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

Key Takeaways

• Accelerated phase-out schedules for key clean energy and decarbonization tax credits will shorten the runway for project development, which could stall or cancel projects.
• Urgency is paramount, and qualified projects should expedite construction and operational timelines to secure eligibility for existing credits.
• A more complicated policy landscape requires concerted effort to navigate, including with the support of policy professionals.

Reconciliation Rolls Back Much of the IRA

On Friday, July 4, 2025, the President signed a sweeping reconciliation bill, H.R. 1, that will add at least $3.3 trillion to the national debt and marks a pivotal, contentious moment for US clean energy policy. The law was enacted through the complex legislative process known as budget reconciliation, requiring only a simple majority of votes in the House and Senate. The new law substantially modifies or terminates many of the Inflation Reduction Act of 2022 (IRA)'s clean energy incentives and has extensive implications for the economic viability of American energy and manufacturing projects.  

In the Senate, three Republicans crossed party lines to vote against the bill, requiring Vice President JD Vance to break the tie. In the House, only two Republicans broke ranks to vote against final passage. While some of the more complex provisions of the bill, such as new foreign entity of concern (FEOC) restrictions, will require more time to fully assess, we've prepared a rapid run-down of key alterations to IRA incentives for carbon management, hydrogen, and clean fuel technologies.

What Is the 2025 Reconciliation Bill?

While the 2025 reconciliation bill is staggering in length, scope, and severity, containing provisions to cut Medicaid, reduce nutrition assistance, raise the debt limit, and cut taxes primarily for the wealthy, some of the most drastic sections of the bill modify tax incentives and other public funding for clean energy and emissions reductions.

‍

Many of the incentives to deploy clean energy that were created or enhanced under the IRA will be phased out early or repealed altogether. Credits with accelerated phase-out schedules include the newly created 45Y clean electricity production tax credit, which will no longer support wind or solar projects after 2027, and the 45V credit for clean hydrogen production for which projects must now commence construction before Jan 1, 2028 (moved up from Jan 1, 2033). 

Since the passage of the reconciliation package, there has been active litigation on several provisions, including an order from a federal district court to vacate IRS guidance that would have prohibited certain wind and solar projects from securing safe harbor.  The table below provides a detailed breakdown of key changes to major tax credits between the original IRA, the draft that moved through Committees in the House, and the final text that was passed by the Senate and signed into law.

Major Tax Credit Changes in the Reconciliation Law

‍

How FEOC Restrictions Threaten Clean Energy Supply Chains

Many clean energy tax credits include ambiguous language restricting projects connected to FEOC, complicating supply chains and creating new problems for developers of clean energy projects. The law also introduces a complex matrix of new definitions, such as "Prohibited Foreign Entities," which includes both "Specified Foreign Entities" and "Foreign-Influenced Entities."

The FEOC restrictions embedded in the reconciliation bill represent a seismic shift for clean energy developers. These new rules, designed to limit the influence of Covered Nations (China, Russia, North Korea, and Iran), will disqualify projects from receiving tax credits if they source components, minerals, or intellectual property from entities tied to these nations. In other instances, the partial ownership or investment of an entity with financial ties to a Prohibited Foreign Entity may also disqualify a project from qualifying for tax credits.

This FEOC language matters for developers and investors because of the resulting global supply chain disruptions, investment uncertainty, and compliance burdens. The clean energy sector is deeply reliant on global supply chains, especially for solar panels, batteries, and wind components, industries where China currently dominates. The IRA intended to counter this by moving the manufacturing and production of these supply chains to the US. Project developers must now thoroughly review their supply chains and capital providers, and may need to quickly pivot to compliant resources. 

In February 2026, the IRS released interim guidance on the FEOC provisions to provide safe harbor guidance for clean energy manufacturing, investment, and production credits to help taxpayers gauge whether material assistance was provided by a prohibited foreign entity. 

