Signals in the Chaos for Energy Traders
- Ron Swartz
- Oct 19
- 5 min read
ERCOT’s five-minute market, reimagined: from instinct and experience to built-in intelligence that embraces how energy traders think.
I’ve spent years on real-time trading floors, and when ERCOT starts to move, it’s anything but quiet. Screens flip between apps. Phones light up. Someone’s asking for a quick sanity check before making a call.
A few traders are shouting across the room, trying to confirm what just changed—was it a unit trip, a constraint, a forecast miss?
People are checking with transmission analysts, or contacting the day-ahead desk to see if there’s room to adjust. It’s organized chaos—the kind that happens when experience, instinct, and teamwork all kick in at once.
If you’ve traded ERCOT, I’m sure you know exactly what I’m talking about. You’ve seen how fast the calm can break—how volatility doesn’t announce itself.
February 2021 — Winter Storm Uri Load forecasts missed by tens of gigawatts, wind froze, and units tripped. The system unraveled faster than anyone expected.
Summer 2023 — Record Heat and Tight Reserves Cloud cover shifted ramp rates, reserves tightened, and RT volatility tested every plan in the book.
April 2024 — Unplanned Thermal Outage During Mild Weather Gas units went offline, solar underperformed, and scarcity pricing showed up where no one expected it.
Each of these moments forced energy traders to react fast— to reassess what they thought they knew just hours earlier.
Those weren’t just stories about weather or outages; they were about judgment, timing, and composure under pressure.
That’s what’s always fascinated me—the moment before the obvious. When the signals are messy, incomplete, and uncertain. When it’s still possible to see something forming, even if you can’t quite name it yet.
What if that moment could be extended? What if you could have a clearer view—earlier—of the conditions building toward those breaks in the market? Not just as data points, but as evolving signals that carry meaning and context.
That’s the question that started me down this path.
From Curiosity to Creation — with AI as the Catalyst
Over the past few years, I’ve been working on ways to better understand what leads up to those kinds of events—how the data evolves before the market diverges and decisions get tested in real time.
At first, I tried to trace it the same way most people would—looking at historical data, patterns, and relationships between load, renewables, constraints, prices, etc. But the deeper I went, the clearer it became: the market moves too fast, and the signals are too intertwined for any manual analysis to keep up.
That’s what led me to AI—not as a buzzword, but as the only way to handle the complexity and speed that define ERCOT’s real-time market.
What I wanted was an intelligence that could constantly read the market — processing every signal, understanding how they interact, and highlighting what actually matters before the rest of the system reacts.
For years, the technology to do that simply didn’t exist. The tools, compute, and modeling approaches weren’t mature enough to make this level of real-time intelligence possible. But that’s changed. Today’s AI finally makes it practical—fast enough, connected enough, and adaptive enough to turn that vision into reality.
That understanding became the foundation for a system we designed from the ground up—one where AI isn’t spliced into existing tools, but embedded into the core architecture itself. Every feature, every calculation, every workflow was built around the idea that intelligence should be native, not layered on.
And for the first time, we could test that idea.
We didn’t just look back at those market-shifting days—we backtested the signals and intel we created—against each one to see what could have been seen sooner. It was eye-opening.
We realized how much value we could have delivered to any real-time trader in those situations—flagging conditions that were building toward divergence, sometimes days in advance.
First it suggests, something’s forming. Then, it’s becoming probable. Next, it’s about to happen. And finally, it happened.
At each stage, the possible courses of action evolve—sometimes it’s awareness, sometimes it’s positioning, and sometimes it’s knowing when to step back and let the market reset.
The AI we’ve developed is watching hundreds of potential signals across the grid, 24×7, in combinations that no human could track in real time. It’s not just delivering situational awareness—it’s interpreting what those conditions mean, explaining what’s driving them or where they could lead, and suggesting possible courses of action for the trader to consider at each stage of evolution.
It doesn’t replace intuition—it sharpens it, giving traders the ability to act faster and with more confidence when conditions change.
If you’re curious, let’s talk sometime. I think you’ll find what we discovered in those backtests game-changing.
Seeing the Grid the Way a Trader Thinks
It’s never just about one metric, is it?
It’s how everything moves in relation to everything else—load forecasts, renewable performance, transmission limits, shadow prices, and price acceleration.
But that’s just the surface.
What if you could see deeper—how operational data interacts with weather systems, generation behavior, regulatory shifts, even sentiment data that reflects short-term market psychology?
That’s where things start to get interesting.
We’ve been working on a way to see the grid the way energy traders actually experience it—not as a list of separate datasets, but as one living, interdependent system.
It starts with data—ERCOT operational feeds, weather forecasts, outages, generation behavior, and even sentiment and regulatory signals. From there, AI continuously evaluates those inputs and identifies signals—conditions or changes worth noticing in real time.
But seeing a signal is only the beginning. The real value comes when AI can connect the dots across multiple factors—understanding how one condition amplifies another, how stress builds, or how a pattern is forming that energy traders can act on.
That’s where intel comes in. Intel isn’t just awareness—it’s AI-driven understanding that interprets what’s happening, explains why it matters, and suggests what a trader might consider doing next.
Over time, the system forms what we call an AI-generated grid status—a live, contextual view of ERCOT’s operational and market state. It reflects not just what’s occurring right now, but what’s evolving beneath the surface.
When that happens, you benefit from trading alpha—the advantage that comes from recognizing context early enough to act before the rest of the market does.
Moments That Matter
Some of the most interesting moments in ERCOT are the quiet ones, just before something breaks open.
— A forecast misses.
— A constraint binds.
— A price run forms.
— You react.
Other times, stress builds slowly until you can feel the flexibility disappearing.
When load starts climbing and wind output underperforms, the system loses maneuvering room fast. When shadow prices start creeping up on a line that hasn’t hit its limit yet, you know congestion isn’t far away. And when multiple factors start stacking—load, reserves, renewables, transmission limits—it’s not coincidence. It’s the system showing strain.
Wouldn’t it be something if your systems could see those moments forming the same way you do—before they’re fully visible?
That’s the kind of intelligence we’re working toward—technology that doesn’t just mirror data but understands intent.
A Personal Note
I’ve spent almost three decades around energy markets, and I’ve seen how fast ERCOT continues to evolve.
What hasn’t changed is the focus it demands.
Every five minutes is another test of preparation, pattern recognition, and judgment.
If you’ll be in Houston for Energy Trading Week, I’d really like to connect—to hear how you read the grid, what signals you trust most, and where the noise still gets in the way. And if you won’t be there, reach out anyway.
I’d value hearing your perspective, because the way you think—the way you see the market—is exactly what’s shaping the next generation of decision intelligence in the energy industry.
by Ron Swartz VP, Product at ennrgy.ai
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