NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

California-Wyoming Grid

Integration Study

Phase 1—Economic Analysis

D. Corbus, D. Hurlbut, P. Schwabe, E. Ibanez, 

M. Milligan, G. Brinkman, A. Paduru, V. Diakov, 

and M. Hand

National Renewable Energy Laboratory

Study conducted for the Wyoming Infrastructure 

Authority 

Technical Report

NREL/TP-6A20-61192

March 2014 
NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

California-Wyoming Grid

Integration Study

Phase 1—Economic Analysis

D. Corbus, D. Hurlbut, P. Schwabe, 

E. Ibanez, M. Milligan, G. Brinkman, 

A. Paduru, V. Diakov, and M. Hand

National Renewable Energy Laboratory

Prepared under Task No. WTFB.1000

Technical Report

NREL/TP-6A20-61192

March 2014 
NOTICE

This report was prepared as an account of work sponsored by an agency of the United States government. 

Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, 

express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of 

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trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, 

or favoring by the United States government or any agency thereof. The views and opinions of authors 

expressed herein do not necessarily state or reflect those of the United States government or any agency thereof.

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Cover Photos: (left to right) photo by Pat Corkery, NREL 16416, photo from SunEdison, NREL 17423, photo by Pat Corkery, NREL 

16560, photo by Dennis Schroeder, NREL 17613, photo by Dean Armstrong, NREL 17436, photo by Pat Corkery, NREL 17721.

Printed on paper containing at least 50% wastepaper, including 10% post consumer waste. 
iii

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Acknowledgements

The National Renewable Energy Laboratory (NREL) thanks the Wyoming Infrastructure 

Authority for sponsoring the California-Wyoming Wind Integration Study. This project involved 

the collaborative efforts of a large interdisciplinary team representing members of various 

organizations. We gratefully acknowledge their contributions. In addition, we would like to 

thank Lawrence Mansueti from the U.S. Department of Energy for his insight and guidance on 

this study. We would also like to thank Kevin Eber and Katie Wensuc of NREL for their

assistance in editing this report.

Contributors include the following:

Technical Review Committee

Barbara Barkovich Barkovich and Yap, Inc.

Merwin Brown University of California – California Institute of Energy 

and Environment

Keegan Moyer Western Electricity Coordinating Council

Jan Strack San Diego Gas and Electric

Jeffery Hein Xcel Energy

Gary Tarplee Utility System Efficiencies, Inc.

NREL Study Team

Anirudh Paduru (AP) National Renewable Energy Laboratory (Project Manager)

Dave Corbus National Renewable Energy Laboratory (TRC Lead)

Gregory Brinkman National Renewable Energy Laboratory

Victor Diakov National Renewable Energy Laboratory

David Hurlbut National Renewable Energy Laboratory

Eduardo Ibanez National Renewable Energy Laboratory

Michael Milligan National Renewable Energy Laboratory

Paul Schwabe National Renewable Energy Laboratory

Sponsor Team

Loyd Drain Wyoming Infrastructure Authority

Dave Smith TransWest Express LLC

Geoff Lambert TransWest Express LLC

William Arthur Pascoe Pascoe Energy Consulting
iv

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

List of Acronyms and Abbreviations

¢ cent

BCA benefit/cost analysis 

BCR benefit/cost ratio

BLM U.S. Bureau of Land Management

CA California

CAISO California Independent System Operator

CPUC California Public Utilities Commission

CREST Cost of Renewable Energy Spreadsheet Tool 

CT combustion turbine

DC direct current

DOE U.S. Department of Energy

EFOR effective forced outage rates 

EIA U.S. Energy Information Administration

ELCC effective load-carrying capability 

EUE expected unserved energy 

FERC Federal Energy Regulatory Commission

GWh gigawatt hour

HVDC high-voltage direct-current

IID Imperial Irrigation District

IPP independent power producer

ITC investment tax credit

kWh kilowatt hour

LBNL Lawrence Berkeley National Laboratory

LCOE levelized cost of energy 

LADWP Los Angeles Department of Water and Power

LMP locational marginal prices 

LOLE loss-of-load expectation

LOLH loss-of-load hours 

LOLP loss-of-load probability 

MACRS Modified Accelerated Cost Recovery System 

MW megawatt

MWh megawatt hour

NPV net present value

NREL National Renewable Energy Laboratory

O&M operations and maintenance 

PCM production cost modeling

PG&E Pacific Gas & Electric

PTC production tax credit

PV photovoltaic

REPRA Renewable Energy Probabilistic Reliability Assessment 

RPS renewable portfolio standard 
v

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

SCE Southern California Edison

SDGE San Diego Gas & Electric

SMUD Sacramento Municipal Utility District

TEPPC Transmission Expansion Planning Policy Committee

TIDC Turlock Irrigation District 

TRC technical review committee

TWE TransWest Express LLC

TWh terawatt hour

VG variable generation 

WECC Western Electricity Coordinating Council

WIA Wyoming Infrastructure Authority

WY Wyoming
vi

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Executive Summary

This report examines the economics of new transmission to deliver wind power from Wyoming 

to electricity customers in California. It looks at possible choices for meeting the last increment 

of California’s renewables portfolio standard (RPS) requirement—33% of retail sales by 2020—

comparing Wyoming wind with in-state renewables likely to be available in 2017.

Other recent studies take a system-wide approach to renewable energy expansion in California 

and the West.1 The purpose of this study is to match these system-wide analyses with one that 

focuses on a possible Wyoming-to-California transmission corridor, which a number of the 

regional studies suggest could accommodate cost-effective renewable energy delivery. By 

focusing on the Wyoming-California transmission corridor, this analysis replaces system-wide 

assumptions with inputs that are more specific, providing a more fine-tuned analysis of costs and 

benefits and enabling a more detailed assessment of risk.

The results suggest that the economic benefits of developing the corridor exceed the costs under 

the array of future conditions tested in the analysis. Benefit-to-cost ratios range from 1.62 to 3.62 

depending on assumptions about federal tax incentives in 2017, and depending on assumptions 

about the future costs of different renewable energy technologies. Where outcomes fall within 

this range will depend on:

• Expectations about future technology costs. If large-scale solar photovoltaic (PV) costs 

fall significantly faster than the cost of wind power, the ratios will tend toward the lower 

end of the ranges reported here.

• Expectations about future federal tax incentives. Reductions in the production tax credit 

(PTC) and investment tax credit (ITC) tend to favor developing the corridor, particularly 

if the reductions are even across all benefitting renewable technologies. If the changes 

significantly benefit solar and geothermal without benefitting wind, the ratios will tend 

toward the lower end of the ranges reported here.

• Avoided transmission build-out in California. The ratios tend toward the higher end of 

the ranges when including the economic benefit of avoided transmission build-out in 

California, regardless of expectations about future generator costs and future federal tax 

incentives.

This study does not offer recommendations about what one should assume regarding future 

costs, future incentives, and avoided transmission build-out. Rather, the aim is to test the extent 

to which the corridor constitutes a “least regrets” proposition for major infrastructure 

development and its long-term benefits, anticipating how some of the most crucial variables 

could change by 2017. 

 1 See, for example, Energy and Environmental Economics, Investigating a Higher Renewables Portfolio Standard in 

California (January 2014); Western Electric Coordination Council (WECC), Interconnection-wide Transmission 

Plan (2013); D. Hurlbut, J. McLaren, and R. Gelman, “Beyond Renewable Portfolio Standards: An Assessment of 

Regional Supply and Demand Conditions Affecting the Future of Renewable Energy in the West,” NREL technical 

report TP-6A20-57830 (August 2013).
vii

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Approach

The analysis tests four renewable resource portfolios and their likely characteristics in 2017. 

Modeling conducted by the California Public Utilities Commission (CPUC) provides the basis 

for defining the portfolios. That modeling, conducted as part of the CPUC’s Long-Term 

Procurement Plan (LTPP) proceedings, identified a number of “net short” procurement scenarios 

for CPUC-jurisdictional utilities.2 This report begins with the resources selected in the 

commercial interest portfolio, which gave weight to projects with power purchase agreements 

and for which permitting applications were substantially complete.

Two portfolios would result in meeting 33% of retail sales with renewable resources:

• the 32,184 GWh/year selected in the commercial interest portfolio (“CA33%”)

• 20,184 GWh/year of the commercial interest portfolio, and 12,000 GWh/year of 

Wyoming wind power (“CA/WY33%”); this portfolio excludes generic projects with no 

specifically demonstrated commercial interest, and assumes that some future in-state 

projects for which developers currently have indicated commercial interest will not meet 

their expected in-service dates

Two other portfolios would result in meeting 35% of retail sales with renewable resources:

• the commercial interest portfolio, plus another 4,433 GWh/year of new California 

resources (“CA35%”)

• 24,617 GWh/year of the commercial interest portfolio, and 12,000 GWh/year of 

Wyoming wind power (“CA/WY35%”); this portfolio excludes generic projects with no 

specifically demonstrated commercial interest, and assumes that all future in-state 

projects for which developers currently have indicated commercial interest will meet 

their expected in-service dates

Figure ES-1 shows the technology breakdown of the renewable resources that change between 

the California and California-Wyoming portfolios. Each change case represents 12,000 GWh of 

annual generation, and is replaced by Wyoming wind power sufficient to generate the same 

amount of energy. Large-scale solar PV accounts for the largest share of the change cases, 

followed by geothermal and in-state wind power.

Table ES-1. California Resources Replaced by Wyoming Wind Power (Share of 12,000 GWh/year)

Technology CA33% CA35% 

Biogas 0.4% 0.2%

Biomass 0.1% 1.1%

Geothermal 29.8% 28.6%

Large-Scale Solar PV 43.5% 43.1%

Small-Scale Solar PV 1.8% 4.4%

Solar Thermal 7.2% 4.0%

CA Wind 17.2% 18.7%

 2 “Net short” refers to the balance of renewable resources that utilities would need to procure in order to satisfy their 

RPS requirements by 2020.
viii

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Each change case entails other measurable effects, most notably the difference in generator costs 

between the two renewable portfolios, changes in resource adequacy, and changes in WECCwide variable production costs between the two portfolios. The availability of Wyoming wind 

power may also affect the need for new transmission projects within California. Combined, these 

component changes constitute the primary measurable economic benefits of developing the 

Wyoming-California corridor. This study uses a benefit/cost analysis (BCA) to compare the 

value of these changes with the cost of developing the corridor.

Transmission Costs

This study uses information from the proposed TransWest Express Project to characterize the 

economics of new transmission along the corridor. The project would include a 600-kV highvoltage direct-current (HVDC) line from south central Wyoming to southern Nevada’s Eldorado 

Valley, with interconnection into the California Independent System Operator (CAISO)

balancing authority. The assumed transfer capability of the transmission corridor is 3,000 MW, 

which in this analysis is assigned entirely to Wyoming wind power. Updated resource 

information on renewable energy zones indicates that wind power facilities near the Wyoming 

terminus of the TransWest Express Project would have a likely annual capacity factor of about 

46%, assuming the use of Type 1 wind turbines at a hub height of 80 meters. Reference case 

capital costs for transmission are assumed to be $3 billion, and total project costs are assumed to 

be 145% of capital costs. Factoring in line losses, annualized transmission costs under these 

assumptions amount to about $29 per MWh delivered.

The ability to connect 12,000 GWh/year of Wyoming wind power could affect the need for two 

new lines and two transmission upgrades in California that would enable additional renewable 

resources. Because treatment of avoided transmission build-out is not straightforward, this 

analysis calculates two sets of benefit/cost ratios: one that includes the benefit of avoided 

transmission build-out, and one that does not.

Generator Costs

The analysis of generator costs relies on three simplifying assumptions.

• a generator’s levelized cost of energy (LCOE) sufficiently represents the fixed-cost 

revenue requirements of a resource with no fuel costs and little variable operating costs

• the stock of generating equipment is replaced at the end of its economic life (assumed 

here to be 20 years) with a new stock of comparable equipment at comparable cost

• generator costs projected to 2017 sufficiently represent the resources examined in this 

analysis. 

A renewable resource’s LCOE provides a more complete picture of generator costs than might 

be shown by capital costs alone. LCOE represents the revenue required from each megawatthour of electricity generated to recover all project costs. Because all resources in the change 

cases are renewable, LCOE in this analysis is almost entirely made up of capital costs, financing 

costs, fixed operating and maintenance costs, and other fixed project costs. LCOE also takes into 

account policy variables such as the PTC and ITC, as well as efficiency improvements that result 

in more output per dollar of capital investment. 
ix

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

The capital cost of the renewable energy technologies required to generate 12,000 GWh of 

electricity in 2017 is a major uncertainty. This study uses projections from two sources to bracket 

a plausible range of potential future technology costs. The 2013 Interconnection-wide 

Transmission Plan published by the Western Electricity Coordinating Council (WECC) provides 

one set of costs. These estimates were vetted and approved through WECC’s Transmission 

Expansion Planning Policy Committee (TEPPC). Another set of cost assumptions based on the 

most recent market intelligence draws on extensive input from technology experts at NREL and 

DOE’s Office of Energy Efficiency and Renewable Energy; these tend to be lower than the 

TEPPC costs. Table ES-2 shows the two sets of capital cost assumptions and their resulting 

LCOEs.

Table ES-2. 2013 Generator Cost Assumptions ($2010)

Technology

Capital Costa ($/kW) LCOEb (¢/kWh)

Reference 

Case

Renewable 

Energy Cost 

Sensitivity

Reference 

Case

Renewable 

Energy Cost 

Sensitivity

Geothermal 6,440 5,675 11.55 9.55

Large-Scale Solar PV 2,685 2,100 9.45 6.95

Small-Scale Solar PVc 3,125 2,100 12.05 6.95

Solar Thermald 5,535 6,900 17.25 11.55

Wind (California) 2,055 1,785 9.85 7.75

Wind (Wyoming) 1,845 1,520 4.75 3.15

(a) Including regional adjustment factors; see WECC, “2013 Plan Data and Assumptions,” p. 93.

(b) Based on current tax credits (calculated after taxes), 9.26% after-tax weighted average cost of capital, 20-year 

cost recovery. LCOE indicates bus bar costs only and does not reflect transmission charges, integration costs, or 

other costs that may arise after power is delivered to the point of interconnection.

(c) Note that CPUC defines small, utility-scale solar as projects between 1 MW and 20 MW in installed capacity. It 

excludes rooftop distributed PV, which does not change in any of the scenarios tested in this analysis.

(d) Cost sensitivity case assumes thermal storage and a higher annual capacity factor.

Another uncertainty is what federal incentives might be in 2017 and later. Current law limits the 

production tax credit (PTC) to wind and other eligible technologies for which construction began 

prior to December 31, 2013. The investment tax credit (ITC) is set to fall to 10% from its current 

30% for solar and other eligible technologies placed in service after December 31, 2016. Both 

incentives have a history of last-minute extensions by Congress, however. To accommodate 

uncertainty about what these incentives will actually be in 2017, the analysis includes three 

sensitivities: one that assumes the PTC and ITC will be phased out or reduced by 2017 consistent 

with current law, one that assumes both incentives will be reauthorized at their 2013 levels by 

2017, and another that assumes both are phased out completely.

Table ES-3 shows the LCOEs for each change case tested in this study. The CA33% and CA35% 

LCOEs represent an average of the technology LCOEs shown in Table ES-2, weighted by the 

technology shares shown in Table ES-1. Note the relatively small differences between the 

CA33% and CA35% change case resources.
x 

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Table ES-3. 2017 Weighted-Average Portfolio LCOEs Used in BCA ($/MWh)

Scenario Sensitivity Reference Case

Renewable 

Energy Cost 

Sensitivity

CA33%

No PTC, 10% ITC (current law) $137 $108

PTC, 30% ITC $107 $82

No PTC, ITC $154 $122

CA35%a

No PTC, 10% ITC (current law) $138 $106

PTC, 30% ITC $109 $81

No PTC, ITC $155 $120

Wyoming Wind

No PTC $68 $51

PTC $48 $32

No PTC $68 $51

(a) Not tested in BCA.

(b) Annual costs are expressed in 2010 dollars to facilitate consistency with WECC's 

2013 Interconnection-Wide Transmission Plan.

Capacity Value

Each portfolio’s capacity value—the contribution that it makes towards planning reserve—is 

sensitive to both generation mix and the underlying weather changes that occur from year to 

year. Wind plants sited in Wyoming have higher capacity values than those sited in California

when California wind is considered alone. When the renewable mix in California comes from a 

blend of wind, geothermal and solar energy, however, the combined capacity value of the instate wind, geothermal and solar exceeds that of Wyoming wind. This is because:

• the capacity factor of the Wyoming wind is much higher than California wind or solar 

photovoltaic (PV). Thus, less installed capacity is required to achieve the same amount of 

energy delivered from the California wind and solar (energy equivalence was the driver 

in the creation of the scenarios). 

• California in-state resources primarily comprise geothermal and solar PV. Geothermal is 

assumed to operate at full installed capacity during critical times, while solar PV operates 

at a high correlation with load during maximum load hours (maximum load hours are 

generally critical reliability periods). By contrast, wind energy tends to have a lower 

correlation with demand during peak load periods. 

With respect to the BCA, the effect of adding Wyoming wind to a 2017 portfolio is a negative 

benefit, in that it would reduce overall capacity value between 919 MW and 957 MW. That 

deficit is priced using the capital cost of a natural gas combustion turbine at an estimated capital 

cost of $800/kW. 

Production Cost Modeling

Production cost modeling (PCM) examines the operational cost of the power system in the 

western United States on an hourly basis over the course of a test year. PCM scenarios for this 
xi

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study modeled operation with and without 12,000 GWh/yr of Wyoming wind power delivered 

via a dedicated DC transmission line. The production costs include only variable costs such as 

fuel costs, variable operation and maintenance costs, and startup costs, with fuel costs being the 

dominant driver.

Production cost modeling of the Western Interconnection shows modest changes in operating 

costs when replacing a fraction of California local renewable resources with Wyoming wind 

power delivered to California by a dedicated transmission line: a savings of 0.2% ($31 million 

annually) for the 33% renewable energy scenario, and 0.1% ($14 million annually) for the 35% 

renewable energy scenario when Wyoming wind is included. The observed small changes in 

operating costs come mostly from lower startup costs when Wyoming wind power is included. 

Wholesale electricity prices are reduced in most areas in response to the Wyoming wind power, 

but tend to be higher farther from the incoming DC line in areas that have renewable resources

that could be displaced by Wyoming wind.

Benefit/Cost Analysis

Benefits exceed cost across all the scenarios and sensitivities tested in this analysis. The 

benefit/cost ratios range from 1.62 to 3.62 across all combinations of assumptions about future 

generator costs, future tax incentives, and avoided transmission build-out within California.

