Reducing Data Center Peak Cooling Demand and Energy Costs with Underground Thermal Energy Storage (UTES)

Jeffrey Winick, Guangdong Zhu, Kevin Kitz, Andy Adams

Research output: Contribution to conferencePaper

Abstract

By recent estimates, data center energy demands are projected to consume between 6.7% and 12% of U.S. annual electricity generation by the year 2028, driven primarily by expanded demands from cloud services, big data analytics, and Artificial Intelligence (AI) (Shehabi et al., 2024). As much as 40% of data center total energy consumption are loads associated with the site infrastructure cooling systems, and these are often highly water consumptive (Aljbour et al., 2024). For energy system planners, this presents significant challenges to meeting and managing the anticipated loads, and especially the peak loads of projected data center deployments. Geothermal technologies offer two unique solutions to these challenges: 1) by serving loads through the deployment of new conventional and/or next-generation geothermal power technologies such as EGS and 2) through an often-overlooked opportunity to reduce data center peak cooling loads. The latter is the focus of this paper which explores Cold Underground Thermal Energy Storage ("Cold UTES") as an emerging industrial-scale geothermal cooling solution. This cooling solution is energy efficient, non-water-consumptive, and utilizes long duration energy storage (LDES) on both diurnal and seasonal time scales. Cold UTES has the potential to also function as a virtual power plant (VPP). The US Department of Energy's Geothermal Technologies Office is supporting R&D to understand the grid and system-wide value, costs, and impacts of deploying this emergent cooling solution at scale.
Original languageAmerican English
Number of pages14
StatePublished - 2025
EventStanford University 50th Workshop on Geothermal Reservoir Engineering - Stanford, California
Duration: 10 Feb 202512 Feb 2025

Conference

ConferenceStanford University 50th Workshop on Geothermal Reservoir Engineering
CityStanford, California
Period10/02/2512/02/25

NREL Publication Number

  • NREL/CP-5700-93607

Keywords

  • AI
  • BTES
  • closed loop
  • data centers
  • grid
  • RTES
  • UTES
  • VPP

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