Demonstrating the data center as a flexible grid asset using a C-HIL setup

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Authors: Saurav Talukdar, Shilpa Marti, Sangsun Kim, Ben Wheeler, Christopher Coco, Matthieu Monsch, Jeremy Rice, Anand Ramesh, Jimmy Clidaras, Deepthi Vaidhynathan, Kumaraguru Prabakar, Gregory Martin
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References: 6

Abstract

Increasing data center demand is outpacing grid infrastructure development.Artificial intelligence workloads and hyperscale cloud growth are creating un-precedented demand for power, while traditional grid expansion faces multiyear development timelines. Verrus is developing an innovative data center solution for this challenge, data centers that act as active grid-supportive assets rather than passive loads. Our approach integrates a novel grid-aware power flow management system with battery energy storage systems(BESS)into a microgrid-controlled, medium-voltage power distribution architecture that delivers critical capabilities, such as: Fast response to grid disturbances such over/ under voltage or over/under frequency Demand flexibility that can service requests from the utility within 10sUninterrupted transition to islanded operation during grid outagesContinuous uptime assurance for compute loads while maintaining all customer service level agreements. Through Verrus’ strategic partnership with the National Renewable Energy Laboratory (NREL), these capabilities were validated using NREL’s Advanced Research on Integrated Energy Systems (ARIES) virtual emulation environment to model a 70-MW grid-interactive data center. This paper outlines the design, methodology, and results of this emulated deployment,demonstrating that data centers can provide both critical load resilience and ancillary grid support without compromising uptime requirements. Specifically, we present a digital real time simulation of a 70 MW data center integrated with a physical microgrid controller, and demonstrate the datacenter response in the event of a grid voltage and frequency event, utility demand response request and utility outage

Fields of Study & Subjects

Distributed and Parallel Computing Systems Advanced Data Storage Technologies Parallel Computing and Optimization Techniques

Publication details

Journal / source
International Journal of Electrical Power & Energy Systems
Volume
178
Pages
111967–111967
Publication date
1 May 2026
Language
EN