The variable load of electric cars is a great match for variable generation.

—Simon Lonsdale, head of ChargePoint’s Business Development

Utilities are dealing with three major trends, Lonsdale noted: digitalization, decarbonization and decentralization. Electric vehicles can provide a big help, he suggested, all the while tied to driver needs.

ChargePoint’s stance is bolstered by a new MIT study that suggests, in part, that electric cars that plug into the grid, could, collectively, act as a massive “virtual battery” for grid energy storage.

ChargePoint envisions that at scale—i.e., with wide deployment of electric vehicles—80% of charging will be at home or at work, while 20% will be public—i.e., in parking lots, streetside or fast charging stations on the highway.

EVs with more than 200 miles of range will accelerate already rapid EV sales growth, and are expected to reach 35% of global new car sales by 2040, according to Bloomberg New Energy Finance. Long-range EVs will need ultra-fast DC charging for long-distance trips, while electric buses and service trucks require high-power charging for their daily routines.

Tesla and its Supercharger network have shown that fast charging is a key factor in acceptance. People need to see the capability for fast charging, to know that if they want to go for a long distance, they could.

—Simon Lonsdale

ChargePoint’s new Express Plus platform can add 300 km (186 miles) of range in 11 minutes, Lonsdale noted.

ChargePoint envisions, however, that the EV market will evolve with a variety of pack capacities, all based on weight, distance and performance ratios for the particular vehicles and their intended uses. For example, he noted, “there will always be a place for 20-30 kWh packs.” Lonsdale suggested that there might be consolidation around three pack categories in the short- to medium term: 20-30 kWh; 60 kWh; and 100+ kWh.

MIT: Utilities and the virtual battery. In the power grid, supply and demand need to match exactly. If consumers demand more power than producers can supply, or if producers provide more power than consumers need, the result can be rolling blackouts. Power producers usually keep turbines spinning at a few offline plants, so they can ramp up production if demand spikes. Or they maintain coal-fueled backup generators that can be fired up quickly. But these approaches are either costly, polluting, or both.

In theory, the grid could employ a battery to keep supply and demand in balance, but existing battery technologies offer no cost savings over power production. In the new paper, the MIT team argues that “smart appliances” in homes and offices, such as thermostats that can be adjusted remotely and electric cars that plug into the grid, could, collectively, act as a massive battery, offering a lower-cost, lower-emission alternative to backup power generation in the grid.

Getting power producers to trust that virtual battery, however, requires rigorously quantifying its capacity and charge and discharge rates. In the paper, the researchers take some initial steps in that direction.

We have a lot of these flexible [electrical] loads, but they’re small, diverse, and scattered all over the place. At the moment, they’re not a well-understood resource. The question is, can we develop algorithms that schedule consumption of these loads in such a way that satisfies all their constraints and makes them appear to the power system as a battery, which can store a certain amount of energy and absorb and release it at a certain rate?

—Daria Madjidian, a postdoc in MIT’s Laboratory for Information and Decision Systems (LIDS)

Madjidian and two of his LIDS colleagues—Mardavij Roozbehani, a principal research scientist; and Munther Dahleh, the William Coolidge Professor of Electrical Engineering and Computer Science and director of MIT’s Institute for Data, Systems, and Society—presented their preliminary answer to that question at the Institute of Electrical and Electronics Engineers’ Conference on Decision and Control.

In treating a collection of flexible electrical loads as a single battery, the researchers identified a fundamental tradeoff between the battery’s capacity and the rates at which it can charge and discharge.

That tradeoff, however, can be renegotiated on a daily or even hourly basis. If, one day, a power provider expects strong but erratic winds, it might want to privilege quick charging, in order to capture the output of its wind turbines. If, on another day, it expects almost all of its customers to begin turning on their home air conditioners in the evening, it might want to privilege capacity, in order to handle a surge of demand.

Although conventional batteries can’t do this, devices with flexible charge rates—such as EVs—can, Madjidian said. “They open a path to designing control policies that tailor their specifications for particular purposes.”

For example, an electric car parked in an office building needs to recharge its battery, but the charge rate can be fast or slow, and the charging might take place at any time within, say, a four-hour window. Slowing the charging rates or deferring the charge times for a group of cars reduces demand on the grid (equivalent to a release of energy from the grid battery). The charge rate of this virtual battery is limited by the available capacity of the cars’ own batteries and by their individual maximum charge rates.

The LIDS researchers first developed a very simple model of a grid with flexible loads, in which the loads were all the same size and came online —the equivalent of electric cars’ being plugged in—at regular intervals. That model suggested the tradeoff between the capacity of the virtual battery and its charge and discharge rates. But in investigating the reasons for that tradeoff, the researchers identified a fundamental principle they believe will hold for almost any collection of flexible loads.

Suppose, for instance, that you have two batteries, one that can be charged or discharged quickly, the other slowly. Now suppose that you’re treating these two real batteries as a single virtual battery, and the virtual battery is half full. How do you distribute the virtual battery’s charge across the two real batteries?

If you want to maximize the charge rate of the half-full virtual battery, you need to keep the faster-charging real battery more depleted than the slower-charging one; that way, it can handle the bulk of any incoming charge. The opposite is true, however, if you want to maximize the discharge rate; then, you need to keep the faster-charging battery fuller than the other, so it can handle the bulk of any discharges.

To see how charge rates trade off against battery capacity, suppose that both of the real batteries are empty. To maximize the charge rate of the virtual battery, you need to use both real batteries; any two batteries can absorb charge faster than either of them can in isolation. But the faster-charging real battery will fill up before the slower-charging one does.

So at the maximum charge rate, the capacity of the virtual battery is the capacity of the faster real battery, plus however much charge the slower battery can absorb by the time the faster battery fills. The remaining capacity of the slow battery must go unused. Lowering the aggregate charge rate, however, allows the slower battery to absorb more charge by the time the faster battery is full, increasing aggregate capacity. In the paper, the LIDS researchers were able to characterize this set of tradeoffs for their simple model. In ongoing work, they are developing more realistic models, in which both the size and the timing of the loads varies.

We need more power-system flexibility as renewable-generation penetration grows. Using electricity demand from EVs [electric vehicles] and HVAC [heating, ventilation, and air conditioning] as a ‘virtual battery’ is a very interesting conceptual jump that many researchers are thinking about. Madjidian et al. build on the work of other researchers to provide some new insight into how to quantify the size of these virtual batteries. Their contribution is to bring new types of electricity loads into the space of things we can quantify as virtual batteries.

—Duncan Callaway, UC Berkeley

Resources

• Battery Capacity of Deferrable Energy Demand Daria Madjidian, Mardavij Roozbehani, and Munther A. Dahleh (2016) “Battery Capacity of Deferrable Energy Demand”