Environmental effects from a large-scale adoption of electric vehicle technology in the city of Los Angeles

Greenhouse gas (GHG) emissions reduction has become an important component of policy decisions on transportation systems design, research and development, and implementation. Particularly in major urban centers, increasing use of electric vehicles (EVs) is being encouraged to support the overall objective of reduction in transportation emissions. This encouragement ranges from consumer tax credits to research and development grants for advanced EV technologies. Assessing the net effect EVs on actual emission mitigation potential, however, depends on three main factors: 1) energy portfolio of power providers, 2) consumer adoption rate, and 3) battery charging patterns. Unfortunately, the current U.S. energy grid is predominantly composed of coal-fired plants that emit high concentrations of GHGs. Therefore, EVs essentially push emissions upstream to the electricity generation sources. EVs represent a dramatic paradigm shift in transportation such that forecasting their adoption requires adaptations to the innovation diffusion models. The charging patterns also affect the emission mitigation potential because the use of "peak" versus "off-peak" power changes the grid energy emissions significantly. This study seeks to quantify the emissions mitigation potential of these three main influencing factors. In order to answer the main research questions, an integrated emissions model is developed for the City of Los Angeles. The model incorporates modules such as changes in population and mobility patterns, consumer technology adoption, vehicle charging patterns, and lifecycle emissions of GHGs from electricity generation. Some of the model's main outputs are the daily EV energy loads, daily system load profile, hourly average marginal grid energy carbon intensity, and the types of energy generation dispatched at every hour. For 2020, model results show that the EV charging loads will be modest with negligible effects on the overall system load profile. Results indicate that high EV adoption results in greater emissions mitigation potential. However, the type of charging has a significant impact on the scale of mitigation at all levels of adoption. Contrary to previous study results, the average marginal carbon intensity is higher if EV charging occurs during off-peak hours. These results demonstrate that the charging decision in terms of the time of day matters in GHG emissions mitigation efforts. The short-term incentives for off-peak charging may not only result in greater emissions but also deter EV technology adoption which would lower the overall emissions mitigation potential. Encouraging restrictive charging behavior in the short-run may be counterproductive to GHG emissions reduction policies. Model results for 2030 show that EV charging loads increase significantly resulting in potential generation shortages. There are also significant grid operation challenges as the region's energy grid is required to ramp up and down rapidly to meet EV loads. For 2030, the average marginal carbon intensity for off-peak charging becomes lower than peak charging. Increasing use of renewable generation sources leads to greater GHG emissions mitigation but the greatest effect arises from the removal of coal generation sources. The study concludes with remarks on further research into the optimal distribution of renewable energy and EV-grid interactions as major research areas to enhance understanding of the EV's effectiveness as GHG emissions mitigating technology.