Batteries solve the availability problem. They do not, by themselves, solve the response problem. That gap is a control problem — and it is worth millions per facility.
The Load Profile Nobody Designed For
Data center power infrastructure was engineered around a set of assumptions that AI workloads have quietly invalidated. Traditional enterprise computing draws power in a way that is high, steady, and broadly predictable. Facility design, utility contracts, and uninterruptible power supply sizing all reflect that profile: keep the load served, ride through the occasional outage, and manage growth in orderly increments.
AI training and inference behave differently. Large training runs synchronize thousands of accelerators, which means power draw rises and falls together rather than averaging out across independent workloads. The result is a load that swings on millisecond timescales with amplitudes conventional infrastructure was never sized to absorb. Utilities see it as instability. Facility operators see it as a hard ceiling on how much compute can be installed behind a given interconnection.
This is now the central bottleneck in AI infrastructure. Data center power demand is forecast to grow at roughly 15% annually through 2030, and the International Energy Agency projects global data center electricity consumption more than doubling to around 945 TWh by 2030. The first gigawatt-scale facilities are arriving in 2026—individual sites drawing as much power as an industrial city.
Why Batteries Became the Answer — And Why That Is Only Half a Solution
The industry’s response has been to put storage on site. Battery energy storage systems now appear on effectively every major data center project. Globally, storage shipments rose more than 75% in 2025 to 421 GWh, with roughly 600 GWh projected for 2026. In the United States, energy storage is projected to reach 41% of total battery demand in 2026, up from 26% two years earlier, and the Energy Information Administration expects 24.3 GW of new storage online in 2026 against a prior record near 15 GW.
Batteries solve the availability problem. They do not, by themselves, solve the response problem.
A battery installation is only as fast and as efficient as the logic that dispatches it. Storing energy adjacent to the load is necessary. Deciding—in milliseconds, correctly, thousands of times per hour—which modules to draw from, which to charge, how to sequence around thermal limits, and how to shape the facility’s demand curve is an entirely different problem. It is a control problem, and it determines whether the installed capacity delivers the economics it was purchased to deliver.
At hyperscale, capacity is procurement. Response is engineering. The gap between them is where the money is.
Where Premergy’s Architecture Applies
Premergy’s Battery Control System was built to manage battery banks under exactly this condition—high-variability load with millisecond-scale transitions. Five applications follow directly from it.
Peak Load Smoothing and Demand Charges
Demand charges, billed against a facility’s highest instantaneous draw, are among the largest line items in a hyperscale operating budget. Intelligent dispatch that shaves peaks without compromising compute availability reduces that charge directly. Because the billing determinant is a peak rather than an average, precision in control translates into savings with unusual immediacy.
UPS Optimization and Partial Replacement
Conventional UPS architecture is centralized and sized for worst-case ride-through, which means substantial capital sits largely idle. The BCS enables distributed, intelligent battery response across a facility, allowing the same storage assets to serve both the ride-through function and active load management. That dual use improves the return on capital already committed.
Power Availability as Revenue
In an AI facility, uptime is not a reliability metric—it is the revenue metric. Improved power stability and faster load response translate directly into higher realized revenue per megawatt deployed. This reframes the value of the control layer from cost avoidance to revenue protection, which is a considerably stronger argument in a capital allocation discussion.
Colocation and Edge Facilities
Power constraints bind hardest where the interconnection is smallest. Colocation and edge sites cannot simply request another 100 MW, so extracting more usable compute from a fixed power envelope has outsized value. These facilities represent an immediate deployment opportunity rather than a long-horizon one.
Renewable Pairing
Intermittent generation and variable load are a difficult match without intelligent storage between them. Better dispatch logic improves the economics of pairing solar or wind with data center load—an increasingly relevant consideration as operators pursue cost and carbon objectives simultaneously.
The Economics of Scale
The reason the data center vertical is Premergy’s highest-priority opportunity comes down to arithmetic.
A greater than 20% efficiency gain in a single electric vehicle—the improvement independently validated at Clemson University’s International Center for Automotive Research across more than 100 dynamometer hours and 700 miles of on-track testing—is a compelling product feature. It sells vehicles. But its value is bounded by the number of vehicles.
In a data center, the multiplier is different in kind. Even a 3% to 5% efficiency improvement across a 100 MW battery installation generates millions of dollars in annual savings at a single facility. The technical achievement is smaller; the economic result is far larger. Licensing structure follows the value accordingly: capacity-based licensing in the range of $5 to $25 per kWh under management, with subscription-based control-layer licensing as facilities become software-defined.
The multi-chemistry architecture is also unusually well matched to this configuration. Grid and data center installations already tend toward mixed deployments—LFP for long-duration capacity, high-power chemistries for rapid discharge spikes. That is the dual-bank architecture, already built and already in the ground, waiting for a control layer designed to exploit it.
The Customers Are Already Moving
The manufacturers converting EV production capacity toward grid and data center storage—LG Energy Solution, Samsung SDI, Tesla Energy, and others—are moving into an application where control architecture, not cell chemistry, governs system economics. They bring the cells. What they need above them is the intelligence layer.
Premergy does not compete with any of them. The platform makes their systems more efficient, more durable, and more valuable in precisely the market they are pivoting toward. That is the definition of a licensing opportunity rather than a competitive collision—and it is why the data center vertical, rather than the automotive market the technology was originally built for, is where Premergy sees its most immediate and highest-value deployment.
