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AI data centers secure power under grid constraints by combining several measures rather than relying on one technology: they forecast demand early, start interconnection and procurement years ahead, shift interruptible computing, coordinate facility controls with batteries, and add carefully governed onsite generation. These measures can bridge a near-term gap while utilities build transmission and generation, but none eliminates the need for long-term bulk power and a transparent cost-allocation process.
The strongest current evidence concerns the United States. Lawrence Berkeley National Laboratory (LBNL) says U.S. data-center electricity use could reach two to three times its 2023 level by 2028, a projection rather than an observed result. Its June 2026 review identifies more than 40 possible solutions across forecasting, interconnection, resource planning and procurement, markets and operations, and cost allocation and rates. LBNL’s large-load connection report
Why AI data centers face a power problem
An AI facility concentrates a very large electrical load in one location. Hundreds or thousands of servers may need high, steady power for training and inference, while the local transmission network, substations, generation fleet, and interconnection studies were designed for earlier demand forecasts. A utility can therefore have electricity available somewhere in its balancing area but still be unable to deliver the requested amount at a particular site on the customer’s schedule.
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The mismatch has several time scales. A data-center developer may want service before a new transmission line, transformer, substation, or generating unit can be permitted, financed, built, and tested. Interconnection studies can also reveal network upgrades that must be assigned and funded before energization. During tight operating conditions, the same facility may be connected physically but asked to reduce its grid draw to protect reliability.
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This is a coordination problem involving the customer, utility, regional transmission organization, generator owners, regulators, and equipment suppliers. LBNL’s “Speed to Power” review counts more than 40 potential solutions; the number refers to options identified in its literature review, not projects already deployed. A credible plan combines options according to the site, workload, market rules, and local network.
The four flexibility levers available to AI facilities
1. Shift computation across time or geography
Some computing can move to another hour or another data center when the local grid is stressed. A batch-model-training run, for example, may be scheduled outside a constrained interval, while a replicated workload can run in a different region. This reduces the facility’s coincident peak without necessarily reducing the total amount of useful computation.
The option depends on service commitments. Interactive inference, safety-critical processing, and workloads with strict latency or data-residency requirements may not be interruptible or movable. Operators must classify jobs by deadline, latency, geographic restrictions, checkpointing capability, and customer penalty before promising flexibility to a utility or market.
2. Adjust facility infrastructure
Cooling, power-conversion equipment, server admission, and other balance-of-plant systems can be controlled so that the site draws less power during a constrained period. Controls can sequence workloads and cooling changes, maintain equipment limits, and restore output when the grid condition improves. These actions are distinct from simply switching off servers: the goal is a coordinated operating envelope that protects hardware and service quality.
Facility controls have to be tested against thermal inertia, restart behavior, redundancy requirements, and local reliability rules. A control signal that looks adequate in a spreadsheet may be unsafe if it causes a temperature excursion, overloads a remaining power train, or creates a simultaneous rebound when all curtailed equipment returns.
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3. Use energy storage
Batteries can charge when the site has headroom and discharge when grid imports must be reduced. A controller can combine battery dispatch with workload movement and cooling adjustments, reserving some state of charge for an outage or a later system emergency. LBNL identifies storage as a flexibility lever, and Pacific Northwest National Laboratory (PNNL) modeled batteries together with onsite generation and coordinated controls.
Storage is finite-duration support, not a permanent energy source. Its useful contribution depends on energy capacity, inverter power, state of charge, recharge opportunities, degradation limits, and the duration and frequency of grid events. A battery sized for a short peak may not cover a prolonged shortage, and repeatedly reserving it for reliability can reduce the energy available for routine peak management.
4. Add onsite or co-located generation
Generation behind the meter can supply part of the facility’s demand and reduce imports over a constrained connection. It may operate continuously, during selected peaks, or as an emergency resource, depending on permits, fuel arrangements, emissions controls, and interconnection terms. PNNL’s September 2026 simulation includes natural-gas generation as one modeled resource; that example does not establish gas as universally preferred, lowest-emission, or appropriate for every project.
Onsite generation changes the regulatory question rather than making it disappear. Protection, synchronization, dispatch rights, fuel security, air permits, noise, water use, and emissions reporting may apply. Whether the resource can export, provide ancillary services, or count toward a utility obligation depends on the applicable tariff. FERC’s current large-load proceedings specifically address co-location and behind-the-meter generation.
