Review of Uncertainty Modeling for Electric Vehicles in Vehicle-to-Grid Interaction

Under the dual-carbon goals, electric vehicles (EVs) and vehicle-to-grid (V2G) technology are crucial for the low-carbon transformation of power systems. However, their practical application is constrained by multiple sources of uncertainty, including user behavior randomness, limited understanding of battery performance degradation mechanisms, and grid fluctuations. This paper systematically investigates uncertainty modeling for V2G systems. First, based on uncertainty theory, the V2G uncertainties are classified into three categories: stochastic uncertainty, epistemic uncertainty, and fuzziness, and their impact mechanisms on system operation are analyzed. Second, a three-dimensional framework of “source-characteristic-method” is established to systematically compare the performance of existing methods, including chance-constrained programming, robust optimization, and Bayesian networks. Optimization strategies are then proposed for energy-oriented and ancillary-service scenarios. Finally, future research directions are outlined from three aspects: data fusion, coordinated vehicle-grid optimization, and market mechanism design, providing theoretical references for large-scale V2G applications.