ERCOT NORTH: A CASE STUDY IN CONSTRAINT


ERCOT North was chosen as a test region for applying the Data Center Reserve Margin (DCRM) model. Not because it is uniquely at risk, but because it offers the right conditions to test the metric’s directional behavior: concentrated hyperscale growth, known transmission constraints, and the richest set of publicly available planning data.


As a focal point for data center and LFL development, ERCOT North is already showing signs of locational strain. Substations near high concentrations of wind, solar, or low-cost gas that were once attractive due to proximity to generation are now facing saturation, as multiple projects compete for limited interconnection capacity. At the same time, statewide reserve margins remain healthy on paper. This makes it a perfect test case for a model designed to isolate whether local infrastructure can actually keep up.


Leveraging May 2025 ERCOT data, DCRM shows that ERCOT North can safely absorb up to 4.59 GW of new AI-scale data center load under base conditions. Based on typical campus-level loads ranging from 100 to 200 MW, this capacity could support roughly 16 to 33 new hyperscaler campuses. However, this margin narrows quickly under stress, highlighting the region’s sensitivity to additional high- demand loads.


Scenario


A B C D E F


G H I


D


0.85 0.9 1


0.9 0.9 1


0.85 0.9


0.85 V


0.05 0.1


0.15 0.1 0.1


0.05 0.15 0.15 0.15


CF


1 1 1


0.9 0.8 0.8 1


0.9 0.8


∆B


(GW) 0 0 6 0 0 0 5 5 5


Condition


Transmission-constrained, mild weather Base Case - normal operations


Optimal transmission, elevated climate risk (summer heat wave) Moderate load flexibility Enahnced load flexibility


Best case -- optimal conditions


Extreme weather, constrained transmission (winter storm Uri) Extreme weather, moderate flexibility Compound stress with load flexibility


Source: data above has been generated by bespoke model built by author Aishwarya Mahesh.


WHERE THE MARGIN MOVES


To test how different constraints interact, I varied three key levers in the DCRM framework:


Deliverability (D): How much of the available power can physically reach high-demand areas, considering grid bottlenecks.


Coincidence Factor (CF): Whether AI demand overlaps with peak grid stress or can shift to off-peak hours.


Risk Derate (V): How much capacity drops (or demand rises) under extreme weather or unexpected outages.


Each reflects a different constraint: physical infrastructure, load behavior, planning uncertainty, and weather-driven uncertainty. While delta base load (ΔB) isn’t varied directly, it emerges as a key output of this analysis, shifting as these levers interact.


The scenarios in the table below explore how combinations of these factors shift the reserve margin:


DCRM (GW)


4.586 4.596 3.577 4.619 4.643 5.484 2.973 3.483 3.301


20 | ADMISI - The Ghost In The Machine | Q3 Edition 2025


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