How Topline Demand Control Reliably Increases Firm Capacity

Topline Demand Control (TDC) increases firm capacity by steering a fleet of distributed energy resources (DERs) to follow a target load shape in real time, instead of sending one fixed command and hoping the fleet responds. In a field comparison across several thousand smart thermostats, a TDC event delivered about 53% more firm capacity than a traditional event, with nearly the same average energy reduction and 25% fewer customer opt-outs.

That difference matters because utilities don’t plan around averages. They plan around reproducible and reliable resources. Fortunately, through granular optimization, TDC can provide a consistent output, using demand flexibility in programs like demand response, EV charging, BYOD programs, or virtual power plants (VPPs) from program managers to a tool for grid operators to employ.

 

In This Article

1. Why Electricity Demand is Rising
2. What Firm Capacity Is and Why the Grid Needs It
3. What is Topline Demand Control?
4. Field Results: Topline Demand Control vs. a Traditional Event
5. What Reliable Capacity Means for Grid Operators
6. FAQs
7. Glossary of Terms

 

Why Electricity Demand is Rising

U.S. peak demand is now forecast to grow 166 GW over the next five years, roughly seven times the 24 GW forecast in 2022, according to Grid Strategies’ 2025 national load growth report. Data centers account for the largest share, alongside new manufacturing, building electrification, and electric vehicles.

For two decades, utilities planned around flat or barely growing demand. Annual energy growth averaged under 1% in the 2000s and 2010s; forecasts now call for about 5.7% per year. At the same time, coal plants are retiring, gas turbines face multi-year procurement backlogs, and new transmission takes years to permit, as Burns & McDonnell notes.

The result is a growing gap between the peak capacity utilities must secure and how quickly conventional supply can be built. Ultimately, this keep the lights on crisis is pushing demand flexibility from a customer program into a resource planning question.

 

What Firm Capacity Is & Why the Grid Needs It

Firm capacity is the amount of power a resource can be counted on to deliver whenever it is called, for as long as it is needed. It is the number a system operator plans around, not the best case or the average.

That distinction is why firm power is back at the center of grid strategy. As intermittent wind and solar grow, the total megawatts on the system matter less than the megawatts available during the tightest hours. Stanford economist Frank Wolak used the August 2020 California and February 2021 Texas supply shortfalls to argue that resource adequacy must account for what each resource can actually deliver, not just its nameplate rating. The Brattle Group has similarly asked whether firm capacity requirements are outpacing replacements as dispatchable plants retire.

Demand flexibility faces the same test. A virtual power plant (VPP) that reduces load by 45% on average but dips to 25% for a few minutes is, to a grid operator, a 25% resource. Closing the gap between average and minimum performance is how existing distributed energy resources become firm capacity.

 

What is Topline Demand Control?

Topline Demand Control is a method of optimizing aggregated DERs so their combined load follows a user-defined load shape for the full length of a demand flexibility event. It pairs AI, demand forecasting, model predictive control (MPC), and real-time control through a Grid-Edge DERMS, and it works across smart thermostats and batteries today.

A traditional event starts with a device command, such as raising every thermostat setpoint by the same offset, and measures the result afterward. Topline Demand Control starts with the result, such as a flat power reduction from 4 to 7 p.m., and works backward to the commands. It runs in three steps:

1. Forecast. Machine learning models estimate the baseline and how much flexibility is available in each hour, based on weather, device behavior, and past events.
2. Plan. The operator sets a target load shape informed by that flexibility estimate.
3. Control. An MPC controller compares live telemetry to the target and determines commands throughout the event, adjusting the commands it plans to send as device responses change or they opt out.

Because the controller acts on the actual change in aggregate demand, it can hold a steady reduction rather than a sharp drop followed by decay. That steadiness is what moves DERs into the control room.

 

Field Results: Topline Demand Control vs. a Traditional Demand Event

Field results: Topline Demand Control vs. a traditional event

In a side-by-side comparison, a Topline Demand Control event raised firm capacity by about 53% over a traditional event using the same fleet of several thousand thermostats. The two events ran on days with similar weather (94°F and 93°F) and an otherwise near-identical setup:

• The same group of customers
• The same pre-cool and load-shed window durations
• One pre-cool command per device of no more than 1 hour, with identical temperature offsets
• One shed command per device of no more than 3 hours, with identical temperature offsets

The traditional event applied the pre-cool temperature offset to every thermostat at the start of the pre-cool window and the shed offset at the start of the load shed window. The Topline Demand Control event applied setpoint changes dynamically, in real time, based on the actual change in aggregate demand, but still only sent at most one command to each device in each window.

Pre-cool and rebound. Real-time offsets during pre-cool appeared more effective than a single upfront command. Rebound after the event was similar in size, but demand returned to baseline faster with Topline Demand Control.

