Note: The following is a redacted version of the original report published April 28, 2024 [43 pages].
EQUITY RESEARCH | April 28, 2024 | 10:34PM EDT

AI, data centers and the coming US power demand surge
Driven by AI, broader demand and a deceleration in the pace of energy efficiency gains, global data center power demand is poised
to more than double by 2030 after being flattish in 2015-20. This growth is the primary catalyst alongside increasing
manufacturing/industrial production and broader electrification trends, to an acceleration in US electricity demand CAGR to 2.4%
through the end of the decade from 0% in the last decade. We believe supporting data center driven load growth will require
investment by Utilities of $50 bn in new power generation capacity. We assume a 60/40 split between gas and renewables, which
we expect to drive ~3.3 bcf/d incremental natural gas demand by 2030. While investor interest in the AI revolution theme is notnew, we believe downstream investment opportunities in utilities, renewable generation and industrials whose investment andproducts will be needed to support this growth are underappreciated.

Carly Davenport+1(212)357-1914
carly.davenport@gs.com
Goldman Sachs & Co. LLC

Brian Singer, CFA+1(212)902-8259
brian.singer@gs.com
Goldman Sachs & Co. LLC

Neil Mehta+1(212)357-4042
neil.mehta@gs.com
Goldman Sachs & Co. LLC

Brian Lee, CFA+1(917)343-3110
brian.k.lee@gs.com
Goldman Sachs & Co. LLC

John Mackay+1(212)357-5379
john.mackay@gs.com
Goldman Sachs & Co. LLC

Goldman Sachs does and seeks to do business with companies covered in its research reports. As aresult, investors should be aware that the ﬁrm may have a conﬂict of interest that could affect theobjectivity of this report. Investors should consider this report as only a single factor in making theirinvestment decision. For Reg AC certiﬁcation and other important disclosures, see the Disclosure
Appendix, or go to www.gs.com/research/hedge.html. Analysts employed by non-US afﬁliates are notregistered/qualiﬁed as research analysts with FINRA in the U.S.
The Goldman Sachs Group, Inc.
See full list of authors insideAuthors
Carly Davenport+1 212 357-1914
carly.davenport@gs.com
Goldman Sachs & Co. LLC

Brian Singer, CFA+1 212 902-8259
brian.singer@gs.com
Goldman Sachs & Co. LLC

Neil Mehta+1 212 357-4042
neil.mehta@gs.com
Goldman Sachs & Co. LLC

Brian Lee, CFA+1 917 343-3110
brian.k.lee@gs.com
Goldman Sachs & Co. LLC

John Mackay+1 212 357-5379
john.mackay@gs.com
Goldman Sachs & Co. LLC

Ati Modak+1 212 902-9365
ati.modak@gs.com
Goldman Sachs & Co. LLC

Brendan Corbett+1 415 249-7440
brendan.corbett@gs.com
Goldman Sachs & Co. LLC

John Miller+1 646 446-0292
john.y.miller@gs.com
Goldman Sachs & Co. LLC

Toshiya Hari
+1 646 446-1759
toshiya.hari@gs.com
Goldman Sachs & Co. LLC

Joe Ritchie+1 212 357-8914
joseph.ritchie@gs.com
Goldman Sachs & Co. LLC

Mark Delaney, CFA+1 212 357-0535
mark.delaney@gs.com
Goldman Sachs & Co. LLC

Jerry Revich, CFA+1 212 902-4116
jerry.revich@gs.com
Goldman Sachs & Co. LLC

Jaskaran Jaiya+1 332 245-7709
jaskaran.jaiya@gs.com
Goldman Sachs India SPL
Varsha Venugopal+1 415 393-7554
varsha.venugopal@gs.com
Goldman Sachs & Co. LLC
Nick Cash+1 212 357-6372
nick.cash@gs.com
Goldman Sachs & Co. LLC
Olivia Halferty+1 801 212-7314
olivia.halferty@gs.com
Goldman Sachs & Co. LLC

Goldman Sachs

Generational Growth

Our views on key questions on the coming data center/AI power surge
How signiﬁcant will the power demand growth from AI/data centers be?
We forecast a 15% CAGR in data center power demand from 2023-2030, driving data
centers to make up 8% of total US power demand by 2030 from about 3% currently.
We now see a 2.4% CAGR in US power demand growth through 2030 from 2022
levels vs. ~0% over the last decade. Of the 2.4%, about 90 bps of that is tied to data
centers.

How much generation and overall capital investment will be required tosupport data center power demand growth?
We estimate about 47 GW of incremental power generation capacity will be required
to support US data center power demand growth cumulatively through 2030, met with
about 60% gas and 40% renewable sources. We expect this to drive about $50 bn ofcapital investment in US power generation capacity cumulatively through 2030.

Will there be bottlenecks in generation or transmission/interconnectionthat constrain growth?
Initial conversations with utilities under coverage do not suggest material concerns on
supply chain on the generation kit or transmission side over the current ﬁve-yearplanning period, though there have been concerns from investors that issues aroundsolar in recent years could reemerge. Further, lengthy interconnection queues remaina challenge to connecting new projects to the grid, and expediting thepermitting/approval process for transmission projects will be key to alleviate it.
Elsewhere, we see similar potential growth constraints from natural gas transmission
infrastructure construction, speciﬁcally long-dated timelines, permitting challenges, andenvironmental / landowner litigation. In our view, the most top of mind constraint for
natural gas is construction and permitting timelines where we see an average lag of~4 years from the project announcement date to in-service date which means theearliest capacity additions, if announced today, would not be in-service until ~2028.

If not, what will be the constraint to growth: AI budgets, demand orneither?
The answer to this will be critical to the trajectory for AI power demand. The lack of any
constraint would be the most bullish for power consumption — i.e., if corporate
technology spending budgets are unlimited, then server shipments would remain
otherwise unchanged even as new generations of chips/servers come available thatcost more money but provide greater compute speed/consume more power (with
efﬁciencies). Demand as the constraint would be the most bearish, as, all else equal,
fewer more efﬁcient servers would be able to meet a ﬁxed level of demand. Ourestimates imply budgets are a constraint, i.e., buyers shift towards more productive
products within their budgets.

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Generational GrowthFinding the downstream beneﬁciaries of AI and data center growth in the
power supply chain
US power demand likely to experience growth not seen in a generation. Not since
the start of the century has US electricity demand grown 2.4% over an eight-year
period, with US annual power generation over the last 20 years averaging less than
0.5% growth. We believe this is on track to change through the end of the decade, led
by a surge in data center demand for power and complemented by electriﬁcation,industrial reshoring/manufacturing activity. Growth from AI, broader data demand and a
deceleration of power efﬁciency gains is leading to a power surge from data centers,with data center electricity use expected to more than double by 2030, pushing data
centers to 8% of US power demand vs. 3% in 2022. We believe this is unlocking
underappreciated equity investment opportunities throughout the power supply chain,and regulated Utilities that will beneﬁt from rate-based growth have continued to trade
more on cyclical factors like interest rates than on the secular generational growth thatis likely to drive earnings. In our report we forecast AI and broader power demand andhighlight 16 Buy-rated stocks across sectors exposed to the US data center power
demand surge.
Where we see opportunity. Investor interest in the data center growth is not new, butdifferentiation among enablers, Utility volume growth beneﬁciaries, Utility capex growth
beneﬁciaries and regionalization will likely be the next area of focus. Technology
companies will likely accelerate underwriting power purchase agreements (PPAs),
where the companies sign contracts to buy power from utilities or independent power
producers, for renewable generation. And infrastructure contractors and industrials
making products that support needed transmission, generation and distribution are likely
to beneﬁt.
Potential drivers of upside and downside. A key driver of AI power demand iswhether the constraint to growth is demand, budgets or neither. New generation AI
servers consume more power and provide more compute speed, even as the power
intensity has fallen meaningfully. There could be meaningful upside to our base case if
appetite for purchase and utilization of servers is unconstrained. There could be
downside to our base case if power efﬁciency is higher than expected or if
power/compute speed efﬁciencies lead to fewer servers purchased than expected.

