IEA Wind Task 26

WP2

The Past and Future Cost of Wind Energy

Leading Authors

Eric Lantz: National Renewable Energy Laboratory

Ryan Wiser: Lawrence Berkeley National Laboratory

Maureen Hand: National Renewable Energy Laboratory
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

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Contract No. DE-AC36-08GO28308

IEA Wind Task 26:

The Past And Future Cost 

Of Wind Energy

Work Package 2

Lead Authors:

Eric Lantz: National Renewable Energy Laboratory

Ryan Wiser: Lawrence Berkeley National Laboratory

Maureen Hand: National Renewable Energy Laboratory

Contributing Authors: 

Athanasia Arapogianni: European Wind Energy Association 

Alberto Ceña: Spanish Wind Energy Association

Emilien Simonot: Spanish Wind Energy Association

Edward James-Smith: Ea Energy Analyses

The IEA Wind agreement, also known as the Implementing Agreement 

for cooperation in the Research, Development, and Deployment of 

Wind Energy Systems, functions within a framework created by the 

International Energy Agency (IEA). Views, findings, and publications of 

IEA Wind do not necessarily represent the views or policies of the IEA 

Secretariat or of all its individual member countries.

Technical Report

NREL/TP-6A20-53510

May 2012
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iii

Acknowledgments

This report has been sponsored by the International Energy Agency (IEA) Wind Implementing 

Agreement for Co-operation in the Research, Development, and Deployment of Wind Energy 

Systems, and funded by the respective entities in the participating countries of Task 26, The Cost 

of Wind Energy, including Denmark, Germany, Netherlands, Spain, Sweden, Switzerland, and 

the United States. The authors of this report would like to thank each of the participating 

countries and their representatives for supporting the work of this task. United States 

participation in Task 26 is supported by the U.S. Department of Energy under Contract No. DEAC36-08GO28308 with the National Renewable Energy Laboratory. 

Special thanks to Graham Sinden (Climate Strategies), Paul Schwabe (National Renewable 

Energy Laboratory), Roberto Lacal Arantegui (European Commission), and Sander Lensink 

(Energy Research Centre of the Netherlands) for detailed comments and input on earlier versions 

of this report. Thanks also to Mark Bolinger (Lawrence Berkeley National Laboratory) for 

reviewing portions of the report and for providing valuable insights and guidance at various 

points in the development of this paper. Finally thanks to Scott Gossett and Linda Huff (National 

Renewable Energy Laboratory) for editing assistance and Lynn Billman, David Kline, Robin 

Newmark, and Brian Smith (National Renewable Energy Laboratory) for additional input and 

direction in completing this work. Of course, any remaining errors or omissions are the sole 

responsibility of the authors. 
iv

Executive Summary

Over the past 30 years, wind power has become a mainstream source of electricity generation 

around the world. However, the future of wind power will depend a great deal on the ability of 

the industry to continue to achieve cost of energy reductions. This summary report, developed as 

part of the International Energy Agency (IEA) Wind Implementing Agreement Task 26, The 

Cost of Wind Energy, provides a review of historical costs, evaluates near-term market trends, 

reviews the methods used to estimate long-term cost trajectories, and summarizes the range of 

costs projected for onshore wind energy across an array of forward-looking studies and 

scenarios. It also highlights high-level market variables that have influenced wind energy costs in 

the past and are expected to do so into the future.

Historical and Near-Term Trends in the Levelized Cost of Wind Energy

Between 1980 and the early 2000s, significant reductions in capital cost and increases in 

performance had the combined effect of dramatically reducing the levelized cost of energy

(LCOE) for onshore wind energy. Data from three different historical evaluations, including 

internal analysis by the Lawrence Berkley National Laboratory (LBNL) and the National 

Renewable Energy Laboratory (NREL) as well as published estimates from Lemming et al. 

(2009) and the Danish Energy Agency (DEA) (1999), illustrate that the LCOE of wind power 

declined by a factor of more than three, from more than $150/MWh to approximately $50/MWh 

between 1980s and the early 2000s (Figure ES-1). However, beginning in about 2003 and 

continuing through the latter half of the past decade, wind power capital costs increased—driven 

by rising commodity and raw materials prices, increased labor costs, improved manufacturer 

profitability, and turbine upscaling—thus pushing wind’s LCOE upward in spite of continued 

performance improvements (Figure ES-1). 

Figure ES-1. Estimated LCOE for wind energy between 1980 and 2009 for the United States and 

Europe (excluding incentives)

Sources: LBNL/NREL (internal analysis), Lemming et al. 2009, and DEA 1999

$0

$50

$100

$150

$200

$250

$300

1980 1985 1990 1995 2000 2005 2010

Levelized Cost of Energy 

(2010 USD/MWh) 

LBNL/NREL Internal Analysis

DEA 1999

Lemming et al. 2009 (Coastal European Sites)
v 

More recently, turbine prices and therefore project capital costs have declined, but still have not 

returned to the historical lows observed earlier in the 2000s. At the same time, however, 

performance improvements have continued. As a result, modeling based on capital cost and 

performance data from the United States and Denmark for projects expected to be built in 2012–

2013 suggests that the LCOE of onshore wind energy is now at an all-time low within fixed wind 

resource classes, and particularly in low and medium wind speed areas (Figure ES-2). Moreover, 

the fact that capital costs remain higher than in the early 2000s but that those increased costs are 

rewarded by improved performance and a lower LCOE demonstrates the fundamental 

interdependence of capital cost and performance in wind turbine and project design. 

Figure ES-2. LCOE for wind energy over time in the United States (left) and Denmark (right) 

Sources: Wiser et al. 2012, James-Smith 2011b 

Long-term Trends in Wind Energy LCOE

Further into the future, the LCOE of wind energy is expected to continue to fall, at least on a 

global basis and within fixed wind resource classes. Performance improvements associated with 

continued turbine upscaling and design advancements are anticipated, and lower capital costs 

may also be achievable. The magnitude of future cost reductions, however, is highly uncertain. 

Estimates of the future cost of onshore wind energy conducted to date have often been the result 

of an iterative process that incorporates some combination of historical trends, learning curve 

analysis, expert elicitation, and engineering modeling. Figure ES-3 presents the estimated cost 

reductions anticipated by 13 recent analyses covering 18 cost scenarios. Many of these studies 

utilize learning curves in combination with expert elicitation, engineering models, and near-term 

market analysis (e.g., EWEA 2009, U.S. DOE 2008, GWEC/GPI 2010, and Lemming et al. 

2009). 

$50

$70

$90

$110

$130

$150

$170

5.5 6 6.5 7 7.5 8 8.5

Levelized Cost of Energy 

(2010 USD/MWh)

50 Meter Wind Speed (m/s)

air density = 1.225 kg/m3

Class 2 Class 3 Class 4 Class 5 Class 6

Estimated 

LCOE: 

2002-03

Estimated LCOE:

2009-10

Estimated 

LCOE: 

2012-13

$50

$70

$90

$110

$130

$150

$170

5.5 6 6.5 7 7.5 8 8.5

Levelized Cost of Energy 

(2010 USD/MWh)

50 Meter Wind Speed (m/s)

air density = 1.225 kg/m3

Class 2 Class 3 Class 4 Class 5 Class 6

Estimated 

LCOE: 

2002

Estimated LCOE:

2009

Estimated 

LCOE: 

2012
vi

Figure ES-3. Estimated range of wind LCOE projections across 18 scenarios

Sources: EREC/GPI 2010, Tidball et al. 2010 (includes modeling scenarios from multiple other 

sources), U.S. DOE 2008, EIA 2011, Lemming et al. 2009, EWEA 2011, EPRI 2010, Peter and 

Lehmann 2008, GWEC/GPI 2010, IEA 2009, and European Commission 2007 

The data presented in Figure ES-3 suggest an approximate 0%–40% reduction in LCOE through 

2030. The single scenario anticipating no further cost reductions assumes that the upward price 

pressures observed between 2004 and 2009 are moderated but remain significant enough to 

prevent future reductions in LCOE. The three studies anticipating a 35%–40% reduction in 

LCOE by 2030 represent ambitious scenarios requiring concentrated efforts to reduce the cost 

wind energy, relatively high rates of global deployment, and levels of investment that exceed 

business as usual. By focusing on the results that fall between the 20th and 80th percentiles of 

scenarios, the range is narrowed to roughly a 20%–30% reduction in LCOE. Cost of energy 

reductions are generally expected to be greater in the early years and then slow over time. Initial 

cost reductions range from 1%–6% per year. By 2030, all but one scenario envisions cost 

reductions falling below 1% per year. 

A large number of technological and market-based drivers are expected to determine whether 

these projections are ultimately realized. Possible technical drivers are summarized in Table ES1 and include reduced component loads and increased reliability. At the same time, a resurgence 

in turbine demand, resulting in supply chain pressures similar to those observed between 2004 

and 2009 could counter cost reductions resulting from continued technical advancements. 

Continued movement toward lower wind speed sites may also increase fleet-wide LCOE, despite 

technological improvements that would otherwise yield a lower LCOE. On the other hand, 

increasing competition among manufacturers and developers could drive down the LCOE of 

onshore wind energy to a greater extent than otherwise envisioned. 

50%

60%

70%

80%

90%

100%

2010 2015 2020 2025 2030

Trends In Wind Power LCOE 

2011 = 100%

20th to 80th

Note: 

-Shaded area represents the full 

range of expectations in the 

literature 

-Each individual line details the 

expected cost of energy pathway 

for a given study
vii

Table ES-1. Potential Sources of Future Wind Energy Cost Reductions

R&D/Learning Area Potential Changes 

(For more detail on technology changes and 

expected impacts, see references below)

Expected Impact

Drivetrain Technology

Advanced drivetrain designs, reduced 

loads via improved controls, and condition 

monitoring (Bywaters et al. 2005)

Enhanced drivetrain reliability and 

reduced drivetrain costs

Manufacturing 

Efficiency

Higher production volumes, increased 

automation (Cohen et al. 2008), and 

onsite production facilities

Enhanced economies of scale, reduced 

logistics costs, and increased component 

consistency (allowing tighter design 

standards and reduced weights) 

O&M Strategy

Enhanced condition monitoring 

technology and design-specific 

improvements and improved operations 

strategies (Wiggelinkhuizen et al. 2008)

Real-time condition monitoring of turbine 

operating characteristics, increased 

availability, and more efficient O&M 

planning

Power 

Electronics/Power 

Conversion

Enhanced frequency and voltage control, 

fault ride-through capacity, and broader 

operative ranges (UpWind 2011)

Improved wind farm power quality and 

grid service capacity, reduced power 

electronics costs, and improved turbine 

reliability

Resource Assessment

Turbine-mounted real-time assessment 

technology (e.g., LIDAR) linked to 

advanced controls systems, enhanced 

array impacts modeling, and turbine siting 

capacity (UpWind 2011)

Increased energy capture while reducing 

fatigue loads, allowing for slimmer design 

margins and reduced component masses; 

increased plant performance

Rotor Concepts

Larger rotors with reduced turbine loads 

allowed by advanced controls (Malcolm 

and Hansen 2002) and application of lightweight advanced materials

Increased energy capture with higher 

reliability and less rotor mass; reduced 

costs in other turbine support structures

Tower Concepts

Taller towers facilitated by use of new 

design architectures and advanced 

materials (Cohen et al. 2008, LaNier 2005, 

Malcolm 2004)

Reduced costs to access stronger, less 

turbulent winds at higher above-ground 

levels
viii

Conclusions and Future Work

Following a long period of historical declines, wind energy costs were increasing for much of the 

past decade. However, today, the cost of onshore wind energy once again appears to be falling 

and is expected to reach a historic low in the near future within fixed wind resource areas. 

Continued cost reductions are expected through 2030, but the anticipated magnitude of those 

reductions varies widely and will ultimately be determined by an array of technical and nontechnical variables. 

Recent capital cost and performance trends have underscored the need for a view of the cost of 

wind energy that equally weighs both trends in capital cost and performance, particularly when 

trying to understand the future cost of wind energy. The technology is now at a point where an 

optimal cost of onshore wind energy may result from little or no further capital cost reductions 

(and perhaps even modest capital cost increases), but continued performance improvements. In 

this environment, it is possible to see capital costs remain relatively flat—with possible modest 

reductions or increases, depending on local market conditions—into the future and to see 

performance increases as the primary target of original equipment manufacturers (OEMs). 

Further improving our understanding of possible future cost trends will require additional data 

gathering and improved modeling capability. Robust data collection is needed across the array of 

variables that must be factored into estimating LCOE (e.g., capital cost, capacity factor, O&M 

costs, component replacement rates and costs, and financing costs) and in each of the wind 

energy markets around the globe. Also needed are data on the many contextual factors that 

impact the overall cost of wind energy and that may also vary with time, such as interconnection 

costs, permitting costs, and the average wind speed of installed wind projects. Such data would 

allow historical LCOE trends to be more closely analyzed, with insights gleaned both through 

more sophisticated learning curve analysis as well as bottom-up assessments of historical cost 

drivers. Additional data could also assist in better distinguishing those cost reductions that result 

from technological improvements from those changes in cost that result from external supply and 

demand market variables or changes in raw material and commodity prices. It is only with this 

improved historical understanding that future possible cost trajectories can be fully understood 

(Dinica 2011). An enhanced capacity to model the cost and performance impacts of new 

technological innovation opportunities, taking into account the full system dynamics that result 

from a given technological advancement, is also essential. Component, turbine, and project-level 

design and cost tools of this nature would allow for more sophisticated cost modeling and 

provide greater insights into possible future costs based on changes in material use and design 

architectures. Together these efforts would enhance our ability to understand future costs, 

facilitate prioritization of research and development (R&D) efforts, and help to understand the 

role and required magnitude of deployment incentives into the future. 
ix

Table of Contents

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

2 Trends in Wind Energy Capital Costs and Performance.................................................................. 3

2.1 Capital Cost Reductions: 1980–2003...................................................................................3

2.2 Capital Cost Increases: 2004–2009 ......................................................................................5

2.3 Performance Increases: 1980–2010 .....................................................................................8

2.4 Recent and Near-term Trends in Capital Cost and Performance .......................................12

3 Impact of Capital Cost and Performance Trends on Levelized Cost of Energy .......................... 14

3.1 Historical LCOE Trends: 1980–2009 ................................................................................14

3.2 Recent and Near-term Trends in LCOE: 2010–2013 ........................................................15

4 Analytical Methods to Project the Future Cost of Wind Energy.................................................... 18

4.1 Learning Curve ..................................................................................................................18

4.2 Expert Elicitation ...............................................................................................................20

4.3 Engineering Model.............................................................................................................21

4.4 Sources of Cost Reduction Identified by Expert Elicitation and Engineering Modeling 

Assessments .......................................................................................................................22

4.5 Quantitative Summary of Future Cost Estimates...............................................................25

5 Conclusions ........................................................................................................................................ 28

6 References .......................................................................................................................................... 30

Appendix A ................................................................................................................................................ 36

Data Inputs for U.S. and Denmark Levelized Cost of Energy Modeling ................................36

Appendix B ................................................................................................................................................ 38
List of Figures

Figure ES-1. Estimated LCOE for wind energy between 1980 and 2009 for the United States and 

Europe (excluding incentives) ........................................................................................... iv

Figure ES-2. LCOE for wind energy over time in the United States (left) and Denmark (right)... v

Figure ES-3. Estimated range of wind LCOE projections across 18 scenarios ............................. vi

Figure 1. Capital cost trends in the United States and Denmark between 1980 and 2003 ............. 4

Figure 2. Capital cost trends in the United States, Denmark, Spain, and Europe from 2003 to 

2009..................................................................................................................................... 5

Figure 3. Wind turbine prices in the United States ......................................................................... 6

Figure 4. Representative turbine architectures from 1980 to 2010 ................................................. 9

Figure 5. Fleet-wide capacity factor data for the United States, Denmark, and Spain from 1999 to 

2010................................................................................................................................... 10

Figure 6. U.S. project capacity factors by vintage and wind power class .................................... 11

Figure 7. Modeled capacity factors for current turbine models relative to historical technology 13

Figure 8. Estimated LCOE for wind energy from 1980 to 2009 for the United States and Europe 

(excluding incentives) ....................................................................................................... 15

Figure 9. LCOE for wind energy over time in the United States (left) and Denmark (right)....... 16

Figure 10. Estimated change in the LCOE between low and high wind speed sites resulting from 

technological advancement ............................................................................................... 17

Figure 11. Estimated range of wind LCOE projections across 18 scenarios ................................ 26

List of Tables

Table ES-1. Potential Sources of Future Wind Energy Cost Reductions ..................................... vii

Table 1. Potential Sources of Future Wind Energy Cost Reductions ........................................... 23

Table 1A. Inputs in Modeling of U.S. LCOE Estimates 2002–2003 through 2012–2013 ........... 36

Table 2A. Inputs in Modeling of Danish LCOE Estimates 2002 through 2012 ........................... 37

x
1 

1 Introduction

Wind energy has been utilized by human society for millennia. The first experiments with windgenerated electricity, however, date to the late 19th century, and it was not until the 1970s that 

wind energy began to penetrate commercial power markets—first in Denmark, then in California 

in the 1980s, and in Germany and Spain in the 1990s (Wiser et al. 2011b). Since the mid-1990s, 

wind energy has evolved into a mainstream source of power generation, with more than 200 

gigawatts (GW) installed around the world (GWEC 2011, BNEF 2011b). A substantial fraction 

of the newly installed total global electric generation capacity each year now comes from wind. 

Wind energy is estimated to deliver the equivalent of just over 2% of global electricity 

consumption (Wiser and Bolinger 2011), though that contribution varies considerably by region 

(GWEC 2011). 

The emergence of wind energy as a major source of power around the world correlates with 

policy support for renewable energy technologies. Policy support has been driven by concerns 

around energy security, economic development, and environmental protection. Without policy 

support, much of the deployment occurring today would not be economically feasible (Wiser et 

al. 2011).1

 Deployment of wind power is also correlated with significant reductions in the cost of 

wind-generated electricity. Between 1980 and the early 2000s, wind power installation costs fell 

by more than 65% in the United States (Wiser and Bolinger 2011) and 55% in Denmark (Nielsen 

et al. 2010). As a result of these dramatic cost reductions, wind energy, in some parts of the 

world, has achieved costs that are competitive with prevailing market prices without policy 

support (Berry 2009, IEA 2009, and IEA and OECD 2010). 

The future of wind energy will depend a great deal on the ability of the industry to continue to 

achieve cost reductions and, ultimately, to achieve cost parity with conventional sources of 

generation (i.e., to compete without direct policy support) across a broad array of contexts and 

locations. The importance of future cost competitiveness has been reinforced by the difficulty in 

developing international consensus around climate change policy (Pielke 2010). Moreover, with 

increasing pressure on governments to reduce spending and debt, long-term policy support for 

wind energy remains uncertain. Estimates by the Global Wind Energy Council (GWEC) and 

Green Peace International (GPI) (GWEC/GPI 2010) suggest that supplying 20% of global 

electricity demand with wind energy is possible; however, achieving such penetration would be 

greatly facilitated by significant future cost reductions.

At the highest level, this summary report provides a review of historical cost, evaluates near-term 

market trends, reviews the methods used to estimate long-term costs trajectories, and summarizes 

the range of costs projected for onshore wind energy across an array of forward-looking studies 

and scenarios. It also highlights high-level market variables that have influenced wind energy 

 

1 Arguably, the need for policy support of wind energy to drive deployment in much of the world is, in part, due to 

the tendency of electricity markets to inherently favor incumbent generation technologies. For example, grid 

systems were not historically designed to integrate variable generation resources, and much of the existing grid 

system was not designed to access geographically constrained renewable energy resources. Moreover, incumbent 

technologies have, in many cases, continued to benefit from direct and indirect policy support in the form of fuel 

production incentives, rail and pipeline development incentives, and socialization of risk (e.g., nuclear power). For 

these reasons—and because wind energy is believed to provide public benefits (e.g., environmental gains)—

advocates argue that policy support for wind energy is often justified.
2 

costs in the past and are expected to do so into the future. More specifically, Section 2 begins by 

summarizing historical capital costs and performance data in the United States and in various 

European countries (for which data were available). Section 3 considers these trends from the 

perspective of the cost of delivered energy and estimates the near-term cost of energy for 

projects in late-stage development in the United States and Denmark today. Section 4 provides 

an in-depth look at the strengths and weaknesses of the various methods used to estimate the 

future cost of wind energy over the long term, summarizes a sample of projections made by a 

variety of institutions under both conservative and optimistic assumptions, and discusses 

potential sources of future cost reductions. 

This report has been developed as part of the International Energy Agency (IEA) Wind 

Implementing Agreement Task 26, The Cost of Wind Energy, and builds on the prior work of this 

task to estimate the 2008 cost of wind energy among the participating countries (Schwabe et al. 

2011). As in the prior report, analysis estimating the levelized cost of energy (LCOE) for wind 

included here utilized the discounted cash-flow model developed by the Energy Research Centre 

of the Netherlands (Schwabe et al. 2011); however, data presented here also rely on LCOE 

estimates generated from other sources, as noted in the text. Additional input to this report comes 

from published data as well as data provided to the Task 26 Working Group by participating 

members. Specifically, Task 26 participant presentations, which provided key data and analysis 

insights to the working group, are included in Appendix B and formed much of the basis for this 

summary.

2

 

To be clear, this report does not make new, long-term cost-of-energy forecasts. Instead, it is 

intended to inform the reader of future possibilities. The focus of the report is on onshore wind 

energy, as the vast majority of historical wind power investments have occurred on land. 

Offshore wind has yet to penetrate many of the world’s commercial power markets and has not 

yet achieved the status of a mainstream source of power generation in much of the world. 

Nonetheless, useful analyses of past and possible future costs for offshore wind are available in 

the literature (e.g., Carbon Trust 2008, Ernst & Young 2009, UKERC 2010, Wiser et al. 2011, 

Levitt et al. 2011, ARUP 2011, and Doyle et al. 2011).3 

 

2 Individual presentations are referenced by author in the body of this report and included in the reference list. 

3 As a less mature technology and industry, offshore wind energy is at a different point on the technology 

development and deployment cost curve. Looking forward, offshore wind costs are generally expected to follow a 

steeper downward trajectory than costs for onshore wind energy (Neij 2008, Wiser et al. 2011, Doyle et al. 2011, 

and ARUP 2011). 
3 

2 Trends in Wind Energy Capital Costs and Performance

The cost of wind energy is a function of the cost to build and operate a wind energy project and 

the amount of energy produced by the facility over its lifetime. For the purposes of this report, 

the cost of wind energy is represented by the levelized cost of energy (LCOE). The LCOE is a 

simple way of comparing the unit costs of different generation technologies and is widely used in 

policy decision-making.4 Additional detail on LCOE and methods for estimating it can be found 

in Schwabe et al. (2011). Precise estimates of the LCOE require detailed data on capital costs, 

financing costs, operation and maintenance (O&M) expenditures, and plant production data. 

However, detailed data across countries on each of these variables are often not in the public 

domain. The most detailed publicly available data sets often capture only capital cost and, in 

some cases, project performance data. Moreover, data on one or both of these two variables are 

available, over an extended time period, for only a handful of countries. 

