EVALUATE: Electric Vehicle Assessment and Leveraging of Unified models toward AbatemenT of Emissions, Phase I

This research explores electric vehicle (EV) and grid interactions with a focus on CO2 emissions for future scenarios where EVs comprise growing market shares (e.g., 10% of the overall fleet mix). A major contribution of this effort has been to develop a methodology that integrates sub-system models and datasets that have previously stood alone, namely models and data that characterize: vehicle energy consumption, travel demands, vehicle charging, and temporal emission profiles associated with electric power generation dispatch. This convergence research helps quantify the relative emissions of light duty vehicle use and charging during various times of day to enable comparison of EV modes against one another and against conventional vehicle baselines. An initial use case involving light duty commuter and recharging scenarios has been explored as a means of validating and tuning the methodology. Under certain simulated scenarios, observed marginal emissions can be as much as 20% lower in the overnight hours compared to marginal CO2 emissions experienced during an identical charging event during the daytime. This study also confirms that marginal CO2 assumptions generally yield higher CO2 impacts than identical simulations that assume weighted average emissions. This variance is broad, ranging from 22% less to 97% greater, depending on a host of case-sensitive factors. These findings suggest that it will be essential to coordinate charging schedules and consider upstream grid implications in order to reduce the environmental impacts of EVs. By quantifying technical parameters related to both the magnitude and the range of possible emissions impacts, the study’s findings can be useful for education and awareness by all EV users, and will help decision-makers consider the importance of emission rate assumptions and the temporal granularity of the tools and data. More specifically, stakeholders should be incentivized to charge when marginal emissions are lowest whenever possible. This idea also has important implications about the location, type, cost and ownership models for tomorrow’s charging infrastructure. Translating and operationalizing this type of guidance will require some combination of education, access to rigorous and clear decision-support tools, signals between stakeholders (e.g., utilities and consumers), and behavioral change. View the NCST Project Webpage