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Exhibit 1: After being ﬂattish for 2015-19, we see power demand from data centers more than tripling in 2030 vs. 2020, with an upside casemore than double the base case depending in part on product efﬁciencies and AI demand
Data center electricity consumption, TWh (LHS) and 3-year rolling average power efﬁciency gains yoy, % (RHS)

1,200

24%

1,100

22%

Efficiency
Gains
Decelerating

900

Power Demand
Increasing

20%
AI

18%

800

16%

700

14%

600

12%

500

Power efficiency
gains

10%

400

8%

Data center power
demand, ex-AI

300

Power efficiency gains, %Data center power demand, TWh

1,000

6%

200

4%

100

2%

0

0%

Source: Masanet et al. (2020), Cisco, IEA, Goldman Sachs Global Investment Research

Exhibit 2: Data centers contribute 90 bps to our 2.4% US power demand CAGR from 2022-2030
Composition of US power demand CAGR, 2022-2030, %Source: Goldman Sachs Global Investment Research, EIA

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Case study: How Dominion is handling power demand growth from data
centers in Northern Virginia
Northern Virginia has been the data center capital in the US by a fairly signiﬁcantmargin. Northern Virginia currently has more data centers than the next ﬁve biggest
markets in the US combined. The explosive growth of data centers in this region overthe last decade or so was due to a number of factors including low cost of energy on arelative basis, and state tax incentives for data centers. Although these data centers area positive for the local and state economy, they present a signiﬁcant challenge andopportunity for the local utility Dominion Energy (D, Not Rated). These data centers areprimarily located within Eastern Loudoun County, which is known as “data center alley”,
and having so many energy intensive buildings in one small area can create issues forthe local utility.
D presented PJM with a 15-year forecast for data centers in 2023, which highlighted
the extreme growth that it has seen in data center alley over the last 7 years. The data
center power demand in D’s service territory grew at a nearly 24% CAGR from2017-2023, and the forecast it presented implies just under a 10% CAGR going forward.
The lower CAGR is largely due to the larger base, as the average MWs added each year
are higher in 2024-2030 than is previous years. The rise of AI is expected to accelerate
the growth of data centers, and some of D’s latest forecasts have the power demand

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Generational Growthfrom data centers increasing by 109% by 2030. The company has stated that although
the extra load from the data centers is great, commercial margins are relatively low, so
the primary beneﬁt for the utility is the supporting investment into the grid and/or into
generation.
Exhibit 3: Northern Virginia is the largest data center market in the
US by a large margin
MW demand by region

Exhibit 4: Dominion’s forecast of load from data centers indicatesthat the rapid growth it has seen will not slow down for theforeseeable future
Dominion 15-year data center forecast
8,000

Data center market size (MWs)
410

7,041
7,000

390

5,516

Demand (MW)

5,099

3,640

698

5,000
4,066
4,000

1,000

784
DallasPhoenix

No. California

New Jersey

Source: JLL, Data compiled by Goldman Sachs Global Investment Research

Atlanta

4,296

3,375
2,767

3,000

2,302

2,000

737

Chicago

6,006

6,000

693

No. Virginia

6,514

931 1,113

1,421

1,808

0
2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Source: PJM , Data compiled by Goldman Sachs Global Investment Research, Company data

In 2022, management stated that it had transmission constraints that could
impact its ability to provide power to data centers in the coming years.
Transmission projects can take years to permit and build, and thus D temporarily paused
connections for new data centers to assess how to proceed. The company’s concern
was not that existing data centers would not receive power, but that if D does notproactively address the constraint it would have issues going forward. The issue only
impacted Data Center Alley, and did not impact D’s ability to service the remainder of its
service territory.
The company determined that two 500-KV transmission lines should be sufﬁcient
to relieve the constraint and support the upcoming power demand, but getting two
transmission projects done in short timeline is not a guarantee. D worked with the PJM
Interconnection (“PJM”, regional transmission organization that coordinates the
movement of wholesale electricity in all or parts of 13 states and DC, including Virginia)
to expedite the process for these transmission projects, and was successful in doing so.
Management told investors on the 4Q2023 earnings call that one of these transmission
projects was already under construction, and the other is in the regulatory process. They
also stated that one of these projects should be enough to relieve the constraint, andthe pause on accepting new data center connections had ended.

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Exhibit 5: PJM’s power demand forecast in the DOM Zone in Virginia
implies signiﬁcant peak power demand growth going forward
PJM DOM Zone load growth forecast
35,000

10%
9%
8%

8%
6%

25,000
4%

4%

4%

4%

7%
6%

5%

6%
5%

20,000

5%
4%
3%

4%

15,000% annual increase

Peak load (MWs)

30,0002%

10,000
2023

2024

2025

2026

1%

1%

2%

1%

2027

2028

2029

2030

1%
0%

2024 forecast (LHS)2022 forecast (LHS)2024 forecast annual Increase (RHS)

2022 forecast annual Increase (RHS)

Source: PJM, Data compiled by Goldman Sachs Global Investment Research

To address long term needs, PJM opened a competitive auction for transmission
projects in that region. Although D was able to reconcile the short term constraints
that data center alley faced, neither the utility nor PJM want to rely on having to
expedite transmission projects to ensure reliability. The end result was $5bn oftransmission projects, split between several notable utilities but with D receiving thelargest portion of the bunch, at ~$2.5 bn. PJM stated that the auction was necessary
not only because of the 7.5 GWs of expected demand from data centers, but also
because of the 11 GWs of generation retirements expected in that region. It also notesthat the majority of the projects utilize existing right of ways, which should reduce costand speed up the process. D highlights some of these projects in its 1Q2023 slide deck,with many being just some form of upgrade to the existing grid. This is expected tosupport system needs through 2028, and should provide a solid baseline for any
necessary incremental grid spend for beyond that time period.
Transmission is one of the major bottlenecks for the clean energy transition, andthe addition of data centers and AI could exacerbate this. Not all RTOs (regionaltransmission organizations, that oversee/coordinate movement of wholesale electricity
across a certain area) will need the same level of investment as PJM, but we look to thetransmission auction that it held as an example of the potential investment needed forthe transmission grid. If we are to assume that the same cost of $5bn to support 7,500
MWs of demand in PJM applies to other RTOs, our base case data center estimate
would imply that the grid would need $19bn of transmission capex through 2030 tosupport growth. Although this is a relatively small portion of industry transmission
spend, this spend will likely be concentrated in speciﬁc regions and therefore could be
impactful for the local utility. This does not include the potential incremental distribution
and generation spend that could be necessary, which again could provide meaningful
upside to utility growth over the long term.

