Technical Report

NREL/TP-6A2-46671

October 2009

Wind Levelized Cost of Energy: 

A Comparison of Technical and 

Financing Input Variables 

Karlynn Cory and Paul Schwabe
National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, Colorado 80401-3393

303-275-3000 • www.nrel.gov

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

Office of Energy Efficiency and Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

Contract No. DE-AC36-08-GO28308

Technical Report

NREL/TP-6A2-46671

October 2009

Wind Levelized Cost of Energy: 

A Comparison of Technical and 

Financing Input Variables 

Karlynn Cory and Paul Schwabe

Prepared under Task No. WER9.3550
NOTICE

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iii

Acknowledgments

This work was funded by the U.S. Department of Energy’s (DOE’s) Wind and Hydropower 

Technologies Program. The authors thank Maureen Hand, of the National Renewable Energy 

Laboratory (NREL), for her guidance and overall direction provided to this project. The authors 

also thank Walter Short of NREL for providing useful insights and helpful input on wind project 

financing. In addition, the authors thank Matthew Karcher of Deacon Harbor Financial for his 

continued support and guidance in using his wind project finance model. Finally, the authors 

thank the individuals who reviewed various drafts of this report including Douglas Arent, 

Maureen Hand, David Kline, Margaret Mann, Michael Mendelsohn, and James Newcomb of 

NREL; Matthew Karcher of Deacon Harbor Financial; and Mark Bolinger and Ryan Wiser of 

Lawrence Berkeley National Laboratory.

The authors also thank Michelle Kubik of NREL for her editorial support and Jim Leyshon of 

NREL for his graphics support. 
iv

List of Acronyms and Abbreviations 

BL back leveraged

Cash Lev cash leveraged

Corp corporate 

DOE Department of Energy 

IEA International Energy Agency 

IIF institutional investor flip

IOU investor-owned utility

IRR internal rate of return

IRS Internal Revenue Service

LCOE levelized cost of energy 

NREL National Renewable Energy Laboratory 

O&M operations and maintenance 

PTC Lev production tax credit leveraged

PTC production tax credit 

REC renewable energy certificate

SIF strategic investor flip
v 

Table of Contents

Acknowledgments.......................................................................................................................... iii

List of Acronyms and Abbreviations ............................................................................................. iv 

Executive Summary ....................................................................................................................... vi

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

2 Six Wind Financing Structures Under Study .......................................................................... 1

3 Analysis Overview and Assumptions ..................................................................................... 3

4 Analysis Methodology, Sensitivity Assumptions, and Multivariable Scenarios .................... 4

5 Results ..................................................................................................................................... 7

6 Conclusions ........................................................................................................................... 14

7 Future Analysis ..................................................................................................................... 15

List of Figures

Figure ES-1. LCOE Sensitivities for capacity factor, installed cost, O&M, and target IRR by 

financing structure ................................................................................................................... vi 

Figure ES-2. Technical- vs. financial-variables scenario analysis ............................................... vii

Figure 1. LCOE ranges by individual variable sensitivities, showing all financing 

structures ....................................................................................................................................8 

Figure 2. Institutional Investor Flip LCOE sensitivities by input variable ......................................9 

Figure 3. Cash Leveraged structure LCOE sensitivities by input variable ....................................10 

Figure 4. LCOE sensitivities for capacity factor, installed cost, O&M, and target IRR by 

financing structure ...................................................................................................................11 

Figure 5. Multivariable scenarios LCOE ranges ............................................................................12 

Figure 6. Technical- vs. financial-variables scenario analysis ......................................................14 

Figure C1. Corporate structure LCOE sensitivities by input variable ...........................................23 

Figure C2. Strategic Investor Flip structure LCOE sensitivities by input variable .......................24 

Figure C3. Cash and PTC Leveraged structure LCOE sensitivities by input variable ..................25 

List of Tables

Table 1. Technical Variables: Individual Variable Sensitivities .....................................................5 

Table 2. Financial Variables: Individual Variable Sensitivities ......................................................5 

Table 3. Technical Variables: Multivariable Scenarios ...................................................................6 

Table 4. Financial Variables: Multivariable Scenarios ....................................................................6 

Table 5. Base-Case LCOE Estimates ...............................................................................................7 

Table A1. Input Assumptions Common to All Structures .............................................................18 

Table A2. Structure-Specific Equity-Financing Assumptions .....................................................19 

Table A3. Structure-Specific Debt-Financing Assumptions .........................................................20 

Table B1. Technical Variables: Individual Variable Sensitivities .................................................21 

Table B2. Financial Variables: Individual Variable Sensitivities ..................................................21 

Table C1. LCOE Results: Individual Variable Sensitivities ..........................................................22 
vi 

Executive Summary

As part of a multinational collaborative study, the National Renewable Energy Laboratory

(NREL) conducted a comparison of the relative impacts of various financial, technological, and 

wind resource variables on the levelized cost of energy (LCOE) 1 from a typical wind project. 

The parametric analysis identified and compared key factors in the cost of wind energy in the 

United States. Analysts used a LCOE pro forma cash flow model to reflect recent U.S. wind 

project financing structures and wind energy market trends. However, the financial crisis of 

2008-2009 and subsequent U.S. federal legislation is not considered in detail because it is still

early to discern and model the exact impacts. 

Analysts first examined the impacts of wind resource, financial, and technical variables on the 

LCOE independently. Each variable’s impact was tested across a range of high- and low-cost

values for six wind project financing structures, which are described in the report. As expected, 

the wind resource variable and all-in installation costs have the largest incremental effect on 

LCOE. These technical variables reveal that incremental improvements through improved R&D 

or manufacturing practices can yield sizeable cost savings, especially for those projects with 

below-average wind resource characteristics. A less obvious, yet moderate LCOE impact 

resulted from variations in the target equity internal rate of return (IRR), the operations and 

maintenance costs (O&M), as well as the return on debt and loan duration (debt-financed 

structures only). The smallest impact resulted from the cost of replacing a specific wind turbine

component. Figure ES-1 plots the input variables with the largest impact on the estimated LCOE. 

Figure ES-1. LCOE Sensitivities for capacity factor, installed cost, O&M, and target IRR by 

financing structure

 

1 As described in the report, LCOE is calculated as the project’s contracted price of energy that yields the required 

internal rate of return for the project’s developer or tax equity investor. 
vii 

In addition to individual variable sensitivities, a multivariable scenario analysis estimated the 

combined effect of varying an entire set of inputs simultaneously on the projected LCOE. For the 

multivariable scenario analysis, the input parameters’ respective highest and lowest cost values 

were modified to be closer to a base-case estimate; this helped model realistic and potentially 

achievable project characteristics, when tested simultaneously. Analysts modeled three cost 

scenarios, which represent the optimal (e.g., high performance, low installed cost, and low 

finance costs), average, and inferior wind projects – each returned markedly different LCOEs. 

The estimated LCOEs from the three cost scenarios ranged from cost-competitive with 2008 

U.S. wholesale power prices to more than twice as high as wholesale prices. Therefore, it is 

difficult to make generalizations about wind LCOE because the cost of a wind project is very 

site-specific.

Lastly, analysts evaluated two subgroupings of the multivariable scenarios (shown in Figure ES2): technical variables (wind resource, installation and equipment replacement costs, O&M) and 

financial variables (IRR, debt terms). The analysis examined which subset of variables had a 

larger impact on the estimated LCOE. For each financing structure tested, the impact of the set of 

technical variables was several times larger than the set of financial variables on the estimated 

LCOE. The relative impacts of the financing variables were maximized under the two structures 

that incorporate debt financing; however, to date, wind projects have not commonly used debt at 

the project level. 

