Abstract

Larger Turbines and the Future Cost of Wind Energy

Eric Lantz & Maureen Hand

National Renewable Energy Laboratory, Golden, CO, USA

Presented at the European Wind Energy Association Annual Event, March 14-17, 2011, Brussels, Belgium

PO. 060

Results

The move to larger turbines has been observed in the United States and around the world. Turbine 15%

Objectives

scaling increases energy capture while reducing general project infrastructure costs and landscape 

impacts, each of which of can reduce the cost of wind energy. However, scaling in the absence of 

innovation, can increase turbine costs. The ability of turbine designers and manufacturers to continue 

to scale turbines, while simultaneously reducing costs, is an important factor in long-term viability of 

the industry. This research seeks to better understand how technology innovation can allow the 

continued development of larger turbines on taller towers while also achieving lower cost of energy. 

Modeling incremental technology improvements identified over the past decade demonstrates that cost 

reductions on the order of 10%, and capacity factor improvements on the order of 5% (for sites with 

annual mean wind speed of 7.25 m/s at 50m), are achievable for turbines up to 3.5 MW. However, to 

achieve a 10% cost reduction and a 10% capacity factor improvement for turbines up to 5 MW, 

additional technology innovations must be developed and implemented.

The effect of the advanced technology inputs on 

specific parameters is represented in Figure 5. Actual 

impacts varied depending on the turbine size (i.e., 

2.5 MW or 5.0 MW). Where direct comparisons can 

be made, these results are shown along with the 

estimates from Cohen et al. (2008). Error bars 

represent the range of impact, or in the case of the 

Cohen et al. (2008) data, estimates of the expected 

range of potential impact.

Figure 5. Resulting Impact from Advanced Technology Inputs 

‐15%

‐10%

‐5%

0%

5%

10%

15%

Blades Tower Drivetrain Manufacturing PE(cost) Array Losses PE(Efficiency)

Chanage from No R&D Scenario

Expected Technology Best Technology Cohen et al. TIOs(For Reference)

Cost Reduction Innovations AEP Innovations

$3,000

Scaling to larger turbines has been an industry trend (Figures 1-3). This research assumes that 

continued scaling is desirable and advantageous for the industry. It evaluates an array of proposed 

technical innovations and quantifies their impact on hypothetical future turbines conceptualized to 

achieve performance targets identified in the literature. It compares the impact of these proposed 

innovations on turbine costs with the cost projections articulated in the 2008 U.S. DOE report, 20% Wind 

Energy by 2030. 

Project Goals: (1) Identify and model the impact of technology innovations that are both incremental 

and widely recognized as viable technological improvements on turbines that scale gradually to 5.0 MW, 

(2) evaluate whether future cost and capacity factor projections in the DOE (2008) report, 20% Wind 

Energy by 2030, are compatible with industry trends towards larger rotors, taller hub heights, larger 

capacity ratings, (3) identify the turbine systems most likely to support cost reductions as the industry 

scales

Fi 6 R lt S A All S i

$0

$500

$1,000

$1,500

$2,000

$2,500

1.5 MW 2.5 MW 3.5 MW 5.0 MW

Turbine Cost (2009, $/kW)

20%Wind Study: Range of Costs 2010 ‐ 2030

No R&D

Expected Technology

Best Technology

Currentmass ratios

90 100 2.0

M th d

Figure 1. Trends in Hub Height in the U.S. Figure 2. Trends in Rotor Diameter in the U.S. Figure 3. Trends Turbine Capacity in the U.S.

Results (Figure 6) show that without technology advancement, continued scaling will be halted by excessive 

costs. However, with incremental changes in technology, turbines up to 3.5 MW are expected to achieve both 

performance increases and lower costs. For 5.0 MW turbines, incremental technology improvements will 

reduce costs. However, to see continued turbine cost reductions and capacity factors on the order of 43% 

(for a mean annual wind speed of 7.25 m/s at 50m), additional innovations will be required. 

Figure 6. Results Summary Across All Scenarios. 