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Exhibit 6: D won a signiﬁcant portion of PJM’s $5bn transmission
auction% split of transmission projects by spend

Source: PJM, Data compiled by Goldman Sachs Global Investment Research

The rapid growth of data centers could pose similar issues for other utilities across the
US without proactive investment. The US power grid is built to maintain some level ofreserve supply in the event of increased demand, but data centers are unusually power
intensive and clusters of these in one region can surpass the reserves available. We
believe that looking how D handled the situation provides useful context for the
potential implications of this trend, and the end result gives us conﬁdence that utilities,
regulators, and RTOs can collaborate to overcome near term challenges. Northern
Virginia remains a unique region due to the incentives that attract data centers, but webelieve other RTOs could require similar transmission auctions to support growth, which
could provide further upside to utility growth going forward.

Data center power demand and the AI catalyst

The combination of AI, ex-AI increases in data demand and a material slowdown
in power efﬁciency gains is making data centers a critical driver of accelerating
global and US electricity demand growth. We assume data center power demand
excluding cryptocurrency will grow by 160% in 2030 vs. 2023 levels (bear/bull range of
80%-240% growth), representing an increase of about 650 TWh by 2030 in our basecase (330/1,000 TWh in our bear/bull cases). Data centers alone will contribute a 0.9%
CAGR to overall US power demand and a 0.3% CAGR to overall global power demand,in our base case, with a bull case contribution of 1.3%/0.5%. Our base case implies data
center power demand moves from 1%-2% of overall global power demand to 3%-4%
by 2030. The increase in the US is even greater — from 3% to 8%. Our estimates foroverall data center power demand are above IEA forecasts (2026), and our outlook for AI
to represent about 19% of data center power demand in 2028 is above recent corporate
forecast.

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Exhibit 7: After being ﬂat for 2015-19, we have seen data center power demand accelerate in 2021-23 andexpect a 160% increase through the rest of the decade
Global data center electricity consumption, TWh; includes AI and excludes cryptocurrency

Data center power demand, TWh

1,200
1,000

RoW AI

800
600

RoW ex-AI

400

US AI

200
US ex-AI

0

Source: Masanet et al. (2020), Cisco, IEA, Goldman Sachs Global Investment Research

Exhibit 8: We see 2030 power use from data centers 1.8x-3.4x 2023 levels in our bear/bull caseElectricity demand from data centers in TWh, base case, bear case and bull case

Data center power use, TWh

1,600
1,400
1,200

1,000
800
600
400
200

0

Bear caseBase case

Bull case

Source: Masanet et. al (2020), Cisco, IEA, Goldman Sachs Global Investment Research

Data demand has been growing, but until recently power demand from data
centers was ﬂattish. Between 2015 and 2019, data center workload demand nearly
tripled while data center power demand was relatively ﬂat. There were two main driversof this:
1. Power consumption efﬁciency gains (i.e., pace of lower power intensity) within
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Generational Growth

cloud and hyperscale data centers. Annual efﬁciency gains averaged about 15%.
2. Mix shift towards cloud and hyperscale data centers which have materially lower
power consumption intensity than traditional data centers. By 2020 more than 90%
of data center workload demand was at cloud/hyperscale centers.
We estimate in 2020 data center power demand was about 240 TWh, based on datafrom the IEA, Cisco and academic sources.
Exhibit 9: Data center workload demand nearly tripled between 2015-2019 but electricity consumption fromdata centers was ﬂat
Data center workload demand (RHS) in million compute instances; data center power demand (LHS) in TWh(mn computeinstances)(TWh)
Data center power sourced from others (LHS)

450

1,500

Data center power sourced from renewables (LHS)

400

1,250

Total data center workload demand (RHS)

350

1,000

300
250

750

200

500

150
100

250

50

0

0
2015

2016

2017

2018

2019

2020

2021

2022

2023E

Source: Masanet et al. (2020), Cisco, IEA, Goldman Sachs Global Investment Research

However, in the last three years, power consumption from data centers has beenon the rise. With much of the mix shift already done, the gains from the shift tocloud/hyperscale have narrowed. Additionally, we have seen efﬁciency gains appear towane within cloud/hyperscale data centers (reported data to calculate efﬁciencies is notconsistently available annually and as such for some historical years are estimated or
implied). The IEA estimates 2022 data center power consumption was about 350 TWh,excluding contribution from cryptocurrency; all else equal this implies cloud/hyperscaleannual efﬁciency gains decelerated to about 1%-2% in 2020-22.

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Exhibit 10: Data center efﬁciency gains and the shift to cloud/hyperscale have been critical drivers of themoderate increase in data center power demand, but decelerating efﬁciency gains have helped to drive a
pickup in power demand from data centers in recent years
Data center power intensity (LHS) in KWh per compute instance; share of cloud/hyperscale data centers (RHS) as% of workload(KWh/computeinstance)(% share)Traditional data center power intensity (LHS)
Cloud/hyperscale data center power intensity (LHS)
Overall data center power intensity (LHS)

2,500

100%

Cloud/hyperscale share of compute instances (RHS)

95%

2,000

90%
85%

1,500

80%
1,000

75%
70%

500

65%
0

60%
2015

2016

2017

2018

2019

2020

2021

2022

2023E

Source: Masanet et al. (2020), Cisco, IEA, Goldman Sachs Global Investment Research

Three key assumptions will help drive data center power demand forecast.
nData consumption outlook — both AI and non-AInPower efﬁciency gainsnPotential infrastructure constraints

AI to accelerate power demand growth
We assume power demand from AI rises about 200 TWh in 2024-30 (bear/bull caseof 110-330 TWh), with AI representing about 20% of overall data center power
demand by 2030 in our base case. We see a wide range in our bear/bull scenario
driven by uncertainty over demand and power efﬁciency. As demand for AI training
grows in the medium term and for inference longer term, we see demand growth wellexceeding the efﬁciency improvements that are leading to meaningful reductions in highpower AI server power intensity. We note that a ChatGPT search consumes around6x-10x the power as a traditional Google search (see Exhibit 11).

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Exhibit 11: ChatGPT queries are 6x-10x as power intensive as
traditional Google searches
Power consumption per query/search (Wh)c.10x

2.9 Wh

0.3 WhGoogleChatGPT

Source: Google, SemiAnalysis

We have seen new AI innovations increase computing speed and max power
consumption per server, representing meaningful reduction in power intensity. As
demand for GPUs grows, there is still notable company-projected intensity reductions.
As an example of how innovations have reduced power intensity per server but
increased overall power per server:n

The NVIDIA DGX A100 system is listed to net 5 petaFLOPS and consuming 6.5 kW
max, or 1.30 kW per pFLOPS.

n

The more recent NVIDIA DGX H100 system is listed at 32 petaFLOPS andconsuming 10.2 kW max, or 0.32 kW per pFLOPS.

n

The new generation NVIDIA DGX B200 system using the new Blackwell chips islisted to net 72 petaFLOPS (training) and consuming 14.3 kW max, or 0.20 kW per
pFLOPS.

