Shifting Playbook for Corporate Power Procurement

Key Takeaways

• Voice your opinion: The Greenhouse Gas (GHG) Protocol is updating its scope 2 guidance with final standards expected in 2027, which may require hourly and regional matching of renewable energy certificates (RECs), potentially changing how companies claim their electricity-related emission reductions. 
• Act now to secure renewable energy contracts: Companies should move forward with their scope 2 climate commitments today. The GHG Protocol is expected to grandfather in contracts entered into under existing rules.
• Beyond the megawatt hour (MWh): High-impact forward REC contracts measure impact beyond the current annual MWh match requirement, maximizing near-term carbon abatement and social impact for every dollar invested.

Why 2027 Rule Changes Matter for 2030 Targets

Companies racing to meet 2030 climate targets face converging pressures: surging electricity demand, constrained renewable energy supply, and scope 2 accounting rules that could undergo significant changes by 2027.

In a recent webinar, power market experts from Relae (formerly Carbon Direct) and Ever.green explored these changes. Patti Smith, former Electricity Decarbonization Lead at Relae; Julia Millot, Senior Power Decarbonization Manager at Relae; and Liz Pearce, Chief Revenue Officer at Ever.green, unpacked what's changing and how companies can respond.

The stakes are high. Based on GHG Protocol Scope 2 Public Consultation materials, companies may need to match RECs to electricity consumption on an hourly and locational basis as early as 2028. However, we expect the GHG Protocol to grandfather forward REC contracts signed before new rules take effect, enabling companies to continue advancing toward 2030 targets amid rule uncertainty.

Big Changes to the Power Grid

The US power grid is entering sustained demand growth for the first time in decades. "Over the next five years, data centers alone are going to put [the equivalent] of four New York Cities onto the grid," Smith explains, citing forecasts that project around 200 terawatt hours of new data center load through 2030 (i.e. cumulative energy consumption). That demand growth also shows up in near-term grid planning. NERC's January 2026 Long-Term Reliability Assessment forecasts North American summer peak demand rising by 224 gigawatts—a 24% increase—over the next decade, with new data centers cited as the primary driver. These figures highlight that peak capacity and total energy consumption are directly impacted by the data center boom.

Meanwhile, new renewable projects face headwinds. Smith points to interconnection queue delays: "Solar and battery projects are taking three to five years from initial request to operation." At the same time, clean energy tax credits, which were driving wind and solar expansion, have been curtailed. New restrictions on foreign supply chain materials, which are critical to renewable project development, are further hampering the development of new clean electricity projects.

The result: Power demand is rising while new renewable electricity supply is getting throttled. 

The Messy Reality of Electricity Emissions Accounting

Quantifying the emissions from an individual power plant is straightforward. Allocating those emissions to the companies that consume power is far more complicated. 

Grid-supplied electricity comes from many generators that shift constantly, sometimes even second to second. Companies can’t directly measure emissions from a grid-connected load because the generators serving it continuously change. 

Without direct measurement, companies need rules to estimate the emissions they are responsible for. The GHG Protocol’s Scope 2 Guidance provides that framework, establishing how companies estimate electricity-related emissions and how to reduce them through renewable energy purchases.

How Companies Currently Claim Renewable Energy

For the past decade, companies have used renewable energy purchases to achieve their scope 2 emission reduction goals. The most widely used mechanism is the REC, each representing clean energy attributes for one MWh of renewable electricity generated and added to the grid. Currently, when a company buys RECs equal to its annual electricity consumption, it can claim 100% renewable electricity. Under current rules, companies can use purchased renewable energy from anywhere in North America and apply it to any load in North America at any time during the year. 

Importantly, emissions from different power grids vary widely across North America depending on time of day, time of year, and the power grid makeup.

This flexibility allows companies to match a REC from a clean grid against electricity consumption from a dirtier one, creating a potential mismatch between emissions claimed and actual emissions avoided. This gap has drawn scrutiny, contributing to the motivations for the scope 2 rules rewrite.

