Enhanced Voltage Stability for DC Microgrids Integrating Hybrid Electric Vehicles with Virtual Inertia and Damping Control

DC microgrids (MGs) have experienced swift growth, driven by the expanding integration of energy storage devices(ESD), renewable energy sources (RESs), and localized loads. However, it suffers from insufficient inertia owing to less rotating mass sources, which can lead to poor voltage stability. This paper introduces a control method that emulates both inertia and damping to mitigate fluctuations in DC voltage, enhance system stability, and address the low inertia concern. The proposed virtual inertia and damping (VID) control system is adopted through using hybrid electric vehicle (HEV). The suggested HEV comprises three power sources: a battery, a fuel cell (FC), and a supercapacitor (SC). In this setup, both the battery and the FC are employed to supply virtual damping owing to their high-energy density, and the SC is utilized to support virtual inertial characteristics due to its high-power density. Through this approach, enhancements in the DC voltage stability of an islanded DC MG can be achieved. Simulations demonstrate that the proposed control system significantly outperforms existing works utilizing virtual inertia (VI) based on an EV’s battery only, as well as over systems lacking VI. The proposed control system effectively reduces voltage fluctuations and improves system stability, specifically in terms of the rate of change of voltage (ROCOV) and accelerated settling time.

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Publication Details

Journal
Transportation research procedia
Published
2025-01-01
DOI
https://doi.org/10.1016/j.trpro.2025.03.110
Citations
1
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
1.21

Funders

Controls
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article

Enhanced Voltage Stability for DC Microgrids Integrating Hybrid Electric Vehicles with Virtual Inertia and Damping Control

Mohamed Zaery, Vijay Vital, S. M. Suhail Hussain, Syed Muhammad Amrr et al.
1 citations
Transportation research procedia
Microgrid Control and Optimization
1.21
article

Enhanced Voltage Stability for DC Microgrids Integrating Hybrid Electric Vehicles with Virtual Inertia and Damping Control

Mohamed Zaery, Vijay Vital, S. M. Suhail Hussain, Syed Muhammad Amrr, Hossam Ali, M. A. Abido
article en
1 citations

Abstract

DC microgrids (MGs) have experienced swift growth, driven by the expanding integration of energy storage devices(ESD), renewable energy sources (RESs), and localized loads. However, it suffers from insufficient inertia owing to less rotating mass sources, which can lead to poor voltage stability. This paper introduces a control method that emulates both inertia and damping to mitigate fluctuations in DC voltage, enhance system stability, and address the low inertia concern. The proposed virtual inertia and damping (VID) control system is adopted through using hybrid electric vehicle (HEV). The suggested HEV comprises three power sources: a battery, a fuel cell (FC), and a supercapacitor (SC). In this setup, both the battery and the FC are employed to supply virtual damping owing to their high-energy density, and the SC is utilized to support virtual inertial characteristics due to its high-power density. Through this approach, enhancements in the DC voltage stability of an islanded DC MG can be achieved. Simulations demonstrate that the proposed control system significantly outperforms existing works utilizing virtual inertia (VI) based on an EV’s battery only, as well as over systems lacking VI. The proposed control system effectively reduces voltage fluctuations and improves system stability, specifically in terms of the rate of change of voltage (ROCOV) and accelerated settling time.

Transportation research procediaVol. 84
King Fahd University of Petroleum and Minerals (SA), King Fahd Military Medical Complex (SA), Sustainable Energy Systems (United Kingdom) (GB), Arizona State University (US), Aswan University (EG)
King Fahd University of Petroleum and Minerals
Affordable and clean energy
Openalex Percentile: Top 24%
Microgrid Control and Optimization
1.21
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