This study applies no threshold test to the resulting benefit/cost ratios, as any determination of 

decision criteria is beyond the scope of this study. We note nevertheless that Order 1000, 

promulgated by the Federal Energy Regulatory Commission in 2011, restricts transmission 

utilities from using a threshold greater than 1.25 in determining whether a transmission facility 

has sufficient net benefits to be selected for a regional transmission plan. 
xii

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Table ES-4. Fifty-Year Streams of Costs and Benefits (Net Present Value)

Element Benefit Cost

Reduction in generator equipment and fixed 

costsa

$6.4 billion to 

$10.9 billion

Change in capacity value of selected resources 

(resource adequacy) -$858 million

Reduction in production costs

(system variable costs) $326 million

Avoided transmission build-out in California zero to

$2.7 billion

Wyoming-California HVDC transmission 

corridor $3.6 billion 

Totals $5.9 billion to

$13.1 billion $3.6 billion

Net $2.3 billion to $9.5 billion

Benefits/Costs 1.62 to 3.62

(a)Assumes equipment has a 20-year life and is replaced twice over 50 years with comparable equipment at comparable 

cost.

(b) NPV streams are discounted over time based on their real annual values in 2010 dollars.

Savings in generator costs constitute the largest component benefit of a portfolio that includes 

Wyoming wind, as shown in Table ES-4. Annual generator cost savings range from around $500 

million to around $1 billion depending on the assumptions about tax incentives and future 

generator costs.

Figure ES-1 shows the resulting benefit/cost ratios based on current laws regarding the PTC and 

ITC in 2017 (no PTC with the ITC reduced to 10%). The range shown on the left is based on the 

reference case capital costs developed by TEPPC; the range on the right uses the renewable 

energy cost sensitivities developed by NREL and DOE technology experts. 

Figure ES-1. Benefit/cost ratios based on current tax law (no PTC, 10% ITC in 2017)

3.01 

2.60 

2.27 

1.85 

 -

 1

 2

 3

 4

Reference case costs Renewable energy

cost sensitivity

Benefit/cost

ratio

Upper value includes benefits

from avoided transmission

build-out in California

Lower value excludes benefits

from avoided transmission

build-out in California
xiii

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Figure ES-2 and Figure ES-3 show the results with the incentives reauthorized at 2013 levels in 

2017, and eliminated in 2017. The ratios tend to be higher without the PTC or ITC, suggesting 

that the corridor may provide a hedge against the risk of reduced federal tax incentives.

Figure ES-2. Benefit/cost ratios based on PTC, 30% ITC in 2017

Figure ES-3. Benefit/cost ratios based on no PTC, ITC in 2017

The BCA also tested whether developing the Wyoming-California corridor could hypothetically 

pay for itself over 20 years. This was done by fully amortizing project costs over 20 years rather 

than the 40 to 50 years that is typical for major transmission investments, and by ignoring any 

benefits that the transmission line may provide over its remaining 20 to 30 years of useful life. 

This equalizes the cost recovery period for the transmission asset and that of the renewable 

energy generation resources. The 20-year benefit/cost ratios with accelerated cost recovery 

ranged from 1.28 (based on the renewable resource cost sensitivity, reauthorizing federal 

incentives to 2013 levels, and excluding the benefit of avoided transmission build-out in 

California) to 2.87 (based on the TEPPC reference case capital costs, no federal incentives, and 

including the benefit of avoided transmission build-out in California).

Increasing transmission project costs by 25% reduced the ratios, but in all cases total benefits 

still exceeded costs. The benefit/cost ratios ranged from 1.29 to 2.89.

2.69 

2.36 

1.95 

1.62 

 -

 1

 2

 3

 4

Reference case costs Renewable energy

cost sensitivity

Benefit/cost

ratio

Upper value includes benefits

from avoided transmission

build-out in California

Lower value excludes benefits

from avoided transmission

build-out in California

3.62 

3.11 

2.87 

2.37 

 -

 1

 2

 3

 4

Reference case costs Renewable energy

cost sensitivity

Benefit/cost

ratio

Upper value includes benefits

from avoided transmission

build-out in California

Lower value excludes benefits

from avoided transmission

build-out in California
xiv

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Next Steps

The economic viability of a major infrastructure project depends on the factors tested in this 

analysis. The benefit/cost ratios indicate economic headroom of between $2.3 billion and $9.5

billion over 50 years on a net present value basis, as shown in Table ES-4. This range of net 

benefits provides an economic benchmark for examining other factors that require additional and 

more complex modeling, and which could not be sufficiently represented in this analysis.

One such additional factor is the cost of interconnection to the CAISO network from the 

converter station at the terminus of the HVDC line, a cost not included in this analysis. This 

would require a detailed, project-specific system impact study similar to what CAISO or a 

transmission utility would conduct. The costs indicated by such a study could be compared with 

the headroom associated with the primary factors tested in this analysis, providing the next step 

in assessing overall project economics.

Another factor is the cost of integrating Wyoming wind power and California renewables, which 

is contemplated for a later phase of this study. An integration analysis would have as its 

objective the optimization of several decision variables, such as where to site flexible resources, 

possible interconnection with the AC network in Wyoming, and participation in an energy 

imbalance market. None of these variables could be quantified sufficiently without an integration 

study, but the economic headroom indicated by this analysis can help evaluate the 

reasonableness of different integration options.

Other factors were excluded from the BCA because of time and resource limitations. These are 

discussed at the end of Section 1.
xv

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Table of Contents

List of Figures .......................................................................................................................................... xvi

List of Tables ............................................................................................................................................ xvi

1 Introduction........................................................................................................................................... 1

Factors Driving Comparison of Renewable Resource Options.............................................................. 1

Report Structure ..................................................................................................................................... 3

Study Scenarios...................................................................................................................................... 3

Metrics Used to Evaluate Cost-Effectiveness of New Transmission Projects....................................... 5

Study Limitations and Future Analysis.................................................................................................. 6

2 Transmission ........................................................................................................................................ 8

The Wyoming-California Corridor......................................................................................................... 8

Avoided Transmission Build-Out in California ..................................................................................... 9

Economic Life of Transmission Versus Economic Life of Generation ............................................... 12

3 Generator Costs.................................................................................................................................. 13

LCOE Analysis..................................................................................................................................... 13

Technology and Weighting Distributions in Project Scenarios............................................................ 13

LCOE Modeling Methodology............................................................................................................. 14

Input Data and Assumptions in Delivered Cost of Energy Analysis.................................................... 15

Reference Case and Renewable Energy Cost Sensitivity Results........................................................ 20

Federal Tax Policy Sensitivities........................................................................................................... 22

Outcomes for BCA............................................................................................................................... 25

4 Capacity Values .................................................................................................................................. 27

Data Sources......................................................................................................................................... 28

Conventional Generation ............................................................................................................... 28

Load Profiles.................................................................................................................................. 29

VG Profiles.................................................................................................................................... 30

Results.................................................................................................................................................. 30

Outcomes for BCA............................................................................................................................... 34

5 Production Cost Modeling................................................................................................................. 35

The PLEXOS Model ............................................................................................................................ 35

Thirty-Three Percent Renewables With and Without Wyoming Wind Power .................................... 36

Thirty-Five Percent Renewables With and Without Wyoming Wind Power....................................... 38

Production Cost Modeling Summary ................................................................................................... 40

Outcomes for BCA............................................................................................................................... 41

6 Benefit Cost Analysis......................................................................................................................... 42

BCA Framework .................................................................................................................................. 42

Cost Component................................................................................................................................... 44

Benefits Components ........................................................................................................................... 45

BCRs .................................................................................................................................................... 45

Sensitivities.................................................................................................................................... 46

Summary .............................................................................................................................................. 47

7 Summary and Future Work................................................................................................................ 49

References................................................................................................................................................. 50

Appendix A : Environmental Siting and Permitting: Wyoming Wind Transmission and Generation53

Appendix B: Supplemental Information on Capacity Value and NREL REPRA Model...................... 55

Appendix C: Supplementary Background on the Wyoming Infrastructure Authority....................... 58

Appendix D: Results of Comparative WECC/TEPPC and CAISO Analysis......................................... 60

From WECC 2011 10-Year Plan.......................................................................................................... 63

From WECC 2013 10-Year Plan.......................................................................................................... 65
xvi

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List of Figures

Figure 1. Factors driving renewable resource and transmission development for the WECC study 2

Figure 2. Factors driving renewable resource and transmission development updated for this 

study ...................................................................................................................................................... 2

Figure 3. Tasks performed in this study................................................................................................... 3

Figure 4. The four study scenarios: CA33%, CA/WY33%, CA35%, CA/WY35% ................................... 5

Figure 5. Location of TransWest Express Project HVDC transmission line......................................... 8

Figure 6. Annual revenue requirements for transmission projects..................................................... 10

Figure 7. Annual revenue requirements of transmission scenarios ................................................... 11

Figure 8. 2017 LCOE results with 10% ITC and no PTC........................................................................ 23

Figure 9. 2017 LCOE results with PTC and 30% ITC ............................................................................. 24

Figure 10. 2017 LCOE results based on no ITC or PTC ........................................................................ 25

Figure 11. ELCC is the horizontal difference between reliability curves, evaluated at the target 

reliability level..................................................................................................................................... 28

Figure 12. Effective forced outage rates for conventional generators................................................ 29

Figure 13. Load duration curves for historical data scaled to 2022 .................................................... 29

Figure 14. Capacity values for each scenario and data set.................................................................. 31

Figure 15. Capacity value (MW) by scenario and data set with disaggregated California resources32

Figure 16. Top net load hours with net load values and incremental contribution from California 

renewables and Wyoming wind ........................................................................................................ 33

Figure 17. Net hourly California power imports for the CA33% (blue) and CA/WY33% (red line) 

cases.................................................................................................................................................... 37

Figure 18. SCE-area hourly profiles of LMP for the two 33% renewable power scenarios: CA33% 

(blue line) and CA/WY33% (red line)................................................................................................. 37

Figure 19. The effects of Wyoming wind power on California net imports......................................... 39

Figure 20. The effects of Wyoming wind power on SCE zone market clearing prices...................... 40

Figure 21. Elements of the benefit/cost framework............................................................................... 43

Figure 22. Benefit/cost ratios based on current tax law (No PTC, 10% ITC in 2017) ......................... 46

Figure 23. Benefit/cost ratios based on PTC, 30% ITC in 2017 ............................................................ 47

Figure 24. Benefit/cost ratios based on no PTC, ITC in 2017 ............................................................... 47

Figure 25. Example of sliding window for wind power generation...................................................... 57

List of Tables

Table 1. Transmission Costs Associated with Tested California Portfolios ........................................ 9

Table 2. Changes in CPUC Portfolio ....................................................................................................... 11

Table 4. Change Case Resources by Capacity ...................................................................................... 14

Table 5. Change Case Resources by Energy Production..................................................................... 14

Table 6. Reference Case Input Assumptions for California Renewable Energy in 2017................... 16

Table 7. Reference Case Input Assumptions for Wyoming Wind in 2017........................................... 16

Table 8. Cost Sensitivity Assumptions for California Renewable Energy in 2017............................. 17

Table 9. Cost Sensitivity Assumptions for Wyoming Wind in 2017..................................................... 17

Table 10. IPP-Based Financing Assumptions........................................................................................ 19

Table 11. Federal Tax Policies for Different Renewable Energy Technologies.................................. 20

Table 12. Reference Case LCOEs in 2017 by Renewable Energy Type (PTC, 30% ITC).................... 21

Table 13. Reference Case LCOE in 2017 for Wyoming Wind (PTC, 30% ITC)..................................... 21

Table 14. Renewable Energy Cost Sensitivity LCOEs in 2017 by Renewable Energy Type (PTC, 

30% ITC)............................................................................................................................................... 22

Table 15. Renewable Energy Cost Sensitivity LCOE in 2017 for Wyoming Wind (PTC, 30% ITC) ... 22

Table 16. 50-Year Streams of Reduced Generator Costs (Net Present Value) ................................... 26

Table 17. Summary of VG Time Series ................................................................................................... 30

Table 18. Capacity Values (MW) by Scenario and Data Set.................................................................. 31

Table 19. Capacity Value (MW) by Scenario and Data Set With California Resources Disaggregated32

Table 20. Fifty-Year Stream of Capacity Value Benefits (Net Present Value) ..................................... 34

Table 21. Annual Production Costs for the CA33% and CA/WY33% Scenarios................................. 36
xvii

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Table 22. The Effect of Wyoming Wind Power on the Annual Average Market Clearing Prices in 

California, by Region, for the Two 33% Renewable Power Scenarios.......................................... 38

Table 23. Simulation Results Summary for CA35% and WY35% Scenarios....................................... 39

Table 24. Fifty-Year Stream of Production Cost Benefits (Net Present Value)................................... 41

Table 25. Fifty-Year Streams of Costs and Benefits (Net Present Value) ........................................... 45

Table 26. Capacity Outage Probability Table for Conventional Units ................................................. 56

Table 27. Capacity Outage Probability Table for Wind Sliding Window ............................................. 57

Table 28. Example of Capacity Outage Probability Table with Conventional and Wind Generation57

Table 29. Summary of the Recent WECC/TEPPC and CAISO Studies and Their Findings............... 60

Table 30. WECC 2011 10-Year Plan Base Case Analysis...................................................................... 63

Table 31. WECC 2011 10-Year Plan—Cost of Transmission and Wyoming Wind CF Sensitivity to 

Match 2013 Base Case ....................................................................................................................... 64

Table 32. WECC 2013 10-Year Plan Base Case Analysis...................................................................... 65

Table 33. WECC 2013 10-Year Plan Cost of Capacity (“CT Type”) Sensitivity................................... 66
1

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1 Introduction

This study presents a comparative analysis of two different renewable energy options for the 

California energy market between 2017 and 2020: 12,000 GWh per year from new California instate renewable energy resources; and 12,000 GWh per year from Wyoming wind delivered to 

the California marketplace. Either option would add to the California resources already existing 

or under construction, theoretically providing the last measure of power needed to meet (or to 

slightly exceed) the state’s 33% renewable portfolio standard (RPS). Both options have 

discretely measurable differences in transmission costs, capital costs (due to the enabling of 

different generation portfolios), capacity values, and production costs. The purpose of this study 

is to compare and contrast the two different options to provide additional insight for future 

planning.

This report adds to other recent studies of transmission and resource planning in the West. In 

2011, the Western Electricity Coordinating Council (WECC) conducted an analysis of the 

benefits of integrating renewable resources from Wyoming through new long-distance 

transmission to California. The results were published in WECC’s 2011 10-Year Regional 

Transmission Plan, which was updated in 2013. The second major study is the California 

Independent System Operator’s (CAISO’s) 2012–2013 Transmission Plan. Appendix D provides

a summary of these recent studies and their findings. Throughout this report, annual costs are 

expressed in 2010 dollars to facilitate consistency with WECC's 2013 Interconnection-Wide 

Transmission Plan. Net present value streams are discounted over time based on their real annual 

values in 2010 dollars.

This study is a detailed analysis of wind power delivered via a proposed Wyoming-to-California 

transmission corridor, which was among the top-ranking cross-region renewable energy 

corridors identified in NREL’s “Beyond RPS” study. Because of its narrower focus, this study 

uses project-specific, site-specific, and load-specific data to an extent not possible in a systemwide analysis. Consequently, this study is an examination of renewable energy alternatives on a 

single corridor; it does not provide comparisons with other regional corridors.

Factors Driving Comparison of Renewable Resource Options

The 2011 WECC Plan found accessing high-quality renewable resources in Wyoming to be 

potentially cost-effective in helping to meet California policy needs, such as the state’s 33% 

RPS, within the 10-year planning horizon. Benefits focused on the comparative capital costs for 

transmission and generation and the production levels (or capacity factors) of the various 

renewable resources. Figure 1 shows the factors driving the comparison of renewable resource 

options based on the TEPPC analysis (WECC 2011).

Figure 2 shows the same factors in the context of this study. Items studied in this report include 

the transmission costs to interconnect and/or deliver energy to a particular load, the resource 

capacity value, and the Western Interconnection production costs. CAISO applies a 145% 

multiplier to transmission capital costs when permitting, siting costs and other project costs are 

not known specifically. This analysis uses the CAISO multiplier to represent total project cost 

for a new transmission line. (See Appendix A for a discussion of environmental permitting and 

siting issues.)
2

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Figure 1. Factors driving renewable resource and transmission development for the WECC study

Figure 2. Factors driving renewable resource and transmission development updated for this 

study
3

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Report Structure

This report begins by describing the assumptions relating to transmission costs, including the 

potential avoided cost of in-state transmission improvements that might not be needed if an 

HVDC transmission line along the Wyoming-California corridor were developed (Section 2). 

The study then examines how the individual components considered in the BCA could change 

between the California-only portfolio and the California-plus-Wyoming-wind portfolio. These 

components include changes in generator costs (Section 3), changes in resource adequacy 

(capacity value analysis, Section 4), and changes in system-wide variable costs (production cost 

modeling, Section 5). The generator cost analysis includes a detailed levelized cost of energy 

(LCOE) analysis for each scenario examined. Future technology developments are uncertain, so 

the LCOE analysis uses two sets of 2017 cost projections to define a plausible range of future 

costs. 

The outcomes from the analyses in Sections 2 through 5 are then combined in the BCA, detailed 

in Section 6. Figure 3 shows a schematic of the different study tasks for this report.

Figure 3. Tasks performed in this study

Study Scenarios

This study compares the relative economics of a plausible California in-state renewable resource

portfolio to a Wyoming wind power portfolio capable of providing the same amount of energy 

annually. A number of major transmission projects currently in advanced planning would 

provide an energy pathway along the Wyoming-to-California corridor. This study uses the 

proposed TransWest Express Project to characterize the economics of electricity delivery from 

Wyoming to the CAISO balancing authority area.

As explained further in Section 2, the analysis assumes 3,000 MW of HVDC transfer capability 

from Wyoming to California, and that all of it is assigned to Wyoming wind power with an 

Benefit/cost analysis

Capacity 

value 

analysis

Production 

cost analysis

Generator, 

transmission 

cost analysis
4

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annual capacity factor of about 46%. With line losses, this equates to approximately 12,000 

GWh per year of renewable energy delivery.

The California resource scenarios used in this study were based on a portfolio of resources 

selected with the RPS Implementation Analysis Tool, which was created by the California Public 

Utilities Commission (CPUC). Following guidance from the CPUC to CAISO, this analysis used

the commercial interest portfolio, in which project rankings gave weight to demonstrated 

commercial interest (evidence by power purchase agreements) rather than cost, environmental 

impacts, or other factors. The tool and its commercial interest output modules, current as of 

February 2013, were downloaded from the CPUC website for this study in November 2013.