0

20

40

60

80

100

120

140

160

180

Corporate Strategic 

Investor Flip

Institutional Investor Flip 

& Back Leveraged

Cash Leveraged Cash & PTC Leveraged

LCOE $/MWh

Technical Variables LCOE Range Financial Variables LCOE Range Base-Case LCOE

Figure ES-2. Technical- vs. financial-variables scenario analysis
1 

1 Introduction

The expansion of wind power capacity in the United States has increased the demand for project 

development capital. In response, innovative approaches to financing wind projects have 

emerged and are proliferating in the U.S. renewable energy marketplace (Cory et al. 2008). Wind 

power developers and financiers have become more efficient and creative in structuring their 

financial relationships, and often tailor them to different investor types and objectives (Harper et 

al. 2007). As a result, two similar projects may use very different cash flows and financing 

arrangements, which can significantly vary the economic competitiveness of wind projects. 

This report assesses the relative impact of numerous financing, technical, and operating variables 

on the levelized cost of energy (LCOE) associated with a wind project under various financing 

structures in the U.S. marketplace. Under this analysis, the impacts of several financial and 

technical variables on the cost of wind energy are first examined individually to better 

understand the relative importance of each. Then, analysts examine a low-cost and a high-cost 

financing scenario, where multiple variables are modified simultaneously. Lastly, the analysis 

also considers the impact of a suite of financial variables versus a suite of technical variables. 

The analysis was performed as part of multiyear, multinational International Energy Agency

(IEA) task to develop a better understanding of the costs of wind energy. The parametric analysis 

identified key factors in the cost of wind energy. Future analysis will compare the countryspecific results in an effort to develop a transparent and accepted methodology for estimating 

wind energy costs and to help project future cost-performance trends.

While this report updates a selected number of assumptions stemming from changes in the 

financial and wind energy markets since 2007, it does not consider the 2008-2009 financial crisis 

in-depth. Wind project development has been impacted by the rapid post-September 2008 credit 

tightening, the simultaneous Wall Street financial turmoil, and subsequent actions by the federal 

government in 2008 and 2009 to bolster the economy. At the time of this writing, it is still early 

to discern and model these exact impacts, which will be the subject of many future studies. 

However, it is apparent that since the financial crisis, the wind industry has seen a dramatic 

“flight to quality” for new project development (e.g., highly experienced developers, thoroughly 

tested technology, creditworthy off-takers, etc.) (Schwabe et al. 2009). The combination of these 

factors impacts the cost of both debt and equity financing; and, thus, the overall LCOE of a wind

project. 

2 Six Wind Financing Structures Under Study

This analysis incorporates the use of a wind levelized cost of energy model developed in “Wind 

Project Financing Structures: A Review and Comparative Analysis” (Harper et al. 2007) as well 

as “A Review of Wind Project Financing Structures in the USA,” (Bolinger et al. 2008). These

reports described seven financing structures used in pre-financial crisis wind project 

development, and were designed to satisfy both investor and wind developer needs. This report 
2 

assesses only six financing structures,

2

 which are briefly addressed below. For more detailed 

descriptions and schematic representations of each structure, see Harper et al. 2007. 

All equity-financing structures:

1) Corporate (Corp). A single developer who is able to use all of the project’s tax benefits, 

and acts as both developer and investor. The corporate developer internally funds 100% 

of the project’s costs and receives 100% of the project’s associated cash flows and tax 

benefits. 

2) Strategic Investor Flip (SIF). In addition to the project developer, a separate tax equity

investor partnered with to fully use a wind project’s tax benefits. The developer and tax

equity investor negotiate their respective equity contribution ratios, an initial or “pre-flip” 

distribution of the project’s cash revenues and tax benefits, and a future or “post-flip” 

distribution. The “flip-point” occurs once the tax equity investor achieves an agreed upon 

internal rate of return based on his/her equity contribution. 

3a) Institutional Investor Flip (IIF). Also uses a third-party tax equity investor with distinct 

pre- and post-flip cash and tax benefits allocations. Distinct from the Strategic Investor 

Flip, the developer in this scenario invests a larger up-front equity investment and tends

to attract passive tax equity investors. 

3b) Back Leveraged (BL).Identical to the Institutional Investor Flip, but uses corporatelevel debt to fund the developer’s equity contribution investment. The loan made to the 

developer is entirely outside of the project, and is not captured within the model; 

therefore, these financial structures (3a, 3b) were combined in this analysis.3

Structures with project-level debt: 

 

4) Cash Leveraged (Cash Lev). Similar to the Strategic Investor Flip (partners, cash flows, 

and tax benefits), except incorporates project-level debt, which is repaid from the wind 

project’s future cash flows and is collateralized by the project’s assets. This structure

boosts equity returns through leveraging of lower-cost capital, and reduces required upfront equity contribution by the developer and/or tax equity investor. In exchange, the 

project’s debt lender receives interest payments on the loan, preferred claims on cash 

flows and the project’s assets, and certain project-approval rights. 

5) Cash and Production Tax Credit Leveraged (Cash and PTC Lev). Similar to the 

Cash Leveraged structure, but additional debt is taken against expected production tax 

credit (PTC) benefits to fund initial project costs. The goal is to further boost returns and 

minimize up-front equity contributions. However, developers and tax investors might be 

 

2 A seventh financing structure, Pay-As-You-Go, has been used primarily as a tool for refinancing wind projects; 

therefore, it is excluded in the current evaluation. 

3 Note that for these analyses, the Institutional Investor Flip and Back- Leveraged financing structure are identical at 

the project level (the Back-Leveraged structure uses corporate-level debt, which is not captured in the model) and, 

therefore, return identical results.
3 

required to make future equity contributions if there are any PTC revenue shortfalls (e.g., 

for project underperformance). 

3 Analysis Overview and Assumptions

This analysis examines the impacts of various input parameters as well as the different financing 

structures on the estimated LCOE. The model used to perform the analysis is an Excel-based pro 

forma financial cash flow model that uses a representative template for each of the financial 

structures investigated (Karcher 2008). For each, the model incorporates the assumed rates of 

return by the developer or tax equity investor, the equity contribution ratios, and the cash and tax 

benefits allocations, and then solves for the LCOE needed to satisfy those input assumptions 

(Harper et al. 2007). Each financial analysis uses a combination of shared and structure-specific 

assumptions to estimate the LCOE. For a detailed description of the model’s input assumptions, 

see Appendix A. 

 

The model estimates the nominal levelized cost of energy, which includes the all-in cost of the 

equipment, as well as the cost of financing (both debt interest rate and equity returns), and other 

ancillary costs (if applicable). Levelized cost of energy also factors in PTC and accelerated 

depreciation benefits. Because this is a generic analysis without a specific location, several 

specific elements are excluded, namely state, local, and utility-based incentives; and renewable 

energy certificate (REC) revenues. 

For validation purposes pertaining specifically to this study, renewable energy attorneys from

Stoel Rives LLP independently audited the model. Stoel Rives also verified that the assumptions 

used in the following analyses are representative of the time period under consideration (Stoel 

Rives 2009). 

3.1 Base-Case Assumptions

Analysts used the base-case assumptions as reference points to measure changes in the estimated 

LCOE from the individual variable sensitivities and the multivariable scenario analyses. In 

reality, regional or market variations move many projects away from the base case toward more 

or less cost-competitive wind projects. These variations are captured in the sensitivity and 

scenario analyses described in Sections 4.1 though 4.3. 

The base-case assumptions model a commercial wind project with an in-service date of January 

1, 2008. Although more recent year-end 2008 wind data is available, January 1 is used to exclude 

the effect of the 2008-2009 financial crisis on wind project development (Wiser and Bolinger 

2009). 

