The New Geothermal Energy: How EGS Unlocks Clean, Firm Power at Scale

Key Takeaways

• Geothermal shares the clean firm category with only three other technologies: nuclear, natural gas with carbon capture and storage (CCS), and solar or wind paired with long-duration energy storage (LDES).
• Buyers comparing geothermal to solar or unabated natural gas power contracts are comparing different products: one isn’t firm, the other isn’t clean, and both remain cheaper options because they satisfy less of what buyers currently need. 
• Geothermal’s cost premium is real, but so is the value it delivers. Geothermal can reach 90%+ capacity factor, delivering dispatchable clean power around the clock, and it sidesteps the gas turbine manufacturing backlogs and pipeline infrastructure delays slowing other firm power technologies. 
• Geothermal is closer to commercial readiness than most buyers realize. As demand for firm, low-carbon power intensifies, geothermal warrants a serious look alongside nuclear and gas with CCS.

Geothermal is Gaining Traction Despite Its Cost Premium

Hyperscalers, corporate energy buyers, and data center developers face unprecedented pressure to procure clean power. Wind and solar have played a major role in meeting this need, but they are intermittent, and load growth is accelerating and changing. This market pressure is creating bottlenecks that cannot be solved without new grid infrastructure and/or substantial additions of energy storage. Geothermal, which does not suffer from intermittency, has gained tremendous momentum as a challenger to wind and solar, yet it carries one seemingly glaring disadvantage: its cost. 

Yet, major utilities and hyperscalers alike are lining up for geothermal offtake anyway: Southern California Edison and Google have signed separate contracts with Fervo, and Meta has two 150 MW agreements in place with Sage and XGS. These contracts are signaling a growing shift in power markets: not all megawatt-hours are created equal, and the cheapest contracts may no longer contain what buyers truly need. 

Fast, Clean Firm Power: What Buyers Need Today 

In the clean firm power evolution, clean came first. Buyers signed solar and wind power purchase agreements (PPAs) and matched their annual consumption with renewable energy contracts, or offset them with renewable energy certificates (RECs). With lax carbon accounting, solar and wind remain unbeatable on price. But annual emissions tallies conceal something important—a buyer can procure as much renewable electricity as it consumes over a year while still depending on the rest of the grid during the hours when those resources are unavailable. To close that gap, buyers need a source that delivers clean power around the clock, firmly in every hour, on demand. 

Two unforgiving demands have followed since: speed and location. With today’s rapid buildout of new load, a power source needs to come online in time to deliver power when the customer needs it. It is also important to connect near a customer’s location because existing transmission assets are often fully subscribed. Failure on either axis can disqualify a project. However, a contract that can meet all attributes: clean in every hour, firm on demand, quick to deliver, and able to reach the load, might be the best (or only) option available. It is also likely to be considerably more expensive than those annual solar benchmarks that only met the clean condition. 

Four Technologies Make the Clean Firm Power Shortlist

Given this strict set of constraints, what power options should buyers consider? The right set of technologies should aim to solve as many of the aforementioned conditions as possible, and that leaves a fairly tight shortlist: 

1. Solar or Wind paired with Long-Duration Energy Storage (LDES): To achieve the level of reliability that many buyers, including data centers, require, solar or wind would have to be paired with LDES, beyond 4–8 hours, to be able to cover evening hours and remain online during increasingly common extreme weather events. While there are promising LDES technologies on the horizon, most remain expensive and not yet widely commercial. Costs rise significantly compared to a standard solar or wind PPA, and viability varies by location. This will be increasingly competitive as storage costs fall, but not yet a mature firm-power solution at scale.
2. Natural Gas with CCS: With large amounts of unabated natural gas generation coming online, CCS is an attractive option to mitigate a large portion of these emissions while retaining time to power (by building gas first and adding CCS later) and the dispatchability of gas-fired power. On the flip side, CCS is operationally complex, geographically restricted, and - unlike other options here - failure at any point in the CCS chain directly impacts the carbon intensity of power delivered. Gas-fired generation is also reliant on the gas delivery pipeline infrastructure, which may face capacity constraints. Finally, natural gas with CCS is 50% more costly per MWh than unabated firm gas.
3. Nuclear: Nuclear is seeing renewed interest from a wide range of corporate buyers and investors. Conventional nuclear and small modular reactors (SMRs) are attracting funding and offtake deals. While nuclear power holds high potential, long development timelines, high capital costs, and technology risk remain barriers to commercializing the next wave of nuclear technologies. 
4. Geothermal: Geothermal can provide firm, dispatchable, and low-carbon power with capacity factors typically above 90%. Development timelines can be faster than nuclear in regions where resources are already proven, with some projects approaching commercial readiness now. Geothermal technology is still geographically constrained, but enhanced geothermal systems (EGS) are poised to  expand their geographical flexibility by reducing the number of siting variables. 

How to Evaluate the Right Clean Firm Power Technology for Your Project

How do these various options stack up? We compared them across six dimensions:

1. Firmness: how reliably a resource can deliver power on the hour, on demand
2. Carbon: the lifecycle emissions intensity when considering the operational emissions of the generating facility and the broader system carbon impacts required to meet any reliability gaps
3. Time to power: a measure of how long it takes to get from “we need power” to delivery, including interconnection and schedule risk
4. Cost (current): the likely cost of a PPA if signed today
5. Execution risk: a measure of how likely a project’s claims regarding price, timing, and performance will hold
6. Siting flexibility: a measure of how constrained a resource is to a specific geography

This frame is ultimately something the buyer should score themselves; below you will find our current read. Red (unfavorable) represents a weakness that can end an evaluation on its own, green (favorable) means the dimension won’t constrain the decision, and yellow (moderate) sits somewhere in between. 

Comparing the clean firm power options is, perhaps ironically, challenging along the “clean” and “firm” lines. Every serious contender delivers low-carbon power around the clock, because that is the price of admission. Not one technology earns an unfavorable rating in those categories. And that is the catch: every weakness in the comparison table sits somewhere else. The dimensions that ultimately win (or end) these contracts may be the ones that the “clean firm” label can obscure or oversimplify.

A resource that cannot be energized for a decade, or sited near load, or delivered at a price a buyer cannot defend internally, may be infeasible regardless of what else it offers. None of the four clean firm generation technology groupings clear every hurdle today, but they can satisfy different buyer priorities.  

Solar or wind with LDES offers more buyer flexibility, at a price. Enough generation, storage, and other firming can deliver the reliability required, and the buyer sets that target. But the increments get expensive quickly, especially when reliability gaps remain long or unpredictable. Covering more hours means more storage and more generation to fill it, so each step toward firmness parity costs more than the one before it. Firming shortfalls also carry carbon consequences, depending on what gets called to compensate. Those emissions may be absent from GHG accounting ledgers, but that doesn’t stop them from hitting the atmosphere. 

Gas with CCS is challenged by execution and siting because the product depends on a chain: gas generation, capture, transport, and a permitted storage site capable of permanent CO₂ disposal at scale. Each piece can be developed and tested, but low-carbon electricity requires all of them to work together, which can only be demonstrated once all are built. A buyer cannot substitute a link, and cannot observe the whole chain before committing. Clean, in this sense, is conditional, and the delivered power could end up being far more carbon-intensive than advertised.