3

The commercial interest portfolio addressed a “net short” (resources required to round out the 

33% RPS goal) of 32,184 GWh/year. About 88% of the net short was from California resources. 

One of the resource scenarios used in this analysis takes the commercial interest portfolio 

without any change. The others replace a portion of the in-state resources with Wyoming wind 

power, resulting in a blend of California and Wyoming renewables. 

To provide some flexibility to the analysis, the study started with two base scenarios. The 

CA33% portfolio comprised the 32,184 GWh/year of resources selected in the commercial 

interest net short portfolio. Known projects with demonstrated progress toward permitting—

“core” projects—constituted about 82% of the commercial interest portfolio; nonspecific generic 

projects comprised the rest. All of the non-core generic projects were assumed to be dependent 

on new transmission or line upgrades within California; 95% of the core projects required no 

transmission improvement beyond those already approved by CAISO. 

The CA35% portfolio added another 4,433 GWh/year of renewable energy from the next-best 

California resources (that is, the 4,433 GWh/year of unselected resources that scored highest 

under the CPUC’s commercial interest weighting). The additional energy would put CAISO’s 

generation portfolio at about 35% renewable energy.

Two hypothetical portfolios were constructed to study the effects of replacing 12,000 GWh/year 

with Wyoming wind power. The CA/WY33% portfolio removes the 7,109 GWh of generic 

projects, and it supposes that 45% of the selected core projects with expected in-service dates of 

2015 or later will not be completed as scheduled. No specific core projects are removed; rather, 

the entire portfolio of core projects expected to be in service in 2015 or later is uniformly derated 

to 55% of their expected annual energy production. Consequently, both the technology 

composition and the LCOE of the derated portfolio are the same as for the fully rated portfolio. 

The CA/WY35% portfolio retains all selected core projects, and replaces all non-core projects 

with Wyoming wind power. Figure 4 illustrates the construction of all four portfolios tested in 

this study.

 3

CPUC, “RPS Calculator for the TPP,” Feb. 7, 2013,

http://www.cpuc.ca.gov/PUC/energy/Procurement/LTPP/2012+LTPP+Tools+and+Spreadsheets.htm
5

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Comparison: 33% Renewables Comparison: 35% Renewables

Commercial Interest 

Portfolio

CA33% 

Portfolio

CA/WY33% 

Portfolio

CA35% 

Portfolio

CA/WY35% 

Portfolio

14,301 GWh

selected core projects in 

service before 2015

All included All included

10,774 GWh

selected core projects in 

service in 2015 or later

5,883 GWh included

All included

4,891 GWh*

12,000 GWh

WY wind 7,109 GWh

selected non-core 

projects

7,109 GWh 12,000 GWh

(selected, plus 

top-scoring nonselected)

12,000 GWh

WY wind

*Core projects selected for the scenario with a planned in-service date of 2015 or later that hypothetically could be 

replaced by Wyoming wind, due to possible contract failure or construction delay. Derived by derating to 55% all 

selected core projects with a planned in-service date of 2015 or later. Included in CA33% scenario, not included in 

CA/WY33% scenario.

Figure 4. The four study scenarios: CA33%, CA/WY33%, CA35%, CA/WY35%

In combination, the four portfolios address the possibility that up to 45% of projects with 

expected in-service dates of 2015 or later would not meet their scheduled completion dates. They 

also accommodate the possibility that some of the 12,000 GWh of Wyoming wind power might 

be sold to California public utilities whose load and energy planning are done outside CAISO but 

are still subject to the state RPS. 

Metrics Used to Evaluate Cost-Effectiveness of New Transmission 

Projects

The cost-effectiveness of a new transmission project is measurable in at least two different ways. 

One common method is to consider the total generation and transmission costs of one portfolio 

and compare those costs to one or more alternative portfolios. This method, sometimes referred 

to as least cost planning, was used in WECC’s 2011 and 2013 Regional Transmission Plans. 

That study compared the costs of various renewable resource portfolios, some of which included 

new inter-regional transmission projects to access lower-cost remote renewable resources. This 

approach found that an inter-regional transmission project could be a cost-effective component 

of a portfolio designed to achieve the lowest combined costs for total generation and 

transmission. 

Another common method is to compare the cost of a new transmission project to the benefits (or 

comparative cost savings) enabled by the new transmission project. This approach typically 

combines the costs and benefits of a new transmission project into a benefit/cost ratio (BCR). 

This approach provides an easily understood score indicating whether the benefits exceed the 

costs. A BCR greater than 1.0 indicates that the benefits of investment in a new transmission 
6

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project exceed the costs. As the BCR increases above 1.0—especially when evaluating the 

project under several sensitivities—decision makers tend to have greater confidence that a 

project will be cost effective under a wide range of future conditions. 

While this study combines the analytical components into a series of BCRs, it does not adopt a

threshold test for them. We note nevertheless that Order 1000, promulgated by the Federal 

Energy Regulatory Commission in 2011, restricts transmission utilities from using a threshold 

greater than 1.25 in determining whether a transmission facility has sufficient net benefits to be 

selected for a regional transmission plan.

Study Limitations and Future Analysis

The economic viability of a major infrastructure project depends on the factors tested in this 

analysis. The benefit/cost ratios show economic headroom, which suggests the reasonableness of 

examining other factors that require additional and more complex modeling.

One such additional factor is the cost of interconnection from the converter station at the 

terminus of the HVDC line to the CAISO network. This would require a detailed, projectspecific system impact study similar to what CAISO or a transmission utility would conduct. The 

costs indicated by such a study could be compared with the headroom associated with the 

primary factors tested in this analysis, providing the next step in assessing overall project 

economics.

Another factor is the cost of integrating Wyoming wind power and California renewable 

resources, which is contemplated for a later phase of this study. An integration analysis would 

have as its objective the optimization of several variables, such as where to site flexible 

resources, possible interconnection with the AC network in Wyoming, and participation in an 

energy imbalance market. None of these variables could be quantified for this analysis prior to an 

integration study, but the economic headroom shown in the benefit/cost ratios can help evaluate 

the reasonableness of different integration options.

Other factors were excluded from the BCA because of time and resource limitations. 

• The study does not directly quantify the benefits of carbon reduction. The effect is 

unlikely to be significant, however, because each portfolio examined in this study 

comprises nothing but renewable energy resources. Consequently, each change case

would result in the offset of essentially the same amount of system-wide emissions. 

Nevertheless, changes in generator cost from one test portfolio to another could be 

interpreted as differences in the cost of mitigating the same amount of emissions. 

• The study does not look at changes in employment, changes in statewide productivity and 

wages, or other macroeconomic indicators. Some of these effects (employment and real 

wages, for example) are likely to have complex interactions and may even trend in 

opposite directions if electricity prices increase significantly. The economic modeling 

required to represent these effects was outside the scope of this study, but could be 

investigated separately.
7

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• Transmission permitting and siting risks were not quantified within this study. Appendix 

A to this study provides an update on the permitting status of the TWE Project and 

reference to sites that list other projects. The permitting status of the in-state transmission 

projects associated with the California portfolios was not included in the CPUC data 

reviewed for this study.

• The study examined only one representative transmission configuration for the 

Wyoming-California corridor. Other configurations (AC rather than HVDC, or a 1,500-

MW HVDC line rather than a 3,000 HVDC line) would have yielded different 

benefit/cost ratios. Results based on the 3,000-MW HVDC configuration, however, 

suggest there may be sufficient economic headroom to warrant examining other 

configurations.

• The portfolios tested in this analysis were constructed from the project rankings in the 

CPUC’s commercial interest portfolio using objective mathematical criteria. No other 

portfolios were tested here, but others could be tested using different selection criteria. 

For example, the mathematical criteria eliminated a transmission upgrade that would 

have enabled an additional 480 MW of geothermal power in the Imperial Valley. This 

affected the total MW included in the California-Wyoming change scenario as well as 

local power prices modeled for the Imperial Valley. 

This study utilized February 2013 results from the CPUC commercial portfolio as the basis for 

constructing the California portfolios examined. The CPUC model contained assumptions about 

Wyoming wind resources (which were not selected for the commercial interest portfolio), but 

these assumptions were not reconciled with those used in this study. In addition, the costs of 

transmission upgrades and new lines associated with the commercial portfolio were adopted “as 

is” from the CPUC’s February 2013 results. This study did not investigate whether these 

upgrades and new lines were entirely dependent on the renewable resources they would enable, 

or whether they might be built for other reasons such as reliability. Instead, the study brackets its 

results by calculating benefit/cost ratios both with and without the savings of avoided 

transmission build-out.
8

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2 Transmission

Transmission enters into the BCA in two ways: the cost of developing a Wyoming-California 

HVDC corridor; and the costs of transmission build-outs in California that might be avoided if 

12,000 GWh/year of Wyoming wind were available to California.

The Wyoming-California Corridor

A number of major transmission projects currently in advanced planning would provide an 

energy pathway along the Wyoming-California corridor. This study uses information from the 

proposed TransWest Express Project to characterize the economics of new transmission along 

the corridor. The project would include a 600-kV high-voltage direct-current (HVDC) line from 

south central Wyoming to southern Nevada’s Eldorado Valley, with interconnection into the 

CAISO balancing authority. Figure 5 shows the general path of the project; additional 

information can be found in Appendix A.

Figure 5. Location of TransWest Express Project HVDC transmission line

The assumed transfer capability of the transmission corridor is 3,000 MW, which in this analysis 

is assigned entirely to Wyoming wind power. Updated resource information on renewable 

energy zones indicates that wind power facilities near the Wyoming terminus of the TransWest 

Express Project would have a likely annual capacity factor of about 46%, assuming the use of 
9

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Type 1 wind turbines at a hub height of 80 meters. (AWS TruePower, 2011) Reference case 

capital costs for transmission are assumed to be $3 billion, and total project costs are assumed to 

be 145% of capital costs. Factoring in line losses, annualized transmission costs under these 

assumptions amount to about $29 per MWh delivered.

4

The analysis includes two cost sensitivities for developing the corridor: transmission cost 

recovery over 20 years rather than 50 years; and project costs that are 25% more than the 

reference case assumptions. By hypothetically recovering transmission costs over 20 years and 

calculating net present value over that same amount of time, the analysis sets aside any benefits 

that could occur beyond 20 years, providing a conservative cost sensitivity.

Transmission projects take years to permit and build before they are energized. Typically the 

generation resources using a transmission line would be brought into service over several years. 

This analysis assumes all resources for the various portfolios come on line in 2017 as a proxy for 

resources coming on-line between 2017 and 2020.

Avoided Transmission Build-Out in California

The two California generation portfolios tested in this analysis depend in part on five 

transmission investments, listed in Table 1. Replacing these California portfolios with Wyoming 

wind power could eliminate the need for their associated transmission projects, at least with 

respect to meeting the last 12 TWh of a 33% or 35% California renewable energy scenario.

Table 1. Transmission Costs Associated with Tested California Portfolios

Transmission 

avoided in 33% 

scenario

Transmission 

avoided in 35% 

scenario

Type of 

investment

Estimated 

annual GWh 

enabled

Annual revenue 

requirement

Kramer Kramer new line 785 $142 million 

Los Banos Los Banos new line 1,926 $78 million 

Imperial Imperial upgrade 3,738 $82 million 

Solano Solano upgrade 554 $6 million

Total, 33% scenario 7,004 $308 million

Westland upgrade 1,033 $14 million

Total, 35% scenario 8,037 $322 million

CPUC, “RPS Calculator for the TPP,” Feb. 7, 2013

The need might not be avoided altogether, however, as reliability benefits not examined in this 

study might justify the projects regardless of wind power from Wyoming. This study did not 

address those reliability issues, making it difficult to judge the validity of treating these avoided 

costs as benefits.

Therefore, this analysis calculates two sets of benefit/cost ratios: one that includes the benefit of 

avoiding the transmission costs shown in Table 1, and one that does not. This creates “bookends” 

 4 Additional information on the TransWest Express Project is on the WECC Transmission Project Portal, 

http://www.wecc.biz/Planning/TransmissionExpansion/Transmission/Pages/default.aspx.
10

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for the BCA results that encompass all possible weightings of avoided transmission costs and 

provides a more fully informed picture of development risks.

Figure 6 illustrates the relative cost impacts of the five avoided transmission projects compared 

to the cost of transmission from Wyoming to Eldorado Valley. The total costs of the California 

projects are within 8% to 13% of the cost of developing the Wyoming-to-Eldorado Valley 

corridor. 

*Replaced in Dec. 2013 update with a potential new line to Riverside East. Annual revenue requirement of the replacement is 

about $49 million less than that of the potential Los Banos line.

Figure 6. Annual revenue requirements for transmission projects

The potential benefits of avoided transmission build-out in California were estimated using the 

CPUC LTPP tool. The tool and its commercial interest output modules, current as of February 

2013, were downloaded from the California PUC website for this study in November 2013.5 In 

late 2013, California PUC staff conducted a newer run of the same model with updated 

transmission assumptions. Although the detailed results of the update had not been published at 

the time of this writing, NREL did confirm with California PUC staff the changes in the selected 

transmission bundles, summarized in Table 2. The timeline for this project prohibited a full recalculation of the entire analysis. Doing so would have required detailed information about the 

portfolio of selected resources using existing transmission, and these details were not yet 

available. Nevertheless, changes in the selected transmission bundles enabled some adjustments 

to the BCA.

 5 http://www.cpuc.ca.gov/PUC/energy/Procurement/LTPP/2012+LTPP+Tools+and+Spreadsheets.htm

 $-

 $100

 $200

 $300

 $400

Wyoming

wind

California

33% portfolio

California

35% portfolio

$Millions

Westland

Imperial

Solano

Kramer

Los Banos*

Wyoming-California
11

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Table 2. Changes in CPUC Portfolio

Segment

MW 

capability 

added

Annual cost Change

Merced (segment 1) 42 $143 million Dropped

Los Banos (segment 1) 370 $143 million Dropped

Riverside East (segment 1) 2,400 $94 million Added

Kramer (segment 1) 700 $78 million Reduced to 580 MW

*35% renewables scenario only

Figure 7. Annual revenue requirements of transmission scenarios

A new line to the Merced renewable energy zone was dropped in the update, but this has no 

effect on the present analysis. The Merced line ranked high in the February 2013 results and was 

not part of any change case on which a BCR was based. Consequently, removing the Merced line 

from the updated portfolio does not affect the results of this study. 

The changes that do affect the analysis are illustrated in Figure 7. The first column shows the 

avoided transmission benefits included in this study’s initial BCR sensitivity. The second column 

shows the effect of dropping the Los Banos line and adding the Riverside East line. It would 

reduce by about $49 million per year (or about 16%) the effect of counting potentially avoided 

transmission in California as a benefit. This reduction was applied as an adjustment to the initial 

analysis; the upper “bookend” for avoided-transmission benefits is 84% of the transmission 

build-out represented in the February 2013 portfolio that could potentially be avoided if the 

Wyoming-California corridor were developed. The discount puts the value of the build-outs in 

$0

$100

$200

$300

$400

Transmission included in

BCR sensitivity

Transmission in revised

CPUC scenario

$ Millions

Westland*

Imperial

Solano

Kramer

Los Banos

Riverside East
12

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

the February 2013 scenario on par with the value of build-outs included in the updated scenario, 

thereby accounting (at least partially) for the latest California PUC updates.

Economic Life of Transmission Versus Economic Life of Generation

Wind turbines, solar installations, geothermal plants, and other utility-scale renewable energy 

facilities typically have economic lifetimes of around 20 years. This analysis makes the 

assumption that all generator financing is based on a 20-year recovery period. This is shorter 

than the assumed economic life of a new transmission line, which for this analysis is 50 years. 

To accommodate the difference, the analysis imposes the assumption that the stock of generation 

equipment is replaced twice during the 50-year period with comparable new equipment at 

comparable cost. The annual revenue requirement for generation (in real dollars) is held constant 

for the entire 50-year period. The original assets are paid off and retired in year 20; those assets 

are replaced in year 21 with a new set of assets whose annual revenue requirement equals that of 

the retired assets. 

In other words, the analysis assumes that three factors generally balance out between years 20 

and 21 and between years 40 and 41. These factors include inflation, technological 

improvements that would tend to push replacement costs lower, and exigencies affecting how 

long equipment is actually used before it is replaced (which for any given piece may be shorter 

or longer than the assumed 20 year financing period). So while the annual revenue requirement 

remains the same from year 20 to year 21, the revenue would be applied to a new and equivalent 

portfolio of generation equipment in year 21. While all these factors could in fact balance out 

differently, the effect of errors about what happens in the 21st year and beyond are blunted by 

the effect of discounting. Under the NPV inputs used here, roughly two-thirds of the total 

benefits and total costs occur during the first 20 years of the analysis.

The short-term BCRs, which assume full recovery of transmission costs over 20 years rather than 

the normal 50 years, provide a conservative sensitivity that tests the robustness of the 

assumptions underlying the 50-year BCRs.

Table 3 summarizes the 50-year streams of transmission costs and benefits for the avoided 

transmission build-out in California and the Wyoming California HVDC transmission corridor.

Table 3. 50-Year Streams of Transmission Costs and Benefits (Net Present Value)

Benefit Cost

Avoided transmission build-out in California zero to $2.7 billion

Wyoming-California HVDC transmission corridor $3.6 billion 
13

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3 Generator Costs

LCOE Analysis

Comparing the cost of energy across different technologies requires careful assessment of a 

number of factors including capital costs, operations and maintenance expenses, fuel costs, 

finance charges, and annual energy production (Schwabe et al. 2010). Representing these 

parameters as a single descriptive value, however, can be accomplished using a variety of 

methods and serves to simplify multi-technology comparisons such as this one. This study uses

LCOE as the primary metric for describing and comparing the cost of renewable energy 

generation for several types of technologies. LCOE represents the sum of all costs over the 

lifetime of a given generator project, discounted to the present time, and divided by the annual 

energy production to arrive at a levelized cost per unit of energy. 

Generation benefits relating to transmission are typically based on savings in variable costs that 

result from dispatching existing resources more efficiently. This analysis requires a different 

approach, however. First, the most significant generator benefits in this analysis are associated 

with the cost of adding new resources, not the redispatch of existing resources. Second, the new 

resources are renewable with no fuel cost and very little variable cost, making a conventional 

analysis based on variable cost unrevealing.

When applied to a renewable resource with no fuel cost, the generator LCOE primarily measures 

the revenue required to recover a project’s fixed and non-fuel operating costs (in dollars per 

MWh, or cents per kWh). The generator LCOE also takes into account other factors that capital 

and operating costs alone do not capture, such as financing expenses, incentives, and locational 

differences affecting a project’s capacity factor. 

