California needs clean firm power, and so does the rest of the world
Three detailed models of the future of California’s power system all show that California
needs carbon-free electricity sources that don’t depend on the weather.
JCS Longa, E Baikb, JD Jenkinsc, C Kolsterd, K Chawlad, A Olsond, A Cohene,
M Colvina, S M Bensonb, RB Jacksonb, DG Victorf,g, SP Hamburga
Affiliations:a: Environmental Defense Fund, b: Stanford University, c: Princeton University, d: Energy &Environmental Economics, e: Clean Air Task Force, f: UC San Diego, g: The Brookings
Institution
Summary
California has committed to achieve a net-zero carbon economy by 2045. To meet the state’s
carbon-neutral goal, California will need to rely primarily on forms of energy that do not emit
greenhouse gases.
Rapid expansion of wind and solar power and electrification of transportation and heating
underlie an affordable path to a net-zero carbon economy. At the same time, extensive
electrification and increased reliance on weather-dependent renewable energy sources could
create new reliability challenges requiring proactive planning. Solar and wind supplies drop bysome 60% from summer to winter. In some cases, the state can get little output from solar andwind generation for weeks at a time. These lulls can also coincide with periods of increased
demand, further exacerbating the challenge. Today, California relies on natural gas power plants(and heating) during these periods. If a net-zero carbon economy cannot continue to depend on
natural gas to meet this demand, what will the state use instead?
Batteries can be very useful in dealing with the variation in solar and wind power overhours-long periods, such as daily cycles in solar power output, but today’s battery technologies
cannot cost-effectively store enough energy to pull the state through a big winter storm lasting
many days. Without another source of energy to generate electricity during these periods,
California’s electrified economy could come to a halt. As renewable energy expands, and as useof natural gas for electric generation is eliminated, the state could face a situation much like what
Texas recently went through in February 2021. Luckily, California has a track record of planningfor these types of events, and with proactive effort, the state can secure an affordable andreliable carbon-free electricity future.
California’s relationship with natural gas is in transition. To ensure grid reliability, California
needs to replace today’s carbon-emitting gas-fired power plants, which supply over 40% of thestate’s electricity, with some alternative source of clean electricity that is available on demand,for as long as it is needed, whenever it is needed. This type of electricity resource is known as“clean firm power.” Many choices could fulfill this need. Geothermal power takes energy fromheat in the ground and is available as needed. California’s geology has provided the largest
geothermal plant in the world and California might expand this resource. Nuclear power can1

provide very large amounts of energy steadily in a small footprint. California could evencontinue to use gas power if the CO2 emissions were captured and sequestered underground.
Alternatively, gas power plants could be converted to burn clean fuels, such as hydrogen, whichmight be made via electrolysis with solar power, reformed from natural gas while leaving waste
CO2 to be sequestered underground, or produced from gasification of agricultural and forestry
residues or crops. All of these clean firm power sources (and perhaps others) would provide
critical reliability that could prevent a Texas-sized tragedy.
Even though these clean firm power technologies currently cost more per kilowatt hour thansolar and wind, this study shows that these resources also deliver greater value, which justifies
their higher cost. A robust investment in a portfolio of clean firm power options will obviate theneed for otherwise-redundant renewable energy. As a result, our modeling finds that California
can reach a 100% carbon-free clean electricity supply by 2045 while keeping consumer costs
similar to those paid today. An ambitious but achievable investment in clean firm power, withinstalled capacity similar in magnitude to our existing gas fleet—or roughly 25-40 gigawatts—
could eliminate the need for ten times that amount of wind and solar capacity, and significantly
reduce associated transmission expansion and the land area required for electricity generation
facilities. What will cost a lot more—both in consumer costs and in reliability—will be nothaving clean firm power at all. A strong portfolio of clean firm power will add reliability to thegrid, and if California can help utilities recover costs for their clean firm power investments now,
we can help keep the lights on in the future without incurring a significant increase in electricity
prices.
Introduction
California’s government has set ambitious goals to eliminate greenhouse gas emissions, startingwith electricity. A 2018 law, Senate Bill 100 (SB100), mandated that all retail sales of electricity
must be provided from carbon free sources by 2045. Jerry Brown, who was then the governor,
issued a companion executive order Executive Order B-55-18 requiring the entire state, not justthe electric sector, to zero-out net emissions also by 2045.
Policymakers in California and also throughout the world have to figure out how to achieve
similar goals. In recent years, wind and solar power have become much cheaper. They have
improved more quickly than even optimistic experts thought possible a decade ago, in part due toaggressive government mandates for purchase that have created larger markets and allowed rapid
learning, induced incremental innovation, and rewarded economies of scale. Furthermore,markedly improving batteries can store the electricity created by wind and solar for later use, andCalifornia, of course, benefits from good renewable resources, especially sunshine.
But how far can wind and solar power alone get us on the path to deep decarbonization of theeconomy through clean electricity? More generally, can California find a decarbonization
strategy that not only cuts emissions, but also does not markedly increase the cost of electricity
while preserving the reliability of the electric grid—without sacrificing other environmental
goals along the way? Recent blackouts in Texas and California especially highlight the need toattend to reliability. If such a strategy succeeds in California, it might be imitated around theglobe.
2

This challenge is growing. California’s energy planners project that electricity demand in
California will nearly double from today to 2045, as more end uses in our economy currently
powered by fossil fuels, from cars to the heating of building, transition to electricity.
Electrification would increase California’s peak demand for electricity from about 50 gigawatts
today to about 100 gigawatts midcentury. How will California maintain the affordability andreliability of electricity supplies while tackling the twin tasks of decarbonization andelectrification?
To answer these questions, we convened a group of energy system experts who used threedifferent optimization models of California’s electricity system to quantify the costs of a numberof different future scenarios for new sources of clean, reliable electric power. Groups from
Princeton and Stanford Universities ran the first two models; the third was by a group from theconsulting firm Energy and Environmental Economics (E3).1
Each model sought to estimate not only how much electricity would cost under a variety of
scenarios, but also the physical implications of building the decarbonized grid. How much new
infrastructure would be needed? How fast would the state have to build it? How much land
would that infrastructure require? Although each of these models offered their own depictions of
the California electricity system and independently explored the ways it would be optimized,
they all used the same data with respect to past conditions and they all used the same estimationsof future technology costs. Despite distinct approaches to the calculations, all the models yieldedvery similar conclusions.
Sun and wind and challenges
Wind and solar have become mature technologies and enjoy substantial public support. Batteries
have also significantly improved. Consequently, wind and solar power and batteries can be thecornerstones in an affordable, carbon-free California electricity system.
However, relying to a much greater extent on solar and wind power also present challenges asthese resources depend on the weather and weather is variable on time frames spanning minutes
to seasons. Average daily output from current California solar and wind infrastructure in thewinter declines to 30-40% of the maximum summer production, for example. Periodic largescale weather patterns extending over 1,000 kilometers or more, known as “dunkelflaute” (theGerman word for dark doldrums) drive wind and solar output to low levels across the region andcan last days, or even several months. Average wind and solar outputs also vary from year toyear, particularly for wind power. Batteries can help make up for fluctuations that last for
multiple hours, but they cannot make up for these longer-duration variations in wind and solar
availability. For this reason, having enough wind and solar power to meet demand during theslack periods would necessitate building an enormous amount of capacity that would otherwise
exceed the grid’s demand during more abundant periods.

1 Appendix A provides a key to the scenario names and Appendix B provides supplemental material for each of themodeling efforts. Data spread sheets are also found at www.edf.org/cleanfirmpower.

3

Since wind, solar and batteries are the most mature and affordable clean energy technologies
available today to help reach California’s carbon-free electricity goals, our project first sought tofigure out just how much overcapacity would be needed to ensure reliable electricity
availability—and how much it would all cost—assuming that wind and solar power and battery
storage were the only options available for new capacity. We thus modeled a carbon-freeelectricity system with wind, solar and batteries as the only new resources available, and tested
this system to ensure reliability over hundreds of possible scenarios for the weather over thecourse of a year.
We found that reliably generating the electricity needed in 2045 from wind and solar power
would require building up to nearly 500 gigawatts of power-generating capacity (along with 160
gigawatts and 1000 gigawatt-hours of new storage). This is roughly half the capacity of the entire
U.S. electricity generating system today and about six times the current total generating capacity
now serving California (about 80 gigawatts), including nuclear, gas and coal generating stations,hydroelectric dams, and everything else.
All of this excess capacity would be expensive. We estimate that wholesale electricity rates
would increase by about 65% over today if currently available renewable energy and storage
technologies alone were to be utilized to meet demand in 2045. It may not be possible to build
wind and solar facilities at this scale, even if consumers were willing to pay that premium. Solardevelopment will likely dominate the renewable energy portfolio in California, and getting tonearly 500 gigawatts by 2045 would require expanding solar capacity at a rate 10 times higher
than has ever been done before. There may not be enough people, supplies, or land to do this.
This is the great challenge with weather-dependent energy sources. On a dollar per kilowatt hourbasis, wind and solar power are now cheaper than carbon-intensive sources of electricity likecoal or even gas. They can thus play a central role in delivering an affordable carbon-free grid.
But if wind and solar are pushed to do all of the heavy lifting themselves, the system requires a
lot of excess generating capacity and storage (most of which is seldom used) to provide reliable
electricity and completely drive out greenhouse emissions. As a result, this strategy ends up
being much more expensive that it might appear at first glance.

4

Figure 1. The growth of photovoltaic (PV) capacity dedicated to California across a range of scenarios deliverying
100% carbon-free and reliable electricity supplies by 2045. All cases shown include variable renewable energy andbatteries. The case in red labeled “Renewables” did not allow any clean firm power to be built. Other cases allow
both renewable energy, batteries and the labeled form of clean firm power. Results shown for mid-range capital costcases and $33/MMBtu generic zero-carbon fuel. The renewable and batteries-only case was only solved by E3’s
RESOLVE and RECAP models and includes otherwise redundant solar capacity necessary to ensure reliability
through all weather-years. All other cases show the range of outcomes from the ensemble of three models(RESOLVE, GenX, and urbs).

