Unlocking AI Data Center Interconnection Capacity Through Coordinated Grid and Data Center Flexibility

Abstract

The rapid growth of AI data centers is creating concentrated electricity demands that can exceed available distribution network headroom and delay interconnection. This paper investigates whether capacity can be used effectively by coordinating flexibility on both sides of the interconnection. A day-ahead mixed integer second order cone programming framework maximizes feasible AI data center IT capacity using utility side conservation voltage reduction and network topology reconfiguration, together with data center workload shifting and reserve constrained uninterruptible power supply storage. Background feeder demand uses voltage dependent ZIP models, while the data center is modeled as constant power demand using Training, Inference, and Mixed workload profiles. A two-stage solution first maximizes interconnection capacity and then minimizes feeder losses for the retained capacity, with numerical relaxation screening. Studies on a 116-bus model derived from the IEEE 123-node feeder show that, at the constrained bus-60 connection point, grid side flexibility, driven almost entirely by network reconfiguration, increases feasible IT capacity by approximately 42.5-45.0%, while data center flexibility alone provides approximately 2.1-5.4% gains. Coordinated operation increases feasible IT capacity from approximately 10.5-10.6 MW under BASE operation to 15.3-16.0 MW, corresponding to gains of approximately 45.9-50.8%; the 16.0 MW Inference result is limited by the adopted UPS reserve requirement. Benefits are strongly location dependent and influenced by workload flexibility, deferral duration, PUE, UPS energy and reserve requirements, and feeder loading. Under a combined adverse operating condition, FULL capacity decreases by only 3.4% relative to the nominal Mixed case while retaining substantial additional capacity over BASE.