Er-Shun Du

Capturing Electrical Windows Through Reconfigurable Optical Networks: Transfer-aware Scheduling of Multi-Region AI Data Centers

Artificial-intelligence data centers (AIDCs) can provide spatiotemporal load flexibility by migrating training workloads in response to regional electricity prices and renewable-energy (RE) availability. However, transfer-blind scheduling may overestimate migration benefits by overlooking transfer time or prevent train...

Spatial LLM Workload Shifting Needs Foresight: Model Commitment for AI Data Center Operation under Power Grid Constraints

Model commitment (MC) is proposed, a mixed-integer linear programming framework that jointly schedules model deployment and cross-site request routing under power constraints and electricity-price signals and enables AI data center operators to achieve a 100% service rate under time-varying grid conditions and reduce t...

Multi-Timescale Risk-Aware Planning for High-Renewable Power Systems: A Review and Analytical Framework