A clean firm solution
There is a better solution. Solar and wind do not need to do the job alone. There exist carbonfree alternatives that do not depend on sunshine or wind. We call these resources “clean firm
power” defined as zero-carbon power that can be relied on whenever it is needed for as long as itis needed. Clean firm resources do not depend on the weather like solar and wind do, and theseresources do not have limitations in how long they can produce power, as batteries do.
For example, geothermal power takes energy from heat in the ground and is available whenneeded. California’s geology already provides the Geysers, the largest geothermal plant in theworld. Advances in geothermal technology could plausibly expand this resource beyond thespecial conditions found at the Geysers. Clean firm power might include “green” hydrogen split
from water using renewable electricity or hydrogen split from gas or biomass gasification,leaving CO2 to be sequestered underground, or it could entail biofuels made from sustainablyharvested agricultural or forestry waste or crops. California could continue to use gas-generated
power if the greenhouse gas emissions were captured and safely stored permanently
underground. Nuclear power can provide very large amounts of energy steadily in a small5footprint; ongoing advances in nuclear technology could allow the deployment of lower cost,much-diminished accident risk with less waste.
Our modeling finds that almost any combination of these resources (or others with supply chainsthat do not result in greenhouse emissions) could deliver a 100% carbon-free electricity supply
with generation and transmission supply costs of about 7-10 cents per kilowatt-hour, which
compares well to today’s average generation and transmission costs for California’s investorowned utilities (9 cents per kilowatt-hour, Figure 2). Renewable energy can inexpensively
provide at least half of the carbon-free energy needed by 2045—and more in most cases (Figure4)—but clean firm technologies provide a critical complement to weather-dependent renewableenergy that ensures reliability while keeping whole system costs low. We also find that having
more than one clean firm power option helps reduce costs even further (Figure 2). This key
insight will help decision makers planning a decarbonized grid, not just in California, but inother parts of the world as well: opening the portfolio to clean firm power as well as wind andsolar energy goes a long way to keeping the total costs and impacts down.

Figure 2. The wholesale generation and transmission costs for 100% carbon-free electricity for the year 2045. All
cases shown include variable renewable energy and batteries. The case in red labeled “Renewables” did not allow
any clean firm power to be built. Other cases allow both renewable energy, batteries and the labeled form of clean
firm power. Results shown for mid-range capital cost cases and $33/MMBtu generic zero-carbon fuel. The
renewable and batteries-only case was only solved by E3’s RESOLVE and RECAP models to ensure reliability ofthis portfolio through all weather-years.

6A range of sensitivity cases demonstrate that the substantial cost savings from having one ormore clean firm resources is robust to a range of possible future technology costs (Figure 3).
Across all modeled sensitivity cases, portfolios with at least one clean firm power option are
32-53% cheaper than the renewable energy and batteries only portfolio.

Figure 3: Price sensitivity results show that modeled 2045 California electricity system costs are relatively
insensitive to the cost of clean firm power; all cases with at least one clean firm power option are 32-53% cheaper
than the variable renewable energy and batteries only portfolio (see Figure 2).

We find that most of the energy supplying California in 2045 comes from inexpensive renewable
resources (Figure 4), mainly solar in California. But when the sun doesn’t shine for many days
at a time, the clean firm resources are worth their relatively expensive price.

7

Figure 4. Percent of modeled 2045 California electricity supply coming from various resources for portfolios withclean firm power.

Figure 5. Installed capacity of California electricity resources in 2045 for portfolios with clean firm power.

8A clean firm power portfolio
California today has 48 gigawatts of total firm power capacity, most of which (42 gigawatts)
come from natural gas fired powerplants. The remaining gigawatts come from nuclear power,
geothermal and a small amount from coal. California plans to decommission its last nuclear
power plant at Diablo Canyon in 2025 taking 2.2 gigawatts of firm and zero-carbon capacity
offline.
Our modeling concludes an ambitious but achievable investment in clean firm power capacity,essentially replacing the gas fleet with 25-40 gigawatts of clean firm power will minimize costs
while maintaining reliability (Figure 6) and substantially reduce the amount of renewable energy
capacity that must be deployed (Figures 1 & 5 above). By 2045 the clean firm power portfolio
could eliminate the need for some 250 to 400 gigawatts of additional renewable energy.

Figure 6. Clean firm power capacity needed to ensure reliability and affordability in 100% carbon-free cases.
All models include variable renewable energy and batteries and account for existing contracts. Results shown for
mid-range capital cost cases and $33/MMBtu zero-carbon fuel. GenX optimizes Western Interconnect-wide
dispatch and thus generally requires less firm capacity in California than other models.

Each of these clean firm power resources would play a different role in eliminating overcapacity(Figure 7). For example, nuclear power would act as a “flexible base” power source, generally
providing a steady amount of electricity but reducing output during the height of solar output,
9enabling nuclear plants to conserve their fuel for longer refueling cycles. Although we did notmodel geothermal generation explicitly, we would expect it to act quite similarly to nuclear
power as it also has large up-front costs and minimal variable expenses. The models find it
economical to ramp output from natural gas plants with CO2 capture and storage down and up
from day to night and to shut these plants down for longer stretches in the spring. Power plants
using more costly carbon-free fuels would be utilized only occasionally when solar, wind andstorage options were unavailable. As a result, we find that having many options for clean firm
power available results in the least cost solutions because each resource is able to operate at theideal utilization rate, resulting in the lowest-cost mix of clean firm resources (Figures 7 & 7).2
Any of these resources could adjust to fill the gap during times of dunkelflaute, resulting in thesubstantial cost savings shown in Figures 2 & 3.

Figure 6. Each type of clean firm power would play a slightly different role in meeting demand. Daily
generation pattern of the three models on an example September day for each scenario. Nuclear power would act asa “flexible base” power source but conserving fuel by reducing output during the height of solar output. Natural gas
generation with CO2 capture and storage would ramp up at night when solar ramps down. Use of costly carbon-freefuels would only run when solar, wind and storage options were unavailable. Any of these resources could fill thegap during times of dunkelflaute. Each clean firm resource shows distinct daily operations that are consistent acrossall three models. Note: Representative September day used for RESOLVE, average generation profile for September
days used for urbs, and average generation profile of representative September weeks used for GenX. GenX imports
indicate net imports into California, and may include imports of power from clean firm resources operating outsideof California. (Figure taken from Baik et al. “What is different about different net-zero carbon electricity systems?”,manuscript EGYCC-D-20-00141 (under review) in Energy and Climate Change).

2 For more, see Baik et al. “What is different about different net-zero carbon electricity systems?”

manuscript EGYCC-D-20-00141 (under review) in Energy and Climate Change.

10Figure 8. Operating shares of various resources in a year. ZCF is gas turbine power plants using zero-carbon fuel;CCS is natural gas power plants with carbon capture and storage. The least cost solution is achieved when many
options are available for clean firm power so that the marginal cost curve can follow the lowest cost resource at eachutilization rate. The lowest cost portfolio is highlighted. The more types of clean firm power that are available, thelower the total cost. At one end, power plants burning high cost zero carbon fuels but with low capital costs are
suited to act as quick flexible response and to operate only occasionally when needed. At the other end, nuclear
power or other options with high fixed costs and low variable costs are suited to operating at high utilization rates(above 70% of the hours of the year). In the middle, CCS provides a good way to regularly provide the least
expensive power option at night and will generally back off during the day when solar is available. Net load is theload net renewables and storage that these clean firm resources have to meet. The figure shows that each of the clean
firm resources takes a chunk of the net load that has to be met. In the net load graph – the shaded area under the
curve (MW * % hours operating) multiplied by 8760 hours in a year should approximately be the MWh ofelectricity each resource generates. (Figure taken from Baik et al. “What is different about different net-zero carbon
electricity systems?”, manuscript EGYCC-D-20-00141 (under review) in Energy and Climate Change).

Advantages of a portfolio with Clean Firm Power
Decarbonized electricity systems with clean firm power have other key advantages over systems
that are solely based on variable renewable energy and batteries (Table 1). Portfolios that include
clean firm power alongside renewables in a 100% carbon-free electric system require between
625 and 2500 square miles dedicated to utility scale solar and wind. Without clean firm power,
3-10 times as much land would be required — more than 6250 square miles. Recent assessmentsof the solar resource in California indicate that 6250 square miles may exceed the amount of land11

fit for utility-scale solar not subject to legal restrictions and without high conservation value.
Other limitations that will make 100% renewable systems difficult to deploy include difficulty inobtaining transmission right of ways or spatially explicit deployment needs or geographic
challenges that have yet been factored in and these would amplify the value of clean firm
capacity. These conclusions would become even more extreme should the existing out-of-state
supplies of hydro and nuclear power become unavailable.
Table 1. Summary of issues related to the need for clean firm power.
With Clean Firm
Power

Issue
Costs for generation and transmission

Without Clean
Firm Power~9 cents/kWh~15 cents /kWh

25 – 200 GW

470 GW

New short-term 20 -100 GW
battery power
capacity

160 GW

New short-term 100-800 GWh
battery energy
storage capacity

1000 GWh

California transmission and distribution costs are
currently about 9 cents/kWhSolar and Wind Capacity
Entire U.S. electric generating capacity is ~1100 GW

New Storage*
Largest battery facility now being
built is 0.6 GW /2.4 GWh.
CA expects to have 2 GW battery
capacity in 2021

Land Use625- 2500 sq miles

6250 sq miles

2 – 3 million MWMiles~9 million MW
Miles

CA land area is ~164,000 sq miles

Transmission
CA currently has ~ 15 million MW-miles (26,000
circuit miles) of transmission*Energy storage beyond existing pumped hydroIncluding clean firm power also reduces the need for millions of megawatt-miles of transmission
lines. California currently has approximately 15 million megawatt-miles of transmission. All
portfolios that include clean firm power add 2-3 million megawatt-miles of new in-statetransmission lines to meet the goal of zero emissions by 2045. Some of this might be built along
existing right of ways, but any siting and permitting this amount of transmission for timely build
out will present challenges. Eschewing new clean firm power could at least triple this need to 9
million megawatt-miles even with West-wide coordination of electricity supplies.

12Our models built a significant amount of battery storage in addition to California’s existing
pumped hydro storage capacities. Models with clean firm power built approximately 20-100
gigawatts of new battery storage with 100-800 gigawatt-hours of energy storage capacity.
Without clean firm power, the models built about 160 gigawatts of short-term battery storage todeliver nearly 1000 gigawatt-hours of energy. California has a large battery-focused policy underway, and some of the world’s biggest battery installations have followed. California will haveabout 2 gigawatts of battery storage in 2021; the largest single battery storage facility being built
anywhere has a capacity of 0.6 gigawatts at Morro Bay and will be able to provide power for 4
hours, or 2.4 gigawatt-hours. Portfolios that depend solely on wind and solar build the equivalentof nearly 1000 energy storage facilities of this size and, even with clean firm power, the models
build hundreds of facilities this size. Future energy storage facilities will likely include a lot ofbatteries but might also include different configurations of technologies, including pumped hydroor novel systems.
Increasingly better batteries play a key role in a carbon-free grid, but like all resources, forcing
them to play roles they are ill-suited to adds cost and challenge. Batteries provide flexibility on
hourly and diurnal time scales, and all three modeling approaches choose to install storage—
modeled as batteries, though in the real world an array of options would be used. But in none ofthese solutions do batteries economically fill the entire need for clean firm resources. Batteries
make sense for shorter duration uses (e.g, shifting solar from midday into the evening) but
cannot cost-effectively sustain discharge for weeks at a time. We did examine a class oftechnologies called “long duration storage” to see if these could substitute for clean firm power.
Long duration storage technologies, such as electrolysis and underground storage of hydrogen or
advances in ultra-cheap metal-air batteries could potentially provide storage for longer than afew days. Modeling for this study and other recent work indicates these resources play their bestrole as partial substitutes or even complements, rather than true alternatives to clean firm power;
they provide another useful arrow in the quiver, but systems with clean firm power remain
meaningfully less expensive (Figure 9).

