Managing Risks from Large Digital Loads Using Coordinated Grid-Forming Storage Network

TL;DR

This paper investigates how coordinated grid-forming storage units can stabilize the power grid amidst the rapid growth of AI data centers, avoiding costly infrastructure upgrades.

Contribution

It introduces a bi-layered control strategy for managing distributed and collocated storage to ensure grid stability with AI data center loads.

Findings

Coordinated control effectively maintains voltage and frequency stability.

Distributed storage can match the performance of larger collocated units.

The approach reduces the need for expensive grid upgrades.

Abstract

Anticipated rapid growth of large digital load, driven by artificial intelligence (AI) data centers, is poised to increase uncertainty and large fluctuations in consumption, threatening the stability, reliability, and security of the energy infrastructure. Conventional measures taken by grid planners and operators to ensure stable and reliable integration of new resources are either cost-prohibitive (e.g., transmission upgrades) or ill-equipped (e.g., generation control) to resolve the unique challenges brought on by AI Data Centers (e.g., extreme load transients). In this work, we explore the feasibility of coordinating and managing available flexibility in the grid, in terms of grid-forming storage units, to ensure stable and reliable integration of AI Data Centers without the need for costly grid upgrades. Recently developed bi-layered coordinated control strategies -- involving fast-acting, local, autonomous, control at the storage to maintain transient safety in voltage and frequency at the point-of-interconnection, and a slower, coordinated (consensus) control to restore normal operating condition in the grid -- are used in the case studies. A comparison is drawn between broadly two scenarios: a network of coordinated, smaller, distributed storage vs. larger storage installations collocated with large digital loads. IEEE 68-bus network is used for the case studies, with large digital load profiles drawn from the MIT Supercloud Dataset.