What each strategy can and cannot deliver
| Strategy | How it responds to grid stress | Dependable capacity and duration | Effect on workloads or service | Permitting, emissions, and grid implications |
|---|---|---|---|---|
| Flexible computing | Moves selected demand to another time or location, lowering coincident imports. | Depends on the amount and timing of work that can be moved; no universal capacity value is established. | Can affect latency, deadlines, location, or interruption commitments; unsuitable for some real-time jobs. | Usually avoids a new generating asset, but requires workload orchestration and an agreed measurement method. |
| Facility controls | Coordinates cooling, power conversion, server admission, and related systems to reduce or reshape load. | Bounded by thermal, electrical, and redundancy limits at the site. | May require controlled performance reductions and careful ramp and recovery logic. | Can reduce grid upgrades needed for peaks, but controls must be validated against equipment and safety requirements. |
| Battery storage | Discharges during constrained intervals and recharges when grid capacity is available. | Finite energy duration; performance depends on inverter power, state of charge, recharge access, and cycling limits. | Little direct workload impact if dispatch is sufficient, but reserve policies can limit routine use. | Requires equipment, fire-safety, and siting approvals; does not remove the need for durable energy supply. |
| Onsite generation | Supplies demand behind the meter and can reduce imports during peaks or emergencies. | Can provide longer support while fuel and equipment are available, subject to operating and permit limits. | Normally preserves computing output, but starts, outages, and maintenance require operating plans. | May involve air, noise, fuel, and emissions obligations; tariff treatment and export rights matter. |
| Transmission, generation, and interconnection investment | Adds durable network or supply capability and establishes the facility’s long-term grid service. | Designed for continuing service rather than a finite event window. | Minimal workload impact once completed, although construction timing controls when service begins. | Requires studies, permits, capital allocation, and decisions about who pays for network upgrades. |
No current official source establishes one option as the cheapest or most reliable across regions. The strategies are complements: a battery can cover a short event while workloads move, and both can reduce the amount of interim generation needed while a transmission upgrade is built.
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The planning sequence that makes reliability credible
1. Build a defensible load forecast
Separate maximum demand, expected energy use, ramp rates, and operating profiles for training, inference, storage, and cooling. Show which loads are continuous, which can be delayed, and which can move geographically. Include phased construction and commissioning rather than presenting the final nameplate load as an immediate requirement. A forecast that utility planners can audit is more useful than a single headline megawatt figure.
2. Start interconnection and site studies early
Ask the utility and regional grid operator what transmission service, distribution work, protection changes, and study milestones apply. Document the requested import level, energization phases, contingency performance, and any conditions under which the facility would curtail. PNNL’s framework for streamlining large-load interconnection highlights the need for a more consistent and fair process; the practical implication is to make assumptions, study inputs, and upgrade responsibilities explicit.
Do not treat an application queue position as guaranteed power. A study can identify network reinforcements, generator dependencies, or operating restrictions that change the schedule and cost. The customer should maintain a decision log showing which requirements are firm, which are proposed, and which depend on future tariff changes.
3. Match procurement to the residual load
After accounting for movable workloads, facility controls, storage, and onsite resources, calculate the load that must be served continuously from the grid. Procure generation and transmission for that residual requirement, with an explicit treatment of peak conditions and contingencies. LBNL places resource planning and procurement alongside forecasting and interconnection because a connection without adequate upstream supply is not a durable solution.
4. Define operating services before equipment arrives
Specify who can send a curtailment or dispatch instruction, how quickly the site must respond, how long an event may last, how recovery is coordinated, and how performance is measured. Distinguish an economic demand response event from an emergency reliability action. Workload owners need an escalation path when a control request conflicts with a customer deadline or a safety limit.
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5. Test the combined control system
Run hardware-in-the-loop or staged operational tests where feasible, and simulate communication loss, battery depletion, generator start failure, and a sudden return of curtailed load. Verify that protective relays, cooling controls, storage dispatch, and workload schedulers do not issue conflicting commands. The objective is a predictable response, not the maximum theoretical flexibility.