As a result of the aforementioned data limitations, independent capital cost and performance data 

have often been used historically as a proxy for the cost of wind energy. Reliance on these two 

variables as an indicator of the historical cost of wind is justified, in part, because they are two of 

the more significant contributors to the LCOE. However, because capital cost and performance 

are interdependent (e.g., newer, better performing technology sometimes costs more), reliance on 

only one of these variables may fail to fully explain historical (or forecast future) LCOE trends 

(e.g., EWEA 2009, Ferioli et al. 2009, Berry 2009, and Dinica 2011). This report includes data 

and analysis of both historical capital costs and project performance to inform our understanding 

of long-term cost trends and the high-level factors that influence the cost of wind energy. 

Moreover, the report does so with greater consideration for the interdependency between capital 

costs and project performance than has been typical in the past. 

Here we examine capital cost and performance trends from the 1980s through present day. 

Capital cost data are broken into three different epochs, from 1980 to 2003, from 2004 to 2009, 

and from 2009 to projects planned for commissioning in 2012 and 2013. These time periods 

cover both significant cost reductions as well as the period of cost increases observed during the 

last decade. Included is a discussion of the drivers of both capital cost reductions and increases. 

Developments in turbine performance since the 1980s are also discussed; however, performance 

data are focused primarily on the more recent period, from the turn of the century to today’s 

current turbine offerings. 

2.1 Capital Cost Reductions: 1980–2003

From the 1980s to the early 2000s, average capital costs for wind energy projects declined 

markedly. In the United States, capital costs achieved their lowest level from roughly 2001 to 

2004, approximately 65% below costs from the early 1980s (Wiser and Bolinger 2011). In 

Denmark, capital costs followed a similar trend, achieving their lowest level in 2003, more than 

55% below the levels seen in the early 1980s (Nielsen et al. 2010) (Figure 1). Over the same time 

period, global installed wind power capacity grew from a negligible quantity to nearly 40,000 

 

4 It can be seen to represent a break-even cost per unit of energy produced for a generating facility and is typically 

based on the cost of capacity, operation and maintenance costs, the expected level of production, and financing costs 

as represented by a discount rate. LCOE typically does not capture or represent all societal costs or benefits resulting 

from wind energy deployment (e.g., grid integration costs and environmental externalities). 
4 

megawatts (MW) (GWEC 2006), with the bulk of this growth (>85%) occurring between 1995 

and the early 2000s. The primary markets for wind energy during this time were Europe and the 

United States. 

Figure 1. Capital cost trends in the United States and Denmark between 1980 and 2003 

Sources: Wiser and Bolinger 2011, Nielsen et al. 2010 

Over this time frame, technological innovations allowed for the development of larger turbines at 

lower costs. Economies of scale resulting from increased turbine size were followed by 

economies of scale in project size and manufacturing (EWEA 2009). More specifically, 

innovations in design, materials, process, and logistics helped to drive down system and 

component costs while facilitating turbine upscaling (EWEA 2009). Between 1980 and the early 

2000s, commercial turbines grew from less than 100 kilowatts (kW) to more than 1 MW in 

capacity, rotor diameters grew from roughly 15 meters to more than 70 meters in some cases, 

and tower heights grew from 20 meters to more than 65 meters in some cases (Wiser et al. 2011). 

Larger turbines provided access to better wind resources while lowering the plant-wide parts 

count and generating turbine-level economies of scale for many components for which costs do 

not vary proportionally with turbine size (e.g., controls). For those components where costs tend 

to increase significantly with size, such as rotors, towers, and generators, engineering design 

innovations mitigated the otherwise-expected cost increases. Turbine upscaling also facilitated 

reductions in required project infrastructure, including roads, total project foundation costs, and 

underground cabling, helping to drive down balance of plant (BOP) costs on a dollars per 

kilowatt ($/kW) basis (EWEA 2009). 

$0

$1,000

$2,000

$3,000

$4,000

$5,000

1980 1985 1990 1995 2000 2005

Installed Project Captial Cost

(2010 USD/kW)

US Capacity-Weighted 

Average

DK Capacity-Weighted 

Average
5 

As costs declined and the technology matured, larger projects also began to emerge. These

projects offered further potential economies of scale, this time in development costs, substation 

and interconnection infrastructure, transmission tie lines, and O&M facilities. Again, efficiencies 

were gained because these types of costs do not vary proportionately with project size (EWEA 

2009). Finally, as the market expanded, greater production volumes allowed for investment in 

manufacturing facilities and created opportunities to increase production efficiencies, thus

offering an additional source for capital cost reduction. 

2.2 Capital Cost Increases: 2004–2009

The initial period of capital cost reductions came to an end in the early-to-mid 2000s. Data from 

the United States, Denmark, Spain, and Europe show capital cost increases beginning around 

2004 and continuing through at least 2007–2009 (Figure 2). Capital costs in the United States

were maintained at peak levels through 2010, although preliminary data indicate that capital cost 

reductions are likely in 2011, (see also Section 2.4) (Wiser and Bolinger 2011). In Denmark and 

Europe capital costs peaked earlier, in 2008 and 2007, respectively, and have declined modestly 

since then (see Figure 2 and Section 2.4). 

An important exception to this general trend of substantially rising costs from 2004 to 2009 was 

China. Specifically, the emergence of a handful of strong domestic original equipment 

manufacturers (OEMs) has resulted in significantly lower capital costs in China (i.e., 

$1,100/kW–$1,500/kW [2010 U.S. dollars] in 2008–2009) than witnessed in Europe or the 

United States (Wiser et al. 2011b). 

Figure 2. Capital cost trends in the United States, Denmark, Spain, and Europe from 2003 to 2009

Sources: Wiser and Bolinger 2011, Nielsen et al. 2010, Ceña and Simonot 2011, and EWEA 

2011 

Although balance-of-plant costs have played a role (e.g., Fowler 2008), the increase in capital 

costs observed between roughly 2004 and 2009 has been largely tied to increases in the price of 

wind turbines (Wiser and Bolinger 2011, Ceña and Simonot 2011). For example, Figure 3 

depicts reported wind turbine transaction prices in the United States. Because visibility 

$0

$500

$1,000

$1,500

$2,000

$2,500

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Installed Project Captial Cost

(2010 USD/kW)

EWEA Capacity-Weighted Average

Spain Capacity-Weighted Average

US Capacity-Weighted Average

DK Capacity-Weighted Average
6 

surrounding wind turbine transactions has declined in recent years, the figure also presents a 

range of reported pricing for U.S. transactions signed in 2010 and early 2011 and includes 

average global turbine prices reported by Vestas for the years 2005 through 2010. Leaving 

discussion of the sizable drop in turbine prices from 2009 to 2011 for Section 2.4, the figure 

clearly depicts the sharp rise in turbine pricing experienced during the mid-2000s.5 

Figure 3. Wind turbine prices in the United States

Sources: Wiser and Bolinger 2011, Vestas (2011a, 2011, 2011c), and BNEF (2011a) 

An array of factors has contributed to this increase in turbine prices. Raw material commodity 

prices and energy prices have been among the “exogenous” factors influencing capital costs over 

this time period. Both materials and energy prices increased substantially up to the time of the

financial crisis in late 2008. Bolinger and Wiser (2011) estimate that materials price increases, 

including steel, iron, copper, aluminum, and fiberglass, resulted in an approximately $71/kW 

price increase for wind turbines in the United States from 2003 through 2008. Increases in 

energy prices were estimated to be responsible for an additional increase of about $12/kW 

(Bolinger and Wiser 2011). A similar Danish study (Nielsen et al. 2010) found that steel prices 

accounted for about $80/kW of the difference in Danish turbine prices over this same time 

period. Excluding steel, Nielsen et al. (2010) found that other raw material prices accounted for 

about $20/kW of the difference in Danish turbine prices from 2003 through 2008. Analysis of 

changing commodity prices in the Spanish wind industry suggest that changes in copper and 

steel pricing accounted for 46% and 34%, respectively, of the overall raw material cost increase 

of wind turbine nacelles from 2006 through 2010, while increases in the price of steel have been 

responsible for about 38% of the increase in the raw material cost of a typical wind turbine tower 

over this same time period (Ceña and Simonot 2011). 

 

5 Other forms of electricity production equipment also experienced price increases over this same time frame 

(Chupka and Basheda 2007).
7 

Growth from approximately 40,000 MW to nearly 200,000 MW of installed wind power 

capacity (GWEC 2011) in a period of about 6 years also resulted in significant supply 

constraints. Coupled with increasing OEM profitability and increasing labor costs, these 

constraints resulted in significant upward pressure on wind turbine prices. Analysis of the 

financial reports from the world’s largest turbine manufacturer, Vestas, shows that manufacturer 

profitability grew notably from 2003 through 2008 and accounted for an estimated $106/kW of 

the increase in U.S. turbine prices during this time (Bolinger and Wiser 2011). Nielsen et al. 

(2010), using a slightly different methodology also based on Vestas financial reports, estimate an 

impact to turbine prices of about $125/kW over this time period. While these two particular 

quantitative estimates are based solely on data from Vestas, there is solid evidence that the 

profitability of other manufacturers also increased during this time (Bolinger and Wiser 2011). 

According to Nielsen et al. (2010), these higher earnings followed substantial price reductions 

associated with excess manufacturing capacity in the early 2000s, as well as financial losses that 

were accrued during the first half of 2000s. 

In addition, Bolinger and Wiser (2011) found that growth in labor costs accounted for roughly 

$60/kW of the observed turbine price increase from 2003 through 2008 for Vestas, with similar 

results for Suzlon (Bolinger and Wiser 2011). Nielsen et al. (2010) estimate that the difference in 

labor costs from 2003 through 2008 was comparable, at about $50/kW. Though not quantified, 

additional upward pressure on turbine prices presumably came from the increased profitability 

and labor costs of component manufacturers. Supply chain bottlenecks also resulted in extended 

development lead times and placed additional upward price pressure on (non-turbine) BOP costs. 

Nielsen et al. (2010) observed that the rapid increase in demand for turbines from 2005 resulted 

in supply side constraints for key components such as bearings and gear boxes, and that these 

constraints may have been compounded by sub-contractors who optimized their production to 

smaller wind turbine models that dominated the market up until 2005. 

Continued turbine upscaling and increasingly sophisticated designs have also resulted in turbine 

price increases due to increased material use, more complex control and sensing systems, and 

enhanced power conversion capabilities (Wiser and Bolinger 2011). Using an NREL scaling 

model described in Fingersh et al. (2006), turbine upscaling alone—not considering other design 

advancements, which have also improved the performance of modern turbines and resulted in 

improved grid interactions—has been estimated to be responsible for $114/kW of the turbine 

price increases observed from 2003 to 2008 in the United States, representing the single largest 

factor driving prices higher over this time period (Bolinger and Wiser 2011). Over the time 

period from 2006 through 2010, Ceña and Simonot (2011) attribute upwards of 50%–70% of 

individual nacelle, blade, and tower raw material cost increases to the impacts of increased 

material use (weight) in the larger turbines of today. Focusing solely on steel costs, Nielsen et al. 

(2010) estimated that the structural strengthening required to accommodate larger rotors and 

higher towers alone resulted in additional cost for wind turbines of approximately $60/kW when 

comparing turbines built in 2003 with those in 2008. As shown in Bolinger and Wiser (2011), 

however, the increased capital costs due to upscaling have been rewarded by higher energy 

yields. Excluding other drivers of turbine prices, upscaling was estimated to yield a lower 

turbine-level LCOE, despite the increase in capital cost associated specifically with upscaling. 

This highlights the interdependence of capital cost and performance. 
8 

Manufacturer warranty provisions also increased over this time period. Estimates by Bolinger 

and Wiser (2011) indicate that Vestas’ increases in warranty provisions were responsible for 

$31/kW of wind turbine price increases from 2003 through 2008, with strong evidence that other 

manufacturer warranty provisions were also increasing over this time period. For wind power 

markets outside of Europe, exchange rate fluctuations may have also contributed to increases in 

observed turbine prices. In the United States, for example, the declining value of the dollar 

relative to the Euro was estimated to have been responsible for $87/kW of the total observed 

increase in turbine prices from 2003 through 2008 (Bolinger and Wiser 2011). As markets 

outside of Europe have begun to build up their own domestic manufacturing capacity, however, 

fluctuations in exchange rates will likely have a less significant impact on turbine prices and 

subsequently capital costs moving forward. 

Further discussion and analysis of the role of some of these factors in driving historical onshore 

wind energy costs is included in Milborrow (2008), Blanco (2009), and Dinica (2011). 

Moreover, other authors note the importance of many of these same factors in driving up the cost 

of offshore wind energy (e.g., Carbon Trust 2008, Greenacre et al. 2010), as well as other forms 

of electricity generation equipment (e.g., Chupka and Basheda 2007, Winters 2008) over a 

similar time frame.

2.3 Performance Increases: 1980–2010

To maximize turbine performance, manufacturers have sought to develop more advanced turbine 

components and larger turbines. More advanced components promise greater efficiency, 

improved availability, and reduced generation losses (EWEA 2009, Wiser et al. 2012). Scaling to 

taller towers allows wind turbines to capture less turbulent and often stronger wind resources. 

Meanwhile, larger turbine rotor diameters allow a turbine to generate more electricity than would 

otherwise be the case. In Figure 4, the growth of turbine nameplate capacity, hub height, and 

rotor diameter, over the past 30 years is illustrated.6

 

 

6 The development of larger turbines may not result in an increased ability to extract energy from a fixed amount of 

wind energy, and as such, scaling does not improve turbine efficiency. However, by increasing the energy 

production of a given turbine, which requires innovations in rotor design, tower design, the drivetrain, and transport 

logistics, turbine scaling and the development of larger turbines is a technological change that has advanced the state 

of the art.
9 

Figure 4. Representative turbine architectures from 1980 to 2010

Source: NREL

A review of annual fleet-wide capacity factor data for the United States, Denmark, and Spain, 

spanning 1999 through 2010, demonstrates the resulting performance improvements, to some 

degree (Figure 5). Specifically, data for the United States and Denmark demonstrate overall 

increases in average fleet-wide capacity factors on the order of 20% or more over this period. 

However, such data are often confounded by inter-annual wind resource variability, dispatch 

curtailment due in part to transmission congestion,7

 and long-term trends toward siting projects 

in lower wind resource areas as the best resource sites are developed. The latter variable has been 

especially significant in Spain, where fleet-wide capacity factor data show relatively flat—and to 

some extent, declining—capacity factors as a result of new developments being pushed to lower 

quality wind resource areas, simply because they are the only readily developable sites that 

remain. 

 

7 Although not shown in the data in Figure 5, this latter issue has emerged in the United States in certain regions 

(Wiser and Bolinger 2011).
10 

Figure 5. Fleet-wide capacity factor data for the United States, Denmark, and Spain from 1999 to 

2010

Source: Wiser and Bolinger 2011, James-Smith 2011a, and Mesa 2011 

Note: U.S. data for 2007, 2008, 2009, and 2010 are estimated based on actual production plus 

estimated curtailment (where curtailment data are available). 

As a result of the limitations noted above, fleet-wide capacity factor data are incapable of 

demonstrating the true level of performance improvement achieved over the past three decades.

However, by evaluating overall multi-year average capacity factor changes within specific wind 

power classes and for specific project vintages, greater insights into the overall magnitude of 

technical improvement can be gained. Such an exercise is particularly important in places like 

Spain (and many other parts of the world) where projects have increasingly been installed in 

lower wind power class sites, and the actual degree of capacity factor improvement over time 

within individual wind resource classes is less apparent (Wiser 2011, Dinica 2011).8 Figure 6 

illustrates the substantial improvements over time in multi-year average capacity factors that 

have been observed from projects installed in the United States when sorted by wind power class 

and project vintage. These improvements have been directly linked to the development of taller 

towers and larger rotors (Wiser 2010). In addition, by relying on modeled performance of 

representative turbines installed between the early 2000s to today, Wiser et al. (2012) show 

improvements in capacity factor on the order of 20% for projects in Class 5 and Class 6 wind 

regimes and as much as 50% for projects in mid-Class 3 wind regimes (see Figure 7).

 

8 Aside from resource exhaustion as has been observed in Spain, drivers of project siting in lower wind resource 

class areas could also include limited transmission availability, environmental and wildlife exclusions, and 

potentially social opposition, among other siting limitations.

20%

25%

30%

35%

40%

1998 2000 2002 2004 2006 2008 2010

Capacity Factor

DK Capacity Factor (Years for which data were provided)

Spanish Capacity Factor

US Capacity Factor
11 

Figure 6. U.S. project capacity factors by vintage and wind power class

Source: Wiser 2010 

Using a different but related metric for wind power plant performance, annual energy production 

per square meter of swept rotor area (kilowatt-hour [kWh]/m2) for a given wind resource site, 

improvements of 2%–3% per year over the last 15 years have been documented (IEA 2008, 

EWEA 2009).9 

 

9 These data suggest that turbines are not only capturing more energy as a result of scaling but are also becoming 

more efficient.

0%

10%

20%

30%

40%

50%

2 3 4 5 6

Capacity Factor

Wind Power Class

Projects installed: 2005 - 2007

Projects installed: 2002 - 2004

Projects installed: 1998 - 2001
12 

2.4 Recent and Near-term Trends in Capital Cost and Performance

Since the financial crisis of late 2008, global turbine production capacity has increased while 

demand in some markets has softened. In combination with a shift in the directional impact of 

many of the turbine price drivers discussed in Section 2.2 (e.g., commodity or raw materials 

prices and manufacturer profitability), the result has been significant turbine price declines (see 

Figure 3). Declining turbine prices (as well as BOP costs) began to reduce average capital costs 

in some markets as early as 2008 (e.g., Denmark), but other markets took more time to work

through higher cost inventory that was contracted before prices began to fall (e.g., the United 

States).10 Data from Denmark show that capital costs fell about 2% from 2008 to 2009, and then 

by more than 22% between 2009 and 2010 (Nielsen et al. 2010); EWEA estimates an 8% 

reduction from 2007 through 2010 in the whole of Europe (EWEA 2011) (see Figure 2). 

Analysts expect capital cost reductions of this magnitude to follow in the near term for the 

United States (Wiser et al. 2012, Wiser and Bolinger 2011)11 and elsewhere, perhaps with some 

exceptions and variability in the overall magnitude of capital cost reduction. Capital cost 

expectations farther into the future suggest continued declines (See Section 4). 

Despite the recent decline in turbine prices, hub heights and rotor diameters have continued to 

trend toward larger machines, suggesting that turbine performance improvements will also 

continue. Preliminary analysis conducted by Wiser et al. (2012) suggests that capacity factors for 

projects to be installed with current state-of-the-art technology in the United States (“Current: 

2012-13” in Figure 7) will improve significantly within a given wind power class, relative to 

older technology (Figure 7). Moreover, also shown in the figure, the most significant 

performance improvements are occurring in equipment designed for low wind speed sites 

(typical average hub-height wind speeds of 7.5 m/s).12 As a result of these technical and design 

advancements, Wiser et al. (2012) find that the amount of U.S. land area that could achieve 35% 

or higher wind project capacity factors has increased by as much as 270% when going from 

turbines commonly installed in the 2002–2003 time frame to current low wind speed turbine 

offerings.

 

10 In the United States, turbine lead times approached 2 years during the peak demand period in the first half of 

2008. Market fundamentals have since changed, and lead times have dropped significantly. Nevertheless, there is a 

natural lag between turbine contract and power purchase agreement signing and project commissioning such that 

turbines ordered in early 2008 were still working their way through projects that were completed in 2010. 11 Initial 2011 data for the United States indicate that average project capital costs will likely fall relative to 2010, 

while greater cost reductions are expected for projects to be completed in 2012–2013, based on current turbine 

orders.

12 As previously noted, performance improvements may not actually result from increased turbine or rotor efficiency 

but rather from new equipment that can access more valuable wind regimes as a result of higher hub heights and 

capture more energy as a result of larger rotors.
13 

Figure 7. Modeled capacity factors for current turbine models relative to historical technology 

Source: Wiser et al. 2012 

Note: Low wind speed turbines are generally certified for IEC Class III wind conditions. IEC 

Class definitions are distinct from and independent of the Wind Power Classes noted in the 

figure. IEC standards define the reference average annual hub height wind speed for Class III 

machines as 7.5 m/s at an air density of 1.225 kg/m3. Although site-specific factors (e.g., 

turbulence, gust conditions, air density) ultimately determine the viability of a specific turbine at 

a particular site, Wiser et al. use the 7.5 m/s (at an air density of 1.225 kg/m3

) reference as the 

maximum average annual wind speed at hub height for these machines. 

2002-03 

Standard 

Technology

2009-10 

Standard 

Technology

Current: 2012-13 

Standard 

Technology

15%

20%

25%

30%

35%

40%

45%

50%

5.5 6 6.5 7 7.5 8 8.5

Capacity Factor

50m Wind-speed (m/s) 

air density = 1.225 kg/m3

Current: 2012-13 

Low Wind-speed (100m Tower)

Current: 2012-13 

Low Wind-speed 

(80m Tower)

Class 2 Class 3 Class 4 Class 5 Class 6
14 

3 Impact of Capital Cost and Performance Trends on Levelized 

Cost of Energy

As noted earlier, attempting to elicit LCOE trends from either capital cost or performance 

independently is tenuous. Both variables are important components of the overall LCOE 

equation. Historical and near-term LCOE trends are highlighted in this section to demonstrate the 

value of focusing on LCOE and the shortcomings associated with having an exclusive focus on 

either capital cost or performance. Data shown here generally reflect the impact on LCOE from 

capital cost and performance changes. More precise estimates would also factor in the effects of 

changes in O&M costs, major equipment replacement costs, financing costs, and actual turbine 

lifetime. However, due to limited data availability, the latter variables were not considered in the 

analysis presented in Section 3.2. Also note that the LCOE figures presented in this section

exclude available incentives that might affect the price of wind energy in wholesale markets.13

3.1 Historical LCOE Trends: 1980–2009

Significant reductions in capital cost and increases in performance between 1980 and 2003 had 

the combined effect of dramatically reducing the LCOE of wind energy. Data from three 

different historical evaluations, including internal analysis by the Lawrence Berkley National 

Laboratory (LBNL) and the National Renewable Energy Laboratory (NREL) as well as 

published estimates from Lemming et al. (2009) and DEA (1999), illustrate that the LCOE of 

wind power declined by a factor of more than three from more than $150/MWh to approximately 

$50/MWh over this period (Figure 8). During this time, capital cost and performance trends were 

both generally aligned with substantial capital cost declines and performance improvements. As 

a result, there was little risk associated with an exclusive focus on one or the other when 

attempting to understand broad trends in the LCOE of wind.14

The increase in capital cost observed between 2004 and 2009, however, pushed the LCOE 

upward despite the continued performance increases noted during this period (see Section 2). 

The observed increase in LCOE was mitigated to some extent by performance increases, 

however, and the overall LCOE trend is only modestly upward within the broader historical 

timeline depicted in the figure. In fact, as discussed earlier, the single largest (but certainly not 

the only) driver for the increased capital costs over this period was turbine upscaling (Bolinger 

and Wiser 2011), which was rewarded with performance improvements. Clearly, from 2004 to 

2009, an exclusive emphasis on either performance or capital costs to understand the trends in 

the cost of wind energy would have been misleading. In the former case, one would have 

assumed costs would have continued to fall due to performance increases, while in the latter 

instance, one might have assumed that the LCOE increases would have been more dramatic than 

they ultimately were.

