Inside NERC’s Level 3 Alert on Data Center Loads

Key Takeaways

• The electricity demand surge is real and accelerating. Just last year, data center load in the US was projected to increase from 25 GW to 120 GW by 2030. Today, Texas’ preliminary long-term load forecast projects over 187 GW of data center load by 2030, more than double today’s total peak demand of approximately 91 GW.
• Flexible loads that respond dynamically to policy signals are becoming a regulatory requirement. Texas Senate Bill 6 (SB6), signed into law in June 2025, is the clearest signal yet. It makes remote curtailment equipment a condition of interconnection for new loads of 75 MW or more, so utilities can disconnect them during declared firm load shed events, and separately creates a voluntary demand response program that those loads can elect to join. 
• Relae’s power system modeling puts a dollar value on what data center load flexibility is worth. Our ERCOT analysis shows that data center demand response can eliminate forced load shedding risk, even at 40 GW 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. 
• Flexible load curtailment and compute uptime do not need to be in conflict. In our modeling, demand response operates as a "ghost battery" at the data center's grid node, absorbing grid stress as a physical battery would. That construct has a direct real-world analog: on-site battery storage lets data centers draw from stored energy during grid stress events rather than curtailing workloads. Technologies such as those demonstrated by Emerald AI have shown 25% load curtailment at cluster scale while preserving compute service quality.

Data Center Electricity Demand is Testing Grid Limits

Electricity demand in the United States is growing at its fastest pace in decades. Leading the surge is a rapid buildout of data centers, driven by the expansion of artificial intelligence. ERCOT, the grid serving most of Texas, is projecting up to 187 GW of new data center load by 2030, against a total peak demand today of approximately 91 GW.

The scale of this shift extends across the country. According to NERC's 2025 Long-Term Reliability Assessment (LTRA), summer peak demand across the US bulk power system is forecast to grow by 224 GW over the next 10 years, 69% above the prior year’s 10-year projection of 132 GW, with data centers as the dominant driver. As previously explored by Relae, data center energy capacity in the US is projected to increase from 25 GW to 120 GW by 2030, characterizing it as the first wave of a longer demand surge that electrification of buildings and transport will reinforce.

Policymakers are responding in real time, and regulatory responses like Texas SB6 are already rewriting the rules for how data centers connect to the grid. The pace of change is fast, but the siting, infrastructure, and interconnection decisions made today will have lasting consequences: they will determine whether data centers are grid assets or grid liabilities, and the financial difference between the two is measured in billions.

New Solutions for Data Center Demand Response: Texas SB6 as a Test Case

The rapid nature of this new wave of load growth means traditional approaches to managing the grid may not be sufficient. In the past, lead times on new sources of electricity demand allowed utilities to procure supply-side resources in advance. The scale and immediacy of data center deployment is revealing limitations of this approach. States, utilities, independent system operators (ISOs), and regulators are actively pursuing novel approaches for grid management in response.

Texas has become a focal point of US data center expansion, with SB6 as a leading policy response. The bill, which took immediate effect upon Governor Greg Abbott's signature on June 20, 2025, is the most significant restructuring of large-load interconnection rules in ERCOT's history. It requires large loads over 75 MW to install remote curtailment equipment as a condition of interconnection, so grid operators can disconnect them during declared firm load shed events, and creates a separate voluntary demand response program, procured competitively with at least 24 hours’ notice, that those loads may elect to join. If data centers are adding substantial new load to the grid, this reasoning goes, they should also contribute to grid stability by demonstrating load flexibility—reducing power draw at times of peak demand.

While demand response programs currently exist, incentivizing voluntary curtailment from data centers is challenging. In an AI compute arms race, the value of uninterrupted compute time far exceeds any available curtailment payment, such as via PJM’s capacity market mechanisms. 

Approaches to bridge that gap are coming to fruition: EPRI’s DCFlex program is working with hyperscalers and utilities to develop the technical protocols, measurement standards, and contractual frameworks that would make large-load demand response a routine grid service. Innovators like Emerald AI have demonstrated a 25% power reduction across a 256-GPU cluster over three hours during an Arizona grid stress event, while preserving compute service quality, helping to bridge the valuation asymmetry between energy and compute. 

Hyperscalers are already putting a flexible load commercial strategy into action. In March 2026, Google announced  1 GW in demand response contracts with multiple US utilities, including Entergy Arkansas, Minnesota Power, and DTE Energy.

The conversation has shifted from whether data centers can be flexible to how that flexibility gets structured and deployed. 

Putting a Dollar Value on Data Center Load Flexibility

For developers, investors, and grid operators navigating data center growth, the challenge has been making high-stakes siting and interconnection decisions without a clear picture of what load flexibility is actually worth, what inflexibility costs the system, or how curtailment requirements will reshape the regulatory landscape. 

Prior research has established that flexible data center load can absorb substantial grid stress. For instance, research from Duke University’s Nicholas Institute found that 22 of the largest US balancing authority areas could absorb approximately 98 GW of new flexible load if 0.5% of that load’s annual energy is curtailed, or 76 GW at a stricter 0.25% curtailment level. 

Relae’s analysis goes further by quantifying the economic cost at each increment of flexible load growth, and the precise threshold at which that flexibility stops being optional. We zeroed in on ERCOT, a region with high data center load growth and immediate regulatory stakes, determining the value of implementing flexibility and, conversely, the system risk of failing to do so.

How We Modeled It 

Assessing the impacts of load growth and flexibility solutions requires a systems-level analysis, best achieved via power market modeling. At Relae, we deploy our in-house power system modeling framework to navigate this complexity. 

Our toolkit includes CD-PyPSA-USA, used for this analysis, which is built on the Python for Power System Analysis (PyPSA) platform. This grid model simulates how power networks operate and evolve over time by solving for the least-cost optimization of the entire power system. Critically, our model is customized to explicitly represent complex, real-world dynamics, including data center load flexibility, co-located generation, and various policy constraints.

For this analysis, we simulated ERCOT operations under a range of data center growth scenarios. We modeled loads from 5 GW up to 40 GW in 5 GW increments, pairing each with sufficient on-site gas generation to cover roughly 70% of data center energy needs–a conservative estimate on the approach developers are taking today. 

We ran each scenario under two conditions: no load flexibility (“flex00”, the baseline) and 25% emergency curtailment capability (“flex25”), consistent with solutions exhibited by Emerald AI. This approach allowed us to determine the system's response to step-changes in electricity demand.

What Our Analysis Reveals

Before doing the analysis, we expected to see load flexibility become more important as more data center load is added to the grid, preventing forced load shedding with high lost-load costs. But we didn’t know how large this effect would be, or what amount of new data center load would start to trigger it.

Our analysis found that without flexible load management, forced load shedding first appears at 30 GW, small in scale at first (4.4 GWh over 3 hours) but growing sharply as load increases. At 35 GW, we observe 50 GWh of shedding across 39 hours. At 40 GW, shedding reaches 158 GWh across 81 hours, equivalent to nearly three times ERCOT’s average hourly energy consumption, with an economic cost at VoLL of approximately $5.5 billion.¹

By enabling on-demand data center load flexibility, forced load shedding is eliminated in every scenario we tested. The same 40 GW case instead sees 165 GWh of controlled, short-duration curtailment spread across 86 hours, less than 1% of hours in a year. Further, the average demand response in these hours was less than 5% of the nameplate data center load, with the largest event reaching 14% of data center load. The grid stays balanced, consumers remain connected, and data centers deliver substantial value to the system via flexible loads.

Load Flexibility Is High Value and Presents an Opportunity for Storage

Our modeling puts a dollar figure on what flexible load is worth. At a VoLL of $35,000/MWh, each hour of demand response in the 40 GW scenario delivers approximately $64 million in avoided consumer welfare losses. Over a full year, that adds up to $5.5 billion, achieved through an average of just 5% demand response across the 86 hours of curtailment needed to eliminate all forced load shedding.