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

2009

122

Projects

9,994 MW

TurbineHub Height (Average, m)

Hub Height (weighted average)

Note: Box plots 

identify 

10th/25th/75th/90th 

percentile project hub 

height values

0

10

20

30

40

50

60

70

80

90

100

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

2009

122

Projects

9,994 MW

Turbine Rotor Diameter (Average, m)

Rotor Diameter (weighted average)

Note: Box plots 

identify 

10th/25th/75th/90th 

percentile project 

rotor diameter values

0.71 MW

0.88 MW

1.21 MW

1.43 MW

1.6 MW 1.65 MW 1.66 MW

1.74 MW

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

1998‐99 2000‐01 2002‐03 2004‐05 2006 2007 2008 2009

1,425

turbines

1,987

turbines

1,757

turbines

1,960

turbines

1,536

turbines

3,190

turbines

5,029

turbines

5,734

turbines

1,016

MW

1,758

MW

2,125

MW

2,803

MW

2,454

MW

5,249

MW

8,350

MW

9,994

MW

Average Turbine Size (MW)

$2,481

$1,236

$91 $59

$229 $99 $100

$261

$179

$1,500

$2,000

$2,500

$3,000

ost ($/kW; 5MW)

Impact from Expected Technology Advancments

Methods

We first conceptualized future turbines that scale to 2.5 MW, 3.5 MW, and 5.0 MW and increase capacity 

factors gradually from 39% to 43% (capacity factors estimates assume a Class 4 wind regime, 7.25m/s at 

50m). This task was carried out using NREL’s Wind Turbine Design Cost and Scaling model (Fingersh et al. 

2006). This model allows basic optimization in developing general turbine parameters (e.g., hub height and 

rotor diameter) and generates turbine cost and performance estimates. Two sets of innovation opportunities, 

expected technology and best technology were also developed. Both scenarios rely on the same basic set 

of technology changes, which originally emerged from the U.S. DOE Wind Partnership for Advanced 

Technology Components (WindPACT) (e.g., Bywaters et al. 2005, Malcolm and Hansen 2002, Cohen et al. 

2008) and are summarized in Table 2. “Expected technology” indicates that a specific technology is likely to 

be widely adopted in the future. “Best technology” is indicative of the maximum beneficial impact from a 

given technology change, as determined by Cohen et al. (2008). One additional scenario was included that 

assumes future turbines could be built with today’s blade and drivetrain mass ratios (i.e., kg/m2 and kg/kW, 

Figure 7. Sources of Cost Reduction for a Conceptual 5 MW Wind Turbine 

The impact of technological advancements in various turbine component areas along with the incremental 

value of the best technology scenario and additional innovations that allow for today’s nacelle (kg/kW) and 

rotor (kg/m2)mass ratios to be achieved with larger future turbines are shown in Figure 7. Examining the 

results by impacts from specific turbine component areas shows that implementation of advanced 

technology across turbine systems is important for continued scaling. Improvements in drivetrains, 

$238

$0

$500

$1,000

No R&D Base case Drivetrain Blades Towers Power Electronics Manufacturing Efficiency (Siting &

Turbine)

Best Technology Current Mass

Ratios

20% Wind 2030

Estimate

Turbine Co

in Specific Turbine Systems

Incremental Cost Reduction

Achieved in other

Technology Scenarios

Conclusions

37%

39%

41%

43%

45%

47%

49%

20

40

60

80

100

120

140

160

180

Capacity Factor

Meters

No R&D Basecase (simple scaling) Expected Technology

Best Technology Estimated Capacity Factor (Right Axis)

respectively). Changes in some parameters (e.g., reduced losses) resulted in increased energy capture. As 

a result, turbines modeled in the expected and best technology scenarios were scaled down so as not to 

exceed the explicit capacity factor targets. Basic design features of the conceptual turbines used in each 

scenario are shown in Figure 4 and Table 1. The general scope of technology change captured in this 

analysis is described in Table 2. Table 3 summarizes other modeling inputs used in this analysis.

Table 2. Technology Concepts Captured by This Analysis

Cost Reducing

Innovations Technology Concepts

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 drive path 

•Single-stage, medium speed gearbox

•Direct drive 

Manufacturing 

efficiency

•Increased automation 

Cohen et al. 

2008

•Improved resins with greater ease of use

•Reduced design margins resulting from more consistent 

fabrication

advanced tower designs, and reductions in array and other turbine losses are especially critical.

