Data Centre Grid Balancing

AI data centres are requesting interconnection at gigawatt scale, and queues that once processed steady industrial load now hold years of flexible, fast-ramping demand that legacy planning models were never built to see. Utilities cannot distinguish firm from flexible load in their forecasts, so they plan capacity for peaks the data centres could shave themselves — while the real-time balancing intelligence to treat these consumers as dispatchable demand-response partners does not exist.

We integrate SCADA, AMI, and GIS signals into a real-time, CIM-aligned grid asset graph, then layer queue-aware load forecasting on top: interconnection-request pipelines, load-flexibility commitments, and weather-normalised feeder profiles feed models that treat large loads as schedulable resources. The balancing layer lets utilities orchestrate peak-shaving and demand-response dynamically — curtailment windows, flexibility dispatch, and grid-stress signals all reading from the same governed network model.