Other Major Rollbacks to the IRA

Beyond clean energy tax credits, the reconciliation package also repeals and rescinds many other IRA provisions. This includes a full rescission of all unobligated IRA appropriated balances at the Department of Energy's Loan Programs Office, and several other programs, including:

• The Tribal Energy Loan Guarantee Program
• Greenhouse Gas Reduction Fund
• Transmission Facility Financing

A complete list of rescissions of energy-related funding is outlined in Sections 60001-60024 and 50402 of the law. These rescissions represent tens of billions of dollars in lost climate investments made under the IRA, which would have provided funds to state, local, and Tribal governments, federal agencies, non-profits, and commercial project developers to reduce emissions and update critical infrastructure.

What Can Project Developers and Other Companies Do?

Developers will need to act quickly to meet updated commence construction and place into service requirements, though circumstances are technology specific (e.g., safe harbor updates to 48E and 45Y). Tax credits generally have advanced commence construction and operational deadlines, resulting in a strong first-movers advantage. Companies should also review their supply chains and revise equipment and material procurement sourcing plans as necessary to address restrictions presented in the reconciliation bill.

An executive order from President Donald Trump issued on July 7 will further complicate how companies proceed. In the EO, the President directs his administration to "strictly enforce the termination of […] 45Y and 48E […] for wind and solar facilities." The Administration will likely issue extremely strict interpretations of "commence construction" clauses and FEOC requirements in forthcoming tax credit guidance issued by the Treasury Department, though these moves are quite likely to face litigation.

The new restrictions being proposed by the Administration, including specific details on FEOC, qualified equipment, commence construction, and other reporting requirements, will require additional guidance from the IRS and provide an opportunity for engagement through public comment. It is important that impacted companies weigh in during these public comment periods, not only to help inform and influence the final rules issued by the Administration, but also to build an administrative record that could support litigation efforts to strike down the final rules.

Staying Ahead of Policy Changes

Given the rapidly shifting landscape of energy policy, it's paramount that companies stay abreast of the latest changes and dedicate resources to understanding how they may be affected. Policy professionals, including the experts at Relae (formerly Carbon Direct), can support organizations as they engage in the regulatory process, anticipate and prepare for new legislation, and navigate the requirements to access essential tax credits and incentives. Even under new constraints, expert guidance can help maximize impact and minimize disruption.

Frequently Asked Questions

How does the reconciliation bill change the timelines for major clean energy tax credits?
Most clean energy tax credits saw their windows shortened relative to the original IRA: 

• The 45Y and 48E credits now terminate entirely for wind and solar facilities placed in service after December 31, 2027, with a separate phase-down (75% in 2034, 50% in 2035, 0% after) for other technologies. 
• The 45V clean hydrogen credit's "commence construction" deadline moved from December 31, 2032 to December 31, 2027. 
• The 45Z clean fuel credit now ends on December 31, 2029 (versus 2027 in the original IRA, but bonuses for SAF have been removed and new emissions-calculation methods favor corn ethanol). 
• Notably, the 45Q carbon capture credit saw little change and retained transferability, with credit values for enhanced oil recovery and utilization raised to match secure geological storage.

What are the FEOC restrictions, and why do they matter so much for developers?
FEOC ("Foreign Entity of Concern") restrictions disqualify projects from tax credits if they source components, minerals, or intellectual property from entities tied to China, Russia, North Korea, or Iran. Even partial ownership or investment ties to a "Prohibited Foreign Entity" can trigger disqualification. The definitions are complex and still being clarified through IRS guidance, meaning developers need to review supply chains and capital providers carefully and may need to pivot to compliant sourcing.

What should project developers do now in response to these changes?
Developers should move quickly to meet the earlier "commence construction" and "placed in service" deadlines, since credits now benefit early actors. This includes reviewing and potentially restructuring supply chains and procurement plans to address FEOC restrictions, and closely monitoring forthcoming IRS/Treasury guidance. 

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.

The AI and Climate Execution Challenge

Data center energy capacity in the US is projected to increase from 25 GW to 120 GW by 2030—a fivefold increase. Hyperscalers are projected to invest $7 trillion globally in data center infrastructure through 2030, with approximately $2.8 trillion invested in the US. 