Exhibit 12: We have seen new AI innovations increase max power consumption per server but increase computing speed per server by aneven greater level, representing meaningful reduction in power intensity
Recent evolution of NVIDIA server system speciﬁcations are indicative of increasing max power per server but with lower power intensity relative tocomputing speed (for training)

Max power required

Compute speed(kW)(petaFLOPS)

6.5

NVIDIA DGX A100

1 petaFLOPS =One quadrillion (onethousand trillion)
operations per second

5(1.30 kW/pFLOPS)

10.2

NVIDIA DGX H100

~1.5x the power~7x the compute speed(0.32 kW/ pFLOPS)NVIDIA DGX B200

14.3

~2x the power~15x the compute speed

32

72(0.20 kW/pFLOPS)

Source: NVIDIA, Goldman Sachs Global Investment Research

Our global TMT team expects AI server units shipped to grow at a 76% CAGR in
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Generational Growth2024-26, representing a 50% CAGR in server revenues; whether demand, budgets
or neither is the constraint to growth is critical for energy use forecasting. A key
question impacting compute demand is whether that demand is pent-up (i.e., available
new servers will be bought regardless of budget), not pent-up and constrained bydemand itself, or constrained by customer budgets. In other words, will customers buy
equal amounts of the more powerful servers as they would the less powerful ones? If ina scenario in which a customer initially desires to buy 10 AI servers, and a newgeneration is announced with 10x the compute speed for 5x the price, will thecustomer:
1. Buy the same number of servers (10), spend more money (5x) and substantially
increase its compute speed as a result of the innovation (10x)? This would represent
no constraints (i.e., unlimited budgets, so demand dictated by available supply).
2. Buy 1/10 the number of servers (1), maintaining the same level of compute power
as prior to the innovation and spending less money (0.5x)? This would represent
demand as the constraint.
3. Buy 1/5 the number of servers (2), thereby maintaining budget allocated for serverpurchases but getting a greater compute speed (2x)? This would represent budget
as the constraint.
Exhibit 13: Extent of pent-up demand for AI server supply and voraciousness of technology capex budgets will be critical for pace of AI
power consumption
Indicative scenario analysis of how demand vs. budget constraints could impact AI compute speed and power use

Assumes power generation, transmission and interconnection are not constraints for indicative purposes
Source: Goldman Sachs Global Investment ResearchConﬁdence in the above question is key to whether forecasting methodology should
be weighted towards server-based (requiring nuance given varied and dynamic power
consumption intensity and compute intensity) vs. demand based (forecasting compute
power and power intensity per unit of compute power).
We have applied both a server supply driven forecast and a compute speed
demand driven forecast, with a heavier weight applied towards the supply-driven
methodology.

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Generational GrowthnOn the server supply-driven side, we apply our global Technology team’s forecast
for server shipments per year mentioned above, divided among high energy vs. low
energy servers. We assume server replacement every 5 years. Our TMT team seesa sharp increase in high power servers (greater electricity consumption per server,
higher ASP) increasing sharply in the next few years to meet demand for AI training,
while the team sees greater weighting of growth from lower power servers (lower
electricity consumption per server, lower ASP) towards the back of the decade as AI
demand weighting shifts more towards inference. We assume 5%-8% annual power
efﬁciency gains per year to reﬂect expected future server innovation — relative todemand-based energy intensities where saw higher annual efﬁciencies early in life
cycle, we assume slower pace in a supply-based approach to reﬂect timing ofadoption. The result is falling power intensity per computing speed and sharp
increases in overall power demand due to the growth in new servers with higher
weighting within higher power servers towards the newer generation that as
mentioned have lower power intensity but higher power consumption per server.

n

On the demand-driven side, we use our China Media, Internet & Telecom team’s AI
compute power forecast to drive global AI compute demand outlook. We assume
power efﬁciency gains of 8%-15% annually in our base case through the end of thedecade — this reﬂects similar efﬁciency gains as seen ex-AI in 2015-20.

Together, this implies sharp increase in electricity use from AI, even as revenues
from servers shipped are implied ﬂattish in 2027-30. Our global TMT team’s server
shipment and ASP forecasts imply ﬂattish revenues in 2027-30, driven by greater
weighting towards lower power we see a greater mix shift towards lower cost lower
power servers within the mix, in addition to expectations for ASP declines. However,with the mix shift and ASP reductions offset by increased volumes — and with theoverall number of servers in operation continuing to grow, we see sharp demand growthfrom AI for electricity even despite expected efﬁciencies, owing to a combination of
maturity and expected overlap with demand via AI. Based on the framework andassumptions described above, we see AI power demand moving up by around 200 TWh
in 2030 vs. 2023.

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Exhibit 14: AI servers in operation expected to grow sharply
through 2030 even as revenues from AI server shipments ﬂattens in
2027-30

Exhibit 15: We see AI power demand growing rapidly even as
power use per AI server falls later in the decade due to mix shift
and expected efﬁciencies

AI servers in operation and implied revenues from our global TMT teamforecasts

AI power use, TWh (LHS); max power use per AI server, KW (RHS)

AI servers in operation$200

Revenues from AI
server shipments

10,000

$150

8,000

Lower power
AI servers inoperation

6,000
4,000

High power
AI servers inoperation

2,000
0

2022

2023E 2024E 2025E 2026E 2027E 2028E 2029E 2030E

$100

$50

$0

AI server shipment revenues, $ bn

14,00012,000AI poweruse, TWh$250

16,000Max power use per
AI server, KW

250

5.0

200

4.0

150

3.0

100

2.0

50

1.0

0

0.0
2022

2023E

2024E

2025E

AI power demand

Source: Goldman Sachs Global Investment Research

2026E

2027E

2028E

2029E

2030E

Power use per AI server

Source: Goldman Sachs Global Investment Research

Data center power demand ex-AI
We expect continued strong growth in workload unrelated to AI. We assume
double digit growth in workload compute instances with deceleration from the
20%-25% of compute instances growth seen since 2017 down to 12%-18% annual
growth in 2026-30, owing to combination of maturity and expected overlap with demand
via AI. A key question on ex-AI demand is whether AI modeling and user demand for AI
will partially replace users’ need for searches or other workload. We are broadly
assuming minimal replacement — i.e., AI is largely incremental to the broader trend ofdata center workload demand.
Power intensity efﬁciency gains among cloud/hyperscale data centers havedecelerated; we assume modest re-acceleration in our base case. We assume
power efﬁciency gains — which as mentioned averaged around 15% annually in 2015-19
but decelerated to around low single digits in 2020-22 — will remain relatively low.
However, we assume a slight re-acceleration to an average of 3% in our base case in
2024-30, as industry discussions suggest continued efforts towards innovation
efﬁciencies, especially around the power intensity given the prospects for signiﬁcant
power needs ahead.
The implications of the efﬁciency gains assumption leads to a wide range
between our bear and bull cases for ex-AI data center demand growth. Based on
the above framework and assumptions, we see about 450 TWh of ex-AI power demand
growth in 2030 vs 2023 in our base case vs. 225/650 TWh in our bear/bull cases. As wehave highlighted earlier, we believe the efﬁciency assumption is a critical driver of power
demand outlook ex-AI — our bear/bull cases assume 7%/0% annual intensity reductions
on average in 2024-30.

28 April 2024

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Generational Growth

Exhibit 16: We expect power efﬁciency gains at cloud/hyperscale data centers to continue, but remain at alower pace going forward relative to 2015-20
3-year rolling average % change in cloud/hyperscale KWh per compute instance(% efficiency
gains)

Cloud/hyperscale power efficiency gains

20%

18%
16%
14%
12%

10%
8%
6%
4%
2%
0%

Source: Masanet et al. (2020), Cisco, IEA, Goldman Sachs Global Investment Research

28 April 2024

18

Goldman Sachs

Generational Growth

US power demand growth expanding to levels not seen in decades
Over the last ten years, power demand growth in the US has averaged ~0%.
Despite economic and population growth over that time period, efﬁciencies have offset
this, with advances like the penetration of LED lighting driving lower power usage. As
mentioned above, power consumption efﬁciency of data centers and the mix shift tocloud and hyperscale data centers from traditional data centers have also contributed tothese efﬁciencies. With that said, we expect power demand to accelerate to levels notseen in the US in many years.
With this note, we raise our US power demand forecast to 5,036 TWh by 2030,
which represents a 2.4% CAGR from 2022-2030. This compares to our prior estimatesof 4,733 TWh demand by 2030 and a 1.7% CAGR from 2022-2030. The driver of thischange is embedding explicit assumptions around data center and AI growth. We
continue to forecast residential, commercial ex-data center, and industrial demand using
historical relationships to macroeconomic factors, and embed a separate forecast forelectric vehicles based on our colleagues’ EV outlook.

