Evaluating Grid Resilience in the Era of Ever-Increasing Data Centers

Jul 16, 2026

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Rapid growth of artificial intelligence workloads is expanding and concentrating data center demand, creating new concerns about power system resilience during disruptive events. This paper extends a validated multi-time-step DC optimal power flow framework to quantify how aggregated data center demand affects contingency-induced unserved energy. Using an IEEE 30-bus test system, the work models energy-matched constant loads and studies capacity-growth levels under generator, transmission-line, and coupled derating. Results show substantial increases in both overall and data-center-bus unserved energy, with temporally concentrated demand amplifying resilience impacts even without higher total energy consumption.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

Introduction

Motivation

Literature Review

arXiv :2607 .06958v1 [ ee ss . SY] 8 Jul 2026 Evaluating Grid Resilience in the Era of Ever-Increasing Data Centers Yuhan Du 1[0009−0004−4874−1235], Erika Ardiles-Cruze2 , and Javad Mohammadi 1[0000−0003−0425−5302] 1 University of Texas at Austin, Austin TX 78705, USA {yuhandu, [javadm}@utexas.edu](javadm}@utexas.edu) 2 Air Force Research Labs, Rome NY 13441, USA [erika.ardiles-cruz@us.af.mil](erika.ardiles-cruz@us.af.mil) Abstract. The rapid growth of artificial intelligence workloads is increasing the scale and concentration of data center demand, creating new concerns for power system resilience under disruptive events. This paper extends a validated multi-time-step DC optimal power flow framework to evaluate the impact of aggregated data center demand on contingencyinduced unserved energy. Using an IEEE 30-bus system with flexible resources, we replace a conventional load at a contingency-exposed bus with an energy-matched constant data center load and examine two capacity-growth levels under generator derating, transmission line derating, and coupled derating. The results show that data center capacity growth substantially increases both system-level and data-center-bus unserved energy under transmission-constrained contingencies. Under coupled derating, the high-growth case increases total unserved energy from 3.203 MWh in the energy-matched case to 22.891 MWh. A supplementary energy-matched coincident-demand case further increases total unserved energy by 34.4%, indicating that temporally concentrated datacenter demand can amplify resilience impacts even without increasing total energy consumption. Keywords: Contingency analysis · Data center · DC optimal power flow · Grid resilience · Unserved energy. 1 Introduction 1.1 Motivation Data centers are becoming an increasingly important source of electric demand as artificial intelligence (AI) and cloud computing services expand. In the United States, data center electricity use reached approximately 176 TWh in 2023, representing about 4.4% of total electricity consumption, and is projected to reach 325-580 TWh by 2028, or approximately 6 .7%-12% of total electricity consumption [12] . Unlike gradual growth in conventional demand, new data center loads can be large, concentrated, and sensitive to interruption. The North American Electric Reliability Corporation (NERC) has therefore identified emerging large 2 Yuhan Du, Erika Ardiles-Cruze, and Javad Mohammadi loads, including data centers, as a growing reliability concern because their magnitude and operational characteristics can affect transmission planning, resource adequacy, and real-time system operation [9] . In addition to their scale, AI-oriented data centers may introduce new temporal demand characteristics. A recent production data study of large AI training workloads reports synchronized power variations due to alternating computation and communication phases [2] . These fast variations are outside the temporal resolution of a dispatch model, but they motivate evaluating whether elevated data center demand overlapping with a grid disruption can further stress system operation. Recent data center modeling guidance also emphasizes that model fidelity should be selected based on the grid interaction being evaluated, ranging from detailed, fast-timescale representations to more tractable system-level abstractions [14] . Accordingly, this work studies data centers as aggregated gridfacing loads over a dispatch horizon and includes a simplified coincident-demand sensitivity case. 1.2 Literature Review Early studies on data center demand response demonstrated that computing workloads and local generation can be coordinated to reduce coincident peak demand and electricity expenditure [8] . More recent studies have extended this line of work toward grid-interactive data center operation. Wan et al. incorporated Internet data center demand response into electricity network transition p

FAQs

What problem does the paper address?

The paper evaluates how the rapid growth and concentration of data center demand affects power system resilience during disruptive contingencies, focusing on contingency-induced unserved energy.

How is data center demand modeled in the study?

It uses a validated multi-time-step DC optimal power flow framework, replacing a conventional load at a contingency-exposed bus with an energy-matched constant data center load, and additionally includes a coincident-demand sensitivity case.

What do the results show about capacity growth and derating?

Data center capacity growth increases both system-level and data-center-bus unserved energy under transmission-constrained contingencies. Under coupled derating, the high-growth case raises total unserved energy from 3.203 MWh to 22.891 MWh.

Jul 16, 2026

Upload by Skyler