Nuclear ranks poorly on time, cost, and execution, but full-scale reactors and small modular reactors carry those weaknesses differently. A large reactor arrives in one piece, and its schedule and final cost come into focus as construction advances. By then, years and billions are committed; a buyer cannot learn from a fraction of a reactor before deciding whether to finish it. Small modular reactors are designed to break that capacity into increments a buyer could add as demand grows, which would ease the one-piece problem. But none are operating commercially in the US yet, and the signed deals target first power in the 2030s, so the modular promise is still years out. 

Geothermal’s weaknesses work somewhat differently. Conventional hydrothermal is tightly constrained by location, which buyers can screen before choosing where to procure, or co-locate where possible. Next-generation geothermal, including EGS, could widen that geography considerably. 

For EGS, the largest remaining uncertainty is execution: whether the developers can replicate successful drilling and reservoir performance at commercial scale. Part of that risk belongs to the rock, and no contract can make a reservoir perform. But drilling also produces information before the entire project has been built, and geothermal capacity can often be developed incrementally as needed. A buyer can require resource evidence before committing, tie later commitments to demonstrated performance, or spread procurement across projects rather than depend on a single field. 

Geothermal’s flexibility has limits. A buyer who needs full capacity by a fixed date may have little room to wait for wells to perform before committing to the next phase. And successful wells do not eliminate risk in project expansion or guarantee performance in other locations. Execution risk stays with the resource. What geothermal offers is more opportunity to learn about that risk while a buyer can still do something about it. 

Revisiting the Geothermal Premium

Geothermal contracts, especially EGS, remain expensive. But that premium looks different on this side of the comparison. Clean has a cost, but so does firmness, speed,  reach, and execution. Every clean firm option pays for those attributes somewhere. Solar and wind contracts will remain cheap by comparison, but it’s the wrong comparison: the market signing geothermal contracts is pricing a much larger product. 

That still leaves a hard decision. Geothermal and gas with CCS carry execution risk and siting constraints. Nuclear asks more of the buyer on cost and time. Firmed solar asks more as reliability requirements rise. The best option will change with each buyer, location, and project. Geothermal’s place in that choice is changing quickly. EGS is expanding where projects can be developed, drilling costs are falling, and commercial experience is growing. Those advances are bringing geothermal into consideration for buyers who previously had little reason to look its way. 

And that may be the more revealing aspect of the geothermal market today: buyers are signing those contracts with the alternatives in full view. Their decisions offer an early indication of what clean firm power delivered in the right place and at the right time is worth, and which risks buyers are willing to carry to get it. 

Frequently Asked Questions

Is Geothermal Cheaper Than a Solar PPA? 

No, a geothermal power contract can be significantly more expensive than a solar PPA. But they're not the same product: solar meets annual clean-energy targets, while geothermal delivers firm, dispatchable power around the clock, which is why buyers are increasingly comparing it to nuclear and gas with CCS instead.

Is Geothermal Cheaper Than Solar with LDES? 

Not necessarily on a per-contract basis, but the cost structures behave differently. Solar with LDES gets more expensive with every additional hour of reliability a buyer needs, since covering more hours means adding both more storage and more generation to fill it. Geothermal's premium, by contrast, is priced into the contract upfront as a firm, dispatchable product from day one.

How Does Geothermal's Risk Compare to Gas with CCS? 

Differently, in a way that matters to buyers. Gas with CCS depends on a chain (generation, capture, transport, and storage) that can only be verified once the whole system is built, so a buyer can't test it piece by piece. Geothermal generates performance data incrementally through drilling, letting buyers require resource evidence or tie commitments to demonstrated results before committing further, though this data does not come free.

Which Deploys Faster, Geothermal or Nuclear? 

Geothermal, in regions with already-proven resources. Fervo's Cape Station, for example, broke ground in 2023 and is on track for first power in late 2026 and 500 MW by 2028: a fraction of a typical new-nuclear large reactor timeline.

Key Takeaways

• Larger power users are securing behind-the-meter (BTM) power to bypass grid constraints, pairing data centers with third-party-owned generation assets that deliver electricity through a private line rather than the grid.
• BTM power arrangements can create confusion about electricity emissions classification: the power users neither own the generating asset nor purchase electricity from the grid, leading some to misclassify those emissions as scope 3 in their corporate GHG inventories. But the GHG Protocol's Corporate Standard is clear: BTM electricity emissions belong in scope 2.
• Misclassifying BTM emissions can create reputational and regulatory risk. Relae can help organizations get this right before the contract closes.

Why Large Power Users Are Turning to Behind-the-Meter Power

Large power users are consuming more electricity due to data center growth and are looking to add capacity faster than the grid can support, which is having a direct impact on corporate emissions. For example, between 2020 and 2024, Microsoft’s location-based scope 2 emissions rose 130%, and Google’s rose 92%, driven almost entirely by soaring electricity demand from AI infrastructure. 

To bypass grid congestion and long interconnection queues, many are turning to behind-the-meter (BTM) power. It’s a pragmatic solution to a real supply problem, but it’s opening an urgent carbon accounting question: when the BTM asset is owned and operated by a third party, where should we account for those emissions?

There has been some confusion that has resulted in companies pursuing an interpretation that would place those emissions in scope 3. The GHG Protocol’s Corporate Standard says otherwise, and the stakes of getting this wrong are high.

What Is Behind-the-Meter Power Generation?

Behind-the-meter refers to electricity generated on the power consumer’s side of the utility meter, bypassing the grid, and typically located on or near the site where the power is consumed. 

In most BTM arrangements for a data center, a third-party developer builds and operates a generation asset, such as natural gas, geothermal, or renewable energy, and delivers electricity directly to the facility through a private transmission line. There is no utility meter, no grid connection, and no standard energy invoice. 

This structure allows companies to access large, reliable blocks of power without waiting years for grid interconnection approvals. Since the company does not own or operate the generation asset and is not purchasing electricity through a conventional utility relationship, this arrangement has created some uncertainty around how to account for the associated emissions. 

Can BTM Electricity Emissions Be Classified As Scope 3?

In this scenario, no. The GHG Protocol's Corporate Standard is unambiguous: BTM electricity emissions belong in scope 2, not scope 3. Yet, some companies have been confused about this classification.

There is broad agreement that since the power users do not own or operate the generating asset, those emissions do not belong in scope 1. Divergence starts when we consider that the company is purchasing BTM power, i.e., not from the grid. Since no electricity is acquired from the grid, some argue that rather than accounting for these emissions in scope 2, they are better placed in scope 3, category 8: emissions from leased assets. 

The appeal is obvious for BTM power consumers. Scope 3 emissions face less scrutiny from investors, auditors, and regulators who focus most of their attention on scopes 1 and 2. Classifying BTM emissions as scope 3 would reduce near-term pressure to act. However, the GHG Protocol is unambiguous in its stance.

What the GHG Protocol Actually Says

The GHG Protocol’s Scope 2 Guidance states that “organizations must quantify emissions from the generation of acquired and consumed electricity, steam, heat, or cooling (collectively referred to as ‘electricity’).” The method of delivery, whether grid or BTM, does not change the classification. 

