Fine-tuning how homes can help the grid as ​‘virtual power plants’

Can thousands of houses equipped with remote-control thermostats and other devices mimic what big power plants do for utility grids?

EnergyHub, a software and services company that operates virtual power plants (VPPs) for about 70 utilities in 30 states, says yes — and it has a trove of data from three major U.S. utilities that shows how it can be done.

This week, EnergyHub is publishing the results of trial runs it conducted this spring and summer with Arizona Public Service in Arizona, Duke Energy in North Carolina, and National Grid in Massachusetts. Over several afternoons and evenings, EnergyHub tested its capabilities to get thousands of smart thermostats — and for National Grid, rooftop solar-charged batteries as well – to help the grid.

The company says its approach, known as dynamic load-shaping, will allow it to more precisely manage the 2 gigawatts of flexible capacity it now controls from about 1.4 million customer devices, said Paul Hines, EnergyHub’s vice president of power systems. The company is hoping that the successful test cases will help convince utilities and regulators that ​“virtual power plants can become a core part of their infrastructure,” he said.

Getting customers to turn down their air conditioners, water heaters, and other appliances to deal with rising electricity demand is a lot cheaper than building new power plants. Hines cited a U.S. Department of Energy report estimating that 80 to 160 gigawatts of VPP capacity could be unleashed across the country by 2030, enough to meet 10 to 20 percent of U.S. peak grid needs and save utility customers roughly $10 billion in annual costs.

“There’s broad agreement that there’s a need for this stuff,” he said. ​“But when you talk to grid operators, they still have a ton of skepticism about whether virtual power plants can be a valuable part of the core resource mix.”

That’s because traditional demand response programs that pay customers to let utilities turn down their thermostats and other appliances have some well-known features that can make them unreliable, he said.

A typical demand response event – say, one reacting to a heatwave — involves two key steps: The first is ​“precooling,” or ordering thermostats to ramp up air conditioning earlier in the day when the grid isn’t yet stressed, so that homes can ride through the hotter hours ahead. The second is the demand response event itself, during which temperature settings on thermostats are raised to reduce air-conditioning power use when overall grid demand reaches its peak. 

Hines pointed out two significant issues that can crop up as a demand response event unfolds. First, there’s the ​“decaying response” factor. ​“You get a ton of load shed in hour one,” he said. But as people grow less comfortable with rising temperatures and start to override their thermostat settings, the energy savings decrease.

Then there’s the so-called snapback effect — the surge in electricity use when a demand response period ends and a large number of thermostats reset, triggering a bunch of air conditioners to turn on at once. ​“You get this big spike in load at the end,” he said — and that secondary peak can cause grid problems of its own.

These are standard problems for a smart-thermostat-based program, Hines said. ​“The question is, can we turn that into a more reliable, schedulable resource?”

EnergyHub, a subsidiary of Alarm.com, says its technology enables it to do that. In 2022, the company purchased Packetized Energy, the startup that Hines co-founded with two fellow University of Vermont professors in 2016. In simple terms, Packetized Energy’s software analyzes how much energy every device connected to the system needs to do its work — in the case of thermostats, keeping air conditioning and heating on to deliver set temperatures — over long periods.

That data is then fed into mathematical models that analyze how much each individual customer can be expected to reduce their electricity use during a demand response event. Some homes have poor insulation or older air conditioners that don’t perform as well when it’s time to precool homes, and some people may be less comfortable putting up with multiple hours of hotter-than-usual indoor temperatures, for example. Other homes are better insulated and have more efficient cooling systems, or their residents have previously demonstrated a willingness to sweat it out for longer periods.

EnergyHub’s system divides all those customers into different categories. ​“We use some fairly sophisticated machine learning, differential equations, to build a mathematical model” to predict what’s going to happen to the power consumption of each group when their setpoints change, Hines said.

That’s very different from simply broadcasting a signal to every customer’s thermostat to turn up by several degrees and then waiting to see how many customers actually let those commands stand for hours at a time.

This grouping approach also allows utilities to create a three-hour load-reduction event out of programs that allow utilities to ask their customers to turn over their thermostat controls only for two hours, he noted.

That’s what EnergyHub did with Duke Energy, the sprawling utility with operations in six states. In North Carolina, dynamic load-shaping test runs this spring and summer ​“combined a whole bunch of two-hour events, staggered them in a smart way so we got a three-hour constant load-shed event, and minimized snapback, to get it as close to zero in that final hour,” he said. ​“We fed that into an optimizer that essentially explored an enormous space of what-if scenarios, to figure out what is the optimal combination of 15–20 events to get the load shape you want.”

