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 · Advances in Applied Energy · 2026
Artificial intelligence (AI) is driving unprecedented growth in data center (DC) scale and power demand. AI workloads impose highly dynamic, difficult-to-forecast power profiles on the utility grid, creating reliability and stability challenges that conventional DC architectures are not designed to address. This paper provides a critical review of energy storage systems (ESSs) as the key enabling technology for reliable grid integration of AI DCs. We organize the review around a four-layer hierarchical taxonomy, namely chip-level buffering, rack/server-level ESSs, facility-level uninterruptible power supply (UPS) systems, and grid-scale battery energy storage systems (BESSs), supplemented by non-battery technologies including fuel cells (FCs) and thermal energy storage (TES). Each layer is analyzed with respect to response timescale, power and energy ratings, operational role, integration challenges, and coordination requirements. Grid-interactive UPS (GiUPS) systems are examined in depth as a critical evolution of passive backup infrastructure into active grid-support assets capable of frequency regulation, fast frequency response, and voltage ride-through. Grid-scale BESSs are reviewed for their roles in peak shaving, renewable integration, and ancillary service provision. Second-life battery energy storage systems (SLBESSs) are evaluated as a cost-effective alternative for large-scale deployment. Key findings include: (i) AI DC load profiles differ fundamentally from traditional loads in their sub-second variability, making conventional ESS dispatch strategies insufficient; (ii) hierarchical, coordinated ESS deployment across all layers is necessary for effective load smoothing and grid support; and (iii) significant gaps remain in simulation tools, degradation modeling, load forecasting, and optimal multi-layer sizing. This review identifies open research challenges and future directions at the intersection of AI computing infrastructure and power system integration.