Although this research emphasizes incremental technology change, and focuses on a relatively 

conservative set of proposed technology advancements, it demonstrates that lower turbine costs ($/kW) 

and increased performance are possible as the industry moves to larger turbines. In addition, when 

focusing on technology impacts to cost, projections of modest cost declines are realizable. Should 

more significant technology developments or emerging technology innovations gain traction, additional 

cost reductions and performance increases are possible. Moreover, balance of plant cost reductions 

and reduced industry impacts to landscape may be achieved through a move to larger capacity 

turbines, which may offer additional cost of energy savings.

These cost reductions should not be interpreted as automatic. Innovation across turbine systems is 

required to realize the level of cost reduction discussed here Significant research and field testing is

References

0 35%

2.5 MW 3.5 MW 5.0 MW 2.5 MW 3.5 MW 5.0 MW

Hub Height Rotor Diameter

Figure 4. Conceptual Future Turbines Scaled to 5.0 MW and 

Designed to increase Class 4 Capacity Factors 10%

For major turbine components (blades, towers, 

drivetrain), advanced technology impacts were 

captured with advanced technology scaling 

functions (Fingersh et al. 2006). These advanced

2008

(WindPACT) •Reduced profit margins as a result of increased volume

Power electronics

•High voltage circuitry

•Multi-switch capacity 

•Semi-conductor devices

AEP Innovations

Reduced losses

•Improved micro-siting to reduce array losses Industry 

estimates •Real-time monitoring and operational modifications

•Low soil blades 

Power electronics 

(higher efficiency) 

•High voltage circuit topologies Cohen et al. 

•Multi-switch capacity 2008

Table 3. Operating Conditions and Parameters 

Operating Wind Class 4 (m/s @50 m) 7.25

Weibull K Factor 2

Base Wind Shear 0.143

Air Density 1225 kg/m3

Max Rotor Cp 0.47

Max Combined Drivetrain Efficiency 90.2%

Max tip speed 70 m/s

Max tip speed ratio 7.25

Availability 98%

Table 1. Drivetrain Designs Employed in the Conceptual Turbines 

Developed for this Study

2.5 MW 3.5 MW 5.0 MW

No R&D Base Case 3-stage geared

Expected Technology multi-generator single-stage geared direct drive

Best Technology 

required to realize the level of cost reduction discussed here. Significant research and field testing is

necessary to move even the most basic new concepts from the design phase towards widespread 

acceptance by manufacturers and investors. In addition, if manufacturers have already begun to adopt 

a subset of the innovations captured here, or if any single innovation is ultimately not realizable, 

additional technology advancements are likely to be necessary to achieve future cost, performance, and 

scaling objectives.

Bywaters, G.; Salata, M; Labath, O; et al . (2005). Northern Power Systems WindPACT Drive Train Alternative Design Study Report.

NREL/SR-500-35524. Golden, CO: National Renewable Energy Laboratory. http://www.nrel.gov/docs/fy05osti/35524.pdf Cohen et 

al. 2008

Cohen, J.; Schweizer, T.; Laxson, A.; Butterfield, S.; Schreck, S.; Fingersh, L.; Veers, P.; Ashwill, T. (2008). Tehnology Improvement 

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National Renewable Energy Laboratory

EWEA 2011, Brussels, Belgium: Europe’s Premier Wind Energy Event

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. • NREL/PO-6A20-50835

functions (Fingersh et al. 2006). These advanced

technology functions were based on empirical data 

for technology in prototype form today or 

conceptual designs developed in the WindPACT 

studies. Because current data do not allow for the 

ability to calculate multiple advanced technology 

scaling curves, the impact of technology change in 

blades, towers, and drivetrains is equivalent for the 

expected and best technology scenarios (Figure 5). 

For other indirect cost drivers including manufacturing 

improvements, reduced siting and array losses, and 

power electronics efficiency, estimates emerged from 

WindPACT study data, as summarized in Cohen et al. 

(2008), and from industry estimates to develop both the 

expected and best technology modeling inputs.

Availability 98%

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National Renewable Energy Laboratory. http://www.nrel.gov/docs/fy07osti/40566.pdf

Malcom, D. and A. Hansen. (2002). WindPACT Turbine Rotor Design Study. NREL/SR-500-32495. Golden, CO: National Renewable 

Energy Laboratory. http://eduhosting.org/windpics/wgstudy.pdf

U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy. (2008). 20% Wind Energy by 2030: Increasing 

Wind Energy Contribution to the U.S. Electricity Supply. DOE/GO-102008-2567