With the rapid development of renewable energy such as solar and wind power, the matching of supply and demand in the power system faces greater challenges. Electric vehicles (EVs) provide distributed energy storage services such as peak and valley regulation and frequency regulation to the power grid through a two-way vehicle-to-grid (V2G) system, which improves the flexibility and stability of the power grid. This paper proposes a unified framework based on artificial intelligence (AI) that integrates load forecasting, battery health-aware reinforcement learning scheduling, dynamic pricing, and multi-agent collaborative control, aiming to achieve efficient V2G operation in large-scale smart grids. By reviewing relevant literature, this paper analyzes the potential of the framework in improving grid stability, reducing operating costs, promoting the use of renewable energy, and extending battery life, and explores key challenges such as battery degradation, network security, system interoperability, and regulatory complexity. The study points out that the current model is mainly based on theory and simulation, lacking the support of large-scale empirical data. In the future, it is necessary to combine actual operation data and pilot projects to improve battery aging modeling and user behavior differentiation analysis to promote the practical application and optimization of the framework.
Q. Shen (Tue,) studied this question.
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