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Carl Kalin
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Electric Vehicle Sales Analysis
CREED’s EV Tracker report
CREED now provides the first-ever reports designed specifically for utilities and program administrators (PAs) to track electric vehicle (EV) adoption and growth, as well as grid impacts, at the zip-code level. Highlights of CREED’s EV Tracker report are summarized below.
Value of the Report
- Monitor and Plan for EV Growth: The EV Tracker report allows utilities and PAs to closely track the growth of EVs and resulting grid impacts, and thus plan grid modernization and charge management strategies accordingly.
- Determine Program Impacts: Tracking EV purchases throughout the U.S.—and comparing EV adoption in regions with incentives vs. those without incentives—provides the most accurate determination of market lift from program offerings.
Research Questions
- What is the market share for EVs, and how is that share changing over time?
- Where are the EV drivers located in each service territory? What are the resulting load impacts on the grid?
- What is the market share by make/model for EVs?
- How does the growth of plug-in hybrid EVs (PHEVs) compare to battery EVs (BEVs)?
- How does EV adoption differ by demographic and household characteristics, such as income, education, predominant housing type, etc.?
Provided Data and Analysis
- Geography: Total of 26 states[1] (over 65% of all U.S. auto sales), available by zip code and county level
- Timeframe: 2018–present, shown by year and by quarter (also available by month)
- Fuel Types: BEVs, PHEVs, hybrids, and internal combustion engines
- Seller Information: Dealer name
- Purchase Type: Individual vs. fleet, new vs. used
Additional Options from Apex Analytics and Rolling Energy Resources
- Impacts of Incentives: Developing a regression model across all zip codes and controlling for demographic and household characteristics to isolate the key drivers (including program efforts) behind EV adoption.
- Charge Station Siting: Utilizing vehicle telematics to determine exactly when and where customers charge their EVs, providing for a more accurate assessment of grid demand impacts.
- EV Load Management: Piloting both active (e.g., demand response or time-of-use assist) and behavioral (e.g., monthly charge reporting) load management strategies to shift charging to off-peak or peak-renewable timeframes.
- Impacts of Incentives: Developing a regression model across all zip codes and controlling for demographic and household characteristics to isolate the key drivers (including program efforts) behind EV adoption.
- EV Forecasting: Developing the forecasted growth rate for EVs in a service territory and assessing how this forecast impacts the grid.