Utilities use analytics from Oracle to manage influx of electric vehicles

The use of electric vehicles is growing at a record rate, with the International Energy Agency predicting that the number of electric cars on the road will rise from 3.1 million in 2017 to 125 million in 2030. Enabling utilities to intelligently manage this new energy demand on the power grid, Oracle Utilities has unveiled a breakthrough in EV detection.

Tapping deep machine learning, Oracle Utilities Analytics Insights is able to identify the presence of an EV, show the time and frequency of charging and disaggregate the energy being consumed by the vehicle with advanced metering infrastructure (AMI) data. 

With this intelligence, utilities can reliably plan for the energy infusion needed to power EVs at scale and engage customers to charge at the times that are the least expensive for them and best for the health of the energy grid. The new EV detection capabilities from Oracle Utilities Analytics Insights are currently being piloted by a number of utilities.

The influx of EVs could represent an average additional growth of 1-4 percent in peak load on the grid over the next few decades, according to a report by McKinsey. While this may seem modest, the impact will be highly volatile and cause unpredictable spikes at the local sub-station and feeder levels in residential areas. This load is projected to reach as high as 30 percent peak growth in certain urban areas that are hotspots for EV adoption.

While this transportation development represents a step forward in reducing carbon emissions, most electricity grids were created long before EVs were a commercially viable consumer product. As transportation continues to evolve from gas to the grid, utilities must plan for an uptick in energy demand that will vary dramatically by area. 

The Oracle EV detection capabilities are powered by more than a decade of research and experience disaggregating household energy data from billions of data points collected from 60 million households across 100 utilities. Oracle’s trained data models can be deployed for each specific household’s usage to understand whether a customer has an EV, how they interact with their EV chargers, and where EVs are clustering on the distribution grid. As such, utilities will be able to better plan for and manage the operational impact of EVs as a new distributed energy resource (DER) on the grid.

From a customer perspective, charging an EV can increase a typical household’s energy usage by 15 percent or more and potentially double usage during peak demand times. With the offering, utilities will have the tools to roll-out intuitive, user-friendly EV adoption customer journeys and time-of-use (TOU) plans to engage, educate and reward owners for charging during non-peak times. In the future, these same kinds of engagement programs can also be used for utilities to buy-back unused energy from their customers’ EV batteries to help balance energy supply and demand in times of need.

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