Grid Integration of AI Data Centers: A Brief Review of Energy Storage Systems Solutions

Sina Mohammadi, Marcus Chen I Wada, Rouzbeh Haghighi, Hualong Liu, Wencong Su, Wayne Wang, Archit Bhatnagar, Ang Chen

Keywords: AI Data Center

TL;DR: This study provides a rigorous analysis of multi-layer energy storage system (ESS) architectures designed to mitigate these grid-integration challenges.

Track: Research Paper

Abstract: The proliferation of artificial intelligence (AI) workloads has necessitated AI-specific data centers (DCs), which have stochastic power profiles and new reliability constraints. Unlike traditional DCs, AI DCs exhibit high-frequency power fluctuations and significant ramping events, because of the tight coupling of high-density and magnitude compute, thermal management, and power electronics. This paper studies the role of energy storage systems (ESS) and provides an analysis of multi-layer ESS architectures that can help mitigate grid-integration challenges. We take a hierarchical view that spans from grid-scale BESS and grid-interactive UPS down to rack-level units and server/GPU-level energy buffers. The analysis reveals the distinct operating timescales and control coordination required for heterogeneous storage integration. Furthermore, we evaluate grid-support functionalities, such as frequency regulation, while addressing the techno-economic trade-offs of degradation and reliability. Our review of ESS capabilities provides roadmaps for designing resilient, sustainable, and grid-compatible AI DCs.

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Submission Number: 2