Multi-Timescale Coordinated Scheduling of Power Systems and AI Data Centers under Renewable Uncertainty

Abstract

The rapid growth of artificial intelligence (AI) applications is making geographically distributed AI data centers (AIDCs) an important class of large and potentially flexible electricity loads. These loads exhibit high power demand and temporal variability, while their geographical distribution enables part of the aggregate AIDC load to be redistributed across multiple sites. Meanwhile, increasing wind and photovoltaic (PV) generation introduces significant uncertainty into power system operations. However, conventional unit commitment (UC) models typically treat computing loads as inelastic demand and cannot effectively coordinate AIDC load flexibility with generation scheduling. To address this issue, this paper proposes a multi-timescale coordinated scheduling approach for power systems and geographically distributed AIDCs under renewable uncertainty. The framework determines weekly day-ahead UC decisions with network constraints and performs 15-min rolling economic dispatch (ED) while coordinating AIDC load migration, deferral, and curtailment. A two-stage robust optimization model with a budgeted uncertainty set is developed to hedge against wind and PV uncertainty. Case studies on the IEEE 39bus system with data-driven AIDC load profiles demonstrate that the proposed approach reduces renewable curtailment and system operating costs under renewable-rich operating conditions with renewable uncertainty.