This section proceeds with a brief description of the LCOE model utilized in the analysis and 

highlights the key input parameters. We then assess the weighted average LCOE of the 

California projects included in the change case being examined and compare that to the LCOE of 

Wyoming wind power The LCOEs are then tested for two sensitivity cases, one measuring the 

effect of changes in future renewable energy generator costs, and one measuring the effect of 

differences in future federal tax incentives.

Technology and Weighting Distributions in Project Scenarios

Table 4 and Table 5 show the installed capacity and energy production compositions of the

change cases, based on the portfolios described in the Study Scenarios section. The total amount 

of installed capacity (in MW) varies slightly across the two California cases, but each one can 

produce 12,000 GWh of electricity annually. The energy production technology distribution of 

the two California change cases is generally consistent. Wind, geothermal, and solar PV (large 

scale and small scale) provide 92% to 95% of the 12,000 GWh modeled. 

LCOE is first calculated by each technology’s unique combination of input parameters. Each 

technology’s LCOE is then weighted according to the amount of energy it contributes to the 

change case. Table 5 shows the weighting factor applied to each technology in each change case. 

Note that the overall LCOE for the California change cases are largely determined by three 

energy technologies: solar PV, wind, and geothermal.
14

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 4. Change Case Resources by Capacity6

Technology CA33% RPS (MWs) CA35% RPS (MWs) WY Wind (MWs)

Biogas 7 3 -

Biomass 2 18 -

Geothermal 504 484 -

Large-Scale Solar PV 2,115 2,222 -

Small-Scale Solar PV 100 252 -

Solar Thermal 348 206 -

Wind 757 819 3,000

Total 3,833 4,004 3,000

Note: The CA33% and CA35% portfolios differ because (a) all selected core capacity with an expected in-service date of 

2015 or later was derated to 55% in the CA33% portfolio, and restored to 100% for the CA35% portfolio; and (b) a 

number of projects were added to the CA35% portfolio so that total annual renewable energy generation could reach 

35% of retail sales.

Table 5. Change Case Resources by Energy Production7

Technology

CA33% RPS CA35% RPS WY Wind

GWh

Scenario 

Weight GWh

Scenario 

Weight GWh

Scenario 

Weight

Biogas 50 .4% 21 .2% - -

Biomass 15 .1% 134 1.1% - -

Geothermal 3,577 29.8% 3,428 28.6% - -

Large-Scale Solar PV 5,217 43.5% 5,167 43.1% - -

Small-Scale Solar PV 217 1.8% 530 4.4% - -

Solar Thermal 863 7.2% 482 4.0% - -

Wind 2,061 17.2% 2,238 18.7% 12,000 100%

Total 12,000 100% 12,000 100% 12,000 100%

LCOE Modeling Methodology

This analysis utilizes NREL’s Cost of Renewable Energy Spreadsheet Tool (CREST) to 

calculate the LCOE for each renewable energy generation technology (CREST 2013). CREST is 

a publicly-downloadable, spreadsheet-based, annual pro-forma model that solves for the 

minimum price of energy sufficient to cover all capital and operating expenditures while 

providing a return to the project’s owners. CREST models assumptions for taxes and financing, 

but does not model partnership-based financing structures that are common to many renewable 

energy projects.

CREST allows the user to select from a number of different modeling options based on the users’ 

preference for input parameter specificity. This analysis uses the tool’s simplified modeling 

option, which is sufficient for the level of detail required to compare across multiple 

technologies. CREST also allows for the selection of both state and federal policy incentive 

mechanisms, such as a production-based tax credit available for wind technologies or an 

investment tax credit available for solar technologies (DSIRE 2013a, DSIRE 2013b). 

 6 Analysis of CPUC 2013

7 Analysis of CPUC 2013
15

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

CREST was not developed as an investment-quality financial model. For this analysis, however, 

it provides a transparent, limited-focused tool for comparing multiple technologies with 

considerably different resource and cost profiles. It also facilitates sensitivity analysis of changes 

in global factors such as the PTC and ITC.

Input Data and Assumptions in Delivered Cost of Energy Analysis

This study uses a range of input parameters to estimate the LCOE under different possibilities for 

future costs, performance, and federal policies. We analyze the LCOE of the renewable 

electricity generation for a reference case as well as a sensitivity test that uses lower renewable 

generation costs.

This analysis assumes installation of the renewable energy generation in the year 2017, so all 

cost and performance values are representative of a project commissioned in 2017. Because 

renewable energy cost projections become more uncertain over the long term, we test how much 

the LCOE estimates might change based on different cost projections. Importantly, neither the 

reference case nor the cost sensitivity analysis (described below) presumes either set will be 

right. Their purpose is to measure how much LCOEs could change across a range of future 

conditions. 

The 2017 reference case uses capital and operating cost assumptions and projections from

WECC’s “2013 Interconnection-Wide Plan Data and Assumptions” (WECC 2013) and provides 

a common input source and vintage for all capital and operating expenses. The underlying costs 

from WECC 2013 were developed through TEPPC, which conducted a transparent, open 

stakeholder process that included diverse perspectives (utility representatives, regulators, 

renewable energy stakeholders, consumer advocates, state energy offices, and others). WECC’s 

board of directors approved the TEPPC cost assumptions used in the 2013 interconnection-wide 

transmission plan; TEPPC has begun a new cycle of data development that is reviewing more 

recent cost information. For consistency with WECC 2013, all values are expressed in 2010 

dollars.8

Table 6 lists the capital costs, operations and maintenance (O&M) expenses, and capacity factor 

input parameters from the 2017 reference case scenarios for the California energy generation.9,10

Capital costs and O&M charges are adjusted using TEPPC state multipliers to account for statebased variations in expenses.

11 Multipliers are applied to both the California and Wyoming 

renewable generation projects.

 8 All values are converted to $2010 using the GDP deflator to be consistent with TEPCC cost assumptions. 9 Capital costs for all scenarios are rounded to the nearest $5. 10 Note that an inverter efficiency factor of 85% is used to convert solar PV cost data from DC to an AC basis. This 

inverter ratio is consistent with the WECC 2013 analysis.

11 Note, however, that solar thermal uses a labor multiplier from NREL’s System Advisor Model that is based on 

Southern California labor rates from the Bureau of Labor Statistics. 
16

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 6. Reference Case Input Assumptions for California Renewable Energy in 201712

Technology

Capital Cost

(2010 $/kW)

O&M Costs 

($/kW - yr)

CA33% RPS

Capacity 

Factor (%)

CA35% RPS

Capacity 

Factor (%)

Biogas 3,055 147 79.9% 79.9%

Biomass 4,975 175 84.5% 85.0%

Geothermal 6,440 169 81.0% 80.9%

Large-Scale Solar PV (AC) 2,685 56 28.2% 26.5%

Small-Scale Solar PV (AC) 3,125 56 24.9% 24.0%

Solar Thermal 5,535 68 28.3% 26.7%

Wind (CA) 2,055 68 31.1% 31.2%

Table 7. Reference Case Input Assumptions for Wyoming Wind in 201713

Technology

Capital Cost

(2010 $/kW)

O&M Costs

($/kW - yr)

WY Wind

Capacity Factor (%)

Wind (WY) 1,845 56 47.7%

Capacity factor data for the reference case’s California scenarios are derived from the CPUC’s 

2013 RPS calculator which identifies the reported generation (GWh) and installed capacity

(MWs) of each project included in the scenario. The capacity factor for each project is then 

simply calculated as the project’s generation divided by its nameplate capacity multiplied by 

8760 (CPUC 2013).14 There are small capacity factor variations between the two California RPS 

scenarios based on geographical and other project-specific considerations. For example, smallscale solar PV shows a wider geographical dispersion relative to the large-scale PV projects, and 

this variation generally includes areas with relative lower quality solar resource.15

Table 7 presents the reference case’s capital costs, O&M expenses, and capacity factor input 

parameters for the 2017 Wyoming wind scenario. Updated resource information on renewable 

energy zones indicates that wind power facilities near the Wyoming terminus of the TransWest 

Express Project would have a likely annual capacity factor of about 46%, assuming the use of 

Type 1 wind turbines at a hub height of 80 meters. (AWS TruePower, 2011)

Forecasting future renewable energy generator costs is inherently uncertain despite the extensive 

industry vetting and analytical attention to renewable energy costs described in TEPPC process.

This uncertainty introduces an element of risk to projects in which these costs are a factor, and 

the approach taken here aims to provide a better measure of the risk by providing additional cost 

perspectives. This is also true for future improvements in generator performance, which is 

recognized, but not considered in WECC 2013.

 12 WECC 2013 and analysis of CPUC 2013 13 WECC 2013 and NREL 2014 14 The capacity factor calculation assumes 8,760 operational hours per year, based on a 365-day calendar year. 15 Note that CPUC defines small, utility-scale solar as projects between 1 MW and 20 MW in installed capacity. 
17

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

The study team elected to develop an alternate set of cost projections with the assistance of 

NREL’s technology experts. The alternative cost values were developed through extensive 

discussion, analysis, and testing by NREL staff. The values included disaggregated capital cost 

trends (i.e., for different types of wind turbines, different geothermal processes, and different PV 

configurations), efficiency improvements (such as wind turbine blade sweeps for applicable 

turbine types), and other technology-specific factors. Information came from industry tracking 

reports, working groups, press releases, government-reported pricing information, and in-house 

cost and modeling analyses. Table 8 and Table 9 present the capital costs, O&M expenses, and 

capacity factor input parameters for the California and Wyoming scenarios for the renewable 

energy cost sensitivity. In general, this sensitivity reflects lower capital and operating expenses 

for the renewable resources. Notably, the key differences represented in the cost sensitivities

affect solar PV, wind, geothermal, and solar thermal technologies.16,17

Table 8. Cost Sensitivity Assumptions for California Renewable Energy in 201718

Technology

Capital Cost

(2010 $/kW)

O&M Costs 

($/kW - yr)

CA33% RPS

Capacity 

Factor (%)

CA35% RPS

Capacity 

Factor (%)

Biogas 3,055 147 79.9% 79.9%

Biomass 4,975 175 84.5% 85.0%

Geothermal 5,675 127 81.0% 81.0%

Large-Scale Solar PV (AC) 2,100 14 24.4% 24.4%

Small-Scale Solar PV (AC) 2,100 14 24.4% 24.4%

Solar Thermal 6,900 73 51.7% 51.7%

Wind (CA) 1,785 59 32.5% 32.5%

Table 9. Cost Sensitivity Assumptions for Wyoming Wind in 201719

Technology

Capital Cost

(2010 $/kW)

O&M Costs 

($/kW - yr)

WY Wind

Capacity Factor (%)

Wind (WY) 1,520 47 52.6%

 16 Note that for each of the technologies, regional variations in costs are included in the renewable energy cost 

sensitivity. For consistency, these use the same regional multipliers as under the reference case assumptions except 

for solar thermal, which uses a labor multiplier within NREL’s System Advisor Model that is based on Southern 

California labor rates from the Bureau of Labor Statistics. 17 All assumptions for biogas and biomass technologies are held constant over both cases. 18 Information provided by NREL and DOE technology program specialists based on industry tracking reports, 

working groups, press releases, government-reported pricing information, and in-house cost and modeling analyses. 

For additional background, see Bolinger, M. and Weaver, S., Utility-Scale Solar 2012: An Empirical Analysis of 

Project Cost, Performance, and Pricing Trends in the United States, LBNL-6408E, Berkeley, CA: Lawrence 

Berkeley National Laboratory, September 2013; Energy Information Administration, “Assumptions to the Annual 

Energy Outlook 2013,” May 2013; Wiser, R. and Bolinger, M., 2012 Wind Technologies Market Report,

Washington, D.C.: U.S. Department of Energy, 2013. 

19 Ibid.
18

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Some of the key differences between the input assumptions in the reference case and those in the 

renewable energy cost sensitivity include:

• Solar PV capital costs are reduced from a range of $2,685/kWAC to $3,125/kWAC (for 

large-scale and small-scale solar PV, respectively) to a single value of $2,100/kWAC. 

This cost sensitivity assumes that even PV projects in the 1-MW to 20-MW range 

(defined as small-scale PV by CPUC 2013) may also realize some economies of scale 

and potentially could be less challenging to develop than much larger projects. This is 

evidenced in Bolinger and Weaver (2013), where nearly all of the lowest cost projects are 

below 20 MW, though the data is limited for the largest projects. Additionally, this PV 

cost sensitivity assumes that real-time cost trends for PV could be falling at a rate that is 

faster than can be vetted through the TEPPC stakeholder committees. Solar PV O&M is 

also reduced from the reference case, and generally in line with estimates from the U.S. 

Energy Information Administration (EIA) and internal NREL estimates. These estimates

assume some continued cost reductions by 2017 (EIA 2013b). Finally, this sensitivity 

tests only a fixed tilt system rather than a mix of fixed tilt and tracking systems based on 

limited data availability across the technology variants. Correspondingly, the capacity 

factor for PV systems in the renewable energy cost sensitivity is also reduced from the 

reference case to account for the lower capacity factor of fixed tilt versus tracking 

systems. The capacity factor data is based on CPUC’s average of ground mounted, fixed 

tilt capacity factor for utility-scale PV systems in California (CPUC 2013). 

• Wind capital costs are reduced from $1,845/kW to $1,520/kW in Wyoming and from 

$2,055/kW to $1,785/kW in California. Notably, this larger cost differential between 

Wyoming and California wind capital costs assumes that different classes of wind 

turbines would be deployed in these different regions. Among other factors, this reflects 

the nature of the different wind regimes in California versus Wyoming. For example, 

Wyoming wind developers may utilize a less expensive, high-wind speed turbine with a 

smaller rotor diameter and hub height compared to the more expensive, low-wind speed

turbines more applicable to conditions in California. This regional cost variation is 

presented in Wiser and Bolinger 2013, but is less evident in the reference case capital 

costs assumptions. Additionally, the renewable energy cost sensitivity assumes some 

level of performance improvement in the Wyoming wind scenarios based on the 

assumption that developers may be trending toward more efficient turbines with greater 

energy capture than previously used. These assumptions are consistent with NREL’s 

forthcoming future wind deployment analysis.

• Geothermal capital costs are reduced from $6,440/kW to $5,675/kW. Geothermal costs 

are highly sensitive to the characteristics of the geothermal resource, making the 

identification of a single representative value especially difficult. Instead, this analysis 

simply uses an average capital cost reported by the EIA between flash and binary 

geothermal technologies (EIA 2013a) and applies the California regional multiplier. In 

other words, we assume both types of geothermal technologies will be deployed within 

California. 

• Solar thermal costs are increased from $5,535/kW to $6,900/kW. This increase is largely 

driven by the assumption that the solar thermal technology deployed in California will 

utilize thermal energy storage. While this shows an increase in upfront capital costs, the 
19

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

inclusion of storage is estimated to result in a significant increase in capacity factor. 

These input assumptions are based on an internal NREL cost update for concentrating 

solar power technologies that was recently referenced by DOE (2014). 

The financing assumptions for energy generation shown in Table 10 are based on the CPUC’s 

2013 values for independent power producers (IPP) and are held constant over all scenarios and 

energy technologies (CPUC 2013). By holding the financial parameters constant, this analysis 

removes the impact of financial differences between one technology or project and another. 

Moreover, the financial variability between wind and solar technologies is likely small because

these technologies have the greatest amount of recent deployment and therefore familiarity 

among financiers.

Table 10. IPP-Based Financing Assumptions20

Financial 

Parameter Value

Debt Ratio 53%

Equity 47%

Debt Interest Rate 7%

Cost of Equity 15%

Debt Term (years) 19

Effective Tax Rate 40.53%

After Tax WACC 9.26%

The federal policy assumptions for each technology are listed in Table 11. These policy 

assumptions are based on the availability and value of federal support mechanisms as of year-end 

2013 for each technology. Sensitivity analysis around the level and availability of various federal 

policy mechanisms are presented in subsequent sections. Additionally, all technologies assume 

the use of five-year Modified Accelerated Cost Recovery System (MACRS) depreciation, though 

bonus depreciation is not considered.21 No state-based policy mechanisms are assumed.

 20 CPUC 2013 21 Note that biogas and biomass are technically eligible for slightly longer accelerated depreciation schedules of 10 

years instead of the five years that wind, solar, and geothermal qualify for. This analysis makes a simplifying 

assumption and utilizes a five-year MACRS depreciation schedule for all technologies for the sake of comparison. 

Overall, the impact is minimal because biogas and biomass account for approximately only 1% of the California 

change case scenarios. 
20

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 11. Federal Tax Policies for Different Renewable Energy Technologies22

Technology Federal Incentive Value

Biogas Production Tax Credit 1.1 ¢/kWh

Biomass Production Tax Credit 1.1¢/kWh

Geothermal Production Tax Credit 2.3¢/kWh

Large-Scale Solar PV Investment Tax Credit 30% of eligible costs

Small-Scale Solar PV Investment Tax Credit 30% of eligible costs

Solar Thermal Investment Tax Credit 30% of eligible costs

Wind (CA and WY) Production Tax Credit 2.3¢/kWh

As noted previously, this study makes the simplifying assumption that each of the renewable 

energy generators has a 20-year useful life and cost recovery period. In general, renewable 

energy technologies with longer useful life than assumed here could benefit from cost recoveries 

spread over a longer period of time. However, this effect is muted somewhat by increasing 

maintenance costs over time and the impact of discounting future cash flows.23 

Reference Case and Renewable Energy Cost Sensitivity Results

The input assumptions presented in previous sections were used to calculate the 2017 LCOE for 

each technology using input parameters from both the reference case and the renewable energy 

cost sensitivity. The overall scenario LCOE was then estimated based on the generationweighted factor of each individual technology.

Table 12 and Table 13 show the 2017 LCOE estimates for the CA33% and CA35% RPS change 

cases and the Wyoming wind change case; each change case is based on the reference case 

costs.

24 The combined LCOE for the California change-case resources ranges from $0.1074/kWh 

for the 33% RPS scenario to $0.1085/kWh for the 35% RPS scenario (10.74 ¢/kWh to 

10.85¢/kWh). Note the relatively small differences between the CA33% and CA35% change 

case resources.

The Wyoming wind scenario’s LCOE is estimated at $0.0475/kWh (4.75¢/kWh), approximately 

$0.060/kWh (6.0¢/kWh) less than the CA33% RPS change case scenario. The sizably lower 

LCOE in the Wyoming wind scenario is due to at least two principal factors. First, the Wyoming 

wind scenario comprises a single technology with the lowest present-day capital cost of all 

technologies tested. Second, the high annual average wind speed in Wyoming leads to a high 

capacity factor for Wyoming wind relative to California solar PV and wind technologies. 