That value points directly to an opportunity for storage. In our model, demand response functions as a “ghost battery” at the data center’s grid node, absorbing grid stress exactly as a physical battery would, without any electrons needing to flow. That virtual battery can become a real one. On-site battery storage allows a data center to dispatch stored energy during grid stress events rather than curtailing workloads, maintaining compute continuity while relieving grid pressure.

The implications point in two directions. 

• For the hyperscaler or data center operator, physical storage converts a compliance obligation into an uptime guarantee: the curtailment event becomes a battery discharge, with negligible impact to the compute stack. 
• For the storage developer, co-location with large data center loads represents a high-value deployment opportunity with a clear commercial case. The avoided welfare costs per curtailment hour our model quantifies is the value a well-positioned battery asset, co-located at a data center node, can credibly claim to preserve.

The Data Centers of Tomorrow Need to be Grid Assets

The data center buildout underway is large enough to reshape grid reliability across entire regions, and the regulatory environment is beginning to reflect that scale. Texas SB6 is the most prescriptive example to date: it requires new large loads above 75 MW to install remote curtailment equipment operable during firm load shed events.

Our modeling quantifies what load flexibility is worth across this landscape: data centers with curtailment capability can provide significant value and avoid billions in consumer welfare losses annually. For developers and investors, designing that capability in from the start can convert a compliance requirement into a long-term grid asset.

The federal picture has moved in the same direction since this analysis was published. In May 2026, NERC issued a rare Level 3 Alert on computational loads; in June, FERC ordered six RTOs and ISOs to revise or justify their large-load interconnection rules, and in July, FERC directed NERC to develop mandatory computational-load reliability standards by the end of the year. We covered what that means for grid modeling and interconnection in Inside NERC’s Level 3 Alert on data center loads. Flexibility is no longer only a Texas statutory question; it is becoming part of the federal reliability framework.

Frequently Asked Questions

What is data center load flexibility, and how does it work?

Load flexibility is a data center’s ability to reduce the power it draws from the grid on short notice, during the small number of hours when the system is under stress. In practice, that means shifting or pausing deferrable compute, drawing on on-site batteries or generation, or pre-cooling the facility ahead of a peak. The point is not to consume less overall—it is to move a thin slice of demand out of the hours when the grid can least afford it.

What does Texas SB6 require of large data centers in ERCOT?

Texas Senate Bill 6, signed June 20, 2025, makes remote curtailment equipment a condition of interconnection for new loads of 75 MW or more in ERCOT, so utilities can disconnect them during declared firm load shed events. Separately, it creates a voluntary, competitively procured demand response program those same large loads can elect to join, with at least 24 hours’ notice. The mandatory piece is the disconnection capability; paid participation in demand response is a choice.

What does data center inflexibility cost? How much curtailment avoids it?

Relae’s ERCOT modeling found that without flexibility, forced load shedding first appears at 30 GW of data center load and reaches 158 GWh across 81 hours at 40 GW—roughly $5.5 billion a year in consumer welfare losses at ERCOT’s $35,000/MWh value of lost load. Enabling curtailment eliminated forced shedding in every scenario we tested, at an average of under 5% of data center demand across 86 hours, less than 1% of the year. Each hour of demand response in the 40 GW case is worth about $64 million in avoided losses.

Is data center load flexibility proven today, and how does it compare to on-site batteries?

Data center load flexibility is past proof of concept and into commercial deployment: Emerald AI cut power to a 256-GPU cluster by 25% for three hours during an Arizona grid stress event without degrading compute service quality; EPRI’s DCFlex initiative is building the protocols and contracts, and Google has signed 1 GW of data center demand response with US utilities. 

On-site batteries reach the same result from the other direction—instead of curtailing workloads, the facility discharges stored energy, which is why our modeling treats demand response as a “ghost battery” at the data center’s grid node. For operators who cannot pause compute, storage turns the same compliance obligation into an uptime guarantee.

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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Key Takeaways

• The Greenhouse Gas (GHG) Protocol’s proposed scope 2 revisions would shift many large power buyers from annual renewable energy certificate (REC) accounting to 24/7 hourly matching and reveal a larger emissions gap than most inventories currently report.
• Of all the US grid regions modeled, the emissions gap between annual and 24/7 hourly matching is widest in PJM Interconnection (PJM) and the Electric Reliability Council of Texas (ERCOT), the markets where data center load is growing fastest.
• Relae's modeling quantifies the shift from annual to 24/7 hourly matching: serving a 4-gigawatt (GW) data center load at 100% hourly carbon-free energy requires 9.6 GW of additional clean capacity in ERCOT and 10.5 GW in PJM, a roughly 800-megawatt premium in PJM that translates directly into cost and siting strategy.
• Closing that gap requires investments in clean, firm generation technologies, like natural gas with carbon capture and storage (CCS), battery storage, and geothermal. The optimal mix varies by market and load profile, which means modeling current and future emissions positions under 24/7 accounting to understand the best procurement options for a specific portfolio.

Annual REC Accounting No Longer Holds at Data Center Scale

For years, large corporate energy buyers have relied on a straightforward approach: purchase renewable energy certificates (RECs) or sign virtual power purchase agreements (VPPAs) to offset market-based scope 2 emissions. Under the current GHG Protocol guidance, these instruments allow companies to claim low or zero emissions regardless of when or where clean energy is actually generated. When corporate clean energy demand was modest, this fueled new renewable project development while aggregate grid emissions were trending down.

That approach worked, until now. Energy demand from data centers and hyperscalers is surging. The Federal Energy Regulatory Commission (FERC) reported more than 50 GW of data center capacity operating in the US at the end of 2025, much of it concentrated in regions where local clean generation cannot keep pace. When corporate clean energy demand was modest, the gap between contractual claims and physical generation was small enough that few questioned this argument. At hyperscaler levels, with load concentrated in a handful of grids, that gap is becoming too large to ignore.

From a climate perspective, well-designed renewable procurement has created real impact by channeling corporate capital into new clean generation, and reducing CO2 emissions anywhere to benefit the climate everywhere. From a grid perspective, power consumption and generation must balance in real time, and the flow of electricity is constrained by the physics of the transmission system. Some regulators, investors, and standard-setters argue that corporate clean energy claims should be grounded in this second, engineering perspective rather than the first. The GHG Protocol's proposed revisions reflect that view, and would force buyers to defend their claims against it.

Relae’s modeling of this 24/7 framework in PJM and ERCOT helps quantify its costs and emissions implications in the markets where the stakes are highest.  

What Does 24/7 Hourly Matching Mean for Scope 2 Accounting?

The biggest proposed change to the GHG Protocol’s current Scope 2 Guidance is the move from annual power reporting and matching to a 24/7 approach. Instead of calculating emissions with an annual emissions factor (EF) based on their independent system operator (ISO) or eGRID region for each megawatt-hour (MWh) consumed, companies would need to use hourly-specific EFs. 

Companies would still be able to retire RECs to reduce their market-based emissions. However, companies would need to show that these RECs came from clean energy that was generated on the same grid, in the same hour as their facilities consumed power. This makes annual, location-agnostic REC retirement, currently the dominant practice, insufficient for 24/7 market-based accounting. 

Both the time restriction (hourly matching) and the location restriction (generation on the same grid as consumption) will make it more difficult for companies to retire RECs. For example, because today's methodology is location-agnostic, a New York-based company can retire RECs from a Texas wind farm (purchased unbundled or via a VPPA) to reduce its reported market-based scope 2 value. This has allowed renewable development to follow the best resource sites rather than the load. Similarly, the time of day that the wind farm generates energy is irrelevant, as long as it is approximately in the same calendar year. 

Under the proposed revisions, retiring these RECs would no longer be acceptable for the New York company, since they would fail both location- and hourly-matching requirements. As a result, companies with large REC portfolios today may no longer be able to retire them in order to reduce their market-based scope 2 emissions, if the proposed revisions take effect. These companies may face significant unmatched consumption under 24/7 accounting, especially during evening peaks or grid stress events when fossil-based generation fills the gap. 

Annual Matching vs 24/7 Hourly Matching

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The figure below illustrates the gap between what a representative large buyer reports under the current annual location- and market-based methodologies, versus what an hourly 24/7 analysis reveals.

