Texas BESS Boom: Where AI Meets Grid Reliability

Texas just added 6.4 GW of energy storage capacity in 2024, and more than 12 GW of storage is already deployed on the ERCOT system. That’s not a “pilot phase.” That’s infrastructure.

Two new announcements underline what’s happening on the ground: a 250 MW / 500 MWh battery near Dallas-Fort Worth and a 150 MW / 300 MWh battery in Hidalgo County. On paper, these look like straightforward buildouts. In practice, they’re proof that modern grid reliability is becoming a software problem as much as a hardware problem.

This post is part of our “AI for Energy & Utilities: Grid Modernization” series. The theme running through every project like this is simple: the grid is getting faster, more volatile, and more decentralized—and AI is becoming the control layer that makes utility-scale battery energy storage systems (BESS) pay off.

What these Texas storage projects really signal

These announcements aren’t just “more batteries.” They signal that ERCOT’s operating reality now expects storage to do real work: manage renewable variability, reduce congestion pain, and backstop reliability during extreme demand.

Here are the two projects, as reported:

• Mallard Energy Storage (near Dallas-Fort Worth): 250 MW / 500 MWh, developed by Peregrine Energy Solutions with technology from Wärtsilä (Quantum2) and construction services from WHC Energy Services. The site is roughly 30 miles northeast of Dallas.
• Gunnar Reliability Project (Hidalgo County): 150 MW / 300 MWh, developed/operated by GridStor under a tolling agreement. Construction is underway, with commercial operation expected by year-end 2026.

Texas officials have also said storage could reach ~30% of the state’s power grid capacity by 2030. If that trajectory holds, the winning utilities and developers won’t be the ones who only know how to procure batteries—they’ll be the ones who can operate fleets of batteries intelligently.

Why “tolling agreements” are showing up everywhere

Both projects include tolling agreements with undisclosed Fortune 500 counterparties. That’s not a throwaway detail.

A tolling agreement generally means a buyer pays for the right to “dispatch” the asset under defined terms—often valuing availability, responsiveness, and performance. This structure is a clue that the market is maturing:

• Buyers want predictable capacity and controllable flexibility, not just merchant exposure.
• Sellers need operational excellence because penalties and performance clauses get real.

And operational excellence is where AI starts to matter.

AI’s role in modern BESS: it’s not optional anymore

A utility-scale battery is a high-speed asset in a high-speed market. Humans can supervise it, but they can’t manually optimize it every five minutes while simultaneously accounting for prices, constraints, weather, outages, and degradation.

AI is the practical way to turn BESS into a reliability tool instead of an expensive box that occasionally arbitrages.

1) Forecasting: the grid runs on predictions, not guesses

The main job of grid-scale analytics is forecasting what will happen next—because batteries are most valuable before the event.

AI-driven forecasting typically combines:

• Short-term load forecasting (minutes to days)
• Renewable generation forecasting (wind/solar ramps)
• Weather-driven risk models (heat waves, winter events, humidity-driven load)
• Price forecasting for energy and ancillary services

In Texas, this is especially relevant because ERCOT sees sharp ramps and localized congestion. A battery that charges at the wrong time can worsen congestion; a battery that discharges too early can miss the most reliability-critical interval.

A stance I’ll defend: “Good forecasts beat bigger batteries.” Not always—but more often than people want to admit. A 300 MWh asset operated intelligently can outperform a larger asset run conservatively.

2) Optimization: dispatch is a math problem with real-world consequences

Once you can forecast, you still need to decide what to do.

BESS optimization is not one problem; it’s several competing objectives:

• Maximize revenue across stacked value streams
• Preserve state of charge for reliability events
• Respect interconnection/export limits and congestion constraints
• Reduce cycling that accelerates battery degradation
• Meet contractual commitments under tolling or capacity-style structures

AI helps by evaluating thousands (or millions) of possible dispatch paths under uncertainty—then selecting actions that balance profit and risk.

A practical pattern we see in grid modernization programs:

1. Day-ahead planning sets a baseline schedule.
2. Intraday re-optimization updates every hour (or more frequently).
3. Real-time control reacts to fast frequency events and dispatch signals.