 22 DSIRE 2013a, DSIRE 2013b 23 For example, a 25-year useful life and cost recovery period reduces the modeled scenario LOCE by less than 2% 

relative to a 20-year useful life. 24 Note that this section refers to LCOE results using the cents per kilowatt-hour notation for reader convenience and 

broader familiarity. Other sections, particular those concerning bulk power system analysis, use the dollar per 

megawatt-hour convention.
21

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 12. Reference Case LCOEs in 2017 by Renewable Energy Type (PTC, 30% ITC)

Technology

CA33% RPS CA35% RPS

LCOE 

(¢/kWh) Weight

Weighted 

LCOE 

(¢/kWh)

LCOE 

(¢/kWh) Weight

Weighted 

LCOE 

(¢/kWh)

Biogas 6.65 0.4% 0.03 6.65 0.2% 0.01

Biomass 9.75 0.1% 0.01 9.65 1.1% 0.11

Geothermal 11.55 29.8% 3.44 11.55 28.6% 3.30

Large-Scale Solar PV 9.45 43.5% 4.11 10.05 43.1% 4.33

Small-Scale Solar PV 12.05 1.8% 0.22 12.45 4.4% 0.55

Solar Thermal 17.25 7.2% 1.24 18.35 4.0% 0.74

CA Wind 9.85 17.2% 1.69 9.75 18.7% 1.82

Total - 100% 10.74 - 100% 10.85

Table 13. Reference Case LCOE in 2017 for Wyoming Wind (PTC, 30% ITC)

Technology

WY Wind Scenario

LCOE 

(¢/kWh) Weight

Weighted 

LCOE 

(¢/kWh)

WY Wind 4.75 100% 4.75

Total - 100% 4.75

Table 15 shows the resulting California LCOEs based on the renewable energy cost sensitivity. 

Note the weighting is consistent between the reference case and the sensitivity. Table 16 presents 

the LCOE for the Wyoming wind scenario based on the renewable energy cost sensitivity

assumptions.
22

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 14. Renewable Energy Cost Sensitivity LCOEs in 2017 by Renewable Energy Type (PTC,

30% ITC)25

Technology

CA33% RPS CA35% RPS

LCOE 

(¢/kWh) Weight

Weighted 

LCOE 

(¢/kWh)

LCOE 

(¢/kWh) Weight

Weighted 

LCOE 

(¢/kWh)

Biogas 6.65 0.4% 0.03 6.65 0.2% 0.01

Biomass 9.75 0.1% 0.01 9.75 1.1% 0.11

Geothermal 9.55 29.8% 2.85 9.55 28.6% 2.73

Large-Scale Solar PV 6.95 43.5% 3.02 6.95 43.1% 2.99

Small-Scale Solar PV 6.95 1.8% 0.13 6.95 4.4% 0.31

Solar Thermal 11.55 7.2% 0.83 11.55 4.0% 0.46

CA Wind 7.75 17.2% 1.33 7.75 18.7% 1.45

Total - 100% 8.20 - 100% 8.06

Table 15. Renewable Energy Cost Sensitivity LCOE in 2017 for Wyoming Wind (PTC, 30% ITC)

Technology

WY Wind Scenario

LCOE 

(¢/kWh) Weight

Weighted 

LCOE 

(¢/kWh)

WY Wind 3.15 100% 3.15

Total - 100% 3.15

As shown in Table 14, the renewable energy cost sensitivity reduces the combined California 

LCOE from $.1074/kWh (10.74¢/kWh) to $.0820/kWh (8.20¢/kWh). Similarly, the LCOE for 

Wyoming wind drops from $.0475/kWh (4.75¢/kWh) to $.0315/kWh (3.15¢/kWh). Overall, the 

differential between the California resource mix and Wyoming wind is estimated at $.05/kWh 

(5¢/kWh). This large differential suggests that under both scenarios there is a considerable 

LCOE premium for the California technology mix that ranges from five to six cents per kilowatthour. Moreover, a sizable LCOE differential persists when solar and wind technologies 

experience cost reductions and improve productivity greater than what is assumed in the 

reference case. 

Federal Tax Policy Sensitivities

The availability and level of federal incentives is also a key driver in the LCOE estimation for 

renewable energy technologies (Cory and Schwabe 2009). Under this sensitivity analysis, federal 

policies are shown to have an unequal effect between the California and Wyoming scenarios due 

to varying federal policies impacts on the underlying technologies. For example, solar 

technologies (i.e., PV and thermal) receive a 30% ITC based on the overall cost of the system, 

whereas wind receives a 2.3¢ PTC for each kilowatt-hour produced, and geothermal is eligible 

for either the 2.3¢/kWh PTC or a 10% ITC. And because the California and Wyoming scenarios 

 25 In both cost cases, the small differential between the LCOEs for the CA33% and 35% scenarios suggests that 

based on the level of detail utilized for this analysis, there is not a discernable difference between the LCOEs of the 

two California RPS change case scenarios. The remaining LCOE results are therefore presented for only the 

California 33% RPS test scenario because it is nearly identical to the 35% test scenario. 
23

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

have different technology mixes, capital costs, and energy production, the impact of federal 

polices is unequal. Therefore, this analysis tests the impact of federal policies on the Californiato-Wyoming LCOE differential for three possible future federal policies: 

1. A 2017 policy environment with the 10% ITC, but not the PTC; 

2. A 2017 policy environment in which the ITC and PTC are reauthorized at their 2013 

levels (shown previously in LCOE Results section); and

3. A 2017 policy environment with neither the ITC nor the PTC.

The first policy scenario represents 2017 federal energy policy as written in 2013 law. Both solar 

and geothermal technologies are eligible for a 10% ITC with no explicitly stated expiration date, 

whereas the production tax credit for wind expired as of year-end 2013.26 Figure 8 shows the 

LCOE for the California mixed renewables and the Wyoming wind scenarios with a 10% ITC 

and no PTC policy with the reference case input assumptions as well as the renewable energy 

cost sensitivity.

Figure 8. 2017 LCOE results with 10% ITC and no PTC

Figure 9 illustrates the 2017 LCOE differential between the California mix and Wyoming wind 

with the reference case input assumptions and the renewable energy cost sensitivity and assumes 

the reauthorization of the PTC and ITC. 

 26 Note, however, that wind energy producers may still qualify for the PTC if certain criteria are met for beginning 

construction in 2013 (DSIRE 2013b). 

13.7

10.8

6.8

5.1

6.9

5.7

Reference case inputs Renewable energy cost sensitivity

0

2

4

6

8

10

12

14

16

LCOE 2010 ¢ / kWh

CA Mixed Renewables WY Wind Scenario Differential
24

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 9. 2017 LCOE results with PTC and 30% ITC

With only the 10% ITC and no PTC in effect (Figure 8), the LCOE for the Wyoming wind and 

California mixed renewable scenario is approximately $0.02/kWh (Wyoming) to $0.03/kWh 

(California) higher than then PTC and 30% ITC case (Figure 9). The magnitude of the LCOE 

increase is consistent for both the reference case input assumption and the cost sensitivity cases. 

The increase is somewhat more pronounced on the mixed renewable California scenario 

compared to Wyoming due to its mix of PTC and ITC eligible technologies, and the capital cost 

profiles of the ITC eligible technologies. This result suggests that a policy environment as 

currently written in 2017 law (10% ITC, no PTC) would generally increase the LCOE 

differential between the California and Wyoming wind scenarios by at least $0.007/kWh 

(0.7¢/kWh). 

Figure 10 shows this LCOE impact for a policy environment with no PTC or ITC for the 

reference case and the renewable energy cost sensitivity. The same trends seen in the 10% ITC 

policy case are extended here to the no-ITC, no-PTC policy case. That is, a policy environment 

without federal tax credits leads to a larger increase in LCOE in the California mixed renewables 

scenario compared to the all-wind Wyoming scenario. Together these results indicate that the 

Wyoming wind scenario could provide a possible hedge against unknown policy futures and that 

a significant LCOE differential exists under the range of policy options tested here. 

10.7

8.2

4.8

3.2

6.0

5.0

Reference case inputs Renewable energy cost sensitivity

0

2

4

6

8

10

12

LCOE 2010 ¢ / kWh

CA Mixed Renewables WY Wind Scenario Differential
25

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 10. 2017 LCOE results based on no ITC or PTC

The likelihood of lower LCOE in the Wyoming wind scenario is one of the key economic 

benefits of developing the Wyoming-to-Eldorado corridor for wind energy. Therefore, the BCA 

analysis presented in the subsequent sections uses the LCOE findings presented here to test how 

the benefit of the reduced Wyoming LCOE relative to the California scenarios compares to the 

added cost of constructing and operating a new transmission line in the corridor.

Outcomes for BCA

The generator cost savings associated with transmission from Wyoming to California is the 

difference between the annual revenue requirement of the Wyoming wind portfolio, and that of 

the California generation portfolio against which it is tested. Annual revenue requirements for 

the entire portfolios are approximated by multiplying the weighted average LCOE by 12 TWh 

per year:

ARRi = LCOEi × 12 TWh

∆ARR = ARRCA − ARRWY

where:

i = the generation portfolio being tested

ARRi = the annual revenue requirement for portfolio i

LCOEi = the levelized cost of energy for portfolio i, in $/MWh

In all cases, the annual revenue requirement of the Wyoming wind portfolio is less than the cost 

of the California resources it would hypothetically replace. Table 16 shows the net present value 

of this benefit stream extended over 50 years. 

15.4

12.2

6.8

5.1

8.6

7.2

Reference case inputs Renewable energy cost sensitivity

0

2

4

6

8

10

12

14

16

18

LCOE 2010 ¢ / kWh

CA Mixed Renewables WY Wind Scenario Differential
26

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 16. 50-Year Streams of Reduced Generator Costs (Net Present Value)

Cost and Policy Assumptions Benefit

Reference case cost assumptions (TEPPC)

Current law (no PTC in 2017, 10% ITC in 2017) $8.7 billion

PTC, ITC reauthorized at 30% for 2017 $7.6 billion

No PTC, ITC $10.9 billion

Renewable energy cost sensitivity

Current law (no PTC in 2017, 10% ITC in 2017) $7.2 billion

PTC, ITC reauthorized at 30% for 2017 $6.4 billion

No PTC, ITC $9.1 billion

Note: 50-year streams assume generator equipment has a 20-year life and is replaced twice over 50 years with 

comparable equipment at comparable cost.
27

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

4 Capacity Values

The capacity value of a generation resource is the contribution that it makes towards planning 

reserve. Capacity value can also be calculated for a group of resources, or for an entire power 

system, as appropriate. The contribution of any resource, or group of resources, to resource 

adequacy can be calculated using the effective load carrying capability (ELCC), which 

represents the maximum load that can be served at a given reliability target—typically a loss of 

load for one day over the course of 10 years. The ELCC calculation is built on one of the more 

fundamental reliability metrics—loss-of-load expectation (LOLE), loss-of-load hours (LOLH), 

or expected unserved energy (EUE)—each of which is based on loss-of-load probability (LOLP). 

A rigorous probabilistic method based on loss-of-load probability (LOLP) analysis is the 

approach recommended by the IEEE Task Force on Wind Capacity value, 27 and by the North 

American Electric Reliability Corporation28 for assessing the capacity value of variable 

generation sources such as wind and solar energy. 

The ELCC represents the additional load that can be supported by the resource in question, 

holding long-term reliability constant, and is also called the capacity credit or capacity value of 

the resource. For example, a 200 MW gas unit with a forced outage rate of 0.10 would have an 

ELCC of approximately 180 MW (LOLE-based analysis considers the convolved contribution of 

all plants; this example vastly simplifies the numerical results to aid in the discussion). A 200 

MW wind plant with a capacity factor of 35% might have an ELCC of 30 MW, or 15% of its 

installed capacity. We note that this example points out a fundamental difference in the ratio of 

capacity value, as measured by ELCC, to installed capacity when we compare resource types. 

We will discuss implications of this in more detail later in this section.

For variable generation (VG), the recommended approach is to utilize time-synchronized power 

production and load data. This will implicitly capture the underlying weather drivers for load, 

solar generation, and wind generation. If data from different years are used for load and VG, one 

could easily envision a situation in which the load on a given day is based on hot sunny weather 

that induces significant air conditioning loads; whereas the wind data is based on a cloudy, 

stormy day. Many other similar examples can result in a mismatch between the implicit weather 

driver of load and the VG resource. 

Figure 11 illustrates the ELCC calculation. The example uses a target LOLE of one day over the 

course of 10 years. The left curve shows the relationship between the level of peak load that can 

be served and the LOLE. At the target LOLE, a 10 GW load can be served, and as the curve 

shows, a lower load will have a higher reliability level and a higher load would have a lower 

reliability level. When a new generator is added to this system, the reliability curve shifts to the 

right, and the distance of this shift depends on a combination of system and generator attributes. 

The example diagram shows that the additional load that can be served while maintaining the 

1d/10y level of reliability is 150 MW; thus the new generator has a capacity value of 150 MW.

 27 A. Keane et al., “Capacity value of wind power,” IEEE Trans. on Power Syst., vol. 26, no. 2, pp.564–572, May 

2011.

28 NERC’s Integration of Variable Generation Task Force (IVGTF), Methods to Model and Calculate Capacity 

Contributions of Variable Generation for Resource Adequacy Planning, Princeton, NJ: North American Electric 

Reliability Corporation, 2011, www.nerc.com/docs/pc/ivgtf/IVGTF1-2.pdf.
28

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 11. ELCC is the horizontal difference between reliability curves, evaluated at the target 

reliability level

NREL developed the Renewable Energy Probabilistic Reliability Assessment (REPRA) to better 

understand how different types of renewable generation—much of which is non-dispatchable—

can contribute to a power system’s resource adequacy with respect to reliability. 29 The tool, 

which is used in this analysis of capacity value, is described in detail in Appendix B.

Data Sources

Necessary data sources for this portion of the study can be divided into three categories: 

conventional generation, load profiles, and variable generation (VG) profiles. We discuss each 

piece separately, although most of them reference CAISO’s PLEXOS model.30

Conventional Generation

The list of conventional generators and their effective forced outage rates (EFOR) are extracted 

from the PLEXOS database. There are 700 generators in the database and their distribution of 

capacity and EFOR are displayed in Figure 12.

 29 E. Ibanez, M. Milligan, “Probabilistic Approach to Quantifying the Contribution of Variable Generation and 

Transmission to System Reliability,” http://www.nrel.gov/docs/fy12osti/56219.pdf; E. Ibanez, M. Milligan, “Impact 

of Transmission on Resource Adequacy in Systems with Wind and Solar Power,” 

www.nrel.gov/docs/fy12osti/53482.pdf. 30 California ISO, “2012 LTPP Plexos Model” (October 2, 2013 version)

0.12

0.11

0.10

0.09

0.08

0.07

Loss of Load 

Expectation 

(days/

years)

8.0 8.5 9.0 9.5 10.0 10.5 11.0

Load (GW)

Original Reliability Curve

Reliability Curve after Adding New Generation

Target Reliability Level

<-150 MW->

Additional Load that 

can be served to 

bring the LOLE back 

to the target

At the same 

load level, 

LOLE is 

reduced
29

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 12. Effective forced outage rates for conventional generators

A portion of the in-state California resources correspond to geothermal, concentrating solar 

power, biogas, and biomass. For the purpose of this section, all these resources are given full 

capacity credit toward the calculation of capacity value, along with a nominal EFOR of 2%.

Load Profiles

The CAISO PLEXOS database is configured to perform simulations in the year 2022 using data 

from the 2005 meteorological year. Our intent is to extend the calculations to the years 2004 and 

2006. To that end, historical data was collected from FERC’s Form 714 for all California 

balancing areas. These historical profiles were scaled to match the 2022 projections in the 

CAISO database to meet the same peak load and energy demand. The resulting load duration 

curves are shown in Figure 13. It is unclear whether the 2022 projections in the PLEXOS 

database had PV rooftop production embedded. Large penetrations of rooftop PV could affect 

the final shape of net load and change the top net load hours, which correspond to the hours with 

most risk. Given that no information was available and the separation of load and rooftop PV 

profiles is not trivial, this issue was ignored in the escalation of load.

Figure 13. Load duration curves for historical data scaled to 2022
30

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

VG Profiles

VG profiles are available in the CAISO PLEXOS database for wind, large PV, and small PV for 

each balancing authority, as well as for out-of-state renewables (wind from BPA and PV from 

NV Energy). According to CAISO documentation, these profiles are based on NREL’s Western 

Wind Dataset31 and Clean Power Research’s SolarAnywhere database.

32 However, the CAISO 

profiles could not be reproduced from the reported source data sets.

Thus, sites from the databases above were selected (in descending capacity factor order), until 

achieving the energy content in the PLEXOS database time series by BA for the year 2005. The 

same sites were used to create the profiles for 2004 and 2006. The CAISO and NREL data series 

show similar variability distribution, with the exception of BPA, which shows more frequent and 

larger (but not extreme) ramps.

Similarly, almost one hundred 30-MW sites close to the TransWest Express interconnection 

point in Wyoming were selected to represent Wyoming wind. Corrections were applied at the 

hourly level to represent increased annual capacity factors resulting from the improvement in 

generator technology since the creation of the database.

At the end of this process four different sets of VG time series are available, summarized in 

Table 17.

Table 17. Summary of VG Time Series

Data Set Met. 

Year CA Wind and PV WY Wind

CAISO05 2005 CAISO site selection

NREL-04 2004 NREL site selection NREL site selection

(matching energy 

delivered)

NREL-05 2005

NREL-06 2006

Results

Before analyzing the capacity value for the different cases it is worth noting that, with the current 

set of inputs, the LOLE is negligible, even with 2022 levels of load. To achieve a LOLE of 1 day 

in 10 (a well-established industry standard), the peak load would have to be 25% to 35% higher. 

This shows that, barring local transmission congestion issues, there is not a shortage of capacity 

in the CAISO plan.

Figure 14 summarizes the capacity value for all the scenarios and VG data set. The box on the 

left represents the capacity value in the 33% penetration scenarios (CA33 in orange and 

 31 3TIER. (2010) “Development of Regional Wind Resource and Wind Plant Output Datasets”, National Renewable 

Energy Laboratory, Golden, CO, Tech. Rep. NREL/SR-550-47676. [Online]. Available: 

http://www.nrel.gov/docs/fy10osti/47676.pdf

32 Perez, R. (2002). “Time-Specific Irradiances Derived From Geostationary Satellite Images.” Journal of Solar 

Energy Engineering—Transactions of the ASME (124:1); pp. 1–1.
31

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

CA/WY33 in green), while the box on the right presents that same information for the 35% 

scenarios (CA35 in orange and CA/WY35 in green). The different year/VG data sets are 

displayed along the horizontal axis. Table 18 shows the same information. According to the 

table, the in-state scenarios present a higher capacity value of 957 MW. Table 19 and Figure 15

disaggregate the California resources.