 

13 In the United States, this is primarily the production tax credit, which is not accounted for in the data below. To 

the extent that other industry support schemes (e.g., feed-in-tariffs and renewables obligations certificates) might 

also affect the price of wind energy, they are not captured here.

14 Of course, an accurate estimate of actual LCOE trends would have required an assessment of both factors, as both 

were improving over this period. 
15 

Figure 8. Estimated LCOE for wind energy from 1980 to 2009 for the United States and Europe 

(excluding incentives)

Sources: LBNL/NREL (internal analysis), Lemming et al. 2009, and DEA 1999

3.2 Recent and Near-term Trends in LCOE: 2010–2013

Turbine prices and therefore project capital costs have recently declined since their peak in the 

late 2000s, but turbine pricing and capital costs have not returned to the historical lows observed 

earlier in the 2000s. Despite this, continual improvements in turbine technology are expected to

result in the industry achieving an apparent historic low in the LCOE of wind, particularly in low 

and medium wind speed sites (i.e., 6.0 m/s to 8.5 m/s average annual hub-height wind speed) 

(Wiser et al. 2012, James-Smith 2011b). 

Applying capital cost and performance data from the United States and Denmark, along with 

standard industry assumptions derived from historical data and industry inquiries for O&M and 

replacement costs, financing costs, and project availability, Figure 9 illustrates the results of two 

analyses completed by the IEA Task 26 working group.15 Both these LCOE estimates rely on the 

same cash flow model used previously by the IEA Task 26 working group model and described 

in detail in Schwabe et al. (2011). The results show that the LCOE of wind increased from 2002 

to 2009 as a result of capital cost increases that outweighed the performance improvements 

otherwise experienced over that time frame. In comparison, current turbine offerings (i.e., those 

that might be installed in the 2012–2013 time frame) also have higher project-level installed 

 

15 The turbine technologies considered in these two analyses include the GE 1.5/1.6 series machines in the United 

States analysis and the NEG Micon NM52/90, Siemens 2.3-93, and Vestas 3.0 V112 turbines in the Danish analysis. 

Capital costs range from $1,300/kW to $2,150/kW depending on the commercial operation date, local market 

conditions, and whether low wind speed turbines are considered (low wind speed turbines are only considered in 

2012–2013 for those wind speed classes where they are IEC certified and are somewhat higher in cost than their 

“standard technology” analogs). Turbine production varies based on the technology being considered and, of course, 

the average annual wind speed. A complete description of technology, cost, and performance modeling inputs as 

well as the constants utilized for annual operations expenditures and financing costs is included in Appendix A.

$0

$50

$100

$150

$200

$250

$300

1980 1985 1990 1995 2000 2005 2010

Levelized Cost of Energy 

(2010 USD/MWh) 

LBNL/NREL Internal Analysis

DEA 1999

Lemming et al. 2009 (Coastal European Sites)
16 

costs than those installed in 2002–2003, but those costs are lower than in 2009–2010 and are 

more than offset by the sizable expected performance improvements, yielding a lower LCOE 

than in 2002–2003, despite the higher capital costs. 

Figure 9. LCOE for wind energy over time in the United States (left) and Denmark (right) 

Sources: Wiser et al. 2012, James-Smith 2011b 

Note: Estimates assume low wind speed turbines are utilized at sites with ≤7.5m/s equivalent sea 

level annual average hub height wind speed for 2012–2013 only. All other estimates are based 

on modeled performance for standard industry technology during each time period. Estimates 

also exclude available incentives and policies that might reduce the price of wind energy in 

wholesale power markets. Finally, these estimates assume constant standardized industry values 

for O&M, financing costs, and losses.

Looking ahead, because LCOE is the primary cost metric that impacts the bottom line of project 

owners, utility purchasers, and to some extent even society at large (although, of course, not all 

societal costs are captured in the LCOE), it is justified that analysis of future wind energy costs 

be based on estimates of LCOE rather than capital cost alone. Moreover, unlike between 1980 

and the early 2000s when both performance and capital cost trends suggested significant 

reductions in the cost of wind energy, the technology is now at a point where an optimal cost of 

onshore wind energy may result from little or no further capital cost reductions (and perhaps 

even modest capital cost increases), but continued performance improvements.16 Continued 

incremental optimization on a cost or performance basis, as is likely into the future, is expected 

to require analysis of future cost based on LCOE.

Aside from simple technological maturation, the potential for limited capital cost reduction is 

further justified by the current trend of OEMs to offer a suite of turbine designs, including an 

increasing number of offerings that are specifically designed for lower wind speed sites. Low 

wind speed turbines typically have higher hub heights and larger rotors (in comparison to their 

capacity rating), necessarily resulting in a higher capital cost product. But those capital cost 

increases are rewarded by improved performance and, in many instances, a lower LCOE than 

standard turbine offerings that feature smaller towers and rotors. In fact, as shown in Wiser et al. 

(2012), by generating more significant performance increases in the emerging fleet of low wind 

 

16 Simultaneous reductions of capital cost and performance improvements do remain a possibility, albeit 

increasingly unlikely.
17 

speed turbines, the difference in the LCOE of wind between low to medium wind speed sites and 

high wind speed sites has declined over time (Figure 10). As a result of the improved economics 

of wind and this narrowing in the LCOE between lower and higher wind speed sites, an 

increasing amount of land area has become economically viable for wind energy development. In 

the United States, for example, the available land area capable of generating wind energy at an 

unsubsidized cost of $62 per megawatt-hour (MWh) (2010 U.S. dollars) is estimated to have 

increased by 42% relative to the land area capable of producing power from wind at this cost in 

the early 2000s (Wiser et. al. 2012). 

Figure 10. Estimated change in the LCOE between low and high wind speed sites resulting from 

technological advancement 

Source: Wiser et al. 2012 

Note: Today’s turbine offerings include typical standard technology with an IEC Class I/II 

rating as well as low wind speed technology designed for IEC Class III sites. Wind speeds noted 

here are average annual sea level equivalent wind speeds at 50 m. “Technology Choice” 

signifies the lowest cost turbine for a given wind regime. At average annual hub height wind 

speeds below the sea level equivalent of 7.5 m/s, Technology Choice results in the use of low 

wind speed technology. 

$44/MWh

$25/MWh

8 m/s

7 m/s

6 m/s

$0

$20

$40

$60

$80

$100

$120

Technology Choice

Current, 2012-13

Standard Technology

2002-03

Levelized Cost of Energy ($/MWh)

No Incentives
18 

4 Analytical Methods to Project the Future Cost of Wind Energy

A historical perspective provides a great deal of insight into the trajectory of the industry and 

likely near-term trends (2–5 years), as well as an understanding of the driving forces behind past 

and therefore potential future cost trends. However, the historical perspective alone fails to 

reflect the specific technologies that remain in the research and development (R&D) pipeline and 

may not adequately reflect the temporal nature of future cost reductions, which are likely to 

depend on the rate of deployment and the amount of public and private sector R&D invested in 

technology development. To complement an analysis of historical trends, three analytical 

methods, the learning curve, expert elicitation, and the engineering model, have been developed 

to provide additional insights into the future cost of wind energy over periods beyond the 

immediate future. 

This section examines the strengths and weaknesses of each of these three methods, highlights 

examples of each as applied today, and summarizes the results of an array of studies utilizing one 

or more of these methods to project the cost of wind energy through 2030. It also discusses 

potential sources of future cost reductions, primarily focusing on changes in wind turbine 

technology anticipated to reduce initial costs and operations expenditures and/or increase turbine 

performance.

4.1 Learning Curve

The learning curve methodology has been the most common approach to forecasting future wind 

energy costs. This approach assumes an explicit cost reduction, typically calculated from 

historical trends, as a function of technology deployment or, in some cases, equipment 

production. By using a learning curve, a specific learning rate, or percentage reduction associated 

with every doubling in the capacity of installed wind energy projects, can be calculated. The 

estimation of a historical learning rate is not in itself a projection, but rather a means of 

evaluating historical trends (Lemming et al. 2009). Nonetheless, learning rates derived from 

historical trends are commonly used to forecast future costs. A recent review of 18 estimates of 

learning rates for onshore wind energy, completed for the Intergovernmental Panel on Climate 

Change Special Report on Renewable Energy (Wiser et al. 2011), found that the overall range of 

learning rates available in the published literature varied from 4%–32%. However, by focusing 

on a more limited sample of studies conducted since 2004 and using common variables (e.g., 

global installations and total investment costs) the range was narrowed to 9%–19%. 

The broad range of learning rates derived from past analysis suggests that a few caveats are 

necessary when interpreting learning rates and using them to forecast future wind energy costs. 

With respect to assessing previously calculated learning rates, the boundaries of a given study 

are important. As the wind industry has only recently become truly global, many past studies 

focused on deployment within a given country or regional market (e.g., Neij 1997, Mackay and 

Probert 1998, Wene 2000, and Söderholm and Sundqvist 2007). Today, however, the wind 

industry serves a multi-billion dollar global market and provides manufacturers and installers 

with opportunities to learn from technology improvements and installation efficiencies from 

around the world. Cost reductions used to calculate learning rates should therefore ideally be 

associated with global installations—not simply those in a specific local market (Ek and 
19 

Söderholm 2010).17 Boundaries around the time period of historical data used are also critical. 

For example, including data from 2002 to 2009 (which observed price increases due to a range of 

factors discussed earlier) will produce a lower learning rate than if the rate were only calculated 

from data that extends to the early 2000s; whether or not using 2002–2009 data is appropriate 

depends on the nature of the drivers that impacted wind energy costs during that period. 

The dependent variable that is studied will also impact the results of a learning curve study. Data 

limitations have resulted in learning curve studies that frequently used project capital costs or 

turbine prices as the dependent variable and as a proxy for wind energy costs, as opposed to the 

LCOE. However, as discussed in Section 3, if capital costs are flat or increasing, but 

performance is also increasing, reductions in LCOE could be occurring that are not reflected in 

capital cost trends. Use of a dependent variable other than LCOE could potentially distort the 

true learning rate for the technology (EWEA 2009, Ferioli et al. 2009, and Dinica 2011). 

Finally, learning rates used to estimate future costs would ideally also be adjusted to account for 

cost reductions resulting from drivers that are unrelated to cumulative installed capacity. For 

example, cost reductions may result from a targeted R&D investment, economies of scale, 

market supply and demand forces, or changes in commodity prices, all factors that are not 

necessarily affected by learning associated with industry maturation and increased deployment. 

Unless such variables are accounted for when estimating a learning rate (which results in a lower 

learning rate) or can be assumed to provide equivalent cost reductions into the future, future 

projections may overestimate the level of cost reduction associated with a specific increase in 

total installed capacity (Wiser et al. 2011). Even when controlling for such variables, however, 

declining returns are possible (Ferioli et al. 2009, Nemet 2009), and an assumption that past 

learning rates can be applied to forecast future costs may be suspect.

4.1.1 Future Onshore Cost Estimates from Learning Curves

Despite their limitations, learning curve techniques can be informative with respect to the general 

magnitude of possible future cost reductions. Learning curves are likely to remain in use due to 

their relatively basic analytical underpinnings, reliance on real historical data, limited data 

requirements, and the lack of obvious alternatives, particularly when looking more than a decade 

into the future. As a result, the European Wind Energy Association (EWEA) and many others, 

including governments, have relied extensively on learning curves to project the cost of wind 

energy into the future. Often studies that apply learning curves and rates include external 

assumptions that adjust for some of the limitations noted above by, for example, assuming a 

reduction in the learning rate over time or only applying the learning curve for a short period of 

time (e.g., EWEA 2009). Others focus the use of learning exclusively on capital costs and 

assume little or no improvement in wind project performance (e.g., IEA 2009, Lemming et al. 

2009), while still others apply the assumed learning rate to LCOE estimates (EWEA 2009). 

Based on a range of learning curve estimates for LCOE of 9%–17%, EWEA (2009) used a 

learning rate of 10% to estimate that LCOE would drop by roughly 1–2 euro cents/kWh between 

2010 and 2015. Using three different learning rates that are assumed to fall with deployment and 

 

17 If a given cost reduction that is the result of learning by a global industry is attributed to only a fraction of global 

installations, and represents a national or regional market, the degree of cost reduction resulting from a given set of 

installations can be over or underestimated. As a result, errors can be introduced into both historical comprehension 

and projections.
20 

industry maturation from 10% to 6%, and ultimately to 3%, in order to represent the diminishing 

returns associated with increased deployment, Lemming et al. (2009) estimate that wind energy 

capital costs will fall by approximately 20% by 2020 and 30% by 2050 while capacity factors 

remain at current levels. The Global Wind Energy Council (GWEC) and Green Peace 

International (GPI) (GWEC/GPI 2010) utilize a learning rate and variable deployment rates to 

estimate that capital costs will fall by 10% to 20% between 2010 and 2030. IEA (2009) applied a 

7% learning curve to land-based wind energy to determine that capital costs will fall by nearly 

20% by 2030 and 25% by 2050. 

By and large, institutions using learning rates to project future costs have often tended toward 

relatively conservative assumptions and largely arrived at capital cost reductions on the order of 

20%–30% by 2030 with somewhat smaller, incremental cost reductions accruing through to 

2050. A review of learning rates in the literature conducted by the IEA Task 26 working group 

suggests that the most aggressive use of learning rates in the literature results in a reduction in 

the LCOE from wind power on the order of 30%–40% by 2030 (see Section 4.5). 

4.2 Expert Elicitation

To bolster the reliability of learning curve estimates and to garner a more detailed understanding 

of how future cost reductions may actually be realized, analysis of the future cost of wind energy 

also sometimes includes expert elicitation. This approach is based on surveying or interviewing 

industry executives and technology design experts. Interviews are typically focused at the turbine 

component and system level and may also attempt to capture trends in various aspects of 

installation costs (e.g., underground cabling, erection costs, and required on-site monitoring 

infrastructure). By evaluating the potential for cost reductions or performance improvements at 

the component or system level and combining the estimated potential from an array of concrete 

possible technological advancements, this approach constitutes a simple but technology-rich, 

bottom-up analysis. In fact, expert elicitation is unique in that it allows for a relatively simple 

bottom-up analysis and for a diverse set of variables (i.e., market pressures or system-level 

turbine interactions) to be considered. However, it also introduces a relatively high level of 

subjectivity into the analysis, as the responses to the elicitation may be affected by the design of 

the data collection instrument and by the individuals selected to submit their views through that 

instrument.

4.2.1 Examples of Expert Elicitation

Expert elicitation is sometimes combined with learning curve analysis and may be used to inform 

public sector R&D investments. In the latter case, explicit quantitative projections may or may 

not be included, but expert interviews are utilized to identify specific areas where focused R&D 

is likely to have the greatest impact on the future cost of energy. The European wind industry, 

with funding from the European Union, has used the expert elicitation approach in its European 

Wind Energy Technology Platform (TP Wind) to identify specific research priorities. The U.S. 

Department of Energy’s study 20% Wind Energy by 2030 (U.S. DOE 2008) utilized the expert 

elicitation derived from insights in the Wind Partnership for Advanced Technology Components 

(WindPACT) design studies (e.g., Cohen et al. 2008) to estimate an approximately 10% 

reduction in wind energy capital costs and a nearly 20% increase in capacity factors (36%–43% 

in a Class 4 wind resource) between 2005 and 2030. Neij (2008), in work conducted for the 

European Union’s New Energy Externalities Developments for Sustainability (NEEDS) project, 

combined expert elicitation with learning curve analysis to estimate that future turbine costs 
21 

could be approximated with a learning rate of about 10% and that reductions in the LCOE could 

be approximated with a 15% learning rate when accounting for increased performance. Although 

applied to offshore wind, Junginger et al. (2004) combine a sophisticated learning curve 

approach (i.e., estimating learning curves for individual turbine components) and expert 

elicitation to estimate future offshore wind energy cost reductions.

4.3 Engineering Model

Similar to expert elicitation, engineering modeling analysis provides a bottom-up alternative or 

complement to the learning curve. Rather than relying on high-level data or expert opinion, this 

approach utilizes detailed modeling of specific possible technology advancements that are 

expected to result in cost reductions or performance increases. Because this approach typically 

models both cost and performance, it inherently emphasizes expected reductions in LCOE. It 

also requires a relatively robust understanding of possible technology advancements and, as a 

result, the opportunities captured by engineering studies are often incremental and generally 

realizable in the near term (5–10 years). 

In addition to primarily being focused on the near to medium term, the main limitation of the 

engineering model approach is that it requires highly sophisticated design and cost models to 

capture the full array of component- and system-level interactions. Often the level of 

sophistication achieved with today’s modeling tools is insufficient to truly capture the systemlevel interactions that are common in wind turbine design. Cost models are also unable to make 

projections about future commodity prices or supply and demand pressures throughout the 

supply chain (of course, learning curves and expert elicitation face similar challenges).18

Accordingly, the projected costs are generally based on the impact of a particular technical 

innovation, all else being constant. 

4.3.1 Engineering Model Examples

One of the prime examples of the engineering modeling approach comes from the U.S. 

Department of Energy’s WindPACT project (e.g., Bywaters et al. 2005, Malcolm and Hansen 

2002). Under that project, an array of system design studies was used to understand how various 

innovation opportunities might affect turbine performance into the future. These results were 

ultimately tied to cost functions to quantify their impact on turbine and project costs (Fingersh et 

al. 2006). More recent NREL modeling work that builds upon these studies suggests that 

performance increases on the order of 20% and cost reductions on the order of 10% over the next 

one to two decades are possible but may require additional technological advancements not 

captured by the WindPACT studies (e.g., Lantz and Hand 2011). Another example of the 

engineering modeling approach being applied to future costs is in the European Commission’s 

UpWind project. In this effort, technical experts identified and analyzed an array of actions 

required to achieve a functional 20-MW turbine, and cost modeling was used to estimate the 

potential cost of this machine and the impact to costs from the various technological 

enhancements (UpWind 2011). 

 

18 Of course cost models are able to analyze the impacts of changes in commodity prices and labor costs, they 

simply cannot forecast or anticipate such changes.
22 

4.4 Sources of Cost Reduction Identified by Expert Elicitation and Engineering 

Modeling Assessments

Incremental innovations in a variety of aspects of wind turbine design as well as in turbine 

erection, O&M strategies, and manufacturing are expected to contribute to reduced wind energy 

costs in the future. Table 1 summarizes the broad categories of opportunities envisioned to apply 

to onshore wind energy projects, based on engineering studies and expert elicitation. Much of the 

opportunity to drive down costs is perceived to be in the design and performance of wind 

turbines because of their critical role in calculating wind energy’s LCOE; initial turbine 

expenditures alone account for roughly 70%–75% of project capital costs (Wiser and Bolinger 

2011, Blanco 2009) and about 60% of lifetime project costs (Blanco 2009). However, O&M 

strategies and manufacturing efficiencies are also anticipated to help reduce the cost of wind 

energy in the future. 
23 

Table 1. Potential Sources of Future Wind Energy Cost Reductions

R&D/Learning Area Potential Changes 

(For more detail on technology changes and 

expected impacts, see references below)

Expected Impact

Drivetrain Technology

Advanced drivetrain designs, reduced 

loads via improved controls, and condition 

monitoring (Bywaters et al. 2005)

Enhanced drivetrain reliability and 

reduced drivetrain costs

Manufacturing 

Efficiency

Higher production volumes, increased 

automation (Cohen et al. 2008), and 

onsite production facilities

Enhanced economies of scale, reduced 

logistics costs, and increased component 

consistency (allowing tighter design 

standards and reduced weights) 

O&M Strategy

Enhanced condition monitoring 

technology, design-specific 

improvements, and improved operations 

strategies (Wiggelinkhuizen et al. 2008)

Real-time condition monitoring of turbine 

operating characteristics, increased 

availability, and more efficient O&M 

maintenance planning

Power 

Electronics/Power 

Conversion

Enhanced frequency and voltage control, 

fault ride-through capacity, and broader 

operative ranges (UpWind 2011)

Improved wind farm power quality and 

grid service capacity, reduced power 

electronics costs, and improved turbine 

reliability

Resource Assessment

Turbine-mounted real-time assessment 

technology (e.g., LIDAR) linked to 

advanced controls systems, enhanced 

array impacts modeling, and turbine siting 

capacity (UpWind 2011)

Increased energy capture while reducing 

fatigue loads, allowing for slimmer design 

margins and reduced component masses; 

increased plant performance

Rotor Concepts

Larger rotors with reduced turbine loads 

allowed by advanced controls (Malcolm 

and Hansen 2002) and application of lightweight advanced materials

Increased energy capture with higher 

reliability and less rotor mass; reduced 

costs in other turbine support structures

Tower Concepts

Taller towers facilitated by use of new 

design architectures and advanced 

materials (Cohen et al. 2008, LaNier 2005, 

and Malcolm 2004)

Reduced costs to access stronger, less 

turbulent winds at higher above-ground 

levels
24 

As suggested by Table 1, fundamental elements of wind turbine innovations include advanced 

control systems to assist in shedding loads, advanced condition monitoring to minimize major 

component failures and unplanned turbine downtime, and forward-looking resource evaluation 

(i.e., seeing the wind that is approaching the turbine) in order to better position turbines to 

maximize production or minimize loads. 

Cohen et al. (2008) estimate that taller towers could increase annual energy production by 11%. 

In the absence of new design innovations, however, the increased costs associated with 

extending towers beyond current industry norms may not be cost effective except in those sites 

with higher-than-average wind shear (Wiser et al. 2012). Advanced design concepts considered 

by Fingersh et al. (2006) suggest that higher tower heights might be achieved in the future with 

only incremental cost increases. Advanced control systems and integrated system design are 

considered critical for reducing tower loading and allowing for the development and application 

of lower-cost tower and foundation designs (UpWind 2011, U.S. DOE 2008). Innovation 

opportunities include hybrid, or concrete and steel towers, application of advanced materials, and 

new structural designs. Ceña and Simonot (2011) estimate that a 125-m hybrid steel and concrete 

tower could result in a total tower materials cost reduction on the order of 40%–50%, although 

they expect this cost savings to be slightly less for an installed tower due to greater labor 

requirements associated with hybrid and concrete towers. Future tower innovations may also be 

targeted at easing crane height requirements, for example, with self-erecting or partially selferecting towers, and minimizing logistics constraints, the latter of which could support reduced 

transportation costs. 

Moving to larger rotor diameters may increase annual energy output from a given wind turbine 

by as much as 10%–30% (Cohen et al. 2008). To achieve larger rotor diameters without 

significant cost increases or efficiency losses, however, new innovations that reduce blade 

weight and loads are necessary (Griffin 2001). Some weight reductions may be achieved by 

incremental design refinements and optimizations or the application of new materials with lower

mass-to-strength ratios. More significant weight reductions might be achieved with designs that 

passively shed loads by twisting (Ashwill 2009) or that include partial blade span actuation (i.e., 

the ability to control different sections of a single turbine blade) coupled with sensing capacities 

that allow the rotor to adapt to variability in wind conditions and turbulence in different parts of 

the rotor disk (Buhl et al. 2005, Lackner and van Kuik 2009, and UpWind 2011). Trailing edge 

flaps that react to the wind as it is moving towards the rotor rather than the wind that has already 

passed by could also assist in load shedding and ultimately weight reductions (Andersen et al. 

2006, Berg et al. 2009). 

Optimization of current drivetrain designs to increase reliability and reduce weight is expected to 

reduce drivetrain cost and increase performance (e.g., Peeters et al. 2006, Heege et al. 2007). 