Understanding this emissions gap is the essential first step for buyers to make informed decisions about which instruments to retain, which contracts to renegotiate, and where new investment will matter most. If the proposed scope 2 revisions are enacted, companies procuring clean energy will be disincentivized from buying RECs sourced from variable renewables in distant locations, and instead will find it more favorable to invest in same-grid clean, firm generation, such as geothermal, nuclear, and renewables plus storage. RECs from these projects would qualify to be retired against market-based scope 2 emissions under the proposed revisions, where today's distant-wind or off-peak-solar RECs would not.

Where Pressure Is the Highest: ERCOT and PJM

Two markets stand out for projected hyperscaler load growth: PJM, which covers the extended mid-Atlantic region, and ERCOT in Texas. Both are on track to absorb massive increases in data center demand over the next decade, and both expose the limits of annual REC accounting in ways that will be hard to ignore under the new proposed framework.

PJM: 60% Fossil Generation Means High Marginal Emissions

PJM is one of the largest and most complex wholesale electricity markets in the world. Its generation mix still includes 60% coal and natural gas, which means hourly emissions intensity remains high, particularly during evening peaks and grid stress events when fossil generation dominates the dispatch stack. 

Buyers relying solely on annual REC retirement may show low market-based scope 2 emissions today, but a 24/7 analysis tells a different story. For PJM-based buyers, this means hourly matching gaps will be largest during evening and overnight hours, when nuclear and storage become disproportionately valuable relative to additional solar.

ERCOT: Solar and Wind Don’t Peak When Demand Does

Texas has abundant wind and solar, with solar generation growing nearly 7x since 2020, but those resources don’t always run when demand peaks. While fossil-based generation has declined since 2020, it still comprises more than half of ERCOT’s generation. Solar dominates midday, wind peaks in the evening, and natural gas fills the gaps, especially during high-demand evenings or extreme weather events. 

Buyers with large ERCOT footprints may find that VPPA portfolios, which generate most of their clean energy in off-peak hours, already satisfy the proposed location-based test but fail on hourly matching. Battery storage and demand flexibility could help bridge the gap.

Figure 3 below quantifies that gap in both markets by showcasing the carbon-free energy (CFE) score in ERCOT and PJM, as well as the additional capacity required for a 4 GW load to achieve a 100% CFE target. The CFE score is the share of grid-supplied electricity in a given hour that comes from carbon-free sources, and is the metric the proposed scope 2 revisions would use to evaluate hourly matching. A 100% CFE target means electricity consumption is matched to carbon-free generation in every hour of the year.

In the left panel, a representation¹ of each market's 2030 hours are sorted by grid (CFE) score, from the dirtiest hour on the left to the cleanest on the right. Neither grid approaches 100% carbon-free on its own, and the shaded areas represent the unmatched hours a buyer claiming 100% clean energy through annual instruments would actually carry under 24/7 accounting. The gap is the maximum unmatched hours a buyer might be exposed to, as some RECs procured through annual matching may qualify under the new rules, if satisfying the locational and hourly requirements.

The right panel translates that gap into action. The additional co-located clean generation and storage required to serve a representative 4 GW load (roughly 5% of the forecast 2030 C&I load in ERCOT and 4% in PJM) at a 100% hourly CFE target, on top of what the underlying grid already provides.

A few patterns are worth highlighting. First, the left panel confirms that PJM’s grid will still spend materially more hours below 100% carbon-free than ERCOT’s in 2030, a direct consequence of the coal- and gas-heavy generation mix described above. Notably, ERCOT's curve reaches 100% in a meaningful share of hours (windows when the grid is running entirely on carbon-free resources), while PJM's never does, meaning some fossil generation is dispatched in every hour.  

Second, the ISO a buyer operates in drives a meaningful difference in build-out: hitting 100% hourly CFE for a 4 GW load takes 9.6 GW of additional capacity in ERCOT and closer to 10.5 GW in PJM. This indicates the advantage of achieving hourly and locational matching in already clean grids, which may influence a buyer choosing where to site new workloads. 

Renewables have the largest share of the additional capacity in both markets (5-6 GW), paired with significant long-duration energy storage (~2 GW), while natural gas with CCS provides meaningful clean, firm capacity (~3 GW). ERCOT’s storage share of capacity is slightly larger, reflecting the midday-solar/evening-load mismatch, while PJM leans a bit more on natural gas with CCS, where clean, firm generation does more of the heavy lifting due to lower wind speeds and solar irradiance than Texas.

The right panel also illustrates why clean, firm technologies (natural gas with CCS, advanced nuclear, and enhanced geothermal) are likely to be included alongside renewables and batteries in any serious 24/7 portfolio. With only renewables and batteries, hitting the same target requires about double the total generation and storage capacity. In both markets, targets that look achievable today on an annual REC basis will require materially more capital and a different mix of resources, under 24/7 accounting. 

Top Questions Large Power Buyers Need to Model Before the Rules Change

The GHG Protocol revisions are not finalized, and the timing of any mandate remains uncertain, which is exactly why modeling cannot wait.

A useful self-test for any large power buyer is: can your team answer the following today with defensible numbers?

• What is your hourly CFE score across your largest load centers, and how far does it sit from your reported market-based emissions?
• Which of your existing VPPAs and REC contracts hold value under 24/7 accounting, and which become effectively stranded?
• What mix of resources delivers the incremental clean, firm capacity that closes your gap in PJM, ERCOT, or wherever your load is concentrated at the lowest cost?
• If your next gigawatt of load were sited in a different ISO, how would your emissions position change?

Clean firm projects do not appear off the shelf. Advanced nuclear, enhanced geothermal, and natural gas with CCS all carry multi-year development timelines, and corporate offtake agreements are often what get these projects financed in the first place. Buyers who engage now help shape the project pipeline that will be available in their target markets in 2030, and can lock in offtake terms before competition for the most valuable sites tightens. Buyers who wait until the methodology is final will be working with shorter lead times, fewer development partners, and less leverage to specify projects that fit their load profiles and hourly matching needs.

Frequently Asked Questions

What is 24/7 hourly matching, and how does it differ from today's REC accounting?

Today's scope 2 accounting lets companies retire renewable energy certificates (RECs) from any grid, at any time of year, to offset their emissions. The GHG Protocol's proposed 24/7 hourly matching would require RECs to come from clean generation on the same grid, in the same hour a facility consumes power, making most of today's location-agnostic RECs ineligible for market-based accounting.

Why are PJM and ERCOT under the most pressure from this shift?

Both markets are absorbing the fastest-growing data center load in the country, and both still lean on fossil generation to meet demand outside peak renewable hours. PJM's generation mix is 60% coal and gas, while ERCOT's solar and wind often don't peak when demand does, so buyers in these markets face the largest gaps between their annual REC claims and their actual hourly carbon-free energy score.

How much additional clean capacity does it take to close the gap?

Relae's modeling finds that serving a 4 GW data center load at 100% hourly carbon-free energy requires 9.6 GW of additional clean capacity in ERCOT and 10.5 GW in PJM. That capacity mix leans on renewables and long-duration storage in both markets, with natural gas with CCS playing a larger role in PJM, where wind and solar resources are weaker.

What should power buyers do before the GHG Protocol revisions are finalized?

Start modeling now. Buyers should know their hourly carbon-free energy score, understand which existing VPPAs and REC contracts hold value under 24/7 accounting, and identify the lowest-cost mix of clean, firm resources that closes their gap. Clean firm projects like advanced nuclear, enhanced geothermal, and natural gas with CCS take years to develop, so buyers who engage early have more influence over the project pipeline and better offtake terms.

Modeling the 24/7 Emissions Gap with Relae

For large power buyers assessing what the proposed GHG Protocol revisions mean for their power procurement portfolio, Relae's Advanced Power Emissions Analysis solution models the gap between current market-based reporting and what 24/7 accounting would reveal—by market, load profile, and technology stack. 

