The AI data means energy suppliers can 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 use of electric vehicles (EVs) is growing at a record rate, with the International Energy Agency (IEA) predicting that the number of electric cars on the road will rise from 3.1 million in 2017 to 125 million in 2030.
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.
“With solar, wind and storage technologies now constituting 90 percent of investment interest, the road is paved for deeper decarbonization of the electricity sector,” said Ben Kellison, director grid research at Wood Mackenzie Power & Renewables. “The case for transport electrification has never been stronger and the rapid growth in investment interest from car manufacturers is a confirmation of the future consumer demand for EVs. Utilities are now faced with an increasingly clean and decentralized system and they need new data and analytic packages to support a new planning paradigm.” While this transportation development represents an important 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.
“With almost every major auto manufacturer releasing new EV models in the coming years, the window of time for utilities to act is closing,” said Dan Byrnes, SVP of product development at Oracle Utilities. “The intelligence our analytics provide is essential for utilities to make needed assessments on grid investments and in tandem, work as trusted advisors to customers who may be in the dark as to how owning an EV is impacting their energy footprint and bill. From utility optimization to proven customer engagement, only Oracle offers a complete package to manage the explosion of EVs.”
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 the journey.”