Hourly Power Grid Variations, Electric Vehicle Charging Patterns, and Operating Emissions

Area of Science:

• Environmental Science
• Transportation Engineering
• Energy Systems

Background:

• Light-duty vehicles contribute significantly to US greenhouse gas emissions.
• Electric vehicle (EV) adoption is a key strategy for reducing these emissions.
• Accurate assessment of EV emissions requires up-to-date grid and charging data.

Purpose of the Study:

• To analyze the impact of EV charging patterns on emissions using recent hourly grid data (2018-2019).
• To quantify the errors introduced by common approximations in EV emission analyses.
• To develop and validate a novel approximation for EV emissions.

Main Methods:

• Utilized hourly charging, driving, temperature, and power grid data from 2018-2019 for 60 US cases.
• Compared emissions from overnight vs. daytime EV charging across different regions.
• Quantified errors from ignoring hourly grid variations and temperature effects on fuel economy.

Main Results:

• Regional variations in EV charging patterns significantly affect emission outcomes.
• Overnight charging in California yields ~70% more emissions, while in New York, it yields ~20% fewer emissions compared to daytime charging.
• Common analytical approximations can lead to over 10% error in 30% of cases, reaching 50% in California.

Conclusions:

• Accurate, hourly grid data is crucial for precise EV emission analysis.
• A validated novel approximation improves the accuracy of EV emission estimations.
• Optimized charging strategies are essential for maximizing emission reduction benefits of EVs.