However, wholly new architectures are also possible and are in development and production, 

including direct drive turbines, single-stage medium-speed geared designs, and multi-generator 

architectures (Cohen et al. 2008). Permanent magnet generators have become increasingly 

common in wind turbine design and may resolve some of the longstanding challenges of weight 

and size typically associated with direct drive machines. UpWind (2011) specifically identified 

permanent magnet transversal flux generators from among 10 specific drivetrain configurations 

to be particularly promising in terms of drivetrain weight reduction. Drivetrain advancements 
25 

were estimated by Cohen et al. (2008) to result in increases in energy production up to 8% while 

also potentially reducing costs by as much as 11%. 

Advanced manufacturing strategies are expected to result in tighter design tolerances, driving 

down weight and increasing product reliability as well as lowering overall turbine costs as a 

result of process improvements and greater economies of scale (Cohen et al. 2008, UpWind 

2011). New manufacturing strategies that allow for on-site production could also be employed to 

lower logistics and transportation costs. Broader use of condition monitoring and increasingly 

refined operational strategies (e.g., use of preventive maintenance strategies and enhanced 

planning for turbine downtime) are expected to facilitate reductions in operations and 

replacement costs. Finally, improvements in power electronics may reduce costs while 

increasing the ability of wind turbines to provide grid services. 

4.5 Quantitative Summary of Future Cost Estimates

Policy analysts, researchers, trade groups, and others have commonly utilized one or more of the 

approaches described above to estimate the future cost of wind energy. Cost projections applying 

these approaches vary, although when considered against at least the higher end of the range of 

historical learning rates observed in the literature and suggested for every doubling of global 

capacity (see Section 4.1 or Wiser et al. 2011), the range is somewhat less dramatic.19 

Figure 11 compiles and normalizes data from 13 relatively recent analyses (including both 

research studies and policy analysis modeling inputs) and 18 scenarios to illustrate the expected 

range of the future costs for onshore wind energy anticipated in the literature. The LCOE 

reduction estimates shown in Figure 11 were developed by extracting data from individual 

studies and scenarios. Raw cost data were converted to common values (i.e., 2009 dollars), and a 

minimum performance starting point. From these normalized data, an estimated LCOE for each 

scenario was calculated. LCOE values for this portion of the analysis were estimated with the 

simplified LCOE methodology also employed in the IPCC Special Report on Renewable Energy 

Sources (Moomaw et al. 2011). Where there were incomplete data for a given scenario (e.g., 

only capital costs available) representative industry data were utilized for other assumptions. 

After calculating the annual percent reduction in LCOE implied by each scenario, scenarios were 

aligned to a common starting point to estimate the percentage reduction in LCOE from 2010 to 

2030. Studies analyzed were dated between 2007 and 2011. Based on this relatively narrow and 

recent time frame, it was assumed that the drivers of specific cost reduction trajectories remain 

generally fully available to be integrated into the average fleet turbine (i.e., if a study was 

completed in 2007, the technological advancements envisioned would not be fully tapped by the 

 

19 Learning rates are a function of cumulative installed capacity, and it is not unreasonable to expect that global 

installed wind power capacity will double more than once over the next 20 years. Achieving 20% wind energy by 

2030 in the United States has been estimated to require 305 gigawatts (GW), or an additional 260 GW from the endof-year 2010 figure (DOE 2008). China expects to have perhaps 200 GW installed by 2020, roughly a 150-GW 

increase (GWEC 2011). Global capacity was 197 GW at year-end 2010 and thus China’s activities alone could 

nearly double global capacity by 2020. The Global Wind Energy Council deployment forecast based on data through 

2010 suggests an average 18% growth annual rate over the next 5 years, which would result in a doubling of global 

capacity by the end of 2014 (GWEC 2011). The higher end of the historical learning rates presented in the literature 

for onshore wind energy, if applied to the anticipated number of doublings of installed wind power capacity by 2020 

or 2030, would, in many cases, yield cost reductions that are greater than those presented in Figure 11. 
26 

2010 starting point in Figure 11).20 Many of these studies utilize learning curves in combination 

with expert elicitation, engineering models, and near-term market analysis (e.g., EWEA 2009, 

U.S. DOE 2008, GWEC/GPI 2010, and Lemming et al. 2009). Some of the more extreme results 

are generated from comparably conservative assumptions (e.g., Tidball et al. 2010) or from

advanced scenarios with the most optimistic assumptions (e.g., EREC/GPI 2010, GWEC/GPI

2010, and Peter and Lehmann 2008). 

The normalized data suggest an absolute range of roughly a 0%–40% reduction in LCOE 

through 2030 (Figure 11). The single scenario anticipating no further cost reductions assumes 

that the upward price pressures observed between 2004 and 2009 are moderated but remain 

significant enough to prevent future reductions in LCOE. The three studies anticipating a 35%–

40% reduction in LCOE by 2030 represent ambitious scenarios requiring concentrated efforts to 

reduce the cost of wind energy and levels of investment that exceed business as usual. In 

addition as virtually all the studies reviewed here incorporate learning curve concepts at some 

level, differences in the respective scenarios also depend on the assumed levels of deployment. 

Nevertheless, by focusing on the results that fall between the 20th and 80th percentiles of 

scenarios, the range is narrowed to roughly a 20%–30% reduction in LCOE. 

Figure 11. Estimated range of wind LCOE projections across 18 scenarios

Sources: EREC/GPI 2010, Tidball et al. 2010 (includes modeling scenarios from multiple other 

sources), U.S. DOE 2008, EIA 2011, Lemming et al. 2009, EWEA 2011, EPRI 2010, Peter and 

Lehmann 2008, GWEC/GPI 2010, IEA 2009, and European Commission 2007 

LCOE reductions are generally expected to be greater in the early years and then slow over time. 

Initial cost reductions range from 1%–6% per year, fall to 1%–4% by 2020, and decline further 

 

20 In some cases, new turbine models or existing prototypes may already include technology improvements that have 

been captured in various studies, suggesting that some portion of the cost reduction projection has already been 

realized. However, when thinking of the industry not in terms of cutting edge, latest technology, but rather in terms 

of the industry standard or average turbine among the existing fleet today, it is reasonable to assume that the LCOE 

represented by the existing fleet average turbine has not incorporated the vast majority of the technology 

improvement opportunities that are assumed to be realized in individual studies or modeling scenarios.

50%

60%

70%

80%

90%

100%

2010 2015 2020 2025 2030

Trends In Wind Power LCOE 

2011 = 100%

20th to 80th

Note: 

-Shaded area represents the full range of 

expectations in the literature

-Each individual line details the expected 

cost of energy pathway for a given study
27 

to 0%–1.5% by 2025. By 2030, all but one scenario envisions cost reductions falling below 1% 

per year. 

A variety of factors are expected to influence whether or not these estimates are realized. 

Resurgence in turbine demand resulting in supply chain pressures similar to those observed 

between 2004 and 2009 could, again, drive wind energy LCOE higher. Renewed upward 

pressure on commodity prices that might be associated with a strong economic recovery could 

also drive prices higher.

At the same time, factors that may not be directly captured in the studies highlighted here, 

including the impact of increasing competition among manufacturers in general, could drive 

down costs further. 
28 

5 Conclusions

Over the past 30 years, the wind industry has become a mainstream source of electricity 

generation around the world. The industry has observed significant cost of energy reductions. 

However, from roughly 2004 to 2009, the cost of wind energy increased. Historically, cost 

reductions have resulted from both capital cost reductions and increased performance. From 

2004 to 2009, however, continued performance increases were not enough to offset the sizable 

increase in capital costs that were driven by turbine upscaling, increases in materials prices, 

energy prices, labor costs, manufacturer profitability, and—in some markets—exchange rate 

movements. Nevertheless, as capital costs have moderated from their 2009–2010 levels, the cost 

of wind energy has fallen and is now at an all-time low within fixed wind resource classes. 

Looking forward, the LCOE of wind energy is expected to continue to fall, at least on a longterm global basis and within fixed wind resource classes. Performance improvements associated 

with continued turbine upscaling and design advancements are anticipated, and lower capital 

costs may also be achievable. The magnitude of future cost reductions, however, remains highly 

uncertain, although most recent estimates project that the LCOE of onshore wind could fall by 

20%–30% over the next two decades. 

As the industry continues to mature and future technology advancement opportunities become 

increasingly incremental, however, LCOE reductions can be anticipated to slow. Moreover, 

continued movement towards lower wind speed sites may invariably increase industry-wide 

LCOE, despite technological improvements that would otherwise yield a lower LCOE. Other 

local factors such as transmission needs may also push towards higher costs. With these factors 

in mind, it is of important to consider the interdependence of capital costs and performance, and 

to evaluate the future cost of wind energy on an LCOE basis. Moreover, such evaluations must 

consider trends in the quality of the wind resource in which projects are located, as well as 

development, transmission, integration, and other cost elements that may also change (and 

increase) with time and deployment levels, but are sometimes ignored in traditional LCOE 

analyses (e.g., Dinica 2011). 

Estimates of the future cost of wind energy conducted to date have often been the result of an 

iterative process that incorporates some combination of historical trends, learning curve analysis, 

expert elicitation, and engineering modeling. Theoretically, each of these approaches could 

independently provide an estimate of the future cost of energy; however, it has often been 

recognized that it is a combination of these different methodologies that is likely to yield the 

most accurate results. The individual strengths and weaknesses of each approach are in some 

ways complementary, so future projections are also expected to employ various combinations of 

these methods. 

Further improving our understanding of possible future cost trends will require additional data 

gathering and improved modeling capability. Robust data collection is needed across the array of 

variables that must be factored into estimating LCOE (e.g., capital cost, capacity factor, O&M 

costs, component replacement rates and costs, and financing costs) and in each of the wind 

energy markets around the globe. Also needed are data on the many contextual factors that 

impact the overall cost of wind energy and that may also vary with time, such as interconnection 

costs, permitting costs, and the average wind speed of installed wind projects. Such data would 
29 

allow historical LCOE trends to be more closely analyzed, with insights gleaned both through 

more-sophisticated learning curve analysis as well as bottom-up assessments of historical cost 

drivers. Additional data could also assist in better distinguishing those cost reductions that result 

from technological improvements from those changes in cost that result from external supply and 

demand market variables or changes in raw material and commodity prices. It is only with this 

improved historical understanding that future possible cost trajectories can be fully understood 

(Dinica 2011). An enhanced capacity to model the cost and performance impacts of new 

technological innovation opportunities, taking into account the full system dynamics that result 

from a given technological advancement, is also essential. Component, turbine, and project-level 

design and cost tools of this nature would allow for more sophisticated cost modeling and 

provide greater insights into possible future costs based on changes in material use and design 

architectures. Together these efforts would enhance our ability to understand future costs, 

facilitate prioritization of R&D efforts, and help to understand the role and required magnitude 

of deployment incentives into the future.
30 

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36 

Appendix A 

Data Inputs for U.S. and Denmark Levelized Cost of Energy Modeling

Modeling inputs from the independent U.S. and Danish levelized cost of energy (LCOE) 

analyses are shown in Table 1A and Table 2A, respectively. In both cases, assumptions are 

intended to represent industry-wide average estimates; actual performance, capital costs, 

operating costs, and other terms may vary widely from one project to the next. All dollar values 

are in real 2010 U.S. dollars, and each scenario assumes a 20-year project or economic life. For 

the U.S. analysis, performance is modeled and based on a 50 meter (m) annual average wind 

speed. As such, the U.S. analysis applied a Weibul k Factor of 2 across all scenarios and utilized 

the 1/7th power law to estimate hub height wind speeds, again across all scenarios. U.S. 

aggregate income taxes are assumed to be 38.9%. In Denmark, modeling relies on estimates of 

the typical full load hours for a wind regime in Eastern Denmark as acquired from industry data 

and sources. A Weibul k factor of 2 is assumed in the Danish analysis, and Danish corporate 

taxes are 25%. 

Table 1A. Inputs in Modeling of U.S. LCOE Estimates 2002–2003 through 2012–2013

Characteristics 2002–2003 2009–2010 Current Turbine Pricing: ~2012–2013

Nameplate capacity 1.5 megawatts 

(MW) 1.5 MW 1.62 MW 1.62 MW 1.62 MW

Hub height 65 m 80 m 80 m 80 m 100 m

Rotor diameter 70.5 m 77 m 82.5 m 100 m 100 m

Installed capital cost $1,300/kilowatt 

(kW) $2,150/kW $1,600/kW $1,850/kW $2,025/kW

Operating costs $60/kW-year $60/kW-year $60/kW-year $60/kW-year $60/kW-year

Losses (availability, 

array, other) 15% 15% 15% 15% 15%

Financing cost/ 

discount rate (nominal) 9% 9% 9% 9% 9%
37 

Table 2A. Inputs in Modeling of Danish LCOE Estimates 2002 through 2012

Characteristics 2002 2009 2012

Nameplate capacity 0.9 MW 2.3 MW 3.0 MW

Hub height 49 m 80 m 80 m

Rotor diameter 52 m 93 m 112 m

Installed capital cost $1,465/kW $1,908/kW $1,857/kW

Operating costs $16/megawatthour (MWh) $16/MWh $16/MWh

Full Load Hours 2209 3102 3602

Financing cost/ 

discount rate (nominal) 8% 8% 8%
Appendix B. The Cost of Wind Energy Presentations

The Cost of Wind Energy

Alberto Ceña & Emilien Simonot

esimonot@aeeolica.org

Technical department

Spanish Wind Energy Association

THE COST OF WIND ENERGY

AEE contribution to

IEA task 26, WP#2

July 2011

Credit: AEE

OBJECTIVES OF THE STUDY

• Identify the factors influencing the evolution of wind 

turbine generator (WTG) design and technical 

characteristics

• Understand the influence of design evolution and 

external factors on the cost of WTG

• Highlight critical issues that could influence the cost of 

WTG in the near future.

38
APPROACH OF THE STUDY

• Study challenges: 

– Assess how WTG costs vary over time

– Determine the factors that influence WTG cost variations, 

including:

• Needed technology changes (e.g., size and weight increase, 

and new technology standards)

• External factors (e.g., raw material costs, energy, and 

manpower)

 Try to identify the effect of each factor

• Data inputs: 

– All sources available, including AEE ́s statistics, WTG 

manufacturers ́ portfolios, and theses

• Study criteria:

– WTG broken down into three main types studied separately: 

Rotor, Nacelle, and Tower. 3

WIND ENERGY 

DEPLOYMENT IN 

SPAIN

39
0

1.000

2.000

3.000

4.000

5.000

6.000

MW

En 2010

En 2009

En 2008

En 2007

En 2006

En 2005

En 2004

Total a 01/01/2004

Fuente: AEE

Autonomous 

community

Accumulated 

capacity end 

2010 (megawatts 

[MW])

Castilla y León 4803,815

Castilla-La 

Mancha 3709,19

Galicia 3289,325

Andalucía 2979,33

Aragón 1764,01

Comunidad 

Valenciana 986,99

Navarra 968,37

Cataluña 851,41

La Rioja 446,62

Asturias 355,95

País Vasco 153,25

Murcia 189,91

Canarias 138,92

Cantabria 35,3

Baleares 3,65

Most of the best spots 

occupied during the early 

development of wind energy 

were in place before 2004.

Today, leading regions (in terms of installed capacity) are not the best 

wind resource regions. As such:

- Developers have to deal with medium and low wind conditions

- There is a need to adapt technology to these sites.

HISTORICAL AND GEOGRAPHIC TRENDS

TREND IS TO INSTALL BIGGER AND BIGGER WTG

0

500

1000

1500

2000

2500

199019911992199319941995199619971998199920002001200220032004

2005

20062007200820092010

Average capactiy (MW)

Installed WTG average capacity (Spain)

(kW)

40
THE OPERATING HOURS OF WIND FARMS DECREASED SLOWLY 

DURING THE LAST DECADE—FROM 2,500 TO 2,000 HOURS AND 

STABILIZED AROUND 2,000 HOURS

Source: CNE 8

Ant98 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Year of instalation 2.440 2.496 2.544 2.366 2.328 2.181 2.201 2.077 2.148 2.147 1.973 1.931 2.170 1.993

0

500

1.000

1.500

2.000

2.500

3.000

Average operating hours

Average operating hours for projects, by year of instalation. 

15%

17%

19%

21%

23%

25%

27%

29%

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

CAPACITY FACTOR

GLOBAL CAPACITY FACTOR EVOLUTION

9

The current average capacity factor is lower than in early 2000 and is oscillating from 

23% to 25%.

Early development of 

wind power: best wind 

condition sites

Worse wind conditions Worse wind conditions but 

technology adaptation

41
FACTORS 

INFLUENCING WIND 

POWER COSTS

10

WTG AND WIND FARM COSTS

11

Aerogeneradores

73,02%

Obra Civil

7,36%

Conexión

10,84%

Otros costes de 

inversión

8,78%

Fuente: Estudio INTERMONEY y AEE

Onshore wind farm investment costs are closely linked to WTG 

costs which represent 73% of the total investment costs.

42
Wind farms costs 2001-2009 Wind turbines costs 2001-2009

Source: CNE Source: CNE

Average installed project costs have increased 100% in 

the period 2001-2009.

Average WTG costs have increased 50% in the period 

2001-2009.

Before 2006: investment costs increase and standard deviation 

remains low. Low cost period.

Before 2004: demand meets offer.

From 2004 to 2006: high demand and strong cost increases.

2007: 3,520 MW installed. The high demand pushes costs up. Wide 

range of costs. Transition period.

2007: Wide range of costs due to very high demand. The variation of 

raw material costs starts.

Since 2008: Stabilization of costs at higher levels. The standard 

deviation decreases but the average costs remain high.

High cost period.

Since 2008: Costs remain high as the result of:

- Lower demand due to the end of the regulatory framework

- Increase of raw material prices (i.e., steel, copper, and fiberglass)

Future trends: - Prices may decrease as a result of more offers at lower prices (new manufacturers especially from Asia)

- Technology evolution and costs of material have to be taken into account.

WTG AND WIND FARM COSTS—NO APPLICATION OF THE 

LEARNING CURVE

LEARNING CURVE AND FUTURE TRENDS

In Spain, it appears that no learning curve exists, from the early

industrial stage of wind power to the present, no cost reductions have

been observed.

• Reduction of production costs must have happened—but were

hidden by a high demand market—which is not an appropriate

scenario to incentivize cost reduction.

• Recent changes introduce new industry challenges and costs:

– The regulatory framework: needs for local production in the new

regional tenders mean a rise in investment costs

– Less and less good wind condition sites: bigger diameter and higher

towers

– Market volatility and supply insecurity for raw materials (i.e., steel,

copper, and rare earth metals)

– Decrease of Spanish market installation rhythm means WTG

manufacturer strategy to look abroad  new investments.

13

43
COST OF MATERIALS

14

WEIGHT AND RAW MATERIAL INFLUENCE ON WTG COST 

VARIATION

Formula used to determine how weight increase and raw material costs 

independently influence WTG cost:

Determination of influence of weight increase W for each material between year 

n-x and year n:

Determination of influence of raw material cost variation C for each material 

between year n-x and year n:

15

( )

( ) ( ) n n xn xn

n xn xn

Cost Weight Cost Weight

Weight Weight Cost W

− −

− −

×−×

− × =

( )

( ) ( ) n n xn xn

n xn n

Cost Weight Cost Weight

CostCost Weight C

− −

−

×−×

×− =

44
RAW MATERIALS COSTS 

16

MATERIAL 2006 PRICE CURRENT PRICE

Iron (Cast Steel) (1) 370 €/t 500 €/t

Steel (2) 460 €/t 620 €/t

Copper ~ 5.500 $/t = ~ 4.500€/t (2) ~ 9.000 $/t = ~ 7.000 €/t (3)

Glass fiber ~ ~ 450 €/t (4)

Resin Unknown composition

(1) Iron price: 80% of steel price

(2) Flat Products/HRC/S. Europe domestic Ex-Works €/t

(3) IMF Commodity Price Survey

(4) Personal communication

Values to be considered in the next slides.

0

100

200

300

400

500

600

700

800

900

ene 06

abr 06

jul 06

oct 06

ene 07

abr 07

jul 07

oct 07

ene 08

abr 08

jul 08

oct 08

ene 09

abr 09

jul 09

oct 09

ene 10

abr 10

jul 10

oct 10

ene 11

€/t

Cost of Steel Flat Products (South Europe domestic)

Monthly min

Monthly max

Monthly Avg

Flat Products/HRC/S. Europe domestic Ex-Works €/t

45.000 €/MW 

85.000 €/MW

70.000 €/MW

42.000 €/MW 

+ 40 €/kW - 43 €/kW + 28 €/kW

STEEL COST EVOLUTION SINCE 2006

45
0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

ene 06

abr 06

jul 06

oct 06

ene 07

abr 07

jul 07

oct 07

ene 08

abr 08

jul 08

oct 08

ene 09

abr 09

jul 09

oct 09

ene 10

abr 10

jul 10

oct 10

ene 11

Stainless Steel €/t (South Europe 

domestic)

Stainless Steel/CR 304 2B 2mm coil transaction/S. Europe domestic delivered €/t

STAINLESS STEEL COST EVOLUTION SINCE 2006

1- NACELLE

19

46
NACELLE WEIGHT: INFLUENCE OF

ELECTRICAL CONVERSION TECHNOLOGY

20

0

50

100

150

200

250

300

350

400

450

500

0 1000 2000 3000 4000 5000 6000

NACELLE WEIGHT (t)

RATED POWER (MW)

TYPE OF ELECTRICAL CONVERSION - WEIGHT

Asíncrono

DFIG

Multipole Wound rotor

PMG

Squirrel Cage

Polinómica (DFIG)

Polinómica (Multipole Wound 

rotor)

Lineal (PMG)

Typology with permanent magnet generator could be the lightest one, 

whereas multipole wound generator technology is the heaviest one.

EVOLVING GRID CODES WILL ENCOURAGE THE USE OF NEW 

CONVERSION SYSTEMS

21

0

5000

10000

15000

20000

25000

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Cumulative installed capacity

Breakdown of electrical conversion tecnology in Spain.

Full Converter

DFIG

Squirel cage

Due to new grid codes requirements, the technological trend is to use 

full converter in new WTG designs.

For installed WTG, 

retrofits are needed to 

comply with new grid 

codes. As a result, 

unplanned costs can 

arise during the 

operation phase.

47
• Trends to build WTG equipped with permanent magnet 

generators or multipole technology include:

– No gearbox = may reduce operation and maintenance (O&M) costs

– Full converter = easier grid integration but higher cost

– Larger generators = heavier

• Permanent magnet generators – use of rare earth:

– Demand of rare earth is high because of lots of other applications

– China generates 97% of world production and risks exist regarding 

future delivery.

ISSUES: GENERATORS AND POWER ELECTRONICS

22

Symbol Name Selected Usages

Pr Praseodymium

Rare-earth magnets, lasers, core material for carbon arc lighting, 

colourant in glasses and enamels, additive in Didymium glass used in 

welding goggles, ferrocerium firesteel (flint) products

Nd Neodymium Rare-earth magnets, lasers, violet colors in glass and ceramics, ceramic 

capacitors

Sm Samarium Rare-earth magnets, lasers, neutron capture, masers

Gd Gadolinium Rare-earth magnets, high refractive index glass or garnets, lasers, x-ray 

tubes, computer memories, neutron capture

Dy Dysprosium Rare-earth magnets, lasers

Rare earth used in the construction of permanent magnets.

