Opportunities for and challenges to further reductions in the “specific power” rating of wind turbines installed in the United States

Abstract

A wind turbine’s “specific power” rating relates its capacity to the swept area of its rotor in terms of Watt per square meter. For a given generator capacity, specific power declines as rotor size increases. In land-rich but capacity-constrained wind power markets, such as the United States, developers have an economic incentive to maximize megawatt-hours per constrained megawatt, and so have favored turbines with ever-lower specific power. To date, this trend toward lower specific power has pushed capacity factors higher while reducing the levelized cost of energy. We employ geospatial levelized cost of energy analysis across the United States to explore whether this trend is likely to continue. We find that under reasonable cost scenarios (i.e. presuming that logistical challenges from very large blades are surmountable), low-specific-power turbines could continue to be in demand going forward. Beyond levelized cost of energy, the boost in market value that low-specific-power turbines provide could become increasingly important as wind penetration grows.

Keywords

Specific power levelized cost of energy capacity factor turbine design long blades large rotors economics geospatial modeling

Introduction

The design of modern wind turbines has evolved significantly over time. Although three-bladed, upwind turbines emerged as the dominant archetype as far back as the late 1980s, this configuration has since undergone numerous design improvements that have contributed toward greater reliability, increased energy capture, and lower costs. Well-known examples of such enhancements include variable-speed turbines, individual blade pitch, and dedicated airfoils with passive load-shedding capabilities.

No less important, wind turbines have also grown physically larger in several key dimensions, including rotor diameter, tower or hub height, and nameplate capacity. There is widespread acknowledgment that this wind turbine scaling has been a primary driver of historical reductions in the levelized cost of land-based wind energy (EWEA, 2009; Lantz et al., 2019; Wiser et al., 2011), by lowering investment costs per unit of capacity, boosting wind plant production, and reducing operations and maintenance (O&M) costs. Growth in turbine size has been enabled by scientific and engineering advancements, as well as enhanced computational tools, controls software, design standards, manufacturing methods, and O&M procedures (Wiser et al., 2011).

In addition to scaling, wind turbine design has become more closely tailored to the specific market conditions in which the technology is being deployed, including the applicable wind regimes, land-use patterns, grid accessibility, and policy environments. In the United States in particular, but also in other countries like China, India, and Brazil, one manifestation of this local optimization has been a trend toward lower “specific power” ratings. Specific power (SP) is defined as the ratio between a turbine’s nameplate capacity and the swept area of its rotor, and is expressed in units of watts of capacity per square meter of swept area (W/m²). Because swept area increases with the square of blade length (i.e. $π r^{2}$ ), mounting longer blades on a turbine with fixed generator capacity reduces that turbine’s SP rating. Hence, this trend toward lower SP turbines is most visible through increasingly longer blades and greater swept area relative to nameplate capacity.

Lower SP turbines have a number of attributes that have driven their deployment in the United States and many other markets around the world. Most obviously, for a given turbine generator capacity, a larger rotor captures more of the energy in the wind flowing past the turbine at any given moment, and therefore runs the generator closer to or at its rated capacity a greater percentage of the time. The result is more megawatt-hours (MW h) of electricity generated per megawatt (MW) of capacity installed, resulting in a higher “capacity factor.”¹

A higher capacity factor is not necessarily an end design goal in and of itself. However, with the sophisticated control systems of modern turbines, extending blade length to increase energy production can often be achieved with relatively limited impact to the rest of the turbine system (e.g. nacelle, tower, and foundation). As a result, larger rotors are often incorporated with a limited impact on overall turbine cost on a per unit capacity (US$/MW) basis. In such cases, boosting capacity factor allows for greater energy production per invested dollar and provides a direct path toward a lower levelized cost of energy (“LCOE,” expressed in US$/MW h). This is particularly true in the United States and other relatively land-rich countries (e.g. China), where wind projects tend to be constrained more by contractual, interconnection, or transmission capacity limits rather than by land availability. Under these conditions, turbine manufacturers and plant owner/operators have an incentive to maximize the MW h generated per installed—and constrained—MW of capacity. In other markets with land constraints but robust transmission networks (e.g. much of Europe), turbine designers and plant owners may instead prefer to maximize the capacity and energy produced per unit land area, resulting in a preference for greater generator capacity and relatively higher SP turbines.²

