The load that arrived without paperwork
Home EV charging is growing faster than most utilities' records of it. A customer buys an electric car, an electrician installs a dedicated charger in the garage, and the household starts drawing a large new load most evenings. Sometimes a permit reaches the utility. Sometimes a rebate application does. Often nothing does.
The utility's picture of EV adoption is usually stitched together from whatever sources happen to exist: rebate and program enrollments, permit feeds, vehicle registration data, customer surveys. Each one captures a slice. None captures the whole. The gap tends to show up in the least convenient way: a service transformer running hot on a summer evening, a voltage complaint from the end of a street, a failure that nobody saw coming.
Yet the evidence is already in hand. Every premise with an AMI meter reports the energy it uses, and a home that charges a car uses energy differently from one that does not. The information planners need is sitting in data the utility already collects, validates, and stores.
Why this is harder than it looks
Finding chargers in meter data sounds like a data science exercise. In practice, the hard parts are operational.
It has to cover every meter. A study of a few hundred premises tells you something about adoption in general. It does not tell you which transformer on which feeder is carrying several new chargers it was never sized for. Planning needs the whole network, not a sample.
The record has to stay trustworthy. A utility's record of known chargers feeds rebates, rate eligibility, program enrollment, and interconnection files. If an analytics tool quietly "corrects" that record, nobody can tell any longer what a customer reported and what a model inferred. The fix creates a new problem.
Disagreement runs in both directions. Some premises show charging that the record does not know about. Others are on the record but show no sign of charging: the charger was removed, the household moved, the car charges at work, or the charger was never installed. Both lists matter, and they call for different follow-up.
Findings have to land where planning happens. A spreadsheet of premise identifiers goes stale the week it is exported. Planners think in transformers, feeders, and headroom. A finding that is not tied to the network is hard to act on.
Missing data is not an answer. A meter with gaps, or a premise that only recently got AMI, cannot prove there is no charger there. Treating "not measured" as "nothing found" makes the results look cleaner than they are.
Principles of a good approach
A sound approach to EV detection follows a handful of principles, whoever builds it.
1. Look at every meter, continuously. Adoption changes month to month, so detection should run across the whole network on a schedule, not as a one-time project.
2. Report on the record, never rewrite it. Detection results should sit beside the utility's record, clearly labeled as findings. Changes to the record go through the utility's own process: outreach, field verification, program review.
3. Show both kinds of mismatch. Chargers the meters show that the record does not, and chargers the record lists that the meters do not support.
4. Tie every finding to the network. Each premise should connect to its service transformer and feeder, so findings flow straight into loading studies and DER planning.
5. Be honest about evidence. Where meter data is missing or incomplete, say so, rather than reporting a clean "no charger."
6. Use one detector everywhere. If the customer-facing view and the grid planning view use different methods, they will eventually disagree, and customers and planners will lose confidence in both.
What it looks like in practice
In Grid Data Enhanced Analytics, EV charger detection runs across every meter on the network. It uses the same detector that Home Energy Management uses for individual households, so the household view and the grid view are working from one method.
The results answer a single question: which premises charge an EV that the records do not know about? The utility's record of known chargers is set beside what the meters show, producing two lists. One holds chargers the meters show that the record does not. The other holds chargers on the record that the meter data does not support. The record itself is reported on, never rewritten. GDEA is monitoring-only by design, so it has no path to change the utility's systems of record.
Every finding is linked to the live network map. A planner can move from a premise to its service transformer and feeder, then to the capacity and headroom analysis for that feeder. The scenario sandbox lets them test what a new wave of chargers would do, and compare options such as upsizing a transformer or moving load, before committing to one. Detection finds today's chargers; headroom analysis and the sandbox help place the next ones.
On the customer side, Home Energy Management supports electrification advice. A household can see what an EV, a heat pump, or solar panels would cost on its own load model, with and without the change, so the utility can offer advice that is personal and honest while its planners work from the grid-side view.
Illustrative example (hypothetical)
A planner reviews a suburban feeder ahead of summer. The utility's records show a handful of home chargers on it. The detection results show noticeably more, clustered on two streets that share one service transformer, and that transformer already runs close to its rating on hot evenings. Instead of waiting for a failure, the planner flags the transformer for a loading study and uses the sandbox to compare an upsized unit against splitting the load. In the other direction, two premises on the record show no sign of charging. The program team adds them to its next verification round rather than deleting them. The record changes only when someone confirms what is actually there.
Questions to ask any vendor
When evaluating EV detection from any provider, ask:
Does detection run across every meter, or only on a sample or an opt-in group?
Does it write to our system of record, or report beside it? Who decides what gets corrected?
Does it show chargers on our record that the meter data does not support?
Can we see findings by transformer and feeder, next to loading and headroom?
What does it report when a meter's data is missing or incomplete?
Do the customer-facing tools and the grid planning tools use the same detection, so they cannot disagree?
Where to go from here
EV adoption will keep outrunning paperwork. The utilities that plan well will be the ones that read the evidence their meters already provide, keep it separate from the record, and put it in front of the people who size transformers and feeders.
Learn more about Grid Data Enhanced Analytics and Home Energy Management, or talk to our team about finding the chargers on your network.