The Five Convergences (Part III of VI): AI as Controller – When the Grid Learns to Steer Itself

The electric grid has long been called “the most complex machine ever built.” For more than a century, it has relied on human judgment, supported by mechanical systems and basic automation. But now, a dramatic shift is underway — one where the grid gains the ability to perceive, decide, and act in real time.

In this in-depth episode of AIxEnergy, host Michael Vincent is joined by Brandon N. Owens — founder of AIxEnergy and author of The Five Convergences of AI and Energy — to explore one of the most transformative changes in the power sector: AI as Controller.

Brandon explains how artificial intelligence is evolving from a passive analytics tool into an active operator of critical energy infrastructure. This is the moment when AI stops simply advising the grid and starts steering it. AI controllers can process thousands of data points at once, adapt instantly to changing conditions, and take action in milliseconds — from dispatching a battery, to rerouting power across an entire region during a disturbance.

Through clear, engaging examples, Michael and Brandon unpack the opportunities and challenges of this new era:

• Hornsdale Power Reserve in South Australia – where Tesla’s Autobidder software runs a 100-megawatt battery with minimal human intervention, earning millions in market revenue while lowering costs for consumers.
• Google’s AI-managed wind farms – where advanced forecasting boosted the value of wind energy by 20 percent without building a single new turbine.
• Virtual power plants – where thousands of homes, batteries, and electric vehicles are coordinated by AI to act like one large power plant, providing vital support during peak demand.
• Global experiments – including a French competition where AI agents learned to reroute power flows more effectively than human engineers in complex simulations.

The discussion also addresses the risks of putting AI in control of the grid:

• “Model drift,” where AI performance declines as grid conditions evolve.
• Cybersecurity threats, in which false data could trick AI into harmful actions.
• “Black box” decision-making, where operators cannot explain why the AI acted as it did.

Brandon outlines the safeguards needed to keep AI-controlled grids safe:

• Constraint governors that limit AI actions to pre-approved safety ranges.
• Supervisory oversight from humans or backup systems that can override AI decisions instantly.
• Transparent logging so every AI decision can be reviewed and understood later.

Looking to the future, the conversation imagines self-balancing, self-healing, and self-optimizing grids — systems that integrate massive amounts of renewable energy, recover from disruptions in seconds, and constantly improve efficiency. But with that vision comes the need for strong governance, ethical safeguards, and market rules that match AI’s unprecedented speed and precision.

The takeaway is clear: AI as Controller could unlock extraordinary efficiency, reliability, and sustainability — but only if it is implemented with transparency, accountability, and human oversight from day one.

Whether you’re an energy professional, a technology leader, a policymaker, or simply curious about how AI will shape the future, this episode offers an accessible yet deeply informed look at one of the defining transformations of our time.

Transcript

Michael: Welcome to AIxEnergy, the podcast where we explore the rising intersection of artificial intelligence and the systems that power our world. I'm your host Michael Vincent, and today we continue our deep-dive series into The Five Convergences — a framework that maps how artificial intelligence, or A-I, is reshaping electric infrastructure from the inside out. This is episode three of six on the topic, and today we begin our first deep dive--this one into the concept of A-I as Controller.

Our guest today is Brandon N. Owens — founder of AIxEnergy dot I-O and the author of not one, but two reports: The Five Convergences of A-I and Energy and Artificial Intelligence and US Electricity Demand: Trends and Outlook to Two Thousand Forty. Together, these reports form the intellectual foundation for understanding A-I’s physical footprint on the American electric grid. Brandon, thanks for joining us.

Brandon: Thanks for having me. It’s an important conversation and I’m excited to unpack it with you.

Michael: Today we’re focusing on one of the most important shifts happening in the energy industry — something called AI as Controller. Can you explain what that means?

Brandon: This is where artificial intelligence doesn’t just help us analyze the grid. It actually takes part in operating it. In some cases, it makes the decisions directly.

Michael: Ok, how did we get from manual controls to a place where AI can actually run parts of the grid?

Brandon: If we go back a century, running the grid was entirely manual. Operators pulled levers on large switchboards. They worked from physical gauges and sometimes phone calls from other stations.