6. Allocate costs transparently
Large-load projects can trigger upgrades that benefit the wider network, serve only the new customer, or do both. Contracts and tariffs should identify the portion assigned to the facility, the treatment of shared network work, and the consequences if the project is delayed or downsized. Unclear allocation can leave utilities exposed to stranded investment or make a viable project appear uneconomic.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What is changing in U.S. interconnection and tariffs
On June 18, 2026, the Federal Energy Regulatory Commission (FERC) directed all six jurisdictional regional grid operators to justify or reform tariff provisions for large loads. The action covers co-location, behind-the-meter generation, flexible service, cost transparency, and study processes. It is a regulatory process, not a single nationwide tariff already in force; the final requirements and implementation details can differ by proceeding and region. FERC’s announcement
FERC Chairman Laura V. Swett said: “We are setting the stage for a resilient, reliable, and forward-thinking grid that empowers communities and safeguards consumers by transforming the way large energy users access the grid. It also is critical that FERC provide certainty for investors by directing the markets to protect existing deals and unlock opportunities for technological advancement and economic expansion. We can facilitate both, which is exactly what we did today.”
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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →For a data-center developer, the practical question is not simply whether co-location is allowed. It is how the tariff defines a network customer, what studies are required, whether behind-the-meter resources can count toward obligations, how flexible service is priced, and who bears costs when a project changes its load profile. Those questions should be resolved in the applicable regional process rather than assumed from a different market.
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What the PNNL modeling shows—and what it does not
PNNL’s September 22, 2026 study modeled coordinated controls for batteries and onsite generation on a modified IEEE 24-bus transmission system. The simulation incorporated AI data-center load profiles, battery storage, and natural-gas generation, and found potential to relieve congestion on a weak grid and assist during stressed conditions. PNNL’s study description
This is modeled potential, not a field-tested guarantee. A real project has different transmission topology, market rules, equipment limits, communications, fuel availability, weather, and operator procedures. The result supports evaluating coordinated controls as part of a portfolio; it does not prove that every data center can provide the same service or avoid the same upgrade.
Common failure modes and how to avoid them
- Counting all computing as flexible: classify workloads by latency, deadline, data location, and interruption tolerance before assigning a response value.
- Treating a battery as firm energy: model state of charge, recharge time, event duration, degradation, and reserve requirements.
- Assuming a generator solves interconnection: verify permits, fuel, synchronization, protection, emissions obligations, and tariff treatment.
- Ignoring rebound load: schedule a controlled recovery so curtailed servers and cooling systems do not return simultaneously.
- Using a study result as a commitment: distinguish a simulation or forecast from an executed interconnection agreement and tested operating capability.
- Leaving cost allocation until the end: identify customer-specific and shared upgrades while the site and load phases are still negotiable.
- Planning only for normal conditions: include contingencies, communication failure, equipment outages, fuel interruptions, and prolonged grid stress.
A decision checklist for project teams
- Have the utility and grid operator accepted a phased, auditable load forecast?
- Which workloads can move in time or geography, and what service commitments limit that movement?
- What facility controls can reduce demand without violating thermal, electrical, or redundancy limits?
- How much battery power and energy is available after reserving reliability margins?
- What onsite resources are permitted, and can they legally export or provide grid services?
- What transmission, substation, protection, and generation upgrades are required before each energization phase?
- Which party pays for each upgrade, and what happens if the project’s schedule or size changes?
- Which current tariff and FERC proceeding govern co-location, flexible service, and behind-the-meter generation at the site?
- Has the integrated control scheme been tested for failure, depletion, curtailment, and recovery conditions?
The durable answer
Reliable power for AI data centers is achieved by coordinating demand flexibility and interim resources with permanent grid investment. Forecasting and early interconnection work determine what the system must deliver; workload shifting, facility controls, storage, and onsite generation can reduce or reshape what it must deliver immediately. Transmission, generation procurement, tariff design, and transparent cost allocation determine whether that arrangement remains reliable as the facility grows.
LBNL’s May 2026 overview, “Integrating AI Data Centers with the Power Grid,” emphasizes that flexibility can accelerate near-term deployment but does not replace long-term bulk-generation needs. That distinction is the central engineering and policy test: bridge the constrained period without treating a temporary workaround as a permanent power system.
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