Advancing Disaggregate Modeling of Electric Vehicle Charging Behaviour

Abstract

The growing adoption of electric vehicles (EVs) poses both opportunities and challenges for electricity grid management, where management strategies vary by region based on demand, climate and portfolio of generation types. In Ontario, Canada where the energy mix is dominated by baseload nuclear and hydroelectric generation and where residential EV charging is common but not universal, understanding the timing and location of EV charging is critical for infrastructure planning and system reliability. This thesis focuses on non-overnight EV charging behavior, charging events that occur during the day, mostly away-from-home, and explores how infrastructure access and electricity pricing may influence the 24-hour distribution of EV electricity demand. The research addresses the question: do infrastructure location and pricing conditions influence the temporal distribution of non-overnight electric vehicle charging demand in Ontario? To answer this, a novel simulation model was developed that integrates real-world travel data from the 2016 Transportation Tomorrow Survey (TTS) with charging decisions predicted using a discrete choice model estimated from a custom stated-preference survey of current EV users. This survey, still ongoing at the time of writing, has collected over 5,900 responses across 300+ participants, capturing variation in price elasticity stop duration, charger type, and other contextual factors that influence away-from-home charging. The simulation assumes a 10% EV adoption scenario across the Greater Toronto and Hamilton Area (GTHA), generating 24-hour electricity demand profiles under six distinct combinations of infrastructure access and pricing. Results suggest that infrastructure availability may be the primary determinant of when charging occurs throughout the day, while pricing has a stronger influence on how much charging takes place. Scenarios with free and widespread public access produce higher daytime demand, while constrained infrastructure and high pricing result in lower, more diffuse load patterns. However, charging patterns, such as the concentration of demand early in the day, appear sensitive to model assumptions, particularly morning state-of-charge (SoC) initialization and simplified home charging representation. These findings underscore the importance of model calibration and choice behaviour realism in demand modeling efforts. The implications of this work, while preliminary, point to the need for coordinated planning of charging infrastructure and pricing policies that consider charging behaviour as well as actual trip patterns and regional energy system characteristics. The research contributes both a flexible simulation framework and a charging choice model estimated from stated charging behaviour that can be expanded for future planning scenarios. Next steps include further model refinement, validation using real-world charging session data, and explicit inclusion of populations without home charging access. As EV adoption continues to grow, the tools developed in this thesis provide a foundation for anticipating and managing its impact on electricity demand.