Exhibit 17: US power demand growth has averaged 0% in the last
10 years, relative to early 2000s growth of ~1.5% on average, andour forecast for over 2% growth on average through 2030

Exhibit 18: Data center/AI growth adds about 80 bps on average to
our annual power demand growth rates from 2024-2030
US power demand growth, %US power demand growth, %
5.0%

3.0%

4.0%
2.5%

3.0%
2.0%

2.0%

1.0%
0.0%

1.5%

-1.0%
-2.0%

1.0%

-3.0%
0.5%

-4.0%
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030

-5.0%

Power demand growth

2000-2007 Average

Source: EIA, Goldman Sachs Global Investment Research

10 year Average

0.0%
2024

2025

2026

2027

Previous Demand Growth

2028

2029

2030

Data Center Growth

Source: Goldman Sachs Global Investment Research

On our updated estimates, we expect data centers to contribute 8% to overall US
power demand by 2030 in our base case, from current levels estimated at ~3%. Thistranslates to a 16% CAGR for data centers in our base case from 2023-2030, andcontributes about 90 bps to our overall power demand CAGR over the same time frame.
We believe this will drive a need for incremental generation capacity to be built in theUS, which we believe in part has been signaled by sizable IRP ﬁlings in recent years
from several companies that have increased load growth forecasts/generation needs, aswell as RTOs that have revised load growth assumptions higher.

28 April 2024

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Goldman Sachs

Generational Growth

Exhibit 19: Data centers contribute 90 bps to our 2.4% power demand CAGR over 2022-2030
Power demand CAGR composition, %Source: EIA, Goldman Sachs Global Investment Research

Exhibit 20: We expect data centers to comprise ~8% of US power
demand by 2030 in our base case, with potential for 11% in our bullcase and 5% in our bear case

Share of Overall US Power Demand (%)

Data center demand as a share of total US power demand, %
12%
10%
8%

6%
4%
2%
0%
2023

2024

2025

2026

2027

2028

Non-AI Data Center Demand

AI Demand

Bull Case

Bear Case

2029

2030

Source: IEA, Goldman Sachs Global Investment Research, EIA

Exhibit 21: Several utilities in our coverage have ﬁled meaningful resource plans on the back on increasing load growthIntegrated Resource Plan by utility
Resource Plans
Company
Plan name
DUK
NC & SC Resource Plan

Details
8.9 GWs of natural gas, 6.5 GWs of solar, 2.7 GWs of storage, 1.2 GWs of windSO

Georgia Power IRP

Up to 10 GWs of renewables

XEL

Colorado Resource Plan - Alternative Portfolio

1.8 GWs of storage, 1.7 GWs of wind, 1.6 GWs of solar, ~700 MWs of natural gas

D

DESC IRP

5 GWs of solar and 1.6 GWs of battery storage

D

DEV IRP

10.8 GWs of solar, 5.9 GWs of natural gas, 3 GWs of wind, and 1 GW of storage

AEP
AEE

Various IRPs
Missouri IRP

8 GWs of wind, 6 GWs of solar, 5 GWs of natural gas, and 1 GW of storage
By 2030: 2.8 GWs of renewables, 800 MWs of natural gas, 400 MWs of storage

Source: Data compiled by Goldman Sachs Global Investment Research

28 April 2024

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Goldman Sachs

Generational Growth

In order to size the generation capacity potential, we have considered expected
regional allocation of data centers, current and future states of reserve margins indifferent regions across the US according to NERC, and assumptions around natural gas
vs. renewables and peakers vs. combined cycle gas plants based on recent IRP ﬁlings.
In order to support data center/AI driven load growth, we assume capacity is met with
60% gas (40% CCGT/20% peaking units) and 40% renewables (25% solar, 15% wind).
This adds 47 GW of capacity cumulatively from 2024-2030, including 28 GW of gas and
19 GW of renewables. This embeds ~9 GW of combined cycle and ~19 GW of peakers,in addition to ~13 GW of solar and ~6 GW of wind. This points to average annual
capacity additions through 2030 of ~1.3 GW for CCGT, ~2.7 GW for gas peaking units,
0.9 GW for wind, and ~1.9 GW for solar. Overall, our power supply/demand model now
embeds ~45 GW annual net capacity additions on average through 2030, including
non-data center driven adds.
While we do not explicitly assume any nuclear capacity is added through the extent ofour model (through 2030), we believe (1) we will see fewer nuclear plant closures going
forward, which could present upside risk to nuclear generation as more plants receive
license extensions, as well as uranium demand, (2) nuclear is an attractive generation
source for data centers given it is zero carbon and reliable, but we view IPPs with
nuclear capacity as best positioned to beneﬁt versus regulated utilities, and (3) small
modular reactors (SMRs) could have a more impactful role in the future, but we viewthat as beyond the life of our current model.
Exhibit 22: We assume the greatest share of data centers are built
out in MISO, followed by SERC, CAISO, and PJM
Regional allocation of data center driven load

Exhibit 23: Based on NERC forecasts for reserve margins acrossregions, we assume just under half of data center driven demand
will need to be met with new generation capacity cumulatively
through 2030
How to meet data center driven power demand, %WECC NW/SW
14%

MISO
19%

ISO-NE
2%
NYISO
3%

CAISO
16%
PJM
15%

ERCOT
12%

SPP
2%

New
Capacity
Need
49%

SERC
17%

Source: BCG, Data compiled by Goldman Sachs Global Investment Research

28 April 2024

Existing
Capacity Length51%

Source: Goldman Sachs Global Investment Research

21Goldman Sachs

Generational Growth

Exhibit 24: We assume an average capacity factor of 63% on CCGT
units, 15% on peakers, 35% on onshore wind, and 27% on utility
scale solar to inform our capacity addition forecasts

Exhibit 25: We see about 47 GW of incremental capacity adds toserve data center driven load through 2030
Data center driven capacity adds, MW

Base case capacity factors, %
80%
2027
2028
Base Case Capacity Factors (%)
2029
2030

70%

60%
Average
50%
40%
30%
20%
10%
0%
2024

2025
CCGT

2026

2027

Peaker

Wind

2028

2029

2030

Solar

Source: Goldman Sachs Global Investment Research

Source: Goldman Sachs Global Investment Research

Exhibit 26: Capacity additions to support data centers on our numbers
represent about 15% of our overall net capacity additions in the US
through 2030
Overall net capacity additions through 2030 by source, MW

Source: Goldman Sachs Global Investment ResearchConsistent with our LCOE model, we assume capital costs to build this generation
capacity is $1,000/kW for CCGTs, $750/kW for gas peakers, $1,600/kW for onshorewind, and $1,300/kW for utility scale solar. This drives incremental capital investmentsof ~$50 bn cumulatively through 2030, or $7 bn on average per year to support data
center growth. Combining data center related power generation capacity needs with ourbaseline capacity addition forecasts to support load growth in other areas as well as
offset retirements of coal units drives total generation capex of $665 bn cumulatively
through 2030, or ~$95 bn on average annually through 2030.
While we recognize that $50 bn industry wide spend through 2030 could be digestible,
we believe spend will be regionally concentrated, particularly in MISO, which could be
more meaningful for companies exposed to MISO. We also believe this is just one pieceof the overall utilities capex opportunity pie. We see investment needed for further
generation capex to support the energy transition, load growth outside of data centers,
as well as needs to support grid reliability. We also note that assumptions around