Even if you don’t brand it as “AI,” any serious approach ends up using machine learning forecasts plus optimization routines. Otherwise, you’re leaving value on the table or adding reliability risk.

3) Asset health: AI protects the battery you just financed

Battery projects live or die on long-term performance: usable capacity, availability, thermal stability, and warranty compliance.

AI contributes through:

• Predictive maintenance (early detection of abnormal cell behavior)
• Anomaly detection on temperature, voltage spread, and inverter metrics
• Degradation-aware dispatch (avoiding cycles that cost more in life than they earn in margin)

This is where grid operators and CFOs suddenly agree: you want every MWh to be profitable, but you also want the battery to still be healthy in year 8.

Why Texas is leading the storage buildout (and what it demands)

Texas is becoming the proving ground for grid-scale batteries because the system rewards fast response and because demand growth is relentless.

The reality is that grid reliability challenges in ERCOT aren’t hypothetical:

• Peak demand is rising (population, electrification, industrial growth, data centers)
• Renewable penetration creates steeper ramps
• Extreme weather compresses margins and stresses transmission

Battery storage fits because it can respond quickly and site flexibly. But as deployments scale, coordination becomes the bottleneck.

The coordination problem: one battery is simple; fleets are not

A single 250 MW / 500 MWh battery can be operated with a small team and decent software. A fleet across multiple nodes, with different interconnection limits and price dynamics, becomes a system-of-systems problem.

AI-enabled fleet orchestration focuses on:

• Node-level congestion and curtailment signals
• Site-specific weather sensitivity
• Differentiated degradation costs by asset and by operating temperature
• Portfolio risk (not overexposing to one market condition)

As Texas heads toward the “storage is 30% of capacity” claim, fleet orchestration isn’t a nice-to-have. It’s grid modernization.

What utilities and developers should do now (before the next summer peak)

Most organizations wait too long to treat BESS operations as a core competency. They procure, interconnect, and commission—then try to bolt on analytics later. That’s backwards.

Here’s a more reliable approach.

A practical checklist for AI-backed BESS readiness

1. Unify your data pipeline

   • SCADA/EMS telemetry, market data, weather feeds, maintenance logs
   • Clean time alignment and standardized tags (this is where projects stall)

2. Define dispatch objectives in plain language

   • “We will prioritize reliability coverage from 4–9 p.m. unless prices exceed X”
   • Translate strategy into constraints the optimizer can actually use

3. Adopt degradation-aware KPIs

   • Track revenue per cycle, revenue per throughput MWh, and availability
   • Put a real dollar value on degradation and include it in dispatch decisions

4. Harden cyber and access controls

   • More automation means more control pathways
   • Treat BESS controls like critical infrastructure, not like an IoT gadget

5. Run stress tests against extreme scenarios

   • Heat wave + low wind, winter storm + gas constraint, transmission outage
   • The goal is to validate your operational playbook before the event

If you’re building in ERCOT, I’d add one more: model congestion explicitly. Congestion isn’t an edge case in Texas; it’s part of the business model.

People also ask: how does AI help stabilize the grid with batteries?

AI helps stabilize the grid with batteries by forecasting short-term demand and renewable generation, then optimizing dispatch to deliver fast response while preserving battery health.

Concretely, AI improves stabilization by:

• Predicting high-risk intervals (peaks, ramps, low reserve margins)
• Pre-positioning state of charge where it’s most valuable
• Managing real-time response for frequency and reliability services
• Reducing forced outages via predictive maintenance

Stability isn’t just about having storage capacity—it’s about having the right state of charge at the right node at the right minute.

Where this is going next: storage as the grid’s control surface

The Texas projects near Dallas-Fort Worth and in Hidalgo County are big on their own—500 MWh and 300 MWh—but the more interesting story is what they represent. Batteries are becoming the grid’s control surface: the thing you modulate to keep everything else stable.

And once that’s true, the industry’s advantage shifts toward whoever can:

• forecast volatility earlier,
• optimize dispatch faster,
• and protect asset health more consistently.

If you’re working on grid modernization—at a utility, developer, or large energy buyer—this is the moment to treat AI-enabled operations as part of the project, not a Phase 2 “nice add-on.”

What would change in your next storage project if you assumed, from day one, that software performance is as material as battery performance?