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Suburban EV adoption ‘hot spots’ could require smarter approach to charging and grid operations

 April 3, 2019 

If you could hold planet Earth in the palm of your hand, it would feel nearly as smooth as a billiard ball. Up close, features such as Mount Everest and the Marianas Trench become more formidable. An August 2018 McKinsey study on the how electric vehicles could impact demand on power grids showed a similar dichotomy between the big picture and the details in which the devil feels so at home.

Using a Monte Carlo simulation of EVs in Germany as a frame of reference, McKinsey researchers found that as EV adoption rises—from <1% of vehicle stock today to 7% by 2030 to 40% by 2050—EV-related electricity demand (kWh of consumption) would add just 1% to total German power demand by 2030 and about 4% by 2050.

So the big picture looks smooth and no cause for major alarm from a grid operations perspective. But EV adoption is unlikely to be uniform, and national averages will hide sharper geographic differences. Especially when it comes to suburban ‘hot spots’ where EV adoption is expected to be greatest.

In suburbia, surging EV demand could also spike grid demand

When the McKinsey team looked at postal-code-level EV penetration, it found that, in suburban areas where EV uptake is expected to be strongest, peak load would spike about 30% in the evening hours after commuters return from work and plug in their wheels.

That model, based on load profiles for a September day in the U.S. Midwest, assumed a typical residential feeder circuit for 150 homes, each home with two cars. One in four cars would be an EV charging at an average plug-in power of 3.7 kilowatts. Without corrective action, the analysists say, the associated 30% bump in peak demand would be enough to require grid upgrades costing several hundred dollars per EV to alleviate overloaded feeder circuits.

A second analysis considered the load profile of a fast-charging station. That one came back with more startling results yet. According to the authors, “a single fast-charging station can quickly exceed the peak-load capacity of a typical feeder-circuit transformer.” No surprise there, when one considers that a single EV using a high-end fast charger sucks as much juice as the peak demand of 80 households.

A role for coordinated EV charging and complementary strategies

Fortunately, there are solutions less formidable than scaling Everest or plumbing the depths east of the Mariana Islands. Among those the McKinsey team suggests include collocating an energy storage unit with the transformer (or, alternatively, pairing batteries with EV charging stations, as ChargePoint is doing with its PowerBlock fast-charging system).

The big one McKinsey suggests, though, is implementing time-of-use rates for electricity users. That at least provides a business case for load shifting in the form of a price signal for EV drivers to bulk shift EV demand from on-peak times to off-peak hours. But that still doesn’t solve the problem of myriad EV drivers all plugging in around the same time—even if at off-peak hours—and surging grid demand beyond what local circuits have been built to handle.

Moreover, for many grids across the country, existing time-of-use pricing doesn’t always correlate with grid emissions, so EV drivers could inadvertently be economically incented to shift their charging to times of higher grid emissions. We need to solve the demand-price-emissions equation as a set, rather than focus on one or two at the expense of the others.

To avoid dozens of EVs simultaneously draining the local grid during what would historically have been an off-peak time, doing this right will require “centrally coordinated, intelligent steering of EV-charging behavior,” the McKinsey team says. Or perhaps a blend of central coordination by utilities, distribution system operators, and EVSE network operators and decentralized coordination via EV drivers, automakers, and others at the grid edge.

Doing more with smart EV charging

Fostering smart EV charging behavior would offer myriad benefits, the authors say. Of course, it would shave peak demand at the heart of McKinsey’s study findings. Second, it would provide a new lever for managing system demand and offer valuable system-balancing services to grid operators (akin to a flavor of vehicle-to-grid services sometimes written about but not yet realized). And, it could crank up charging at times of high solar and wind generation (or, similarly, times of low marginal emissions rates) and ramp it back down when generation and marginal emissions get dirtier.

This last point is important. McKinsey is saying that centrally coordinated, smart EV charging done right should involve more than shaving peaks and taming the roller coaster of grid demand. It should take into account the mix of electricity being produced in a way that favors renewables over fossil energy sources, a need that will persist even as coal-fired electricity generation continues to wane. McKinsey has separately estimated that roughly 80 percent of the forecast growth in U.S. electricity demand is expected to be met with natural gas generation.

Here is where technologies such as WattTime’s Automated Emissions Reduction (AER) system can play a vital role.

It’s not hard to estimate average EV emissions based on overall grid mix in a particular region—calculators such as this one by the Union of Concerned Scientists can do that for you. But that’s like viewing Earth as cue-ball smooth; averages show a general picture but lack the details necessary for impactful decision making.

The electricity mix—and especially those generators that are on the margin—in a given locale varies minute-by-minute with the ebbs and flows of demand, winds gusting through turbine blades, clouds passing between solar panels and the sun, and so on.

Built into EVSE networks, EV chargers, and EVs themselves (or some combination), WattTime’s AER software keeps tabs on power generation and marginal emissions rates in real-time. That opens the door to the tracking marginal emissions of power consumption in fifteen-minute increments—and, more importantly, using that knowledge to optimize charging not only based on peak-load considerations, but also on the environmental impact of charging at a given moment.

There are, after all, counterintuitive times when it could actually better to charge an EV during times of peaking grid demand. For one example, imagine that peak demand aligns with a particularly windy day, where surplus wind generation is being curtailed. Or perhaps early afternoon on a late spring or early fall day, when solar PV is cranking but even the grid’s peaking demand isn’t enough to suck up all those sun-powered electrons. Normal time-of-use rates might push EV charging demand off peak and miss an opportunity to charge during that time of surplus wind or solar.

Without smart, flexible charging and the insights of technologies such as WattTime AER, peaks and troughs will be as ingrained in EV charging as they are in our planet’s crust. Fortunately, we have the tools to navigate the ups and downs in the cleanest possible way.