Spatially Heterogeneous Resilient V2G-Enabled Grid Frequency Control via an Adversarially Trained Structural Switching Framework

With the increasing penetration of renewable energy, power systems require fast and reliable frequency regulation resources. Vehicle-to-grid (V2G) aggregation can provide fast response capability. However, it relies heavily on communication networks and is vulnerable to communication degradation and false data injection attacks (FDIAs). To address this challenge, this paper proposes a detection-free resilient control method for V2G-based frequency regulation. Rather than relying on explicit attack detection or compensation, the proposed method achieves decision-level adaptation from closed-loop system feedback through dynamic selection and switching of aggregator subsets. In this way, unreliable or compromised aggregators are implicitly avoided, improving system robustness under uncertain communication and cyber conditions. To further enhance robustness, a diffusion-based adversarial reinforcement learning framework is developed. A conditional diffusion model is used to generate diverse capacity scenarios with spatial heterogeneity. Adversarial training formulates the interaction between the attacker and the defender as a zero-sum game. This enables the learning of robust selection–switching policies under worst-case disturbances. Simulation results on the IEEE 39-bus system show that the proposed method improves frequency regulation performance under communication degradation and FDIA. The RMS frequency deviation is reduced from 0.13426 Hz to 0.09174 Hz compared with the no-defense case.