What's Changing in GHG Protocol Scope 2 Accounting?

On October 19, 2025, after years of consultation, the GHG Protocol released two separate proposals for public consultation: 

1. Scope 2 changes: Moving away from annual REC matching to an ‘hourly and regional’ REC matching requirement.

2. New consequential methodology: A new approach to estimating emissions caused by a company’s consumption and avoided by its renewable energy contracts. 

The hourly matching proposal (24/7): Companies would match RECs to consumption hour by hour within the same grid region, rather than annually across any North American grid. 

"A REC generated on a Texas wind farm would not be able to be used for electricity consumed in New York," Millot explains.

The consequential approach: This proposes a carbon matching methodology, which estimates emissions caused by a load and estimates the emissions a renewable project displaces. 

"Projects in the Carolinas are avoiding 0.6 or 0.7 tons of CO₂ per megawatt hour, whereas a California project is probably closer to 0.2 or 0.3," Smith explains. 

Projects in the Carolinas deliver more than double the climate impact per REC under the consequential rules. In this methodology, the load and generator do not need to be located in the same region.

While the proposed rules and new methodologies work through the public consultation process, it will be important for companies to start to anticipate the potential impacts on their climate goals and strategies. 

Timeline for Scope 2 Accounting Changes

Both the Scope 2 and Consequential Electricity-Sector Emissions consultations closed January 31, 2026, after GHG Protocol extended the original deadline. The GHG Protocol is analyzing feedback with a second consultation and final standards expected by 2027, though the exact timeline is still being finalized.

Companies are encouraged to participate in the public consultation. The GHG Protocol is asking for comments on critical questions, such as: 

• Should proposed rules apply to energy consumers of all sizes? 
• Which geographical boundaries should be used for locational matching? 
• Should existing contracts be grandfathered in? 

The public consultation period is an opportunity to shape the standards that will govern electricity-related emission accounting for years to come.

Why Act Now Instead of Waiting

With final rules still in development, companies with scope 2 emission reduction goals or science-based targets face a decision: Wait for clarity or act now.

Several factors favor early action:

• Inclusion of legacy contracts. "There are a lot of indications from the committees that existing long-term contracts will be grandfathered in," Pearce notes. The draft considers a legacy clause that would allow organizations to apply pre-existing contractual agreements, even if they don’t comply with new rules. 
• Throttled renewable project development. Interconnection delays for new renewable energy projects, elimination of clean energy tax credits by 2028, and limitations on foreign materials needed to develop renewable energy project components mean that new REC supply may be harder to access in future years.
• Renewable project development timelines. "There's generally a lag, sometimes six to 18 months" between contract signing and project operation, Pearce explains. That means even if you sign today, the RECs won’t be generated for up to 18 months from the signing date.
• High-impact opportunity. Through careful project selection, renewable energy investment can go beyond the annual energy match requirement and incorporate additional impactful metrics, such as higher avoided emissions and positive social impacts.

Renewable Energy Buying Options for Companies

Previously, companies have been able to buy renewable energy through the following three paths; however, they all come with their own tradeoffs.

Traditional REC Buying Options

1. REC spot markets make up most corporate renewable procurement. However, they mainly come from existing projects rather than financing new development, which is critical to expanding renewable energy supply to meet rising decarbonization needs.
2. Virtual power purchase agreements (VPPAs) are highly impactful but require large power loads and the ability to manage long-term financial risks. Unavailable to most companies.
3. Utility green tariffs have limited availability throughout the US (depending on the utility(s) that serve your load) and vary in quality. 

Alternative REC Procurement Approach

For companies that want to go beyond the REC spot market and are not large enough to pursue a VPPA, there’s an alternative procurement option available: a high-impact forward REC contract. These multi-year contracts commit to purchasing RECs from specific new projects before they're built, providing the upfront revenue certainty developers need to secure financing at a fraction of the scale and complexity of a VPPA.