In general, the analysis used assumptions from Harper et al. 2007; however, some of the basecase assumptions were modified to reflect subsequent developments in wind and financing 

markets. The analysis revised base-case assumptions for the average wind resource, project size,

installation costs, equipment replacement costs, and interest and inflation rates. Appendix A 

includes a detailed listing of the model’s input assumptions. 
4 

4 Analysis Methodology, Sensitivity Assumptions, and 

Multivariable Scenarios

To estimate the relative impacts of technical and financing variables on the resulting wind power 

LCOE, analysts modeled both individual variable sensitivities and multivariable scenarios. To 

model individual variable sensitivities, they developed a range of high-cost, base-case, and lowcost values – based on a variety of industry sources – for each variable studied. They also 

developed distinct high-cost and low-cost estimates for the multivariable scenarios. 

The input variables analyzed in the individual and multivariable analyses include: 

Technical variables Financial variables

 Capacity factor  Target IRR,

4

 Total installed cost 

and

 For the two finance structures that use 

 Operations & maintenance5, and project-level debt:

 Levelized replacement cost6  Return on debt (interest rate), and

 Loan duration

The analysts selected these variables for study because they are most likely to impact LCOE, and 

they must be carefully considered in project development. The Department of Energy (DOE) 

highlights technical variables of capacity factor, operations and maintenance, and total installed 

costs as drivers in wind power prices (Wiser and Bolinger 2008, Wiser and Bolinger 2009). 

Levelized replacement cost, which recovers the expense of technical equipment replacement 

over the project’s contract life, has also been reported as an increasingly significant component 

of annual cost of wind energy (Hand 2009) . 

Because the development of a wind project is capital-intensive, a project’s financing terms are 

critical to securing investment while developing economically successful projects. For example, 

debt and equity providers have identified a project’s target IRR as crucial in attracting tax equity 

that could otherwise flow to separate tax investment opportunities such as affordable housing 

(Harper et al. 2007); yet high IRRs also increase financing costs. Similarly, the debt terms are

key cost factors for developers considering debt financing to fund new project development 

(Schwabe et al. 2009).

4.1 Individual Variable Sensitivities

For the individual variable sensitivity analysis, the analysis modifies a variable individually to 

test the stand-alone sensitivity of each input variable on the LCOE (the output). As these 

variables were individually tested, all other variables were held fixed to the base-case 

assumption. These sensitivities were performed to identify which individual variables had the 

biggest overall impact on LCOE, if modified within a reasonable range. 

 

4 Target IRR is the negotiated internal rate of return received by the wind project’s investor(s) or developer.

5 Operations and maintenance charges are typically distributed between fixed and variable components of the total 

O&M cost. For this analysis, variations in O&M expenditures are tested as changes within the variable charges only. 6 Levelized replacement cost captures expenses associated with technical equipment restitution such as a gear box 

replacement or rebuild, but does not account for lost revenue due to equipment downtime. 
5 

To perform the individual variable sensitivities, analysts developed a range of high-cost, basecase, and low-cost assumptions for each of the input variables. These ranges were based on 

estimates of realistic wind project characteristics.

7 The financial variable ranges were set wide 

enough that it is possible that they encompass the investment conditions that might result from

the financial crisis (without attempting to model exactly where the market will finally settle). 

Table 1 details the high-cost, base-case, and low-cost tested values for the technical input 

variables. 

Table 1. Technical Variables: Individual Variable Sensitivities

Scenario

Capacity 

Factor 

(%)

Installed 

Cost 

($/kW)

Operations & 

Maintenance 

($/MWh) 

Levelized 

Replacement Cost 

($000)

High-Cost 22 2,600 17 25,600

Base-Case 34 1,710 6 12,800

Low-Cost 48 1,240 3 0 

Table 2 similarly lists the high-cost, base-case, and low-cost tested values for each of the 

financial variables. Note that the target IRR values are differentiated by financing structure, 

which is due to each structure’s unique risk and return characteristics among the project 

investors (Harper et al. 2007). For example, structures with debt carry a greater default risk for

the tax equity investor because the project’s debt lenders get priority rights in the case of project 

default. Therefore, the tax equity investor in the debt-leveraged structures requires a higher 

target IRR relative to the all-equity financing structure, which compensates for their increased 

risk exposure. Furthermore, the financing terms (both debt and equity) required by project 

investors will also vary from project to project within a structure, depending on overall quality 

of the project, developer reputation, underperformance risk, and other factors. 

Table 2. Financial Variables: Individual Variable Sensitivities 

Scenario

Corp 

(%)

SIF, IIF 

& BL 

(%)

Cash 

Lev 

(%)

Cash & 

PTC Lev 

(%)

High-Cost 15.00 / 12.00 / 14.00 / 14.25 13.00 10

Base-Case 10.00 / 6.50 / 9.00 / 9.25 5.80 15

Low-Cost 8.00 / 6.00 / 7.50 / 7.75 4.20 18

Loan 

Duration 

(Years)

Return 

on Debt 

(%) 

Target IRR*

 *Varies by financing structure

In addition to the high-cost, base-case, and low-cost scenarios indicated in Table 1 and Table 2, 

the analysts tested equally spaced intermediate points between the high- or low-cost scenario and 

the base case to more clearly assess potential trends in the input variables’ LCOE impact.

Appendix B shows intermediate tested points. 

 

7 Note that the range of levelized replacement costs were developed based on varying estimates for the frequency of 

turbine equipment failure. 
6 

4.2 Multivariable Scenario Analysis

Analysts also modeled high-cost and low-cost multivariable scenarios to estimate the combined 

impact of varying all tested variables (technical and financial) simultaneously on the projected 

LCOE. However, the input parameters’ highest- and lowest-cost values are distinct from the 

values used in the individual variable sensitivities. The analysis adjusted the input parameters’ 

high- and low-cost values of the input parameters to be closer to the base case; this allowed

analysts to model realistic and potentially achievable project characteristics when tested 

simultaneously. In other words, it is unlikely that any one project would have all of the most 

favorable (i.e., extreme low-cost) or least favorable (i.e., extreme high-cost) values together – 

some mix is likely more realistic. Table 3 illustrates the multivariable scenarios for the technical

variables. Similarly, Table 4 illustrates the multivariable scenarios for the financial variables. 

Appendix B describes the methodology for determining these input values. 

Table 3. Technical Variables: Multivariable Scenarios

Scenario

Capacity 

Factor 

(%)

Installed 

Cost 

($/kW)

Operations & 

Maintenance 

($/MWh) 

Levelized 

Replacement Cost 

($000)

Adjusted High-Cost 26.8 2,244 12.6 20,480

Base-Case 34.0 1,710 6.0 12,800

Adjusted Low-Cost 42.4 1,428 4.2 5,120

Table 4. Financial Variables: Multivariable Scenarios

Scenario

Corp 

(%)

SIF, IIF 

& BL 

(%)

Cash 

Lev 

(%)

Cash & 

PTC Lev 

(%)

Adjusted High-Cost 13.00 / 9.80 / 12.00 / 12.25 10.12 12.00

Base-Case 10.00 / 6.50 / 9.00 / 9.25 5.80 15.00

Adjusted Low-Cost 8.80 / 6.20 / 8.10 / 8.35 4.84 16.80

Target IRR*

Return 

on Debt 

(%) 

Loan 

Duration 

(Years)

 

 *Varies by financing structure

4.3 Technical vs. Financial Multivariable Scenario Analysis

Analysts examined a final scenario that divided the multivariable scenario analysis into two 

subgroupings. They tested technical variables (capacity factor, installed cost, O&M, and 

levelized replacement cost) separately from financing variables (target IRR, return on debt, and 

loan duration). The LCOE was estimated with the technical variables set to their respective 

adjusted high-cost (or low-cost) multivariable scenario values while the financial variables were 

fixed to their base-case values and then vice versa. Analysts performed this test to see which 

subset of variables had a larger impact on the estimated LCOE. 
7 

5 Results 

Table 5 shows the base-case LCOE estimates from the individual variable sensitivities, based on 

financial structure. The base-case LCOE provides a baseline measure to compare results from the 

sensitivity and scenario analyses that follow.

