What is Grid Orchestration? Learn more
EVs are a promising growth opportunity for utilities, but also create major challenges for DER managers who need to plan ahead as continued adoption drives increased demand.
We outlined some ways grid operators can start getting ready for this increased pressure in a previous blog post, Preparing for EVs: 3 Actions Every Utility Can Take Today. All of them aim to, at least in part, help grid operators know not just how many EVs they’re dealing with but also how those EVs are impacting utility equipment.
Unfortunately, clear visibility into EV adoption is hard to come by. Unless the EV owner is one of as few as 30% enrolled in an EV program or specific tariff, there’s no easy way to know if there’s an EV at a particular meter. At present, many utilities simply pore over EV registration or sales data, group it by zip code, and create a ballpark estimate from there. And even the utilities that can detect EVs with software solutions have no insight into how those DERs are impacting their grid.
With Camus’ grid orchestration software, however, utilities can not only detect EVs in their network based on patterns in their usage data, they can see exactly how each EV charger impacts upline equipment, from distribution transformers to line conductors. This increased EV awareness allows utilities to more precisely target efforts to enroll EV owners in managed charging programs and reduce the risk of overloading upline equipment.
In the video clips below, you’ll see two ways our software allows utilities to uncover EVs and assess their impacts. First, we’ll track an EV charger upline from an individual meter to reveal how a specific charger impacts nearby equipment. Then, we’ll show how grid-level analysis can be used to understand EV impacts on the system as a whole.
To find EV chargers that haven’t been registered with the utility, our platform analyzes meter-level load data from advanced metering infrastructure (AMI). The machine learning algorithm is trained on known EV chargers to understand how an EV draws power and searches for that pattern across all meters. Typically, this lets us detect and report up to 70% of EVs in a given area.
Here’s how to quickly identify EVs and their impacts on utility equipment using the Camus platform.
First, we select a specific service area. Then, we turn on both transformers and EVSEs (an acronym for Electric Vehicle Supply Equipment), which displays known EVs (in blue) and detected EVs (in red).
Clicking on one of the red (detected) EV icons lets us see details from the meter data, which shows regular spikes of ~7kW. Camus’ platform recognizes this pattern as typical of a level 2 charger and flags it as a detected EVSE.
We then move upline from this view to look at the distribution transformer and its performance. There’s a slight overload on December 31st, but the load duration curve in the second chart shows that it has been within capacity for 99% of the last month.
Scrolling further down the page, we see detailed information and a graph of how each meter on this transformer is drawing power. That graph again shows one meter with the distinctive ~7kW spikes of the detected EV, while the list below marks it with the icon that appears on the map.
The granular view demonstrated above is useful when you’re investigating the impacts of a specific charger or looking into known anomalies on a specific section of the grid. At other times, you might want to search for the areas where EV impacts are putting strain on the grid. You can do so by analyzing the most heavily utilized transformers, filtering down to those with known or detected downline EV chargers.
The videos below demonstrate how this works. It starts with showing a table and map of the most heavily-loaded transformers on the grid. We can sort the transformer data by a number of values, including their overall utilization factor.
With the data sorted, we know which transformers are overloading with some frequency. To get a more nuanced view on where that extra load is coming from, we can activate a number of search filters, including the number of downline EVSEs. This makes it easy to identify heavily loaded transformers with existing EVSEs, where additional EV chargers may severely strain the infrastructure.
From here, we can click into any of the transformers and return to the detailed view of transformer-level data from the first video. In addition to looking at loading on the transformer over time, we can scroll down for a detailed view that shows all DERs on a given meter, including EVs.
Widespread EV adoption will have an enormous impact on the grid, and utilities need to prepare. Fortunately, Camus’ grid orchestration software can help improve capital planning, giving grid operators a better sense of what the future holds. Pinpointing individual overloaded transformers makes it easier to identify which upgrades are essential and which could be deferred or avoided through proactive mitigation. With the current wait time for a new service transformer now over three years, having a clear picture of your equipment’s lifespan is more valuable than ever.
Our platform bridges the gap between planning and operations so that utilities do not feel forced to choose between critical upgrades and ensuring affordability. By integrating and analyzing disparate data sources in our orchestration platform, you can understand where EVs are, when they’re drawing power, and where they’re drawing it from. This creates opportunities to lower capital costs and increase reliability. It’s part of how Camus’s grid orchestration platform is illuminating the path to a more dynamic, flexible grid.
Ready to see what our orchestration platform can do for your grid? Request a demo today
Not ready for a demo yet? Download our playbook Is your utility ready for EVs? This playbook, published in collaboration with Utility Dive, discusses advanced grid intelligence and how it can help utilities understand the relationship between DERs and the grid, and plan accordingly.
