Stochastic Modeling of EV On-Board Chargers for Fast Frequency Response Under Communication Delays

Abstract

The rapid electrification of the transportation sector offers a promising avenue for ancillary services through Vehicle-to-Grid (V2G) applications. This is particularly critical for low-inertia systems, such as the U.K. grid, where the transition toward converter-based renewable generation necessitates very fast frequency response. Therefore, the viability of V2G for high-value frequency markets is constrained by strict latency requirements (e.g. one second)). Existing literature has predominantly focused on high-level economic aggregation models or communication network delays, largely neglecting the stochastic physical response dynamics of the EV On-Board Charger (OBC). This paper addresses this gap by developing a discrete-time Markov chain model that specifically characterizes the internal dynamics and response latency of OBC hardware. We integrate this model into a discrete-event simulation framework to evaluate end-to-end system latency, coupling stochastic OBC constraints with Over-the-Air (OTA) communication delays. We analyze the performance of fleets comprised of three common OBC ratings: 10 kW, 22 kW, and 43 kW. Contrary to the intuition that higher power ratings yield superior agility, our results demonstrate that high-capacity chargers may exhibit lower success rates in fast frequency markets due to insufficient ramp-rate-to-capacity ratios. Furthermore, we demonstrate that the frequency of mode switching events (switching between charging and discharging) is a dominant factor in performance degradation due to hardware hysteresis. These findings underscore that the efficacy of V2G applications requires precise EV-level control logic rather than relying solely on fleet-level optimization. Finally, the proposed models are evaluated against the PJM interconnection’s composite score methodology. The results demonstrate high accuracy, suggesting the proposed framework can serve as a preliminary, EV-specific V2G assessment tool for market operators.