• The weight of nacelle rises from 30 tons in 1 MW WTG up to 

more than 120 tons in multi MW WTG

Need to consider specific logistic solutions: higher load trucks 

and heavier lifting capacities

• Nacelle raw material structure:

ISSUES RELATED TO NACELLE MATERIALS

23

28%

57%

9%

2% 3% 1%

Iron

Steel

Copper

Glassfiber

Resin

Quartz

WTG nacelle raw material breakdown (GAMESA G8X)

Raw material Tons

Iron 10, 5

Steel 21, 69

Copper 3, 5

Glass fiber 0, 8

Resin 1, 2

Quartz 0, 35

Gamesa G8X Nacelle (real data)

Source: Life cycle assessment of a multi-megawatt wind turbine - E. Martínez, F. Sanz - Renewable Energy 34 (2009) 667–673

48
EVOLUTION OF COST OF MATERIALS IN NACELLE

24

Use of raw material in WTG nacelle

Period 2004 – 2010 Present and 

Future

WTG type 2 MW – doubly

fed induction 

generator (DFIG)

Cost Estimation for a 

3 MW– DFIG or 

similar

Cost Variation

Due to raw 

material cost 

changes

Due to WTG

weight increase

Iron (cast 

steel)

~10.5 ton 3.885 € ~33 tons 16.500 € 34% 66%

Steel ~22 ton 10.120 € ~68 ton 42.160 € 34% 66%

Copper ~3.5 ton 15.750 € ~10 ton 70 000 € 46% 54%

Glass fiber ~0.8 ton ~2.3 ton 1100 € No historical data available

Resin ~1.2 ton ~3.5 ton Unknown composition

Quartz ~0.3 ton ~1.2 ton No data available

The cost increase of raw material in WTG nacelles from steel:

– Approximately 1/3, from the cost increase of raw material.

– Approximately 2/3, to the size and weight increase of the WTG.

For copper, these two factors each share 50% of the variation.

2- BLADES

25

49
ROTOR SIZE: BIGGER AND BIGGER

0

20

40

60

80

100

120

140

199019911992199319941995199619971998199920002001200220032004200520062007200820092010

Rotor diameter (m)

Rotor diameter of installed WTG (Spain)

Mean Diameter

Min Diameter

Máx Diameter

• The increase of diameter has been constant since the earliest stages of wind 

power development.

• Latest WTG designs with rotor diameter of more than 100 meters (m) show that 

this tendency should continue.

300

600

500 660750

850

1000

1250

1500

1650 1500

2000

2500

3000 4200

5000

3000 3000

2500 2300

2000

5540

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

0 10 20 30 40 50 60 70

BLADE WEIGHT (Kg)

BLADE LENGTH (m)

BLADE

BLADES ARE ALSO GETTING HEAVIER AND HEAVIER

Continuous growth is

observed, however,

length seems to grow

quicker than weight.

From 1 to 18 tons

G8X (~40m) Blade material

breakdown

Resin 3.9 tons

Fiberglass 2.6 tons

Total 6.5 tons

RATED POWER (kW)

0

1000

2000

3000

4000

5000

6000

7000

0 10 20 30 40 50 60 70

WTG rated power (kW)

Blade length (m)

0

1000

2000

3000

4000

5000

6000

7000

0 5000 10000 15000 20000

WTG rated power (kW)

Blade weight (kg) 27

50
ISSUES RELATED TO BLADE MATERIALS

• Wind turbine blades are made of a main frame and several different layers:

– Frame and internal layers are designed to be as light as possible

– External layers responsible for protection and wind harnessing are 

composed of glass fiber and resins

– Chinese fiberglass is gaining market share due to a low price policy.

• Technological trends:

– Segmented blades, optimized logistics

– New materials, including use of carbon fiber, resins improvement, and 

special coatings (e.g., anti biofouling and stealth)

• WTG blades have not experienced very important changes in their 

structural designs since the 90 ́s. The possibility to reach a maximum 

length/weight ratio has to be considered and R&D efforts are planned to 

address this issue (including research on new materials and designs).

EVOLUTION OF COST OF MATERIAL FOR BLADES 

29

Use of raw material in WTG blades

Period 2004 – 2010 Present and 

Future

Blade type 40 m long/ 

7, 5 ton 

Cost 60 m long/

17, 7 ton

Cost Variation

Due to raw 

material cost 

changes

Due to WTG

weight increase

Glass fiber ~ 2.6 ton No historical 

data available ~6.2 ton 2790 € No historical data available

Resin ~1.2 ton ~3.5 ton Unknown Composition

51
3-TOWERS

30

2500

2000

1800

1500

1300

1250

1000 1250

1000

1000

1000

1000

900

3000

3000

2000

2000

4500

5540

5000

0

200

400

600

800

1000

1200

0 20 40 60 80 100 120 140

TOWER WEIGHT (tons)

HUB HEIGHT (m)

TOWERS

WEIGHT INCREASE IN WTG TOWERS

31

STEEL TOWER

CONCRETE TOWER

220 tons

45 tons

RATED POWER (kW)

Enercon E-112 1

(1) http://www.enercon.de/en-en/633.htm for E-112 reference

(2) Lower hub heights

OFFSHORE SPECIFIC

DESIGN2

52
32

(Difficult interpretation: WTG Class I/II and WTG Class II/III mixed)

Higher class (lower wind speeds) means larger rotor diameters and higher 

towers.

CORRELATION BETWEEN DIAMETER/TOWER HEIGHT AND WTG 

IEC CLASS

0

20

40

60

80

100

120

140

0 20 40 60 80 100 120 140 160

Rotor Diameter (m)

Hub Height (m)

IEC WTG Class: Rotor Diameter and Hub Height

I

II

III a

0

20

40

60

80

100

120

140

0 20 40 60 80 100 120 140 160

 

 

Clase I

Clase II

Clase III

Class I

Class III

Class II

33

Use of raw material in towers

Period 2006 2010

WTG type •2 MW – DFIG

•Hub height: 80 m 

(steel tower)

Cost

•Estimation for a 3 

MW– DFIG or similar

•Hub Height: 125 m 

(steel or hybrid 

tower)

Cost

Variation

Due to raw 

material cost 

changes

Due to WTG

weight 

increase

TOWER

STEEL 

Steel

~ 180 ton 82.800 € ~ 425 ton ~ 263.000 €

+ 38% + 62%

HYBRID TOWER

Steel

- 162 ton (including steel 

segment and steel 

reinforcement in the 

concrete segment)

100.400 €

- 62% (compared to full steel 

structure)

Concrete - 740 ton 46 000 €

Total Hybrid Design 146.400 € - 44% (compared to full steel 

structure)

EVOLUTION OF COST OF MATERIAL FOR TOWERS

The cost increase of raw material in WTG towers from steel:

– Approximately 1/3, to the cost increment of raw material

– Approximately 2/3, to the size and weight increment of the WTG.

When using hybrid designs, the raw material cost reduction resulting from the use of 

concrete is important, however, it must also consider an increase in manpower costs (not 

counted here).

53
SUMMARY

34

35

Summary: Use of raw material in WTG

Period 2006 2010

WTG type •2 MW – DFIG

•Blade 40 m long/

7, 5 ton

•Hub height: 80 m 

(steel tower)

Cost

•Estimation for a 3 

MW– DFIG or similar

•Blade: 60 m long/ 

17, 7 ton

•Hub Height: 125 m 

(steel or hybrid 

tower) Cost

Variation

Due to raw 

material cost 

changes

Due to WTG

weight 

increase

NACELLE

Iron (Steel Casts) ~10.5 ton 3.885 € ~33 tons 16.500 € + 34% + 66%

Steel ~22 ton 10.120 € ~68 ton 42.160 € + 34% + 66%

Copper ~3.5 ton 15.750 € ~10 ton 70 000 € + 46% + 54%

Glass fiber

~0.8 ton No historical 

data available

~2.3 ton 1100 € No historical data available

Resin ~1.2 ton ~3.5 ton Unknown composition

BLADE

Glass fiber

~ 2.6 ton No historical 

data available

~6.2 ton 2790 € No historical data available

Resin ~1.2 ton ~3.5 ton Unknown composition

TOWER

STEEL 

Steel

~ 180 ton 82.800 € ~ 425 ton ~ 263.000 €

+ 38% + 62%

TOWER

HYBRID1

Steel - 162 ton 100.400 € - 62% (compared to full steel 

structure)

Concrete - 740 ton 46 000 €

Total Hybrid Design 146.400 € - 44% (compared to full steel 

structure)

(1) Data for hybrid tower extracted from tall tower for large wind turbines, report from Vindfork project V-324 Höga torn för vindkraftverk – Elforsk rapport 10:48

54
THANK YOU FOR YOUR 

ATTENTION

Alberto Ceña & Emilien Simonot

esimonot@aeeolica.org

Technical department

Spanish Wind Energy Association

Credit: AEE

55
Danish Wind Project Performance, Capital Cost, and Price Trends

Danish Wind Project Performance, 

Capital Cost, and Price Trends

IEA Wind Task 26: Cost of Wind Power

Edward James-Smith

Golden, March 29, 2011

Explanatory Notes

• Price data is from actual projects in Denmark and is published 

in the report Vindmøllers Økonomi, February 2010

• Updates for 2010 from projects and turbine manufacturer

• Data for 2004 to 2007 is from a very small pool of turbines 

due to the low deployment in Denmark during this period

• Production data is from the Danish Energy Agency wind 

turbine register

• Wind data is from the Danish Wind Turbine Owners’ 

Association

• Performance data from 2004 is high as most turbines erected 

that year were demonstration turbines with large rotors and 

high hubs.

2

56
PRICE TRENDS IN DENMARK

3

General Trends in Denmark - Price

• Capital costs have fallen sharply since 2008

• Some turbines have been available at lower

prices due to orders being cancelled or defaulted – can mask true price of turbines

• Overcapacity in manufacturing – downward

pressure

• Turbines are getting higher and rotor diameters 

larger

• Manufacturing base is moving away from 

Denmark.

4

57
Breakdown of costs

5

Turbine

80%

Foundations

6.4%

Internal grid

1.3%

Roads

2.1%

Project 

development

4%

Financing 

costs

3%

Land

3.7%

Diverse

0.4%

Other

20%

On average, wind projects spend 9% of the total budget on 

purchasing old turbines for repowering. These costs are

considered to be covered by the repowering subsidy and are

therefore not included.

Data from Vindmøllers Økonomi (2010)

Turbine Prices in Denmark

6

0

500

1000

1500

2000

2500

2008 Euro/kilowatt (kW)

Data From: Vindmøllers Økonomi (2010)

58
Learning Rates and Prices

7

0

2000

4000

6000

8000

10000

12000

14000

16000

1990 1995 2000 2005 2010 2015 2020 2025 2030

DKK/kW installed capacity

LR 6,7 % 1990 - 2009 Faktiske priser/prisprognose LR 9 % 1990 - 2001

Source: Vindmøllers Økonomi (2010)

Prices in the European Union Using European 

Wind Energy Association Targets and 

Learning Rates

8

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

2010 2015 2020 2025 2030

DKK/kW installed capacity

Høj estimat Mellem estimat Lav estimat

Source: Vindmøllers Økonomi (2010)

59
Price Prognosis: Total Investment (Danish Krone per 

Megawatt-hour [MWh]) in Year One – Equivalent of 

One Share in Danish Turbine Cooperative

9

3,228

2,990

2,808 2,711 2,688

0

500

1,000

1,500

2,000

2,500

3,000

3,500

2010 2015 2020 2025 2030

Investment per 1000 kWh production in year 1 (2008-kr.)

Source: Vindmøllers Økonomi (2010)

PERFORMANCE OF DANISH

TURBINES

10

60
General Trends in Performance

• Turbines have higher hub heights and larger

rotors

• Resulted in marked increase in capacity factor 

since 2000 – average capacity factor for 

onshore turbines has increased by ~ 50%

• The increase in capacity factor appears to 

have resulted in lower prices per kilowatthour (kWh).

11

Hub Heights

12

0

10

20

30

40

50

60

70

80

90

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Average hub height for new capacity (m)

Source: Vindmøllers Økonomi (2010)

61
Rotor Diameters

0

20

40

60

80

100

120

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Average rotor diameter for new turbines (m)

13

Source: Vindmøllers Økonomi (2010)

Annual Wind Energy in Denmark

14

Production data from January 1 to September 30, 2010 was used for all 

onshore wind turbines commissioned from 2000 to September 2010 and 

adjusted to normal wind year to determine average performance per 

commissioning year.

Source: Danish Energy Agency Wind Turbine Register

62
Performance of Danish Onshore Turbines From 

January to September 2010 

(Adjusted to Normal Wind Year)

15

0

10

20

30

40

50

60

70

80

90

100

110

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Year of commissioning

Rotor diameter

Hub height

Capacity factor

Price Equivalent of One Share in Danish Turbine 

Cooperative, Total Investment Euro/MWh in 

Year One

16

0

100

200

300

400

500

600

700

800

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Investment in EURO/MWh of first year production

63
ADDITIONAL SLIDES ON COSTS AND 

PRODUCTION

17

Change in Energy Production and Price Per Megawatt for SWT 

2.3 MW with 82, 93, and 101 Meters at Different Wind Speeds

18 Source: Vindmøllers Økonomi (2010)

64
Production in Relation to Weight

19

Source: Vindmøllers Økonomi (2010)

Cost of Steel

20 Source: Vindmøllers Økonomi (2010)

65
Labor Costs

21

Source: Vindmøllers Økonomi (2010)

Summary of Costs

22 Source: Vindmøllers Økonomi (2010)

66
Development of LCOE for Wind Turbines in Denmark

Development in LCOE for Wind 

Turbines in Denmark

Edward James-Smith

Methodology

• Input

– Power curves for typical turbines built in 2002 and 2009 and an 

example of a high-performance turbine available for delivery in 2012 

– Capacity factor calculated for turbines at average wind speeds of 7.5 

meters (m) per second (s), 7.2 m/s, 6.9 m/s, and 5.9 m/s and Weibull 

shape parameter of 2.3 

– Cost data from IEA Wind Task 26 and turbine manufacturer

– Historical generation data for 2002 and 2009 turbines and first year 

generation data for two turbines of type available for delivery in 2012

– Historical data used to validate calculated data.

• Output

– Levelized cost of energy (LCOE) determined in 2008 Euro using IEA 

Wind Task 26 LCOE calculator

– Comparison of relative price and relative value in Denmark using 

current subsidy program.

2

67
Capacity Factor for Turbines

2002 2009 2012

7.5 m/s 34% 37% 48%

7.2 m/s 31% 34% 45%

6.9 m/s 28% 32% 43%

5.9 m/s 20% 24% 33%

Actual Danish 

Average

25% 35% 41%

3

LCOE 2002

4

0

10

20

30

40

50

60

70

80

90

7.5 m/s 7.2 m/s 6.9 m/s DK avg 5.9 m/s

LCOE (2008 Euro/MWh) Capacity factor (%)

68
LCOE 2009

5

0

10

20

30

40

50

60

70

80

90

7.5 m/s DK avg 7.2 m/s 6.9 m/s 5.9 m/s

LCOE (2008 Euro/MWh) Capacity factor (%)

LCOE 2012

6

0

10

20

30

40

50

60

70

7.5 m/s 7.2 m/s 6.9 m/s DK avg 5.9 m/s

LCOE (2008 Euro/MWh) Capacity factor (%)

69
Remarks

• The average LCOE for turbines commissioned 

or ordered for commissionining fell from €65 

in 2002 to €60 in 2009 and €53 in 2012.

• A comparison of calculated LCOE for 2002, 

2009, and 2012 indicates that higher wind 

sites were required in 2009 to bear the higher 

costs of turbines despite higher performance 

than in 2002.

7

Comparison of Turbines with Danish 

Support Program Price (2008 

Euro)/

Kilowatt

[kW])

Turbine Megawatthour 

(MWh)/

Megawatt 

(MW)

EUR/MWh Relative

price

Income: 

subsidy 

(M€)

Income: 

market 

(M€)

Total

(M€)

Income/

project

price

Relative 

value

2002 1104 0.9 kW 2209 500 1 0.66 2.12 2.78 2.79 0.85

2009 1438 3 MW 3102 464 0.93 2.2 9.9 12.13 2.81 0.86

2012 1400 3 MW 3602 389 0.78 2.2 11.52 13.73 3.27 1

8

Average market price used: EUR53/MWh

Subsidy: EUR33/MWh for 22,000 full load hours

Balancing subsidy and balancing costs not included

Current subsidy regime used for 2002 turbines. Another subsidy regime was in place 

at the time.

70
Engineering Model-Based Technology Projections

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.

Engineering Model-Based Technology 

Projections

IEA Wind Task 26 Working 

Group Meeting

E. Lantz (NREL) and M. Hand 

(NREL)

March 29, 2011

Innovation for Our Energy Future

NREL PIX 16813

Innovation for Our Energy Future

Historical Costs and Future Projections Appear at Odds

2

DOE 2008

AEO 2010

Lemming et al. 2009

EREC/GPI 2008

EPRI 2009

EPA 2009

$0 

$500 

$1,000 

$1,500 

$2,000 

$2,500 

$3,000 

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Capital Cost (2009$/kW)

Wiser and 

Bolinger 2010

71
Innovation for Our Energy Future

Attempting to Reconcile Recent Trends and Cost 

Projections Generates Critical Questions

3

Working Premises

• Cost of energy (not installed cost) is the design driver

• Turbine manufacturers must balance lower installed costs 

against increased performance

Applicable Questions

• Are projections of lower installed costs and increased 

performance realistic?

• What level, and in what turbine system(s), of innovation is 

required to achieve installed cost and performance 

projections?

Innovation for Our Energy Future

Learning Curve Alternatives: Expert Elicitation

4

• Survey industry experts to 

gather a range of possible 

technology outcomes

• Develop probability 

distributions associated with 

various technical outcomes

• Example 

• U.S. Department of Energy 

(DOE) Risk Analysis 

project conducted in 

association with 

WindPACT analytic studies

Turbine Capital Cost

O&M Costs

Levelized Replacement Cost

Balance of Station Cost

Site-Specific Design/Reduced 

Design Margin TIOs

Advanced (Enlarged) Rotor TIOs

Reduced Energy Losses and 

Increased Availability TIOs

Advanced Tower TIOs

Manufacturing TIOs

New Drive Train Concept TIOs

Advanced Power Electronics TIOs

Learning Curve Effects

-30 -20 -10 +10 +20 +30 +40

% Change

-30 -20 -10 +10 +20 +30 +40

% Change

TIOs’ Potential for Improvement (% change from reference turbine)

Annual Energy Production

Note: TIO =Technology Improvement Opportunity; 

O&M = Operations and Maintenance

Source: Cohen et al., 2008

72
Innovation for Our Energy Future

Learning Curve Alternatives: Engineering Model

5

Bottom-up, component-level, system analysis 

1. Evaluates tangible technology advancements: proposed and 

anticipated technology advancements, with a focus on realizable 

opportunities

2. Measures the potential value of individual innovations: specific 

opportunities are quantified independently, and results are combined 

to arrive at a cost estimate

3. Considers installed cost and technology performance: allows 

weighting of tradeoffs between cost increases and improvements in 

energy capture

4. Offers the opportunity to substantiate a learning curve 

projection: creates a pathway to a future point

Requires simplification of complex engineering problems

Does not typically represent economy of scale or volume-based 

cost improvements explicitly

Innovation for Our Energy Future

NREL’s Wind Turbine Design and Cost Scaling Model

Flexible, modular, spreadsheet 

model:

• Perform trade-off studies of turbine 

technology options

o Determine technology changes with 

the greatest potential to reduce cost of 

energy (COE)

• Generate wind technology cost 

and performance trajectories 

o Used in generation capacity 

expansion modeling

73
Innovation for Our Energy Future

Cost Model Features

Permits scaling of components to analyze turbine 

configurations

• Costs based on DOE WindPACT analysis (and 

development subcontracts)

• Includes simple and advanced scaling curves

Illustrates some technology pathways in relation to 

industry

• Pathways based on industry trends and WindPACT 

analysis

Validated using industry data where possible

Innovation for Our Energy Future

Cost Model Functional Overview

8

Spreadsheet tool consists of two primary elements:

1. Cost and scaling calculator(s)

• Series of models for individual turbine components (e.g., blades, 

hub, low-speed shaft, pitch mechanisms, and mainframe)

• Most relationships are simple functions of rating, rotor diameter, 

and hub height

• Does not directly consider variable cost of capital or supply and 

demand market price pressures

2. Simplified energy production calculator

• Assumes equivalent blade geometry and airfoil performance

• Only utilizes the peak coefficient of power (Cp)

• Operational range divided into 1⁄4 meter (m) per second (s) bins, 

for each bin hub power, drive-train efficiency, and total energy are 

calculated, energy capture and capacity factor are computed from 

totals

74
Innovation for Our Energy Future

Wind Turbine Blade Innovation Pathway

WindPACT Baseline Design

0

5000

10000

15000

20000

25000

30000

0 10 20 30 40 50 60 70

Mass (kg)

Rotor Radius (m)

Commercial Blade Data

Source: Based on Fingersh et al., 2006

Innovation for Our Energy Future

Wind Turbine Blade Innovation Pathway

WindPACT Baseline Design

LM Glasfiber Blades

0

5000

10000

15000

20000

25000

30000

0 10 20 30 40 50 60 70

Mass (kg)

Rotor Radius (m)

LM Glasfiber Blades

Commercial Blade Data

Source: Based on Fingersh et al., 2006

75
Innovation for Our Energy Future

Wind Turbine Blade Innovation Pathway

WindPACT Baseline Design

WindPACT Final Design

LM Glasfiber Blades

0

5000

10000

15000

20000

25000

30000

0 10 20 30 40 50 60 70

Mass (kg)

Rotor Radius (m)

LM Glasfiber Blades

Commercial Blade Data

TPI Innovative Blade 

Concepts

Source: Based on Fingersh et al., 2006

Innovation for Our Energy Future

NREL Engineering Model Analysis Scope

12

Objective

• Quantify the value of potential technological advancements on larger turbines

• Evaluate the feasibility of achieving the U.S. 20% wind cost and performance 

trajectory while simultaneously moving to 5-megawatt (MW) turbines

Approach

• Develop hypothetical turbines that are in line with the 20% wind study, to 

achieve 20% wind performance trajectory, and scale gradually to 5-MW

• Apply individual technological improvements to those turbines to evaluate their 

impact on installed cost and turbine capacity factors

Caveats

• Emphasis on incremental technology and marketplace changes

• Innovations must be adopted widely

• Continued analysis of how balance-of-plant (BOP) costs are affected by larger 

turbines and consideration of emerging innovation strategies would further 

enhance our understanding

76
13 Innovation for Our Energy Future

Notes: Technology scenario labels are generally defined by WindPACT risk analysis and summarized in Cohen et al. 

2008. Individual turbine designs are optimized to reach capacity factor targets of the 20% wind study; capacity factor 

targets assume continued scaling of turbines, hence, higher capacity factor targets are expected for larger machines.