8

 

Table 5. Base-Case LCOE Estimates

Levelized Cost of 

Energy ($/MWh) Corporate

Strategic 

Investor Flip

Institutional 

Investor Flip & 

Back Leveraged

Cash 

Leveraged

Cash & PTC 

Leveraged

Base-Case 74 71 63 58 54

5.1 Individual Variable Sensitivities 

Figure 1 shows the resulting range of estimated LCOE for the individual variable sensitivities, 

which plots each variable’s highest- and lowest-cost input values for all of the financing 

structures tested. The input variables in Figure 1 are ordered according to impact on the LCOE, 

from largest impact to smallest (top to bottom). The input parameters with the largest variation 

between the highest- and lowest-cost LCOE estimates generally have the greatest impact on 

overall wind project costs. Appendix C lists the complete results of the individual variable 

sensitivity analysis, including all interim tested values between the highest- and lowest-cost input 

parameters. 

 

 

8

 Again, note that for these analyses, the Institutional Investor Flip and Back-Leveraged financing structure are 

identical at the project level (Back-Leveraged structure uses corporate-level debt, which is not captured in the 

model) and, therefore, return identical results. 

All-equity financing

structures

Structures with

project-level debt
8 

20 40 60 80 100 120 140 160

Cash & PTC Leveraged

Cash Leveraged

Institutional Investor Flip & Back Leveraged

Strategic Investor Flip

Corporate

Cash & PTC Leveraged

Cash Leveraged

Institutional Investor Flip & Back Leveraged

Strategic Investor Flip

Corporate

Cash & PTC Leveraged

Cash Leveraged

Institutional Investor Flip & Back Leveraged

Strategic Investor Flip

Corporate

Cash & PTC Leveraged

Cash Leveraged

Institutional Investor Flip & Back Leveraged

Strategic Investor Flip

Corporate

Cash & PTC Leveraged

Cash Leveraged

Institutional Investor Flip & Back Leveraged

Strategic Investor Flip

Corporate

Cash & PTC Leveraged

Cash Leveraged

Institutional Investor Flip & Back Leveraged

Strategic Investor Flip

Corporate

Cash & PTC Leveraged

Cash Leveraged

Institutional Investor Flip & Back Leveraged

Strategic Investor Flip

CorporateLevelized Replacement Cost Loan Duration Return on Debt O&M Target IRR Installed Cost Capacity Factor

LCOE $/MWh

Input Variable

Financing Structure Base-Case Input Values

High-Cost: 22%

Base-Case: 34%

Low-Cost: 48%

High-Cost: 25,600 ($000)

Base-Case: 12,800 ($000)

Low-Cost: 0 ($000)

High-Cost: 10 Years

Base-Case: 15 Years

Low-Cost: 18 Years

High-Cost: 13.0%

Base-Case: 5.8%

Low-Cost: 4.2%

High-Cost: $17/MWh

Base-Case: $6/MWh

Low-Cost: $3/MWh

High-Cost: See Table 2

Base-Case: See Table 2

Low-Cost: See Table 2

High-Cost: $2,600/kW

Base-Case: $1,710/kW

Low-Cost: $1,240/kW

Figure 1. LCOE ranges by individual variable sensitivities, showing all financing structures 

The results of the individual variable sensitivity analysis indicate that among the input 

parameters tested, capacity factor and installed cost have the largest impact on the estimated 

LCOE. Target IRR has the largest impact among the financing variables tested, particularly for 

financing structures that use 100% equity. O&M costs had a moderate impact on the LCOE. For 

the financing structures that use debt at the project level (Cash Leveraged and Cash and PTC 

Leveraged), the loan terms have a modest effect on LCOE. The levelized replacement cost has 

the lowest impact on projected LCOE. 

The relative impacts of each input variable on the projected LCOE are depicted in greater detail

in Figures 2 and 3 and in Appendix C (Figures C1-C3 are included in Appendix C, because the 

results are similar to those in the figures presented here). Each figure illustrates a different 

financing structure tested. 

In Figures 2 and 3, the percentage change of the input variable from the base case is shown along 

the X-axis. The two Y-axes show the resulting estimated LCOE that corresponded with the 

change in the input variable in percentage terms (from the base case) as well as in dollars per 

megawatt-hour ($/MWh). For each input variable, the slope of the plotted line shows the 
9 

variable’s impact on the LCOE. The steeper the slope, the greater the impact an input variable 

has on the LCOE.

 

Figure 2 shows the LCOE sensitivity to the tested individual variables in the all-equity, 

Institutional Investor Flip financing structure. This structure represents the majority of thirdparty (non-corporate) financing deals from 2003-2006 (Harper et al. 2007). As shown, installed 

cost and capacity factor have the largest impact on LCOE, even though the ranges tested were 

relatively small (in percentage terms from the base-case input value).9

 

Target IRR also shows a 

large impact on the LCOE – an approximate 40% increase in the target IRR increases the 

estimated LCOE by around 20%. 

$30 

$40 

$50 

$60 

$70 

$80 

$90 

$100 

$110 

$120 

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

-125% -100% -75% -50% -25% 0% 25% 50% 75% 100% 125% 150% 175% 200%

LCOE $/MWh

Change In LCOE

Capacity Factor O&M Levelized Replacement Cost Target IRR Installed Cost

Change In Input Variable

Figure 2. Institutional Investor Flip LCOE sensitivities by input variable

 

9 Installed cost, target IRR, O&M, return on debt, and levelized replacement cost are directly proportional to the 

estimated LCOE. Capacity factor and loan duration (Figure 3) are inversely proportional.
10 

Figure 3 shows the LCOE sensitivity to individual variables in the Cash Leveraged financing 

structure, which incorporates the use of debt financing. It shows that most of the variables have 

the same relative relationships as the Institutional Investor Flip financing structure (Figure 2).

The exception is target IRR, which has about the same percentage change in input variable, but a 

significantly lower impact on LCOE. This makes sense due to the addition of debt, which 

reduces the portion of equity investment in the project from 100% to approximately 55%. The 

addition of debt also adds two new variables – the loan duration and return on debt (or interest 

rate), both of which appear to have moderate impacts on LCOE.

$30 

$40 

$50 

$60 

$70 

$80 

$90 

$100 

$110 

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

-125% -100% -75% -50% -25% 0% 25% 50% 75% 100% 125% 150% 175% 200%

LCOE $/MWh

Change In LCOE

Capacity Factor O&M Levelized Replacement Cost

Target IRR Installed Cost Loan Duration

Return on Debt

Change In Input Variable

Figure 3. Cash Leveraged structure LCOE sensitivities by input variable

Figure 4 plots the most impactful input variables for all financing structures. The range of each 

variable’s tested values is plotted against the estimated LCOE. 

The shapes of the curves are noteworthy. With the exception of the target IRR parameter, the 

overall slopes of the projected LCOE are similar among the six financing structures, which 

indicates that the impacts of the variables are fairly consistent across financing types. Installed 

cost and O&M appear to have a mostly linear relationship to the estimated LCOE. This suggests 

that reducing installation costs or O&M expenditures will have a fairly consistent impact on the 

LCOE. Capacity factor, on the other hand, has a dramatic impact on the LCOE for belowaverage projects but levels somewhat as average or above-average capacity factors are achieved. 

This suggests that especially large reductions in LCOE can result from capacity factor 

improvements, particularly from substandard values to average capacity factor levels. The 
11 

impact of the target IRR financing variable fluctuates by financing structure because each has a 

unique combination of equity capital investment and associated risk characteristics for the 

project investors. 

 

The actual level ($/MWh) of the estimated LCOE varies by financing structure. The structures 

involving the use of debt (Cash Leveraged as well as Cash and PTC Leveraged) show lower 

estimated LCOEs compared to the all-equity financing structures. The Cash and PTC-Leveraged 

structure shows the lowest estimated LCOE because it uses the greatest percentage of debt 

financing, which is generally cheaper than equity financing (assuming debt interest rates are less 

than the equity target IRR). 

