Learnings

Outcomes

The project has delivered against its objectives as detailed below. 

Developing a strategic transport and energy demand model

• An innovative agent-based model has been developed that illustrates the impact of competition for public chargers on V2G uptake.
• The model and associated report provides a deeper understanding of the electric vehicle driver behaviours that might influence V2G uptake and how they vary across current EV drivers.

Quantifying the smart charging and V2G contributions to delaying/displacing transmission network reinforcement

• Model outputs include various scenarios and sensitivities that illustrate how different future behaviours may affect V2G uptake. 
• The results show that smart charging can reduce the impact of vehicle charging on peak demand, but if consumers are willing to provide energy back to the grid via V2G, then substantial more reductions in peak demand are possible. There are some differences in the size of this reduction in peak demand depending on the price signals received by consumers and the number of electric vehicles that can provide these services.
• We identified the main barriers to providing significant reductions in peak demand are a combination of ensuring battery degradation is minimal for consumers, allowing consumers to access V2G-enabled chargers and reducing the hassle associated with plugging in frequently. 
   

Determining the impact of large EV charging stations on future system operation and investment

• The modelling shows how EVs have the potential to provide significant power for ancillary services. 

Overall, key learnings included:

• There is significant variation in EV driver’s behaviour, journeys made and access to infrastructure. These differences will ultimately drive the differences in V2G outcomes for each individual driver. The survey provided evidence on the extent to which specific behaviours could affect the uptake of V2G. 
• The consumer behaviour research and the modelling results, demonstrated that the following may be required for V2G to have a significant impact on peak demand:
  • Cost reductions in V2G-enabled chargers will be required since the current costs are significantly higher than non-V2G-enabled chargers and lifetime returns are uncertain.
  • EV drivers will need to be confident that intensive discharging and recharging will not degrade their batteries.
  • Reducing the hassle associated with plugging in vehicles frequently will be required. We find that some drivers may respond to a small incentive to plug in every night. There may also be behavioural pushes or technological solutions that can increase the frequency of plugging in.
  • Vehicles may provide significant availability for system services, but technical uncertainties over response times remain.
  • The impact of non-domestic charging points is likely to be lower than domestic charging, particularly at peak hours.  

Recommendations for further work

• Focus on non-domestic and fleets: commercial vehicles may have more predictable driving behaviours and may be more likely to optimise their charging strategy. 
• Implications of business models for autonomous vehicles: in one future scenario in 2050, we find a large uptake of autonomous vehicles. Depending on the business models, these vehicles could provide significant V2G. 
• Business models for V2G: more research could be done to help understand how to offer V2G to consumers in the most effective way (e.g. incentives that will cover the cost of the up-front charger). 
• Integrating further differences in V2G into future energy scenarios: it might be useful to reflect differences in consumer behaviours across these scenarios. 

Lessons Learnt

The following lessons were learnt: 

• Data and assumptions are crucial to ensure model robustness.
• Energy market is important while analysing the system benefits for V2G, new market designs will be needed to encourage more participants into V2G to scale ups the benefits.
• The security design of V2G systems should consider potential issues arising from the use of different devices and communication protocols by various stakeholders in a V2G ecosystem. Given the complexity of cyber-physical systems interconnections in V2G environments, further research is needed to better characterize threat sources and understand their origins and motivations. 

Dissemination 
Internal workshop to hand over the model and identify key model results with National Grid and the National Energy System Operator on 17 January 2024. Furthermore, an innovation article was published within National Grid to share learnings.