If a company consumes electricity from a BTM source, the emissions from generating that electricity belong in scope 2. Section 5.4 of the Scope 2 Guidance addresses BTM power generation directly: “the company with operational or financial control of the energy generation facility reports those emissions in scope 1, following the operational control approach, while the consumer of the energy reports the emissions in scope 2.”

This resolves the question completely. The emissions sit in scope 1 if the company has operational or financial control of the asset, or in scope 2 if a third party controls it.

The GHG Protocol’s Corporate Value Chain (Scope 3) Accounting and Reporting Standard reinforces this conclusion. “Category 8 includes emissions from the operation of assets that are leased by the reporting company in the reporting year and not already included in the reporting company’s scope 1 or scope 2 inventories.”

Because BTM electricity emissions are captured by the Scope 2 Guidance, the scope 3 category 8 does not apply.

Get the Accounting Right Before the Contract Closes

The GHG Protocol is unambiguous: behind-the-meter electricity emissions belong in scope 2 for companies that consume, but do not control the generating asset. This means that BTM contract terms are crucial to determining how the emissions will be classified, since the GHG Protocol assigns scope based on who holds operational or financial control of the generating asset. 

Companies that move fast on BTM capacity without understanding this distinction risk locking in a scope 1 or scope 2 obligation they didn't anticipate or building a reporting strategy around a scope 3 interpretation the GHG Protocol doesn't support. This can become a reputational or even a regulatory liability that is far harder to address after the contract is signed.

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Key Takeaways

• Community opposition has blocked, withdrawn, or stalled more than $170 billion in announced AI data center investment across 20 US states since January 2024. The pace is accelerating: 6 cancellations in 2024, 25 in 2025, and more than 20 additional cancellations by May 15, 2026.
• Data center opposition is bipartisan. It spans red and blue counties, every region, and multiple grid operators, with nearly two-thirds of the blocked investment sitting in counties that voted for Donald Trump in 2024. 
• Process and transparency, more than resource concerns alone, drive the fastest and most durable opposition. How a developer runs the engagement process shapes both community sentiment and the project’s ultimate success.

How Many AI Data Center Projects Have Been Cancelled? 

Between January 1, 2024, and May 15, 2026, community opposition blocked, withdrew, or stalled 46 announced AI data center projects across 20 US states, representing more than $170 billion in announced investment. These values are disclosed or derived for 35 of the 46 projects; the remaining 11 carry no public figure.

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The pace of successful opposition has accelerated sharply. Cancellations rose from 6 in 2024 to 25 in 2025. The first five months of 2026 added more than 20 additional cancellations, the fastest stretch on record. 

Virginia leads the state count with 11 blocked projects, followed by Indiana with 7 and Texas with 5. Together, those three states account for roughly half of all cancellations in the dataset. The PJM grid region carries the largest single share of blocked investment at $70 billion across 13 projects, followed by MISO at $37 billion.

Is Opposition to Data Centers Bipartisan?

Yes. The opposition wave crosses party lines on every measure we examined. Republican-leaning counties hosted 28 of the 46 projects (61%), Democratic-leaning counties hosted 16 (35%), and 2 fell within five points.

Weighted by announced investment, about two-thirds of blocked dollars sat in Republican-voting counties. Strong Republican counties (those Trump won by more than 15 points) account for $99 billion across 23 projects. Strong Democratic counties account for $29 billion across 9 projects. The remaining $44 billion spans Lean Republican, Tossup, and Lean Democratic counties.

Why Are Communities Opposing Data Centers?

Communities raise a consistent set of concerns across the country: water demand, electricity rates, air quality where developers propose gas co-generation, rural character, and a lack of transparency in the development process. 

Across the seven cases that we studied in depth, process, and transparency concerns were the most consistently cited factors associated with opposition. Non-disclosure agreements between developers and local officials, ownership structures in which the ultimate end-user was not publicly identified, and closed-door pre-application negotiations produce opposition faster and more durably than any other concern.

The pattern holds across very different communities: a diffuse civic mobilization in rural Georgia, an NGO water coalition in a Texas college town, a conservation coalition anchored by the Southern Environmental Law Center in Southside, Virginia, and an institutional civic organization with legal-expert and celebrity support in northern Virginia. Each produced the same outcome, and each flagged process and transparency as a dominant or top-three concern in the public record.

By the time a project reaches its first public hearing against organized community opposition, the political path of the project is largely set. Late-stage benefits packages consistently fail to reverse that trajectory. Communities read them as concessions, not commitments.

Assess Community Opposition Risk Before You Site

Community opposition is now a structural feature of the AI data center siting environment. The patterns are clear enough to act on now. Relae's Community Impacts team helps developers and capital partners implement responsible development standards through pre-siting community intelligence, calibration of benefits design to specific community contexts, and building the verification scaffolding that turns commitments into outcomes. 

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Frequently Asked Questions 

Which states are banning data centers in the US?

The first statewide moratoriums on data centers have arrived. In July 2026, Governor Hochul signed the country's first statewide moratorium, an executive order pausing state environmental permits for up to one year for new data centers of 50 MW or more. Texas followed weeks later, halting approvals of new data center grid connections until state regulators audit their power, water, tax, and ownership details. This is a snapshot from August 2026, and we will likely see additional changes in the months ahead.

The local picture is more developed. Individual municipalities and counties have adopted moratoria and zoning ordinance amendments that block or restrict data centers within their jurisdictions. The City of Peculiar, Missouri, removed data centers from its light-industrial zoning entirely. Monroe County, Georgia, and Jones County, Georgia, both adopted moratoria after project denials in 2025. Cassville Township, Wisconsin, and San Marcos, Texas adopted zoning and code amendments in 2026. State legislatures in Virginia, Indiana, Texas, and Missouri have taken up data center siting, ratepayer, and permitting legislation, though most bills remain in progress rather than enacted.

How much data center investment has been blocked in the US?

More than $170 billion in announced AI data center investment has been blocked, withdrawn, or stalled by community opposition across 46 projects and 20 US states between January 1, 2024 and May 15, 2026. 

Relae arrived at this figure from data on 35 of the 46 projects; 11 have no publicly disclosed investment value. The pace has accelerated sharply: 6 cancellations in 2024, 25 in 2025, and more than 20 additional cancellations in the first five months of 2026 alone. Virginia leads the state count with 11 projects. The PJM grid region carries the largest single share of blocked capacity at $70 billion across 13 projects.

What causes a data center project to be cancelled by community opposition?

As of August 2026, communities cite a consistent set of concerns across cancelled projects: water demand, grid strain and residential rate impacts, air quality where developers propose gas-fired co-generation, rural character and farmland conversion, and lack of transparency in the development process. 

In the seven cases we studied in depth, process and transparency were together the most consistent driver of opposition. Non-disclosure agreements between developers and local officials, shell LLC ownership structures that conceal the end-user, and closed-door pre-application negotiations produce faster and more durable opposition than any single resource concern. Late-stage benefits packages consistently fail once that transparency-driven frame has formed.

