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