Electric vehicle charging : understanding driver behaviour and charging controls to improve impacts on the electricity grid

The electricity grid and transportation sector are undergoing simultaneous, rapid, and unprecedented transformations to reduce emissions. Coupled through electric vehicle charging, the two transformations can both hinder and support each other: the grid must provide electric vehicle drivers with reliable, affordable electricity and convenient access to charging stations; electric vehicle charging can impact the grid's transformation in turn by increasing demand, accelerating equipment aging or forcing upgrades, aligning or misaligning with renewable generation, or even providing grid services. This dissertation focuses on that coupling, by understanding what shapes electric vehicle charging demand and how it should be reshaped to improve the impacts on the electricity grid. Studying drivers' charging behaviour is the first step toward understanding charging demand. Electric vehicle charging behaviour is highly heterogeneous, shaped by individuals' travel patterns, access to charging infrastructure, and personal preferences. For example, charging data reveals that some drivers are risk averse and prefer to top-up at every opportunity while others prefer less frequent, higher energy sessions. We propose a novel methodology to include driver behaviour in a model of large-scale electric vehicle charging demand for applications in long-term planning. The methodology builds in knobs for future scenario design based on data-driven modeling of driver behaviour, clustering drivers and charging sessions. We calibrate the methodology using a large data set of nearly four million charging sessions from Northern California in 2019. Charging control is a powerful tool widely used to modify charging profiles. Studying the connections between charging control, electricity rate design, and drivers' charging behaviour is the second step toward understanding charging demand. We first investigate controlled charging at a small scale, studying the impact of workplace charging control for different electricity rate schedules on the aging of a distribution transformer. Then, we propose a novel methodology for representing such control in large-scale models of charging demand. The proposed methodology uses machine learning to directly model the mapping from uncontrolled to controlled aggregate demand. Finally, we apply this understanding of how drivers' charging behaviour, charging control, and access to charging infrastructure shape and reshape demand to study the future large-scale impacts on the electricity grid. We focus on the Western US, and model grid dispatch in 2035 under a range of charging scenarios to evaluate the effects of increasing or decreasing the deployment of home or workplace charging infrastructure and of the widespread deployment of charging control in response to different electricity rate schedules. An important contribution of this dissertation is its emphasis on open-source, highly scalable tools. Long-term planning for electric vehicles requires scenario analysis of the range of possible futures, and faster simulation run times allow planners to test assumptions or interact with new scenarios in near real-time. More than anything, the results of this dissertation urge policymakers to consider the coupling of grid and electric vehicle planning. Careful electricity rate design and better build-out of away-from-home charging infrastructure could yield meaningful improvements for both sectors' transformations

Keywords: Mechanical Engineering.
Advisor: Majumdar Arunava
Rajagopal Ram
Azevedo Inês M. L.