Injection Sensitivity-Based Damping Improvement Model Using V2G and Cooperative Game Theory

With growing concerns about global climate change, the traditional utility grid is transitioning towards cleaner and greener energy generation through renewable energy resources. However, integrating these renewable resources into the utility grid presents significant challenges, adding operational complexities such as low inertia, intermittent generation, and locational inflexibility. Among these, locational inflexibility affects the damping of grid oscillations, particularly low-frequency electromechanical ones. Poorly damped oscillations can lead to reduced Area Transfer Capability (ATC), malfunctioning of protection devices due to erratic power swings, and in the worst case, large-scale outages and blackouts within the power network. This paper proposes a solution to improve the damping of oscillations by introducing an active power injection sensitivity-based damping enhancement using Plug-in Electric Vehicles (PEVs). A cooperative game-theoretic approach is employed to balance the interests of both the Grid and the PEVs while addressing the critical issue of maintaining the oscillation damping factor within a desirable range for secure grid operation. The effectiveness of the proposed method was validated using the standard IEEE 16-Machine 68-Bus test system, as well as a modified version of this test bed. The modification involved integrating wind energy to assess the performance of the proposed method under the influence of renewable energy sources. This adjustment enabled the method to effectively capture scenarios ranging from conventional power grids with minimal renewable integration to modern power systems with high levels of renewable energy integration. The results demonstrate that the method can significantly improve the damping of inter-area oscillations in both scenarios.

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3557944
Primary Topic
Iterative Learning Control Systems
Type
article
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article

Injection Sensitivity-Based Damping Improvement Model Using V2G and Cooperative Game Theory

Panagiotis N. Papadopoulos, Pratyasa Bhui, Faheem Ul Haq
IEEE Access
Iterative Learning Control Systems
article

Injection Sensitivity-Based Damping Improvement Model Using V2G and Cooperative Game Theory

Panagiotis N. Papadopoulos, Pratyasa Bhui, Faheem Ul Haq
article en

Abstract

With growing concerns about global climate change, the traditional utility grid is transitioning towards cleaner and greener energy generation through renewable energy resources. However, integrating these renewable resources into the utility grid presents significant challenges, adding operational complexities such as low inertia, intermittent generation, and locational inflexibility. Among these, locational inflexibility affects the damping of grid oscillations, particularly low-frequency electromechanical ones. Poorly damped oscillations can lead to reduced Area Transfer Capability (ATC), malfunctioning of protection devices due to erratic power swings, and in the worst case, large-scale outages and blackouts within the power network. This paper proposes a solution to improve the damping of oscillations by introducing an active power injection sensitivity-based damping enhancement using Plug-in Electric Vehicles (PEVs). A cooperative game-theoretic approach is employed to balance the interests of both the Grid and the PEVs while addressing the critical issue of maintaining the oscillation damping factor within a desirable range for secure grid operation. The effectiveness of the proposed method was validated using the standard IEEE 16-Machine 68-Bus test system, as well as a modified version of this test bed. The modification involved integrating wind energy to assess the performance of the proposed method under the influence of renewable energy sources. This adjustment enabled the method to effectively capture scenarios ranging from conventional power grids with minimal renewable integration to modern power systems with high levels of renewable energy integration. The results demonstrate that the method can significantly improve the damping of inter-area oscillations in both scenarios.

IEEE AccessVol. 13
University of Manchester (GB), Indian Institute of Technology Dharwad (IN)
Openalex Percentile: Top 93%
Iterative Learning Control Systems
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