Abstract

The energy demands from data centers contribute greatly to water scarcity footprint and carbon emissions. Understanding the use of on-site renewable power generation is an important step to gain insight into making data centers more sustainable. This novel study examines the impact of on-site solar or wind energy on data center water scarcity usage effectiveness (WSUE) and carbon usage effectiveness (CUE) at a U.S. county scale for a given data center size, water consumption level, and energy efficiency. The analysis uncovers combinations of specific metrics associated with grid-based carbon emissions and water scarcity footprint that enable predictions of the improvements anticipated when implementing on-site solar or wind energy. The implementation of on-site renewables has the most benefit in reducing carbon footprint in areas with high existing grid-based emissions such as the western side of the Appalachian Mountains (e.g., central and eastern Kentucky). The largest benefit in reducing water scarcity footprint is generally seen in counties with low water scarcity compared to adjacent areas (e.g., northern California).

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References

1.
Jones
,
N.
,
2018
, “
How to Stop Data Centres From Gobbling Up the World's Electricity
,”
Nature
,
561
(
7722
), pp.
163
166
.
2.
Bashroush
,
R.
, and
Lawrence
,
A.
,
2020
, “Beyond PUE: Tackling IT’s Wasted Terawatts,” Uptime Institute Intelligence, UII-34.
3.
Chen
,
L.
, and
Wemhoff
,
A. P.
,
2019
, “
Economic and Environmental Analysis of U.S.-Based Data Centers Containing Photovoltaic Power Generation
,”
Presented at the 2019 IEEE ITherm
,
Las Vegas, NV
,
May 28–31
, p.
107
.
4.
Miller
,
R.
,
2020
, “
Amazon Buys Solar, Wind Power to Speed Shift to a Renewable AWS Cloud
,” Data Center Frontier, https://www.datacenterfrontier.com/cloud/article/11428521/amazon-buys-solar-wind-power-to-speed-shift-to-a-renewable-aws-cloud, Accessed September 11, 2023.
5.
Miller
,
R.
,
2019
, “
Renewable Rooftops: Iron Mountain Plans Massive NJ Solar Array
,” Data Center Frontier, https://www.datacenterfrontier.com/energy/article/11429356/renewable-rooftops-iron-mountain-plans-massive-nj-solar-array, Accessed September 11, 2023.
6.
Qu
,
S.
,
Wang
,
H.
,
Liang
,
S.
,
Shapiro
,
A. M.
,
Suh
,
S.
,
Sheldon
,
S.
,
Zik
,
O.
,
Fang
,
H.
, and
Xu
,
M.
,
2017
, “
A Quasi-Input–Output Model to Improve the Estimation of Emission Factors for Purchased Electricity From Interconnected Grids
,”
Appl. Energy
,
200
, pp.
249
259
.
7.
Marriott
,
J.
, and
Matthews
,
H. S.
,
2005
, “
Environmental Effects of Interstate Power Trading on Electricity Consumption Mixes
,”
Environ. Sci. Technol.
,
39
(
22
), pp.
8584
8590
.
8.
Chen
,
L.
, and
Wemhoff
,
A. P.
,
2022
, “
Assessing the Impact of Electricity Consumption on Water Resources in the U.S
,”
Resour. Conserv. Recycl.
,
178
, p.
106087
.
9.
de Chalendar
,
J. A.
,
Taggart
,
J.
, and
Benson
,
S. M.
,
2019
, “
Tracking Emissions in the US Electricity System
,”
Proc. Natl. Acad. Sci. U.S.A.
,
116
(
51
), pp.
25497
25502
.
10.
Koffler
,
C.
,
Hengstler
,
J.
,
Thellier
,
L.
, and
Stoffregen
,
A.
,
2019
, “
On the Relevance of Scope 3 Emissions and Power Trade for Regional Life Cycle Inventories of Electricity Consumption in the USA
,”
Int. J. Life Cycle Assess.
,
24
(
8
), pp.
1360
1375
.
11.
NREL
,
2021
, “
Life Cycle Greenhouse Gas Emissions From Electricity Generation: Update
,” https://www.nrel.gov/docs/fy21osti/80580.pdf, Accessed September 11, 2023.
