Grid Integration of AI Data Centers: A Critical Review of Energy Storage Solutions
Sina Mohammadi, Wayne Wang, Marcus Chen I Wada, Rouzbeh Haghighi, Ali Hassan, Hualong Liu, Archit Bhatnagar, Ang Chen, Wencong Su
TL;DR
A structured view of how energy storage can improve reliability, flexibility, and sustainability when connecting future AI data centers to the power grid is provided.
Abstract
Artificial intelligence (AI) is driving a rapid expansion of data centers (DCs). These facilities consume large amounts of electricity and introduce new challenges for power systems. AI workloads cause rapid power changes and high peak demand. These behaviors are different from traditional data centers (TDCs) and can affect grid stability and reliability. This paper reviews how energy storage systems (ESSs) can help integrate AI data DCs with the electric grid. We examine storage solutions at multiple levels, including grid-scale batteries, UPS systems, rack-level storage, and chip-level buffering. Each layer operates at a different time scale and serves a different purpose. Grid-interactive UPS (GiUPS) systems can respond quickly to disturbances and assist with frequency regulation or voltage ride through. Large battery energy storage systems (BESSs) can smooth power demand, support renewable on-site generation, and provide grid services. Rack-level and server-level storage help manage fast power fluctuations close to computing hardware. We also discuss other technologies such as fuel cells (FCs) and thermal energy storage (TE) that can support co-generation and reduce emissions. In addition, second-life battery energy storage (SLBESS) are reviewed as a lower-cost option for large installations whether supporting UPS battery or as a backup generation. The paper compares the benefits, challenges, and coordination requirements of these solutions. Overall, the study provides a structured view of how energy storage can improve reliability, flexibility, and sustainability when connecting future AI data centers to the power grid.