In 2025, one in four new cars sold worldwide was electric – over 20 million vehicles, according to the IEA. The agency projects an increase to 23 million in 2026, and in the US, there are now roughly 5.8 million EVs on the road.
The load is growing fast. You may have started looking at Department of Transportation (DOT) data in an attempt to understand how it will impact the grid and therefore your bottom line – such as through the revenue opportunity it presents, the GHG reduction potential, or the grid challenges that come with it. However, drawing insights from DOT data can lead utilities astray due both to the nature of electrification and the data itself.
The DOT data answers the wrong question
Registration data was built to answer a tax question: who owns a vehicle within a given jurisdiction, so the government knows who to bill. It was not designed to tell a utility anything useful about grid impact.
For example, someone could register their car in Montana, where taxes are lower, and charge it every night in California. The Californian DOT data won’t know the car exists. The Montana DOT has the car registered to a Mondana address – but the load is on California’s grid.
And then you need to take into account that people often forget to update their address when they move.
So, DOT registration data is only where EVs are registered. What the utility needs to know is where those vehicles are charging, and the DOT isn’t meant to answer that.
Factoring in location and size
Even if registration data was accurate on location, it would still miss half the picture: the size of the charging equipment.
A level 1 charger draws roughly the same power as a large household appliance. A typical new-install level two charger draws 9.6-11.5 kW (40-48 amps), though chargers 19.2 kW exist. For reference, in the US, average residential peak demand can be around 9.7 kW. So a single level 2 charger can easily more than double the peak draw of a home. The grid needs to be built to meet that peak, even if it only occurs for an hour.
So a single level two charger can more than double the peak draw of a home, and the grid needs to be built to meet that peak, even if it only occurs for an hour.
Registration data tells you none of this. It tells you a vehicle exists, it doesn’t tell you whether that vehicle is plugged into a standard wall socket or a level 2 charger, whether it’s charging in your territory, or what that means for the transformer serving that street.
Novel and uncontrolled
Multiply this hidden grid impact across thousands of streets and you start to see the planning and operational problem.
Utilities have always dealt with load growth. Historically, it’s been predictable and manageable using established planning methods, growing steadily enough that planners could model when a substation would need replacing or when service transformers would need upgrading. That predictability is what long-range planning is built on.
EVs break that model, because it’s a novel and uncontrolled or “permission-less” load; customers can simply plug in or install level 2 chargers, so that power draw can happen anywhere.
With large-scale distributed generation, like megawatt-scale solar and wind, utilities have natural visibility over the pace of connection and the ability to plan accordingly. With behind-the-meter EV adoption, it happens at an unpredictable pace.
And history has shown that it doesn’t happen evenly, it clusters. One person on a street buys an EV, then a neighbour does, then another. This socio-economic clustering means that certain areas electrify faster, and utilities will need to develop efficient strategies that can pro-actively monitor and manage these impacts.
Registration data was designed to answer someone else’s question; you need something that answers the questions important to utilities: where is electrification happening on my grid?
What accurate detection gives you
The nature of the EV growth means that meeting that new load is therefore a process, not a single event or pilot. Monitoring, reacting, and incorporating data back into planning on an ongoing basis is the key to recapture control.
AMI-based EV detection gives you data driven insights that can directly accelerate your EV programs, while also enabling more informed planning. It answers where are vehicles actually charging, what size charger are they using, and where is that creating real stress on the network right now?
Using ElectronCompass, we recently analysed around 150,000 residential meters for an EV team at a privately owned utility, and found that over 80% of the detected level 2 chargers exist in less than 2% of the zip codes. This insight is now allowing the EV team to re-approach their customer outreach team with a vastly more targeted request, opening up new marketing opportunities that focus their effort and spend on where it counts.
When you know both the location and size of the EV load, you can make decisions on setting up new programs armed with data around the realistic amount of flexible load that exists behind the meter. Or you can accelerate the existing EV programs by getting more customers involved in beneficial load shaping.
That precision is important to utilities, from those program teams trying to target EV outreach efficiently, to planners updating their tools to account for novel load, to operations teams managing the grid in real time.
The data is already sitting in your AMI. The question is whether you’re using it to ask the right questions.