12.
Hadian
,
S.
, and
Madani
,
K.
,
2015
, “
A System of Systems Approach to Energy Sustainability Assessment: Are All Renewables Really Green?
Ecol. Indic.
,
52
, pp.
194
206
.
13.
Patterson
,
M.
,
Azevedo
,
D.
,
Belady
,
C.
, and
Pouchet
,
J.
,
2011
, “Water Usage Effectiveness (WUE): A Green Grid Data Center Sustainability Metric,” The Green Grid, 35.
14.
Solon
,
O.
,
2021
, “
Drought-Stricken Communities Push Back Against Data Centers
,” NBC News. https://www.nbcnews.com/tech/internet/drought-stricken-communities-push-back-against-data-centers-n1271344, Accessed September 11, 2023.
15.
Kumar
,
N.
,
Aujla
,
G. S.
,
Garg
,
S.
,
Kaur
,
K.
,
Ranjan
,
R.
, and
Garg
,
S. K.
,
2019
, “
Renewable Energy-Based Multi-indexed Job Classification and Container Management Scheme for Sustainability of Cloud Data Centers
,”
IEEE Trans. Ind. Inf.
,
15
(
5
), pp.
2947
2957
.
16.
Li
,
C.
,
Qouneh
,
A.
, and
Li
,
T.
,
2011
, “
Characterizing and Analyzing Renewable Energy Driven Data Centers
,”
Presented at the Performance Evaluation Review
,
San Jose, CA
,
June 7–11
, pp.
131
132
.
17.
Wang
,
H.
, and
Ye
,
Z.
,
2016
, “
Renewable Energy-Aware Demand Response for Distributed Data Centers in Smart Grid
,”
Presented at the 2016 IEEE Green Energy and Systems Conference, IGSEC 2016
,
Long Beach, CA
,
Nov. 6–7
.
18.
Wan
,
T.
,
Tao
,
Y.
,
Qiu
,
J.
, and
Lai
,
S.
,
2023
, “
Internet Data Centers Participating in Electricity Network Transition Considering Carbon-Oriented Demand Response
,”
Appl. Energy
,
329
, p.
120305
.
19.
Kwon
,
S.
,
2020
, “
Ensuring Renewable Energy Utilization With Quality of Service Guarantee for Energy-Efficient Data Center Operations
,”
Appl. Energy
,
276
, p.
115424
.
20.
Peer
,
R. A. M.
,
Grubert
,
E.
, and
Sanders
,
K. T.
,
2019
, “
A Regional Assessment of the Water Embedded in the US Electricity System
,”
Environ. Res. Lett.
,
14
(
8
), p.
084014
.
21.
Meldrum
,
J.
,
Nettles-Anderson
,
S.
,
Heath
,
G.
, and
Macknick
,
J.
,
2013
, “
Life Cycle Water Use for Electricity Generation: A Review and Harmonization of Literature Estimates
,”
Environ. Res. Lett.
,
8
(
1
), p.
015031
.
22.
Cai
,
X.
,
Wallington
,
K.
,
Shafiee-Jood
,
M.
, and
Marston
,
L.
,
2018
, “
Understanding and Managing the Food-Energy-Water Nexus—Opportunities for Water Resources Research
,”
Adv. Water Res.
,
111
, pp.
259
273
.
23.
Chen
,
L.
, and
Wemhoff
,
A. P.
,
2022
, “
Characterizing Data Center Cooling System Water Stress in the United States
,”
Presented at the 2022 ASHRAE Winter Conference
,
Las Vegas, NV
,
Jan. 29–Feb. 2
.
24.
Azevedo
,
S.
,
Patterson
,
M.
,
Pouchet
,
E.
, and
Tipley
,
R.
,
2010
, “
Carbon Usage Effectiveness (CUE): A Green Grid Data Center Sustainability Metric
,” The Green Grid, 32.
25.
U.S. Energy Information Administration
,
2020
, “
Annual Net Generation By All Energy Sources and By Producing Sector
,” https://www.eia.gov/electricity/data.php#generation.
26.
