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<p>Vehicle-to-grid (V2G) technology offers a transformative approach to integrating electric vehicles (EVs) into smart grids, enhancing grid stability, supporting renewable energy integration, and reducing reliance on fossil fuels. By enabling EVs to store and discharge energy, V2G systems help balance supply and demand, especially during peak hours, while providing services like frequency regulation and energy storage. This study systematically reviews V2G advancements from 2019 to 2024 using the PRISMA methodology and Biblioshiny tool to identify research trends, gaps, and opportunities. Current optimization models, such as linear programming, agent-based models, and machine learning, face key limitations, including scalability issues, high computational demands, battery degradation, and challenges in real-time adaptability. To address these gaps, this review proposes novel solutions, including hybrid optimization techniques, advanced predictive models, blockchain-enabled energy trading, and dynamic pricing strategies. The findings emphasize the need for scalable, efficient models and robust policies to unlock V2G’s full potential for a sustainable and resilient energy future.</p>

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Advancements and challenges in vehicle-to-grid technology: a systematic literature review of energy trading and optimization models and future directions

  • 期刊论文

  • 发表日期：

    2026年1月17日

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  • Ubaid ur Rehman

摘要

Vehicle-to-grid (V2G) technology offers a transformative approach to integrating electric vehicles (EVs) into smart grids, enhancing grid stability, supporting renewable energy integration, and reducing reliance on fossil fuels. By enabling EVs to store and discharge energy, V2G systems help balance supply and demand, especially during peak hours, while providing services like frequency regulation and energy storage. This study systematically reviews V2G advancements from 2019 to 2024 using the PRISMA methodology and Biblioshiny tool to identify research trends, gaps, and opportunities. Current optimization models, such as linear programming, agent-based models, and machine learning, face key limitations, including scalability issues, high computational demands, battery degradation, and challenges in real-time adaptability. To address these gaps, this review proposes novel solutions, including hybrid optimization techniques, advanced predictive models, blockchain-enabled energy trading, and dynamic pricing strategies. The findings emphasize the need for scalable, efficient models and robust policies to unlock V2G’s full potential for a sustainable and resilient energy future.