Key Takeaways

• Batteries have competed away the revenue that financed their first wave. In Texas, ancillary prices have fallen about 90% since 2023, and price spikes driving arbitrage revenue are becoming ever less frequent.
• The business has moved from trading energy to selling capacity. In Texas, tolls work as private capacity contracts, and resource adequacy payments make up roughly 70% of battery revenue in California. Project economics increasingly pencil on a fixed $/kW-month basis rather than price spikes.
• Long-term contracts help shift the risk, but they don’t fully close the gap. Under a 15-year toll, a new battery needs about 22% less revenue, but that is still nearly three times what Texas batteries earned in the market in 2025.
• Buildout now depends on who will pay for capacity. Merchant revenue alone no longer covers a new battery, so growth hinges on how many buyers will sign capacity contracts worth nearly three times what Texas batteries earn in the market. 

Battery Revenues Are Falling While Deployments Keep Rising

US developers are expected to install about 24 GW of utility-scale battery storage energy systems (BESS) in 2026, about 60% more than the record 15 GW added in 2025, according to the EIA. Yet in Texas, where batteries earn most of their money in the market, a typical battery earned roughly 85% less in 2025 than in 2023. 

Is this unjustified overbuilding by an industry that has grown too fast? Some battery executives argue this is not overbuilding, because merchant revenue earned in wholesale markets is no longer the business. In their view, falling merchant revenue matters less because longer-term offtake contracts, such as 15-year tolls with utilities and large power buyers, make lower revenue financeable. As a result, today’s installations bank on batteries being paid as capacity assets rather than as pure energy arbitrageurs. 

We tested those claims against market data from Texas (ERCOT) and California (CAISO) between 2022 and 2026. Our core finding is that project economics in both markets are increasingly reliant on fixed $/kW-month earnings rather than arbitrage revenue.

How Does a Grid Battery Make Money?

A battery has three ways to earn:

1. Energy arbitrage (merchant: paid per MWh traded, hour-by-hour): charging when power is cheap and discharging when it is expensive. Its value depends on the daily spread between the highest- and lowest- priced hours.
2. Ancillary services (merchant: paid per MW held in reserve, bought a day ahead): payments for standing ready to keep the grid stable, such as frequency regulation and reserves, whether or not the battery is called on to deliver energy.
3. Capacity contracts (contracted: fixed $/kW-month for many years): a utility or large buyer pays a fixed monthly fee, often called a toll, for the right to use the battery, and takes on the market risk itself. It is the most stable of the three.

Texas batteries earned almost entirely from the first two. California batteries have earned from all three, and as market revenue has fallen, resource adequacy (RA) contracts–the mechanism through which load-serving entities meet the state’s capacity requirements–have become their main source of income. 

That additional revenue stream is crucial in CAISO, making up roughly 70% of battery revenue by 2025, as net market revenue declined from $103/kW-year in 2022 to under $40/kW-year in 2025. Outcomes differ, too: California still curtails large amounts of midday solar despite its growing battery fleet, while ERCOT’s batteries have flattened price peaks. 

How Texas Batteries Lost Their Merchant Revenue

Ancillary services filled up first. In 2022-23, ancillary services supplied more than 80% of Texas batteries’ revenue. But these markets are small and fixed in size. ERCOT buys about 3 GW of the fast reserves batteries excel at, and 6–8 GW of ancillary services in total. 

The battery fleet passed the first mark in mid-2023 and the second in spring 2024; it is now about 19 GW of registered capacity. Average ancillary services prices fell 85-95% between 2023 and 2025, and ERCOT's day-ahead ancillary services spending fell from about $1.5 billion to $174 million. California’s ancillary markets saturated even earlier: batteries already won about 70% of its regulation-up procurement in 2022, and about 90% since 2024. Its ancillary services spending, much smaller than ERCOT’s to begin with, fell by about 60%, from $237 million in 2022 to $95 million in 2025.

Price spikes disappeared, too. ERCOT's average daily spread between its four highest- and four lowest-priced hours fell from $163/MWh in 2023 to $48/MWh in 2025, even as natural gas prices rose. Yet the typical day, adjusted for gas prices, looks much as it did in 2022. What vanished is scarcity: hours when real-time prices topped $1,000/MWh fell from about 60 in 2023 to 2 in 2025. 

Batteries discharge into exactly those hours, and there are now enough of them to flatten the peaks. Controlling for gas prices, renewables, and demand, each additional gigawatt of batteries is associated with a 9% narrower daily spread. 

Merchant revenue collapsed. Texas batteries earned about $193 per kW-year in 2023 and only about $29 in 2025, according to Modo Energy’s fleet index. California batteries’ market revenue fell from about $80 to under $40 per kW-year, before resource adequacy payments. Merchant revenue alone is no longer enough to finance a new battery in either market. 

Costs fell too, but not by as much: NLR’s benchmark installed cost for a four-hour system fell about a third from its 2022 peak to 2024, from roughly $517 to $334 per kWh in 2024 dollars. Costs then turned up again: Lazard’s levelized cost of storage rose in 2026, to near the top of its range since 2020, as tariffs cut access to cheaper Chinese cells and new foreign-entity-of-concern (FEOC) rules pushed buyers toward other suppliers.

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The Battery Business Has Moved From Energy to Capacity

As merchant revenue declines, storage developers are increasingly turning to capacity contracts to support revenue requirements. This has an important advantage: contracted cash flow can carry more debt at a lower cost. Under illustrative financing assumptions, moving a battery coming online in 2028 from merchant to a 15-year toll lowers its cost of capital from about 12.6% to 7.2%. 

Using NLR’s projected installed capital costs for 2028, that cuts the annual revenue a two-hour system needs to break even by about 22%, from $105 to $82 per kW-year ($64-101 across NLR’s low- and high- cost cases). However, that is still nearly three times what Texas batteries earned in the market in 2025. Given this gap, new BESS installations might appear to be uneconomic.

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Why Are Developers Still Building BESS? 

However, new BESS installations continue to be built. This is partly because many battery storage projects coming online now were financed on 2022-23 revenue (the average project can take over four years from entering the interconnection queue to operation), and partly because new contracts, such as tolls, are priced on capacity, not energy: the supplier is obligated to keep certain amounts of capacity available over a given time period, independent of energy delivery. 

In Texas (which has no capacity market), such tolls are, in effect, private capacity contracts. In California, resource adequacy already plays that role: batteries’ RA contracts averaged about $9 per kW-month in early 2025. 

An owner needs roughly $7 per kW-month for a two-hour battery (about $11 for four hours) to break even, in line with the roughly $7 per kW-month cap on a 20-year battery storage agreement Austin Energy approved in May 2026. By contrast, the energy and ancillary revenue an offtaker could capture by dispatching it is about $2.50–3.50 per kW-month. The difference is what buyers pay for firm capacity: insurance against scarcity events like Winter Storm Uri, capacity to meet their own load obligations, or a hedge against scarcity returning as demand grows. 

The Next Battery Wave Depends on Who Pays for Reliability

Batteries have done what they were built to do: absorb volatility and steady the grid. In doing so, they competed away the revenue that financed the first wave. At today’s prices, merchant revenue no longer covers a new battery. The build continues on projects financed in 2022–23 and on capacity contracts worth nearly three times what Texas batteries earn in the market. Whether the buildout continues depends on how many buyers will pay that premium. 