Key Takeaways

• The opportunity: Clean, firm power is a strategic priority for large electricity buyers. Natural gas-fired generation equipped with carbon capture and storage (CCS) is emerging as a key tool in meeting this demand. The existing gas-fired power fleet in the US should be assessed to identify plants well-positioned for carbon capture retrofits that would benefit grid decarbonization. 
• The challenge: The climate benefits of CCS-equipped natural gas plants depend entirely on how often they actually run. Adding carbon capture technology increases the cost to operate the equipment. These higher running costs can make the plant less competitive in auctions where the grid operator picks the cheapest power first. Without mechanisms to keep these plants running continuously, they may be outbid by cheaper, higher-polluting plants, causing grid-wide emissions to stay the same or even increase. 
• The solution: Hyperscalers and other large energy buyers are creating a robust market for clean, firm power. By paying a "clean, firm premium" through long-term offtake agreements, these buyers can offset the higher operational costs of CCS, ensuring these plants are continuously utilized. This corporate leadership not only maximizes the grid-wide climate impact of each retrofit but also provides an important hedge against policy volatility, securing the investment case for clean innovation even when the future of subsidies like the 45Q tax credit is uncertain.

We Need Clean, Firm Power Now

The market signals for clean, firm power are clear. Meta’s nuclear energy projects and Microsoft’s Crane Clean Energy Center demonstrate growing interest in reliable, low-carbon electricity to support the rapid expansion of AI. Similar commitments by Google and Meta to advanced geothermal power also illustrate this trend. 

One of the near-term options to meet this demand is natural gas with carbon capture and storage (CCS). As explored by Relae (formerly Carbon Direct), retrofitting existing gas facilities offers a path to reliable baseload power with low direct emissions, leveraging existing infrastructure to bypass the years-long delays typical of new grid interconnections. 

Recent initiatives from Google and Calpine are already working to prove this concept at scale. This type of corporate leadership is driving the market; over the last decade, voluntary corporate procurement led to more than 40% of new clean energy capacity in the US. Further, recent procurement decisions illustrate that these players are willing to pay a “clean, firm premium” to secure round-the-clock, low-emissions sources of power.

Why Systems-Level Analysis Matters for CCS

While news of corporate procurements often makes headlines, recent analysis finds the number of supply contracts for natural gas power with CCS may outpace the number of secured offtake agreements. Without a power purchase agreement (PPA) to ensure competitive operation, or strong policy support, a generator may need to operate as a “merchant plant” in power markets, competing solely on cost.

A power plant’s ultimate climate impact is determined primarily by how it is positioned in the market, not just its facility-level technology. 

How Power Markets Determine Which Plants Run

Understanding the potential of CCS to deliver clean, firm power and grid-wide decarbonization requires looking beyond the technology performance at a single facility. A retrofitted plant does not operate in isolation; its impact depends on how it interacts with the broader power market’s merit order.

The merit order is the ranking system in competitive power markets where the grid operator dispatches the cheapest offers first. Since carbon capture units are energy-intensive, the retrofitted natural gas plant incurs higher operating costs. This cost increase can inadvertently price the lower-emitting plant out of the market. Without mechanisms to ensure continuous utilization, the CCS plant is potentially outbid by cheaper, more carbon-intensive resources. This creates a risk of increased overall grid emissions.

To illustrate this dynamic, we’re sharing the results of our detailed grid modeling analyses of the Electric Reliability Council of Texas (ERCOT), which serves most of Texas, and the Southwest Power Pool (SPP), which covers parts of 14 states across the central US. Our analysis highlights the value of corporate “clean, firm premiums” in achieving maximum climate benefit and mitigating policy risk present in government subsidy support. 

This type of systems-level grid modeling is necessary in understanding how facility-level reductions translate into real climate benefits. Support to incentivize continuous operation, such as corporate offtake agreements or the 45Q tax credit, is key to ensuring that retrofitting a gas power plant with CCS reduces overall grid emissions. 

Offtake Agreements and Policy Support as Solutions

Power offtake from CCS retrofitted gas plants can meaningfully reduce system-level emissions. By directly matching electricity demand with the supply of power, large energy buyers – the offtakers – ensure the power plant is effectively utilized. This type of arrangement helps ensure any changes to reduce emissions intensity at the facility level translate into broader emissions reductions on the grid.

For these offtakers, the decision to pay a premium for clean power is driven by the goal of additionality – ensuring their procurement has a measurable, additional emissions reduction impact. Beyond physical energy, buyers secure Energy Attribute Certificates (EACs) for CCS, which serve as the verified proof of low-carbon generation required to satisfy corporate zero-emissions targets. As seen in the recent Google and Calpine agreement, these certificates allow buyers to claim the specific climate benefit of the CCS retrofit, justifying a premium over standard wholesale market rates to secure firm, clean delivery.

In the absence of offtake agreements, policy frameworks like the 45Q tax credit (up to $85 per ton of CO₂ sequestered) serve a similar function by offsetting production costs.

However, access to this credit is not a guarantee and carries operational hurdles. To unlock the full credit value, facilities must meet stringent prevailing wage and apprenticeship requirements. Furthermore, the credit is limited to a 12-year window once the facility is placed in service, and requires construction to commence by 2033.

Beyond these eligibility requirements, the long-term outlook for 45Q involves inherent uncertainty. Recent regulatory shifts, including potential changes to the Greenhouse Gas Reporting Program (GHGRP), pose risks to the verification mechanisms required to substantiate captured tons. 

Corporate offtake agreements offer a crucial private-sector complement to this landscape; they provide a stable revenue model independent of policy cycles, ensuring the investment case remains robust over the full life of the asset.

Understanding the Merit Order in Power Markets

Most US power plants operate in competitive deregulated markets, where grid operators dispatch generators based on their marginal cost of production – the cost of generating one additional unit of electricity. The operator ranks these offers from lowest to highest price, creating the "merit order.”

In these auctions, the cheapest resources (typically renewables and base load) are dispatched first. Progressively more expensive units (gas and peaking plants) are called upon until demand is met. The price of the final, most expensive unit required sets the market-clearing price received by all generators in that period. 

The Figure below shows an example generation merit order in the ERCOT energy market.

Case Study: How Support Structures Influence Dispatch

The merit order figure illustrates a hypothetical scenario for a natural gas generator, showing how its market position changes based on technical and policy variables:

• Pre-Retrofit (Stage A): The plant operates with standard marginal costs, sitting competitively in the middle of the supply stack.
• Post-Retrofit (Stage B): Retrofitting with CCS introduces higher operating costs due to the energy-intensive nature of carbon capture. Without external support, the plant’s marginal cost increases (A to B), making it less competitive. The retrofitted plant may be utilized less while cheaper units are dispatched to meet demand.
• Post-Retrofit + policy or offtake support (Stage C): Financial support, whether through the 45Q tax credit (approx. $33/MWh¹) or a corporate offtake agreement, can effectively offset the plant’s higher operational costs (B to C). This effect restores the plant’s competitiveness, ensuring it dispatches consistently.

Testing This With Grid Modeling

At Relae, we apply state-of-the-art grid analysis tools to answer these and more complex analytical questions related to the future energy system. Our custom modeling framework has been used to simulate clean power strategies, assess data center demand response programs, and understand how procurement decisions today impact the future energy system.

While the theoretical impact of a CCS retrofit, a PPA agreement, and the 45Q tax credit on a plant’s dispatch is clear, it’s important to put the theory to the test by modeling their effects on system-wide emissions.

Our Modeling Approach

Because each grid region has distinct power plants and load requirements, they must be modeled separately. For this analysis, we chose to model the ERCOT and SPP power markets to determine the region-specific, grid-wide emissions impact of hypothetical CCS retrofits of natural gas power plants. 

As part of this modeling, we:

• Deployed detailed hourly simulation: We used our custom PyPSA-USA grid model to produce a set of hourly simulations of the ERCOT and SPP electricity markets.²
• Identified suitable retrofits: We identified suitable combined cycle gas power plants for a CCS retrofit in each of the markets, based on key commercial and operational criteria, including size, age, generation profile, and proximity to CO₂ transport/storage.
• Modeled plant and energy assumptions: To reflect the retrofit, we adjusted generator cost and energy use for the identified plants (up to 1.4 GW capacity), fitting all combustion turbines with capture and requiring each plant to consume 20% more fuel per unit of electricity produced to power CCS.³
• Carried out comparative scenario analysis: We simulated several scenarios, including (1) pre-retrofit, business-as-usual, (2) post-retrofit, with and without a PPA, and (3) post-retrofit, with and without the 45Q tax credit, to isolate the impact of different procurement agreements and policy landscapes on grid-wide emissions. 

