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<p>Vehicle-to-Grid (V2G) technology facilitates bidirectional energy flow between electric vehicles (EVs) and the power grid, offering a promising solution for grid stability and renewable energy integration. Despite progress, the optimization models supporting V2G remain a key barrier to its widespread adoption. This systematic review examines the evolution of V2G optimization models from 2019 to 2025, analyzing their strengths, limitations, and future potential. Conventional optimization models, including mixed-integer programming, dynamic programming, and heuristic approaches, dominate current research. While effective for specific scenarios, these methods often struggle with scalability, real-time processing, and integration with diverse grid infrastructures. Additionally, they lack user-centric features and robust data security mechanisms. To address these challenges, hybrid optimization frameworks are suggested that combine mathematical rigor with machine learning (ML) and artificial intelligence (AI) for adaptive, real-time decision-making. Privacy-preserving techniques such as federated learning are suggested to enhance data security. Standardization efforts and interdisciplinary approaches integrating technical, economic, and social dimensions are critical to overcoming interoperability and user adoption barriers. By outlining a roadmap for advancing V2G optimization models, this study provides actionable insights for researchers and practitioners aiming to accelerate the deployment of sustainable energy systems.</p>

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A Systematic Literature Review of the State of the Art Vehicle-to-Grid Based Energy Conservation and Optimization Models: Current Research Trends, Challenges, and Future Directions

  • 期刊论文

  • 发表日期：

    2025年11月27日

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  • Ubaid ur Rehman

摘要

Vehicle-to-Grid (V2G) technology facilitates bidirectional energy flow between electric vehicles (EVs) and the power grid, offering a promising solution for grid stability and renewable energy integration. Despite progress, the optimization models supporting V2G remain a key barrier to its widespread adoption. This systematic review examines the evolution of V2G optimization models from 2019 to 2025, analyzing their strengths, limitations, and future potential. Conventional optimization models, including mixed-integer programming, dynamic programming, and heuristic approaches, dominate current research. While effective for specific scenarios, these methods often struggle with scalability, real-time processing, and integration with diverse grid infrastructures. Additionally, they lack user-centric features and robust data security mechanisms. To address these challenges, hybrid optimization frameworks are suggested that combine mathematical rigor with machine learning (ML) and artificial intelligence (AI) for adaptive, real-time decision-making. Privacy-preserving techniques such as federated learning are suggested to enhance data security. Standardization efforts and interdisciplinary approaches integrating technical, economic, and social dimensions are critical to overcoming interoperability and user adoption barriers. By outlining a roadmap for advancing V2G optimization models, this study provides actionable insights for researchers and practitioners aiming to accelerate the deployment of sustainable energy systems.