The EV detection capabilities use Oracle's experience of disaggregating household energy data from billions of data points collected from 60 million households across 100 utilities. The trained AI 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.
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 AI capability, 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.
"EVs will have an impact on every part of a utility's operations – from grid stability and regulatory affairs to customer billing and engagement," said Byrnes. "With Oracle, our customers have the tools and intelligence they need to make better decisions, maximize outcomes, and increase customer satisfaction every step of