Comparing Renewable Energy Procurement Options

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The Path Forward

Despite rapidly increasing grid demand, renewable project headwinds, and changing accounting rules, companies can still meet 2030 scope 2 goals. 

What companies should do now:

• Watch for the next round of GHG Protocol consultation on Scope 2 revisions
• Evaluate forward REC contracts to lock in terms before rule changes
• Prioritize high-impact RECs that deliver measurable climate and social benefits

Key Takeaways

• Trust in carbon credits remains low. In part, this is because many mistakenly treat every credit type as interchangeable.
• Reduction, removal, and avoidance credits are verified against fundamentally different baselines, so credit quality must be judged based on type and project specifics, never with one blanket standard.
• The Integrity Council for the Voluntary Carbon Market's Core Carbon Principles now give buyers an independent bar to check against, including 44 methodologies approved across 13 eligible crediting programs, as of August 2026.
• Removal credits still make up only 5–6% of the market, even as compliance-driven demand accelerates, per our 2026 State of the Voluntary Carbon Market report.

Three Types of Carbon Credits: Reduction, Removal, and Avoidance

A carbon credit is a mechanism that allows one party to compensate another for their carbon mitigation activities. Based on their net emissions impact, there are three types of carbon credits: reduction, removal, and avoidance. 

1. Reduction credits reflect activities that decrease greenhouse gas emissions, compared to prior practices. 
2. Removal credits reflect activities that remove carbon dioxide already present in the atmosphere and oceans and lock it away for decades, centuries, or millennia.
3. Avoidance credits reflect activities that prevent greenhouse gases from being emitted in the first place. For all three types, credits are assessed and issued by measuring or estimating how much carbon is reduced, removed, or avoided as a result of a credit purchase and its associated activities.

While credits are assessed in different ways depending on the type, one of the most important indicators of quality is a project's baseline - the emissions that would be present in a business-as-usual scenario, without action being taken to reduce, remove, or avoid them. Project developers use baselines as a means of comparison to assess the net emissions impact of a project. 

• Baselines must be accurately set and data-driven. 
• Emissions impacts of a project must be correctly calculated against its baseline. 

Reduction, removal, and avoidance projects involve very different activities and, for some types of projects, it may be more challenging to establish an accurate baseline. However, without accurate baselines, climate impacts cannot be reliably determined.

Across the voluntary carbon market, Reduction credits represent roughly 20% of the purchases. Removal credits represent roughly 5% of the purchases. The remainder, roughly 75%, are avoidance credits.

Carbon Reduction Credits

Carbon reduction must drive the majority of our push to net zero, but translating carbon reduction activities into carbon credits that can be purchased is challenging. Examples of activities represented by carbon reduction credits include reducing fossil fuel use by improving fuel efficiency, or programs that reduce the methane that is generated from farms or municipal waste processing. 

Reduction credits are measured and quantified against the baseline emissions of an existing technology or process. Some reduction credits are easy to track and measure, such as efficiency investments or destruction of fugitive methane. Other projects are more complex. For example, low-emission cookstove projects in developing regions rely on tracking patterns of cookstove use and quantifying emission factors for various fuel and stove combinations, both of which are hard to do. The result, as studies have shown, sometimes leads to overcrediting in reduction projects. 

Superpollutant Credits

One class of reduction credit, superpollutant credits, has garnered recent attention. These credits involve the reduction of non-CO2 greenhouse gases with very strong radiative forcing, such as methane, nitrous oxides, or fluorinated gases like chlorofluorocarbons. These are not a substitute for CO2 removal, but can provide rapid and profound reductions at modest cost. Many different kinds of superpollutants exist in today’s market, worth roughly $60M today. Recent purchases by tech companies and others have highlighted the potential of these reduction credits.