Figure 4. LCOE sensitivities for capacity factor, installed cost, O&M, and target IRR by financing 

structure 

5.2 Multivariable Scenarios 

The multivariable scenario analysis estimated the combined impact of varying an entire set of 

inputs simultaneously on the projected LCOE. Figure 5 shows the results of the multivariablescenario analysis between the high-cost, base-case, and low-cost scenarios. 
12 

178 177

162

155 151

36 34 33 30 26

74

71

63

58 54

0

20

40

60

80

100

120

140

160

180

200

Corporate Strategic 

Investor Flip

Institutional Investor 

Flip & Back 

Leveraged

Cash Leveraged Cash & PTC 

Leveraged

LCOE $/MWh

Base-Case

Figure 5. Multivariable scenarios LCOE ranges 

Figure 5 shows the extent to which the estimated LCOE varies among optimal, average, and 

substandard wind projects. To put these LCOEs into context compared to electricity prices, it is 

important to know that the average wholesale power prices ranged regionally from about 

$45/MWh to more than $80/MWh in 2008 (Wiser and Bolinger 2009). Comparatively, the lowcost multivariable scenario is on the low end of that scale with an LCOE of $26-$36/MWh, 

depending on financing structure. This indicates that wind projects with superior resource

attributes, low-cost equipment, and excellent financing terms can be cost-competitive with U.S. 

wholesale power prices.10 The base-case scenario’s estimated LCOE range of $54-$74/MWh is 

comparable to the range of wholesale power prices. The high-cost scenario has an estimated 

LCOE range of $151–$178/MWh, more than double wholesale power prices. As such, high-cost 

and poor-performing wind projects with higher financing terms are not directly competitive with 

wholesale power prices across most of the United States.

Because there is a wide range of site-specific costs and technology drivers for individual 

projects, it is difficult to make generalizations about LCOE for wind that hold true for wind 

projects, as a whole, across the nation. Therefore, site-specific considerations (size of project, 

economies of scale, resource quality, etc.) are critical when considering the cost of wind energy. 

 

10 This ignores the current financial uncertainties surrounding the financial crisis, the current limitations on access to 

debt and equity, as well as increased competition among projects.
13 

5.3 Technical- Versus Financial-Variable Scenarios

Figure 6 shows the results of varying the suite of technical and financial variables separately. 

The range of projected LCOE that results from varying the technical variables as a whole (and 

holding the financial variables constant) is illustrated in the lighter region, while the range of 

projected LCOE from varying the financial variables is shown in the darker region. Additionally, 

the base-case value for each structure is illustrated as a band within the LCOE ranges. As 

anticipated from the individual variable results, varying the set of technical variables as a whole 

has a larger effect on the estimated LCOE than the set of financial variables. This result shows 

why it is critical that developers and utilities site and select wind projects at locations with 

excellent resources and technical cost characteristics before considering financing. These 

characteristics typically have a much larger impact than a project’s financing terms. 

Furthermore, the impact of financing variables varies by the structure of financing, and the set of 

financial variables have their largest impact on the LCOE within the Cash Leveraged as well as 

the Cash and PTC Leveraged structures. To date, however, these debt-leveraged models have 

seen limited use in wind project funding despite being among the cheapest options available 

(assuming debt interest rates are less than the equity target IRR). The implication is that market 

players might be willing to accept slightly higher project costs to avoid the involvement of 

conservative project lenders. This choice to avoid cheaper sources of financing is less of an issue 

for the highest-quality wind projects because the production benefits far outweigh the financing 

savings. However, for moderate-quality wind projects, the savings from better-than-average 

financing terms can have more impact. The debt-leveraged structures – the cheapest structure of 

wind financing – would offer an opportunity for overall project cost reductions in the face of 

wind resource, technology, or installation conditions that are less than ideal.

11 

 

 

11 And with the contraction of capital available for equity investment due to the financial crisis, project-level debt 

might be needed to get all of the projects under development built. 
14 

0

20

40

60

80

100

120

140

160

180

Corporate Strategic 

Investor Flip

Institutional Investor Flip 

& Back Leveraged

Cash Leveraged Cash & PTC Leveraged

LCOE $/MWh

Technical Variables LCOE Range Financial Variables LCOE Range Base-Case LCOE

Figure 6. Technical- vs. financial-variables scenario analysis

6 Conclusions

The results of the individual variable sensitivities suggest several insights. As expected, changes 

in a project’s capacity factor and installed cost have such a significant impact on the LCOE that 

small improvements through improved R&D or manufacturing improvements can yield major 

benefits. The most dramatic reductions in LCOE occurred for projects that had below-average 

wind resource quality and that had incremental improvements in capacity factor. This suggests 

that increasing the capacity factor from substandard values to average capacity-factor levels can 

achieve especially large reductions in LCOE. 

A less obvious, yet moderate impact on the LCOE resulted from a change in the target IRR 

financing term. As previously noted, in the 100% equity financed Institutional Investor Flip 

structure, an approximate 40% increase in the target IRR increases the estimated LCOE by about 

20%. This indicates that the target equity return could have a sizable effect on LCOE if the range 

of high- and low-cost values widens beyond historical industry practice, a likely outcome of the 

financial crisis (Schwabe et al. 2009). 

The analysis also examined two subsets of the input variables to compare the overall impact of 

technical versus financial variables. For each financing structure tested, the impact of technical

variables on the overall estimated LCOE was noticeably larger than that of the financing terms. 
15 

Because debt generally costs less than equity, the relative impacts of the financing variables were

maximized under the two structures that incorporate debt financing (Cash Leveraged and Cash 

and PTC Leveraged structures). However, to date, the leveraged deals have been quite rare; but 

they could be an opportunity for some, albeit smaller, cost savings if other cost reductions in 

wind and technical characteristics are difficult to achieve. 

The move away from debt financing may have resulted from the project team wanting to 

maintain some of the “upside potential” for themselves. Compared to equity investors, debt 

lenders usually have more stringent risk-mitigation requirements (e.g., require long-term 

contracts, require creditworthy off-takers, etc.) and have priority rights on project assets. Prior to 

the financial crisis, these measures may have been seen as detrimental to project economics – 

locking-in energy prices over the life of the project means that the project cannot take advantage 

of any potential upward swings in market prices. However, the sources for capital investment – 

tax equity and debt – have shrunk under current market conditions. Depending on how quickly 

and thoroughly these markets recover, the use of debt might emerge as an increasingly prevalent 

way to securing project financing going forward. 

 

7 Future Analysis

While conducting the research for this study, the analysts discovered some points of interest that 

may warrant further in-depth analysis. Potential topics include:

1) Inflation sensitivities. This seems to be especially appropriate given the uncertainty 

surrounding the current economic environment. It would be helpful to model the effect as 

an individual sensitivity parameter relative to the other input variables. 

2) Utility ownership. Utilities can own wind projects and use the PTC (Cory et al. 2008). 

Following a 2008 clarification by the Internal Revenue Service (IRS), utilities can own 

between 51% and 99% of a project, and take the full value of the PTC over the life of the 

project (Mann 2008). Although this ownership structure has seen limited use, investorowned utilities (IOUs) may decide to own more wind projects going forward. Due to 

their regulated rate of return, IOUs could develop wind projects using better financing 

terms than developers, particularly in today’s troubled market where equity returns are 

expected to increase substantially. Therefore, because this type of ownership was not 

considered in the current analysis and could become more relevant, it should be 

investigated going forward.