13

Figure 9. The impact of long duration energy storage (LDES) on 2045 California electricity generation andtransmission cost. Costs on the left are for systems that either have clean firm power or clean firm power plus
LDES. Those on the right have only variable renewable energy, batteries and long duration storage. Having LDES
does not substantially reduce costs if clean firm power is available, but low-cost long-duration storage options candiversify the portfolio of options and reduce reliance on clean firm capacity moderately. Systems which substitute
LDES for clean firm power entirely are significantly more expensive. Note that these scenarios without clean firm
power were not modeled across multiple weather years, which could require additional storage and renewableenergy capacity (and greater cost) to meet reliability requirements across years.

Finally, some have argued that California could continue to deploy its existing gas fleet toprovide firm, but not clean, power and then offset the resulting emissions with negative emission
technologies (NETs) such as direct air capture and sequestration or bioenergy with carbon
capture and storage (BECCS) to meet its economy-wide carbon neutrality goal. We found thissolution will likely cost more and produce more direct CO2 emissions than a system that includes
both NETs and clean firm power. Even if the state wants to consider NETs, developing clean
firm power is a “no regrets” strategy that positions California to meet its economy-wide
decarbonization goals by retaining available NETs to offset more-difficult-to-decarbonize sectors
such as agriculture, industry, or heavy-duty transport. Also, NETs technologies have common
ground with CCS and hydrogen from gas or biomass for clean firm power in that they all requiresequestration of CO2. Having CO2 sequestration capacity in California will therefor enable bothclean firm power and several negative emission technology options.

14

Conclusion
Weather-dependent renewable resources like wind and solar will play a starring role in
California’s low carbon energy future. Even with substantial clean firm power installations, ourmodels generally find that at least 60% of California’s electricity in 2045 could be produced withrenewable resources. Our model results show that, moving towards 2045, even without cleanfirm power, California can abate a lot of emissions by building out renewable energy andproviding firm, but not clean, power from gas plants. Until then, existing natural gas-fired
generators could act as firm power, albeit with continued emissions, and thus used only whenthese generators are essential. But by 2045, emissions need to drop to zero and California willneed to replace these carbon-emitting resources or retrofit them to either capture and store CO2 orburn clean fuels in a zero-carbon economy. Our model results show that squeezing out the last
increments of CO2 from power generation while maintaining affordability and reliability will
require clean firm power.
An ambitious but achievable investment in clean firm power, on the order of California’s
existing gas fleet could, on the upside, eliminate the need for ten times that amount of renewableenergy and thus help keep generation and transmission costs in line with today, cut the land area
needed for utility scale solar facilities and energy storage by a factor of ten, and reducetransmission infrastructure needs by a factor of four by 2045. These advantages will help
increase the likelihood of achieving climate goals in California.
California needs to start planning early to obtain clean firm power supplies. It may seem as
though 2045 is a long way off, but from an infrastructure investment and technology
development perspective, it is really the day after tomorrow. California could initially target
deployment of approximately 30 gigawatts of clean firm power by 2045, with interim milestones
along the way in order to avoid high system costs and loss of reliability in the future.
Deployment will require policy support because these technologies are currently more expensive
per kilowatt hour than wind and solar energy and all face implementation challenges. Managing
this issue requires early innovation, investment, and political conversations to choose viableclean firm power systems. It takes a long time to develop new technologies and to get regulators
to approve them; waiting a decade to get to work will put California at risk of failure. As allthese technologies keep costs low, California can work to scale several of these clean firm
options simultaneously and expand whichever ones ultimately prove most feasible and costeffective.
We don’t yet know the best choices and mixes of clean firm power. Consequently, the stateshould design an adaptive investment strategy—one that proactively deploys and tests a diverse
portfolio of clean firm power choices until experience identifies the best and most feasible
choices. A broad portfolio approach increases chance of success, helps to avoid technologicalcul-de-sacs, and thus will help ensure affordability and reliability in the long run. California’s
government could require utilities to build some form of clean firm power now and allow cost
recovery for their implementation. Leaving the form of clean firm power up to the utilities
themselves—with oversight from California’s regulators focused on evaluating what the utilities
do on the ground—will allow experimentation and learning.

15

The federal government can also help, not just California, but the entire nation, by making
sizeable investments in clean firm power—including investment in innovations needed for the
next generations of these sources.
There’s a lot at stake not just for California. Across the planet—in diverse places such as India,
China, Chile, Europe and many others—governments and electric system planners are pushinghard to deploy vast quantities of renewable power. California can help the planet achieve thosevisions—and thus help the planet cut global emissions and slow climate change—by showing
how these power sources can be integrated reliably with clean firm power while meeting othergoals such as wise land stewardship, environmental justice, and containment of costs.

16

Acknowledgements
J. Jenkins and E3 received support from EDF. J Jenkins also received support from the Clean
Air Task Force, and the Harvard Center for the Environment. E. Baik received support from the
Stanford Center for Carbon Storage. D. Victor receives base support from Electric Power
Research Institute. Neha Patankar contributed to the supplemental information for the GenX
model.
Appendix A. Scenario definitions
Scenario Names
Renewables (ReB)
Gas/CCS (ReBC)
Nuclear (ReBN)
Fuels (ReBF)
All (ReBCNF)
Gas/CCS+Nuclear(ReBCN)
Gas/CCS+Fuels(ReBCF)

Technologies allowed for expansion
Solar PV+Wind+Battery Storage
Solar PV+Wind+Battery Storage+CCS
Solar PV+Wind+Battery Storage+Nuclear
Solar PV+Wind+Battery Storage+Zero Carbon Fuel
Solar PV+Wind+Battery Storage+CCS+Nuclear+Zero Carbon Fuel
Solar PV+Wind+Battery Storage+CCS+Nuclear
Solar PV+Wind+Battery Storage+CCS+Zero Carbon Fuel

17

Supplementary Information
Contents
A.

Common Modeling Assumptions ........................................................................................................ 19

B.

RESOLVE ............................................................................................................................................... 25

C.

urbs ...................................................................................................................................................... 29

D.

GenX..................................................................................................................................................... 38

18

A. Common Modeling Assumptions
All three models shared common assumptions on load, future resources availability and future
resource costs. The following section summarizes the common assumptions made across all
three models. All common assumptions are derived from E3’s RESOLVE modeling work.
1. Load Growth Assumptions

California electricity loads and hourly load shapes assumed in this study result from sector
allocated emissions based on E3’s modeling of economy-wide greenhouse gas emissions
reduction targets as set by California to achieve an 80% greenhouse gas emissions reduction
below 1990 levels by 2050 3. Table 1 shows loads expected to be considerably higher in 2030
and 2045 than today due to electrification of vehicle and building loads.
Table 1: Load growth assumptions for modeled years
California Load Growth
Assumptions

20194

2030

2045

Annual Load [TWh/yr](includes BTM PV)

~250

317

475

2. Resources Considered for Expansion

California has several resources that can be considered for its future decarbonized electricity
sector. All three models’ optimization capabilities allow them to select from among a wide rangeof potential new resources. In this study, the options for new investments considered are definedby the scenarios and include technologies that are commercially available today as well as
technologies that are assumed available in the near and long-term: by 2030 or 2045. The fullrange of resource options considered in this study is shown in Error! Reference source not found..
Table 2: Candidate resource options considered
Candidate
Resource
OptionNatural Gas
Generation

Available Options

Functionality

Simple cycle gas

Dispatches economically based on heatturbines

rate, subject to ramping limitations

Reciprocating engines

3 https://www.ethree.com/wp-

content/uploads/2018/06/Deep_Decarbonization_in_a_High_Renewables_Future_CEC-500-2018-012-1.pdf
4 https://efiling.energy.ca.gov/Lists/DocketLog.aspx?docketnumber=20-IEPR-03

19

Combined cycle gas

Contributes to meeting reserveturbines (CCGT)

requirements and ramping needs

Combined cycle gas

CCGT combined with CCS is an RPS

turbines with Carbon

eligible resource

Capture & Storage

CCGT with CCS has flexible ramping(CCS)

constraints and can capture 90-100% of
greenhouse has emissions

Nuclear
Generation

New advanced nuclear

New nuclear plants are assumed to begeneration

dispatchable pressure-water-reactors5

Renewable
Generation

Geothermal

Variable generation generates asWind (inc. Out-of-Stateavailable; geothermal assumed to run as

and Offshore)baseload

Utility Scale Solar PV

Dynamic downward dispatch of variable(inc. Out-of-State)

renewable resources to help balance loadDistributed Solar PV
Energy Storage

Flexible Loads

Batteries (> 1 hour)Stores excess energy for later dispatch

Pumped hydro storage

Contributes to meeting reserve(> 12 hours)

requirements and ramping needs

Advanced shift demand

Allows the model to shift load from one

response (e.g.,

timepoint to anothercontrollable AC)

5 https://www.eia.gov/outlooks/aeo/pdf/electricity_generation.pdf

20

The modeling exercise includes the following generation technologies6:o‘‘Fuel-saving’’ variable renewable energy (VRE) resources. They harness renewable energy inputs(wind, solar insolation, water) that vary on timescales ranging from seconds to hours to seasons, havezero (or near-zero) variable costs, and have no fuel costs. At lower penetration levels, they may
displace the need for firm capacity, but, at higher shares, capacity needs are driven by periods withlow VRE availability. At high energy shares, these technologies therefore contribute value primarily
by displacing other higher variable cost generating technologies whenever available and reducing thetotal fuel consumption and variable costs of the system. These include
wind power,
solar photovoltaics (PV),concentrating solar power
run-of-river hydropower.

o‘‘Fast-burst’’ balancing resources. These are either energy constrained (storage, demand flexibility)or have very high variable cost (demand curtailment). These technologies are therefore poorly suited
to operating continuously over long periods of time and are better used during high-value periods
when relatively fast bursts of power or quick demand adjustments are needed to balance supply anddemand. These include
short-duration energy storage (e.g., Li-ion batteries),
flexible demand (or schedulable loads), anddemand response (or price-responsive demand curtailment).

o‘‘Firm’’ low-carbon resources. These are technologies that can be counted on to meet demandwhen needed in all seasons and over long durations (e.g., weeks or longer) and include
nuclear power plants capable of flexible operations,hydro plants with high-capacity reservoirs,
coal and natural gas plants with CCS and capable of flexible operations,geothermal power, andbiomass- and biogas-fueled power plants.
3. Technology Costs

Most technology cost assumptions for available resources are taken from NREL’s 2018 Annual
Technology Baseline. Assumptions on capital and fixed O&M costs for resources, as well as
associated financing assumptions are included in Supplementary Information file Cost and
Financing Assumptions.xlsx. An example of assumed costs for resources in 2045 are summarizedin Table 3.
Note that modeled costs for the modeled timesteps are taken as averages of capital costs for thetime frame for which they are being modeled. For example, for modeling time frames from 2030
to 2045, capital costs assumptions for 2031-2045 are averaged and are input into the model.