While 2025 was defined by a 'scale at all costs' scramble for compute, in 2026, the new mandate is responsible scale: reconciling voracious power demands with aggressive net-zero commitments and rising energy costs. 

Grid constraints determine the geography and velocity of growth, forcing companies into complex trade-offs between speed-to-market and “clean, firm” power, which can take years to develop. Evolving carbon accounting rules are shifting procurement strategies and infrastructure choices at this trillion-dollar scale, creating a “carbon debt”—embodied emissions that will stay on the books for decades. In 2026, the competitive advantage likely belongs to those who integrate power, hardware, and climate strategy from day one. 

Powering AI: Grid Reliability, Constraints, and Interconnection

Grid infrastructure faces reliability challenges from aging systems and capacity constraints.  Interconnection queues stretch three to five years for renewables, while large electrical load interconnection lacks consistent standards.

Federal Regulatory Response

The federal government is moving to standardize these processes, with a critical decision point in 2026. For companies planning data center deployments in 2026, understanding these regulatory shifts is likely essential to realistic timeline and site selection planning.

On October 30, 2025, the US Department of Energy (DOE) leveraged Section 403(a) of the DOE Organization Act to direct the Federal Energy Regulatory Commission (FERC) to issue a rulemaking to “ensure efficient, timely, and non-discriminatory load interconnections” for large (>20 MW) electrical loads. 

By April 30, 2026, FERC is expected to issue a final rule on large electrical load interconnections for grid operators, providing federal regulations for approval pathways,  timelines, and rates. 

Public comments on DOE’s advanced notice of proposed rulemaking were due on December 5, 2025, and grid operators, utilities, NGOs, and customers submitted over 150 comments reflecting a wide range of perspectives. 

While federal standardization should reduce procedural uncertainty, it doesn't create new grid capacity. Even with clearer approval pathways, the underlying supply-demand mismatch remains a primary gating factor for growth.

Bridging the Supply-Demand Gap

Data center energy demand is surging, but new clean electricity generation takes years to build. This mismatch between accelerating demand and slow-building supply is forcing the industry to pursue solutions on two timelines: near-term load flexibility strategies that unlock existing capacity, and long-term generation investments that build new power supply.

Load Flexibility: Near-Term Grid Access

Load flexibility is emerging as a possible path to faster grid connection. Oracle, NVIDIA, Emerald AI, and Salt River Project's joint research demonstrated 25% power reduction during peak hours through workload tiering. The demonstration shows that if data centers reduce consumption during peak times (roughly 1% of the year), it unlocks 126 GW of currently constrained capacity that could be available now.

Large power loads increasingly face incentives or mandates to demonstrate flexibility as part of interconnection agreements, making this an access requirement, not an optional efficiency measure. For example, Senate Bill 6 in Texas mandates that data centers and other large loads must reduce their consumption during certain grid peak times. Many other state legislatures are passing legislation that will impact data centers.

Storage has shifted from smoothing renewables to enabling multiple strategies: making intermittent renewables firmer, providing grid reliability services, and supporting 24/7 matching. Storage may emerge as a solution to allow data centers to reduce grid consumption during peak hours while maintaining operations.

Relae helps clients design load flexibility strategies under evolving regulatory frameworks: evaluating behind-the-meter generation options, sizing storage for peak reduction scenarios, and structuring interconnection configurations that preserve optionality across accounting methodologies.

Clean Firm Power: Long-Term Generation

Hyperscalers remain committed to clean, firm generation that’s reliable: power that's both low-carbon and dispatchable 24/7. Natural gas with carbon capture and storage (CCS) is emerging as a critical bridge technology. Google's 400 MW CCS power agreement with Broadwing, expected online in 2029, demonstrates commercial demand at scale. 

In our analysis evaluating CCS pathways, commercial viability depends on rigorous assessment of permitting timelines, capital and operating costs, storage geology, vendor compatibility, and 45Q tax credit optimization. Execution has been most prevalent where technology intersects with regulatory approval and storage access.