Deployment continues to grow in both markets. ERCOT registered about 4.2 GW of new batteries in January to July 2026, against 2.8 GW a year earlier. But the pace is slowing: Texas’s fleet grew about 50% in 2025 after nearly doubling in 2024, and interconnection queues are thinning: ERCOT battery applications fell by half in the second half of 2025. 

Wholesale markets financed the first boom. The next wave depends on buyers who value reliability, including utilities, retailers, and large loads such as data centers, and on how cheaply that risk can be priced and capitalized. Data centers in particular put a high value on firm power: research inferring their willingness to pay from cloud computing prices puts it at up to $13,600 per MWh for frontier AI chips on guaranteed service, far above ERCOT’s $5,000 per MWh price cap. 

For developers and investors, the question is no longer how much the market will pay, but who will sign, and for how long. For more on how interconnection queues shape how many storage projects get built and how queue wait times vary by technology, see Relae’s interconnection research.

Relae’s Power & Energy advisory team helps developers, investors, and large energy buyers understand and price the value of storage. This includes behind-the-meter modeling, resource portfolio optimization, and interconnection queue analysis. 

Frequently Asked Questions

Why are battery storage revenues falling if deployment keeps growing? 

Batteries have become victims of their own success. They earned most of their early revenue from two sources: ancillary services and high wholesale market prices. Texas now has more batteries than ancillary services the grid needs, so those prices have collapsed. And because batteries discharge during the most expensive hours, there are now enough of them to flatten the price spikes that used to pay the most.

What is a battery tolling agreement? 

A toll is a long-term contract in which a utility or large buyer pays a battery owner a fixed monthly fee for the right to direct how the battery is used. The buyer keeps whatever the battery earns in the market and takes on the risk if that falls short. The owner gets a steady income it can borrow against.

Who is signing these contracts? 

Mostly utilities, retailers and power traders, who need capacity to serve their customers or want protection against price spikes. In California, utilities sign capacity contracts with batteries to meet the state’s resource adequacy requirements. Large loads such as data centers place a high value on reliable power, but few have signed contracts with grid batteries so far; most of their battery deals are for on-site power.

Will battery storage buildout slow down? 

Not yet. Texas is still adding more batteries each year, largely projects that were planned and financed when revenues were higher. But the pipeline behind them is thinning: new battery applications in ERCOT’s interconnection queue halved in the second half of 2025. Whether buildout holds up depends on how many buyers will pay for capacity at a premium to what the market pays today.

Key Takeaways

• Power demand is outrunning buildout. Meeting large load growth requires more than new generation; it requires faster interconnection and congestion relief on existing transmission lines. 
• Dynamic line rating (DLR) is available today, deploys in months, and enables faster speed-to-power. On the right thermally congested lines, DLR can unlock more capacity at a fraction of new infrastructure cost. In one utility demonstration, 5% to 10% of additional capacity was enough to clear most of the congestion on the lines studied.
• DLR has been held back by weak incentives, but that is changing. Utilities earn a regulated return on capital they invest in new assets, which favors building new infrastructure over lower-cost solutions like DLR. Load growth and new Federal Energy Regulatory Commission (FERC) mandates are starting to shift the calculus.

The Grid Cannot Expand Fast Enough for AI Demand, But It Can Carry More

Power demand is booming as data centers scale across the US grid, and current grid infrastructure cannot supply it. This constraint is physical, not financial. Meeting this demand requires a significant amount of power generation and infrastructure upgrades. More than 2 terawatts of generation and storage sit in interconnection queues, roughly 1.5x the total installed generation capacity in the US. 

Regional markets are working to accelerate generation buildouts, but connecting that generation to the transmission network remains expensive and slow to match speed-to-power needs. New high-voltage lines take years to permit, cost between $2 million and $6 million per mile to build, and major projects routinely take five to ten years from identification to energization. For example, PJM Interconnection LLC (PJM) identified the Doubs–Goose Creek 500 kilovolt (kV) corridor as a bottleneck feeding Data Center Alley in 2023 and set June 2027 as the date a fix was needed. Dominion Energy's published schedule for its portion of that rebuild anticipates a completion date of 2031.

A number of studies^1,2 show there is headroom in the bulk transmission system. Grid-enhancing technologies, such as dynamic line rating (DLR), can convert part of that headroom into capacity today while new generation and transmission are being built. DLR lets suitable transmission lines increase their carrying capacity in real time, unlocking that headroom at a fraction of the cost of a buildout. Realizing that value is a targeting exercise with a key question: On which thermally limited lines can DLR actually relieve congestion? 

What Is Dynamic Line Rating?

Dynamic line rating is a method for calculating a transmission line's real-time carrying capacity using live weather and conductor-temperature data. It lets grid operators safely carry more power whenever weather conditions allow.

Most transmission lines operate under a static rating: a fixed, conservative limit on current, set for worst-case weather and held all year. The limit is based on temperature, because pushing too much current can overheat the conductor wire. Metal conductors expand as they heat, which can make them sag and touch trees or other obstacles, causing short circuits or fires. Real conditions almost always cool a conductor better than the worst-case assumption a static rating is built on. That means the line can carry more current while staying at the same maximum conductor temperature, and therefore within the same sag and clearance envelope. That headroom is exactly what DLR captures: instead of leaving it on the table, DLR recalculates the line's rating in real time so operators can use the extra capacity safely.

Beyond a static rating is the ambient-adjusted rating (AAR), which many utilities have begun adopting. An AAR recalculates the rating from forecast ambient air temperature, typically hourly and out to several days. DLR goes further, adding wind speed and direction, solar heating, and in some deployments the conductor's measured temperature.

DLR technologies rest on a heat-balance algorithm: how fast a line heats up (from electric current and sunshine) versus how fast it cools off (from wind and cold air). The calculations are standardized in IEEE 738 in North America and CIGRE 601 internationally. The data feeding those calculations can come from line-mounted sensors, weather models, or both, depending on a tradeoff between per-span accuracy and the cost of installing sensors along every span.

Even so, DLR remains limited in the US, and AAR has been slow to arrive. FERC's Order 881 required the transmission providers it regulates to adopt AAR by July 2025, but FERC has granted numerous extensions. PJM became the first to fully implement AAR in March 2026, while Midcontinent Independent System Operator (MISO) and New York Independent System Operator (NYISO) are not expected until 2028.

The Near-Term Value of DLR: Reducing Grid Congestion

DLR's value is immediate. It can be installed in months, not years, so a currently congested line can start carrying more power the moment conditions allow, reducing congestion right away. When cheaper generation is available upstream of that line, DLR cuts costs directly, because grid operators no longer need to dispatch pricier generation downstream of the congestion to supply load. That means DLR can reduce congestion costs in the current delivery year, compared to a transmission line rebuild that sits in a decade-long queue. 

Over a longer horizon, utility planners can build that headroom into long-term capacity models. This is important, because current capacity-expansion and integrated resource plan (IRP) models still run on static or seasonal ratings, and typically leave out the potential gains from grid-enhancing technologies like DLR. 

NERC's large loads white paper and FERC's RM26-4 rulemaking both raise the issue of how utilities can absorb multi-hundred-megawatt data center requests without a decade-long transmission build. Solutions like DLR are one of the few tools that can compress that timeline. 