What Our Analysis Reveals

Results of this analysis reveal how CCS deployment in the power grid interacts with market economics and the role mechanisms that drive high utilization of CCS retrofit plants can have in ensuring system-wide emissions reductions:

CCS With a Firm Offtake Agreement Can Significantly Reduce Grid-Wide Emissions

Pairing a retrofitted plant with a dedicated offtaker can drive meaningful emissions reductions in both ERCOT and SPP compared to business-as-usual (-0.8% to -1.7% CO₂ in ERCOT; -5.2% to -7.3% CO₂ in SPP). Under these arrangements, system-wide emissions fall because the PPA acts as an operational anchor, ensuring the retrofitted plant maintains high utilization rates despite its higher running costs. Ensuring the plant stays utilized prevents the grid from reverting to more carbon-intensive generation to fill the gap.

Our analysis finds the value of the operational “clean, firm premium” for natural gas with CCS power is up to $60 per MWh. This value varies by hour, region and scenario but results generally align with our previous estimate of a $30 per MWh value associated with this type of generation. Other estimates put this value between $19 and $72 per MWh.

CCS Without an Offtake Agreement Can Reduce Emissions, But Is More Reliant on Policy Support

Without a dedicated offtake agreement or policy support, retrofitting natural gas plants with CCS runs the risk of a small increase in grid emissions (+0.7% CO₂ in ERCOT; -0.0% CO₂ in SPP). System-wide emissions are higher because other power plants displace the plants with carbon capture. The higher operational costs of CCS mean the CCS plants have a less competitive place in the merit order and run for fewer hours in the year.

The story changes with the application of 45Q, and grid-wide emissions are lower for both ERCOT and SPP (-1.7% CO₂ in ERCOT; -3.4% CO₂ in SPP). Access to the 45Q tax credit improves each CCS plant’s position in the merit order, meaning that it runs for more hours and successfully displaces higher-emitting generation with clean, firm power.

The Path Forward for Clean, Firm Power

Our analysis illustrates that in competitive power markets, the overall carbon emissions impact of natural gas generation with CCS cannot be measured solely at the power plant level. While clean, firm power remains a strategic priority for large electricity buyers, and CCS is a key tool to meet this demand, the overall climate value of a successful retrofit is linked to the availability of offtake agreements and the plant’s position in the merit order. 

A systems-level perspective captures what facility-level analysis misses: how market dynamics determine the true climate impact of decarbonization investments. Support mechanisms for the continuous operation of low-carbon power plants, like PPAs and the 45Q tax credit, are important tools that ensure clean, firm power reaches the grid, effectively bridging the competitiveness gap.

Frequently Asked Questions 

How can companies ensure CCS retrofits actually reduce grid-wide emissions? 

By securing the plant’s dispatch through a long-term offtake agreement, or by utilizing a policy incentive like 45Q. Relae’s modeling found that offtake agreements have a substantial impact on the emissions reduction potential of CCS retrofits. 

Why would the dispatch decisions of one power plant affect others? 

Power plants dispatch according to marginal cost, and grid stability requires that total supply remain constant at any given moment. So, if one large plant suddenly dispatches less (say, because its operating costs have increased), other potentially dirtier plants may ramp up to fill the gap, increasing total system emissions.

Key Takeaways

• Data centers are driving surging demand for new, firm electricity supply, accelerating natural gas-fired power generation.
• Carbon capture and storage (CCS) offers a practical way to balance long-term climate commitments with the need for new electricity generation in the near term.
• New natural gas-fired power plants must be capture-committed, not just capture-ready, potentially delivering power in 18 months and decarbonized power 18-24 months later.
• Capture-committed plants integrate planning and finance for the CO₂ capture, transport, and storage value chain from the start.
• Relae believes early investment in engineering, infrastructure, and community engagement is essential to meet capture commitments.

A New Era of Electricity Demand and Climate Pressure

The US and much of the developed world are experiencing profound growth in electricity demand. Two main forces are driving this trend: (1) the push to electrify existing uses, such as vehicles and heating, to improve energy security, enhance system efficiency, and reduce air pollution; and (2) the growth of energy-intensive sectors like manufacturing, telecommunications, and AI data centers.

Among these drivers, AI is creating unique demands that catalyze specific investments in electric power generation. Astonishing AI data center buildout, led by a handful of large technology firms (sometimes called “hyperscalers”) and their utility and construction partners, is accelerating energy consumption. These firms prioritize speed. When asked for their top five criteria for bringing new AI infrastructure online, one executive responded: “Speed, speed, speed, cost, and carbon emissions.”

Data centers require reliable, always-on power (referred to as “firm power”). This differs from other use cases, such as residential or commercial, which do not need the same amount of power across all hours. While hyperscalers and their partners are investing in renewables, nuclear, and geothermal energy at a remarkable pace, renewable resources alone do not yet meet the exploding demand for firm power. 

Natural Gas Provides Firm Power but Drives Emissions Higher

The mismatch between data center power needs and variable renewable generation is fueling a boom in natural gas-fired power generation. The pipeline of new natural gas-fired power plants is enormous. Plants under construction in 2025 would, by themselves, add roughly 25 million tonnes of greenhouse gases each year to the air and oceans. The full suite of plants in planning is at least 10 times larger. Existing gas plants are also being used more and staying online longer.

This rapid buildout is creating tension with corporate climate goals. Hyperscalers remain seriously committed to reducing emissions, but their ability to hit those targets is undermined by the need to procure new, large-scale electricity generation quickly.

Carbon Capture Aligns with Data Center Energy Demands

Carbon capture and storage is one way to bridge the gap. Data centers operate continuously and may have the ability to shift or curtail load. This demand profile suits the duty cycles of natural gas turbines and CCS facilities well. The potential to reduce direct emissions is profound: today’s CCS technology can capture 95% or more of CO₂ emissions at competitive costs in many markets.

This has led to a resurging interest in the concept of capture-ready gas power generation. New natural gas power plants can be built and brought online in 18 months. In a capture-ready plant, the developers integrate the necessary interfaces and reserve additional land, water, and energy to enable a carbon capture project to be built at a future date. In favorable locations, carbon capture can be added to a capture-ready plant in 18-24 months.  

However, past experience shows that capture-ready plants rarely deliver. The ambition and commitment of the developers were contingent on policy and market signals that were either too small or never materialized. While the base plant may have made economic sense in terms of energy value for investment, it does not appear anyone was willing to pay the climate premium for CCS.  

As David Hawkins of the Natural Resource Defense Council famously said, “If your plant is capture ready, my driveway is Ferrari ready.” To bring David’s humorous analogy back to the specifics here: don’t build a new driveway without at least a downpayment on the car.

How to Build Capture-Committed Power Plants for CCS

A better approach is building capture-committed plants, namely facilities that integrate CCS from the start. To be capture-committed, project developers must:

• Identify geologic storage for the many millions of metric tons of CO₂ that these plants will produce each year over the next 20-30 years.
• Plan reliable CO₂ transportation from power generation to geologic storage by pipeline, rail, barge, or truck.
• Engage credible vendors of carbon capture technology that serve their needs and fit their goals.
• Fund front-end engineering design (FEED) studies.
• Arrange, or help to arrange, financing for the construction, commissioning, and operation of all necessary components in the CO₂ capture, transportation, and storage value chain.
• Ensure natural gas supply has near-zero fugitive methane emissions.
• Partner with local and frontline stakeholders to incorporate community impact into project planning, design, and financing.

Capture-committed plants send strong market signals. They help build the permitting pathways and develop the workforce, infrastructure, and community acceptance needed to avoid extra expense and delays. Done well, early commitments and investments will likely create repeatable models that reduce build times and costs.  