Another class of reduction credit, transition credits, involves the deliberate early closure of emitting assets like coal-fired power plants or heavy manufacturing facilities. Early efforts by governments, banks, and companies around the world under the Just Energy Transition Partnership agreement jump-started transition credits as a concept at COP26. The Kinetic Coalition, in partnership with many groups including Relae, has launched work to bring transition credits to market with several pilot efforts, including closing a coal plant in the Philippines 10 years early. 

Carbon Removal Credits

Projects that remove carbon come from a diverse set of solutions, from nature-based solutions like reforestation, to hybrid solutions like biochar, to engineered solutions such as direct air capture and storage. Roughly 5–6% of credits on the voluntary carbon market today are classified as removals, up from roughly 3% a few years ago.

Carbon removal baselines are determined differently depending on whether a project uses an engineered, hybrid, or nature-based solution. For engineered removals, the baseline is zero, because no carbon removal was occurring in the absence of the project. The credited removal will be the difference between the quantity of carbon removed and any emissions that occur to facilitate the removal (determined through a carbon credit life cycle assessment). Baselines for hybrid and nature-based removals can be more challenging. In natural systems, changes in carbon stocks created by removals must be measured and approximated over time, and creditable removals represent the additional carbon removed by the intervention relative to the baseline (e.g., fallow land versus a reforestation project).

Another important consideration for carbon removal credits is project durability, a measure of the likely duration of carbon storage. Stored carbon can re-enter the atmosphere either through deliberate actions (e.g., deforestation) or accidental ones (e.g., wildfires). Nature-based removals are especially vulnerable to being re-released and are usually considered less durable (i.e., stored for less than 50 years). In contrast, engineered solutions offer high durability (i.e., stored for hundreds to thousands of years), and hybrid removals also offer durability periods that are typically longer than those of nature-based removals.

While less durable, nature-based solutions are effective, cost-effective, and widely available today. They made up over 95% of all carbon removal credits issued in 2025. Engineered and hybrid solutions are more expensive and scarce but offer longer durability. Prices of engineered carbon removal are likely to fall with innovation and increased market participation, but are currently much higher than most nature-based credits. With SBTi's finalized Corporate Net-Zero Standard V2.0, this balance is likely to shift. Large companies must now purchase removal credits covering 1–100% of scope 1, scope 2, and scope 3 emissions between 2035 and their net-zero year, with an explicit focus on more durable CO2 removal credits.

Carbon Avoidance Credits

Examples of carbon avoidance include avoiding deforestation that would result in the release of carbon dioxide into the atmosphere or clean energy projects that avoid the release of emissions from burning fossil fuels in possible facilities. This can be confusing, since many avoided credits are called reduction credits, as is the case with projects under the Reducing Emissions from Deforestation and Forest Degradation (REDD+) framework. Avoidance credits make up roughly 75% of certified credits on the voluntary carbon market today - an overwhelming majority - in part due to high availability and low price. 

Avoiding emissions is an important goal with numerous environmental, climate, community, and other benefits. Relae works with clients and customers across industries on developing and implementing strategies to avoid emissions within their value chain. However, there are significant challenges with the way that many carbon avoidance credits are created.

• Carbon avoidance credits are based on an estimate of the emissions that might have existed had a project not been funded. Because it is impossible to observe what might have happened in the absence of a project, carbon avoidance estimates are determined by considering historic data and contextual information. Statistical models can be used to create a presumed baseline that represents what would have happened in the absence of the project.

• Because the baseline is not observed in an avoided emissions project, there is uncertainty in calculating the number of carbon credits it produces. If the baseline is not set accurately, a project can overcredit. While the lack of a directly observed and measured baseline means avoidance credits will always have some degree of uncertainty, high-quality avoidance projects present compelling evidence to support their baselines, greatly reducing uncertainty. 

New datasets, statistical techniques, and methodologies are providing opportunities for developing avoidance credits with more certainty. Three REDD+ methodologies have now cleared the Integrity Council for the Voluntary Carbon Market (ICVCM) assessment for its Core Carbon Principles (CCPs), a concrete sign that credit quality standards for avoidance projects are maturing.

