If you’ve read our previous post on why registration data isn’t sufficient for grid planning, you already know the type of information utilities need from their EVs: where they’re charging, and the size of the load they’re placing on the network.
This blog is about what that data makes possible for the different teams inside your utility (we also talked through this topic in our recent webinar).
The data that helps one team do its job better is often the same data another team needs, it’s just used in different ways to meet different goals.
EV program teams: Know where to start
EV program teams typically operate with limited marketing bandwidth. They can’t blanket their entire service territory hoping to find EV owners, and without detection data, it’s a chicken-and-egg problem. The program needs EV owners to be effective, but they don’t know where the EV owners are.
Accurate EV detection data breaks that open. In one example we’ve worked on with ElectronCompass, all detected EVs could be reached by targeting just 4% of postal codes across a service territory – and over 80% of them by targeting less than 2%.
This gives a small team with limited bandwidth a specific place to start and a way to measure whether their outreach is working. It lowers recruitment costs, removes an element of guesswork, and gives the program a path to scale.
What’s useful in that situation is that this level of insight works at two levels:
- A geographical one, where you’re targeting specific zip codes for outreach campaigns
- A customer level, where you can identify individual detected EV owners directly
Privacy regulations vary by jurisdiction and will determine how granular that customer-level targeting can get, but the geographical layer alone is a significant operational boost for most program teams.
Grid monitoring and planning: Target your capital programs
In aggregate, EVs often look manageable, and in many service territories they are. But the aggregate picture can sometimes mask what’s happening at the distribution level.
The key questions are about colocation and coincidence: are multiple EVs sitting on the same transformer? Are they charging at the same time? Being able to answer those questions with meter-level data, showing where utilization is climbing and where it isn’t, is what separates reactive management from proactive planning.
That same detection capability is what feeds into longer-term planning. Knowing where EV adoption is concentrated and at what scale, rolled up from meter level to feeder and substation level, allows planners to update their models with what’s on the network rather than their assumptions.
The difference between a well-targeted capital program and one that’s over or under-built increasingly comes down to that.
Operations teams: Manage what’s already happening
For operations teams, there are transformers on the network that are being pushed beyond their limits by EV load. The question is which ones, and what to do about it.
This is also where the limits of most demand response (DR) participation models become to light. The majority of demand response signals in the market today are systemwide i.e. a time-of-use price, a curtailment event, a market signal.
Those signals don’t account for the fact that EV load creates hyper-local problems on the distribution network. A transformer serving a street with five level 2 chargers doesn’t care what the systemwide signal says. It cares about what’s happening on that street at that moment.
For DR participation models to scale, the distribution grid has to be part of the equation. Operations teams need data that tells them not just that load needs to move, but where it needs to move from and where it safely can go.
That’s a different capability from what most participation models currently provide. Plus, it’s one that only becomes possible when you have accurate, granular detection data underneath it.
Getting this visibility of what’s on your network and what it’s doing – what ElectronCompass is built to provide – is key for all utility teams. The above list is not exhaustive. The next step is putting an economic value on that picture, so that the decisions each of these teams make can enable a common language across planning, operations and, ultimately, regulatory conversations about how to manage this load cost-effectively and reliably.