Drivetrain Design

2.5 MW 3.5 MW 5.0 MW

No Research and Development (R&D) 

Basecase (Simple Scaling) 3-stage geared

Expected Technology multi-generator single-stage geared direct drive

Best Technology 

Land-based Wind Technology Development 

Pathway to Achieve 20% Wind Targets

35%

37%

39%

41%

43%

45%

47%

49%

0

20

40

60

80

100

120

140

160

180

2.5 MW 3.5 MW 5.0 MW 2.5 MW 3.5 MW 5.0 MW

Capacity Factor

Meters

No R&D Basecase (simple scaling) Expected Technology 

Best Technology Estimated Capacity Factor (Right Axis)

HubHeight Rotor Diameter

Innovation for Our Energy Future

Technology Captured by Inputs Range

14

Turbine Cost Reduction 

Innovations Reflected in the Advanced

Technology Parameters Input Data Source

Blade Technology •Enhanced structural design, targeted reinforcement

Cost and scaling model 

advanced technology 

scaling curves

•High-tech composites possibly including carbon fiber

Tower Technology

•Tower feedback to blade pitch controls

•Flap-twist coupling in blade design

•Reduced blade chord with tip speed increase

Drivetrain Technology

•Multi-generator drivepath 

•Single-stage, medium-speed gearbox

•Direct drive 

Manufacturing Efficiency

•Increased automation 

Cohen et al. 2008 TIOs 

(WindPACT Risk 

Analysis)

•Improved resins with greater ease of use

•Reduced design margins resulting from more consistent 

fabrication

•Reduced profit margins as a result of increased volume

Power Electronics

•High-voltage circuitry

•Multi-switch capacity 

•Semi-conductor devices

Annual Energy Production

(AEP) Increase 

Reduced Losses

•Improved micro-siting to reduce array losses

•Real-time monitoring and operational modifications Industry estimates

•Low soil blades 

Power Electronics (Higher 

Efficiency) 

•High-voltage circuit topologies Cohen et al. 2008 TIOs •Multi-switch capacity 

77
Innovation for Our Energy Future

Modeling Changes to Cost and Performance

15

Note: One additional scenario considers best technology improvements combined with scaling at today’s blade 

and nacelle mass ratios (i.e., scaling with proportionally comparable masses for blades and nacelles to those 

observed in the industry today).

Innovation for Our Energy Future

Advanced Technology Permits Scaling to 3.5-MW with 

Reduced Costs and Increased Performance

16

78
Innovation for Our Energy Future

Incremental Advancements Across All Turbine 

Components/Systems Are Necessary 

17

$2,149

$1,236

$220 $57

$186 $86 $80

$171 $143 $29

$0

$250

$500

$750

$1,000

$1,250

$1,500

$1,750

$2,000

$2,250

$2,500

No R&D Base case

Drivetrain

Blades

Towers

Power Electronics

Manufacturing

Efficiency (Siting & 

Turbine)

Best Technology

Current Mass 

Ratios

20% Wind 2030 

Estimate

Turbine Cost ($/kW; 3.5 MW) 

Impact from Expected Technology Advancments 

in Specific Turbine Systems

Reduction 

from Other 

Technology 

Scenarios

Innovation for Our Energy Future

Additional Technology Advancement is Required to 

Achieve Cost and Performance Targets at 5.0-MW

18

$2,481

$1,236

$91 $59

$229 $99 $100

$261 $179

$238

$0

$500

$1,000

$1,500

$2,000

$2,500

$3,000

No R&D Base case

Drivetrain

Blades

Towers

Power Electronics

Manufacturing

Efficiency (Siting & 

Turbine)

Best Technology

Current Mass 

Ratios

20% Wind 2030 

Estimate

Turbine Cost ($/kW; 5 MW) 

Impact from Expected Technology Advancments 

in Specific Turbine Systems

Reduction 

from Other 

Technology 

Scenarios

79
Innovation for Our Energy Future

Conclusions

19

1. It is not unreasonable to expect significant turbine cost reductions 

(20%) while increasing turbine performance, when excluding shortterm market dynamics.

• Based on technological change, we can generally reconcile future cost 

reductions with recent trends.

2. Modest cost/performance projections (i.e., 20% wind) can be 

achieved, but 5.0-MW machines will require all of the advanced 

technology captured in this analysis and then some.

3. All turbine systems can contribute to future cost reductions.

• Advanced tower designs and reduced losses appear to offer the greatest 

potential to minimize costs while maintaining performance.

4. If existing prototypes and recently commercialized equipment prove 

viable, these cost reductions may be closer than we think.

20 Innovation for Our Energy Future

Eric Lantz

Research Analyst

Strategic Energy Analysis Center

National Renewable Energy Laboratory

http://www.nrel.gov/analysis/

http://www.windpoweringamerica.gov/

1617 Cole Blvd.

Golden, CO 80401-3393

P: (303) 384-7418

email: Eric.Lantz@nrel.gov

Questions

NREL PIX 05593

80
On-Shore Wind in Spain

Heikki Willstedt Mesa 

hwillstedt@aeeolica.org

Dirección Políticas Energéticas

Asociación Empresarial Eólica

ON-SHORE WIND IN SPAIN

Web2Present Webinar Series- Wind Power 2011

Credit: AEE

MAIN ISSUES

• WHY A SUCCESS STORY?

– Early mover (90s)

– Feed-in tariff (no system changes)

– Utilities among the main developers

– Own industry with research and development (R&D) and investment

• CURRENT SITUATION 2010

– Fourth country in installed power in the world (second in the European 

Union [EU]) with 20,676 megawatts (MW)

– First country in the EU with a wind power generation of 42.7 terawatt-hours 

(TWh)

– Second country in the world with a wind power penetration of 16.4% 

– Spanish companies had 9,200 MW installed outside the country 

• CHALLENGES AHEAD

– Achievement of the National Renewable Energy Action Plan (NREAP) 

objective of 35,000 MW on-shore and 3,000 MW off-shore

– Grid and storage integration (e.g., pumped storage, electrical vehicles, and 

other storage)

81
1.

Wind energy: Context and Situation

ENERGY AND ENVIRONMENTAL CONTEXT

• The external energy dependence of the European Union is over 50%

and it is forecasted to be above 70% by 2020. Energy diversification is a

priority in the European energy policy and reducing energy dependence

is a geostrategic necessity.

• In 2007, the rate of external energy dependence of Spain reached 85.1%

which is a strategic weakness and a burden on the trade balance.

The current rate is 79%.

• According to the commitments made within the Kyoto Protocol, Spain

aims to limit carbon dioxide (CO2) emissions by more than 15%

(reference 1990), but it closed 2007 with a 52% increase. Currently

(2010), the rate is at +24%–28%.

• The long construction period for conventional power stations and the

uncertainty of fossil fuel prices prices triggers the need to use wind

energy as a source of electrical generation.

82
IMPORTANT AND CONTINUOUS GROWTH OF

WIND POWER IN SPAIN

5

• Renewable Energies Plan (2005–2010): 20,155 MW

• Official network planning for 2016: 29,000 MW

• Further increase expected for compliance with the 

goal of 20% of energy demand with renewable 

technologies ≅ 40% -> 38,000 MW.

723 1.408 2.358

3.522

5.033

6.185

8.462

10.013

11.595

15.115

16.699

19.160

20.676

0

5.000

10.000

15.000

20.000

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

MW

AÑOS

ANUAL ACUMULADA

 

Fuente: AEE

6

WIND POWER PRODUCTION AND CAPACITY FACTOR

• Wind production increases due to the 

growth of installed power, but individual 

wind farms have reduced their capacity 

factor up until 2008. In 2009 and 2010, 

the capacity factor has increased.

• Wind production remains constant for 

long periods of time.

• The maximum coverage was 53% of the 

total demand.

2004 2005 2006 2007 2008 2009 2010

Generación eólica 15.744 20.520 22.684 27.169 31.136 36.188 42.702

Tasa de variación eólica (%) 34,3% 30,3% 10,5% 19,8% 14,6% 16,2% 18,0%

Cobertura de la demanda (%) 6,7% 8,3% 8,9% 10,3% 11,8% 14,4% 16,4%

0%

5%

10%

15%

20%

25%

30%

35%

40%

0

5.000

10.000

15.000

20.000

25.000

30.000

35.000

40.000

45.000

GWh

Fuente: REE

0,00%

5,00%

10,00%

15,00%

20,00%

25,00%

30,00%

35,00%

40,00%

Factor de Capacidad

Factor de capacidad mensual promedio, mínimo y máximo 

1998-2010

MÁXIMO

PROMEDIO

MÍNIMO

2009

2010

Fuente: Elaboración AEE

83
7

DEMAND COVERAGE HISTORIC HIGH 9/11/2010

Source: REE

2.

Main Technical Challenge: Integration of Wind 

Energy into the Grid

Spain is an “Electrical Island”

84
THE INTEGRATION OF WIND ENERGY RAISES SOME 

CHALLENGES

• Technical challenges:

– Reaction of wind farms to voltage drops: contribution to grid 

stability

– Voltage control

– Grid security and safety.

• System operation challenges:

– Wind production program: affects the deviations and the use of 

ancillary services

– Coordinated operation of generation units.

9

10

Turbine 

capabilities: 

Manufacturers

Cost: Promoters

Power 

system 

needs: TSO

Universities and 

technological 

centres

TECHNICAL

REQUIREMENTS

Laboratories, 

testing and 

certification 

entities

THE ANSWER TO THOSE CHALLENGES REQUIRES THE 

DEFINITION OF THE TECHNICAL REQUIREMENTS

85
SINCE 2004, THE WIND SECTOR AND THE TSO HAVE CLOSELY 

COLLABORATED TO DEFINE THOSE REQUIREMENTS IN A REALISTIC AND 

PRACTICAL WAY

11

COORDINATION AND FOLLOW 

UP

WORKING GROUP FOR THE 

COORDINATION BETWEEN 

THE WIND SECTOR (6 

REPRESENTATIVES) AND REE

18 MEETINGS

UP TO MARCH 2010

COORDINATED SYSTEM 

OPERATION: 

TSO: CECRE

CONTROL CENTRES (18)

WIND FARMS (AROUND 700)

LVRT REQUIREMENTS

DEFINITION OF THE VERIFICATION 

AND CERTIFICATION PROCEDURE 

(PVVC)

FOLLOW UP AND UPDATE OF THE 

PVVC

STUDY OF WIND ENERGY 

INTEGRATION

95% OF POWER ADAPTED TO 

LVRT

CREATION OF THE 

TECHNICAL VERIFICATION

COMMITTE (CTV):

•PROMOTERS

•MANUFACTURERS

•Wind turbine 

generator (WTG)

•FACTS

•LABORATORIES

•AEE

•REE

STUDY OF WIND ENERGY INTEGRATION (WITH THE PORTUGUESE TSO 2004 INVOLVEMENT)

12

0

2000

4000

6000

8000

10000

12000

14000

16000

0

10

20

30

40

50

60

70

80

90

100

2005 2006 2007 2008 2009

Number of Power Losses

Number of Wind Power Losses Bigger than 100 MW Wind Power NO Fulfilling PO 12.3

WIND POWER CUTS > 100 MW

86
1

CECRE

TSO: COORDINATION CENTRE OF 

RENEWABLE ENERGIES

• CECRE: REE Control Centre 

of Special Regime

• CC: Control Centre 

...

Parques

R.E. 1

Parques

R.E. 2

Parques

R.E. n

CC1

Enlace 

CC2

Enlace 

CCn

Enlace 

Solución Provisional

A COORDINATED OPERATION IS IMPORTANT TO GUARANTEE 

WIND ENERGY INTEGRATION 

CECRE: OBJECTIVES AND FUNCTIONS

14

• Main function: To achieve a greater level 

of integration for renewable energy 

sources without compromising the 

system s security.

– Improves security and effectiveness in 

the system s operation

– Allows substitution of permanent 

preventive criteria for real-time 

production control.

• Real-time risk assessment due to 

voltage dip wind generation losses

• Calculation of wind production 

limitations

• Filter limits for stable solutions in 

accordance with legislation.

GEMAS

87
15

AN UNDESIRED SITUATION: WIND POWER REDUCTION 

AFTER CECRE ORDER (24/2/2010)

3.

The Spanish Market

88
MAIN PLAYERS IN THE SUPPLY CHAIN OF 

WIND ENERGY IN SPAIN

Main players in major activity areas include:

Raw materials 

Energy 

Shipping 

Etc.

Manufacture of 

key components

Manufacture of 

wind turbines

Project 

development, 

construction, 

financing, and 

so on 

Service 

operation, 

maintenance, 

and sale of 

electricity

HANSEN

WINERGY

MOVENTAS

ECHESA

SKF

FAG / INA

TIMKEN

LM GLASSFIBER

FIBERBLADE

...

VESTAS

GE Wind

GAMESA 

ENERCON

SIEMENS

NORDEX

ALSTOM

ACCIONA 

M TORRES

REPOWER

SUZLON

...

IBERDROLA

ACCIONA

NEO ENERGÍA (HC)

ECYR (ENDESA)

EUFER (UN. FENOSA)

GAS NATURAL

ENERFÍN

EYRA (ACS)

EON

EOLIA

MOLINOS DEL EBRO

.....

GES

IMFUTURE

GUASCOR SERVICIOS

INGETEAM

REETEC

EFACEC

SBS

BREAKDOWN BY MANUFACTURERS

18

BREAKDOWN BY MANUFACTURERS 

OF INSTALLED WIND POWER IN 2010

BREAKDOWN BY MANUFACTURERS 

OF ACCUMULATED WIND POWER AT 

THE END OF 2010

More than 700 companies—

from manufacturers to 

financial services—have 

some kind of involvement in 

the wind energy sector.

GAMESA

53,72%

VESTAS

17,07%

ALSTOM-WIND

7,54%

ACCIONA 

WIND POWER

7,04%

GE

5,82%

SIEMENS

3,52%

ENERCON

2,34%

SUZLON

1,05%

NORDEX

0,65%

DESA

0,49% LAGERWEY

0,18%

M-TORRES

0,18%

KENETECH

0,18%

REPOWER

0,12%

FUHRLANDER

0,06%

OTROS

0,03%

Fabricantes. Potencia Acumulada 

Fuente: AEE

GAMESA

50,18%

VESTAS

33,01%

ALSTOM-WIND

9,35%

GE

6,23%

Fuhrlander

0,79%

M-TORRES

0,44%

Fabricantes. Potencia 2010

Fuente: AEE

89
A SOLID INDUSTRIAL NETWORK TO SUPPORT THE NATIONAL 

AND EXTERNAL MARKETS

Spanish wind sector industrial centres

PROMOTERS: ACCUMULATED INSTALLED POWER IN 2010

20

IBERDROLA RENOVABLES

25,00%

ACCIONA ENERGÍA

19,52%

EDPR

9,01%

EUFER

4,42%

ENEL GREEN POWER 

ESPAÑA

3,91%

EyRA

3,74%

OLIVENTO, S.L.

2,04%

ENERFÍN

1,96%

AGRUPACIÓ DE ENERGÍAS 

RENOVABLES, S.A. (AERSA)

1,89%

GAS NATURAL

1,84%

EOLIA 

RENOVABLES

1,78%

E. ON Renovables

1,55%

GAMESA 

ENERGÍA

1,36%

MEDWIND

1,19%

MOLINOS DEL 

EBRO

1,14%

GOVADE

1,12%

GECAL, 

S.A.

1,12%

IBEREÓLICA

0,77%

EÓLICA DE 

NAVARRA

0,72%

FERSA

0,69%

ELECDEY

0,68%

RENOVALIA

0,51%

OTROS

14,06%

Promotores. Potencia Acumulada

Fuente: AEE

90
4.

An Adequate Legal Framework Until 2009

22

A STABLE LEGAL FRAMEWORK IS ESSENTIAL FOR THE 

DEVELOPMENT OF THE WIND SECTOR

• The 2007 Royal Decree, in force from 06/01/07, allows two 

remuneration options:

– Regulated tariff

– Market price and premium: with a cap and floor for the 

premium

• Wind farm installations governed by the previous regulation 

(RD 436/2004) had until 01/01/2009 to decide whether they will 

continue with it or choose the new RD

• Remuneration updated using the RPI, less an adjustment factor 

(0,25% until 2012, 0,5% from then on).

91
WIND POWER HELPS KEEP POWER MARKET PRICES LOWER 

THAN DURING THE PREVIOUS OIL SHOCK IN 2008

23

0.00

5.00

10.00

15.00

20.00

25.00

30.00

Jan-05

Mar-05

May-05

Jul-05

Sep-05

Nov-05

Jan-06

Mar-06

May-06

Jul-06

Sep-06

Nov-06

Jan-07

Mar-07

May-07

Jul-07

Sep-07

Nov-07

Jan-08

Mar-08

May-08

Jul-08

Sep-08

Nov-08

Jan-09

Mar-09

May-09

Jul-09

Sep-09

Nov-09

Jan-10

Mar-10

May-10

Jul-10

Sep-10

Nov-10

Jan-11

% of demand covered by wind versus market and gas 

prices in the Spanish power sector

% of demand covered by wind Pool price (c€/KWh) Natural gas price (€/MWh)

Sources: CNE, OMEL, and REE

In 2008, the price of the market was so 

high that during 3% of the hours, wind 

power did not receive premiums.

WIND FARMS REMUNERATION

24

102,723

74,073 72,420

85,941

77,048 76,896 75,681

78,183 77,471

0

20

40

60

80

100

120

2008 2009 2010

Pool + prima DT 1a RD 661/2007

Pool + prima RD 661/2007

Tarifa regulada RD 661/2007

€/MWh

Fuente: AEE

92
“For each Euro spent in wind power the Spanish Economy 

has gained three in return”

Datos 2007-09

THE SPANISH FEED-IN TARIFF HAS MADE POSSIBLE THE ACHIEVEMENT OF THE 2010 

RES ELECTRICAL OBJECTIVE, AND AS A BONUS HAS ENABLED THE SPANISH WIND 

INDUSTRY TO BECOME A WORLD PLAYER

3.705 M€

Premiums

(Tot. 2007-2009)

=

Monthly 

contribution to 

wind power by 

each Spanish 

household

Other social benefits

Jobs 37.900

Average (07-09)

R&D&I

556 M €

PIB Indirect 

4.087 M € Fiscal balance

648 M€

CO2 Emissions

+ 1.035 M €

Fuel 

Imports

+4.463 M€

PIB Direct

+ 6.192 M €

Exports +7.554 M €

Power

14,4 % of national 

demand (2009)

2

5

5.

Challenges Ahead

93
0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

MWh

Wind and CCGT generation (Jan-Feb/2011)

Wind CCGT Demand

WIND POWER AND OTHER TECHNOLOGIES: INTEGRATION

27

Source: REE

WIND POWER AND STORAGE: ARE THERE INCENTIVES TO 

MAKE IT WORK TO ITS FULL POTENTIAL?

28

-100,000

-50,000

0

50,000

100,000

150,000

200,000

250,000

300,000

Wind power generation and pumped storage (Jan-Feb/2011)

Wind Pumped storage

Source: REE The electric car: 

a reality after 2015?

94
RD 1565/2010

RD 1624/2010

RDL 14/2010 RD 661/2007

Tarifa regulada 

prima variable

RD-L 7/2006

THE SPANISH WIND POWER MARKET IS SLOWING DOWN ITS GROWTH DUE TO 

EXCESSIVE REGULATION AND LACK OF LONG-TERM STABILITY 

29

287

692

950

1.165

1.511

1.152

2.276

1.551 1.582

3.520

1.584

2.461

1.516

65,8%

94,6%

67,4%

49,4%

42,9%

22,9%

36,8%

18,3%

15,8%

30,4%

10,5% 14,7%

7,9%

0,0%

10,0%

20,0%

30,0%

40,0%

50,0%

60,0%

70,0%

80,0%

90,0%

100,0%

0

500

1.000

1.500

2.000

2.500

3.000

3.500

4.000

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

MW

Incremento anual (MW)

TASA DE VARIACIÓN %

Fuente: AEE

Ley 54/1997

RD 2818/1998

RD-L 6/2000

RD 436/2004

Tarifa regulada 

Prima fija

Ley 17/2007

RD-L 6/2009

RES SEE 19/11

THE OBJECTIVE FOR WIND POWER OF 38,000 MW IN 2020 WILL BE 

DIFFICULT TO ACHIEVE WITHOUT THE RIGHT POLICIES

0

5000

10000

15000

20000

25000

30000

35000

40000

19981999200020012002200320042005200620072008200920102011201220132014201520162017201820192020

Potencia instalada 2011-2020 (senda paralización sector 2010-15)

! 

Frontrunner

Noncompliance 

with NREAP 

obj.= Gap 

5.000‒9.000 

MW

?

Uncertain 

future

95
31

• The on-shore wind energy sector has become a world player due to the

extraordinary development of the Spanish market, spurred by ambitious

objectives, an efficient feed-in tariff system, and the involvement of the

main utilities.

• The collaboration between the Spanish wind sector and the TSO has been

very fruitful and essential to guarantee the integration of wind energy in

the electrical system.

• The development of wind energy in Spain has also been backed by an

important industrial capacity and now the main challenge is to maintain a

competitive position in the global market.

• To achieve the 2020 NREAP objectives for wind power, a long-term policy

framework is necessary to attract investments in an increasingly

competitive European wind market.

CONCLUSIONS

Heikki Willstedt Mesa 

hwillstedt@aeeolica.org

Dirección Políticas Energéticas

Asociación Empresarial Eólica

THANK YOU FOR YOUR 

ATTENTION!

