Vehicle-to-Grid (V2G) solutions are gradually being deployed, offering significant potential to provide ancillary services that enhance grid stability while creating revenue opportunities for electric vehicle (EV) owners. Aggregators play a central role in this process by coordinating EV fleets to participate in electricity markets. However, their scheduling strategies often rely on aggregated fleet data that can obscure substantial behavioral diversity among individual users. This creates a risk of overestimating fleet flexibility, particularly in intraday electricity markets where short-term availability is critical.

This research explores the potential of V2G systems to participate in intraday electricity markets through a detailed analysis of real-world EV operational data. Moving beyond conceptual definitions and dataset presentations, the study aims to identify discrepancies between aggregated and individual EV behaviors by applying statistical methods to evaluate the role of private EV fleets as flexible energy resources within short-term market environments.

The study uses field data collected from private EV users in Germany, including connection status, State of Charge (SoC), and user-defined SoC targets. Through statistical methods comparing key indicators such as plug-in and plug-out patterns per vehicle, arrival and departure SoC levels, and availability distributions across various time scales, the research evaluates how and when EVs can offer flexibility to the grid, extracting critical metrics such as connection windows and available energy capacity among individual users.

The findings highlight the value of statistical analysis not only for describing data but also for supporting the development of strategies for market integration. The analysis reveals significant differences between fleet-level averages and individual vehicle behaviors, providing valuable insights into flexibility potential and informing the development of V2G optimization strategies.

Overall, this study assesses the potential of private users by utilizing data analysis to develop algorithms for optimized V2G operation under uncertainty. Furthermore, it aims to offer insights for system operators, aggregators, and policymakers to facilitate the integration of electric vehicles into real-time energy markets, particularly in scenarios where flexibility is critical for effective renewable energy integration.