6 This taxonomy was originally developed in Sepulveda, et al. 2018, “The Role of Firm Low-Carbon Electricity

Resources in Deep Decarbonization of Power Generation,” Joule 2(11). https://doi.org/10.1016/j.joule.2018.08.006

21

Table 3: Candidate cost assumptions for 2045

While California has a wide variety of resources for its decarbonization, it has limited capacity ofsome resources. Notably, onshore wind, geothermal, and offshore wind resources are limited, so
most of future capacity growth in-state is dominated by solar PV and battery storage. Assumed
availability of future resources are summarized in Table 4.

22

Table 4: Resource Assumptions
Category
Candidate Resource Limits

Assumption
Solar PV

Wind

Geothermal
Pumped storage
Battery storage
Demand Response
CCS
Zero Carbon Fuel
Other

Behind-the-meter PV

23

Study Assumption
Limited to 266,963 MW in-stateand 45,684 MW out-of-state(UT/NV/NM/AZ)
Limited to 2,586 MW in-state and12,000 MW out-of-state(WY/NM/PNW)
Limited to 1,808 MW in-state and1,152 MW out-of-state (NV/PNW)
Limited to 4,000 MW
Unlimited availability
Up to 4.9 GW
Available in specific scenarios
Incremental cost of $33/MMBtu
in 2045
Baseline installed capacity of
15,335 MW by 2030 and 24,742
MW by 2050 (forced in). Modelcan select up to 36,749 MW of
additional BTM PV.

Table 5: Three models used and summary of key differences
RESOLVE

Model Type

Temporal
Resolution
SpatialResolution
Capacity
Decisions
Optimized
PolicyAssumptions for
Neighboring
Regions

Imports/Exports
to/from CA

37 representative days

1 year in hourly time
steps (8760 hours)

3 zones: CA, SW, NW

10 CA zones; 2 out ofstate zones (SW, NW)

GenXLinear Programming
Model with linear
relaxation of unit
commitment
constraints
16 representative
weeks with hourly
resolution time steps(2,688 hours)
2 CA zones; 7 out ofstate WECC zones

California

California

WECC-wide

Neighboring statesassumed to adopt
deep decarbonization
measures, which arereflected in theirassumed resourcebuild2000 MW of firm
imports assumed
Unspecified imports
are treated as gas
resources with a CA
carbon adder applied
at 0.43 tCO2/MWh

Neighboring statesassumed to adopt
deep decarbonization
measures, which arereflected in theirassumed resource
build

All states within WECC
adopt the same energy
and carbon policies

Firm imports modeled
and unspecified
imports treated as gas
resources with a CA
carbon adder applied
at 0.43 tCO2/MWh

Co-optimized subject
to inter-regionaltransmission network
flow constraints andendogenoustransmission capacity
expansion

Linear Programming

Model

urbs
Linear Programming
Model

24

B. RESOLVE
E3’s RESOLVE (Renewable Energy Solutions) is a resource investment model that is wellknown to utilities and utility commissions across the US, having been used extensively forresource planning needs including most recently: in California (CPUC 2017), the Northwest(Energy Northwest 2020, PSE IRP 2017), Hawaii (PSIP 2016), the Southwest (El Paso Electric
Utility 2020), the Midwest (Xcel Minnesota 2019 IRP) as well as jurisdictions on the East Coast.
RESOLVE is a linear optimization model that identifies optimal long-term generation andtransmission investments in an electric system, subject to reliability, technical, and policy
constraints such as annual greenhouse gas emissions levels and clean energy portfolio standards(E3 2020, Ming et al 2019). In this study RESOLVE optimizes California’s resource portfolio
for least system cost while modelling interactions with external regions (including the Northwest
and Southwest) based on price signals from their generation profiles. Resource portfolios forneighboring regions are input based on E3’s Aurora Market Pricing work for the WECC
assuming environmental policy constraints like that of California by 2045. E3 also complements
this analysis using their Renewable and Energy Capacity Planning Model (RECAP) to develop azero carbon-emitting portfolio built solely on renewables and battery-storage additions (durationof 4-8 hours), referred to in the text as “ReB” scenario. RECAP assesses generation resource
adequacy for a power system based on loss of load probability analysis and through an iterative
process can be used to build a portfolio of variable and storage resource that will meet its
probabilistic limits on reliability. All E3 portfolios presented in this study are assessed for theirreliability using RECAP and meet the 2.4 hours per year loss of load hours electric reliability
standard.
RESOLVE is a resource investment model that identifies optimal long-term generation andtransmission investments in an electricity system, subject to reliability, technical, and policy
constraints. In this study, it is used to develop least-cost resource portfolios for California thatmeet SB100 defined by achieving 100%+ RPS by 2045 and meeting various decarbonization
targets.
This study utilizes the California-wide RESOLVE version that was developed for the CEC’s
deep decarbonization project to evaluate long-run (2045) electricity portfolios for the state. A
separate version of RESOLVE is used by the California Public Utilities Commission (CPUC) toevaluate near-term (2030) optimal resource portfolios for the CAISO footprint within the context
of the CPUC’s Integrated Resource Planning (IRP) proceeding 7. The principal differences
between the models are the footprint (state of California vs. CAISO area) and the time frame(2045 vs. 2030) over which resource investment is optimized.
Neighboring regions to California, the Northwest (NW) and Southwest (SW), are also modelled
as part of the system with optimized investment. Given recent policy adoptions by Washington
State, Colorado, New Mexico and Arizona, we assume that these regions will have a resource
build that matches an equivalent to California’s 80% greenhouse gas emissions reduction target
by 2050 from 1990 levels. RESOLVE assumes that 10,000 MW of resources from out of stateregions can contribute to meeting Resource Adequacy needs in California.
7 http://cpuc.ca.gov/General.aspx?id=6442457210

25

RESOLVE considers both the fixed and operational costs of different portfolios over the lifetimeof the resources and is specifically designed to simulate power systems operating under highpenetrations of renewable energy and electric energy storage. By co-optimizing investment andoperations decisions in one stage, the model directly captures dynamic trade-offs between them,such as energy storage investments vs. renewable curtailment/overbuild. The model uses
weather-matched load, renewable and hydro data and simulates interconnection-wide operations
over a representative set of sample days in each year. The objective function minimizes netpresent value (NPV) of electricity system costs, which is the sum of fixed investment costs andvariable plus fixed operating costs, subject to various constraints. Figure 1 provides an overview
of the model.
Figure 1: Overview of RESOLVE ModelRESOLVE scenarios are designed to ensure reliability under all high renewable penetration
cases with the following features:•

Economic Retirement Functionality: This logic allows RESOLVE to retire existing resources if
the going-forward costs of maintaining the resources is greater than the fuel, O&M, ancillary
service and capacity savings the resources produce when operating.

•Seasonal Energy Sufficiency Requirement: This constraint ensures the system can produce
sufficient energy across extended periods (up to 3 weeks) and anomalous periods of low
renewable output that are not captured in the limited set of sample days used for operations in themodel. In most electricity systems today, which meet significant shares of demand with firm
resources that can be dispatched throughout the year when needed, this type of constraint is not
significant. However, in a system that relies heavily on intermittent renewables, the capability toserve load during prolonged periods of low renewable output is a key reliability consideration.

In addition to the common assumptions and resources modeled, E3 considers 55 GWh/day offlexible loads in the form of Advanced shift demand response (e.g., controllable AC) that allows
the model to shift load from one timepoint to another.
It is worth noting that RESOLVE is not designed to answer detailed reliability questions insystems without sufficient firm capacity. The RESOLVE modeling framework is limited to a setof 37 representative sample days, which does not have enough data points to make robust
conclusions on reliability events that happen infrequently, potentially less than once per year. In

26

addition, the sample days are independent (not connected) and therefore do not capture thepotential need for multi-day or seasonal storage.
RESOLVE does however include both a Planning Reserve Margin (PRM) constraint to ensurethat sufficient resources are maintained to meet an assumed long-run reliability standard as wellas an Energy Reserve Margin (ERM) constraint to ensure an equivalent loss of load expectation(LOLE) below 2.4 hours/year (per CAISO’s definition of a reliable system). The PRM andERM standards are developed exogenously and incorporated into RESOLVE as an assumption.
For systems in which firm capacity is available to be selected, the capacity expansion model
selected sufficient resources to meet energy and capacity needs plus a defined reserve margin toensure sufficient capacity during adverse weather events.
For the ReB scenario in which firm capacity was not available for selection, a more robust
approach to resource adequacy was required in which a much wider range of potential load, windand solar conditions was considered. For this case, E3 used RESOLVE to develop an initial
portfolio that meets energy needs during the 37 selected days. E3 then used its RECAP model,
which combines loss-of-load-probability modeling with a capacity expansion heuristic, toidentify additional wind, solar and battery resources needed to meet a 1-day-in-10-year Loss-ofLoad Expectation standard. This more exacting approach was not utilized for all scenarios
because (1) the analysis is very time- and resource-intensive and was beyond the scope of thisproject, and (2) E3’s experience in prior studies has been that the simpler reserve margin
approach generally produces portfolios that meet resource adequacy standards when sufficient
firm capacity is available for selection by the capacity expansion model.
The Renewable Energy and Capacity Planning (RECAP) model is a loss-of-load-probability
model developed by E3 that has been used extensively to test the resource adequacy of electric
systems across the North American continent, including California, Hawaii, Canada, the PacificNorthwest, the Upper Midwest, Texas, and Florida. RECAP was developed specifically toaddress the needs of a changing electricity sector by incorporating the unique characteristics ofdispatch-limited resources such as wind, solar, hydro, battery storage, and demand response into
the traditional reliability framework.
RECAP calculates a number of reliability metrics by simulating the electric system with a
specific set of generating resources and loads under a wide variety of weather years, renewablegeneration years, and stochastic forced outages of electric generation resources and imports ontransmission. Correlations enforced within the model capture linkage among load, weather, andrenewable generation conditions. Time-sequential simulation tracks the state of charge andenergy availability for dispatch-limited resources such as hydro, energy storage, and demand
response. By simulating the system thousands of times with different combinations of thesefactors, RECAP provides a robust, stochastic estimation of loss of load expectation (LOLE),
target planning reserve margin (PRM), individual resource effective load carrying capability(ELCC), and other reliability statistics. An overview of this process is provided below. RECAP
conducts a Monte-Carlo time-sequential simulation of loads and resource availability, and thegeneral steps in RECAP’s algorithm are shown below.