Credit: AEE

Recent Developments in the Levelized Cost of Energy from U.S. Wind Power 

Projects

96
1

Recent Developments in the 

Levelized Cost of Energy from 

U.S. Wind Power Projects

Ryan Wiser, Lawrence Berkeley National Laboratory

Eric Lantz, National Renewable Energy Laboratory

Mark Bolinger, Lawrence Berkeley National Laboratory

Maureen Hand, National Renewable Energy Laboratory

February 2012

This analysis was funded by the Wind & Water Power Program, Office of 

Energy Efficiency and Renewable Energy of the U.S. Department of 

Energy under Contract No. DE-AC02-05CH11231

2

Background and Motivation

• Some recent focus has been placed on the increasing capital cost 

of wind projects observed from the early 2000s to 2010, and the 

apparent flattening of fleet-wide capacity factors in recent years

• These trends are important, but ignore other developments:

– Continued improvements in capacity factors within individual wind resource 

classes (if not fleet-wide) due to hub height and rotor diameter scaling

– Significant recent improvements in low-wind-speed technology, resulting in 

increased capacity factors and increased land area open to development 

– Steep reductions in turbine prices negotiated in last 2 years, and some 

evidence of reductions in balance of plant costs  expected (with some lag) to 

lead to sizable near-term reductions in project-level capital costs

– Possible longer-term (i.e., since early 2000s) reductions in the cost of operating 

and maintaining as well as financing wind projects, as well as potentially 

improved turbine/project availability

– Incentive choice with respect to 30% ITC/1603 Treasury grant in lieu of PTC

97
3

Background and Motivation

• Exclusive focus on historical capital cost and fleet-wide capacity 

factor trends fails to convey recent improvements in the levelized

cost of energy (LCOE) from wind projects, the opening of new 

lower-wind-speed areas for development, and the fundamental 

interdependency of capital costs and capacity factors

• Wind turbine manufacturers, wind project developers, and wind 

power purchasers are not focused solely on optimizing capital costs 

and capacity factors, individually, but are more interested in:

– Levelized cost of wind energy across all wind resource classes 

• Lower LCOE  lower power sales prices  greater demand for wind energy

– Amount of land area that might be reasonably developed

• Transmission/siting/policy influences can constrain development in high-windresource regimes  opening new lower-wind-resource regimes for development 

can significantly increase potential development opportunities 

4

Objectives of Present Work

(1) Develop consistent levelized cost of energy estimates for wind in 

the U.S. in various wind resource regimes for:

– Projects installed in 2002-2003: The low-point of wind capital costs 

– Projects installed in 2009-2010: The likely peak of wind capital costs

– Projects to be installed in 2012-2013: When current turbine pricing is likely to 

more-fully make its way into observed capital costs

Focus on direct costs, accounting primarily for capital cost trends and 

trends in estimated capacity factors; conduct analysis with/without 

PTC/MACRS; emphasize, only as an example, GE turbines

(2) Estimate the amount of available land area that would exceed 

certain capacity factor and LCOE thresholds using the same 

assumed technology, assumptions, and time periods as above 

(3) Conduct two side-case analyses: (1) impact of incentive choice 

between PTC and ITC/Section 1603; and (2) impact of possible 

O&M, financing, and availability trends

98
5

Caveats

• This is a preliminary assessment of the impact of various trends on 

LCOE; the analysis does not consider all factors, and the results have 

not undergone rigorous peer-review or been published

• The analysis uses GE turbine technology only as an example to 

facilitate assessment

• This work only seeks to understand and estimate recent and near-term 

developments

• LCOE estimates for 2012-2013 are based on current turbine pricing, 

but are nonetheless speculative

• This work has not attempted to track developments over a longer 

historical record or to forecast longer-term future trends

• The present analysis is focused on the U.S., though the basic findings 

should hold for other regions of the world as well

\ 

6

Presentation Outline

Recent 

Trends

Analysis 

Assumptions

Analytical 

Results 

99
7

Datasets Used To Explore Recent Capital 

Cost and Performance Trends

• 488 projects built from 1983-2011, 34.6 GW 

• 81 turbine transactions from 1997-2011, 23.9 GW

Project- and 

TurbineLevel Capital 

Costs

• 338 projects built from 1983-2009, 32.0 GW

ProjectLevel 

Performance 

/ Capacity 

Factors

Data shown here are primarily from the U.S. DOE’s 2010 Wind 

Technologies Market Report (“U.S. DOE 2011”)

8

Installed Wind Project Capital Costs 

Increased from Early 2000s through 2010

Project costs bottomed out in 2001-2004; rose by $850/kW 

on average through 2009; held steady in 2010 ($2,155/kW); 

based on limited available data, may have dropped in 2011

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

198219831984198519861987198819891990199119921993199419951996199719981999200020012002200320042005200620072008200920102011

Installed Project Cost (2010 $/kW)

 Individual Project Cost (488 projects totaling 34,616 MW)

 Capacity-Weighted Average Project Cost

 Polynomial Trend Line

Source: U.S. DOE 2011

100
9

Fleet-Wide Capacity Factors (CF) Have 

(Generally) Increased Over Time

BUT:

Some 

leveling off in 

fleet-wide

capacity 

factors in 

recent years 

is also 

apparent

0%

5%

10%

15%

20%

25%

30%

35%

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

6 12 41 85 98 118 144 169 212 256 358 338

549 1,005 1,545 3,285 3,826 5,182 5,894 8,726 10,712 15,686 24,403 31,986

Capacity Factor

Year:

Projects:

MW:

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Pre-1998 1998-99 2000-01 2002-03 2004-05 2006 2007 2008 2009

9 projects 22 projects 25 projects 35 projects 31 projects 19 projects 34 projects 76 projects 87 projects

647 MW 780 MW 1,553 MW 1,906 MW 3,414 MW 1,591 MW 4,718 MW 8,409 MW 8,967 MW

 Capacity-Weighted Average 2010 Capacity Factor (by project vintage)

 Individual Project 2010 Capacity Factor (by project vintage)

2010 Capacity Factor (by project vintage)

Sample includes 338 

projects totaling 32 GW

Source: U.S. DOE 2011

10

Historical Trends Important, But Ignore 

Other Notable Developments

• Sizable historical/continued increases in hub heights 

and rotor swept area (in proportion to nameplate 

capacity), leading to improvements in capacity factors 

within individual wind resource classes, especially in 

lower-wind-speed sites

Project 

Performance 

• Steep reductions in wind turbine prices negotiated over 

the last 2 years, with some evidence of a simultaneous 

reduction in balance of plant costs, which are expected 

to lead to sizable near-term capital cost reductions

Installed 

Capital Costs

• Operation and maintenance: Potential reduction in the 

cost of O&M for newer wind power projects

• Financing: Notwithstanding the impact of the financial 

crisis, generally improved financing terms over the 

longer-term as the sector and technology have matured

• Availability: Potential reduction in losses due to 

improved turbine/project availability 

Other Possible 

Advancements 

(since early 2000s)

See following slides for more details...

101
11

Recent Moderation of CF Increase Driven In Part 

By Move Towards Lower Wind Speed Sites...

Projects increasingly sited in poorer wind regimes at 50m:

• 1998-2001: Class 4-5 common

• 2006-2009: Class 3-4 common

Trend likely driven by transmission/siting limitations and policy 

influences, as well as improvement in low wind speed technology

0

100

200

300

400

500

600

700

800

900

1998-99 

48 projects 

988 MW 

(97%)

2000-01 

43 projects 

1725 MW 

(98%)

2002-03 

50 projects 

2067 MW 

(97%)

2004-05 

54 projects 

2611 MW 

(93%)

2006 

38 projects 

2455 MW 

(100%)

2007 

53 projects 

5250 MW 

(100%)

2008 

108 projects 

8169 MW 

(98%)

2009 

88 projects 

9479 MW 

(95%)

Wind Power Density 

(Average, W/m2 @ 50 m)

Wind Power Density at Project Level

Project Level Capacity Weighted Mean

Project Level Median Class 

1

Class 

2

Class 

3

Class 

4

Class 

5

Class 

6

Class 

7

Note:

Box plots identify 10th/25th/75th/90th 

percentile values

Percentages in labels represent % of 

total in LBNL database Class 

1

Class 

2

Class 

3

Class 

4

Class 

5

Class 

6

Class 

7

Source: Lawrence Berkeley National Laboratory

12

...and Growing Amounts of Wind Energy

Curtailment in Some Regions

Wind Curtailment, GWh 

(percent potential generation) 2007 2008 2009 2010

Electricity Reliability Council of Texas 109

(1.2%)

1,417

(8.4%)

3,872

(17.1%)

2,067

(7.7%)

Southwestern Public Service Co. N/A 0

(0.0%)

0

(0.0%)

0.9

(0.0%)

Public Service Company of Colorado N/A 2.5

(0.1%)

19.0

(0.6%)

81.5

(2.2%)

Northern States Power Co. N/A 25.4

(0.8%)

42.4

(1.2%)

42.6

(1.2%)

Midwest ISO, less NSP N/A N/A 250

(2.2%)

781

(4.4%)

Bonneville Power Administration N/A N/A N/A 4.6

(0.1%)

Total Across These 6 Areas: 109

(1.2%)

1,445

(6.4%)

4,183

(10.4%)

2,978

(5.1%)

U.S. fleet-wide capacity factors from 2008-2010 would have 

been 1-2 percentage points higher absent curtailment 

(curtailment largely caused by inadequate transmission and minimum load)

Source: U.S. DOE 2011

102
13

Move to Lower Wind Speed Sites and Increased 

Curtailment Hide the Very Real Increases in CFs 

Witnessed in Individual Wind Resource Classes 

0

10

20

30

40

50

60

70

80

90

1998-99 2000-01 2002-03 2004-05 2006 2007 2008 2009 2010

1,418 1,982 1,686 1,942 1,515 3,190 5,004 5,733 2,855 

1,014 MW 1,756 MW 2,080 MW 2,779 MW 2,440 MW 5,249 MW 8,348 MW 9,993 MW 5,113 MW

Rotor Diameter

Hub Height

Average Rotor Diameter and 

Hub Height (m) COD:

Turbines:

Capacity:

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

1 2 3 4 5 6 7 8

Capacity Factor (Weighted Average)

Wind Resource Class @ 50m

2005-2007 Projects

2002-2004 Projects

1998-2001 Projects

Historical increases in avg. 

hub height and rotor diameter 

have been substantial

Leading to sizable increases 

in avg. capacity factors within 

specific resource classes 

Source: U.S. DOE 2011

Source: Lawrence Berkeley National Laboratory

14

Wind Turbine Prices Have Softened 

Since Their Highs in 2008

Turbine price quotes in 2011 for “standard” technology are reportedly as low 

as $900/kW (Tier 1: ~$1,100-1,250/kW, with average at ~$1,100/kW); higher 

costs typical for smaller orders, larger rotors/towers, etc. 

(also more-favorable terms for buyers and improved technology; balance-ofplant costs also reportedly lower than in recent past)

Lag between 

turbine prices 

and project costs 

should lead to 

substantial 

project-level 

installed capital 

cost reductions 

Source: Bolinger and Wiser 2011 by 2012-2013

103
15

Some Evidence of Lower O&M Costs, 

Higher Availability, Improved Financing

O&M costs from 

limited sample of US 

wind power projects 

(U.S. DOE 2011)

GE’s reported median 

availability by model year 

(Mesh et al. 2011, “GE 

Wind Turbine Availability) 

Strength of evidence 

supporting specific 

improvements in 

O&M, availability, and 

financing somewhat 

lower than trends in 

capacity factor and 

capital cost

16

Recent “Chatter” Suggests that, As a Result of 

These Trends, Delivered Wind Energy Costs 

Have Declined Substantially

BNEF 2011: "The cost of wind generation has been driven to record lows by declines in turbine 

prices and the cash grant, which eliminates the cost of securing tax equity financing.”

“Austin Energy officials say those wind contracts are among the cheapest deals available, when 

the cost of building power plants is taken into account, and comparable to what the historically 

volatile natural gas market has been offering recently.” (Statesman.com article)

“Our contract with NextEra Energy Resources is one of the lowest we’ve ever seen and results 

in a savings of nearly 40 percent for our customers,” said David Eves, president and CEO of 

Public Service Company of Colorado. “The addition of this 200-megawatt wind farm 

demonstrates that renewable energy can compete on an economic basis with more traditional 

forms of generation fuel, like natural gas, and allows us to meet the state’s Renewable Energy 

Standard at a very reasonable cost to our customers.” (Reuters article)

Consumers Energy, Michigan: "Lower wind power costs mean $54m saving for Consumers 

Energy.“ (newspaper article)

Westar, Kansas: Signed more wind contract than needed "...because pricing is so attractive now 

and to minimize tax risk to our investors" (Westar Q4 earnings call)

104
18

Analysis Objective

Estimate Capacity Factors, LCOE, and Developable 

Land Area By Selecting Representative Assumptions 

for Turbines Used in U.S. Wind Projects

• The low-point of wind project capital costs Installed in 

2002-2003 

• The likely peak of wind project capital costs Installed in 

2009-2010 

• When current turbine pricing is likely to morefully make its way into observed capital costs

(To be) 

Installed in 

~2012-13 

19

Basic Approach

In each period, account for common actual/expected trends in: 

(1) installed capital costs (based on actual/estimated cost)

(2) capacity factors in different wind resource classes (estimated based on 

available power curves, assuming sea level air density of 1.225 kg/m3)

For simplicity, in most cases hold constant: O&M costs; financing 

structure and costs; project/turbine availability and other losses; 

project life, income taxes, decommissioning, etc.

Estimate LCOE: (1) without PTC or 5-yr MACRS (depreciation 

assumed to be 12-yr straight line); and (2) with PTC and 5-yr MACRS

Conduct two side analyses: (1) impact of incentive choice between 

PTC and ITC/Treasury Grant; and (2) impact of possible O&M, 

financing, and availability trends

Use IEA Task 26 Work-package 1 LCOE model

105
20

For Analytical Simplicity, Focus on 

GE Turbine Evolution

GE has been the dominant supplier of turbines to the U.S. market over this 

timeframe, ensuring that a focus on GE as an example of the evolution of 

cost, performance, and LCOE trends is appropriate

Typical GE Turbine Offerings Over Time 

(does not include 2.5-MW turbine platform)

Credit: Al Hicks, NREL

21

Summary of Core Input Assumptions

Characteristics 2002-2003 2009-2010 Current Turbine Pricing: ~2012-2013

Technology type Standard Standard Standard* Low Wind* Low Wind*

Nameplate capacity 1.5 MW 1.5 MW 1.62 MW 1.62 MW 1.62 MW

Hub height (HH) 65 m 80 m 80 m 80 m 100 m

Rotor diameter (RD) 70.5 m 77 m 82.5 m 100 m 100 m

Installed capital cost $1,300/kW $2,150/kW $1,600/kW $1,850/kW $2,025/kW

Operating costs $60/kW-yr $60/kW-yr $60/kW-yr $60/kW-yr $60/kW-yr

Losses (availability, 

array, other) 15% 15% 15% 15% 15%

Financing (nominal) 9% 9% 9% 9% 9%

All assumptions intended to reflect 

representative actual/common 

conditions; installed costs, operating 

costs, losses and other assumptions 

can vary considerably from one 

project to the next

• Dollar values are all real 2010$

• Financing cost / discount rate reported in nominal terms

• Air density = 1.225 kg/m3 (sea level wind speed)

• Weibul K Factor = 2 in all scenarios

• 1/7th power law scaling to estimate hub height wind speed

• 20-year assumed project/economic life in all scenarios

• Aggregate income taxes assumed to equal 38.9%

*These turbines are assumed viable in sites up to the respective IEC Class II and Class III reference average annual 

wind speed. Depending on site specific gust, turbulence, and air densities these turbines in actuality may be 

reasonably placed in sites with higher average annual wind speeds than applied in this analysis.

106
22

Basis For Core Assumptions

Turbine Characteristics (capacity, hub height, rotor diameter)

– Common GE designs for relevant installation years

Installed Capital Cost

– 2002-2003 and 2009-2010: based on actual average costs for installed projects

– 2012-2013: assumes $550/kW drop in turbine/BOP costs since high-point for 80 m HH / 

82.5 m RD turbine based on earlier data on turbine cost trends, BNEF (2011), and 

discussions with wind developers/manufacturers; $250/kW assumed increase for 100 m RD 

upgrade and additional $175/kW increase for 100 m HH upgrade based on discussions with 

wind developers/manufacturers; result is an estimate of the average installed cost of 

projects based on current turbine orders; actual project costs will have a large spread 

around the average, with both lower- and higher-cost projects anticipated

Other Costs and Characteristics

– Operating costs: Assumed to be exclusively denominated in $/kW-yr, though, in practice, 

some costs may be more-appropriately denominated in $/MWh; all-in operating costs 

assumed to equal $60/kW-yr in core analysis based on review of available data/literature

– Losses: 15% losses assumed in core analysis based on review of available data/literature, 

as well as matching actual and estimated project-level capacity factors

– Financing: 9% assumed in core analysis based on review of available data/literature

23

Summary of Side-Case Input 

Assumptions

• Assumes that project owners choose between the PTC 

and ITC/Treasury Grant based only on the face-value 

of those incentives (i.e., the incentive that minimizes 

the LCOE with no additional changes and ignoring any 

ancillary benefits of the ITC/Treasury Grant)

Incentive 

Choice

• O&M: Cost reduction from $64/kW-yr in 2002-2003 

$57/kW-yr in 2012-2013 based on review of available 

data/literature, including 2010 Wind Technology 

Market Report, BNEF (2011), etc. 

• Financing: All-in cost of finance decreases as 

technology matures from 10.5% in 2002-2003  9% 

in 2012-2013 based on review of available 

data/literature and discussions with wind developers

• Availability: Reduction in average project-level 

losses from 16.5% in 2002-2003  14% in 2012-

2013 based only on assumed improvement in 

availability, itself based on review of limited available 

data/literature and discussions with wind consultants 

and developers

O&M, 

Financing, 

Availability

107
24

Presentation Outline

Recent 

Trends

Analysis 

Assumptions

Analytical 

Results 

25

Outline of Key Results

Estimated Capacity Factors in Varying Resource Classes

Levelized Cost of Energy in Varying Resource Classes

Side Analysis: Impact of Improvements in O&M, Financing, and Availability 

Designing Turbines for Low Wind-Speed Sites: Narrowing the Gap in LCOE

Side Analysis: Impact of ITC/Treasury Grant Option In Lieu of PTC 

Increased Land Area Exceeding Capacity Factor Thresholds

Increased Land Area Exceeding LCOE Thresholds

108
26

Turbine Design Advancement Leads To 

Enormous Increase in Capacity Factors

Estimated capacity factor improvement driven by larger rotor swept area in 

proportion to nameplate capacity, as well as higher hub heights; increase is 

especially apparent with newest batch of low-wind-speed turbines

2002-03 

Standard Technology

2009-10 

Standard Technology

Current: 2012-13 

Standard Technology

15%

20%

25%

30%

35%

40%

45%

50%

5.5 6.0 6.5 7.0 7.5 8.0 8.5

Capacity Factor

50m Wind-speed (m/s) air density = 1.225 kg/m3

Current: 2012-13 

Low Wind-speed (100m Tower) Current: 2012-13 

Low Wind-speed (80m Tower)

Class 2 Class 3 Class 4 Class 5 Class 6

Note: Low wind-speed turbine performance is modeled up 

to a 7.5 m/s hub height wind speed, per IEC standards; 

site specific conditions may allow these machines to be 

installed in wind regimes that exceed the IEC average 

annual wind speed, leading to higher capacity factors (at 

higher average 50m wind speeds) than shown here.

27

Levelized Cost of Wind Energy Is Estimated 

To Be at An All-Time Low for 2012-2013

Accounting only for assumed capacity factor and capital cost trends, LCOE increased 

substantially from 2002-2003 to 2009-2010 because capital cost increases were not 

fully compensated by CF improvements; estimated LCOE has since dropped below its 

earlier lows within individual wind resource classes because capital cost increases from 

2002-2003 to the present have been more-than offset by CF improvements, yielding a 

lower LCOE than at any time in the past within individual resource classes

Without Federal Incentives With Federal PTC/MACRS

Mid-Class 3 

Mid-Class 5

$0

$20

$40

$60

$80

$100

$120

2002-03: 

Standard Tech.

2009-10:

Standard Tech.

Current, 2012-13

Tech. Choice

Levelized Cost of Energy ($/MWh)

No Incentives

Mid-Class 3 

Mid-Class 5

$0

$20

$40

$60

$80

$100

$120

2002-03: 

Standard Tech.

2009-10:

Standard Tech.

Current, 2012-13

Tech. Choice

Levelized Cost of Energy ($/MWh)

Includes Federal PTC & MACRS

Note: “Tech. Choice” assumes that IEC Class III machines are only available for sites up to 7.5 m/s average 

wind speed at hub height (sea level air density)

109
28

Levelized Cost of Energy in Varying 

Resource Classes (without PTC/MACRS)

Based on current pricing and assumptions: 100m rotor diameter is found to be 

economically attractive in comparison to 2012-2013 ‘Standard Technology’ where it 

can be deployed; a wind sheer higher than 1/7th is found to be needed for the 

100m tower to be least cost in comparison to the 80m option (with the 100m rotor)

2002-03: Standard 

Technology

2009-10: Standard 

Technology

Current, 2012-13: 

Standard 

Technology

Current, 2012-13: 

Low Wind-speed 

(80m Tower)

$0

$20

$40

$60

$80

$100

$120

$140

$160

5.5 6.0 6.5 7.0 7.5 8.0 8.5

Levelized Cost of Energy ($/MWh) 

No Incentives

50m Wind-speed (m/s) (air density = 1.225 kg/m3)

Class 2 Class 3 Class 4 Class 5 Class 6

Current, 2012-13: 

Low Wind-speed 

(100mTower)

1

2

29

Levelized Cost of Energy in Varying 

Resource Classes (with PTC/MACRS)

LCOE increased from 02-03 to 09-10, but has since dropped below its 

earlier lows despite the increase in capital cost from 02-03 to the present

With PTC/MACRS and with current turbine pricing and other specific assumptions, 

the highest wind speed sites evaluated below can support LCOEs as low as 

~$33/MWh (real$), while the lower wind speed sites have LCOEs of ~$65/MWh

2002-03: Standard 

Technology

Current, 2012-13: 

Standard Technology

Current, 2012-13: 

Low Wind-speed 

(80m Tower)

2009-10: Standard 

Technology

$0

$20

$40

$60

$80

$100

$120

5.5 6.0 6.5 7.0 7.5 8.0 8.5

Levelized Cost of Energy ($/MWh) 

Includes Federal PTC & MACRS

50m Wind-speed (m/s) air density = 1.225kg/m3

Class 2 Class 3 Class 4 Class 5 Class 6

Current, 2012-13 

Low Wind-speed 

(100mTower)

2

1

110
30

All Else Being Equal, Capital Cost Increases 

Would Have Led To Much Higher LCOEs 

(Mid Class 3 Wind Resource, with PTC/MACRS)

$0

$10

$20

$30

$40

$50

$60

$70

$80

$90

$100

2002-03:

Standard Tech.

2009-10:

Standard Tech.

2012-13:

Standard Tech.

2012-13: Low Wind

(80m Tower)

2012-13: Low Wind

(100m Tower)

Levelized Cost of Energy ($/MWh)

Includes Federal PTC & MACRS

Capital Cost Impacts on LCOE

Assumes Fixed Performance circa 2002-03 and Class 3 Wind Resource

Current Pricing: Variable Turbine Designs

31

All Else Being Equal, Performance Improvements 

Would Have Led To Much Lower LCOEs 

(Mid Class 3 Wind Resource, with PTC/MACRS)

$0

$10

$20

$30

$40

$50

$60

2002-03:

Standard Tech.

2009-10:

Standard Tech.

2012-13:

Standard Tech.

2012-13: Low Wind

(80m Tower)

2012-13: Low Wind

(100m Tower)

Levelized Cost of Energy ($/MWh)

Includes Federal PTC & MACRS

Performance Impacts on LCOE from Advanced Turbine Designs 

Assumes Fixed Capital Cost circa 2002-03 and Class 3 Wind Resource

Current Pricing: Variable Turbine Designs

111
32

Same Tradeoff Between Capital Cost and 

Performance Exists in Other Wind Resource Classes 

(Mid Class 5 Wind Resource, with PTC/MACRS)

$0

$5

$10

$15

$20

$25

$30

$35

$40

2002-03:

Standard Tech.

2009-10:

Standard Tech.

2012-13:

Standard Tech.

Levelized Cost of Energy ($/MWh)

Includes Federal PTC & MACRS

Performance Impacts on LCOE from Advanced 

Turbine Designs Assumes Fixed Capital Cost circa 2002-03 and Class 5 Wind 

Resource

$0

$10

$20

$30

$40

$50

$60

$70

2002-03:

Standard Tech.

2009-10:

Standard Tech.

2012-13:

Standard Tech.

Levelized Cost of Energy ($/MWh)

Includes Federal PTC & MACRS

Capital Cost Impacts on LCOE

Assumes Fixed Performance circa 2002-03 and Class 5 Wind 

Resource

33

Same Tradeoff Between Capital Cost and 

Performance Exists without PTC/MACRS

$0

$10

$20

$30

$40

$50

$60

$70

$80

$90

2002-03:

Standard Tech.

2009-10:

Standard Tech.

2012-13:

Standard Tech.

