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Accelerating time to power: Why utilities are prioritizing faster, data center interconnection
AI-driven data centers are scaling faster than power infrastructure planning and delivery cycles can support. Global data center capacity demand is expected to more than triple by 2030, with U.S. demand expected to grow from approximately 25 GW in 2024 to more than 80 GW. At the same time, large load interconnection requests are surging, with AI playing a major role and accounting for a substantial share of future load growth.
Utilities are challenged to support rapid data center growth while safeguarding grid reliability and meeting the expectations of regulators, customers and communities.
The pressure to balance risk, reliability and investment timing is something utilities are actively navigating today. Eaton’s new report explores the current surge in load growth—driven by AI and hyperscale data centers—and how utilities are adapting planning, interconnection and stakeholder coordination.
Utilities are facing unprecedented growth in large load data center interconnection queues. In some regions, large load interconnection requests have surged by as much as 700% in a single year, reflecting the speed and scale of data center development. Much of this demand reflects requested capacity at interconnection rather than committed or near-term load. At the same time, utilities have limited visibility into the timing and rate at which load will actually ramp.
This creates two simultaneous challenges:
Infrastructure timelines, by contrast, remain long and relatively fixed. New transmission lines, substations and generation assets can take more than five or even ten years from planning to operation.
This mismatch forces utilities to make critical decisions about when and how to invest. Building for full projected demand too early risks stranded assets if load does not materialize as expected. Delaying investment, however, risks slowing economic growth and missing near-term load opportunities.
As a result, utilities are beginning to explore approaches such as phased or conditional interconnection, where initial capacity is delivered based on near-term, evidenced load requirements, with additional capacity aligned to actual demand growth and system readiness. Rather than planning around theoretical future load, this model allows infrastructure to grow alongside demonstrated demand, reducing the risk for stranded assets while enabling meaningful early connection.
For utilities, this represents a fundamental shift - traditional, static planning assumptions are no longer sufficient in an environment defined by rapid, large-scale load growth.
Planning complexity is increasing across the electrical grid
At the same time, planning is becoming more complex. The timelines that govern utility infrastructure investment, regulatory approval and large load interconnection are increasingly non-linear and interdependent. Utilities must also manage multidirectional power flow across the electrical grid, including interactions between centralized generation, distributed energy resources (DERs) and high-density loads. Together, these dynamics are fundamentally changing how system planning is approached, requiring earlier coordination, improved demand visibility and more adaptive planning frameworks.
For utilities, time to power is shaped by a combination of structural and process-related factors.
At the grid level, available transmission and distribution capacity directly limits how quickly new large loads can be connected to the electrical grid. In parallel, permitting timelines and increasing community and land-use considerations are extending project deployment cycles.
A significant portion of time to power is also influenced by non-technical constraints:
Together, these factors introduce structural friction that extends beyond engineering challenges and affects the overall delivery timelines.
At the same time, decisions about where data centers are being built are evolving. Power availability and electric grid capacity, access to renewable energy as well as cooling and land advantages are playing a more prominent role in determining location. This is contributing to geographic diversification toward regions with more favorable power and operating conditions, as seen in emerging regional models for data center development. Broader electricity system trends also reinforce these constraints, with demand growth and infrastructure pressures highlighted in many forward-looking energy outlooks.
Beyond physical capacity, time to power is increasingly shaped by the performance envelope of the electrical grid—the operating conditions under which power can be delivered safely and reliably. This includes constraints such as congestion, contingency risk and system flexibility.
Taken together, these factors illustrate that time to power is shaped by a combination of infrastructure limitations, regulatory processes and site-specific factors that vary significantly by region. Reducing the time to power is not about removing constraints, it is about planning, coordinating and investing effectively within them.
Time to power is becoming a strategic issue and an increasingly important consideration across the energy ecosystem.
It directly influences:
Extended timelines have tangible consequences. They can slow the pace of AI deployment, delay infrastructure investment and create misalignment across planning cycles for utilities, developers and policymakers. As these pressures converge, time to power is playing a growing role in how infrastructure delivery is phased, how utility grid capacity is allocated and how quickly AI-driven growth can be realized.
“Utilities are facing a fundamentally different era of load growth. For decades, Eaton has partnered with electric utilities and data centre operators as a trusted advisor, bringing together deep expertise in grid infrastructure, regulatory frameworks and long‑term planning with a practical understanding of data center operations and large‑load grid impacts.”
Through its grid infrastructure expertise, power management technologies and experience working across both utilities and data centers, Eaton helps enable more flexible, coordinated approaches to large-load integration and speed to power.
For utilities, the question is no longer whether time to power needs to improve, but how to do so while managing risk. What approaches are proving effective and how can they be applied in practice? That is where the next stage of the conversation focusses.
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