Why this matters now

The AI revolution is here, and it comes with a very real and growing demand for power. As AI capabilities become embedded into everyday tools and industrial systems alike, the energy required to train and operate these models has grown dramatically.

Emerson experts Brett Benson and TJ Surbella explain that the increasing reliance on generative AI and advanced analytics brings with it unprecedented demands on electrical infrastructure and energy planning.

In an article in Power magazine, they outline why AI training facilities operate very differently from traditional data centers:

“These facilities leverage banks of hardware like graphical processing units (GPUs) and tensor processing units (TPUs) to process tremendously large amounts of data. As they process data, these GPUs, TPUs, and their associated support systems—HVAC, server cooling systems, and more—also consume massive, but unpredictable, amounts of power. Large numbers of processors switch on and off in unison, creating instantaneous swings of hundreds of megawatts (MW) at a time.”

The swings these sites generate, Benson and Surbella explain, create significant challenges for traditional utilities,

“AI training loads are massive, fast-changing, and unlike anything utilities have historically served, and the facilities that run these loads are fundamentally different from traditional data centers. They also operate differently from other massive power consumers utilities have historically supported.”

Typically, this means AI-training data centers cannot connect directly to the grid the same way traditional data centers and other customers would.

Takeaway: AI training data centers introduce unprecedented power volatility that traditional grid designs were never built to support.

TL;DR

• AI training introduces massive, unpredictable power fluctuations.
• Traditional grids are not designed for rapid load swings.
• Many AI data centers must deploy islanded or hybrid power strategies.
• Battery energy storage systems are essential buffers for AI loads.
• Advanced automation and control are critical to stability.

Bring-your-own-power

Because traditional grids struggle to support AI training loads, many organizations are building islanded or partially islanded power generation as part of their data center capital investments.

However, islanded generation introduces its own risks. Equipment must be protected against large swings in load, and sites must eventually prepare for grid interconnection as infrastructure evolves.

“One way data center operators are meeting this challenge is via their battery energy storage systems (BESS). While small data centers often have a UPS array for facility power backup, the need for a BESS is unique to hyperscalers performing AI training. A BESS can act as a shock absorber to make AI training loads more grid compatible. First, and most importantly, a properly engineered and configured BESS can deliver power to alleviate the instantaneous load spikes of processor arrays because the excess load required when the processors ramp up is fed by the BESS instead of the grid. Conversely, excess generation (or suddenly reduced load) can be used to recharge the batteries. The load profile to the grid is more even because the BESS manages excess demand and generation, instead of passing it along.”

But managing a BESS in this environment is not something that can be done manually. It requires precise automation.

Takeaway: BESS systems are a critical buffering layer that make AI training loads manageable and grid-compatible.

Control is key

Managing power in an AI training environment requires millisecond-level response times that exceed human capability.

“The ability to instantaneously balance loads and manage control is complicated when operators must navigate a wide array of OEM control systems on each generation asset. Millisecond decision making is critical for balance, because the amount of energy that can move in a cycle is very large, enough to trigger relays and upstream devices to trip larger portions of the system offline. Human operators cannot make such decisions and changes fast enough without assistance from automation.”

A built-for-purpose SCADA system such as Ovation Green SCADA enables operators to manage diverse power assets through a single, unified control interface.

Takeaway: Automation and centralized control are essential for maintaining stability in AI-driven power systems.

Preparing for the future

The grid may not be ready today for widespread AI training adoption, but it will be. Organizations that move early can gain competitive advantage by putting the right power strategies and control systems in place now.

By leveraging the right automation technologies and power architectures, teams can operate effectively today while preparing for a more connected future.

Takeaway: Early investment in power and control strategies positions organizations for long-term AI-driven growth.