2012-13: Low Wind

(80m Tower)

2012-13: Low Wind

(100m Tower)

Levelized Cost of Energy ($/MWh)

No Incentives 

Performance Impacts on LCOE from Advanced Turbine Designs 

Assumes Fixed Capital Cost circa 2002-03 and Class 3 Wind Resource

Current Pricing: Variable Turbine Designs

$0

$10

$20

$30

$40

$50

$60

$70

$80

$90

$100

2002-03:

Standard Tech.

2009-10:

Standard Tech.

2012-13:

Standard Tech.

Levelized Cost of Energy ($/MWh)

No Incentives

Capital Cost Impacts on LCOE

Assumes Fixed Performance circa 2002-03 and Class 5 Wind 

Resource

$0

$10

$20

$30

$40

$50

$60

$70

2002-03:

Standard Tech.

2009-10:

Standard Tech.

2012-13:

Standard Tech.

Levelized Cost of Energy ($/MWh)

No Incentives

Performance Impacts on LCOE from Advanced 

Turbine Designs 

Assumes Fixed Capital Cost circa 2002-03 and Class 5 Wind 

Resource

Class 5

$0

$20

$40

$60

$80

$100

$120

$140

2002-03:

Standard Tech.

2009-10:

Standard Tech.

2012-13:

Standard Tech.

2012-13: Low Wind

(80m Tower)

2012-13: Low Wind

(100m Tower)

Levelized Cost of Energy ($/MWh)

No Incentives

Capital Cost Impacts on LCOE

Assumes Fixed Performance circa 2002-03 and Class 3 Wind Resource

Current Pricing: Variable Turbine Designs

Class 3

112
34

Side Analysis: Impact of Improvements in O&M, 

Financing, and Availability (with PTC/MACRS)

8 m/s

7 m/s

6 m/s 

$0

$20

$40

$60

$80

$100

$120

2002-03 Current, 2012-13

Standard Technology Technology Choice

Levelized Cost of Energy ($/MWh)

Includes Federal PTC & MACRS

26% Cost Reduction

5% Cost Reduction

8 m/s

7 m/s

6 m/s

$0

$20

$40

$60

$80

$100

$120

2002-03 Current, 2012-13

Standard Technology Technology Choice

Levelized Cost of Energy ($/MWh)

Includes Federal PTC & MACRS

39% Cost Reduction

24% Cost Reduction

Core Assumptions

only varies capital cost and capacity factor

Side Analysis Assumptions

also varies O&M, availability, and financing

Assumed improvements in O&M costs, financing rates, and availability lead to 

substantial additional estimated LCOE reductions from 2002-2003 to 2012-2013 in 

comparison to core analysis that only varies capital cost and capacity factor 

Note: “Technology Choice” assumes that IEC Class III machines are only available for sites up to 7.5 m/s 

average wind speed at hub height (sea level air density)

35

Side Analysis: Impact of Improvements in O&M, 

Financing, and Availability (without PTC/MACRS)

Core Assumptions

only varies capital cost and capacity factor

Side Analysis Assumptions

also varies O&M, availability, and financing

Assumed improvements in O&M costs, financing rates, and availability lead to 

substantial additional estimated LCOE reductions from 2002-2003 to 2012-2013 in 

comparison to core analysis that only varies capital cost and capacity factor 

8 m/s

7 m/s

6 m/s

$0

$20

$40

$60

$80

$100

$120

2002-03 Current, 2012-13

Standard Technology Technology Choice

Levelized Cost of Energy ($/MWh)

No Incentives

20% Cost Reduction

3% Cost Reduction 8 m/s

7 m/s

6 m/s

$0

$20

$40

$60

$80

$100

$120

$140

2002-03 Current, 2012-13

Standard Technology Technology Choice

Levelized Cost of Energy ($/MWh)

No Incentives

31% Cost Reduction

17% Cost Reduction

Note: “Technology Choice” assumes that IEC Class III machines are only available for sites up to 7.5 m/s 

average wind speed at hub height (sea level air density)

113
36

Designing Turbines for Low Wind-Speed 

Sites: Narrowing the Gap in LCOE 

The proliferation of turbines designed for lower wind speeds has narrowed the 

gap between the LCOE of high- and low- wind speed sites, increasing the 

economic attractiveness of developing wind projects in lower wind speed areas

Without Federal Incentives With Federal PTC/MACRS

Notes: Does not consider Treasury Grant program / 30% ITC (see later results); “Tech. Choice” assumes that IEC Class III 

machines are only available for sites up to 7.5 m/s average wind speed at hub height (sea level air density)

$32/MWh $25/MWh

8 m/s

7 m/s

6 m/s

$0

$10

$20

$30

$40

$50

$60

$70

$80

$90

$100

Current, 2012-13:

Standard Technology

Current, 2012-13:

Tech. Choice Including Low Wind-speed

Levelized Cost of Energy ($/MWh)

No Incentives

$31/MWh

$23/MWh

8 m/s

7 m/s

6 m/s

$0

$10

$20

$30

$40

$50

$60

$70

$80

$90

$100

Current, 2012-13:

Standard Technology

Current, 2012-13:

Tech. Choice Including Low Wind-speed

Levelized Cost of Energy ($/MWh)

Includes Federal PTC & MACRS

37

Side Analysis: Incentive Choice Has Also Impacted 

the Economics of Low Wind Speed Sites

• 30% ITC/Grant applied to capital cost is relatively more lucrative than PTC on “facevalue” basis when a project has a low capacity factor and high costs  see results 

for 2009-2010 ‘Standard Technology’ in chart on left

• Current turbines / pricing have higher assumed capacity factors and lower costs, 

making PTC the better choice in virtually all developable wind regimes on “face 

value” basis  see results for 2012-2013 turbines in chart on right

Note: Results ignore benefits of ITC/Treasury grant beyond direct face value;

“Technology Choice” assumes that IEC Class III machines are only available for sites up to 7.5 m/s average wind 

speed at hub height (sea level air density)

2012-13: 

Technology 

Choice 

(ITC/Cash Grant)

2012-13: 

Technology 

Choice 

(PTC)

$30

$35

$40

$45

$50

$55

$60

$65

$70

5.5 6.0 6.5 7.0 7.5 8.0 8.5

Levelized Cost of Energy ($/MWh) 

50m Wind-speed (m/s) air density = 1.225 kg/m3

Class 2 Class 3 Class 4 Class 5 Class 6

2009-10: 

Standard 

Technology (PTC)

2009-10: 

Standard 

Technology 

(ITC/Cash Grant)

$30

$40

$50

$60

$70

$80

$90

$100

$110

$120

5.5 6.0 6.5 7.0 7.5 8.0 8.5

Levelized Cost of Energy ($/MWh) 

50m Wind-speed (m/s) air density = 1.225 kg/m3

Class 2 Class 3 Class 4 Class 5 Class 6

114
38

Result Was Narrowing in LCOE Between High & 

Low Wind Sites in 2009-2010, Not in 2012-2013

ITC/Grant improved the economics of low wind speed sites in 2009-2010; 

estimated lower capital costs and improved capacity factors result in face 

value of PTC > ITC/Grant in 2012-2013 even in low wind speed sites 

(ancillary benefits of ITC/Grant may still outweigh loss in face value in such sites) 

$45/MWh $38/MWh

8 m/s

7 m/s

6 m/s

$0

$10

$20

$30

$40

$50

$60

$70

$80

$90

$100

2009-10:

Standard Technology

PTC & MACRS

2009-10:

Standard Technology

Incentive Choice

Levelized Cost of Energy ($/MWh)

Includes Federal Incentives

$23/MWh $23/MWh

8 m/s

7 m/s

6 m/s

$0

$10

$20

$30

$40

$50

$60

$70

$80

$90

$100

2012-13:

Tech. Choice Incl. Low Wind-speed

PTC & MACRS

2012-13:

Tech. Choice Incl. Low Wind-speed

Incentive Choice

Levelized Cost of Energy ($/MWh)

Includes Federal Incentives

Note: “Tech. Choice” assumes that IEC Class III machines are only available for sites up to 7.5 m/s average 

wind speed at hub height (sea level air density)

39

Land Area Exceeding Capacity Factor 

Thresholds Has Increased Dramatically

Notes: Wind speed data come from the 50 m long-term assessments produced by AWS Truepower, MN Dept of Commerce, Iowa State Energy Center, 

Alternative Energy Institute (Texas), and NREL. Alabama, Louisiana, Mississippi, and Florida were not covered by any of these datasets. Standard wind 

resource exclusions were applied, as documented on the Wind Powering America website. Low wind-speed turbines are assumed to be utilized in sites 

up to 7.5 m/s sea level equivalent average annual wind-speed, per IEC standards. Site specific conditions may allow these machines to be placed in 

higher average annual wind-speed sites, which would further increase the percentage increase in available land area beyond what is estimated here. 

2002-03: Standard 

Technology

2009-10: Standard 

Technology

Current, 2012-13: Standard 

Technology

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

28% 30% 32% 34% 36% 38% 40% 42% 44%

Available Land Area (km2)

Capacity Factor

Current, 2012-13: 

Low Wind-speed (100m Tower)

[+ other 2012-13 Turbines]

Current, 2012-13: 

Low Wind-speed (80m Tower)

[+ 2012-13 Standard Tech]

Percentage Increase Since 2002-2003:

> 30% CF: 80% – 140% 

> 35% CF: 130% – 270% 

> 40% CF: 600% – 1400%

115
40

Land Area Exceeding LCOE Thresholds 

Has Also Increased Substantially

Notes: Increase in land area meeting a LCOE threshold is lower than the increase from 

a CF threshold because increased capital cost trends impact LCOE estimates, but not 

CF; “Tech. Choice” assumes that IEC Class III machines are only available for sites up 

to 7.5 m/s average wind speed at hub height (sea level air density)

2002-03:

Standard Technology

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

$30 $40 $50 $60 $70 $80 $90

Available Land Area (km2)

Levelized Cost of Energy ($/MWh)

2012-13:

Lowest Cost

Tech Choice

w/: PTC/MACRS

w/o: PTC/MACRS $57 $67 $77 $89 $99 $110 $121 

Percentage Increase Since 2002-2003:

< $35/MWh w/ PTC & MACRS: 42% 

< $55/MWh w/ PTC & MACRS: 48% 

< $75/MWh w/ PTC & MACRS: 25%

41

Conclusions

• Economic attractiveness of wind projects in recent past was reduced due 

to increased capital cost, move toward lower wind speed sites, and lower 

electricity prices

• Examination of historical trends in capital costs and capacity factors, 

individually, gives an incomplete picture of technology advancement as 

well as historical & current developments

• Recent declines in turbine prices & improved technology have reduced 

the estimated LCOE of wind; LCOE for projects being planned today in 

fixed resource areas is estimated to be at an all-time low

• Considering plausible assumptions for not only capital cost and capacity 

factor, but also O&M, financing & availability, the LCOE for 2012-2013 

projects is estimated to be as much as ~24% and ~39% lower than 

the previous low in 2002-2003 in 8 m/s and 6 m/s (at 50 m) resource 

areas, respectively (with the PTC/MACRS); when only considering 

capital cost and capacity factor, the reduction is ~5% and ~26% 

116
42

Conclusions 

• Technology advancement for lower wind speeds has narrowed the gap in 

LCOE between lower and higher wind speed sites; choice of 30% 

ITC/Treasury Grant may have further encouraged development in lower wind 

speed sites, especially in 2009-2010

• The amount of land area meeting or exceeding certain capacity factor and 

LCOE thresholds has substantially increased as a result of these technology 

improvements  helps alleviate to a degree transmission and siting barriers

• Technology advancement & learning still applies to onshore wind, despite its 

relative maturity, but all modes of technical advancement must be 

considered rather than emphasizing individual parameters

• Despite these recent and impressive technological advancements, three 

counter-veiling factors may intervene to raise LCOE: 

– potential for increased pricing if demand for wind turbines begins to catch up with supply, or 

if other exogenous influences are triggered (e.g., higher commodities and/or labor costs)

– potential continued move towards lower wind speed sites as a result of severe 

transmission/siting limitations

– potential near-term loss of federal PTC/ITC/Treasury Grant

Unpacking the Drivers Behind Recent U.S. Wind Project Installed Cost and Performance Trends 

Environmental Energy Technologies Division • Energy Analysis Department

Unpacking the Drivers Behind 

Recent U.S. Wind Project Installed 

Cost and Performance Trends

Ryan H. Wiser

Lawrence Berkeley National Laboratory

IEA Cost of Wind Energy Workshop

Denver, Colorado

March 29, 2011

This analysis was funded by the U.S. Department of Energy’s (DOE) Office of Energy 

Efficiency and Renewable Energy, Wind and Hydropower Technologies Program

117
2 Electricity Markets and Policy Group • Energy Analysis Department

Understanding Trends in 

Wind Project Costs

This portion of the presentation has been 

published. Please see the final report and 

presentation at:

http://eetd.lbl.gov/ea/emp/re-pubs.html

3 Electricity Markets and Policy Group • Energy Analysis Department

Understanding Trends in 

Wind Project Performance 

- Preliminary Analysis -

118
4 Electricity Markets and Policy Group • Energy Analysis Department

Wind Project Capacity Factors (CF) in the 

United States Vary Substantially By Project

Focusing on 14,722 megawatts (MW) installed from 1998‒2007, capacityweighted fleet-average CFs from 1999‒2008 equaled 33.7%

14,722 MW 

equals 95% 

of all wind 

capacity 

added over 

this time 

frame 0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

<15% 15-20% 20-24% 25-29% 30-34% 35-39% 40-44% 45-49% 50-54%

Installed Capacity (MW)

Capacity Factor (Project Average)

5 Electricity Markets and Policy Group • Energy Analysis Department

Average Capacity Factors Have Increased 

Over Time, by Project Vintage

2% of projects installed from 1998‒2003 had average CFs > 40%

25% of projects installed from 2004‒2007 had average CFs > 40%

0

500

1,000

1,500

2,000

2,500

3,000

3,500

<15% 15-20% 20-24% 25-29% 30-34% 35-39% 40-44% 45-49% 50-54%

Installed Capacity (MW)

Capacity Factor (Project Average)

1998-2003

2004-2007

119
6 Electricity Markets and Policy Group • Energy Analysis Department

Increase In Capacity Factors Is Widespread

(Not Limited to Certain Regions of the Country)

Note: Years represent 

project vintage (i.e., COD); 

capacity factors reflect 

multi-year average

Northwest

California

Mountain

Texas

Heartland

Great Lakes

East

New England

Southeast

0%

10%

20%

30%

40%

98-03 04-07

0%

10%

20%

30%

40%

98-03 04-07

0%

10%

20%

30%

40%

98-03 04-07

0%

10%

20%

30%

40%

98-03 04-07

0%

10%

20%

30%

40%

98-03 04-07

0%

10%

20%

30%

40%

98-03 04-07

0%

10%

20%

30%

40%

98-03 04-07

n/a

0%

10%

20%

30%

98-03 04-07

0%

10%

20%

30%

98-03 04-07

7 Electricity Markets and Policy Group • Energy Analysis Department

Average CFs Have Improved with Project 

Vintage, but Flattened from 2004‒2007

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

1998-99 

32 projects 

935 MW

2000-01

33 Projects

1,446 MW

2002-03 

34 Projects 

2,227 MW

2004-05 

37 Projects

2,680 MW

2006

31 Projects

2,394 MW

2007 

42 Projects 

5,001 MW

Capacity Factor (Average)

Capacity Factor (Weighted Average)

Note: Box plots identify 

10th/25th/75th/90th

percentile CF values

120
8 Electricity Markets and Policy Group • Energy Analysis Department

Possible Drivers of Project Performance

• Project siting: Projects located in stronger wind regimes, as well as 

better micro-siting, may increase capacity factors

• Higher hub heights: Given wind shear, taller towers will access 

better wind speeds

• Larger rotor diameters (relative to rated capacity): Longer 

blades will increase energy capture

• More-efficient turbine designs: Evolutionary gains in turbine and 

blade design/electronics may increase energy capture

• Improved availability: Possible improvement in project-level 

availabilities over time 

• Annual wind resource variation: Wind resource quality can vary 

from one year to the next, nationally and regionally. 

• Output curtailment: Physical or financial curtailment of output due 

to transmission limitations and minimum generation conditions

9 Electricity Markets and Policy Group • Energy Analysis Department

Objective: Evaluate a Subset of These 

Drivers to Better Understand Overall Trends

• For now, focus only on projects built from 1998‒2007 (older 

projects not representative, and have data issues)

• Determine turbine coordinates, hub height, and rotor diameter 

using Federal Aviation Administration (FAA) database and other 

sources

• Correlate turbine/project coordinates to respective average 

estimated wind resource using available wind maps

• Collect and organize project performance data from the Federal 

Energy Regulatory Commission (FERC), Energy Information 

Administration (EIA), and other sources for data years 1999‒2008

• Document trends in project performance nationally, by region, by 

turbine type, by wind resource class, etc.

• Analyze resulting data (i.e., average wind resource, hub height, 

and rotor diameter) to discern relative impact on capacity factors

121
10 Electricity Markets and Policy Group • Energy Analysis Department

Current Data Sample: Projects Installed from 

1998‒2007, with Turbines >200 kilowatts (kW)

Project Sample Description Nameplate 

Capacity

Total sample of possible projects 15,537 megawatts 

(MW)

Projects for which we have rotor diameter, 

hub height, and location/resource 15,268 MW

Projects for which we have above data 

and project-level capacity factor (for at 

least one year)

14,722 MW

95% of possible 

sample

Rotor diameter, hub height, and location/modeled resource data 

have also been collected for 8,376 MW built in 2008; currently

collecting data for 2009‒2010 

11 Electricity Markets and Policy Group • Energy Analysis Department

Results: CFs Increased Despite Significant Decrease 

in Average Wind Resource at 50 Meters (m)

Projects 

increasingly 

sited in 

poorer wind 

regimes at 

50 m:

High Class 5 

in 1998‒1999

High Class 3 

in 2008

0

100

200

300

400

500

600

700

800

900

1998-99 

32 projects 

935 MW

2000-01

33 Projects

1,446 MW

2002-03 

34 Projects 

2,227 MW

2004-05 

37 Projects

2,680 MW

2006

31 Projects

2,394 MW

2007 

42 Projects 

5,001 MW

2008

110 Projects

8,376 MW

Wind Power Density 

(Average, W/m2 @ 50 m)

Wind Power Density (Weighted Average)

Note: Box plots identify 

10th/25th/75th/90th 

percentile values

Class 

2

Class 

3

Class 

4

Class 

5

Class 

6

Class 

7

Class 

1

122
12

This Must Be a Story of Technical Change: Hub 

Height, Rotor Diameter, Efficiency, Etc.

Capacity factors have increased with project vintage within 

fixed estimated wind resource regimes at 50 m

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

1 2 3 4 5 6 7 8

Capacity Factor (Weighted Average)

Wind Resource Class @ 50m

2005-2007 Projects

2002-2004 Projects

1998-2001 Projects

13

0

10

20

30

40

50

60

70

80

90

1998-99 

32 projects 

935 MW

2000-01

33 Projects

1,446 MW

2002-03 

34 Projects 

2,227 MW

2004-05 

37 Projects

2,680 MW

2006

31 Projects

2,394 MW

2007 

42 Projects 

5,001 MW

2008

110 Projects

8,376 MW

Turbine Hub Height (Average, m)

Hub Height (weighted average)

Note: Box plots identify 

10th/25th/75th/90th 

percentile project hub 

height values

Electricity Markets and Policy Group • Energy Analysis Department

Average Hub Heights (HH) Have 

Experienced a Dramatic Increase

123
14

0

100

200

300

400

500

600

700

800

900

1998-99 

32 projects 

935 MW

2000-01

33 Projects

1,446 MW

2002-03 

34 Projects 

2,227 MW

2004-05 

37 Projects

2,680 MW

2006

31 Projects

2,394 MW

2007 

42 Projects 

5,001 MW

2008

110 Projects

8,376 MW

Wind Power Density (Average, W/m2)

Wind Power Density @ HH (weighted average)

Wind Power Density @ 50 m (weighted average)

Electricity Markets and Policy Group • Energy Analysis Department

But...Assuming 1/7th Power Law, Average 

Wind Resource at HH Has Still Declined

15

Rotor Diameter Has Increased; Specific 

Power (W/m2 Swept Area) Has Decreased

• Larger rotors optimized 

for lower wind speeds

• Rotor swept area has 

increased more rapidly 

than turbine capacity, 

leading to reductions in 

average specific power

(W/m2)

• Turbines with lower 

specific power will tend 

to have higher CFs (not 

greater kWh/m2)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

0

10

20

30

40

50

60

70

80

90

100

98-99 00-01 02-03 04-05 06 07 08

Rotor Diameter (weighted average)

Turbine Capacity (weighted average)

Turbine Rotor Diameter (Average, m)

Turbine Capacity (Average, MW)

0

100

200

300

400

500

98-99 00-01 02-03 04-05 06 07 08

Specific Power (weighted average)

Specific Power (Average, W/m2)

124
16 Electricity Markets and Policy Group • Energy Analysis Department

More Efficient Turbine and Blade Designs, 

Electronics, and Project Availabilities

In combination, the following factors (among 

others) may have contributed to an increase in 

CFs over time:

• Variable speed technology

• Improved blade design

– More aerodynamically efficient airfoils

– Lower soiling losses

• Better control systems and algorithms

• Better understanding of wake effects

• Drive-train efficiency improvements

• Higher project-level availabilities

17

Curtailment Needs to Be Considered As 

One Factor That Might Drive CFs Lower

Curtailment has been greatest in Texas, and increased 

substantially in 2009. Our performance data extends through 

2008, so curtailment is less of an issue in the present analysis.

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

1 2 3 4 5 6 7 8 9 101112 1 2 3 4 5 6 7 8 9 101112 1 2 3 4 5 6 7 8 9 101112

2007 2008 2009

zonal curtailment

local curtailment

wind production

17% of Potential Wind 

Generation Curtailed

8% of Potential Wind 

Generation Curtailed

1% of Potential Wind 

Generation Curtailed

125
18 Electricity Markets and Policy Group • Energy Analysis Department

Regression Analysis Helps To 

Piece Together the Story

Regression analysis shows relative influence of: (1) wind power density 

at 50 m; (2) hub height; (3) specific power; (4) installation year (COD) to 

account for efficiency/availability; (4) Texas/state dummy variables to 

account for curtailment, errors in state wind maps, etc.

( ) ββ ( ) β β  β β +⋅+⋅+ ε 

 



 ⋅+



















 



 ⋅+⋅+= ∑ +

i

avg COD i Statei SP

HH CF WPD 3 4 4

7

3

10 2

1 ln

50

ln ln ln

Texas

Beta 0.527 0.989 0.584 0.014 0.075

Stand. 

Error 0.066 0.324 0.194 0.008 0.033

P-value 0.000 0.003 0.003 0.153 0.023

ln( ) WPD 





































 7

3

50 ln HH

















SP

1 ln COD

19

Decoding the Influence of the Variables: 

Graphic Presentation of Regression Results

Actual data show absolute increase in CF of 5.8% from 

1998‒1999 to 2007 projects; regression analysis estimates 4.6% 

-6%

-4%

-2%

0%

2%

4%

6%

Hub Height Specific 

Power

Installation 

Year

Wind Density ALL

Predicted Absolute Change in CF 

from 1998 to 2007 (modeled)

126