and robust response. Notably, the microgrid transitions to islanded mode and disconnects from the main grid with the BESS shifts to grid-forming mode only when the rate of change of all monitored parameters simultaneously exceeds their respective thresholds. This coordinated approach ensures reliable decision-making while minimizing unnecessary disconnections. Simulation results validate the proposed strategy, demonstrating its effectiveness in enhancing microgrid resilience, reducing false positives, and ensuring smooth transitions between grid-connected and islanded modes. The case study demonstrate the proposed model's superior fault detection and recovery capabilities over traditional fixed threshold strategies. This research provides a robust solution to improve microgrid stability and reliability, addressing the complexities associated with the increasing penetration of RESs.

Durham University (GB)
Engineering and Physical Sciences Research Council
Affordable and clean energy
Openalex Percentile: Top 21%
Microgrid Control and Optimization
1.21
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