The rapid growth of artificial intelligence workloads is increasing the scale and concentration of data center demand, creating new concerns for power system resilience under disruptive events. This paper extends a validated multi-time-step DC optimal power flow framework to evaluate the impact of aggregated data center demand on contingency-induced unserved energy. Using an IEEE 30-bus system with flexible resources, we replace a conventional load at a contingency-exposed bus with an energy-matched constant data center load and examine two capacity-growth levels under generator derating, transmission line derating, and coupled derating. The results show that data center capacity growth substantially increases both system-level and data-center-bus unserved energy under transmission-constrained contingencies. Under coupled derating, the high-growth case increases total unserved energy from 3.203 MWh in the energy-matched case to 22.891 MWh. A supplementary energy-matched coincident-demand case further increases total unserved energy by 34.4%, indicating that temporally concentrated data center demand can amplify resilience impacts even without increasing total energy consumption.
As hyperscale and colocation AI data centers continue to expand, the electric grid is increasingly required to support large, concentrated loads, with individual facilities ranging from 500 MW to 2 GW. Current projections estimate that approximately 50 GW of AI data center capaci…
Thermal management in AI data centers is increasingly challenged by bursty workloads and uncertain heat generation. To prevent thermal violations, existing cooling strategies either enforce conservative, rigid bounds that severely limit grid responsiveness, or rely on forecast-dr…
Large AI data center loads can introduce persistent sub-synchronous active-power oscillations that may impact nearby generators by exciting torsional modes and increasing shaft stress. This paper presents a model-based framework for evaluating hydro-generator shaft fatigue risk u…
Resilient-by-design smart grid control demands frameworks capable of maintaining stability under physical disturbances and communication failures, without reliance on centralized coordination. While Centralized Training Decentralized Execution (CTDE) enables a learning-based cont…
AI data centers are emerging as a dominant new load class with their power dynamics fundamentally from conventional industrial loads. Inside a training job, the bulk-synchronous-parallel algorithm moves each node through compute, sync, and checkpoint steps, which swings power bet…
Eco-friendly energy management for artificial intelligence data centers (AIDCs) is crucial because of the significant increase in energy consumption-induced carbon emissions from AIDCs resulting from the rapid expansion of AI applications. This paper proposes a hierarchical carbo…