Spatio-temporal load shifting for truly clean computing

Companies operating datacenters are increasingly committed to procuring renewable energy to reduce their carbon footprint, with a growing emphasis on achieving 24/7 Carbon-Free Energy (CFE) matching—eliminating carbon emissions from electricity use on an hourly basis. However, variability in renewable energy resources poses significant challenges to achieving this goal. This study investigates how shifting computing workloads and associated power loads across time and location supports 24/7 CFE matching. We develop an optimization model to simulate a network of geographically distributed datacenters managed by a company leveraging spatio-temporal load flexibility to achieve 24/7 CFE matching. We isolate three signals relevant for informed use of load flexibility: (1) varying average quality of renewable energy resources, (2) low correlation between wind power generation over long distances due to different weather conditions, and (3) lags in solar radiation peak due to Earth’s rotation. Our analysis reveals that datacenter location and time of year influence which signal drives an effective load-shaping strategy. By leveraging these signals for coordinated energy procurement and load-shifting decisions, clean computing becomes both more resource-efficient and cost-effective—the costs of 24/7 CFE are reduced by 1.29 ± 0.07 €/MWh for every additional percentage of flexible load. This study provides practical guidelines for datacenter companies to harness spatio-temporal load flexibility for clean computing. Our results and the open-source optimization model offer insights applicable to a broader range of industries aiming to eliminate their carbon footprints. • Spatio-temporal load flexibility enables resource-efficiency of clean computing. • A set of signals for effective load shifting between regions and hours is identified. • Optimal load-shaping strategy depends on datacenter location and time of year. • Additional percent of flexible loads reduces costs by avg. 1.29 ± 0.07 EUR/MWh. • Spatial load shifting yields maximum utility for datacenters located 300–400 km apart.

Authors

Institutions

Publication Details

Journal
Advances in Applied Energy
Published
2024-12-20
DOI
https://doi.org/10.1016/j.adapen.2024.100202
Citations
21
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
7.33

Funders

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article

Spatio-temporal load shifting for truly clean computing

Iegor Riepin, Tom Brown, Ví­ctor M. Zavala
21 citations
Advances in Applied Energy
Distributed and Parallel Computing Systems
7.33
article

Spatio-temporal load shifting for truly clean computing

Iegor Riepin, Tom Brown, Ví­ctor M. Zavala
article en
21 citations

Abstract

Companies operating datacenters are increasingly committed to procuring renewable energy to reduce their carbon footprint, with a growing emphasis on achieving 24/7 Carbon-Free Energy (CFE) matching—eliminating carbon emissions from electricity use on an hourly basis. However, variability in renewable energy resources poses significant challenges to achieving this goal. This study investigates how shifting computing workloads and associated power loads across time and location supports 24/7 CFE matching. We develop an optimization model to simulate a network of geographically distributed datacenters managed by a company leveraging spatio-temporal load flexibility to achieve 24/7 CFE matching. We isolate three signals relevant for informed use of load flexibility: (1) varying average quality of renewable energy resources, (2) low correlation between wind power generation over long distances due to different weather conditions, and (3) lags in solar radiation peak due to Earth’s rotation. Our analysis reveals that datacenter location and time of year influence which signal drives an effective load-shaping strategy. By leveraging these signals for coordinated energy procurement and load-shifting decisions, clean computing becomes both more resource-efficient and cost-effective—the costs of 24/7 CFE are reduced by 1.29 ± 0.07 €/MWh for every additional percentage of flexible load. This study provides practical guidelines for datacenter companies to harness spatio-temporal load flexibility for clean computing. Our results and the open-source optimization model offer insights applicable to a broader range of industries aiming to eliminate their carbon footprints. • Spatio-temporal load flexibility enables resource-efficiency of clean computing. • A set of signals for effective load shifting between regions and hours is identified. • Optimal load-shaping strategy depends on datacenter location and time of year. • Additional percent of flexible loads reduces costs by avg. 1.29 ± 0.07 EUR/MWh. • Spatial load shifting yields maximum utility for datacenters located 300–400 km apart.

Advances in Applied EnergyVol. 17
University of Wisconsin–Madison (US), Technische Universität Berlin (DE)
National Science Foundation, Google
Industry, innovation and infrastructure
Openalex Percentile: Top 2%
Distributed and Parallel Computing Systems
7.33
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