​“The result was a bunch of different schedules from different devices,” he said.

Below is a chart displaying this, with target load reductions in yellow and actual load reductions in black, followed by a second chart that color-codes the different groups that EnergyHub assembled to do the job. (Note that this chart uses positive numbers to indicate how much load reduction is happening and negative numbers to show greater-than-normal electricity consumption during precooling and postevent snapback.) 

Duke Energy has more than 1,500 megawatts of controllable load across its six utilities, and more than 100,000 smart thermostats under EnergyHub control, said Brian Lusher, the utility’s manager of residential demand response. But as with many other demand response programs, those resources have typically ​“been called all at once, and you only have a length of time before that home heats up or cools off beyond a certain limit and customers are opting out,” he added.

EnergyHub’s work behind the scenes helps Duke’s grid operators get more clarity into how much of the controllable load at their fingertips will actually persist through multiple hours, he said. ​“One specific group may only be able to last with that temperature drop for 30 minutes, while another group with higher insulation level can last for two hours.”

The actual results didn’t perfectly match the targets that Duke Energy established, he noted. But it’s closer than what a typical demand response event would yield, he added — and the feedback being delivered from EnergyHub’s internet connections to individual thermostats helps true up grid operators’ understanding of what’s happening in real time.

“As a grid operator, I don’t need to know those nuances,” he said. ​“I need to schedule an event and know how long it will last.”

National Grid, a utility that offers its customers in Massachusetts lucrative incentives for signing up their thermostats, batteries, and electric vehicles chargers to its Connected Solutions program, decided to test EnergyHub’s dynamic load control chops on batteries as well as thermostats.

“That’s a natural request from a utility — ​‘I’ve got a group of thermostats, they do load shed mostly in the first hour of the event and then they’re done. Can we use a bunch of batteries to fill in that gap for a four-hour event?’” Hines said. 

The result, he said, ​“was a four-hour, fairly constant delivery of power. That’s particularly important for National Grid,” which is part of a New England grid region where utilities must pay significant capacity charges based on their energy demand during peak hours that they can’t precisely predict.

National Grid still has relatively few batteries in its Connected Solutions program, said Paul Wassink, a lead engineer and manager for the utility’s distributed energy resources programs. Most of its roughly 100 megawatts of controllable load in Massachusetts is connected via smart thermostats, and normally ​“we’re sending the exact same setpoints to everyone in the state.”

“EnergyHub looked at all those thousands of thermostat customers and found that with this exact mix, with different setpoints, they could get a pretty firm dispatch from here to there,” he said.

The utility also tends to ​“call very long events, for the whole afternoon, to make sure we’re not causing a problem with precool or snapback,” he said. Those secondary peaks aren’t a huge problem across its grid at large, he said — but they can cause problems on local grid circuits serving lots of customers with smart thermostats all responding at once.

As for Arizona Public Service (APS), it’s been working with EnergyHub for years to fine-tune its smart-thermostat-based Cool Rewards program. To combat increasingly long bouts of 100-degree-plus Fahrenheit summers, APS has been aggressively dispatching thermostats to precool homes using the gigawatts of solar power available on its grid during midday hours. It also wants to start turning down as much air conditioning as possible starting at 4 p.m., when the utility’s time-of-use rates begin charging customers more for power, Hines said.

This summer’s tests were aimed at proving that EnergyHub could successfully curtail an increasing amount of electricity from APS’s Cool Rewards customers as peak demands rose during the day. ​“You kind of ramp it up as the stress from the evening load increases, and then ramp it down when the stress decreases,” he said. 

Kerri Carnes, APS’s director of customer-to-grid solutions, said that getting this kind of granular control will be increasingly important as the utility’s share of smart-thermostat-enabled capacity grows from about 200 megawatts as of this year to nearly 1 gigawatt by the end of the decade.

“To successfully manage that, we have to leverage machine learning and automation to optimize these assets for the maximum effect,” said Carnes. ​“We said, ​‘Here’s the shape we want,’ and [EnergyHub] not only met it but exceeded it for several of the hours. That felt like a precursor to what we’ll be able to do with this in the future.”

Not all customers can participate in Cool Rewards — homes need a central air-conditioning system for a smart thermostat to be useful. But APS calculates that the cost of the bill credits and discounted or free thermostats is a bargain compared with the alternative, which is firing up a power plant to supply the missing electricity demand.

“These are savings that flow to all our customers,” Carnes said