Figure 14. Capacity values for each scenario and data set

Table 18. Capacity Values (MW) by Scenario and Data Set

Scenario CAISO-05 NREL-04 NREL-05 NREL-06 Average

CA33 1345 2086 1150 997 1395

CA35 1235 2200 1138 946 1380

CA/WY33 306 946 332 166 437

CA/WY35 385 960 332 166 461

Difference 33% 1040 1140 818 831 957

Difference 35% 850 1240 806 780 919
32

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Table 19. Capacity Value (MW) by Scenario and Data Set With California Resources Disaggregated

Scenario Type

CAISO05 NREL-04 NREL-05 NREL-06 Average

CA33

CA PV 640 1396 509 330 719

CA Wind 106 102 43 67 79

CA Other 599 588 599 601 597

CA/WY33 WY Wind 306 945 332 166 437

CA35

CA PV 642 1635 489 392 789

CA Wind 104 86 160 62 103

CA Other 489 479 489 492 487

CA/WY35 WY Wind 385 960 332 166 461

Figure 15. Capacity value (MW) by scenario and data set with disaggregated California resources

These results show that the capacity value is sensitive to both generation mix and the underlying 

weather changes that occur from year to year. Wind plants that are sited in Wyoming have higher 

capacity values than wind plants in California; however, when the renewable mix in California 

comes from a blend of wind and solar energy, the combined capacity value of the wind and solar 

exceeds the Wyoming wind alone. Although we don’t compare wind-only cases for Wyoming

and California (because the California case includes solar) it is useful to discuss other differences 

that arise. To achieve the same wind energy target by developing wind energy in California as 

compared to wind energy in Wyoming, additional installed wind capacity would be needed in 

California to compensate for the lower annual capacity factors in California compared to 

Wyoming.
33

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 16 is an attempt to better understand the previous results. Each panel represents a data 

scenario for either the CA33 or the CA/WY33 scenarios. The hours with highest net load are 

displayed. These hours typically happen in the summer evenings. Net load is represented in gray 

for each case. On top of the net load we plot the generation corresponding to the last 12,000 

GWh of renewable generation, be it resources in California or in Wyoming. The net load curve 

in 2004 is significantly lower and flatter, due to higher PV generation during those hours (not 

shown here). The incremental PV added to the system also presents high levels of output during 

hours of relatively high load, leading to the largest capacity value. Coincidentally, wind 

generation in Wyoming is also the most significant during that period. For the rest of cases, net 

load shapes and generation levels from wind and PV are similar, especially in the 2005-based 

cases. In 2006, generation from Wyoming wind is particularly small during the first few top-netload hours, which leads to a smaller capacity value.

Figure 16. Top net load hours with net load values and incremental contribution from California 

renewables and Wyoming wind

It is clear from the several scenarios that capacity value is a function of resource timing relative 

to demand, renewable energy mix and location, and year. Weather systems drive electricity 

demand and wind and solar energy. To examine the contribution of the various renewable 

scenarios to overall resource adequacy, we used the NERC- and IEEE-recommended approach, 

which is built on LOLP modeling. The modeling tool, REPRA, was developed at NREL and uses 

an advanced sliding window method to convolve the VG into the LOLE calculation. For the 

NREL data we applied this method to the three available data years.

The California in-state resources present a significant higher capacity value in terms of MW than 

the Wyoming wind. This is to be expected because:
34

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

• Capacity factor of the Wyoming wind is much higher than California wind or PV. Thus, 

less capacity is required to achieve the same energy delivered, which was the driver in the 

creation of the scenarios.

• California in-state resources are primarily comprised of geothermal and solar PV. The 

output of geothermal resources is assumed to be 100% of the installed geothermal 

capacity during critical reliability hours. The output of solar PV is highly correlated with 

load during maximum load hours; hours that are generally critical from a reliability 

standpoint. By contrast, wind energy has a lower correlation with demand during peak 

periods.

The difference in capacity value is fairly consistent across scenarios and averages 957 and 919 

MW for the 33% and 35% penetration scenarios, respectively. The NREL-05 and CAISO-05 

results are quite similar, in spite of the different siting procedures. 

Outcomes for BCA

On average, replacing the California portfolio with Wyoming wind power would increase the 

need for dispatchable capacity by about 957 MW for the 33% renewable energy scenario, based 

on an average of the models examined in this section. This equates to annual benefit reductions 

of $81 million. The dollar equivalents represent the cost of building new natural gas combustion 

turbines in California to provide local capacity value equivalent to what would be lost by 

acquiring Wyoming wind, and is priced using the capital cost of a natural gas combustion turbine

at an estimated capital cost of $800/kW. Table 21 shows the 50-year stream of capacity value 

benefits.

The analysis assumes that the difference in capacity value will remain constant over the 50-year 

period. California’s overall resource adequacy picture could change up or down depending on 

trends affecting generator retirement, the availability of hydroelectric resources, and load growth. 

The assumption here is that system changes would affect both portfolios (12,000 GWh/year from 

the tested California portfolio versus the same amount of energy from Wyoming wind) in the 

same direction. Discounting reduces the impact of deviations from this assumption that might 

occur over time.

Table 20. Fifty-Year Stream of Capacity Value Benefits (Net Present Value)

Benefit

Change in capacity value of selected resources 

(resource adequacy) -$858 million
35

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5 Production Cost Modeling

This section examines the operational cost of the California power system, on an hourly basis, 

with and without 12 TWh/yr of Wyoming wind power delivered via a dedicated DC transmission 

line. The production costs include only variable costs such as fuel costs, variable O&M costs, 

and start-up costs. Capital cost differences are discussed in previous sections and are not 

considered in the production cost modeling.

The PLEXOS PCM was used for this study, and the primary goal was to understand any 

production cost differences between the scenarios. PLEXOS simulates the operation of the 

electric power system by optimizing the commitment and dispatch of the electric power system 

for one year with a time resolution of one hour. This is similar to how market operation software 

works, but simplified for planning purposes.

The PLEXOS Model

To perform the PCM, we drew on the publicly available PLEXOS database,33 recently developed 

by CAISO based on the 2012 CPUC Long-Term Procurement Plan process for the Western 

Interconnection. We made several changes to the database for the specific purposes of the 

present study:

• Added 12 GWh of Wyoming wind power

• Added a 3,000-MW DC transmission line connecting the Wyoming wind resources with 

the CAISO balancing authority in southern Nevada. 

• Modeled two levels of renewable energy demand in California, 33% and 35% of retail 

energy consumption. To accommodate these levels, several California renewable 

generation facilities were scaled up or down depending on scenario. In the cases that 

included Wyoming wind power, the locations and quantities of the renewable resources 

that were scaled down were determined as described previously in this report for the two 

different Wyoming wind power scenarios (CA/WY 33% and CA/WY 35%).

This study analyzed system production costs for all of the Western Interconnection. PLEXOS 

accounts for the costs of power that is imported into or exported out of California by including 

those areas in the model. Several convergence tests were performed to find the numerical 

uncertainty of the optimized system-wide production costs, finding the precision to be 

approximately 0.02%.34 This does not account for uncertainties in market assumptions and in the 

parameters of the generators and transmission infrastructure, but the 0.02% provides a basis for 

understanding whether production cost differences are significant.

The load and VG profiles are from the CAISO PLEXOS model. The hourly profiles for load, 

wind, and solar are based on the 2005 meteorological year and are described in more details in 

Section 4, where they are referred to as CAISO-05. These profiles were scaled down as 

necessary to displace energy for the Wyoming wind power such that the comparable scenarios 

had the same amount of wind and solar generation. The wind profiles for the additional wind 

 33 CAISO. “2012 LTPP PLEXOS Model” (October 2, 2013 version). 34The system production cost of the PLEXOS solution will be within approximately 0.02% of the optimal system 

production cost based on all of the model inputs. 
36

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from Wyoming are based on the NREL Western Wind and Solar Integration Study data set and 

the methodology for selecting sites is described in Section 1. Parameters for generation and 

transmission infrastructure are based on the CAISO assumptions for the database. Transmission 

was modeled zonally, with each balancing authority area in the Western Interconnection 

representing a single zone. 35

Thirty-Three Percent Renewables With and Without Wyoming Wind 

Power

Table 21 shows the operating costs for the two 33% renewable power scenarios. The WY33% 

scenario costs approximately $31 million less than the CA33% scenario. While this value 

exceeds the numerical error margin (0.02% x $15,000 million = $3 million), it is relatively small 

compared to the capital cost differences between the scenarios discussed in Section 3. The 

majority of the difference ($23 million) comes from a reduction in start-up costs. This result is 

consistent with previous results from the Western Wind and Solar Integration Study Phase 2

(Lew 2013), which show that high solar penetrations can cause more start-up costs compared to a 

similar mixture of wind and solar.

Table 21. Annual Production Costs for the CA33% and CA/WY33% Scenarios

Costs, $M CA/WY33% CA33% CA33% –

CA/WY33%

Generation Cost 13,679 13,687 8

Startup Costs 1,363 1,386 23

Total Costs 15,042 15,073 31

The Wyoming wind power affects the California power import/export balance (Figure 17). The 

difference between the two cases in Figure 17 is represented by the green line, which shows 

mostly negative values, meaning that in the WY33% scenario California is indeed importing 

more energy compared to CA33%. Bringing 12,000 GWh of Wyoming wind power to California 

increases the total net California imports by 12,640 GWh, meaning that an additional 640 GWh 

accompany the Wyoming wind power imports. The additional imports come into California 

because of price changes, as described below.

 35 Zonal modeling accounts for nominal transfer limits between zones but does not consider the capabilities of 

individual transmission system elements (lines, transformers) and does not reflect all of the specific WECC-defined 

Path limits. In addition, a zonal model predicts hourly market clearing prices at the zonal level. It does not produce 

hourly Locational Marginal Prices (LMPs) at the nodal level (such as occurs at nodes within the CAISO balancing 

authority). The WECC electric system is a mixed contract-path/nodal market and, in this sense, a zonal model can 

be considered as an approximation of both. Because not all transmission elements and paths are modeled in a zonal 

model, there may be congestion-related impacts and costs that are not identified in the two scenarios considered in 

the instant study. It is judgment of the authors that these impacts are relatively small compared to the other 

uncertainties inherent in the analysis. 
37

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Figure 17. Net hourly California power imports for the CA33% (blue) and CA/WY33% (red line) 

cases.

The effect of Wyoming wind power on zonal market clearing prices in California is shown in 

Figure 18, which compares SCE-area hourly market clearing price profiles for the two scenarios 

(CA33% and CA/WY33%). The SCE zone is one of the two areas, along with the Los Angeles 

Department of Water and Power (LADWP) zone, directly connected to the southern terminus of 

the TransWest Express Project. The price reduction effect of Wyoming wind is the strongest in 

these two zones. As expected, the SCE zone market clearing prices mostly decrease, by 

$0.8/MWh on average. However, the CA/WY33% case shows little effect on price variability, as 

the SCE zonal market clearing prices’ standard deviation is $67/MWh for the CA/WY33% 

scenario vs. $74/MWh for the CA33% scenario.

Figure 18. SCE-area hourly profiles of LMP for the two 33% renewable power scenarios: CA33%

(blue line) and CA/WY33% (red line)

The effect of Wyoming wind power on market clearing prices in other California zones is 

summarized in Table 22. The regions include the Imperial Irrigation District (IID), the LADWP, 

Pacific Gas and Electric Company’s (PG&E) Bay and Valley regions, SCE, San Diego Gas and 

Electric Company (SDGE), Sacramento Municipal Utility District (SMUD), and the Turlock 

Irrigation District (TIDC).
38

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Transmission congestion causes market clearing prices to vary depending on zone; some regions 

are better connected and show similar prices (PG&E Bay/Valley, SCE/SDGE, SMUD/TIDC). In 

general, adding variable renewable generation (which has very little marginal cost) lowers prices 

in nearby areas because it will bid into the market with a low price/quantity offer. For this study, 

it was assumed that all variable renewables bid into the market with a zero price/quantity offer, 

although in reality some generators will submit negative price/quantity offers into the market if 

there is a tax credit or other incentive to generate. A generator would submit a negative 

price/quantity offer to reflect the opportunity costs the generator would incur if its offer did not 

clear the market.

Most regions show lower prices in the CA/WY33% scenario. Prices rise in IID because there are 

fewer resources with zero marginal costs in the CA33% case compared to the CA/WY33% case. 

The particular scenarios tested in this analysis removed about 480 MW of geothermal resource 

potential from IID. These resources were associated with a possible transmission upgrade that 

was selected in the CPUC’s commercial interest portfolio and eliminated in the test scenario for 

Wyoming wind power. Because geothermal has low variable costs, market clearing prices tend 

to be lower when it is included and higher when it is not. The increase in prices in IID also leads 

to additional imports coming into IID from outside California in the CA/WY33% case, which 

explains why California imports increase by more than the additional 12 TWh of Wyoming wind 

power.

Table 22. The Effect of Wyoming Wind Power on the Annual Average Market Clearing Prices in 

California, by Region, for the Two 33% Renewable Power Scenarios

Average Market 

Clearing Prices by 

California Zone CA/WY33% CA33% CA33% – CA/WY33

IID 39.1 37.3 -1.8

LADWP 38.3 39.6 1.3

PG&E Bay 39.1 39.7 0.6

PG&E Valley 39.1 39.7 0.6

SCE 40.3 41.1 0.8

SDGE 40.3 41.1 0.8

SMUD 49.1 49.6 0.6

TIDC 49.1 49.6 0.6

Thirty-Five Percent Renewables With and Without Wyoming Wind 

Power

The simulation results for the CA35% and CA/WY35% scenarios show similar trends to the 33% 

renewable power cases (Table 23). The annual production costs were $14 million lower in the 

CA/WY35% case compared to the CA35% case, which is less than the difference in the 33% 

cases (Table 21). The difference is probably due to a slight change in the type of energy that is 
39

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

displaced to make room for the Wyoming wind power. Again, the start-up costs comprised a 

majority of the difference between the CA/WY35% and CA35% scenarios.

Table 23. Simulation Results Summary for CA35% and WY35% Scenarios

Costs, $M CA/WY35% CA35% CA35% –

CA/WY35%

Generation Cost 13,545 13,551 6

Startup Costs 1,373 1,382 8

Total Costs 14,918 14,933 14

Similar to the 33% RPS cases, bringing 12,000 GWh of Wyoming wind power to California 

increases the total net California imports by 12,677 GWh, meaning that an additional 677 GWh 

accompany the Wyoming wind power (primarily as additional imports to the IID). The effects of 

Wyoming wind power on California net power imports and on the SCE zone market clearing 

prices are shown in Figure 19 and Figure 20, and are similar to the 33% renewable power case.

Figure 19. The effects of Wyoming wind power on California net imports
40

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 20. The effects of Wyoming wind power on SCE zone market clearing prices

Production Cost Modeling Summary 

Production cost modeling of the Western Interconnection shows modest changes in operating 

costs when replacing a fraction of California local renewable resources with Wyoming wind 

power delivered to California by a dedicated transmission line: a savings of 0.2% ($31M/yr) for 

the 33% renewable energy scenario, and 0.1% ($14M/yr) for the 35% renewable energy 

scenario. The benefit/cost analysis includes this production cost savings as a benefit, albeit a 

small one.

The observed small changes in operating costs come mostly from smaller startup costs when 

Wyoming wind power is included, which is consistent with results from the Western Wind and 

Solar Integration Study Phase 2. Prices are reduced in most areas in response to the Wyoming 

wind power, but prices are higher in areas that have fewer renewable resources and are not near 

southern Nevada (the southern terminus of the DC line in the scenarios with Wyoming wind 

power).

Future work could analyze the potential integration issues associated with the proposed scenarios 

involving high penetrations of renewable generation in California and coming from other parts of 

the Western Interconnection. These include sub-hourly analyses to understand the power system 

flexibility requirements, potential impacts of load, solar and wind forecast errors, and changes to 

ancillary service requirements due to variability and uncertainty inherent in the wind and solar 

generation.

These issues depend on the parameters of the power system in California and throughout the 

West. Additional scenarios could study the integration impacts of local California resources and 

imported wind with different assumptions. For example, market structure changes (such as an 

Energy Imbalance Market in the Western Interconnection) would impact many of these 

integration issues, and have an impact on production costs in these scenarios.

Other future work could include modeling replacement portfolios other than those tested here. 

One that keeps the potential transmission upgrade to IID, for example, could result in different 

market clearing prices for that area if it enables additional geothermal power locally. 
41

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Outcomes for BCA

The production cost analysis suggests that using Wyoming wind to replace a California portfolio 

of renewable generation would have little effect on overall production costs. In the 33% 

renewable energy scenario, the higher capacity factors provided by Wyoming wind power would 

reduce production costs by about $28 million per year. This comprises about 3% of the total 

benefits included in the BCA. Table 24 shows the 50-year stream of production cost benefits.

Table 24. Fifty-Year Stream of Production Cost Benefits (Net Present Value)

Benefit

Reduction in production costs

(system variable costs) $326 million
42

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6 Benefit Cost Analysis

BCA is a tool for testing a transmission project’s economic merit. It is a test of societal welfare, 

in contrast to the LCOE analysis that focuses on a given generation technology’s cost per unit of 

energy produced. BCA and LCOE analysis each have different limitations due to what each does 

and does not capture. When used in conjunction with one another, however, they can provide a 

more fully informed picture of how a project might affect the economy overall and what it might 

mean to a utility’s customers.

This section describes the components used to examine the societal benefits and societal costs of 

developing the Wyoming-California transmission corridor with a major HVDC line. Results 

described in other sections of this report constitute inputs to the BCA.

The BCA focuses on the 33% CAISO renewable portfolio (some contract failures for projects 

expected to be in service in 2015 or later, and no procurement beyond current RPS 

requirements). The analyses detailed in other sections of this report showed little difference 

between a 33% California portfolio with some future project delays, and a 35% California 

portfolio where all future core projects met their expected in-service dates. Consequently, the 

BCA results for a 33% renewable portfolio would also be reasonably indicative of energy 

procurements that would go slightly beyond what is required under the current California RPS.

Results from this study applied to a BCA framework suggest that developing the Wyoming-toCalifornia corridor for wind power could be economic under most assumptions about future 

market conditions, although changes in federal incentives and other market factors could affect 

the magnitude of the societal benefits.

Order 1000, promulgated by FERC in 2011, prohibits transmission utilities from using a BCR 

threshold greater than 1.25 in determining whether a transmission facility has sufficient net 

benefits to be selected for a regional transmission plan.36 This study applies no threshold, 

however. Any determination of decision criteria is beyond the scope of this study.

BCA Framework

BCA results are normally expressed as a BCR.