A Path Toward Power That’s Clean Firm and Future-Ready

Eventually more carbon-free power in the form of renewables, nuclear, and geothermal energy will be deployed to serve national and international electric load growth for all types of electrification. Over time, these resources will likely displace natural gas. Until then, hundreds of millions of tons of CO₂ will be emitted each year unless commitments are made to take tangible action now. 

Capture-committed natural gas-fired plants offer a pragmatic solution. With the right planning, financing, and community engagement, they can provide reliable power without locking in emissions, and they can deliver enormous benefits compared to uncontrolled operation. Federal and state governments can accelerate this transition by honoring and increasing CCS grants, supporting shared infrastructure, and streamlining permitting for CCS plants as they have for other clean energy supplies. These investments will enable the construction of cleaner, more resilient power infrastructure for the industries driving demand, from AI data centers to heavy industry.

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Frequently Asked Questions

What is the difference between a capture-ready and a capture-committed power plant?

A capture-ready plant creates an option to add carbon capture in the future, whereas a capture-committed plant treats capture as part of the project from day one. In a capture-ready plant, developers install the right interfaces and reserve extra land, water, and energy, but nothing obligates them to build the capture project, ever. A capture-committed developer secures options for CO₂ transportation and geologic storage, relationships with capture equipment vendors, funding for engineering studies, and financing across the full value chain before the base plant comes online.

Why have capture-ready plants historically failed to add carbon capture?

Nobody was willing to pay the climate premium. Capture-ready developers built plants that made economic sense on energy value alone, then waited for policy and market signals to justify carbon capture. Those signals were either too weak or never arrived, so the option went unexercised and no capture project was ever designed. The base plant runs uncontrolled for decades while the reserved land sits empty. David Hawkins of the Natural Resources Defense Council captured the problem well: "If your plant is capture ready, my driveway is Ferrari ready." Preserving an option costs very little. Exercising it costs a great deal, and capture-ready facilities rarely came with the funding to do so.

If renewables, nuclear, and geothermal will eventually displace gas, why invest in CCS for gas plants now?

Because greenhouse gas emissions happen in the meantime. Gas plants being built today will operate for 20 to 30 years, long before carbon-free resources scale enough to displace them. Left uncontrolled, they will emit hundreds of millions of tons of CO₂ over that span. Capture on those plants avoids most of it. Today's technology can capture 95% or more of CO₂ emissions at competitive costs in many markets.

What can federal and state governments do to accelerate capture-committed projects?

Three kinds of support matter most: funding, infrastructure, and permitting. Governments should honor and extend existing CCS incentives. Developers make capture commitments years before any revenue arrives, so uncertainty in government funding undermines the confidence these projects require. Governments should also support shared CO₂ transport and storage infrastructure. Common pipelines, rail terminals, and storage hubs make it easier for developers to secure physical CO₂ offtake. Finally, permitting for CCS should be streamlined the way it has been for other clean energy supplies. Permitting delay is a leading cause of cost overruns, and a capture-committed plant should be able to pursue capture and storage with the same intensity and speed as electricity generation.

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

• Enhanced geothermal systems (EGS) overcome traditional geothermal energy limitations by engineering subsurface conditions rather than searching for them, enabling widespread deployment of clean firm renewable power.
• Induced seismicity from high-pressure injection has caused major EGS project cancellations, but advanced approaches like Sage Geosystems’ gravity-assisted fracturing mitigate this risk by avoiding overpressures and directing fractures downward away from fault zones.
• Sage’s $97 million Series B financing, co-led by Ormat Technologies and Carbon Direct Capital, will fund the first commercial EGS facility at an existing Ormat plant—accelerating the transition from innovation to grid-scale deployment.
• For hyperscalers racing to power AI infrastructure, EGS offers a credible path to firm, 24/7 low-carbon power at scale.

Geothermal Energy: The Heat (And Pressure) Is On

For decades, geothermal energy has occupied a compelling yet narrow place in the clean energy landscape. It offers what the grid increasingly needs— firm, renewable, low-carbon power—yet has remained constrained by limited siting flexibility, high upfront resource risk, and persistent concerns around induced seismicity. 

Enhanced geothermal systems (EGS) change that equation. Instead of searching for ideal subsurface conditions, EGS engineers them directly. In doing so, EGS rewrites the rules of where geothermal energy can be deployed and how far it can scale, with the potential to transform this historically niche resource into a widely deployable form of clean firm power.

One such solution, Sage Geosystems, uses a pressure-managed EGS approach to extract geothermal energy from engineered subsurface reservoirs, while explicitly addressing the seismicity risks that have constrained earlier projects. 

How EGS Scales Geothermal Energy

Conventional geothermal power relies on a narrow set of subsurface conditions: sufficiently high temperatures, naturally occurring fluid, and enough permeability to circulate fluid through hot rock. In practice, those conditions coexist in only a few places—nearly all US commercial geothermal power generation is concentrated in California, Nevada, and a handful of sites across Utah and Hawaii.

EGS reduces this constraint by engineering permeability and fluid access rather than relying on their natural presence. While fluid access and permeability are harder to find, heat is not: the Earth’s natural geothermal gradient ensures that hot rock exists almost everywhere at sufficient depth. 

By reducing the number of variables that must be discovered rather than designed, EGS expands siting flexibility and lowers the resource risk that has historically constrained geothermal development. The Department of Energy (DOE) estimates this approach could unlock more than 5,500 GW annually of US resource potential, which, when converted to electric power, is roughly comparable to the total installed power capacity of the US today.

One remaining challenge has been induced seismicity. When you inject pressurized water into rock and create fractures, you are adding lubrication to geological systems that have been static for millions of years. If those fractures propagate into existing fault zones, the faults can slip, producing earthquakes. Projects in Basel, Switzerland (2006) and Pohang, South Korea (2017) triggered magnitude 3.4 and 5.4 events, respectively, both leading to project cancellations and regulatory backlash that set the industry back years.

Sage's approach to EGS is designed to address this risk directly. Rather than relying on high-pressure hydraulic stimulation, Sage uses a gravity-assisted fracturing approach that helps avoid the high overpressures that can drive fault slip. Further, its approach biases fracture growth downward and away from shallow, critically stressed fault systems. By understanding causes and conditions, Sage aims to work with the subsurface, not against it. 

This is not a minor technical detail. It is the difference between a technology that can scale with community acceptance and one that faces opposition at every site. For a hyperscaler evaluating geothermal offtake agreements, seismicity risk translates directly into permitting risk, timeline risk, and reputational risk. 

The Clean Firm Power Gap Driving EGS Adoption

To understand why this matters, start with the problem hyperscalers are trying to solve. Solar and wind have scaled dramatically, but they face a structural limitation: they do not generate power when the sun is not shining or the wind is not blowing. Batteries help bridge short gaps, but current technology cannot economically cover multi-day periods of low renewable output. Nuclear provides firm generation, but faces permitting timelines that extend well past 2030.

This creates what might be called the 'clean firm power gap'—the difference between what hyperscalers need (24/7, low-carbon, scalable to gigawatts) and what current markets can supply. A single large AI training cluster can consume more than 100 MW continuously. Meta, Google, and Microsoft are planning data center campuses that will require gigawatts of capacity. The gap between demand and available clean firm power supply is widening, not narrowing.

Geothermal energy aligns closely with this need. Unlike solar or wind, geothermal power plants run continuously, with capacity factors that routinely exceed 90%. And unlike nuclear, geothermal projects can, in principle, be permitted and built on shorter timelines. The challenge has never been performance, rather availability: with the emergence of EGS, geothermal power is expanding where clean firm power can realistically be built, arriving at a moment when the grid’s need for dependable, low-carbon supply has never been greater.

Sage Raises $97 Million to Deploy Geothermal at Ormat Site

Sage Geosystems announced $97 million in Series B financing co-led by Ormat Technologies, the world's largest geothermal operator, and Carbon Direct Capital, a leading energy investing firm. Ormat will host Sage's first commercial facility at an existing Ormat plant.

