Understanding user preferences regarding vehicle-to-grid (V2G): A latent class choice analysis

• Estimated a latent class choice model for V2G contracts, revealing four user groups. • Examined how numerous user characteristics influence class membership. • Flexibility attributes (guaranteed range and plug-in time) outweigh monetary incentives. • Guaranteed range emerges as the most important attribute for the majority (72%). • Financial compensation is the top priority only for the smallest group (10%). The vehicle-to-grid (V2G) innovation—which enables electric vehicles to return stored electricity to the grid—holds significant potential to facilitate the integration of intermittent renewable energy and support climate goals. However, user preferences and how they vary across different user groups remain poorly understood, even though V2G’s success depends on driver participation. This study addresses this gap by conducting a stated choice experiment with 1,018 participants in the Netherlands. Participants chose between hypothetical V2G contracts based on four key attributes: financial compensation, guaranteed driving range, minimum plug-in time, and battery degradation—each varied at three levels. Using a latent class choice model, the analysis identified four distinct user preference profiles (or classes). Overall, guaranteed range and plug-in time were found to outweigh financial incentives for most users. The largest class (43% of users) prioritizes guaranteed range and shows the lowest sensitivity to financial incentives. The second-largest class (29%) also prioritizes guaranteed range, while assigning the least importance to plug-in time. The third class (18%) places the greatest importance on reducing plug-in time, followed by increasing guaranteed range. The smallest class (10%) is primarily motivated by financial compensation. The study further examines how user characteristics—such as socio-demographic, household, car use, and attitude factors—relate to class membership. The analysis provides a comprehensive overview of how these characteristics influence user preferences. These findings offer valuable insights into the diversity of V2G user preferences and inform targeted policy recommendations.