Integration of Electric Vehicle Charging Loads in Residential Building Stock Energy Modeling

The rapid adoption of electric vehicles (EVs) has resulted in significant new household electric loads that have the potential to change how energy costs are incurred by homeowners and the landscape of utility operations and energy infrastructure. Whereas adoption patterns and magnitudes of residential building and EV charging loads are influenced by distinct factors, the loads themselves are tightly coupled with the behavior of the individual occupants and EV owners. This correlated relationship motivates the use of a common approach to analyze coincident energy impacts, whereas domain-specific tools are required to accurately characterize the EV and housing parameters separately. In this report, the authors describe an approach to integrate data and assumptions from the Transportation Energy & Mobility Pathways Options (TEMPO)(TM) tool into the ResStock(TM) model to generate accurate and flexible datasets that capture the coincident behavior and energy outputs of residential building loads and EV charging. We present methods to characterize the national baseline EV ownership, EV charger, and charging behavior consistent with TEMPO assumptions and within the context of ResStock's existing housing characteristics data. We also describe a new approach to modeling EV batteries and charging loads in ResStock, which incorporates regional and demographic details of the EV stock characterization translated into individual building models. The modeling approach leverages a detailed lithium-ion battery model and incorporates the stochastic behavior of occupants to generate realistic charging and discharging profiles. Finally, we analyze a national-scale simulation of ResStock with EVs and present trends and impacts to the baseline housing stock, which we then further compare to external datasets. The main outcome of our implementation is the enhancement of building stock modeling capabilities to account for the operation and adoption of EVs in ResStock's national baseline. We provide a solution for modeling current EV charging behavior that aligns with other occupant-driven home loads while offering numerous pathways toward analysis of future scenarios. Through internal validation, we confirm that EV energy use and schedules align with the expected trends across housing stock segments. The results also indicate alignment of EV adoption, charging energy, and schedules against national survey data, vehicle registrations, modeled TEMPO data, and the U.S. Federal Highway Administration's National Household Travel Survey.