How Virtual Power Plants Teach Utility Grids to Manage Themselves
The modern power grid works so well that most people never think about it. At least until it doesn’t. Lights turn on instantly, homes stay cool during heat waves and electric vehicles charge overnight, as if electricity were infinite.
In reality, the system is under historic strain, absorbing new demand from electrification, climate volatility and artificial intelligence (AI)-driven data centers.
“There’s a lot of nuance here and a lot of things that are just backstage,” Justin McCammon, VP of Engineering at EnergyHub, told PYMNTS. “The fact that all of our lights are on right now, there’s so much behind that.”
What’s increasingly behind it is software.
Virtual Power Plants and the Grid Consumers Never See
Virtual power plants, or VPPs, have recently crossed from industry jargon into public discourse, showing up in policy conversations and even morning television.
A VPP is not a physical facility. It’s an orchestration layer that aggregates distributed energy resources, such as smart thermostats, electric vehicles, home batteries and other flexible electrical loads, and operates them as a single grid asset.
“These are typically the largest electrical loads you have in your house,” McCammon said. “And increasingly, they’re internet-connected things.”
“Imagine a territory with a hundred thousand or 500,000 of those all working in orchestration,” he noted, adding that at scale, connectivity changes everything. “That becomes the virtual power plant.”
The business case for virtual power plants starts with peak demand. Utilities are required to serve electricity whenever customers need it, even if demand spikes only a few hours per year.
“If everyone’s ACs are on, they’ve got to serve that load,” McCammon said. “If that means building a whole new power plant for five hours every summer, they’ve got to do it.”
Historically, that obligation has driven massive capital investments in infrastructure that sits idle most of the year. VPPs offer a more flexible alternative. Instead of adding supply, they shape demand by slightly adjusting thermostats, shifting EV charging or dispatching home batteries at precisely the right moments. The result is lower costs, reduced emissions and a grid that stretches further without breaking.
“You can still be comfortable,” McCammon said. “And maybe they don’t have to go build a whole new power plant.”
AI Represents Both the Problem and the Fix
Just as utilities gain new flexibility, electricity demand is accelerating again, this time from artificial intelligence itself. Data centers have become one of the most visible and controversial new loads on the grid.
“Where are they going to get power from?” McCammon asked, giving voice to a question rising up around the U.S. “Is my bill going up because there’s one down the street?”
That tension puts AI in a paradoxical role. It is driving demand even as it becomes indispensable for managing it. Coordinating millions of devices across a dynamic grid simply isn’t possible with static rules or manual controls.
Forecasting is one of AI’s most valuable contributions to virtual power plants. Energy demand, especially from heating and cooling, is deeply tied to weather, making prediction both essential and uncertain.
AI models combine weather forecasts, historical usage, and live device telemetry to estimate future conditions and determine what flexibility is available to utilities. But McCammon is careful about where probabilistic systems are allowed to operate.
“There’s more tolerance for error in forecasting,” he said. “But when it comes to billing or incentives, we’ve got to get that 100% right.”
Maintaining that precision is complicated by the messy reality of consumer devices. Unlike utility-grade smart meters, home technologies vary widely in accuracy, connectivity, and behavior. To compensate, the organization runs thousands of automated data-quality tests inside Snowflake, continuously validating inputs and outputs.
“Every variant of human behavior you can imagine shows up,” McCammon said. “Is the EV plugged in? Is it out of Wi-Fi range?”
“Garbage in, garbage out,” he added. “That’s not optional for us.”
The Autonomous Grid Ahead
McCammon sees the future of virtual power plants moving steadily toward autonomy. Internally, his team uses a framework inspired by self-driving cars, defining maturity levels based on how much human intervention is required.
“Right now, there are still a lot of humans inputting, ‘Do this tomorrow,’” he said. “But the frontier is a grid that reacts on its own … alive and flowing all the time as conditions change.”
In that future, AI continuously absorbs sensor data, evaluates conditions and responds in real time, much like an autonomous vehicle navigating traffic.
The challenge, as with autonomous driving, is confidence. Richer data, deeper integration and more experience with rare extreme events will determine how far autonomy can go.
What emerges is not a dramatic reinvention of energy, but something more consequential: a grid that quietly learns how to manage itself, so the rest of us don’t have to think about it at all.
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