The hardware itself is cheap: sensors and data management cost a small fraction of any physical upgrade. That means the economics comes down to identifying the lines that benefit most from DLR. This is particularly important because on most US grids, congestion concentrates on a small number of lines that repeatedly reach their limits. On those lines, DLR can cut congestion costs directly and defer costlier upgrades, while its potential on other lines may be far lower. As a result, identifying those high-potential, thermally congested lines is essential.

Proven DLR Examples in the Industry 

Real deployments show DLR can reduce a meaningful share of transmission congestion costs, with extra carrying capacity above the static rating running roughly 5% to 30%, depending on how often that capacity is available. In Oncor's ERCOT demonstration, 5% of additional capacity would have relieved up to 60% of congestion on the target lines, and 10% would have practically eliminated it. PPL Electric in Pennsylvania/PJM reports annual customer savings of $23 million after deploying DLR across its initial three lines. The DLR installation cost about $250,000, against a rebuild alternative that would have cost about $50 million and taken far longer. 

The contrast abroad is instructive. Austria's grid operator, APG, recorded about $13 million a year in congestion savings across roughly 15% of its network. While these savings are real, it's important to recognize that these results come from single, well-chosen, badly congested lines. 

The UK's National Grid began with a two-year DLR trial on a single 275 kV circuit in 2022, expanded to more than 275 kilometers of its network by 2025, with estimated consumer savings of about $26 million a year. In April 2026, National Grid signed a five-year contract covering 585 kilometers more, with most installations due by 2028 and potential savings of up to $66 million. Each expansion followed measured results from the stage before it.

Where the Headroom Is: Screening PJM's Data Center Alley

To illustrate the congestion savings from DLR, Relae screened PJM's five-minute real-time market record for every binding transmission constraint in 2025. For each one, we captured the shadow price, the marginal value of relaxing that constraint.³

Our analysis focused on thermal constraints, and then identified lines that bind frequently, in conditions milder than the worst case their static rating was set for, which is when a conductor's true rating sits above its static assumption. For the lines that we identified, congestion costs were added over the binding hours to set a bound on the savings that could result from DLR. That full amount would not necessarily be realized in practice, because the shadow price values only the next megawatt freed, and relieving one line can shift the constraint to the next. However, it serves as a useful estimate for the scale of savings that could be achieved.

We ran the analysis on the Dominion (DOM) zone in PJM, home to Data Center Alley in Loudoun County, Virginia. Figure 2 shows a high-level section of the grid. The 500 kV bulk grid steps down through transformers to the 230 kV substations feeding the data centers, with the lines that experience recurring congestion highlighted. A handful of those 230 kV lines showed up as binding thermal constraints again and again. 

The congestion in DOM isn't constant, and it concentrates in particular months and within the day in particular hours. Figure 3 shows three transmission lines within the DOM zone and the number of hours each was thermally congested in each hour-of-day slot over 2025. Binding concentrates in the warm months and, within the day, from late morning through early evening. 

At first glance, this period looks like the wrong window for DLR. The local weather record says otherwise. These periods turn out to be some of the windiest hours of the day, not the stillest. Median wind speed at Dulles ran about 3.5 m/s, above the 0.6 m/s crossflow a static rating conventionally assumes, with fewer than 5% of observations falling below that threshold. Median ambient temperature in those hours was about 26°C, against the 35–40°C a static summer rating is typically built for. Across all three lines, the large majority of congested hours coincided with weather that would have supported a materially higher rating. 

Valuing just one megawatt of DLR relief at each five-minute shadow price, the estimated savings are worth roughly $300,000 in this three-line example across about 263 line-hours.⁴

Because the value concentrates on a handful of thermally limited, heavily congested lines, and because the operational case has to be made line by line, capturing the opportunity is fundamentally an analytics problem: find the right lines, and prove the savings.

What Does It Take to Scale DLR?

DLR is cheap and effective, but two things stand between it and broader adoption: incentives and advanced grid analytics.

The utility cost-of-service model recovers investment in generation and transmission assets and earns its profit as a regulated return on the capital deployed. Because rates recover capital rather than power delivered, utilities have a stronger incentive to build or upgrade lines than to move more power across the ones they already own. That bias toward capital investment over optimization is why a mature technology has stayed niche in the US for years. Regulators have started to look more closely at this, but the main federal rule still mandates the milder AAR, not DLR, and leaves the return model untouched.

Contingency analysis compounds the problem. Current models are built around fixed line limits. A rating that changes hour to hour adds real modeling work, and more importantly, the system still has to hold under worst-case contingencies. So while operators already forecast weather daily for wind and solar, the harder step is trusting a forecast enough to commit a transmission limit against it. That takes significant predictive analytics built into system planning, not bolted on after.⁵

How Policy Is Starting to Shift the Calculus

Policy is starting to move the incentive problem. FERC's Order 881 made AAR the minimum for the transmission providers it regulates (effective July 2025, with several operators on extended timelines) and required markets to be capable of accepting dynamic ratings. PJM has started to implement this: PPL Electric has run sensor-based DLR on nine congested lines since 2022, feeding PJM's day-ahead markets. 

Order 1920, FERC's first long-term transmission-planning overhaul in more than a decade, now requires planners to formally evaluate grid-enhancing technologies like DLR against conventional builds. It stops short of mandating deployment, but it forces a comparison utilities used to skip. That comparison is now written into filed tariff processes (PJM filed its plan in December 2025). Those first cycles only began in 2026, and the order allows up to three years to reach a selection, so the results are still pending. 

A shared-savings incentive, letting a utility keep a slice of the congestion savings it creates, has been proposed to FERC and championed in the Advancing GETs Act, but it isn't yet a rule, so the core misalignment stands. DOE's GRIP program has funded grid-enhancing deployments, and by early 2026, 16 states had some form of advanced transmission technology requirement, with Colorado adding its Grid Optimization Act in April 2026.

The newest pressure is coming from the demand side. Through 2025–26, FERC began overhauling how large loads connect to the grid, and while none of it touches the utility's return on capital, it changes who sees the costs. FERC issued show-cause orders directing all six grid operators to justify or reform their large-load rules. This tees up consideration of alternative transmission technologies in study processes and greater transparency into costs. 

And the rules are moving toward making the large load pay for the upgrades its connection requires. Pennsylvania's model large-load tariff, for example, recommends utilities charge data centers for the upgrades their interconnection makes necessary. It also instructs utilities to let those customers self-construct certain upgrades, including some affecting the wider grid. That combination is what matters. The party paying the bill now has a reason to ask whether a cheaper fix exists and, in at least one state, a route to build it. We have not yet seen a DLR deployment selected this way, because these frameworks are only months old, but the cost gap between a DLR fix and a rebuild is becoming visible to the party who pays the difference.

How Relae Helps Find the Value of DLR  

Through our Power, Data, and Innovation practice, Relae combines transmission congestion data, line-level thermal constraints, and short-term weather forecasts into a single view of where dynamic ratings would actually pay. The output is a short list of candidate lines, each with a modeled capacity uplift and an estimated dollar value, turning a vague “DLR is promising” into a priced, line-by-line decision. It is the transmission-side complement to our work on the interconnection queue and demand-side flexibility. All three are ways of closing the gap between demand and delivered capacity faster than new construction allows.