 

Load Shed

This is where the difference shows. Both events produced nearly the same average reduction, but the Topline Demand Control event held a far more consistent reduction from baseline. Here, firm capacity is defined as the minimum demand reduction from baseline over any 5-minute interval in the load-shed window, which is the reduction a system operator can count on. We use the maximum baseline value during each event to convert those power reductions to a percentage.

• Firm capacity (minimum 5-min reduction): 24.7% traditional vs. 37.7% with Topline Demand Control, +13.0 pts (52.6% higher)
• Last-hour reduction: 28.7% traditional vs. 44.9% with Topline Demand Control, +16.2 pts (56.3% higher)
• Average reduction: 45.7% traditional vs. 43.4% with Topline Demand Control, −2.3 pts (5.0% lower)
• Customer opt-outs: 25% fewer with Topline Demand Control

The slightly lower average is a deliberate trade. By spacing out setpoint increases instead of front-loading them, commands are staggered, which helps maintain the desired, consistent output. In turn, this reduces overall customer discomfort or disruption. Less total time customers spend with their setpoint changed is likely a reason 25% fewer customers opted out.

In practical terms, Topline Demand Control can turn the firm capacity of a 10,000-thermostat program into that of a roughly 15,000-thermostat program, without recruiting a single new device.

 

What Reliable Capacity Means For Grid Operators

For grid operators, reliability turns demand flexibility from a hopeful estimate into affordable firm capacity they can schedule. Program managers and grid operators judge the same event differently:

• Program managers look at participation, average kW per device, customer satisfaction, and whether the program hit its enrollment and savings targets.
• Grid operators look at the worst interval. They need to know how many megawatts will be there at 6:45 p.m. on the hottest day of the year, and whether that number will hold for the full window.

A traditional event can look successful to a program manager and still be discounted by an operator, because its output sags. Topline Demand Control narrows that gap. A flatter, more predictable reduction can be credited closer to its full value in operations and resource planning, much like a supply block from a conventional generator.

The economics follow. With peak demand rising faster than new plants and infrastructure can be built, every megawatt of dependable capacity from devices customers already own is a megawatt utilities don’t have to build or buy at peak prices. Raising firm capacity by roughly half, with the same energy and fewer opt-outs, gives operators reliable access to affordable capacity from existing DERs, and gives customers a program they are more likely to stay in.

 

FAQs

What is Topline Demand Control (TDC)? Topline Demand Control is a method of dispatching aggregated DERs so their combined load follows a user-defined load shape. It uses AI-based forecasting and model predictive control to adjust device commands in real time.

How does Topline Demand Control increase firm capacity? It holds a steady reduction instead of a sharp drop that fades. In a thermostat field comparison, the minimum 5-minute reduction rose from 24.7% to 37.7% of baseline, about 53% more firm capacity.

Does Topline Demand Control shift more energy than a traditional event? No. Average reduction was nearly the same (43.4% vs. 45.7%). Topline Demand Control spreads that energy more evenly across the event.

Does Topline Demand Control affect customer comfort? It spreads the same offsets out over the event instead of all-at-once. In the comparison, it produced 25% fewer opt-outs.

Which devices work with Topline Demand Control? Smart thermostats and batteries today. Any controllable DER with telemetry can contribute to an aggregate load shape.

Why does firm capacity matter more than average reduction? Grid operators plan around the minimum a resource will deliver. A resource that dips during an event is credited at its lowest point, not its average.

 

Glossary of Terms

• Aggregate load shape: The combined demand curve of a group of devices over time.
• Baseline: The estimated demand a group of devices would have consumed without an event.
• Demand flexibility: The ability to shift, shed, or shape electricity use in response to grid needs.
• Demand response: Programs that ask customers or devices to reduce or shift use during peak periods.
• Distributed energy resource (DER): A small-scale, customer-sited device that generates, stores, or controls electricity use.
• Firm capacity: The amount of power a resource can reliably deliver when called; here, the minimum 5-minute reduction from baseline during load shed.
• Grid-edge DERMS: Software that connects to and controls behind-the-meter devices.
• Load shed: The event window when devices reduce demand.
• Model predictive control (MPC): A control method that uses a model, optimization, and live feedback to keep controlling toward a future target.
• Opt-out: A customer overriding or leaving an event.
• Pre-cool: Cooling a home before an event so it stays comfortable while the thermostat setpoint is raised.
• Rebound: The rise in demand after an event as devices recover.
• Topline Demand Control (TDC): Real-time control of aggregate DER demand to follow a user-defined load shape.
• Virtual power plant (VPP): An aggregation of DERs coordinated to act as a single grid resource.