By the mid-1900s, we had mechanical devices that could adjust power automatically. Then came digital control rooms, where operators could see data from across the network and send instructions remotely.

But all of that was rule-based. The system followed “if this happens, then do that” logic. It didn’t learn. It didn’t adapt.

Today, artificial intelligence is giving the grid something closer to reasoning. These systems can process thousands of signals at once, recognize patterns, and adjust in ways humans cannot match in speed or complexity.

Michael: So when you say “AI as Controller,” what does that really mean?

Brandon: It means AI is not just watching the system. It’s making active choices about how equipment runs. That could be deciding when a battery charges or discharges. It could be adjusting power flows across the network. It might even coordinate thousands of small devices — like home batteries, rooftop solar panels, and electric vehicle chargers — so they all work together as a single resource.

And these systems can do this in real time, within safety limits set by humans.

Michael: Let’s make it real. Can you give us an example?

Brandon: Sure. In South Australia there’s a large battery called the Hornsdale Power Reserve. It uses a system from Tesla called Autobidder.

Autobidder decides on its own when the battery should charge, discharge, or sell energy into the market. In its first year, it made about 24 million dollars in revenue and reduced costs for consumers.

That was possible because it could read the grid’s conditions and the market in real time — and act instantly.

Michael: What about something outside of batteries?

Brandon: Google worked with its DeepMind AI to control how some of its wind farms sell electricity. The AI learned to predict the next day’s wind output using weather forecasts and past performance data.

By selling power in advance instead of just in real time, the value of that energy went up by about 20 percent. That’s AI acting as a controller — in this case, controlling when and how the electricity is sold.

Michael: You’ve also talked about virtual power plants. What are those?

Brandon: Think of a thousand homes. Some have rooftop solar panels. Some have batteries in their basements. Some have electric cars in their driveways.

A virtual power plant uses AI to coordinate all of those devices so they act like one large power station. In California, one project used AI to charge and discharge building batteries at the right moments. In its first year, it sent two gigawatt-hours of energy back to the grid during peak demand. That’s enough to power hundreds of homes for months.

Michael: This sounds powerful, but also risky. What could go wrong?

Brandon: Two main things. The first is what we call “model drift.” That’s when the AI’s environment changes — for example, new types of power plants are added — and the AI’s learned behavior no longer works as well.

The second is cybersecurity. If someone can feed bad data into the system, they might trick the AI into making harmful choices. Because these systems act so fast, a small manipulation could have a big impact.

Michael: How do we keep an AI controller from going too far?

Brandon: We use something called a “constraint governor.” That’s a built-in limit that keeps the AI from taking actions outside a safe range.

We also have supervisory systems — human or automated — that watch the AI’s decisions in real time and can override them.

It’s like the relationship between a pilot and autopilot in an airplane. The autopilot can fly most of the time, but the pilot can take over instantly.

Michael: Are other countries doing this?

Brandon: Yes. In France, the national grid operator ran a competition called “Learning to Run a Power Network.” AI systems learned how to reroute electricity in response to problems. Some of them performed better than human engineers in simulations.

It’s early days, but it shows AI can find solutions we might not think of ourselves.

Michael: Fast forward ten years. What could AI as Controller look like?

Brandon: We could see grids that balance themselves, isolate faults automatically, and constantly optimize how energy flows — all in real time.

That means more renewable energy, fewer blackouts, and lower costs. But it only works if we have strong oversight and clear rules for how the AI operates.

Michael: What needs to happen on the policy side?

Brandon: We need standards for transparency. AI systems should explain their decisions in plain language. We need clear limits and the ability for humans to override them.

Markets may also need new rules. AI-controlled resources can react faster than humans in energy markets. Without safeguards, that could cause price swings or even allow manipulation.

Michael: Brandon, this has been a fascinating look into a future where the grid can, in a sense, think for itself. For listeners who want to learn more?

Brandon: Visit AIxEnergy. The “Five Convergences” report goes deep into AI as Controller with more examples and governance recommendations.

Michael: Thanks for joining us, Brandon.

Brandon: Thanks, Michael. Always great to talk.

Michael: And thank you for listening to AIxEnergy. If you enjoyed today’s episode, share it with a colleague, subscribe, and join us next time as we explore the convergences shaping our energy future.