The investment signals that EGS has become investable to the industry built to scale it. For Ormat, the logic is clear: conventional geothermal is constrained by resource availability. EGS expands the addressable market, but requires the subsurface capabilities that conventional operators don't typically possess by Sage does.

Why the Partnership Structure Works

EGS proposes that the fastest way to scalable power is to eliminate the resource risks that beset conventional geothermal projects. These risks do not simply disappear: they are transferred into subsurface and remain unproven at scale. Conventional operators locate naturally permeable reservoirs. EGS requires creating permeability in crystalline rock and managing induced seismicity risks that don't exist in hydrothermal systems. Sage is actively addressing the seismicity problem that ended projects in Basel and Pohang. Ormat brings everything else: turbines, plant operations, grid expertise, and six decades of operational knowledge.

Building at an existing Ormat site provides another advantage: established subsurface characterization, proven geological stability, and grid infrastructure already in place. For a first commercial deployment, this de-risks demonstration in ways greenfield sites cannot.

Both companies move faster together because the technical capabilities required to make EGS work don't naturally exist within a single organization.

What Hyperscaler Demand Means for the Power Sector

Meta's 150 MW power purchase agreement with Sage—announced in August 2024, with delivery planned for sites east of the Rocky Mountains—adds another dimension to this story. Hyperscalers have concluded that waiting for clean firm power technologies to mature before signing contracts means those technologies may not be available when needed. So they are becoming anchor customers, providing the revenue certainty that enables projects to secure financing.

For geothermal power specifically, this demand signal is transformative. Contracted offtake from creditworthy counterparties changes project economics fundamentally. It lowers the cost of capital, enables debt financing, and de-risks the investment case for additional capacity. The hyperscaler model has already accelerated deployment in solar, wind, and battery storage. Its application to geothermal power may prove similarly catalytic.

The Final Constraint

EGS is not a silver bullet, but it is beginning to look like a credible answer to a growing-problem: how to deliver clean firm power at scale, in more places, and on timelines that match accelerating demand. Advances in subsurface engineering are reducing the resource and seismicity risks that once confined geothermal to a narrow footprint, while partnerships with incumbent operators are showing how those advances can be integrated into existing energy infrastructure. 

At the same time, hyperscalers are reshaping the market by signaling demand early, underwriting first deployments, and pulling technologies forward rather than waiting for them to mature on their own. That combination of technical progress, industrial adoption, and committed buyers is what turns promising concepts into deployable systems. 

Whether EGS ultimately fulfills its potential will depend on repeatable and continued performance under real-world conditions. But the recent alignment of science, incumbents, and demand suggests EGS is moving beyond possibility and into a phase where the final constraint is no longer what the Earth can provide, but what the energy system is prepared to build. 

Frequently Asked Questions

What is an enhanced geothermal system?

An enhanced geothermal system, or EGS, produces geothermal energy by engineering underground conditions needed to circulate fluid through hot rock. Unlike conventional geothermal projects, which depend on naturally occurring heat, fluids, and permeability occurring together, EGS can create or enhance permeability and fluid circulation, greatly expanding the locations where geothermal power may be developed. 

Why is EGS important for data centers and AI infrastructure?
AI and data centers require large amounts of electricity around the clock, creating demand for power sources that are both low-carbon and firmly available. EGS could provide high capacity factor (greater than 90%), 24/7 clean electricity in more locations than conventional geothermal, making it a potentially valuable complement to intermittent renewable resources. 

What is induced seismicity, and how are new EGS technologies addressing it?

Induced seismicity refers to earthquakes caused by changes in underground pressures or stresses caused by human activities. Earlier EGS projects demonstrated that high-pressure fluid injection can activate existing faults and in some cases triggered noticeable earthquakes and intense public backlash. New EGS approaches are being designed to better control reservoir pressure, fracture development, and proximity to faults, reducing seismicity risk while maintaining the fluid circulation needed to extract geothermal energy.

Can EGS be deployed anywhere?

EGS substantially expands geothermal’s geographic potential, but it does not make every location equally suitable. Projects still depend on factors including underground temperature, how deep they need to drill to access that temperature, water availability, seismic risk, and whether the rocks are of type suitable to hold and sustain engineered fracture networks.

Key Takeaways

• Scope 2 emissions (indirect emissions from energy use) are increasingly critical to address. With surging electricity demand, especially from data centers, scope 2 is a growing share of corporate emissions and a priority for decarbonization.
• Approaches to scope 2 accounting are evolving—and formal changes are now on the table. Both location-based and market-based methods remain accepted under the Greenhouse Gas Protocol. Still, the Protocol's recently closed public consultation proposes more granular approaches, including 24/7 power and carbon matching, that would better reflect the realities of modern power markets.
• Proven decarbonization levers, such as reducing energy use, entering power purchase agreements, procuring green tariffs, and buying high-quality renewable energy certificates, are already available and impactful. Decarbonization, not just measurement, must be the goal. Companies don’t need to wait to decarbonize. 

Accounting for Indirect Emissions From Energy Use

As businesses and organizations strive to reduce their environmental impact, carbon accounting has become an essential tool for tracking and managing greenhouse gas (GHG) emissions. Carbon accounting helps organizations measure, report, and mitigate their emissions across various activities. A key framework for categorizing these emissions is the Greenhouse Gas Protocol (GHG Protocol), which classifies emissions into three scopes:

Each scope presents unique challenges and opportunities for reduction. Among them, scope 2 emissions are particularly significant because they stem from purchased energy, which is often generated using fossil fuels. However, numerous reduction mechanisms exist today to help organizations eliminate these emissions, such as improving energy efficiency in order to use less energy, and transitioning to renewable energy sources through market-based mechanisms. Understanding scope 2 emissions is crucial for businesses looking to contribute meaningfully to the global energy transition and achieve sustainability goals.

What Are Scope 2 Emissions?

Scope 2 emissions refer to indirect GHG emissions associated with the consumption of purchased energy. Unlike scope 1 emissions, which result from direct fuel combustion, scope 2 emissions arise from the generation of electricity, steam, heat, or cooling that a company procures from external sources.

The primary sources of scope 2 emissions include:

Purchased electricity: When businesses buy electricity from a utility provider, the emissions from power plants that generate this electricity are classified under scope 2.

Purchased heat, steam, and cooling: Some companies purchase heat, steam, or cooling services instead of generating them on-site. These services often come from centralized facilities that may rely on fossil fuels, thereby contributing to scope 2 emissions.

What sets scope 2 emissions apart from other scopes is the presence of market-based mechanisms that offer multiple pathways for organizations to reduce their carbon footprint. Unlike scope 1, where emissions reductions often require technological shifts or operational changes, scope 2 reductions can be achieved through strategic procurement decisions. The transition to renewable energy sources is an essential component of sustainability strategies, setting the stage for a broader energy transition across industries and economies.

How Are Scope 2 Emissions Measured Today?

The GHG Protocol currently outlines two primary approaches for calculating scope 2 emissions: the location-based method and the market-based method.

Location-Based Method

The location-based method calculates emissions for electricity consumption based on the average emissions intensity of the grid where the energy consumption occurs. This approach is mandatory under various reporting frameworks and does not take into account a company’s procurement choices.

• Relies on grid averages: Emissions are calculated based on regional grid emissions factors rather than specific energy purchases.
• Time-delayed data: Since grid emissions factors are typically updated annually, this method may not reflect real-time energy sourcing changes.
• Limited control: Companies using this method have less direct influence over their reported emissions, as they depend on the overall energy mix of their region.

Market-Based Method

The market-based method, on the other hand, reflects an organization’s actual procurement decisions and energy-sourcing strategies. It accounts for specific contracts, such as power purchase agreements (PPAs), renewable energy credits (RECs), and green tariffs, which allow businesses to claim lower emissions from their purchased electricity.

• Reflects company choices: Emissions calculations take into account contractual agreements for renewable energy purchases.
• Mechanism for electricity transition: Encourages organizations to invest in low-carbon electricity options and actively support the transition to renewables.
• Multiple reduction options: Companies can reduce their scope 2 emissions through a portfolio of mechanisms like PPAs, RECs, and green tariffs, making this method a flexible and strategic tool for decarbonization.