28 April 2024

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Goldman Sachs

Generational Growth

capacity mix/factor and share of demand that requires new capacity are key drivers of
our capex forecast.
Exhibit 27: We see capital costs for different generation sources
ranging from $750-$1600/kW, which informs our capex forecast for
generation

Exhibit 28: We expect to see ~$50 bn of incremental capital
investment in generation to support data centers through 2030, or
about 8% of total generation capex

Capital cost for different generation sources, $/kW

Capital investment in generation to support data center growth, $mn/share of total generation spend, %Capex, $ mn

Solar

CCGT

Peaker

$9,00010%$8,0009%$7,0008%
7%$6,0006%$5,0005%$4,0004%$3,0003%$2,0002%$1,000

1%
0%

$-

$-

$200

$400

$600

2024

$800 $1,000 $1,200 $1,400 $1,600 $1,800
Capital cost ($/kW)

Source: EIA, Goldman Sachs Global Investment Research

Share of Total Generation Capex, %

Wind

2025

2026

2027

Gen Capex Tied to Data Centers

2028

2029

2030

Share of Total Gen Capex

Source: Goldman Sachs Global Investment Research

Exhibit 29: We outline three scenarios around US power demand/capacity adds/capex based on our
SUSTAIN team’s data center model
Data center/AI scenarios and key metrics

Data Center/AI Scenarios

Base Case

Bull Case

Bear Case

2024-30 Capacity Adds (MWs)

47,126

57,13822,9442024-30 Generation Capex ($ mn)$49,979$64,612$24,3262030 Data Center Demand (GWh)397,979

533,026

276,2512030 DC Share of Demand (%)

8%

11%

5%

2023-2030 DC Demand CAGR (%)

15%

20%

9%

Source: Goldman Sachs Global Investment Research

We have not seen signiﬁcant stock price movement for regulated utilities on thistheme, despite the fact that we have seen strong stock price performance for selectindependent power producers (IPPs) that are expected to beneﬁt from increased
demand for power and potentially higher power prices going forward. We believe there
are two potential avenues for regulated utilities to beneﬁt from this data center
driven load growth (recognizing this is not the only driver of load growth). First, is thevolumetric beneﬁts from higher commercial load growth for companies that operate inregions tied to data center growth that are not decoupled. We recognize this is likely
muted given the lower margin nature of commercial sales. Second, is higher rate basegrowth via the increased capital investment required to facilitate this growth. This
could come in the form of generation capex, which we quantify for US demand tied todata centers above, or grid investment to support transmission/reliability needs.

28 April 2024

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Goldman Sachs

Generational Growth

Exhibit 31: Across customer classes, residential sales are
generally highest margin, followed by commercial and industrial

Commercial share of electric revenue (%)

Average price by customer type (cents/kWh)

45%

18.0

40%

16.0
Average Price by Customer Type(cents/kWh)Commercial share of electric revenues(%)

Exhibit 30: Within our coverage, D/NEE/WEC have higher than
average commercial volumes and have no exposure to decoupling

35%
30%
25%
20%
15%
10%
5%

Data Centers
Included inCommercial

14.0
12.0
10.0
8.0
6.0
4.0
2.0

0%

0.0
PEG XELD

NEE AEE SRE

ES

WEC

SO

EXC DUK

FE

AEP

We exclude covered companies that are fully decoupled (CA, NY), and highlight those that haveno exposures at subsidiaries to full or partial decoupling mechanisms

Residential

Commercial

Industrial

Source: S&P Global Market Intelligence, Goldman Sachs Global Investment Research

Source: S&P Global Market Intelligence, Goldman Sachs Global Investment Research

As mentioned above, in addition to incremental power generation needs to support data
center/AI driven load growth, we also expect this load growth to support investment ingrid reliability, in particular for transmission. While there is certainly existing capacity thatcan be utilized or enhanced, we continue to believe transmission is an area where
incremental investment is needed. As we have written in our Future of Utilities Capexseries, we expect to see a signiﬁcant step up in capital investment across our coverage
going forward (+36% cumulatively from 2024-2027 vs. prior 4 year period), supporting~7% earnings growth across our coverage.
Exhibit 32: We forecast 2024-2027 capex across our coverage to increase ~36% versus than the previous
four year period
Capex by year, $ bn

$160

$563bn$414bn

$140

$120
$100
$80

$126

$129

2023

2024

$143

$143

2025

2026

$148

$105

$92

$92

2020

2021

$60
$40

$20
$0
2022

2027

Source: Company data, Goldman Sachs Global Investment Research, SNL

On grid spending speciﬁcally, we introduce an industry spend estimate of ~$720
bn cumulatively through 2030, including ~$260 bn on transmission and ~$465 bn on

28 April 2024

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Goldman Sachs

Generational Growth

distribution. The historical baseline is driven by EEI industry capex estimates, and theforecast is based on the growth in grid spend we expect for our coverage. We assumetransmission grows at a slight premium to the average, offsetting areas of spend decline(e.g. regulatory compliance/other), and that distribution grows more in line with theaverage. This points to average annual spend through 2030 of ~$66 bn on distribution
and ~$37 bn on transmission. Including the generation spend noted above, the total
capital investment across transmission, distribution, and generation is up ~38% across2024-2027 relative to the prior four year period, broadly in line with the step up in our
coverage capex outlined above.
Exhibit 33: We see $720 bn of grid spend across the industry required
through 2030, based on the capex growth outlook for our coverage
Grid capex, $ bn
140.0
120.0

Capex, $ bn

100.0

80.0
60.0
40.0
20.0

Transmission Capex

2030E

2029E

2028E

2027E

2026E

2025E

2023

2024E

2021

2022

2019

2020

2017

2018

2016

0.0

Distribution Capex

Source: EEI, Goldman Sachs Global Investment Research

Capacity Additions - Gas Demand Growth from Data Centers
We see incremental data center power demand driving ~3.3 bcf/d of new natural
gas demand by 2030. This estimate assumes gas provides ~60% of the ~28.7 GW of
total data center power demand expected through the rest of the decade, and implies
roughly a ~10% increase in the amount of gas consumed in the power market vs.
today. More notably, this represents a ~50% increase vs. our prior growth expectations
for power demand for gas - and adds to the broader constructive backdrop for gas
demand growth alongside new LNG export capacity, coal plant retirements, andrenewables backstopping.
We expect this growth to be relatively ratable (~0.47 bcf/d on average) throughthe decade, though this can swing based on price, regional build-out, andinfrastructure delays (we discuss these below). While natural gas currently serves
40% of power demand, and we expect that overall share to hold relatively ﬁrm throughthe rest of the decade, we assume a higher share (60% as mentioned above) serving
data center demand growth speciﬁcally given gas’s relatively low cost and highreliability. For this estimate, we assume ~40% of this demand is met through CCGTs
with heat rates of 7,000 kWh/btu and ~20% of this demand is met through peakers
with heat rates of 10,000 kWh/btu - a swing more towards the former could reduce