BCR = NPV�∑i,t benefitsi,t�

NPV(∑t costst)

where:

i = the type of benefit

t = the year during which either a cost or a benefit accrues

benefiti,t = the value of benefit i that accrues during year t

costt = the cost of the transmission project assigned to year t 

 36 FERC, Final Rule, Transmission Planning and Cost Allocation by Transmission Owning and Operating Public 

Utilities, 136 FERC 61,051 (Order 1000) at 461. Any jurisdictional entity must justify to FERC its use of a threshold 

greater than 1.25.
43

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Change in generator costs ⇒

Benefit/Cost 

Analysis

⇐ Cost of 

proposed 

transmission 

project

Change in capacity value ⇒

Change in production costs ⇒

Other transmission costs avoided ⇒

Benefits not accounted for

Change in integration costs (apart from capacity value)

Change in generator emissions

Change in employment

Change in wages and productivity (due to changes in electricity prices)

Figure 21. Elements of the benefit/cost framework

The net present value (NPV) of benefits and costs are summed over a period of time at a 

common discount rate. This analysis uses a 7% real discount rate and a 50-year cost recovery 

period, following the assumptions used by CAISO.

37 The ratio of the two values indicates a 

project’s economic merit relative to the identified benefits: if the BCR is greater than 1, the 

project’s measured societal benefits exceed its costs. Note that in this analysis, “benefit” 

represents the difference between two actions that would achieve the same outcome—12,000 

GWh/year of renewable energy—rather than the difference between procuring or not procuring 

12,000 GWh/year of renewable energy.

A complementary calculation looks at benefits and costs over a shorter period. This supplemental 

analysis asks whether a transmission investment could hypothetically pay for itself if 

transmission cost recovery occurred over 20 years rather than the normal 40 or 50 years, and 

computes net present value over 20 years rather than 50. It ignores the benefits that would occur 

during the later twenty to thirty years of the transmission line’s useful life, making it a 

conservative measure of consumer benefit relative to alternatives. 

In most applications in the electric sector, BCA tests whether investing in a given capital 

expansion project is more economic than not making the investment. For example, suppose the 

existing network is congested along a certain corridor. The congestion causes system costs to 

increase, but those costs could be alleviated by upgrading the lines along the congested path. 

BCA tests the value of the upgrade against the status quo. If the upgrade costs more than the 

value of the congestion relief (that is, if its BCR is less than 1), then it is not economical from a 

societal perspective; society would be better off paying for the congestion and not incurring the 

cost of the upgrade. Some customers and some producers directly affected by the congestion 

would continue to bear the brunt of the impact locally, but the distribution of impacts generally 

does not affect how the societal benefits compare to societal costs.

An ideal BCA reconciles a project’s total cost with all related social benefits. In practice, BCA 

applied to transmission investments focuses on benefits directly related to operating the electric 

system that can be quantified or modeled with accepted planning tools. As Figure 21 illustrates, 

this analysis focuses on four benefits: changes in the amount of generation capacity needed to

 37 CAISO, 2012–2013 Transmission Plan, approved by Independent System Operator Board of Governors March 

20, 2013, p. 315. In this analysis, NPV is calculated over a 53-year time horizon on the assumption that there would 

be no costs or benefits until 2017.
44

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produce 12,000 GWh/year of electricity; differences in capacity value between one generation 

portfolio and another that would affect the cost of maintaining resource adequacy; changes in 

production costs; and the cost of other transmission investments that would not be needed if the 

tested project were built. (Note that a “benefit” could have a negative value, as would be the case 

if capacity value were to diminish and the cost of maintaining resource adequacy were to 

increase.) These inputs flow into the BCR formulation as follows:

BCR = NPV ∑ (∆gencostt + ∆capvaluet + ∆prodcostt + [avoidedt]) 50

t=1

NPV ∑ costt

50

t=1

where:

t = the year during which either a cost or a benefit accrues

∆gencostt = gencostCA,t – gencostCA/WY,t

gencostCA,t = the annualized fixed costs in year t of the California renewable 

generation test portfolio 

gencostCA/WY,t = the annualized fixed costs in year t of the test portfolio comprising 

California renewable generation and Wyoming wind power

∆capvaluet = capvalueCA,t – capvalueWY,t

capvalueCA,t = the California capacity value in year t of the California renewable 

generation test portfolio (megawatts, multiplied by the annualized permegawatt fixed cost of a natural gas combustion turbine)

capvalueWY,t = the California capacity value in year t of the test portfolio comprising 

California renewable generation and Wyoming wind power

(megawatts, multiplied by the annualized per-megawatt fixed cost of a 

natural gas combustion turbine)

∆prodcost,t = prodcostCA,t – prodcostWY,t

prodcostCA,t = annual production cost in year t using the California renewable 

generation test portfolio without the Wyoming wind generation 

portfolio

prodcostWY,t = annual production cost in year t using the test portfolio comprising 

California renewable generation and Wyoming wind power

[avoidedt] = the avoided cost in year t of transmission projects in California that 

would not be built if the California renewable generation test portfolio 

were replaced with the test portfolio comprising California renewable 

generation and Wyoming wind power [benefit included in one BCR 

sensitivity calculation and excluded in another] 

costt = the cost of the Wyoming-to-California transmission project assigned to 

year t 

Cost Component

The cost component of the BCR (the denominator) includes only the cost of the transmission line 

in the Wyoming-to-California corridor. To simplify the analysis, costs are applied evenly over 

the economic life of the transmission project, assumed here to be 50 years. The assumed 

weighted cost of capital is 7.7%.
45

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The reference case assumes an up-front capital cost of $3 billion; total project costs over the 50-

year life of the project are assumed to be 145% of the capital costs. 

The analysis includes two cost-related sensitivities: total costs that are 25% more than the 

reference case, and accelerating the economic life of the project (and cost recovery) to 20 years.

Benefits Components

Previous sections of this study describe the details behind most of the benefit components 

included in the numerator of the BCR. Table 25 summarizes the results of each section as they 

apply to the BCA.

Table 25. Fifty-Year Streams of Costs and Benefits (Net Present Value)

Element Benefit Cost

Reduction in generator equipment and fixed costsa

$6.4 billion

to $10.9 

billion

Change in capacity value of selected resources 

(resource adequacy) -$858 million

Reduction in production costs

(system variable costs) $326 million

Avoided transmission build-out in California zero to

$2.7 billion

Wyoming-California HVDC transmission corridor $3.6 billion 

Totals $5.9 billion to

$13.1 billion $3.6 billion

Net $2.3 billion to $9.5 billion

Benefits/Costs 1.62 to 3.62

a

Assumes generator equipment has a 20-year life and is replaced twice over 50 years with comparable equipment at 

comparable cost. NPV streams are discounted over time based on their real annual values in 2010 dollars.

BCRs

Savings in generator costs are the largest component benefit, as shown in Table 25. These 

benefits range from around $500 million annually (with greater reduction in generator costs and 

continued PTC and ITC) to around $1 billion annually (moderate reductions in generator cost 

with no PTC or ITC). The variations in BCRs are due almost entirely to inputs and assumptions 

relating to generator costs.

Figure 22 shows the resulting BCRs based on current laws regarding the PTC and ITC in 2017 

(no PTC with the ITC reduced to 10%). The range shown on the left is based on the reference 

case capital costs developed by TEPPC; the range on the right uses the renewable energy cost 
46

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sensitivities developed by NREL and DOE technology experts. Each range is defined by the 

inclusion or exclusion of avoided transmission build-out in California.

Figure 22. Benefit/cost ratios based on current tax law (No PTC, 10% ITC in 2017)

Figure 23 and Figure 24 show the resulting BCRs with the incentives reauthorized at 2013 levels 

in 2017, and with the incentives eliminated in 2017. The ratios tend to be higher without the PTC 

or ITC, suggesting that the corridor may provide a hedge against the risk of reduced federal tax 

incentives.

Sensitivities

Accelerating transmission cost recovery and shortening the NPV calculation to 20 years reduced 

the benefit/cost ratios, but none fell below 1.25. They ranged from 1.28 (based on the renewable 

resource sensitivity costs, reauthorizing federal incentives to 2013 levels, and excluding the 

benefit of avoided transmission build-out in California) to 2.87 (based on the TEPPC reference 

case costs, no federal incentives, and including the benefit of avoided transmission build-out in 

California).

If project costs for the Wyoming-to-California corridor were 25% higher than assumed in the 

reference case, BCRs become more sensitive to generator costs and to tax incentives. The ratio 

drops to 1.29 based on the renewable resource cost sensitivity, reauthorizing federal incentives to 

2013 levels, and excluding the benefit of avoided transmission build-out in California (the 

scenario returning the lowest ratio). The highest ratio in the sensitivity is 2.89 using the TEPPC 

reference case costs, excluding federal incentives, and including the benefit of avoided 

transmission build-out in California.

3.01 

2.60 

2.27 

1.85 

 -

 1

 2

 3

 4

Reference case costs Renewable energy

cost sensitivity

Benefit/cost

ratio

Upper value includes benefits

from avoided transmission

build-out in California

Lower value excludes benefits

from avoided transmission

build-out in California
47

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Figure 23. Benefit/cost ratios based on PTC, 30% ITC in 2017

Figure 24. Benefit/cost ratios based on no PTC, ITC in 2017

Summary

The BCA results suggest that the economic benefits of developing the corridor could exceed the 

costs under the array of future conditions tested in this analysis. Benefit-to-cost ratios range from 

1.62 to 3.62 in Figures 22-24 depending on assumptions about federal tax incentives in 2017, and 

depending on assumptions about the future costs of different renewable energy technologies. 

Where outcomes fall within this range will depend on:

• Expectations about future technology costs. If large-scale solar photovoltaic (PV) costs 

fall significantly faster than the cost of wind power, the ratios will tend toward the lower 

end of the ranges reported here.

• Expectations about future federal tax incentives. Reductions in the production tax credit 

(PTC) and investment tax credit (ITC) tend to favor developing the corridor, particularly 

if the reductions are even across all benefitting renewable technologies. If the changes 

significantly benefit solar and geothermal without benefitting wind, the ratios will tend 

toward the lower end of the ranges reported here.

• Avoided transmission build-out in California. The ratios tend toward the higher end of 

the ranges when including the economic benefit of avoided transmission build-out in 

2.69 

2.36 

1.95 

1.62 

 -

 1

 2

 3

 4

Reference case costs Renewable energy

cost sensitivity

Benefit/cost

ratio

Upper value includes benefits

from avoided transmission

build-out in California

Lower value excludes benefits

from avoided transmission

build-out in California

3.62 

3.11 

2.87 

2.37 

 -

 1

 2

 3

 4

Reference case costs Renewable energy

cost sensitivity

Benefit/cost

ratio

Upper value includes benefits

from avoided transmission

build-out in California

Lower value excludes benefits

from avoided transmission

build-out in California
48

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California, regardless of expectations about future generator costs and future federal tax 

incentives.

The analysis does not imply any recommendation about what one should assume regarding 

future costs, future incentives, and avoided transmission build-out. Rather, the aim of the BCA 

was to test the extent to which the corridor constitutes a “least regrets” proposition for major 

infrastructure development and its long-term benefits, anticipating how some of the most crucial 

variables could change by 2017. 
49

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7 Summary and Future Work

This study compares the relative economics of two options for providing 12,000 GWh/year of 

renewable energy to electricity customers in California: a mix of California renewable resources 

likely to be available between 2017 and 2020, and Wyoming wind power. Either option would 

add to the California resources already serving customers in the state. In the second scenario, 

Wyoming wind power is delivered to the California marketplace via an HVDC transmission line. 

Both options have discretely measurable differences in transmission costs, capital costs (due to 

the enabling of different generation portfolios), capacity values, and production costs. 

The BCA used to examine the economic difference between the two options suggest that the 

benefits of Wyoming wind could exceed the cost of the transmission required to deliver it under 

the array of future conditions tested in this analysis. Moreover, this conclusion remains robust 

over all future scenarios for generator cost and federal incentives that were tested in the analysis. 

It also remains robust to accelerated (20-year) cost recovery for transmission, and for 

transmission costs 25% above the reference case assumptions.

The main scenarios tested in the BCA suggest economic headroom—i.e., benefits in excess of 

costs—amounting to between $2.3 billion and $9.5 billion over 50 years on a net present value 

basis. This degree of headroom warrants further examination of costs that could not be included 

in this analysis. For example, the network upgrades and other system impact costs involved with 

connecting the southern terminus of an HVDC line to the CAISO balancing authority require 

more specialized analysis of CAISO flow data. The headroom indicated by the BCA provides a 

benchmark for evaluating the magnitude of such costs once they have been determined.

Similarly, the amount of headroom shown in this analysis provides a benchmark for measuring 

the integration costs that would be associated with Wyoming wind power. Because of the many 

options, integration issues have been set aside for a subsequent analysis. 

The difference in generator costs—i.e., the capital investment required to generate 12,000 

GWh/year—makes up the largest share of overall benefits. Large-scale solar PV and geothermal 

power are expected to be the two primary renewable resources available for new development in 

California after 2017, but on a dollar/MWh basis they are both more expensive than Wyoming 

wind. In addition, Wyoming wind power generally has a higher capacity factor than does 

California wind power, resulting in more energy per dollar of capital investment. 

The analysis contained in this report supplements previous studies, providing a more detailed 

look at the transmission costs to interconnect and deliver Wyoming wind to the California 

market as part of a California RPS or a post-RPS scenario. Future work efforts could look at the 

benefits of geographic diversity and the impact on reliability. Impacts on the distribution system

could also inform the cost analysis and highlight any potential reliability impacts. An analysis of 

the potential integration issues associated with the proposed scenarios could also further inform 

system costs and could include sub-hourly analyses to understand the power system flexibility 

requirements, potential impacts of load, solar and wind forecast errors, and changes to ancillary 

service requirements due to variability and uncertainty inherent in the wind and solar generation.
50

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CPUC. (2013). “RPS Calculator for the TPP (Transmission Planning Process).” California Public 

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EIA. (2013b). “Assumptions to the Annual Energy Outlook 2013.” 

May. http://www.eia.gov/forecasts/aeo/assumptions/. 

Energy and Environmental Economics (2014). Investigating a Higher Renewables Portfolio 

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Hurlbut, D.; McLaren, J.; Gelman, R. (2013), Beyond Renewable Portfolio Standards: An 

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This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Appendix A : Environmental Siting and Permitting: 

Wyoming Wind Transmission and Generation

Most new electricity generation and transmission projects in the Western United States are 

subject to federal and state environmental and regulatory permitting processes before they can be 

built. 

The risks associated with securing these necessary permits are difficult to quantify or to compare 

across various renewable scenarios. However, several attempts have been made by WECC and 

the CPUC. Environmental/permitting status information is included and quantified in the CPUC 

Long-Term Procurement Planning calculator; however, the status of the transmission projects is 

not included.

NREL determined that attempting to quantify permitting risk factors falls outside the scope of 

this study. That said, it is worthwhile to note that for some Wyoming-based wind projects, these 

federal permitting processes are not only well underway but also nearly complete. 

For example, the TransWest Express Transmission Project is a planned 600-kV, 3,000-MW 

direct current power line between Wyoming and the CAISO market entry point in southern 

Nevada. Because the approximately 725-mile transmission line must traverse federally owned 

land primarily managed by the U.S. Bureau of Land Management (BLM), the TransWest 

Express Project is the subject of an Environmental Impact Statement (EIS) being jointly prepared 

by the BLM and Western Area Power Administration (a power marketing administration that is 

part of DOE). 

Public scoping for the EIS was completed in early 2011; the Draft Environmental Impact 

Statement was completed in July 2013; and a 90-day Draft EIS review period concluded in 

September 2013. BLM and Western scheduled the Final EIS and their Records of Decision for 

release in 2014. 

Meanwhile, the Chokecherry and Sierra Madre Wind Energy Project site has been authorized for 

wind energy development via a Record of Decision signed by former Interior Secretary Ken 

Salazar in October 2012. This central Wyoming wind farm ultimately will deploy up to 1,000 

turbines to harness 3,000 MW of clean power from areas with the nation’s highest-capacity 

onshore wind resources. 

The BLM now is preparing two site-specific Environmental Assessments (EAs) on the wind 

project elements, to be tiered to the project-wide EIS. Both EAs are scheduled for completion by 

fall 2014. Project proponent Power Company of Wyoming LLC (PCW) also has been working 

with the U.S. Fish and Wildlife Service since 2010 on an eagle conservation plan and permit 

program. The Service announced in December 2013 that it will prepare an EIS to analyze PCW’s 

eagle permit application and will complete the process in 15 months.

Both the TransWest Express Project and the Chokecherry and Sierra Madre Project have been 

identified under various federal programs and initiatives as priorities to permit and construct.
54

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Additional information on other Wyoming transmission projects is available on the Wyoming 

Infrastructure Authority’s website at http://wyia.org/projects/. Additional information on other 

wind energy projects proposed on federal land in Wyoming can be found on the BLM website 

at http://www.blm.gov/wy/st/en/programs/energy/renewable/wind.html.
55

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Appendix B: Supplemental Information on Capacity 

Value and NREL REPRA Model

The usual mathematical formulation for LOLE is based on the daily or hourly estimates of 

LOLP/LOLH,

38 and the LOLE is the sum of these probabilities, converted to the appropriate 

timescale. The annual hourly LOLE can be calculated as:

where P() denotes the probability function, N is the number of hours in the year, Ci represents 

the available capacity in hour i, and Li is the hourly load. To calculate the additional reliability 

that results from adding VG, we can write LOLE' for the LOLE after renewable capacity is 

added to the system as:

where gi is the power output from the generator of interest during hour i. The ELCC of the 

generator is the additional system load that can be supplied at a specified level of risk (LOLP or 

LOLE).

Calculating the ELCC of the renewable plant amounts to finding the values ∆Ci that satisfy this 

equation, which says that the increase in capacity that results from adding a new generator can 

support ∆Ci more megawatts of load at the same reliability level as the original load could be 

supplied (with Ci megawatts of capacity). To determine the annual ELCC, we simply find the 

value ∆Cp, where p is the hour of the year in which the system peak occurs after obtaining the 

values for ∆Ci that satisfies the equation. Because LOLE is an increasing function of load, given 

a constant capacity, we can see from the above equation that increasing values of ∆Ci are 

associated with declining values of LOLE. Unfortunately, it is not possible to analytically solve 

the equation for ∆Cp. The solution for ∆Cp involves running the model for various test values of 

∆Cp until the equality in the equation is achieved to the desired accuracy. However, there are 

several approaches that can be applied to the search that can significantly decrease solution time, 

and modern computers can easily manage the computations in a short amount of time.