27

Figure 2. Overview of RECAP model28

C. urbs
urbs is a dispatch and capacity expansion model that designs large-scale power systems to meet
policy standards while minimizing total costs. For capacity expansion, urbs determines what typeand how much of generation, transmission, or storage capacities need to be added to each region
to construct the optimal power system in California. For hourly dispatch, urbs utilizes generation,transmission, or storage resources from all regions cost-optimally to cost-optimally meet thehourly demand profiles in each region. Modeling reliability on urbs is limited to meeting the
given load in the modeled year.
The model solves a linear optimization problem that is formulated in Python and solved by
Gurobi. The optimization goal is to minimize the societal costs of the system which involves
annualized capital cost for new capacities built, as well as fuel costs, and fixed and variable
operational costs for a given year. The optimization is subject to operational constraints ofgeneration, transmission, and storage technologies, as well as policies such as a RPS, a CO2 tax,
or a CO2 regulation. In this study, urbs is used to model California’s power system to meet it
2045 SB100 goals. A more detailed description of the model can be found on the urbs GitHub
page (TUM, 2017).
1

Geographical Extent

Within the model, the California power system is represented by ten distinct regions. Each region
is a set of contiguous counties that are grouped to resemble California’s balancing authority areas
and local reliability areas. The designated regions also take into consideration the distribution oftransmission networks such that there are well-connected transmission lines within a single region.
Electricity transmission and distribution within a region is assumed to be lossless. Each region has
its own distinct set of generation capacities, transmission line capacities, storage capacities, anddemand load profile.

29

Figure S1. Modeled extent of California and its surrounding regions in urbs
The out-of-state (OOS) regions are divided into two separate regions: the Northwest (Washington,
Oregon, Montana, Idaho, and Wyoming) and the Southwest (Arizona, Nevada, and Utah). Energy
imports from these regions occur through import from out-of-state power plants owned by
California balancing authority areas, power purchase agreements, and general electricity imports
from which the generation source is unknown. The California Air Resources Board (CARB)
imposes an 0.428 ton/MWh emissions intensity for unspecified imports. Two additional OOS
regions are modeled to capture defined imports from the Northwest and Southwest, which don’t
have an emissions intensity involved such as hydro and wind power from the Northwest, and PV
imports from the Southwest. The transmission capacity between an out of state region andCalifornia is divided equally for defined and undefined resources. Existing and future generationand storage technology capacities for the out of state regions are taken from E3’s assumptions,which assume 80% emissions reduction in the Northwest and Southwest regions by 2045 relative
to 1990 levels.
Expansion of resources dedicated to California in out of state regions are limited to 10,000 MW
of wind (5,000 MW each from the Northwest and Southwest) and 10,000 MW of Southwest PV.
Additional 1,152 MW of geothermal from the Northwest is also allowed as an expansion
candidate.
30

2

System Scenarios

The modeling is conducted in a two-step process- one from 2020-2030 and another from 20302045. A single 2020-2030 capacity expansion and dispatch model is run with a policy constraintof meeting expected load in 2030 as well as the 60% RPS requirement as set out by SB100. The
results of the 2030 modeling run are then utilized as inputs for the 2045 capacity expansion
modeling. Figure S2 summarizes the results of the 2030 modeling.

Figure S2. Optimized system capacity and generation in California 2030.

3

System Data and Assumptions
3.1 Generation

Generation technologies are divided into dispatchable and non-dispatchable resources.
Dispatchable generation sources are conventional energy sources such as coal, nuclear, gas,biomass, geothermal, and CCS-enabled power plants from which generation can be controlledfrom an operator’s perspective. Non-dispatchable generation resources, such as solar and wind,
are generation resources that cannot be turned on or off to meet the operator’s needs. For nondispatchable generation sources, hourly capacity values are provided for generation source andregion. Generation technologies are also divided between renewable and non-renewable resources
as defined under California’s Renewable Portfolio Standard, and is summarized in Table S5.

31

Table S1. Delineation of renewable generation under California’s RPS
Renewable

Solar, Onshore Wind, Biomass, Geothermal, Offshore Wind, Small
Hydro (<30 MW), Biomass-CCS

Not RenewableGas, Coal, Large Hydro, Nuclear, Gas-CCS

Regional distribution of generation resources are based on EIA’s 2018 Form 860, information on
generators in 2018 (EIA, 2018). The capacities of generating power plants are aggregated basedon regions and generation source as defined by urbs. In cases when the total capacity did not match
RESOLVE inputs, the overall capacities in each region were scaled to match the total generation
capacity in RESOLVE. A 15% planning reserve margin is included in the analysis as well.
Hydro is modeled in a two step-process to represent its seasonal variation and limitations, but also
the flexibility in generation. 70% of the total hydro capacity in a region is considered a nondispatchable resource that has must-run generation patterns based on historical 2018 generationfrom EIA (US Energy Information Administration, 2018). 30% of the total hydro capacity ismodeled as a flexibly dispatchable resource, only limited in operation by an annual capacity factorof 25%.
3.2

Transmission

Transmission lines between regions are distinguished between Alternating Current (AC) andDirect Current (DC) lines, and existing transmission line capacities above 115kV are aggregatedto one large transmission corridor between two different regions. Depending on the length of thetransmission lines between regions, AC transmission lines have a range of efficiency loss of 8999%, while the DC transmission lines have a range of efficiency loss of 93-97% (Clair, 1953).
Existing transmission line data in Western U.S. is obtained from Hart Energy Publishing (Hart
Energy Publishing, 2015). Transmission line capacities above 115 kV are used and the capacitiesof the transmission lines are calculated based on voltage level and length (Kerala State Electricity
Board Engineers’ Association, 2015). The transmission capacities between regions are aggregatedas a single corridor between regions. See attached modeling input excel sheet to see transmission
capacities between modeling regions. Spur line costs for solar and gas-CCS are calculated basedon GenX spur line cost assumptions and added to the total system cost post optimization.
3.3

Translating E3 RESOLVE Resources Zones in urbs

Each region in urbs has 1-2 wind and solar profiles that are consistent with E3 RESOLVE’s
candidate renewable resource zones. Table S2 summarizes the how the solar and wind resourcesfrom E3 RESOLVE’s candidate zones were applied to modeling regions in urbs. For an urbs region
with two RESOLVE zones, additional solar and wind profiles are modeled as ‘Added Solar’ and‘Added Wind’.

32

Table S2. Wind and Solar profiles from E3 utilized in urbs model per region. Two zones that are
applicable two possible potentials and resources profiles were input.
Urbs region

RESOLVE ZONE 1

RESOLVE Zone 2

CCT

SolanoGreater Carrizo

CVA

Central Valley North Los Banos

ECA

California

ELU

Riverside East Palm Springs

Southern California Desert

FRE

Westlands

Tehachapi

LAX

Tehachapi

Kramer Inyoken

NCT

Northern California

NVA

Northern California

PAC

Northern California

SDG

Greater Imperial

SW

SW

Arizona

NW

NW

Utah

3.4

Load

The load in each region is assessed by dividing the aggregate California load assumptions fromE3, by the population density in each region as summarized in Table S3. The aggregate capacity
for distributed solar is also proportionally attributed to each region by its population. In eachregion, the net load profile is obtained by subtracting the assumed distributed PV generation (basedon regional solar profiles from E3) from the overall load.

33

Table S3. Population distribution across urbs regions
urbs region% of Population

CCT

18.2%

CVA

11.4%

ECA

0.1%

ELU

19.7%

FRE

4.0%

LAX

33.2%

NCT

2.7%

NVA

1.7%

PAC

0.3%

SDG

8.9%

Figure S3 shows the location of each region modeled in urbs.

Figure S3. Location of each region modeled in urbs
34

3.5

Calculating System Cost

The total system cost calculated for California consists of capital, fixed, and variable costs for instate generation and out of state resources dedicated to California. In addition, costs/revenues fromimport and export of electricity with neighboring state is assessed. In each region the marginal costof generation in that region is multiplied by the energy level of exports in that region to find thesystem import/export costs. For California, exports from California are considered a net revenue,
while imports from Northwest and Southwest regions are considered costs. Note that in 2045, there
are no unspecified imports due to the CARB carbon adder, so only net revenue from exports is
considered within the total system cost.
4

Additional Technology Assumptions
4.1

CCS Retrofit Cost Assumptions

In addition to building new NGCC units, urbs allows California’s existing NGCC units to be
retrofit with post-combustion carbon capture. Capital costs for retrofitting existing NGCC plants
with carbon capture and storage (CCS) is assumed to be the difference in capital costs between
and NGCC units and NGCC units with CCS. The capital cost assumptions for NGCC-CCS areconsistent with RESOLVE and GenX, while the capital cost for NGCCs are based on NREL’s
2018 Annual Technology Basis for NGCCs. Other fixed and variable operational costs for
retrofit CCS plants are assumed to be consistent with RESOLVE and GenX. Once retrofit, the
NGCC power plant that was retrofit has its peak capacity reduced by 25% to account for theenergy penalty of carbon capture. New NGCC-CCS units are modeled by allowing the system tobuild more NGCC capacity that can then be retrofit by the system.
4.2

Offshore Wind Assumptions

The capacity factors and generation profiles are taken from a BOEM analysis on potential
offshore resources in California (Musial et al., 2016). Average capacity factors for each region istaken from the BOEM study and the offshore wind generation profiles for the five regions thathave potential (CCT, LAX, NCT, NVA, PAC) taken from Renewable.Ninja is scaled to matchthe capacity factor identifies in the BOEM study.
4.3

BECCS Assumptions

In modeling BECCS, only existing biomass power plants in California are allowed to be retrofit
with carbon capture, and so expansion capacity is limited by the existing biomass capacity instate. Cost assumptions for bioenergy retrofit with carbon capture are taken from the 2012 NETL
Analysis on coal and biomass analysis. Fixed O&M costs for BECCS power plants are takenfrom the fixed costs for case P.A.1 power plant with 100% biomass feed with 90% Capture.
Retrofit capture costs are taken to be the difference in overnight capital costs from Case P.A.1.
(100% biomass feed) and Case P.N.1. (100% biomass feed with 90% Capture).
All 2007 $s are converted to 2018 $ to be consistent with capital cost assumptions for othergeneration and storage resources. No learning curve is assumed for BECCS across time.