Key Takeaways

• Accurate load forecasting is needed to distinguish and prioritize real demand, align capital deployment, and reduce delays in bringing new power capacity online. Traditional load forecasting was built for predictable, gradual demand growth, not for the scale, uncertainty, and dynamic behavior of data centers. 
• The system-level fix to data-center load forecasting requires probabilistic, more frequent, category-specific methods paired with mandatory data standards and policy alignment. Together, these give planners visibility into the range of possible futures and the likelihood of each. 
• Without that fix, today's forecasts conflate real demand with speculative submissions, reducing accuracy. Inaccurate forecasting in either direction is expensive: underbuild adds friction to economic development; overbuild risks raising retail rates. Both can erode public trust in planning.
• Behind-the-meter generation (BTM) and load flexibility can help achieve speed-to-power in the near term. Just 1% data-center flexibility could unlock 100 GW—more than the entire US nuclear fleet.

Load Growth Is Increasing, Uncertain, and Concentrated

For two decades, US electricity demand was flat. Utilities, transmission planners, and corporate buyers built their planning models around that reality. Then AI workloads changed it.

AI load growth is large, uncertain, and concentrated in major power markets. While load forecasting projections vary across studies, the trajectory is clear: electricity demand is scaling faster than the bulk power grid was designed to handle. Accurate load forecasting is needed to distinguish and prioritize real demand, align capital deployment, and reduce delays in bringing new power capacity online.

On April 30, 2026, Relae (formerly Carbon Direct) hosted a Trellis Group panel on load forecasting in the AI era. Panelists included Derya Eryilmaz, PhD, Vice President of Power Commercialization at Relae; John Miller, Director of Transmission Policy at the Corporate Energy Buyers Association (CEBA); Daniel Padilla, Strategy and Business Development Lead at Emerald AI; and Sam Hodas, Head of US Government Affairs at National Grid. Jake Mitchell, Director of Climate Tech Innovation at Trellis Group, moderated.

The conversation explored where load forecasts fail, what they cost, how to fix them, and near-term solutions to overcome grid constraints. Here is what the panel found.

What Is Load Forecasting?

Load forecasting is the practice of predicting how much electricity will be consumed across a region, at what times, and under what conditions. It informs the major capital and procurement decisions on the grid: where to build transmission, how much generation to procure, what capacity to bid into wholesale markets, and how corporate buyers secure clean, firm power.

Long-term forecasts inform multi-year decisions about transmission and generation. Short-term operational forecasts inform real-time grid operations and trading. The two often sit in separate workflows, but short-term operational forecasts should feed into long-term system planning to improve accuracy as demand patterns shift.

The Bulk Power Grid Is Under Strain

Large power users face constraints on clean, firm power, transmission capacity, multi-year interconnection queues, and aging infrastructure. The strain is most acute in PJM Interconnection (PJM) and the Electric Reliability Council of Texas (ERCOT), the two US markets expected to see the most significant load growth. Each constraint raises the cost of getting load forecasts wrong.

Hodas from National Grid describes the operational reality on the utility side: aging infrastructure inherited from a different demand era. “We’ve got transmission lines that are 70 to 100 years old in New York and Massachusetts, some of the oldest in the country, still in operation.” Replacing or upgrading that infrastructure requires investment, and ratepayers are already pressed. 

Why Today’s Load Forecasts Fail

Traditional load forecasting was built for predictable, gradual demand growth, not for the scale, uncertainty, and dynamic behavior of data centers. 

Most utilities and Independent System Operators (ISOs) produce load forecasts on annual or biannual cycles. They aggregate submissions from individual customers, run that data through a deterministic single-peak load estimate against a single capacity scenario, and pass the consolidated forecast up to regional planners. Regional Transmission Organizations (RTOs) roll those bottom-up utility forecasts into a regional view. 

This worked when demand was flat and predictable. It no longer works with nonlinear growth driven by data centers. Eryilmaz from Relae identifies key structural limitations. 

Four Structural Limitations to Traditional Forecasting Methods

• Over-stating and double-counting. Data centers bid into multiple regions while shopping for power, inflating regional forecasts and blurring the line between real and hypothetical demand—the speculative-load problem.
• Deterministic models (vs probabilistic models). Most planning runs a single peak load estimate against a single capacity scenario, missing the geographic concentration and uncertainty inherent in integrating large loads into the system.
• Aggregated submissions. Utilities report large loads as a single block of gigawatts, with no resolution into workload type, ramp schedule, or operational shape. Planners reverse-engineer peak-demand assumptions rather than measure them.
• Infrequent cadence. Annual or biannual forecasts cannot catch an 80% queue reduction or a multi-gigawatt addition between cycles.

The Speculative-Load Problem

The core challenge in load forecasting is distinguishing real versus hypothetical load. While data center electricity demand is projected to grow by 13-27% annually through 2028, the majority of the projects in the data center queue may not materialize, inflating regional load forecasts.

American Electric Power's Ohio utility (AEP Ohio) introduced a tariff requiring data centers to put up firm financial commitments before getting in line for grid connection. Its interconnection queue dropped from 30 gigawatts to 5.6 gigawatts. More than 80% of the submitted load was speculative: projects that disappeared once commitment became required.

ERCOT shows the same overstatement problem on a larger scale. Roughly 225 gigawatts of data center demand sits in the ERCOT queue against a historic system peak of 85 gigawatts. Texas Senate Bill 6 introduced similar financial obligations for new loads, but those rules apply only to interconnections after 2025, and the cleanup of speculative demand has not yet materialized.

The speculative-load problem shows up in interconnection times. An average new project in PJM can wait 4 to 5 years to become operational. Some of that delay is a real backlog. The rest comes from the inability to distinguish real submissions from speculative ones.

As Eryilmaz puts it, “Load forecasting is actually the center of all of these problems. It is a tool to help planners make the right investment decisions.”

The Cost of Inaccurate Forecasting

As Miller from CEBA notes, “A single misforecasted project can swing a transmission plan by hundreds of megawatts.” Significant inaccuracies can erode public trust in the planning process in two main ways. Underbuilding adds friction to economic development and can limit corporate access to clean power markets. Conversely, overbuilding risks raising retail rates if capacity remains underutilized. 

The goal is to achieve right-sized infrastructure investment. When planning aligns with actual large load growth, it can be net beneficial to retail rates. By spreading fixed costs across more usage, significant new demand can put downward pressure on the rates via the “denominator effect.” 

On the other hand, forecasting variability can distort capacity procurement and interconnection queue prioritization. When load forecasts spike upward, grid operators like PJM have to scramble to buy additional electricity capacity on short notice. These emergency procurements lock in major dollar commitments on the basis of unstable forecast numbers. 

PJM, Midcontinent Independent System Operator (MISO), and Southwest Power Pool (SPP) have also reshaped their interconnection queues to make room for new large loads, but those queue priorities depend on the same forecasts that are unreliable in the first place. 

“There is no substitute for good backbone regional transmission planning,” Miller says. “Full stop. That is the enabler of all of the load growth that we’re talking about.”

