posted on 2019-06-07, 00:00authored byYijun Gai, An Wang, Lucas Pereira, Marianne Hatzopoulou, I. Daniel Posen
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.