35

Table S4. Modeling assumptions for BECCS in urbs
Description

ValuesOvernight Capital Costs$2,483,000/MW (NETL, 2012)

Fixed O&M

$128,000/MW-year (NETL, 2012)

Variable O&M$12.4/MWh (NETL, 2012)

CRF

0.095

Heat Rate

15,100 MMBtu/MWh (NETL, 2012)

CO2 emissions-841 kg/MWh (NETL, 2012)

Retrofit Power Penalty

25%

5

Cost Sensitivities

A range of cost for expansion technologies were assessed as sensitivity cases. The high, low, andreference cost cases assessed are summarized in Table S5. Additional lower and higher zerocarbon fuel prices at $16.5/MMBtu and $50/MMBtu were also modeled.
Table S5. Range of cost sensitivities for technologies considered for expansion. The costs are
representative average capital costs between 2030 and 2045.
High/Low relative to reference costs
Nuclear+/- 25%
Capital Cost
CCS (retrofit)+/- 20%Capital Cost
Zero Carbon Fuel+/- 25%, 50%
Fuel Cost
Solar
Based on NREL’s High and Low cost estimates
Capital CostWind
Based on NREL’s High and Low cost estimates
Capital Cost
Battery+25% /-33%
Capital Cost

36

Reference
Clair, H. P. (1953). Practical Concepts in Capability and Performance of Transmission Lines.
Transactions of the American Institute of Electrical Engineers. Part III: Power Apparatus
and Systems, 72(2), 1152–1157. https://doi.org/10.1109/AIEEPAS.1953.4498751
EIA. (2018). Preliminary Monthly Electric Generator Inventory.
https://www.eia.gov/electricity/data/eia860M/
Hart Energy Publishing. (2015). Electric Transmission Lines: United States, 2015. Hart Energy
Publishing.
Kerala State Electricity Board Engineers’ Association. (2015). What limits power flow through anoverhead transmission line ? http://www.ksebea.in/wp-content/uploads/2015/09/SIL.pdf
Musial, W., Beiter, P., Tegen, S., & Smith, A. (2016). Potential Offshore Wind Energy Areas in
California: An Assessment of Locations, Technology, and Costs. Boem, December, US
Bureau of Ocean Energy Management.
https://doi.org/http://dx.doi.org/10.1016/j.jpaa.2011.12.005
NETL. (2012). Greenhouse Gas Reductions in the Power Industry Using Domestic Coal andBiomass Volume 1 : IGCC. 1(February), 205.
TUM, C. of R. and S. E. S. (ENS) at. (2017). Overview of urbs.
http://urbs.readthedocs.io/en/latest/overview.html
US Energy Information Administration. (2018). Electricity Data Browser.
https://www.eia.gov/electricity/data/browser/

37

D. GenX
GenX Model Overview
GenX is an electricity resource capacity expansion planning model. It is a highly configurable
optimization modeling framework designed to incorporate a wide range of state-of-the-art
methods to provide improved decision support capabilities for a changing electricity landscape.
For a detailed description of the model, including complete mathematical formulation, see Jenkins
and Sepulveda (2017) and Jenkins (2018) Chapter 3. A brief description of the model and its
configuration and use in this study follows.
GenX is a constrained optimization model that simultaneously optimizes the mix of electricity
generation, storage, and demand-side resource investments and retirement decisions, network
investment decisions, and operational decisions (at hourly resolution) to meet electricity demand
and maximize social welfare in a future planning year. The objective of the model is to minimize
the cost of electricity supply subject to hourly demand-balance constraints at all locations in thesystem and a set of engineering and policy-related constraints on power system operations andinvestments.
GenX uses a least cost optimization framework as a proxy for maximizing social welfare byincluding the opportunity cost of any non-served electricity demand in the objective function.
The hourly own-price elasticity of electricity demand is approximated through a series of priceresponsive demand segments, each representing a set of consumers with different willingness topay for electricity consumption. In other words, if the marginal cost of supply rises above thewillingness to pay of any segment in a given hour, demand is reduced by the aggregate
consumption of that segment of consumers, and the foregone utility of consumption isincorporated as a cost in the objective function.
GenX is a ’static’ investment planning model, in the sense that its objective is not to determine
when investments should take place over time, but rather to produce a snapshot of theminimum-cost generation, storage, and transmission capacity to meet some future planning year.
The model can be run sequentially and myopically (without foresight), with outputs from oneplanning period serving as inputs (e.g. starting generation, storage, and network capacity) for
subsequent planning periods. In this study, we run the GenX model in sequence for the period
from 2020-2030 and then use results from the 2030 planning year as inputs for a 2031-2045
planning period. In this paper, we only present the results for 2045.

38

Fig 1: Diagram of sequential (myopic) expansion planning periods used in GenX SB100
Pathways Project cases
The appropriate level of model resolution with (1) regards to chronological variability ofelectricity demand and renewable energy availability; (2) power system operational detail andunit commitment constraints; and (3) transmission and distribution network representation eachvary for a given planning problem or policy question. As such, GenX is designed to be highly
configurable, with several different degrees of resolution possible on each of these three key
dimensions.
In this study, we employ a simplified zonal transport model, which explicitly represent key
transmission network constraints between zones or regions (with 9 regions, as detailed in Section
1.1). Transmission power flows between regions are constrained to a maximum transfer capacity,which can be expanded via endogenous transmission expansion decisions. As these paths
represent a simplified abstraction of real AC networks, we do not apply optimal power flow
constraints (e.g. parallel flow constraints or voltage angle limits). We do not model distribution
networks in this study (e.g. modeled demand represents bulk power system demand at the
primary transmission substation level, inclusive of estimated losses in distribution voltage levels).
Additionally, to improve computational tractability we configure the model herein to consider
a reduced number of representative hours within the future planning year, selected using k-meansclustering technique along with a selection of peak period based on the method in Mallapragada
et al. (2018). We first identify the week containing the peak load and remove it from the originaltime series data. We then apply k-means clustering on the remaining time series to obtain the setof typical time periods. Instead of selecting the center of the cluster as the typical period, we
track the data point that is closest to the center. The k-means clustering algorithm provides theweight of each cluster, which signifies the total number of hours in the year represented by eachhour in the cluster (with the sum of weights in each hour equal to 8,760, or a full year ofoperations). In this study, we select 16 representative 7-day periods (weeks), based on analysis
of a 3-zone Western Interconnect model using the same inputs as this study that demonstrated
that with 16 representative weeks, cost results are accurate within 0.5% of the cost of a case using
a full year (52 weeks) and capacity results for any resource type are accurate within 2% of peakdemand (Figure 2).

39Fig 2: Abstraction error by resource for western electricity coordinating council (WECC) wide
system Finally, we employ a linear relaxation of discrete unit commitment decisions forthermal generators
Thermal power plants (e.g. large coal, gas, and nuclear power plants) face important constraints
on cycling decisions (start-up and shut-down) and minimum operating levels when online or“committed.” In addition, large thermal units can only be added in discrete increment sizes(e.g. a 450 MW combined cycle power plant of 1,100 MW nuclear reactor), are thus mostaccurately modeled as integer expansion decisions. In this study, these integer unit
commitment and capacity addition variables for thermal units are replaced with continuous
variables while maintaining the full set of unit commitment constraints (e.g. minimum up anddown times after cycling, minimum stable output levels while committed) and incorporating
start-up costs in the objective function. That is, the model is configured as a linear program (LP)where the feasible region represents the convex hull of the mixed integer linear programming(MILP) formulation inclusive of discrete unit commitment and investment decisions for thermal
units. Jenkins (2018) demonstrates that this linear relaxation of unit commitment decisions offersa significant improvement in computational tractability (run-times are improved by roughly 6080%) with minimal error in key outcome variables (e.g. less than 0.25% error in total cost andcapacity outcomes within 1% of peak demand for most resources, see Jenkins (2018), Chapter 3).
Additionally, by employing the full set of unit commitment constraints and associated costs, thislinear relaxation produces more accurate results than a simple linear economic dispatch
formulation that entirely ignores unit commitment decisions more typically applied in capacity
planning models (i.e., ignoring start-up, shut-down, minimum output, etc. and constraining only
ramp rates and maximum output).

40

Fig 3: Summary of GenX model configuration for this study. Possible configurations are depicted
in light blue, and the specific configuration used in this study in dark blue.
1.1 Data and Assumptions

The power system model analyzed in this paper is a case study that approximately represents
the U.S. portion of the Western Electricity Coordinating Council (WECC) or WesternInterconnection that includes California, New Mexico, Oregon, Washington, Montana, Arizona,
Utah, Wyoming, Nevada, Idaho, and Colorado. We divide WECC into 9 transmission zones orregions as shown in Figure 4. Each region represents an aggregation of the base model regions in
the EIA IPM model database (EPA, 2018). Table 1 provides the mapping between IPM zones andthe regions defined for the modeling of WECC system.

41

Fig 4: Western Electricity Coordinating Council map aggregated into 9 zones with transmission
paths showing inter-regional transmission limits for the existing network
Table 1: Mapping from IPM zones to GenX model regions for this study
Model Region

IPM zones

CA N
CA S
AZ
CO NM
ID MT
NNV
SNV
PNW
UT WY

WEC CALN, WEC BANC,WECC SCE, WEC LADW, WEC SDGE, WECC IIDWECC AZWECC CO, WECC NM
WECC ID, WECC MT
WECC NNVWECC SNVWECC PNWWECC UT, WECC WY

42

1.2 Transmission data

The power flow within each zone is unconstrained and power flow between the zones issubject to explicit transmission flow constraints. The 9 regions are connected by 17 transmission
paths. We define length, existing capacity, transmission loss and maximum potential
reinforcement for each transmission line. Based on Cohen et al. (2019), we assume transmission
losses are 1% per 100 miles, transmission expansion cost is 1350 $/MW-mile and weighted
average cost of capital is 7%. The cost assumption in Cohen et al. (2019) is converted from
reported 2013 USD to 2018 USD using 1.0792 inflation rate based on the BLS Consumer Inflation
Index. We assume that the financial asset life of transmission lines is 40 years. We apply a cost
multiplier for transmission reinforcement cost within California of 2.25 times the cost in the restof WECC based on (Cohen et al., 2019). The reinforcement cost for transmission paths between
California and other regions in WECC is assumed to be average of the the within California andrest of the WECC costs. The following table 2 provides details of the transmission network
considered in this study. We limit transmission capacity expansion to 200% of the starting capacity
along each path in each of the two sequential planning periods (2020-2030 and 2031-2045). As
such, a path can be expanded by no more than 400% (but only if the model chooses to expand
the line to the maximize extent allowed in both planning periods).