28 April 2024

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Goldman Sachs

Generational Growthincremental gas demand all else equal. On timing, we would expect the existing ﬂeet topull more gas in nearer years and then new plants - via new pipelines (see below) would serve new demand in later years. .
For context, the US gas market for power sits at roughly ~35 bcf/d and makes up
roughly a third of the total gas market of ~100 bcf/d - making this a notable, but
not yet revolutionary, change in the outlook. Looking forward, we expect natural gas
for power demand to grow ~3% annually, of which, data centers make up a meaningful
portion of that growth (~40%). Versus the market size today, our estimate of anincremental ~3.3 bcf/d of gas demand growth from data centers represents a ~10%
increase in the amount of gas consumed in the power market. For context, thiscompares to the gas market for LNG exports which currently sits at ~12 bcf/d and is set
to nearly double to ~25 bcf/d by the end of the decade based on currently FID’ed
projects - and the ResComm and industrial demand of 21 bcf/d and 23 bcf/d,
respectively. All else equal, while we see the potential for data center driven power
demand to accelerate gas demand for power growth generally, and it could represent ameaningful slice of the pie by the end of the decade, we see the potential magnitude for
growth on a smaller scale than the ramp of LNG set for the second half of the decade.
Exhibit 34: We expect gas demand growth to be relatively ratable(~0.47 bcf/d on average) through the end of the decade

Exhibit 35: We see incremental data center power demand driving~3.3 bcf/d of new natural gas demand by 2030

Potential incremental annual physical gas demand (2024E-2030E)

Potential incremental physical gas demand, cumulative (2024E-2030E)
3.5

0.5

0.5
0.4

0.5

0.5

0.5

0.5
0.5

0.4

0.3
0.2
0.1

3.3
2.9

3.0
2.3

2.5
1.8

2.0
1.3

1.5
0.8

1.0
0.5

0.4

0.0

0.0
2024

2025

2026

Source: Goldman Sachs Global Investment Research

28 April 2024

Cumulative physical gas demandpotential (bcf/d)

Total annual physical gas demandpotential (bcf/d)

0.6

2027

2028

2029

2030

2024

2025

2026

2027

2028

2029

2030

Source: Goldman Sachs Global Investment Research

26Goldman Sachs

Generational Growth

Exhibit 36: We expect data center power demand to result in a
~10% increase in the amount of gas consumed in the power market
vs. today

Exhibit 37: We expect natural gas for power demand to grow ~3%
annually, of which, data centers make up a meaningful portion ofthat growth (~40%)

Gas demand for power generation base plus data center power demand(2020-2030E)

Gas demand growth - base vs data centers (2020-2030E)

300,000

14,00012,00010,000200,000

8,000

Demand (MW)Demand (MW)

250,000

150,000
100,000
50,000

6,000
4,000
2,000
0

0

(2,000)(4,000)Gas Base Demand

Source: Goldman Sachs Global Investment Research

Gas Data Center(6,000)Base Annual Gas Growth

Data Center Annual Gas Growth

Source: Goldman Sachs Global Investment Research

We believe this growth could require the construction of ~6.1 bcf/d of new
pipeline capacity. Taking the Utilities team’s forecast that roughly half of overall new
data center demand requires new powergen capacity (~14.2 GW out of the 28.7 total)
with the balance being able to be served by existing facilities, we assume that only theformer needs incremental pipeline build-out. Given our assumed capacity factors for
CCGT and peakers ~60% and 15%, respectively on average, the required pipeline
capacity to generate the ~3.3 bcf/d of physical demand by 2030 could be ~6.1 bcf/d if
contracted out to 100%. While this may not be required in all cases, contracts for new
capacity are now often sized to be able to accommodate peak load - this has been a
consistent message from management teams in the sector, most notably by WMB attheir 2024 Analyst Day. A qualiﬁer to this view includes the potential for data centers toconnect directly to gas transmission systems as discussed recently by KMI on its 1Q24
call and WMB at its Analyst Day. Direct connectivity could result in lower pipeline
capacity requirements given likely higher average utilizations at data centers vs average
gas-ﬁred power plants – though, these types of projects could be lower capex and/or
higher return, mostly offsetting this impact in our view. As mentioned, our estimate
assumes roughly an equal mix between higher ﬂows on existing assets and new
pipeline construction to accommodate new ﬂows; tighter than expected utilization on
the former could drive the investment requirements for the latter higher. We discussupside scenarios below.

28 April 2024

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Goldman Sachs

Generational Growth

Exhibit 38: We believe data center power demand growth could
require the construction of ~0.9 bcf/d of new pipeline capacity
annually, on average

Exhibit 39: We believe this data center power demand growth
could require the construction of ~6.1 bcf/d of new pipeline
capacity

Total annual gas capacity additions potentially needed (2024E-2030E)

Total gas capacity potentially needed, cumulative (2024E-2030E)

0.9
0.9

Total annual gas transmission
capacity additions potentiallyneeded (bcf/d)

0.9
0.8

0.9

0.9

7.0

0.9
0.8

0.7

0.7
0.6
0.5
0.4
0.3
0.2
0.1

6.1
6.0

5.3

5.0

4.3

4.0

3.4

3.0

2.5

2.0
1.0

1.6
0.7

-

2024

2025

2026

Source: Goldman Sachs Global Investment Research

28 April 2024

Total gas transmission capacity
potentially needed, cumulative (bcf/d)

1.0

2027

2028

2029

2030

2024

2025

2026

2027

2028

2029

2030

Source: Goldman Sachs Global Investment Research

28Goldman Sachs

Generational Growth

We acknowledge the range of outcomes is wide and potential gas and pipeline
capacity demand growth is still being determined - and we highlight potential
upside cases below. While we see recent commentary from both midstream and E&P
companies on the prospect for natural gas to serve signiﬁcant data center power
demand growth as positive for the gas macro backdrop generally, the magnitude of
demand and how that can manifest on a micro level for each company is still being
determined.
We provide two bull case scenarios to illustrate potential upside to our physicalgas and gas pipeline capacity demand estimates - there could be up to ~5.5 bcf/
d of natural gas demand and up to 20.5 bcf/d of gas pipeline capacity demand by
2030:

n

Base Case: As described above, our base case assumes that by 2030, 3.3 bcf/d ofgas could be needed to serve ~60% of the ~28.7 GW of total data center power
demand, with the remaining 40% served by renewables. Our incremental pipeline
capacity estimate of ~6.1 bcf/d assumes roughly half of overall new data center
demand requires new powergen capacity (~14.2 GW out of the 28.7 total) with thebalance being able to be served by existing facilities - we assume that only theformer needs incremental pipeline build-out. Given our assumed capacity factors for
CCGT and peakers ~60% and 15%, respectively on average, the required pipeline
capacity to generate the ~3.3 bcf/d of physical demand by 2030 could be ~6.1 bcf/d
if contracted out to 100%.

nBull Case 1: Our ﬁrst bull case assumes that incremental pipeline capacity could be
required for all incremental gas-ﬁred powergen demand (i.e. 60% of total 28.7 GW,
not just new powergen capacity as we assume in our base case). Assuming pipeline
capacity is contracted to peak plant capacity, this could result in pipeline capacity
needs of ~12.3 bcf/d and ~$15b of transmission capex. By 2030, this scenario could
add up to ~$1.7b-$2.5b of annual incremental EBITDA to the midstream sector.

nBull Case 2: Our second bull case assumes that instead of gas/renewables serving
60%/40% of incremental data center powergen demand, gas will serve 100%. We
maintain our heat rate and capacity factor assumptions embedded in our base caseas well as the proportion of new powergen capacity that will be supplied by CCGT(~67%) and peakers (~33%). Our second bull case assumes that 100% of data
center power demand requires new pipeline capacity (~28.7 GW total). This could
result in ~5.5 bcf/d of incremental physical gas demand, and assuming pipeline
capacity is contracted to peak plant capacity, this could result in ~20.5 bcf/d of gas
pipeline capacity needs. On capex, 20.5 bcf/d of pipeline capacity could require$24.9b of transmission capex by 2030 - this could add ~$2.8b-$4.1b of annual
incremental EBITDA for the midstream sector.