3) Financial crisis of 2008-2009 and select U.S. federal legislation. The scarcity of debt 

and equity financing in late 2008 and 2009 is likely to increase the costs of financing 

capital-intensive wind projects. It could be temporary, but sustained cost increases are

certainly a possibility. Government officials have enacted new and unfamiliar programs 

in response to the crisis, which will likely alter the financing structures included in this 

analysis. While it is still too early to discern exact impacts of these exceptional market 

conditions on wind energy cost determinants, the financing terms or the structures 

themselves should be revisited. 
16 

References 

Bolinger, M.; Harper, J.; Karcher, M. (2008). “A Review of Wind Project Financing Structures 

in the USA,” Wind Energy Journal. September. 

http://www3.interscience.wiley.com/cgi-bin/fulltext/121414698/PDFSTART

Cory, K.; Coughlin, J.; Jenkin, T.; Pater, J.; Swezey, B. (2008). “Innovations in Wind and Solar 

PV Financing,” National Renewable Energy Laboratory technical report NREL/TP-670-42919, 

February. http://www.nrel.gov/docs/fy08osti/42919.pdf

Hand, M. (2009). Personal interview with Maureen Hand, National Renewable Energy 

Laboratory in January 2009. 

Harper, J.; Karcher, M.; and Bolinger, M. (2007) “Wind Project Financing Structures: A Review 

& Comparative Analysis,” Lawrence Berkeley National Laboratory technical report LBNL63434, September. http://eetd.lbl.gov/ea/ems/reports/63434.pdf

Inflationdata.com (2008). “Inflation Rates for Jan 2008 – Present.” Inflationdata.com Web site, 

accessed December 2008. http://inflationdata.com/inflation/ 

IRS (2008). Internal Revenue Bulletin 2008-21, Internal Revenue Service, U.S. Department of 

the Treasury, October. http://www.irs.gov/irb/2008-21_IRB/ar11.html 

Karcher, M. (2008). Wind Financing Structures Pro Forma Model. Deacon Harbor Financial. 

karcher@deaconharbor.com

Mann, L. (2008). “IRS Reinterpretation Will Benefit Utilities RE Investments,” North American 

Windpower, September 2008, pp. 72-73. 

Schwabe, P.; Cory, K.; Newcomb, J. (2009). “Renewable Energy Project Financing: Impacts of 

the Financial Crisis and Federal Legislation,” National Renewable Energy Laboratory technical 

report NREL/TP-6A2-44930, July. http://www.nrel.gov/docs/fy09osti/44930.pdf

Stoel Rives (2009). Review of Pro Forma Wind Model. Memorandum from representatives of 

Stoel Rives LLP, March 25, 2009. 

Wiser, R.; Bolinger, M. (2009). “2008 Wind Technologies Market Report,” U.S. Department of 

Energy, Office of Energy Efficiency and Renewable Energy, July. 

http://eetd.lbl.gov/ea/ems/reports/2008-wind-technologies.pdf

Wiser, R.; Bolinger, M. (2008). “Annual Report on U.S. Wind Power Installation, Cost, and 

Performance Trends: 2007,” U.S. Department of Energy, Office of Energy Efficiency and 

Renewable Energy, May. http://www1.eere.energy.gov/windandhydro/pdfs/43025.pdf
17 

Appendix A. Equity and Debt-Financing Assumptions

Tables A1-A3 list the assumptions used in the Harper et al. 2007 analysis and identify 

assumptions that were updated for this analysis. Table A1 details the assumptions that are 

common across all financing structures that were tested; Table A2 details the structure-specific 

assumptions in the equity-based financing structures; and Table A3 lists the assumptions for the 

structures that incorporate debt financing only. 

Specifically, base-case assumptions updated from the Harper et al. 2007 analysis include: 

1) Average installed wind project size was set at 120 MW to be consistent with the 

average installed wind project size in 2007 (Wiser and Bolinger 2008). 

2) Capacity factor is assumed at 34%, because the weighted average capacity factor for 

projects installed in 2006-2007 was reported as 33%-35% (Wiser and Bolinger 2008). 

3) Average installed capital cost of a wind project built in 2007 increased to $1,710/kW 

(Wiser and Bolinger 2008). The increase of installed capital costs was allocated to wind 

turbines and balance of plant components equally. 

4) 2008 PTC adjustment factor was updated to the actual 2008 reported value of 1.3854 

(IRS 2008). 

5) All-in annual interest rate (return on debt) was set at 5.8% to be consistent with an 

average long-term debt interest rate used to finance a wind project as of January 1, 2008. 

This rate was based on conversations with renewable energy industry attorneys (Stoel

Rives 2009). 

6) Inflation rate was set at 4%, which was the average monthly inflation rate in 2008 based 

on the consumer price index (Inflationdata.com 2008). 

7) Levelized replacement cost was set at $12.8 million over the life of a 120 MW wind 

project, based on discussions with wind project developers and equipment manufacturers

(Hand 2009). 

 

 
18 

Table A1. Input Assumptions Common to All Structures 

Assumption

Harper et al. 2007 

value

2008 

Modification Source/Note

 Year of Initial Commercial Operation 2007 2008 The project becomes operational January 1st 2008.

 Project Capacity 100 MW 120 MW

 Annual Net Capacity Factor 36.00% 34.00%

 Inflation Rate 2.00% 4.00% Inflationdata.com 2008.

 Interest on Reserves 2.00% 4.00% Set to be consistent with assumed inflation rate.

 Hard Costs

 Development Costs 5,000 Unchanged

 Wind Turbines 120,000 160,200

 Balance of Plant 25,000 30,000

 Interconnection 10,000 Unchanged

 Soft Costs

 Interest During Construction

 Interest Rate 6.70% 5.80% Stoel Rives 2009.

 Construction Period 12 Months Unchanged

 Construction Debt Closing Fee

 (% of debt amount)

 Soft Cost Totals

 Interest During Construction Calculation Unchanged

 Equity Closing Costs 400 Unchanged

 Developer Fee 3,500 Unchanged

 Working Capital 1,000 Unchanged Harper et al. 2007.

 Contingency 8,000 10,260 5% of Hard Costs.

 Operations & Maintenance Costs

 Fixed O&M ($/kW-yr) 11.50 Unchanged Harper et al. 2007.

 Variable O&M ($/MWh) 6.00 Unchanged Harper et al. 2007.

 PPA Letter of Credit (LOC)

 LOC AMOUNT ($000) 5,000 Unchanged

 Annual LOC Rate 1.50% Unchanged

 Insurance ($000-yr) 0 Unchanged Harper et al. 2007.

 PTC Base Year ($/MWh) 15.00 Unchanged

 2007 Inflation Adjustment Factor 1.3433 N/A

 2008 Inflation Adjustment Factor 1.3702 1.3854 Internal Revenue Service Notice 2008-48.

 2008 PTC Rate ($/MWh) 21.00 Unchanged $15/MWh multiplied by 1.3854 & rounded to nearest integer.

 Years Available 10 Unchanged Harper et al. 2007.

 State 6% Unchanged

 Federal 35% Unchanged

 Hard Costs

 Development Costs Indirect Unchanged

 Wind Turbines

 Balance of Plant

 Interconnection Direct 20-yr SL Unchanged

 Soft Costs

 Interest During Construction (IDC) Indirect Unchanged

 Debt Closing Costs (when debt is used) Direct 15-yr SL Unchanged

 Debt Closing Fee (when debt is used) Indirect Unchanged

 Debt Service Review

 Equity Closing Costs

 Working Capital

 Developer Fee Indirect Unchanged

 Contingency (5% of Hard Costs) Indirect Unchanged

Harper et al. 2007.

Harper et al. 2007.

Unchanged

Unchanged

Taxes

Deprecation Allocation

Harper et al. 2007.

Harper et al. 2007.

Harper et al. 2007.

Harper et al. 2007.

Wiser and Bolinger 2008 ($1,710/kW in aggregate).

