Smart appliances can collectively act as a massive battery, offering a lower-cost, lower-emission alternative to backup power generation in the grid.
Researchers at MIT are taking on a new approach to actively manage the power grid to help balance supply and demand.
The notion of using the aggregate battery capacity of thousands or millions of plugged-in electric cars—as and when they become sufficiently numerous—as an energy reserve to balance peak demand on the power grid is far from new. Nor is the concept of giving the generator—or, at least, the grid operator—discretionary control over classes of loads within the industrial and domestic consumer base, to limit those peaks. The detail of how to manage such a scheme has, however, been sketchy.
One option is to store power in bulk, but schemes such as pumped-storage hydro are limited, and there is a lack of battery technology to implement the most obvious solution. While we might contemplate assembling a massive battery bank, in available technology it would offer no economic advantage over keeping additional generation running. A paper from MIT offers a new perspective on the problem. The researchers argue 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.
“We have a lot of these flexible [electrical] loads, but they’re small, diverse and scattered all over the place,” said Daria Madjidian, a postdoc in MIT’s Laboratory for Information and Decision Systems (LIDS). “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?”
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 trade-off between the battery’s capacity and the rates at which it can charge and discharge.
That trade-off, 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.
“Conventional batteries can’t do this, but these guys can,” Madjidian said, referring to devices with flexible charge rates. “They open a path to designing control policies that tailor their specifications for particular purposes.”
To get a sense of how a collection of loads can act as a battery, consider a smart thermostat in a large office building. For any given temperature setting, the building’s inhabitants can probably tolerate a swing of a half-degree in either direction without discomfort. Toggling the thermostat a half-degree so that the building’s temperature-control system consumes more energy is the equivalent of charging the virtual battery. Toggling it the opposite direction so that the system consumes less energy equates to releasing the battery’s charge into the grid.
Similarly, 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 the 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 trade-off between the capacity of the virtual battery and its charge and discharge rates. But in investigating the reasons for that trade-off, the researchers identified a fundamental principle they believe will hold for almost any collection of flexible loads.
To see how charge rates trade off against battery capacity, suppose that both of the real batteries are empty, researchers said. To maximise the charge rate of the virtual battery, both real batteries must be used; 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 characterise this set of trade-offs 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.