Behind-the-Meter Energy Hub Dispatch for Data Centers

Mon, 01 Jan 0001 00:00:00 +0000

Problem

AI workloads can push a data center’s power demand up by tens of megawatts in minutes, often faster than its grid connection can supply. On-site gas turbines can cover the gap, but they respond over minutes while computing load changes in seconds. Keeping the two in balance without tripping any plant limit is a hard control problem.

Approach

Each side of the facility gets its own learned world model: one for the turbine fleet and one for the AI data hall. A single planner looks ahead across both at once and decides how far the turbines should ramp and how much work the data hall can safely defer. Every command is checked against the turbines’ physical limits before it reaches the plant, so the learned components never act unchecked.

Contract-Aware SLA Compliance Monitoring

Mon, 01 Jan 0001 00:00:00 +0000

Problem

Colocation contracts promise each tenant specific limits on power, temperature and humidity, with penalty credits when those limits are broken. The terms that matter, such as a measurement window, an exclusion or a deadline for claiming credits, are scattered across long contracts and worded differently by every vendor. Monitoring is usually reactive: a breach is noticed only after it has already cost money.

Approach

AI agents read each SLA contract and turn it into structured, machine-checkable rules. Every rule carries the exact contract text it came from, and a verification step checks that quote against the source before the rule is used.

Data-Centers on Omanshu Thapliyal

Behind-the-Meter Energy Hub Dispatch for Data Centers

Problem

Approach

Contract-Aware SLA Compliance Monitoring

Problem

Approach