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.

The vehicle-to-grid (V2G) innovation—which enables electric vehicles to return stored electricity to the grid—holds significant potential to support renewable energy integration and electric vehicle adoption. Despite growing interest in V2G, there is still limited understanding of user preferences and the factors influencing decision-making. To explore this, we conducted a stated intention study with 1018 participants, examining their likelihood of participating, and their primary drivers and barriers. Our analysis—using a random parameters order probit model and mixed logit models—revealed that most respondents were likely to participate (42%) or remained neutral (32%). Financial incentives were the primary driver (49%), followed by electricity grid-stability (26%) and environmental (25%) factors. The main barrier for most was loss of flexibility (55%), followed by battery degradation (27%) and data concerns (18%). The study highlights how user characteristics—including socio-demographic, household, car use, and attitude factors—influence these preferences. Finally, we provide policy recommendations, including targeted education and communication, income-based incentives, accessible charging infrastructure, and a regulatory framework supportive to technology development and user protections.

This paper systematically reviews the literature on sociotechnical multi-system innovation frameworks that broaden the usual focus on one sociotechnical system to encompass influences from multiple systems. The review includes 75 peer-reviewed papers that span a broad range of energy-demanding systems and mainly build upon the core frameworks of the Multi-level Perspective (MLP) and Technological Innovation Systems (TIS). The analysis identifies three key aspects to consider in multi-system frameworks. The first aspect is the importance of considering the overarching directionality of multiple sociotechnical systems and how they influence each other. The second is to explicitly analyse the phase of each transitioning system. The third aspect is a need for explicit system configuration analysis. This includes analysing the value chain and the number and types of sectors linked to it, typifying the distinct characteristics of sectors internally and how they interact, and analysing complementary or competitive technologies. The paper concludes by providing recommendations for future research, with a particular focus on the further development of new multi-system frameworks that include one or more of the prior-mentioned three key takeaways. Firstly, focusing on dynamics within multi-system niches. Secondly, performing actor-level analysis, including demand-side analysis. Finally, applying quantitative methods, such as computer simulation modelling.

After the outbreak of COVID-19 pandemic, crowding has been highlighted as a risk factor for contracting acute respiratory infections (ARIs) such as COVID-19, which has affected the demand for public transport. Although several countries, including the Netherlands, have implemented differential fare systems for peak and off-peak travel to reduce crowding during the rush hours, the problem of overcrowding on trains has remained prevalent and is expected to cause more disutility than even before the pandemic. A stated choice experiment in the Netherlands is conducted to understand the extent to which people can be motivated to change their departure time to avoid crowded trains during rush hours by offering them real-time information on on-board crowding levels and a discount on the train fare. To gain further insights into how travelers respond to crowding and capture unobserved heterogeneity in the data, latent class models have been estimated. Unlike the previous studies, the respondents were segregated into two groups before the start of the choice experiment based on their indicated preference to schedule a delay earlier or later than their desired departure. To study the change in travel behavior during the pandemic, the context of different vaccination stages was also provided in the choice experiment. Background information collected in the experiment was broadly categorized as socio-demographic, travel and work-related factors, and attitudes towards health and COVID-19. It was found that the coefficients obtained for the main attributes which were presented in the choice experiment (on-board crowd levels, scheduled delay and discount offered on full fare) were found statistically significant, and in line with previous research. It was concluded that when most of the people are vaccinated in the Netherlands, the travelers become less averse to on-board crowding. The research also indicates that certain groups of respondents, such as those who are highly crowd averse, and are not students, can be motivated to change their departure time if real-time crowding information was provided. Other groups of respondents who were found to value fare discounts can also be motivated to change their departure by similar incentives.