Quantifying grid flexibility provision of virtual vehicle-to-vehicle energy sharing using statistically similar networks

Wei Gan, Yue Zhou and Jianzhong Wu

Applied Energy, 2025, vol. 390, issue C, No S0306261925005483

Abstract: The rapid rise in electric vehicle (EV) adoption presents significant capacity challenges for power grids, but with effective charging management, EVs can also serve as flexible resources, underscoring the need for relevant innovative solutions. This paper proposes a virtual vehicle-to-vehicle (V-V2V) framework, enabling EVs to share energy with each other, either at public charging stations or home, as long as they are connected to the same distribution network. The framework eliminates the need for physical proximity and peer-to-peer matching seen in traditional V2V, enhancing grid flexibility and reducing capacity pressures by harmonizing EV charging with other demands and photovoltaic generation. To quantify the flexibility provision of the V-V2V framework, this paper implements and enhances the statistically similar networks method, where simulations are based on generated networks that share similar electrical and topological characteristics, rather than relying on a single network. Using graph theory, the method preserves statistical similarity in both electrical and topological features, along with their internal correlations, ensuring the practicality of the network simulations. To improve flexibility quantification accuracy, this paper introduces a bottom-up, high-granularity model of EV travel and plugging patterns that accounts for diverse user archetypes. Monte Carlo simulations are employed to provide a detailed analysis of travel and charging behaviors by categorizing EV users. The effectiveness of the proposed method is tested through numerical results using real-world UK distribution networks.

Keywords: Electric vehicles integration; Vehicle-to-vehicle energy sharing; Bottom-up modeling; Flexibility provision; Statistically similar networks (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.125818

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