BTM Generation and Load Flexibility: A Near-Term Bridge

Hyperscalers’ need for power is way faster than that of utilities and RTOs. Generation alone cannot scale fast enough to meet this new demand, and hyperscalers need speed-to-power.

As Eryilmaz frames it, behind-the-meter generation and load flexibility are interim solutions to the timing mismatch between data center urgency and the grid's slower build cycles. BTM generation and flexibility work differently:

• BTM is power generated on the data center's side of the utility meter, bypassing grid interconnection entirely. The structure gives operators large, reliable blocks of power without waiting years for grid approval.
• Load flexibility is the demand-side approach. A data center modifies its grid draw in response to grid signals. In practice, that can mean curtailing compute workloads during stress events, pre-cooling facilities ahead of a heat wave, drawing from on-site batteries or generators, or shifting workloads to data centers in less-constrained regions.

The Value of Load Flexibility

Relae’s power system modeling quantifies the dollar value of load flexibility in ERCOT. Load flexibility can eliminate forced load shedding risk, even at 40 gigawatts of data center buildout, preventing $5.5 billion in annual consumer welfare losses by curtailing an average of 5% of demand for under 1% of operating hours.

Padilla from Emerald AI reinforces the scale and value of load flexibility: “With just 1% flexibility, we can unlock 100 gigawatts of data centers across the US. That’s more than the entire US nuclear fleet.”

Silicon Valley Power, a municipal utility, is the first US utility to tie flexibility to interconnection speed: flexible data centers get connected faster. NVIDIA, EPRI, Digital Realty, and PJM are partnering on the Aurora AI Factory, the first purpose-built reference design for flexible AI data centers. 

But standardized policy for load flexibility is lagging. Padilla highlights this challenge: “Today, if a data center wants to be flexible, they have nowhere to point. We need standardized tariffs, interconnection rules, and product definitions for large loads that reward them with upsizing interconnection in response to flexibility.” 

Flexibility Takes Many Forms, but it Isn't Universal

Flexibility means accepting brief, predictable downtime, and some workloads can't tolerate it. Hospital systems and mission-critical enterprise applications need 99.999% uptime, the "five nines" standard. As Padilla puts it: "99.9% uptime, with brief and predictable curtailments, is plenty" for most AI workloads. That distinction determines which data centers can participate in flexibility programs.

Miller points out that compute-level flexibility is not always feasible. BTM batteries and virtual power plants (VPPs) are among the alternatives that can offset what data centers withdraw when the grid is stressed, even at facilities whose compute workloads cannot pause directly.

Better Load Forecasting: The Longer-Term Fix

While BTM generation and load flexibility can help address near-term speed-to-power, the longer-term fix is improving load forecasting methods and the standardization of data provided by the data centers themselves.

Eryilmaz outlines three technical shifts for better load forecasting:

• Embed short-term operational forecasting into long-term planning. Short-term spikes, weather risk, and reserve considerations carry direct implications for multi-year capital decisions. The line between operations and planning breaks down when growth is nonlinear.
• Replace deterministic models with probabilistic methods. Risk metrics like loss of load hours (expected hours per year that demand exceeds supply) and expected unserved energy (total expected energy shortfall) measure both how much capacity the system has and the conditions under which it might fall short. The North American Electric Reliability Corporation (NERC) has suggested both metrics as part of its reliability framework.
• Forecast load by category. Treating all data center load as a single block hides the differences in load profiles, operational schedules, and ramp-up timing that drive system planning.

Policy Alignment

Technical forecasting improvements only scale with policy alignment, and Miller proposes a two-part fix:

On the top-down side, RTOs need authority to take an independent view of utility-submitted forecasts. They should require milestones, such as firm financial commitments and secured financing, before counting a submitted load against the regional forecast. 

On the bottom-up side, state regulators set the rules that govern how individual utilities prepare their forecasts. Large load tariffs play a big role in how utility-level forecasts come together. Federal and state authorities need to row in the same direction. Hodas frames the same alignment from the utility side: “Grid investment unlocks economic growth, but for us to make those investments, we need regulatory certainty.”

Standardizing Large-Load Data

The Federal Energy Regulatory Commission (FERC) has since moved: in June 2026 it issued show cause orders directing six RTOs and ISOs—CAISO, ISO-NE, MISO, NYISO, PJM, and SPP—to revise or justify their large-load interconnection rules, and in July 2026 it directed NERC to develop computational-load reliability standards and registration criteria by the end of the year. Both are useful first steps. But as Eryilmaz argues, voluntary disclosure has not closed the gap.

The industry cannot meaningfully compare ISO forecasts when each utility submits load data in different shapes (e.g., using different methods and data standards) on different schedules. Mandatory submission requirements and published methodologies, applied consistently across utilities, ISOs, and state regulators, are the only path to forecasts whose components are actually comparable.

Getting Load Forecasting Right Starts Now

The system-level fix to improving forecasting is through probabilistic, category-specific methods paired with data standardization and policy support. Together, these account for the scale, uncertainty, and dynamic behavior of data center loads, and give planners visibility into the range of possible futures and the likelihood of each.  

All forecasts will be wrong to some degree, but as Miller puts it, “It's ultimately not about having a perfect prediction. It's about baking in methods to account for uncertainty.” These system-level improvements won't eliminate errors entirely, but they will minimize them, leading to more confident investment decisions and a grid better prepared for what's ahead.

Frequently Asked Questions

What is load forecasting, and why is it harder now with AI data center loads?

Load forecasting predicts how much electricity a region will consume, when, and under what conditions—the basis for where to build transmission, how much generation to procure, and how corporate buyers secure clean, firm power. It was designed for two decades of flat, gradual demand growth. Data center load is none of those things: it is large, geographically concentrated, arrives in gigawatt blocks with no disclosed operating shape, and can be withdrawn as quickly as it appeared.

What is speculative load in an interconnection queue, and how do planners tell it apart from real demand?

Speculative load is capacity requested by projects that may never be built — often the same data center bidding into several regions at once while shopping for power, which counts the same gigawatts more than once. The tested filter is a financial commitment: when AEP Ohio required firm commitments before queue entry, the utility's reported data center pipeline fell from about 30 GW to roughly 5.7 GW. Milestone requirements, independent RTO review of utility submissions, and mandatory data standards are the tools planners have.

Is load flexibility proven and scalable today, or still emerging?

The modeling case for load flexibility is strong; the commercial case is still early. Duke's Nicholas Institute found the 22 largest US balancing authority areas could absorb roughly 76–126 GW of new load if it accepts modest curtailment, and Relae's ERCOT modeling shows demand response eliminating forced load shedding risk at 40 GW of data center buildout, avoiding $5.5 billion in annual consumer welfare losses. What is still missing is the market plumbing—standardized tariffs, interconnection rules, and product definitions—so a data center willing to be flexible has somewhere to sign up.

What should a company look for when evaluating a region's load forecast?

Ask whether the forecast is probabilistic or a single deterministic peak, how often it is refreshed, and whether large loads are broken out by category and operating shape rather than reported as one block of gigawatts. Then ask what milestone or financial commitment a project must clear before its megawatts count toward the forecast. A forecast that cannot answer those three questions cannot tell you how much of the queue ahead of you is real.

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