Co - Authored by: By Michael Cote, Project Manager | Grid Modernization and Distribution Planning, 1898 & Co.
As our society transitions away from internal combustion vehicles toward electric vehicles (EVs), one thing is certain — utility grids will bear the brunt of the challenge of building out and upgrading grid infrastructure to support EV charging.
This raises a number of fundamental questions chiefly related to the fact that grids were not designed to handle this type of dynamic load. An enormous amount of capital will inevitably be required to accommodate this demand, though sophisticated planning and modeling tools can help utilities manage the timing and optimize certain investments to be sure demand is met at the right time.
Forecasting where the first waves of EV adopters are most likely to live becomes a complex exercise in demographic analysis with reliance on many of the principles espoused by the diffusion of innovation (DOI) theory. The DOI has been used for decades as an explanation of why an idea or product gradually gains acceptance among the general population. The analysis we’ve performed for utilities looks at income and educational patterns within the service territory, while also factoring in certain patterns that help identify the sentiments and overall psychological behavior that might make certain population groups adopt a new idea such as EVs before the general population.
Once those pockets of EV owners are mapped out, the forecasting exercise turns to projections of distribution system topology. Where are the feeders and substations that are most likely to be choke points that could become overloaded? Where are the transformers that are most likely to break first?
With this data fed into a forecasting model, we turn to DOI again to map out various forecast scenarios. Utilizing machine learning techniques, we overlay the grid map with polygons that cover the entire service territory populated with all the relevant data needed to rank and prioritize where investments should be made.
Pace of Adoption
Even with sophisticated modeling and forecasting algorithms, developing precise forecasts of rates of EV adoption is still a dice roll. To account for uncertainty, the planning exercise turns to scenarios that lay out various rates of adoption, ranging from slow to moderate growth to aggressive accelerated growth.
According to a recent study by National Renewable Energy Laboratory, there will be between 30 million and 42 million EVs on the U.S. roads by 2030, requiring nearly 27 million Level 1 and Level 2 charging stations at homes, apartments and other residential locations. This compares to around 1.5 million such chargers available as of mid-2023.
The widely quoted study also notes that another 182,000 public direct-current fast chargers would be needed as well as 1 million Level 2 public chargers at commercial locations and high-density neighborhoods.
What It Will Take
The capital investment required for larger capacity transformers and upgraded distribution feeders could easily reach into the hundreds of millions for many utilities. But before those numbers start becoming a reality, most utilities are urgently looking at ways to optimize the grid with many types of lower cost improvements or interconnections as well as implementing time-of-use rates and demand response plans for voluntary curtailments to help mitigate and control demand.
In fact, with predictable charging patterns for EVs, time-of-use rates that provide incentives to allow utilities to control when EVs can charge may become more widely adopted as a means to mitigate enormous capital investments.
With most utilities engaged in five-year planning cycles for distribution capital investments, many are still looking at ways to delay the ramp-up dates as long as possible. With forecasting models still showing relatively moderate rates of EV adoption in most regions of the country, utilities still have time to plan and track expected rates of EV adoption and then converting those forecasts in to annual budgets. However, as the 2030-35 time frame approaches, capital plans to build out the EV charging network should be ready to go, as most models show that rates of public adoption will steadily grow to become a significant portion of the load utilities must serve by this time.