The ELCC of wind power plants range from about 5% to about 40% (Milligan and Porter 2008; 

Rogers and Porter 2008), although this range is not absolute. Capacity contributions of any 

generator will be subject to inter-annual variations, although the properties of this variability will 

differ among technologies and aggregation levels. A thermal plant may have an ELCC of 90% to 

95% of its installed capacity value, but if that plant experiences a forced outage event during 

high-LOLE peak periods, it could conceivably contribute nothing toward reserves in that year. 

 38 This discussion is based on hourly LOLE (LOLH) but can easily be adapted to daily LOLE.
56

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Similarly, wind and solar generation is a function of the weather and thus may vary from year to 

year around the long-term value. More details on LOLE and ELCC for systems with VG can be 

found in the NERC Integration of Variable Generation Task Force 1.2 document (NERC 2011a).

REPRA is a tool has been developed at NREL (Ibanez and Milligan 20012a, 2012b) to better 

understand how different types of renewable generation, which are usually non-dispatchable 

sources of power, can contribute to a power system’s adequacy from a reliability point of view. 

This section describes the tool, and is taken from Ibanez and Milligan (2012a, 2012b).

At the core of the model resides a fast-convolution algorithm that combines the probability 

distribution of the traditional generators. These are represented by a finite number of states. The 

most simple case is whether the unit is available or not, with a probability that it is not equal to 

the EFOR.

After the convolution of the traditional units has been performed, the result is a capacity outage 

probability table, which indicates the LOLP for all levels of load the system can serve. For 

instance, Table 26 shows the result when considering six 50-MW units with an EFOR of 8%. 

The third row shows that the probability of an outage of 100 MW is 0.0688, which is equivalent 

to the probability of any two units being out of service. Similarly, the cumulative probability of 

an outage exceeding 100 MW is 0.0773; alternatively, one can interpret this cumulative 

probability as the LOLP associated with a 200-MW load level.

Table 26. Capacity Outage Probability Table for Conventional Units

MWOUT MW-IN Probability LOLP

0 300 0.6064 1.0000

50 250 0.3164 0.3936

100 200 0.0688 0.0773

150 150 0.0080 0.0085

200 100 5.20E-04 5.38E04

250 50 1.81E-05 1.84E05

300 0 2.62E-07 2.62E07

VG can be convolved with the capacity outage probability table in a similar fashion. The main 

difference is the determination of the probability distribution used in the convolution. Unlike 

traditional generators, VG production is limited by available resources such as wind speed or 

solar irradiance that are governed by weather patterns. To preserve this variation, we made use of 

a sliding window technique for all hours of the year. Figure 25 shows a graphical representation 

of a sliding window, which included the current and adjacent hours. The width was 

predetermined and, in this case, included a total of 5 h.
57

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Figure 25. Example of sliding window for wind power generation

Power outputs in the window were then given equal probability and sorted, providing the 

necessary probability distribution that would be included in an equivalent outage table (Table 

27). This table was then convolved with the results in Table 26 to obtain the total system outage 

table (Table 28). This table was truncated for LOLP values below 0.001.

Table 27. Capacity Outage Probability Table for Wind Sliding Window

MWOUT MW-IN Probability LOLP

0 100 0.4 1.0

10 90 0.4 0.6

20 80 0.2 0.2

REPRA allows the study of resource adequacy for different levels of geographic aggregation. 

This will contribute to a better understanding of the contribution of VG and also, as in this case, 

to better determine the benefits of a more interconnected system.

Table 28. Example of Capacity Outage Probability Table with Conventional and Wind Generation

MW-OUT MW-IN Probability LOLP

0 400 0.243 1.000

10 390 0.243 0.757

20 380 0.121 0.515

50 350 0.127 0.394

60 340 0.127 0.267

70 330 0.0633 0.141

100 300 0.0275 0.077

110 290 0.0275 0.050

120 280 0.0138 0.022

150 250 0.0032 0.008

160 240 0.0032 0.005

170 230 0.0016 0.002

These examples illustrate how the LOLP is calculated with the REPRA tool. The ELCC is then 

calculated using the procedure outlined above.
58

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Appendix C: Supplementary Background on the 

Wyoming Infrastructure Authority 

On August 22, 2003, Wyoming Governor Dave Freudenthal and Utah Governor Mike Leavitt 

announced the formation of the Rocky Mountain Area Transmission Study (RMATS). The 

governors found that: 

For many years, utilities and other entities have been reluctant to make 

investments in needed electric transmission infrastructure. This was due to a 

number of factors, including protracted uncertainties in the regulatory 

environment and nascent regional transmission organizations under development. 

As a consequence of this lack of transmission expansion, transmission congestion 

and bottlenecks were increasing. While this was a problem throughout the 

western interconnect, it was becoming an acute issue in areas of the Rocky 

Mountain sub-region (State of Wyoming 2004, pp. 1-5–1-6).

The governors directed that a charter be developed for the study that specified goals, principles, 

and operating procedures. The study covered several western states, including Colorado, Idaho, 

Montana, Utah, and Wyoming.

Emerging from these efforts, the Wyoming Infrastructure Authority (WIA) was established by 

the Wyoming Legislature in 2004 to diversify and expand the state’s economy through 

improvements in the electric transmission system to resolve constraints and create new capacity 

for the export of Wyoming resources in the form of electricity. The legislation authorizes the 

WIA to plan, finance, construct, develop, acquire, own, maintain, and operate transmission 

infrastructure within and outside the state of Wyoming.

The legislation also provided the WIA with bonding authority of $1 billion and other powers to 

promote transmission development in the state and throughout the region. It also provided the 

state treasurer with the approval of the State Loan and Investment Board and the authority to 

invest in WIA bonds. To date, the WIA has closed a private placement of $34.5 million in bonds 

with the Wyoming State Treasurer for a transmission-related project.

In order to encourage and assure the development of new transmission originating in Wyoming, 

the WIA, in support of the findings and recommendations from the RMATS report (State of 

Wyoming 2004), became a partner in various planning and project efforts within two years of the 

release of the report. In addition to its operating budgets, the legislature authorized the state 

treasury to advance up to $10 million to the WIA in the form of loans to be used for project 

development purposes. Two million dollars has been drawn to date and has been expended on 

specific project development initiatives.

The governing body of the authority is composed of a five-member Board of Directors appointed 

by the governor, with the advice and consent of the Wyoming State Senate. Current board 

members and staff are as follows: 

• Mike Easley (Chairman), CEO of Powder River Energy Corporation in Sundance, 

Wyoming
59

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

• Kyle White (Vice-Chairman), Vice President of Regulatory Affairs for Black Hills 

Corporation in Rapid City, South Dakota

• Bryce Freeman (Treasurer), Director of the Wyoming Office of Consumer Advocate in 

Cheyenne, Wyoming

• J.M. Shafer (Member), Professional Engineer in Windsor, Colorado, and former 

executive with Western Area Power Administration and Tri-State Generation and 

Transmission

• David Sparks (Member), Executive Vice President of TransCore in Jackson, Wyoming

Current staff consists of:

• Loyd Drain, Executive Director

• Holly Martinez, Administrative Manager
60

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Appendix D: Results of Comparative WECC/TEPPC

and CAISO Analysis39

Table 29. Summary of the Recent WECC/TEPPC and CAISO Studies and Their Findings

Category / 

Study

WECC 2011 10-Year 

Plan

WECC 2013 10-Year 

Plan CAISO 2012 2013 Tx Plan

Objective

Compare capital and 

production costs for 

alternative renewable 

resource areas to serve 

California at lower 

ratepayer cost

Compare capital and 

production costs for 

alternative renewable 

resource areas to serve 

WECC region at lower 

ratepayer cost

Compare production costs 

with and without 

transmission investment to 

identify economically-driven 

upgrades to reduce 

ratepayers’ costs

Horizon 10-Year Plan, 2019 10-Year Plan, 2022 5- and 10-Year Plan, 

2017 & 2022

Base Case

Assume all RPS goals are 

met in 2019 based on 2010 

Study Program for 

resources area and 

transmission plans

Assumes all RPS goals 

in 2022 with expected 

load growth are met 

from utility or state 

resource and 

transmission plans. 

Assumes 12,000 

GWh/year of additional 

renewables needed to 

meet 2022 RPS goals 

with high load growth 

(8% per year WECCwide) are met by 

resources concentrated 

in various states (or 

combinations of states) 

CAISO base case with 

approved transmission and 

generation mix

Alternatives

Remove 12,000 GWh/yr of 

resources from CA 2010 

Study Program and replace 

this renewable energy with 

resources from 8 different 

resource areas, using 24 

different transmission 

project configurations

Compare 7 different 

resource areas to 18 

different transmission 

project configurations

Include Delaney-to–Colorado 

River 500-kV line

Data Sources

Various, including WECC 

modeling data, capital costs 

from various groups 

including WGA, California

resource assumptions from 

CPUC (2010)

Various, including 

WECC modeling data, 

renewable capital costs 

from E3, transmission 

cost from B&V, 

California resource 

CAISO databases

 39 Compilation provided by TransWest Express
61

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

assumptions from CPUC 

(2012)

Calculate

Annual renewable energy 

costs for approx. 12,000 

GWh/yr of renewables, 

capacity cost savings from 

base case provided by 

added renewables, 

production cost differences 

from base case, and 

transmission costs of 

identified projects 

Annual renewable 

energy costs for approx. 

12,000 GWH/yr of 

renewables, capacity 

cost savings from base 

case provided by added 

renewables, production 

cost differences from 

base case, and 

transmission costs of 

identified projects 

Annual system production 

costs savings and 

transmission costs of 

identified projects. Utilize the 

ISO Transmission Economic 

Assessment Methodology 

(TEAM).

http://www.caiso.com/Docu

ments/TransmissionEconomi

cAssessmentMethodology.pd

f

Compare

Total cost (energy, 

capacity, production and 

transmission) difference 

from resource/transmission 

alternative to base case 

Total cost (energy, 

capacity, production and 

transmission) difference 

from resource/

transmission alternative 

to base case 

Production cost and capacity 

benefits as benefits versus 

transmission investment costs 

Sensitivities

Wyoming wind capacity 

factor (39% to 47%) and 

transmission cost (+/-30%) 

on single graphic, see 

Figure 35, page 75

Monetary value of 

capacity (“CT Type”

Aero vs. Frame tech.) 

see below, transmission 

cost (+/- 20%)

Twenty-three different 

sensitivities run, see Figure 

5.7-27, page 357

Reports

Plan 

Summary

http://www.wecc.biz/librar

y/StudyReport/Documents/

Plan_Summary.pdf 

http://www.wecc.biz/co

mmittees/BOD/TEPPC/

External/2013Plan_Plan

Summary.pdf 

http://www.caiso.com/Docu

ments/Draft2012-

2013TransmissionPlan.pdf

(This is a draft plan that was 

later revised materially with 

the recommendation for the 

project removed due to data 

errors.)

Other 

Information

2019 Study 

: http://www.wecc.biz/libra

ry/StudyReport/Documents

/2019%20Study%20Report

.pdf

2020 Resource 

Option: http://www.wec

c.biz/committees/BOD/T

EPPC/Pages/2013Plan_1

0-Year.aspx

Need to click on 

“TEPPC_2022_StudyRe

port_PC19-

NONE
62

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25_Resource”

Findings/

Observations: 

(From 

Summary 

Presentation)

Cost-effective remote 

renewable resources—

Some long-distance 

transmission to access 

remote renewable 

resources appears to be 

cost-effective when 

compared to some of the 

local renewable generation 

assumed in the Plan’s 

Expected Future. Based on 

the high level of analysis 

performed, results from the

resource relocation plus 

transmission expansion 

alternatives evaluated as 

part of the 10-year 

planning studies suggest 

total cost savings result 

under the alternative 

resource futures when 

compared to generation 

assumed in the Expected 

Future case.

1) When long-distance 

transmission costs are 

considered, 12,000 GWh 

of Wyoming wind 

transmitted by a DC line 

to southeast Nevada is 

similar in cost to 12,000 

GWh of California 

renewable resources.

2) The lowest cost 

alternative for adding 

12,000 GWh of 

additional renewable 

resources is Wyoming 

wind.

3) After evaluating how 

different firming 

techniques could 

improve the integration 

of VG and reduce the 

production cost in the 

Western 

Interconnection, 

combined cycle gas 

units, when added in 

Wyoming in conjunction 

with long-haul DC lines, 

represented the most 

economic option for this 

set of studies.

1) Draft Plan, later revised—

the Study found that the ... 

Delaney-to–Colorado River 

500-kV line had economic 

benefits greater than [the] 

costs.

2) Draft Plan, later revised—

Recommendation: The ISO 

recommended that the 

proposed Delaney-to–

Colorado River 500-kV line 

be approved as an 

economically-driven network 

upgrade.

Detailed findings and observations of WECC 2011 and 2013 10-year plan are listed below: 
63

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

From WECC 2011 10-Year Plan40

Table 30. WECC 2011 10-Year Plan Base Case Analysis

Capital Cost Comparison of Potentially Cost-Effective Resource Relocation Alternatives with Large-Scale Transmission Expansion

Base Analysis – See Figure 32, Page 71

WY/TWE 

(PC8, EC8-2)

California 

(PC1A, Base) Differential Comments Cost 

Category Cost Drivers Scope Cost/Year Scope Cost/Year Cost/Year

Benefits

Renewables energy -

capital

2,913 MW 

@ 47% CF $568 4,785 MW @ 29% CF $1,810 $998 Table 13, Page 43, Table 23, 

page 62 CTs capacity 

- capital

1,637 MW 

incr. $244

Production energy -

O&M $1 $0 -$1 Table 26, page 70

Total $813 $1,810 $997

Costs Transmission

Capital 

and 

O&M

$2.3B, 3 

GW, 730 

mi.

$337 $0.0B, 0 

GW, 0 mi. $0 -$337

WY: Table 26, Page 70; 

Assumes non-TWE costs (CA + 

WY Tx costs) are the same for 

both cases

Net 

Total $1,150 $1,810 $660

WECC finding is that California 

renewable resources are less 

expensive ($138M/yr)

Benefits 

to Costs 

Ratio

2.96

Transmission investment is 

extremely economic (3-year payoff)

 40 Source: http://www.wecc.biz/library/StudyReport/Documents/Plan_Summary.pdf and 

http://www.wecc.biz/library/StudyReport/Documents/2019%20Study%20Report.pdf 
64

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Table 31. WECC 2011 10-Year Plan—Cost of Transmission and Wyoming Wind CF Sensitivity to Match 2013 Base Case

Capital Cost Comparison of Potentially Cost-Effective Resource Relocation Alternatives with Large-Scale Transmission Expansion

Cost of Transmission and Wyoming Wind CF Sensitivity to match 2013 base – See Figure 35, Page 75

WY/TWE (PC8, EC8-2) California (PC1A, Base) Differential

Cost Comments

Category Cost Drivers Scope Cost/Year Scope Cost/Year Cost/Year

Benefits

Renewables energy -

capital

3,317 MW 

@ 41% CF $728 4,785 MW @ 29% CF $1,810 $838 Table 13, Page 43, Table 23, 

page 62 CTs capacity 

- capital

1,637 MW 

incr. $244

Production energy -

O&M $1 $0 -$1 Table 26, page 70

Total $973 $1,810 $837

Costs Transmission

capital 

and 

O&M

$3.1B, 3 

GW, 730 

mi.

$443 $0.0B, 0 

GW, 0 mi. $0 -$443

WY: Table 26, Page 70; 

Assumes non-TWE costs (CA + 

WY Tx costs) are the same for 

both cases

Net 

Total $1,416 $1,810 $394

WECC finding is that California 

renewable resources are less 

expensive ($138M/yr)

Benefits 

to Costs 

Ratio

1.89 Transmission investment is very 

economic
65

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From WECC 2013 10-Year Plan41

Table 32. WECC 2013 10-Year Plan Base Case Analysis

Change in Total Cost to Achieve RPS-Compliance under High Loads

Base Analysis – See Figure 91, Page 102

WY/TWE (EC21-2) California (PC19) Differential

Cost Comments

Category Cost Drivers Scope Cost/Year Scope Cost/Year Cost/Year

Benefits

Renewables energy -

capital

3,317 MW 

@ 41% CF $556 4,372 MW @ 31% CF $1,376 $820 WY: Table 11, Page 38; CA: Table 8, Page 21

CTs capacity 

- capital 300 MW -$57 2,500 MW -$595 -$538

WY: Table 11, Page 38; CA: 

Table 8, Page 21, CA based CT 

- Aero ($1,150/kW)*

Production energy -

O&M

Delta from 

Base -$424 Delta from Base -$413 $11

Table 31, Page 98, Base Case is 

total production cost for nonRPS compliant 2022 high load 

case

Total $75 $368 $293 *Table 5 (includes all 

resources), Page 8

Costs Transmission

capital 

and 

O&M

$2.9B, 3 

GW, 725 

mi.

$431 $0.0B, 0 

GW, 0 mi. $0 -$431

WY: Table 14, Page 44; 

Assumes non-TWE costs (CA + 

WY Tx costs) are the same for 

both cases

Net 

Total $506 $368 -$138

WECC finding is that 

California renewable resources 

are less expensive ($138M/yr)

 41 Source: http://www.wecc.biz/committees/BOD/TEPPC/External/2013Plan_PlanSummary.pdf and 2020 Resource Option: 

http://www.wecc.biz/committees/BOD/TEPPC/Pages/2013Plan_10-Year.aspx. Need to click on “TEPPC_2022_StudyReport_PC19-25_Resource”
66

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Benefits 

to Costs 

Ratio

0.68 Transmission investment is not 

economic

Table 33. WECC 2013 10-Year Plan Cost of Capacity (“CT Type”) Sensitivity

Change in Total Cost to Achieve RPS-Compliance under High Loads

Cost of Capacity (“CT Type”) Sensitivity – See Figure 92, Page 104

WY/TWE (EC21-2) California (PC19) Differential

Cost Comments

Category Cost Drivers Scope Cost/Year Scope Cost/Year Cost/Year

Benefits

Renewables energy -

capital

3,317 MW 

@ 41% CF $556 4,372 MW @ 31% CF $1,376 $820

CTs capacity 

- capital 300 MW -$40 2,500 MW -$414 -$374

Sensitivity based on 

changing CT technology 

assumption to Frame @ 

($800/kW)*

Production energy -

O&M

Delta from 

Base -$424 Delta from Base -$413 $11

Total $92 $549 $457

Costs Transmission

capital 

and 

O&M

$2.9B, 3 

GW, 725 

mi.

$431 $0.0B, 0 

GW, 0 mi. $0 -$431

Net 

Total $523 $549 $26

WECC finding is that 

Wyoming renewable 

resources are marginally 

less expensive ($26M/yr)

Benefits 

to Costs 1.06 Transmission investment is marginally economic