Hyperscalers are also investing across geothermal, nuclear, including Small Modular Reactors (SMR), hydrogen, and fusion. Long-duration energy storage has also been an area of focus. Each has different risk profiles and opportunities across technical maturity, permitting, commercial viability, emission accounting methodology, dispatchability, and political support. 

Evaluating these pathways requires multi-dimensional frameworks. Each technology faces distinct challenges: SMRs struggle with execution complexity, geothermal with extended development periods, and hydrogen with production-dependent carbon intensity. Tax credit eligibility (particularly 45Q for CCS and 45V for hydrogen) significantly impacts project economics.

Both power generation and data center Infrastructure site selection require integrating environmental and social vulnerability data to avoid community conflicts that delay or stop projects.

Power Accounting Rules Determine Clean Energy Procurement

The Greenhouse Gas (GHG) Protocol extended the public consultation period for proposed scope 2 guidance changes to January 31, 2026. The results will determine clean energy procurement strategies and the carbon value of load flexibility for the next decade.

The proposed shift in electricity emissions accounting could increase clean energy procurement costs for buyers. The accounting methodological debates matter for hyperscalers: 24×7 energy matching versus carbon matching. The issues of deliverability (being located in the same grid region) and additionality (being new, rather than repurposed, generation) are also hotly debated.

These different frameworks strongly influence whether natural gas with CCS, nuclear, geothermal, or battery-backed renewables are considered optimal for a site, and whether load flexibility has carbon value.

Companies need to model scenarios across advanced power emissions methodologies, evaluating portfolio costs and carbon performance before final standards are published in 2027. Companies are also signing forward renewable energy certificate contracts (RECs) and structuring power purchase agreements (PPAs) now to preserve optionality across scenarios.

AI Infrastructure Emissions at Scale

Data center construction creates substantial scope 3 emissions, and their relative importance depends on grid carbon intensity. For facilities powered by average-carbon grids, scope 2 operational emissions dominate. But for data centers powered by very low-carbon electricity (renewables or nuclear), scope 3 embodied emissions can represent 40% of total lifetime greenhouse gas emissions.

In AI data centers, IT equipment drives the majority of embodied emissions. Chips and memory account for 67%, followed by structural materials at 17%, with server power supplies, aluminum, and other components comprising the final 16%. 

Direct procurement of low-carbon materials faces constraints: limited supply, geographic concentration, and contracting complexity. Environmental Attribute Credits (EACs) provide an interim pathway by decoupling environmental benefits from physical materials, but require rigorous quality standards and verification to ensure real emissions reductions.

Our high-quality EAC criteria, developed with Microsoft, establish standards that separate market-making from greenwashing. Levelized Cost of Carbon Abatement frameworks make materials decisions comparable to power decisions, treating infrastructure decarbonization as portfolio optimization, not separate workstreams.

Carbon Removal: Addressing Residual Emissions

Complete supply chain decarbonization by 2030 isn't feasible. Despite aggressive efforts to procure clean power and reduce construction emissions, residual emissions will remain significant. For hyperscalers with net-zero commitments, carbon dioxide removal (CDR) has shifted from an optional component to a structural necessity. Microsoft remains the world's largest CDR buyer, and Google increased purchases 14-fold from 2023 to 2024.

CDR credit quality varies widely. Companies must apply science-based principles to evaluate credits. Our Criteria for High-Quality CDR, developed in collaboration with Microsoft, establishes six science-based principles for evaluating credits—critical as emerging hyperscaler and other corporate demand high-integrity supply.

AI and Climate: Looking Ahead

The window for strategic maneuvering is narrow. The AI infrastructure buildout is happening now, and the decisions made in 2026 will impact a company’s cost structure and carbon profile for years. 

Companies treating power, infrastructure, and decarbonization as separate workstreams will face compounding constraints. The winners of the AI era will be those who integrate power, infrastructure, and carbon strategy into a single, cohesive system. 

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

• 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.

[cta]

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

• 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.

[cta]