Typical architecture of an AI data center
The rapid growth of artificial intelligence (AI) is driving an unprecedented increase in the electricity demand of AI data centers, raising emerging challenges for electric power grids. We provide a comprehensive review and vision of this evolving landscape. Specifically, our paper (i) presents an overview of AI data center infrastructure and its key components, (ii) examines the key characteristics and patterns of electricity demand across the stages of model preparation, training, fine-tuning, and inference, (iii) analyzes the critical challenges that AI data center loads pose to power systems across three interrelated timescales, including long-term planning and interconnection, short-term operation and electricity markets, and real-time dynamics and stability, and (iv) discusses potential solutions from the perspectives of the grid, AI data centers, and AI end-users to address these challenges. By synthesizing current knowledge and outlining future directions, we aim to guide research and development in support of the joint advancement of AI data centers and power systems toward reliable, efficient, and sustainable operation.
Featured Publications:
[1] Xin Chen, Xiaoyang Wang, Ana Colacelli, Matt Lee, Le Xie, “Electricity Demand and Grid Impacts of AI Data Centers: Challenges and Prospects“, arXiv:2509.07218, 2025.
High-fidelity dynamic modeling of large data center loads is essential for investigating their disturbance (such as low-voltage) ride-through capabilities, subsynchronous resonance, and grid stability impacts. In collaboration with ERCOT, we are developing PSCAD-based electromagnetic transient (EMT) models of large loads, including crypto mining facilities, data centers, and hydrogen electrolyzers. Both switching models and average models are developed. These models are validated through our lab experiments and real-world field data. We have also performed small-signal impedance tests, low-voltage ride-through tests, and real grid event playback tests on these models. Check our Consortium on AI and Large Flexible Load (CALL) for more research work and events.
PSCAD-Based EMT Model of Crypto Mining Load
Low-Voltage/High-Voltage Ride-Through Curves