43

Table 2: Transmission network details for WECC
Transmission
Name

Path

Path
Max
Flow [MW]Path distance[Miles]Transmission
Loss [%]
3.1

Path Max Reinforcement
[MW]
7350

Path Reinforcement Cost [2018
USD/MW-yr]
81716

CA N - CA S

3675

309.33

CA N - NNV

100

319.46

3.2

200

60950

CA N - PNW

4200

248.80

2.5

8400

47469

CA S - AZ

4794

387.22

3.9

9588

73878

CA S - PNW

2858

401.48

4

5716

76598

CA S - SNV

6697

159.78

1.6

13394

30484

CA S - UT WY

1920

591.065.9

3840112767

AZ - CO NM

2400

393.32

3.9

4800

46179

AZ - SNV

4785

237.18

2.4

9570

27847

AZ - UT WY

250

247.67

2.5

500

29078

CO NM - UT WY

2400

232.19

2.3

4800

27261

ID MT - NNV

350

347.23

3.5

700

40767

ID MT - PNW

4850

541.91

5.4

9700

63625

ID MT - UT WY

4675

255.44

2.6

9350

29991

NNV - PNW

300

307.56

3.1

600

36110

NNV - UT WY

360

533.89

5.3

720

62683

SNV - UT WY

250431.34

4.3

500

50643

1.3 Load data

To model the hourly load variation for 2030 and 2045, the historical load data for year 2011
from EPA (2018) are scaled up using region-specific growth rates. To consider the regionalvariation in demand growth, we aggregate the 9 modeled zones into 3 broader regions:
California (CA), WECC Northwest (WECC NW) and WECC Southwest (WECC SW). WECC NW
includes Oregon, Washington, Montana, Utah, Wyoming, Northern Nevada, and Idaho while
WECC SW includes Arizona, New Mexico, Southern Nevada, and Colorado. To synchronize inputs
with RESOLVE and urbs models, we use the same regional growth rates for WECC NW and WECC
SW from E3, assumed to be 14.8% and 17.6% through 2030 and 31.7% and 38.3% through 2045,
44

respectively. For these two regions, we use historical demand profiles and increase demand eachhour by the regional percent growth rate, which reflects a business as usual growth projection.
For California, demand projections are also from E3 (the same as used in RESOLVE and urbs
modeling for this study) and assume significant end-use electrification of transportation andheating loads. As such, we cannot use the historical demand profiles from EPA (2018), and instead
use the hourly per unit profiles from E3’s RESOLVE inputs for this study. As E3 input data is for total
California load, we disaggregate the total statewide load into CA N and CA S model regions basedon each region’s share of total 2011 demand in EPA (2018). As a result, CA N and CA S has thesame load profile shape and growth rates with different absolute load value. Figure 5 depicts thetotal annual modeled load in each model region in 2030 and 2045 planning years.
To consider distributed generation (DG) adoption reflective of solar policies in California,
Arizona and Colorado, we remove estimated production from DG solar from the load profile (asmodeled demand reflects load at the transmission substation). E3 assumes total T&D losses of
7.33% for California and EPA’s IPM model assumes 2.88% transmission losses in WECC. As such, we
model the reduction in transmission-level load from behind the meter solar production assuming
average avoided distribution losses of 4.53%. The total capacity of DG solar for California isassumed to be same as E3 RESOLVE assumptions used in this study while the capacity for Arizona
and Colorado is assumed to be sufficient to meet DG solar carve-out requirements in the existingstate RPS policies. We assume 35 degree fixed tilt, south facing systems, with 15% system losses
and generate DG solar profiles from Renewables.Ninja for weather year 2009 (Pfenninger andStaffell, 2016). The modeled locations for DC solar in CA N, CA S, WECC AZ and WECC CO are
Sacramento, Los Angeles, Phoenix and Denver, respectively.

45

Fig 5: Total annual modeled load in 2030 (purple) and 2045 (red) by model region
We assume that the value of lost load (or involuntary demand curtailment) is 9000 $/MW.
Additionally, we assume 7.5% of load in each hour is willing to voluntarily curtail demand(demand response) at an opportunity cost of 600$/MWh, as per the ‘CA Shed DR’ resources
modeled by RESOLVE.
1.4 Generation and Storage

For this analysis, we model the existing generator fleet by clustering individual generators
within each model region into 10 generator types and one storage type per model region based on
2018 installed capacity data from (EIA, 2018): coal, nuclear, natural gas combined cycle (NGCC),
natural gas ‘peakers’ (combining existing combustion turbine, steam turbine, and reciprocating
engine generators), geothermal, solar PV (single axis tracking), onshore wind, small hydro (run of
the river units less than 30 MW), large hydro (combining reservoir and run-of-river units),
biomass and hydroelectric pumped storage. We create two clusters each of NGCC and peaker
plants for CA N and CA S to consider the variation in heat rates. We omit less than 2 GW of
installed concentration solar power (CSP) capacity to reduce model dimensionality. We also model
all existing small hydro, biomass, and geothermal energy resources as must-run generators withmodeled capacity equal to the average output of these units to reduce decision variables.
46

Additionally, we consider new build construction of onshore wind (10 different
locations/profiles across all model regions), solar PV (20 different locations/profiles across allmodel regions), offshore wind (3 locations/profiles, one each in PNW, CA N, and CA S), geothermal(4 locations/profiles in CA N, CA S, PNW, and SNV), and lithium-ion batteries (all regions) in allcases. In select cases, we also permit construction of natural gas combined cycle (NGCC), natural
gas combustion turbine (NGCT), natural gas combined cycle with post-combustion carbon
capture and sequestration (NGCCS) at both 90% and 100% net capture rate, natural gas Allamcycle with CCS (Allam cycle NG) at 100% net capture rate, new advanced nuclear (small modular
reactors), long-duration hydrogen storage, and long-duration aqueous-air/metal-air storage. Due
to geological constraints (lack of available geologies suitable for underground storage), NGCCS
and Allam cycle NG are not permitted in AZ, NNV, SNV or PNW and hydrogen storage is not
permitted in the low cost cases (which assume underground storage) in ID MT, NNV, and PNW.
Finally, in zero-carbon fuel (ZCF) sensitivity cases, we assume existing or new build NGCC andNGCT plants can be reconfigured (at nominal cost) to run on biogas, hydrogen (e.g. produced via
electrolysis or methane reforming with CCS) or another zero-carbon fuel (e.g. ammonia orsynthetic methane) (see Section 1.5 for cost details).
The following table 3 shows the availability of new build technologies for each model region.
Depending on the sensitivity case, the resulting WECC power system model has up to 179 eligible
resources with 75 thermal units subject to linearized unit commitment constraints, 14 energy
storage resources, 9 long duration energy storage resources, 9 large hydro resources, 22 biomass,
geothermal and small-hydro resources (modeled as must-run generators), and 50 dispatchable
variable renewable resource sites/profiles.

47

Table 3: Availability of new technologies by region
Technology

CA N

CA S

AZ

CO NM

ID MT

NNV

PNW

SNV

UT WY

Onshore wind

✓

✓

✓

✓

✓

✓

✓

✓

✓

Offshore wind

✓

✓

✓

✓

✓

✓

✓

✓

✓

Solar PV

✓

✓

✓

✓

✓

✓

✓

✓

✓

Geothermal

✓

✓

✓

✓

✓

✓

✓

✓

✓

NGCC

✓

✓

✓

✓

✓

✓

✓

✓

✓

NGCT

✓

✓

✓

✓

✓

✓

✓

✓

✓

Li-ion battery

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

Allam cycle NG∗

✓

✓

✓

✓

✓

✓

✓

✓

✓

Nuclear∗^

✓

✓

✓

✓

✓

✓

✓

✓

✓

Hydrogen long duration energy storage∗$

✓

✓

✓

✓

✓

✓

✓

✓

✓

Aq-air/Metal-air long duration energy storage∗

✓

✓

✓

✓

✓

✓

✓

✓

✓

NGCC CCS∗* - Available only in select cases; ^ - only available in California after 2030. $ - hydrogen storage is not permitted in the low cost cases (whichassume underground storage) in ID MT, NNV, and PNW.

The projected capital, fixed and variable operations and maintenance (O&M) costs for most
new-build resources are consistent with those detailed above for E3 RESOLVE inputs andgenerally based on NREL (2018). Lithium-ion battery storage costs are based on Lazard (2018).
Costs are converted to region-specific costs using NREL Annual Technology Baseline regional cost
multipliers (NREL, 2019).
In addition to the mid-range cost scenarios included in RESOLVE runs, we model low nuclear
cost and low CCS cost sensitivity cases with investment costs 25% and 20% lower than the basecase assumptions, respectively. We also consider three scenarios with availability of Allam cycle
natural gas plants with CCS (at 100% capture rate). Our high-cost Allam cycle scenario uses
investment cost (2014 $/kW), fixed O&M cost (54 $/kW), and heat rate (7.08 MMBtu/MWh) fromWhite and Weiland (2018), with reported costs converted from 2011 to 2018 USD using 1.13 factor(based on BLS CPI). The variable O&M cost for Allam cycle is assumed to be 70% of the variable
O&M for NGCT with post combustion CCS based on the ratio of O&M costs for Allam cycle andpost-combustion capture reported in White and Weiland (2018) (or 4.42 $/MWh). A low-cost
Allam cycle scenario assumes 25% lower investment and fixed O&M costs than the mid-cost case,and an improved heat rate (6.82 MMBtu/MWh) based on Scaccabarozzi et al. (2017). A very low48

cost Allam cycle case is run based on installed cost (1000 $/kW) and heat rate (6.42
MMBtu/MWh) claims from NET Power, an Allam cycle developer, for Nth of a kind installations.
We scale up the fixed O&M of existing power plants by 1.5 times the cost for new build plants
to reflect increased costs of maintenance for aging units, and we scale variable O&M of existing
power plants proportionate to the ratio of heat rate for existing thermal unit clusters and theheat rate of new build units of the same type.
We also assume minimum output power for thermal units with unit commitment decisions,which is 20%, 30%, 50% and 60% for NGCC, NGCT, nuclear and NGCC CCS plants, respectively,
consistent with E3 RESOLVE assumptions. Moreover, thermal units, biomass plants, geothermaland small hydro are assumed to have 100% availability.
A full description of all cost and performance parameters for the new generation technologies
are given in the “Resources 2030” and “Resources 2045” worksheets of the GenX input data
supplement provided along with this SI. Long duration energy storage assumptions are detailed
below in Section 1.8 below.
1.5 Fuel data