Defining and Standardizing Quality in the Voluntary Carbon Market

Carbon credits are intended to reduce, remove, or avoid emissions. They pay for an environmental service that must be delivered. Companies working to generate climate benefits through credit purchases must grapple with the differences and uncertainties of credit quality to ensure that the intended benefits are realized. 

While high-quality credits exist for all types of projects, a rich understanding of the differences in methodologies, geographies, physics, and ecology is required to identify high-quality projects and understand varied certainty, durability, and risk terms. Identifying high-quality carbon projects demands extensive and project-specific diligence beyond carbon market certification.

Our own diligence work reveals that high-quality projects can be hard to find. Fewer than 10% of the carbon removal projects we assessed for our 2026 State of the Voluntary Carbon Market report met our quality criteria. This diligence work now has an additional, independent backstop. As of August 2026, the ICVCM has approved 44 methodologies across 13 programs, as eligible for its CCP label, including ACR, Gold Standard, and VCS. For buyers, considering projects that are CCP-eligible should be used as a first filter—not a substitute for project-level diligence, but representing a legitimate floor.

While realized emissions impacts may be difficult to prove in some cases, it is important to remember that some projects provide additional co-benefits. REDD+ projects, for example, may have a positive impact on conservation and biodiversity, and cookstove projects may offer clear human health and social welfare benefits. However, these benefits should be assessed separately from carbon reduction, removal, or avoidance benefits.

Frequently Asked Questions

What's the difference between a carbon credit and a carbon offset? 

A carbon credit represents one verified tonne of emissions reduced, removed, or avoided. A  carbon offset describes how a buyer uses that credit, typically to counterbalance emissions it has not yet accounted for. The credit itself does not change type based on how it's claimed.

Which type of carbon credit—reduction, removal, or avoidance—is highest quality? 

No single type is inherently highest quality. Each project, regardless of type, is verified against a defined baseline. Quality depends on how rigorously that specific baseline was set and how well the project meets other quality criteria. Removal credits from engineered sources have the most straightforward baseline (zero), and avoidance credits carry the most baseline uncertainty by design, but these are only one determinant of credit quality.

Do carbon removal credits automatically meet the ICVCM’s Core Carbon Principles? 

No. The ICVCM assesses methodologies and programs, not individual projects. If a project uses a methodology that is eligible for the CCP label, this can be a useful floor but is not a guarantee of high quality.

How does SBTi's near-term removal mandate change which credits I should buy now?

SBTi's finalized Corporate Net-Zero Standard V2.0 doesn't require removal purchases until 2035, but it sets the ramp now (i.e., 1% of scope 1, scope 2, and scope 3 emissions in 2035, scaling to 100% by the net-zero year). This means the highest-durability removal supply that is scarcest and most in demand today is the same supply that many companies will need later. The SBTi guidelines are still changing, so buyers and project developers must track them closely.

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

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

How Many AI Data Center Projects Have Been Cancelled? 

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

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

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

Is Opposition to Data Centers Bipartisan?

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

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

Why Are Communities Opposing Data Centers?

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

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

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

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

Assess Community Opposition Risk Before You Site

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

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Frequently Asked Questions 

Which states are banning data centers in the US?

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

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

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

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

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

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

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

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

Key Takeaways

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

Geothermal is Gaining Traction Despite Its Cost Premium

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

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

Fast, Clean Firm Power: What Buyers Need Today 

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

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

Four Technologies Make the Clean Firm Power Shortlist

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Revisiting the Geothermal Premium

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

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

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

Frequently Asked Questions

Is Geothermal Cheaper Than a Solar PPA? 

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

Is Geothermal Cheaper Than Solar with LDES? 

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

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

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

Which Deploys Faster, Geothermal or Nuclear? 

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

Key Takeaways

• 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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