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Using marginal emissions factor estimates to
assess social benefits of data center load‐shifting
Nathaniel Horner
CEDM Annual Meeting
23 May 2016
CE
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Variability in electricity prices and damages
Variability
Marginal Prices
Marginal Damages
Temporal
✔ Merit-order dispatch
✔ Nuclear & renewables
at bottom of dispatch
curve, fossil at top
Spatial
✔ Grid constraints
✔ Regional fuel mixes
Arbitrage?
• Difficult to transmit
electricity long distances
and into congested areas.
• Expensive to store
electricity.

• What about
shifting the load?
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Research Question
“Physics tells us it's easier to ship photons than electrons; that is,
it's cheaper to ship data over fiber optic cables than to ship
electricity over high-voltage transmission lines.”*
What are the potential private and external savings from shifting
computing load among nodes in a network of geographically
distributed data centers using different cost minimization
strategies?
*Armbrust,
et al. 2009. Above the clouds: a Berkeley view of cloud computing.
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What is a CDN?
• Large, distributed
network of servers
Users
Content providers
• Optimizes content
delivery (web pages,
video, etc.) over the
Internet
• Typically caches
replicates of content
closer to the network
“edge”
Adapted from https://support.rackspace.com/how-to/what-is-a-cdn/
Minimum cost optimization problem
cost
energy
traffic
Satisfy demand
Node capacity
Nonnegativity
Change c for different scenarios:
• Capitalistic: minimize private
cost (LMP)
• Green: minimize external cost
(marginal damages)
• Utilitarian: minimize total cost
• (Baseline): proximity (nearest
node)
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Electricity prices from RTOs/ISOs
• Locational marginal
prices (LMPs) for
real-time wholesale
market
• At relevant
aggregate pricing
nodes (APNs) or
pricing hub in most
states
• Aggregated to hour
intervals
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LMPs by eGRID subregion and seasonal hour of day
January
Y-axes truncated at [0,200]
July
Marginal damages as function of fossil generation
by eGRID subregion*
*Siler-Evans,
et al. 2013. Regional variations in the health, environmental, and climate benefits of wind and
solar generation. PNAS 110.29.
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Network traffic from Akamai CDN
8000
Sep 02
Sep 07
Sep 12
Sep 17
Sep 22
Sep 27
Oct 02
hour
0
4000
8000
October
Oct 02
Oct 07
Oct 12
Oct 17
Oct 22
Oct 27
Nov 21
Nov 26
Nov 01
hour
4000
0
traffic (Gbph)
8000
November
*Malmodin
J, et al (2014). Life Cycle Assessment of ICT: Carbon
Footprint and Operational Electricity Use from the Operator,
National, and Subscriber Perspective in Sweden. Journal of
Industrial Ecology, 18(6), pp.829–845.
4000
0
traffic (Gbph)
September
traffic (Gbph)
• ~900 data centers in ~300
U.S. cities
• Hourly load data over 3
months
• Aggregated at U.S. state
level
• Simulate 1 year of traffic
• Convert traffic to energy: 1
kWh/GB*
Nov 01
Nov 06
Nov 11
Nov 16
hour
Dec 01
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Not a static solution
Proximal
State
Min Total
% Load
State
% Load
CA
19%
CA
38%
FL
13%
VA
18%
TX
11%
GA
13%
VA
6%
TX
12%
IL
6%
FL
6%
Min Private
Min External
State
State
% Load
% Load
TX
24%
CA
51%
IL
17%
VA
24%
CA
12%
GA
10%
GA
7%
TX
5%
FL
7%
MA
3%
*Preliminary results; do not cite.
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Electricity cost comparisons among strategies
Annual savings vs. Baseline
Strategy
Capitalistic
Green
Private
32%
-10%
20%
External
-6%
34%
29%
9%
17%
26%
Total
Private vs. External Electricity Costs
Utilitarian
Millions
Cost
$20
$18
$16
$14
$12
External
$10
Average private electricity cost ($/MWh)
$8
Private
$6
Strategy
Avg. WHSL cost
Proximal
30.76
Capitalistic
20.85
Green
33.79
Utilitarian
24.69
$4
$2
$-
Proximal Capitalistic
Green
Utilitarian
*Preliminary results; do not cite.
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Capacity impacts
Effect on
utilization rates
Strategy
Aggregate effect on peak
bandwidth cap
Mean util
Strategy
95th BW (Gbph)
Change
B/E BW ($/Gbph)
Proximal
83%
Proximal
5950
--
--
Capitalistic
45%
Capitalistic
9340
3390
$730
Green
30%
Green
6340
390
(negative)
Utilitarian
34%
Utilitarian
5950
0
NA
*Typical bandwidth
expansion costs:
~$50/Gbph
*Preliminary results; do not cite.
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Early Conclusions
• Akamai’s savings opportunity is $2.5M/year
– Percentage savings is not as high as indicated here due to omission of
transmission, distribution, and other charges
– “Win-win” solution possible: external and private savings not mutually
exclusive
• Green strategy costs operators 60% premium to capitalistic
strategy; 10% premium compared to proximal baseline
Next:
• Look at shadow prices of capacity constraints
• See how results change with other damage model estimates
*Preliminary results; do not cite.
Marginal Emissions & Damage Factors
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• Updates to Siler-Evans, et al. work on regional marginal emissions
factors and marginal damage factors.
– More recent CEMS data
– Accounting for inter-region transfers (imports/exports)
• Online app for data filtering, visualization, and download
(currently in prototype)
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Acknowledgements
I am affiliated with CEDM and funded by the
Department of Engineering and Public Policy.
I appreciate input on this work from from members of
my thesis committee and researchers in CEDM.
Thanks for listening!
[email protected]
@NatCHorner
http://andrew.cmu.edu/~nhorner
Photo source: http://www.google.com/about/datacenters/gallery/#/tech
Photo source: http://www.google.com/about/datacenters/gallery/#/tech