PI: Ram Rajagopal; Co-PI's: Liang Min and Ines Azevedo; Student: Siobhan Powell
To prepare for the future envisioned by EV50, a crucial first step is developing forecasts and scenarios. Forecasts of EV load will enable us to quantify the energy and capacity impacts of fast charging, to study long-term grid planning, and to define a framework for other key EV50 projects. The picture of EV charging in 2030 and beyond is affected by a multitude of uncertainties. The adoption of fast charging, changing rate schedules and market structures, drivers’ choice to charge on-the-go rather than at work or at home, changing driver profiles, available charging infrastructure, new charging technologies drawing power at higher rates, and longer range vehicles are all important variables that will impact the EV load of the future.
With this project we propose to develop a set of detailed, up-to-date, realistic scenarios for how these effects may play out in California and other regions around the world. Large sets of charging, mobility, and census data will be mined to understand key driving patterns and factors in charging behaviour. These factors will be used to project future changes and reflect a range of behavioural and technical constraints on the load, including charger input and the impact of infrastructure constraints. The scenario generation will be data-driven and utilize a graphical modeling framework to capture the uncertainties, assumptions, and conditioning of the features impacting EV load.
With case-studies in California and other regions, a wide range of scenarios will be presented in publications exploring the large-scale potential impacts of EV charging. We will also develop and publish an open-source software tool implementing the model. An online interface will allow readers to interact with the model, alter assumptions, and generate new scenarios. We believe this will help communicate the uncertainties and assumptions impacting the scenarios, as well as offer insights to inform planning processes for a range of stakeholders.
The project will also include analysis of large-scale grid impacts. For which charging scenarios and levels of adoption will the grid have sufficient capacity? The addition of EVs will interact with other changes including grid decarbonization. EV adoption may not proceed uniformly across California and the grid and health impacts will not be evenly distributed. Policies supporting access to different types of charging can create scenarios with very different profiles. As part of this project, we will compare the different scenarios in terms of total and marginal emissions, damages due to emissions, costs, capacity limits, and equity.