U.S. Environmental Protection Agency
,
2020
Emissions & Generation Resource Integrated Database (eGRID)
,” https://www.epa.gov/egrid.
27.
Tidwell
,
V. C.
,
Macknick
,
J.
,
Zemlick
,
K.
,
Sanchez
,
J.
, and
Woldeyesus
,
T.
,
2014
, “
Transitioning to Zero Freshwater Withdrawal in the U.S. for Thermoelectric Generation
,”
Appl. Energy
,
131
, pp.
508
516
.
28.
Lee
,
U.
,
Xu
,
H.
,
Daystar
,
J.
,
Elgowainy
,
A.
, and
Wang
,
M.
,
2019
, “
AWARE-US: Quantifying Water Stress Impacts of Energy Systems in the United States
,”
Sci. Total Environ.
,
648
, pp.
1313
1322
.
29.
Boulay
,
A.-M.
,
Bare
,
J.
,
Benini
,
L.
,
Berger
,
M.
,
Lathuillière
,
M. J.
,
Manzardo
,
A.
,
Margni
,
M.
, et al
,
2018
, “
The WULCA Consensus Characterization Model for Water Scarcity Footprints: Assessing Impacts of Water Consumption Based on Available Water Remaining (AWARE)
,”
Int. J. Life Cycle Assess.
,
23
(
2
), pp.
368
378
.
30.
Schlomer
,
S.
,
Bruckner
,
T.
,
Fulton
,
L.
,
Hertwich
,
E.
,
McKinnon
,
A.
,
Perczyk
,
D.
,
Roy
,
J.
, et al
,
2014
, “
Annex III: Technology-Specific Cost and Performance Parameters
,”
Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change
,
Cambridge, UK
,
Cambridge University Press
, pp.
1329
1356
.
31.
Chen
,
L.
, and
Wemhoff
,
A. P.
,
2021
, “
Predicting Embodied Carbon Emissions From Purchased Electricity for United States Counties
,”
Appl. Energy
,
292
, p.
116898
.
32.
U.S. Energy Information Administration
, “Real-Time Operating Grid,” https://www.eia.gov/electricity/gridmonitor/dashboard/electric_overview/US48/US48, Accessed May 31, 2020.
33.
Chen
,
L.
, and
Wemhoff
,
A. P.
,
2022
, “
Predictions of Airside Economization-Based Air-Cooled Data Center Environmental Burden Reduction
,”
Presented at the 2022 ASME InterPACK Conference
,
Anaheim, CA
,
Oct. 24–28
, p.
92005
.
34.
Muangnoi
,
T.
,
Asvapoositkul
,
W.
, and
Wongwises
,
S.
,
2008
, “
Effects of Inlet Relative Humidity and Inlet Temperature on the Performance of Counterflow Wet Cooling Tower Based on Exergy Analysis
,”
Energy Convers. Manage.
,
49
(
10
), pp.
2795
2800
.
35.
Vengosh
,
A.
, and
Weinthal
,
E.
,
2023
, “
The Water Consumption Reductions From Home Solar Installation in the United States
,”
Sci. Total Environ.
,
854
, p.
158738
.
36.
Hsu
,
J.
,
2022
, “How Much Water Do Data Centres Use? Most Tech Companies Won’t Say,” New Scientist Magazine, https://www.newscientist.com/article/2342490-how-much-water-do-data-centres-use-most-tech-companies-wont-say/.
37.
Lei
,
N.
, and
Masanet
,
E.
,
2022
, “
Climate- and Technology-Specific PUE and WUE Estimations for U.S. Data Centers Using a Hybrid Statistical and Thermodynamics-Based Approach
,”
Resour. Conserv. Recycl.
,
182
, p.
106323
.
38.
Austin Black
,
2021
, “
What Is STEEP Analysis—5 Factors to Predict the Future
,” WideNarrow. https://www.widenarrow.com/blog/what-is-steep-analysis-5-factors-to-predict-the-future, Accessed February 22, 2022.
39.
Bizo
,
D.
,
Ascierto
,
R.
,
Lawrence
,
A.
, and
Davis
,
J.
,
2021
, “Uptime Institute Global Data Center Survey 2021,” Uptime Institute, UII-511.
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