1.25%

Direct 5-yr MACRS

Non-Depreciable

Unchanged

Project Information

Capital Costs ($000)

Annual Operating Expenses

PTC 

Wiser and Bolinger 2008.

Source: Adapted from Harper et al. 2007
19 

Table A2. Structure-Specific Equity-Financing Assumptions 

Developer Tax Developer Tax Source

 Equity Contributions

 Corporate 100% N/A Unchanged Unchanged Harper et al. 2007.

 Strategic Investor Flip

 Cash Leveraged

 Cash & PTC Leveraged

 Institutional Investor Flip

 Back Leveraged

 Cash Allocations

 Pre-Flip Cash

 Corporate 100% N/A Unchanged Unchanged Harper et al. 2007.

 Strategic Investor Flip

 Cash Leveraged

 Cash & PTC Leveraged

 Institutional Investor Flip

 Back Leveraged

 Post-Flip Cash

 Corporate 100% N/A Unchanged Unchanged Harper et al. 2007.

 Strategic Investor Flip

 Cash Leveraged

 Cash & PTC Leveraged

 Institutional Investor Flip

 Back Leveraged

 Tax Benefits Allocations

 Pre-Flip Tax

 Corporate 100% N/A Unchanged Unchanged Harper et al. 2007.

 Strategic Investor Flip

 Cash Leveraged

 Cash & PTC Leveraged

 Institutional Investor Flip

 Back Leveraged

 Post-Flip Tax

 Corporate 100% N/A Unchanged Unchanged Harper et al. 2007.

 Strategic Investor Flip

 Cash Leveraged

 Cash & PTC Leveraged

 Institutional Investor Flip

 Back Leveraged

Harper et al. 2007.

1% 99%

0% 100%

Unchanged Unchanged

1%

90% 10%

Unchanged

Unchanged

Unchanged

0% 100% Unchanged

Assumption

Harper et al. 2007 value 2008 Modification

1% 99%

40% 60%

Unchanged Unchanged

Unchanged

95% 5% Stoel Rives 2009.

Harper et al. 2007.

Stoel Rives 2009.

Unchanged Unchanged

99%

90% 10% Harper et al. 2007.

Partnership Allocations

1% 99%

Harper et al. 2007.

Harper et al. 2007.

Harper et al. 2007.

Unchanged

 Source: Adapted from Harper et al. 2007
20 

Table A3. Structure-Specific Debt-Financing Assumptions

Assumption

Harper et al. 2007 

value

2008 

Modification Source/Note

 Cash Flow Debt

 Debt Tenor (Years) 15 Unchanged Harper et al. 2007.

 All-In Annual Interest Rate 6.70% 5.80% Stoel Rives 2009.

 Debt Service Coverage Ratio 1.45 Unchanged Harper et al. 2007.

 PTC Debt

 Debt Tenor (Years) 10 Unchanged Harper et al. 2007.

 All-In Annual Interest Rate 6.70% 5.80% Same assumption as term debt.

 Debt Service Coverage Ratio 1.45 Unchanged Harper et al. 2007.

 Debt Closing Costs 400 Unchanged Harper et al. 2007.

 Total Debt Closing Fee Calculation Unchanged Harper et al. 2007.

 Debt Service Reserve Calculation Unchanged Harper et al. 2007.

 Annual Debt Agency Fee ($000 flat) 25 & 40 Unchanged Harper et al. 2007.

 Debt Tenor (Years)

Calculation (5.5 

years in the base 

case)

Unchanged Harper et al. 2007.

 All-In Annual Interest Rate 6.70% 5.80% Same assumption as term debt.

 Debt Service Coverage Ratio 1.45 Unchanged Harper et al. 2007.

TERM DEBT (project-level debt backed by cash flows or a pledge of PTCs)

 Both “Cash Leveraged” and “Cash & PTC Leveraged” Structures

BACK LEVERAGE DEBT (debt secured by the developer, rather than by the project itself)

Source: Adapted from Harper et al. 2007
21

Appendix B. Input Values

The following tested input values, including interim points between the high-cost (low-cost) and 

the base case, were used in the individual variable sensitivity analysis.

Table B1. Technical Variables: Individual Variable Sensitivities

Scenario

Capacity 

Factor 

(%)

Installed 

Cost 

($/kW)

Operations & 

Maintenance 

($/MWh) 

Levelized 

Replacement Cost 

($000)

High-Cost 22 2,600 17.00 25,600

25 2,378 14.25 22,400

28 2,155 11.50 19,200

31 1,933 8.75 16,000

Base-Case 34 1,710 6.00 12,800

38 1,593 5.25 9,600

41 1,475 4.50 6,400

45 1,358 3.75 3,200

Low-Cost 48 1,240 3.00 0

Table B2. Financial Variables: Individual Variable Sensitivities

Cost

Corp 

(%)

SIF, IIF 

& BL 

(%)

Cash 

Lev 

(%)

Cash & 

PTC Lev 

(%)

High-Cost 15.00 / 12.00 / 14.00 / 14.25 13.00 10.00

13.75 / 10.63 / 12.75 / 13.00 11.20 11.25

12.50 / 9.25 / 11.50 / 11.75 9.40 12.50

11.25 / 7.88 / 10.25 / 10.50 7.60 13.75

Base-Case 10.00 / 6.50 / 9.00 / 9.25 5.80 15.00

9.50 / 6.38 / 8.63 / 8.88 5.40 15.75

9.00 / 6.25 / 8.25 / 8.50 5.00 16.50

8.50 / 6.13 / 7.88 / 8.13 4.60 17.25

Low-Cost 8.00 / 6.00 / 7.50 / 7.75 4.20 18.00

Target IRR*

Return 

on Debt 

(%) 

Loan 

Duration 

(Years)

 *Varies by financing structure

For the multivariable scenario analysis, the adjusted input values (see Table 3 and Table 4 in 

section 4.2) were calculated as a two-step process. First, the delta between the input variable’s 

highest- (lowest-) cost value used in the individual sensitivities and its base-case value was 

derated by 0.6 (e.g. 60%). The derate was based on judgment of realistic project assumptions. As 

previously mentioned, it is unlikely that any particular wind project would have all the best 

technology and financial assumptions (or worst) at the same time. Rather it is more likely that a 

single project would have a more modest set of variables together, hence the presumed derate. 

Second, the resulting reduced delta was added (subtracted) to the base-case value to give the 

input variable’s highest- (lowest-) cost value for the multivariable sensitivity analysis. This was 

repeated for all variables.
22

Appendix C. Additional Results

Table C1. LCOE Results:

12

Input 

Variable

Sensitivity 

Adjustments

LCOE 

($/MWh)

Δ From 

Base-Case

LCOE 

($/MWh)

Δ From 

Base-Case

LCOE 

($/MWh)

Δ From 

Base-Case

LCOE 

($/MWh)

Δ From 

Base-Case

LCOE 

($/MWh)