Regional values for fuel cost for coal, natural gas, and uranium and their CO2 content areconsistent with E3 RESOLVE inputs and taken from EIA Annual Energy Outlook (EIA, 2018) anddefined for 3 aggregated regions: California (CA), WECC Northwest (WECC NW) and WECC
Southwest (WECC SW). Table 4 presents fuel price and CO2 content for fuels by region. Biomass
fuel costs are assumed to be 10 $/MMBtu as per E3 RESOLVE assumptions.
In select cases, we also model two levels of zero-carbon fuel (ZCF) costs. A high cost scenario
assumes a ZCF cost of approximately 33 $/MMBtu reflective of E3’s RESOLVE cost assumptions for
marginal biomethane prices in 2045. Additionally, low-cost ZCF cases with prices ofapproximately 15 $/MMBtu are based on techno-economic assessment of potential hydrogenproduction costs from steam methane reforming of natural gas with carbon capture andsequestration (so-called “blue hydrogen”) based on IEA GHG Pro- gramme (2017) and assuming
a 50% reduction in CAPEX for the carbon capture module by 2040. While modeled based on atechno-economic study of hydrogen from methane reforming with CCS, this low cost ZCF
case is consistent with any clean hydrogen supplied at a delivered cost of approximately$1.60-1.75/kg (e.g. including renewable electrolysis). Regional hydrogen fuel costs in this lowcost case parallel natural gas regional price differences to reflect similar cost of fuel delivery. CO2
emissions for hydrogen are assumed to be zero based on direct combustion emissions (note that
upstream GHG emissions for natural gas-based hydrogen production paths may be non-zero). For
derivation of these fuel cost assumptions, see the “Blue H2 Costs” sheet in the GenX input data
supplement provided along with this SI.

49

Table 4: Price and CO2 content assumptions for fuels by region
Fuel[tons/MMBtu]

Price [$/MMBtu]

2030
CA

CO2 content

2045
SW

CA

NW

SW
NW

Coal

2

2

2

2

2

2

0.094

Uranium

0.7

0.7

0.7

0.7

0.7

0.7

0

Natural gas

5.8

5.06

5.3

6.9

6.05

6.3

0.053

Biomass

10

10

10

10

10

10

0

Zero-carbon fuel - High cost (e.g. biomethane)

-

-

-

32.73

32.73

32.73

0

Zero-carbon fuel - Low cost(e.g. H2 at ~$1.60-1.75/kg delivered cost)

-

-

-

15.84

14.58

15.04

0

SMR w/CCS - Steam methane reforming with carbon capture and sequestration; NW - WECC NW; SW - WECC SW

The fuel prices above are modified by applying a 45 $/ton CO 2 price to all fuels in 2030 basedon E3’s mid-range CO2 price trajectory from California IRP proceedings (rounded to the nearest
whole dollar). Additionally, fuel costs for CCS plants built in 2021-2030 planning period receive a
50 $/ton CO2 subsidy to reflect the 45Q tax credit for carbon capture and sequestration. For 2045
cases, either a 0 emissions cap is applied (in baseline cases), with no carbon price, or a 200 $/ton
CO2 price is applied to all fuels to reflect the potential marginal cost of negative emissions to offset residual emissions from natural gas units in 2045 in a net-zero emissions (carbon neutral)
economy, consistent with California Executive Order B-55-18.
1.6 Wind and solar resource clusters

For potential capacity expansion of solar and onshore wind, we define multiple clusters per
region based on E3 RESOLVE inputs (see Table 5). Additionally, three offshore floating windturbine resource clusters are added with profiles from Pfenninger and Staffell (2016) andmaximum capacity per site from Musial et al. (2016). See the “Generator Variability” sheet in the
GenX input data supplement provided along with this SI for hourly capacity factor profiles foreach resource cluster.
To consider the cost of electricity transmission from solar and wind sites to demand centers,
we add spur line costs to inflate the base investment cost. In this way, we implicitly account fortransmission costs within model regions, where transmission constraints are not explicitly
considered. Spur line distances are based on driving distances from sites to major metropolitan
areas as estimated by Google Maps. See comments in “Resources 2045” sheet of the GenX data
supplement for route assumption details. We used driving distances (as opposed to straight-line
distances) to approximate realistic routes considering topological and jurisdictional constraints
that may face transmission routes. Where renewable sites are assumed to deliver to metropolitan
50

areas in another model region (e.g. Montana wind delivering to Portland, Oregon metro area), we
subtract the explicitly modeled inter-regional transmission distance from the site to metro
distance. Spur line costs per mile are from Cohen et al. (2019), and as per the reference, spur line
costs in California are assumed to be 2.25 times more costly than in other regions. Table 5
presents the assumption for the new solar and wind clusters.
(Note that spur line costs are also added to new nuclear and CCS units, reflecting expected
siting locations for these resources. See “Resources 2045” sheet of the GenX data supplementfor details.)

51

Table 5: Solar and wind resource clusters
Technology

Onshore wind

Solar

Offshore wind

RegionCluster

Max Capacity

Spur Line Dis-

Transmission[MW]tance [miles]

cost [$/MW-yr]

CA N

1

643

*

*

CA N

2

146

*

*

CA S

1

1094

*

*

CA S

2

416

*

*

AZ

1

2897

185

56522

CO NM

1

10000

150

45829

CO NM

2

34580

110

33608

ID MT

1

5633

108

33024

PNW

1

10048

120

36663

UT WY

1

33816

223

68024

CA N

1

78817

20

13749

CA N

2

14914

20

13749

CA N

3

5056

20

13749

CA N

4

28088

20

13749

CA S

1

15237

20

13749

CA S

2

4318

20

13749

CA S

3

17337

20

13749

CA S

4

15448

20

13749

CA S

5

14310

20

13749

CA S

6

36553

20

13749

AZ

1

15020

20

6110

CO NM

1

664

20

6110

CO NM

2

15000

20

6110

ID MT

1

1065

20

6110

PNW

1

7556

20

6110

PNW

2

9211

180

54994

SNV

1

15000

20

6110

UT WY

1

15020

20

6110

CA N

1

2397

75

53160

CA S

1

3702

34

46970

PNW

1

5256

144

78700

* - Spur line cost for
California wind sites
included in annualized
wind cluster investment
cost provided by E3.

521.7 Hydropower

The input data for reservoir hydro includes initial level of the water in the reservoir, inflow
data and minimum reservoir level. We assume that hydro reservoirs are half full at the beginningand constrain the water level at the end of the year to be same as at the beginning of the year.
The EIA-923 report provides monthly historical inflow data for reservoir hydro for each state. Dueto lack of hourly profiles, we aggregate the state level data into model regions and equallydistribute the inflow over each hour. Minimum power share from hydro reservoirs is set equal tothe minimum of hourly values. Based on validation of modeled dispatch against historical 2009
CAISO aggregate hydro dispatch, we set reservoir capacity for California resources to 2 times theaverage hourly average inflow rate. Lacking data for validation for other states, we assume
reservoir capacity in the Southwest states (AZ, NV, CO, NM) are equal to that in California (2 times
average hourly inflow) and reservoir capacity in the Northwest states (OR, WA, ID, MT, UT, WY) aretwice that of California (4 times the average hourly inflow) based on the relative flexibility of hydrounits in E3’s RESOLVE inputs (expressed in RESOLVE as daily water budgets). See the ’Hydro
Variability’ sheet in the GenX data supplement for more detail.

1.8 Long duration energy storage

In select sensitivity cases, we include two types of potential long duration energy storage
technologies. First, we model a long duration hydrogen energy storage pathway. Capacity
decisions are made independently for charge power capacity (electrolysis), energy storage
capacity (underground or tank H2 storage), and discharge power capacity (combustion turbine).
Three cost assumption levels are considered for hydrogen electrolysis and storage capacity
costs. Low storage capital costs represents underground storage in large salt caverns based on
estimate from Lord et al. (2014); Mid cost represents underground storage based on “future”
underground cost estimate from Steward et al. (2009). High cost represents above ground steeltanks “future” cost from Steward et al. (2009). Electrolysis costs for 2040 represent interpolationof 2025/2030 and 2050 cost projections from the literature. Low electrolysis cost is based on
2030 estimate from Saba et al. (2018) (397 EUR/kW) and 2050 forecast from Dolf Gielen andMiranda (200 $/kW). Mid cost is based on the lowest 2025 estimate from Taibi et al. (480EUR/kW) and lowest 2050 estimate from Michalski et al. (2017) (334 EUR/kW). High cost isbased on the highest 2025 estimate from Taibi et al. (700 EUR/kW) and highest 2050 estimate
from Michalski et al. (2017) (607 EUR/kW). EUR converted to USD at an exchange rate of 1.11:1.
Electrolysis fixed operations and maintenance costs vary across sensitivity scenario as well, basedon Michalski et al. (2017). As discharge costs reflect mature combustion turbines, no costsensitivity is considered for this cost component and capital, fixed O&M and variable O&M costs
are the same as those modeled for new build stand-alone combustion turbines from NREL (2018).
Capital cost and performance assumptions for hydrogen storage are summarized in Table 6.

53

Table 6: Cost and performance sensitivity assumptions for hydrogen long durationenergy storage
Attribute

Low

Mid.

High

Storage component (underground caverns or above-ground tanks)
Capex [$/kWh]

1

5

10

Asset Life [years]

30

Energy to power ratio permitted

48–2000

Charge power component(electrolysis)
Capex [$/kW]
321
452
OpEx [$/kW-yr]
14
30
Single-trip Efficiency [%]67
Asset life [years]
25
Discharge power component(combustion turbine)
Capex & Opex [$/kW-yr]

726
46

123

Single-trip Efficiency [%]40

Additionally, we model cases exploring a speculative range of cost and performance
assumptions that may be achieved by low-cost aqueous-air or metal-air electro-chemical long
duration energy storage technologies, as detailed in Table 7. These technologies are assumed tohave use the same capacity for charging and discharging and have independently sizable energy
storage capacity decisions.
Table 7: Cost and performance sensitivity assumptions for aqueous-air or metal-air electrochemical long duration energy storage
Attribute

Low

High

Energy Capex [$/kWh]

4

12Power Capex [$/kW]

300

1300

Energy to power ratio permitted

100-200

12-48

Round Trip Efficiency [%]45

50

OpEx [$/kW-yr]

30

40

Lifetime [years]

25

25

54

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B. A., Gagnon, P. J., Ho, J. L., et al. Regional energy deployment system (reeds) model
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Dolf Gielen, E.T. and Miranda, R. Hydrogen: A renewable energy perspective.
https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2019/Sep/
IRENA_Hydrogen_2019.pdf.
EIA.
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Data Supplement Inputs on GenX
Data Supplement Inputs on URBS
Data Supplement Inputs on Resolve
Data Supplement Results on GenX
Data Supplement Results on URBS

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