SHIELDX™ brings together two complementary battery-free technologies that work in tandem to address the very behaviours that make AI-driven loads so disruptive.
AI and large-scale machine-learning clusters are transforming global power architectures at unprecedented speed. Not long ago, a 20 MW data centre was considered significant. Today, power-dense AI compute campuses routinely scale from 100 MW to multiple gigawatts, with the world’s largest operators planning even higher densities.
In this new landscape, AI-scale workloads introduce rapid, multi-megawatt load-step changes – 80%, down to 40%, back to 90% – all within sub-second timeframes. These extreme load swings propagate upstream, placing growing strain on transmission grids and, in many regions, beginning to destabilize them.
As grid-connection queues lengthen and grid power availability becomes increasingly constrained, operators are turning to behind-the-meter (BTM) onsite generation to secure the power they need. But this necessary shift brings its own challenges.
In a traditional data centre, the public grid acts as a vast shock absorber – instantly providing power during spikes, and absorbing demand when workloads drop.
With independent onsite generation, that buffer no longer exists. Engines and turbines must directly handle abrupt, high-magnitude load changes, a task they were never designed to manage at scale. This is where the true challenge begins.
Today, only 1% of data centres operate solely on BTM islanded power plants. But within the next five years, this is expected to rise to 30%, driven by power-availability constraints, soaring AI demand, and the need for self-sufficient, high-reliability energy architectures.
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