Δ From 

Base-Case

22.0% 125 70% 126 77% 121 92% 97 69% 95 74%

25.0% 109 48% 107 51% 102 62% 84 45% 80 48%

28.0% 94 28% 92 30% 88 39% 73 27% 70 28%

31.0% 83 13% 80 13% 72 15% 64 12% 62 15%

34.0% 74 0% 71 0% 63 0% 58 0% 54 0%

37.5% 65 -11% 61 -14% 54 -14% 51 -12% 47 -13%

41.0% 58 -21% 53 -25% 46 -27% 45 -21% 42 -23%

44.5% 52 -29% 47 -34% 39 -38% 41 -30% 37 -31%

48.0% 47 -36% 41 -42% 33 -47% 37 -37% 33 -39%

$2,600/kW 118 61% 119 67% 108 72% 90 57% 87 60%

$2,377.5/kW 107 46% 106 50% 98 55% 82 43% 79 45%

$2,155/kW 97 32% 94 33% 86 36% 74 28% 71 30%

$1,932.5/kW 85 16% 82 16% 74 18% 66 14% 62 15%

$1,710/kW 74 0% 71 0% 63 0% 58 0% 54 0%

$1,592.5/kW 68 -7% 64 -9% 57 -10% 54 -5% 51 -6%

$1,475/kW 62 -15% 58 -18% 51 -19% 51 -12% 46 -16%

$1,357.5/kW 57 -23% 51 -28% 45 -29% 46 -21% 41 -24%

$1,240/kW 51 -31% 45 -36% 38 -40% 40 -30% 37 -32%

See Table B2 110 49% 102 43% 90 42% 65 12% 59 8%

See Table B2 100 36% 94 32% 82 30% 63 10% 58 6%

See Table B2 91 24% 86 21% 75 19% 61 7% 57 4%

See Table B2 82 12% 78 10% 69 9% 59 3% 56 2%

See Table B2 74 0% 71 0% 63 0% 58 0% 54 0%

See Table B2 70 -4% 70 -2% 63 -1% 57 -1% 54 -1%

See Table B2 67 -8% 69 -3% 62 -2% 56 -2% 53 -2%

See Table B2 65 -12% 69 -3% 61 -3% 56 -3% 53 -2%

See Table B2 62 -16% 68 -4% 61 -3% 55 -4% 53 -3%

$17/MWh 89 21% 84 18% 76 21% 72 25% 69 26%

$14.25/MWh 85 16% 80 12% 73 16% 69 20% 65 20%

$11.5/MWh 81 11% 77 8% 69 10% 66 14% 61 13%

$8.75/MWh 78 6% 74 4% 66 5% 62 8% 58 7%

$6/MWh 74 0% 71 0% 63 0% 58 0% 54 0%

$5.25/MWh 73 -1% 70 -2% 62 -2% 57 -2% 53 -2%

$4.5/MWh 72 -2% 69 -3% 61 -3% 56 -3% 52 -4%

$3.75/MWh 71 -4% 68 -4% 60 -5% 55 -5% 51 -5%

$3/MWh 70 -5% 67 -6% 59 -6% 54 -6% 50 -7%

13.0% 76 32% 77 41%

11.2% 70 22% 71 31%

9.4% 66 15% 65 19%

7.6% 62 7% 59 9%

5.8% 58 0% 54 0%

5.4% 57 -1% 53 -2%

5.0% 56 -3% 52 -4%

4.6% 55 -4% 51 -6%

4.2% 54 -6% 50 -8%

10 Years 74 29% 67 24%

11.25 Years 70 21% 65 20%

12.5 Years 65 13% 61 12%

13.75 Years 62 7% 58 7%

15 Years 58 0% 54 0%

15.75 Years 57 -1% 54 -1%

16.5 Years 56 -3% 53 -2%

17.25 Years 55 -4% 51 -6%

18 Years 54 -6% 51 -6%

25,600 ($000) 76 3% 72 2% 65 3% 59 3% 56 3%

22,400 ($000) 75 2% 72 2% 64 2% 59 2% 56 2%

19,200 ($000) 75 2% 71 1% 64 1% 58 2% 55 2%

16,000 ($000) 74 1% 71 0% 63 1% 58 1% 55 1%

12,800 ($000) 74 0% 71 0% 63 0% 58 0% 54 0%

9,600 ($000) 73 0% 70 -1% 63 -1% 57 -1% 54 -1%

6,400 ($000) 73 -1% 70 -1% 62 -2% 57 -1% 54 -2%

3,200 ($000) 73 -1% 69 -2% 62 -2% 56 -2% 53 -2%

0 72 -2% 69 -2% 61 -3% 56 -3% 53 -3%

O&M 

N/A

Levelized Replacement 

Cost Loan Duration Return on Debt

Largest Impact On LCOE Moderate Impact On LCOE Smallest Impact On LCOE

Capacity Factor Installed Cost Target IRR

N/A

Cash & PTC 

Leveraged

Strategic Investor 

Flip

Institutional Investor 

Flip & Back 

Corporate Leveraged Cash Leveraged

Individual Variable Sensitivities

 

12 LCOE ($/MWh) shown rounded to nearest integer; percentages calculated from precise LCOE values.
23

$35 

$45 

$55 

$65 

$75 

$85 

$95 

$105 

$115 

$125 

$135 

$145 

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

-125% -100% -75% -50% -25% 0% 25% 50% 75% 100% 125% 150% 175% 200%

LCOE $/MWh

Change In LCOE

Capacity Factor O&M Levelized Replacement Cost Target IRR Installed Cost

Change In Input Variable

Figure C1. Corporate structure LCOE sensitivities by input variable
24

$35 

$45 

$55 

$65 

$75 

$85 

$95 

$105 

$115 

$125 

$135 

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

-125% -100% -75% -50% -25% 0% 25% 50% 75% 100% 125% 150% 175% 200%

LCOE $/MWh

Change In LCOE 

Capacity Factor O&M Levelized Replacement Cost Target IRR Installed Cost

Change In Input Variable

Figure C2. Strategic Investor Flip structure LCOE sensitivities by input variable
25

$25 

$35 

$45 

$55 

$65 

$75 

$85 

$95 

$105 

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

-125% -100% -75% -50% -25% 0% 25% 50% 75% 100% 125% 150% 175% 200%

LCOE $/MWh

Change In LCOE

Capacity Factor O&M Levelized Replacement Cost

Target IRR Loan Duration Return on Debt

Installed Cost

Change In Input Variable

Figure C3. Cash and PTC Leveraged structure LCOE sensitivities by input variable
F1147-E(10/2008) 

REPORT DOCUMENTATION PAGE Form Approved

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1. REPORT DATE (DD-MM-YYYY)

October 2009

2. REPORT TYPE

Technical Report 

3. DATES COVERED (From - To)

 

4. TITLE AND SUBTITLE

Wind Levelized Cost of Energy: A Comparison of Technical and 

Financing Input Variables

5a. CONTRACT NUMBER

DE-AC36-08-GO28308

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. AUTHOR(S)

K. Cory and P. Schwabe

5d. PROJECT NUMBER

NREL/TP-6A2-46671

5e. TASK NUMBER

WER9.3550

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

National Renewable Energy Laboratory

1617 Cole Blvd.

Golden, CO 80401-3393

8. PERFORMING ORGANIZATION

REPORT NUMBER

NREL/TP-6A2-46671

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)

NREL

11. SPONSORING/MONITORING

AGENCY REPORT NUMBER

12. DISTRIBUTION AVAILABILITY STATEMENT

National Technical Information Service

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13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 Words)

The expansion of wind power capacity in the United States has increased the demand for project development capital. In 

response, innovative approaches to financing wind projects have emerged and are proliferating in the U.S. renewable 

energy marketplace. Wind power developers and financiers have become more efficient and creative in structuring their 

financial relationships, and often tailor them to different investor types and objectives. As a result, two similar projects 

may use very different cash flows and financing arrangements, which can significantly vary the economic 

competitiveness of wind projects. This report assesses the relative impact of numerous financing, technical, and 

operating variables on the levelized cost of e (LCOE) associated with a wind project under various financing 

structures in the U.S. marketplace. Under this analysis, the impacts of several financial and technical variables on the 

cost of wind electricity generation are first examined individually to better understand the relative importance of each. 

Then,analysts examine a low-cost and a high-cost financing scenario, where multiple variables are modified 

simultaneously. Lastly, the analysis also considers the impact of a suite of financial variables versus a suite of technical 

variables. 

nergy

15. SUBJECT TERMS

levelized cost of energy; LCOE; NREL; analysis; wind energy; wind project; wind financing; wind developers; wind 

markets; technical variables; wind modeling; production tax credit; Paul Schwabe; Karlynn Cory

16. SECURITY CLASSIFICATION OF: 17. LIMITATION 

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a. REPORT

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