Marginal Greenhouse Gas Emissions of Ontario's Electricity System and the Implications of Electric Vehicle Charging.

Abstract

To estimate greenhouse gas (GHG) emission reductions of electric vehicles (EVs) deployment, it is important to account for emissions from electricity generation. Since such emissions change according to temporal patterns of electricity generation and EV charging, this study operationalizes the concept of marginal emission factors (MEFs) and uses person-level travel activity data to simulate charging scenarios. Our study is set in the Greater Toronto and Hamilton Area in Ontario, Canada. After generating hourly MEFs using a multiple linear regression model, we estimated GHG emissions for EV charging at two EV penetration rates, 5% and 30%, and five charging scenarios: home, work and shopping, night, downtown vs suburb, and an optimal low emission charging scenario, matching charging time with the lowest available MEF. We observed that vehicle electrification substantially reduces GHG emissions, even when using MEFs that are up to seven times higher than average electricity emission factors. With Ontario's 2017 electricity generation mix, EVs achieve over 80% lower fuel cycle emissions compared with equivalent sets of gasoline vehicles. At 5% penetration, night charging nearly matches low emission charging, but night charging emissions increase with 30% EV penetration, suggesting a need for policy that can smooth out charging demand after midnight.