While market-based mechanisms provide flexibility in reducing scope 2 emissions, they also highlight the need for more precise and updated carbon accounting methodologies. For example, some decarbonization strategies, such as time-shifting energy consumption to better match renewable generation, are not accounted for under these methods. This and other limitations mean that the traditional methods outlined in the GHG Protocol are increasingly seen as outdated in an era of rapid changes in energy generation and grid dynamics. As a result, the market is shifting toward more advanced power emission accounting methodologies that provide a more accurate reflection of emissions associated with electricity use.

Proposed Changes to the GHG Protocol Scope 2 Guidance

The current GHG Protocol Scope 2 Guidance provides a market-based instrument methodology, originally designed in the early 2000s, that allows US-based companies to procure renewable energy at any point within a year from anywhere in North America and apply it to any of its annual electricity consumption within that same year. This methodology, as written, allows for a potentially significant mismatch of “emissions caused” (by consuming electricity) versus “emissions avoided” (by generating renewable electricity) in that it does not account for any of the realities of electric grids and generators, which vary significantly over different regions, seasons, and time of day. 

In response to this, the GHG Protocol Scope 2 Guidance is currently undergoing a revision process, which will include how emissions associated with electricity consumption are calculated. A focus of the revision process is on how to better account for the real emissions associated with a corporate’s electricity consumption, and more impactful ways of mitigating them through market-based instruments and other approaches. Advanced power emission accounting methodologies, such as 24/7 power matching and carbon matching, are being explored as ways to better represent the GHG emissions associated with electricity consumption. 

• 24/7 power matching emphasizes matching electricity consumption with an equivalent amount of renewable energy production on an hourly basis.
• Carbon matching emphasizes measuring the emissions impact of incremental electricity consumption or production at a specific time.

These emerging methodologies propose a shift toward more granular temporal and region-specific matching, which could require companies to rethink their emissions reporting approach and explore more advanced tracking tools. They may also introduce new strategies beyond market-based instruments for reducing scope 2 emissions, such as time-shifting energy consumption.

As power grids continue to decarbonize and new digital tools emerge, businesses will need to adapt to these evolving methodologies to remain compliant, enhance sustainability strategies, and achieve meaningful reductions in emissions. Companies that proactively integrate advanced power emission tracking into their carbon accounting strategies will be better positioned to lead in the transition to a low-carbon economy.

How to Reduce Scope 2 Emissions

The GHG Protocol provides multiple mechanisms for reducing scope 2 emissions, allowing organizations to shift their energy consumption toward lower-carbon alternatives. These include:

• Reducing energy consumption: Improving energy efficiency in operations can significantly lower electricity use. In some cases, this involves capital investments in more energy-efficient equipment, but in other cases, it can be based on operational changes such as reducing unnecessary lighting, HVAC, and other services during non-working hours. (Electrification efforts, such as shifting from fossil fuel-powered systems to electric alternatives, may actually increase scope 2 emissions, but this can ultimately reduce overall emissions by correspondingly decreasing scope 1 emissions and allowing for renewable energy procurement.) 
• RECs: Companies can purchase unbundled RECs (emissions “attributes” separated from the actual electricity product) to offset emissions associated with purchased electricity. While there has been criticism of RECs due to their significant range in quality, high-quality RECs are available, which may include ensuring regional matching, financial additionality, on-line date additionality, or tighter temporal generation to consumption matching. The use of high-quality unbundled RECs is the most accessible and realistic option for most smaller-scale companies to address scope 2 emissions. 
• On-site generation and co-location: Installing on-site renewable energy generation, such as solar panels, allows companies to directly offset their electricity consumption from the grid. In some commercial settings, such as companies using leased real estate or co-located data centers, partnering with facilities that prioritize renewable energy procurement can help reduce scope 2 emissions for the facility owner while the facility occupant reduces scope 3 emissions. 
• PPAs: Entering into long-term contracts with renewable energy providers ensures companies receive electricity from clean energy sources while supporting the expansion of renewable generation capacity. PPAs are available with standardized contract terms, and some service providers will aggregate demand from multiple smaller companies to reach the minimum required amount for typical PPA contracts. Hedging products are also available to reduce market risks.
• Green tariffs: Many utilities offer green tariffs that enable businesses to purchase renewable energy directly through their electricity provider, often at a premium but with lower emissions impact. For many smaller companies, this is a more viable approach than a PPA with a single renewable generator.

By adopting a combination of these strategies, businesses can significantly lower their scope 2 emissions while aligning with broader sustainability goals and regulatory requirements. The path to decarbonization requires proactive investment in cleaner energy sources, efficient consumption practices, and leveraging market-based instruments to drive the transition toward a low-carbon future.

Why Does Reducing Scope 2 Emissions Matter?

Reducing scope 2 emissions is the underpinning of decarbonizing the power sector and enabling the global energy transition. In 2025, S&P reported that corporate buyers added 15.2 GW of renewable capacity in the US, up from 9.1 GW in 2024, illustrating the growing impact of the corporate sector on the electricity grid. Cleaner grids translate to lower emissions for all energy users. Organizations that actively reduce their scope 2 emissions can contribute to decreasing demand for fossil fuel-based electricity and accelerate the deployment of renewable energy infrastructure.

For companies that own and operate data centers, this transition is especially important. AI data centers consume large amounts of electricity, and their reliance on purchased power makes them a significant source of scope 2 emissions. Since many businesses rely on third-party data center services, reducing emissions from these facilities also helps lower scope 3 emissions across industries. Corporates can influence data centers by requiring that they have a clear and explicit low-emission power strategy in place before procurement.

Beyond direct corporate benefits, reducing scope 2 emissions has a tangible long-term impact on power grids. Increased investment in renewable energy procurement sends a strong market signal, encouraging utilities and developers to expand clean energy projects. As more companies commit to sourcing renewable energy, the overall mix of grid power shifts, making low-carbon electricity more accessible and reducing reliance on fossil fuel-based generation. Ultimately, widespread corporate action in scope 2 emissions reduction supports the broader decarbonization of power markets and strengthens global climate commitments.

Frequently Asked Questions

Will RECs (renewable energy certificates) still count toward scope 2 reductions under the GHG Protocol's proposed changes?

Under the current Scope 2 Guidance, yes—RECs remain a valid market-based instrument. The proposals from the GHG Protocol's recent consultation range from retaining market-based accounting with stricter quality criteria to restructuring how instrument-based claims are reported altogether, and nothing is final until the revised standard is published. What's clear is that scrutiny is rising, particularly for unbundled RECs with weak temporal or geographic connection to a company's actual consumption, so prioritizing high-quality RECs now is the best way to future-proof a procurement strategy.

How would the proposed hourly and regional matching requirements affect companies that rely on unbundled RECs today?

Hourly (24/7) and regional matching would require renewable generation claims to line up much more closely with when and where a company actually consumes electricity. Companies relying on annually matched, unbundled RECs sourced from distant grids would likely see their reported market-based emissions rise under such requirements. The practical preparation is to start collecting more granular (ideally hourly) consumption data and shift toward RECs and contracts with tighter regional and temporal matching.

What's the practical difference between location-based and market-based scope 2 accounting, and will that distinction survive the GHG Protocol's revision?

The location-based method calculates emissions using the average emissions intensity of the local grid, regardless of procurement choices, while the market-based method reflects a company's actual contracts, such as PPAs, RECs, and green tariffs. The consultation explored options from strengthening the criteria for market-based claims to reporting emissions and market instruments in separate, complementary statements. Both concepts will exist in some form, but companies should expect the requirements behind market-based claims to tighten.

When is the new Scope 2 Guidance expected to take effect, and what should companies do now to prepare?

Per the GHG Protocol's July 2026 development plan, a draft of the revised consolidated Corporate Standard is expected for public consultation in 2027, with a final published standard currently estimated for late 2028, and adoption timelines will follow publication. Companies should take action now. Energy efficiency, PPAs, green tariffs, and high-quality RECs reduce real emissions under any accounting regime. Building hourly consumption tracking and auditing the quality of existing REC portfolios now will make any future transition smoother.