28 April 2024

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Goldman Sachs

Generational Growth

Exhibit 45: We see a wide range of potential pipeline capacity needs and therefore capex requirements and EBITDA additions through 2030
Base Case vs Bull Case - Gas Demand, Pipeline Capacity Demand, Capex, and EBITDA Implications
Base Case:- Gas share of power demand: 60%

Bull Case 1:- Gas share of power demand: 60%

Bull Case 2:- Gas share of power demand: 100%

- Share of new gas demand requiring
new pipeline capacity: 50%- Share of new gas demand requiring new
pipeline capacity: 100%

- Share of new gas demand requiring
new pipeline capacity: 100%

Physical Gas Demand Adds (bcf/d)

3.3

3.3

5.5

Gas Pipeline Demand Adds Assuming
Pipes are Contracted to Peak Capacity(bcf/d)

6.1

12.3

20.5

Gas Pipeline Capex by 2030 ($m)$7,353$14,930$24,884

Potential Annual Midstream EBITDA Adds
by 2030 ($m)$817-$1,226$1,659-$2,488$2,765-$4,147

Data Center Physical Gas andTransmission Capacity Scenarios

Source: Goldman Sachs Global Investment Research

Exhibit 46: Our Base case call for up to 3.3 bcf/d of gas demandthrough 2030, but our bull case call for up to ~5.5bcf/d through 2030
Base Case vs Bull Cases - Cumulative Natural Gas Demand Potential(bcf/d)Exhibit 47: Our base case calls for ~6.1 bcf/d of pipeline capacity
additions, but our bull cases call for up to ~12.3-20.5 bcf/d ofpipeline capacity additions
Base case and Bull Cases - Cumulative gas pipeline capacity additions
potentially needed (bcf/d)

5.0
4.0
3.0
2.0
1.0
0.0
2024

2025
Base Case

2026

Bull Case 1

Source: Goldman Sachs Global Investment Research

28 April 2024

2027

2028
Bull Case 2

2029

2030

Cumulative gas pipeline capacity
additions potentially needed (bcf/d)

Cumulative physical gas demandpotential (bcf/d)

6.0
20.0

15.0

10.0

5.0

0.0
2024

2025

Base Case

2026

2027

Bull Case 1

2028

2029

2030

Bull Case 2

Source: Goldman Sachs Global Investment Research

30Goldman Sachs

Generational Growth

Disclosure Appendix
Reg AC
We, Carly Davenport, Brian Singer, CFA, Neil Mehta, Brian Lee, CFA, John Mackay, Ati Modak, Brendan Corbett, John Miller, Toshiya Hari, Joe Ritchie,Mark Delaney, CFA, Jerry Revich, CFA, Jaskaran Jaiya, Varsha Venugopal, Nick Cash and Olivia Halferty, hereby certify that all of the views expressed inthis report accurately reﬂect our personal views about the subject company or companies and its or their securities. We also certify that no part of ourcompensation was, is or will be, directly or indirectly, related to the speciﬁc recommendations or views expressed in this report.
Unless otherwise stated, the individuals listed on the cover page of this report are analysts in Goldman Sachs’ Global Investment Research division.

GS Factor Proﬁle
The Goldman Sachs Factor Proﬁle provides investment context for a stock by comparing key attributes to the market (i.e. our coverage universe) and its
sector peers. The four key attributes depicted are: Growth, Financial Returns, Multiple (e.g. valuation) and Integrated (a composite of Growth, Financial
Returns and Multiple). Growth, Financial Returns and Multiple are calculated by using normalized ranks for speciﬁc metrics for each stock. The
normalized ranks for the metrics are then averaged and converted into percentiles for the relevant attribute. The precise calculation of each metric mayvary depending on the ﬁscal year, industry and region, but the standard approach is as follows:
Growth is based on a stock’s forward-looking sales growth, EBITDA growth and EPS growth (for ﬁnancial stocks, only EPS and sales growth), with ahigher percentile indicating a higher growth company. Financial Returns is based on a stock’s forward-looking ROE, ROCE and CROCI (for ﬁnancial
stocks, only ROE), with a higher percentile indicating a company with higher ﬁnancial returns. Multiple is based on a stock’s forward-looking P/E, P/B,
price/dividend (P/D), EV/EBITDA, EV/FCF and EV/Debt Adjusted Cash Flow (DACF) (for ﬁnancial stocks, only P/E, P/B and P/D), with a higher percentile
indicating a stock trading at a higher multiple. The Integrated percentile is calculated as the average of the Growth percentile, Financial Returns
percentile and (100% - Multiple percentile).
Financial Returns and Multiple use the Goldman Sachs analyst forecasts at the ﬁscal year-end at least three quarters in the future. Growth uses inputs
for the ﬁscal year at least seven quarters in the future compared with the year at least three quarters in the future (on a per-share basis for all metrics).
For a more detailed description of how we calculate the GS Factor Proﬁle, please contact your GS representative.

M&A Rank
Across our global coverage, we examine stocks using an M&A framework, considering both qualitative factors and quantitative factors (which may varyacross sectors and regions) to incorporate the potential that certain companies could be acquired. We then assign a M&A rank as a means of scoring
companies under our rated coverage from 1 to 3, with 1 representing high (30%-50%) probability of the company becoming an acquisition target, 2representing medium (15%-30%) probability and 3 representing low (0%-15%) probability. For companies ranked 1 or 2, in line with our standard
departmental guidelines we incorporate an M&A component into our target price. M&A rank of 3 is considered immaterial and therefore does notfactor into our price target, and may or may not be discussed in research.

Quantum
Quantum is Goldman Sachs’ proprietary database providing access to detailed ﬁnancial statement histories, forecasts and ratios. It can be used forin-depth analysis of a single company, or to make comparisons between companies in different sectors and markets.

Disclosures
Financial Advisory Disclosure
Goldman Sachs and/or one of its afﬁliates is acting as a ﬁnancial advisor in connection with an announced strategic matter involving the following
company or one of its afﬁliates: Dominion Energy, Inc.

Distribution of ratings/investment banking relationships
Goldman Sachs Investment Research global Equity coverage universe

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31Goldman Sachs

Generational Growth
Rating Distribution

GlobalInvestment Banking Relationships

Buy

Hold

Sell

Buy

Hold

Sell

48%

36%

16%

64%

56%

41%

As of April 1, 2024, Goldman Sachs Global Investment Research had investment ratings on 2,885 equity securities. Goldman Sachs assigns stocks as
Buys and Sells on various regional Investment Lists; stocks not so assigned are deemed Neutral. Such assignments equate to Buy, Hold and Sell forthe purposes of the above disclosure required by the FINRA Rules. See ‘Ratings, Coverage universe and related deﬁnitions’ below. The Investment
Banking Relationships chart reﬂects the percentage of subject companies within each rating category for whom Goldman Sachs has provided
investment banking services within the previous twelve months.

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Goldman Sachs

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