The energy generation profiles that result from lower SP turbines (as well as taller towers) have also been found to have the knock-on effect of improving the wholesale market value of wind energy (Molly, 2011). Research shows that the marginal value of wind energy to the electric grid declines with increasing wind penetration, as greater amounts of wind generation flow onto the grid concurrently during windy periods, thereby depressing wholesale power prices (e.g. Hirth, 2013; Mills and Wiser, 2014). By having power curves that more evenly distribute when wind generation occurs—for example, relatively less generation during high wind hours and relatively more generation during low wind hours—turbines with lower SP and taller towers can partially mitigate these declines. Specifically, a range of studies finds that at high wind penetrations, such turbines can boost the grid system value of wind energy by 8%−30% (∼US$3−US$15/MW h) (Dalla Riva et al., 2017; Hirth, 2016; Hirth and Müller, 2016; Johansson et al., 2017; May, 2017). Conceptually, this finding is a function of increased wind generation during periods of relatively lower wind speeds, which are, in some markets, at least partially correlated with higher electricity prices.

Despite their potential benefits, low SP turbines involve tradeoffs. Operating a greater percentage of time at a turbine’s rated capacity results in spilling energy that could otherwise be captured with a larger generator. Rather than further increasing the rotor swept area, boosting the generator capacity can also be a cost-effective way to increase annual electricity production and—particularly if achieved with limited impact to cost per unit capacity—reduce LCOE. Longer blades may also experience greater physical loads and transfer them to the rest of the turbine, perhaps with implications for tower and foundation design and cost as well as long-term reliability. Although, to date, wind turbine manufacturers have—through use of innovative design, sophisticated controls, and new materials—been able to stay ahead of mass and cost curves that compromise the economics of scaling (Garrett and Rønde, 2011a, 2011b; Razdan and Garrett, 2015a, 2015b, 2018a, 2018b), the extent to which they will be able to do so going forward is uncertain (Sieros et al., 2010). Moreover, additional growth in turbine size may be limited by not only engineering and materials usage constraints, but also by social acceptance and regulatory hurdles as well as the logistical constraints and costs of manufacturing, transporting, and erecting ever-larger blades, towers, and nacelle components by road and rail (Cotrell et al., 2014; DNV GL, 2019; McKenna et al., 2016; U.S. Department of Energy (DOE), 2015a).

In part because of these tradeoffs, the extent to which this recent trend toward lower SP turbines will continue is far from certain. Further upward scaling in turbine size is anticipated, but uncertainty remains on both the magnitude and relative focus (e.g. rotor vs generator vs tower) of that scaling (Wiser et al., 2016; Wood Mackenzie, 2018). Research demonstrates that taller towers and larger rotors can potentially enable economic wind development in lower wind-speed areas that have not seen much or any development to date (Burt et al., 2017; Capps et al., 2012; DOE, 2015a; Lantz et al., 2019; Rinne et al., 2018). But in more-seasoned wind development areas with better wind resources, recent product offerings from the major turbine manufacturers, as well as various analyst projections, suggest that the next concerted move in turbine design will be toward higher capacity turbines, which—given the current limits on blade length—will have higher, not lower, SP ratings.

This article examines the tradeoffs posed by low-SP turbines, particularly with an eye toward evaluating the opportunities for and challenges to continued SP reduction in the future. Using empirical data, we document the historical trend toward lower SP turbines in the United States, and link that trend with higher capacity factors and lower LCOE. We also review recent research out of Europe that finds that a lower SP rating can increase the value of wind generation in the wholesale market, particularly under high-wind-penetration scenarios. Next, in light of the tradeoffs involved, we discuss the extent to which this trend toward lower SP is likely to continue in the United States. This discussion is informed by geospatial analysis that explores the relative economic attractiveness of several different wind turbine designs—including low-, business-as-usual (or reference), and high-SP turbines—across the United States. As explained in more detail later, the geospatial analysis relies on wind-speed data from the National Renewable Energy Laboratory (NREL) Wind Integration National Dataset (WIND) Toolkit in order to quantify the capacity factors associated with these three turbine configurations at a 2-km grid resolution across the continental United States. We then couple these capacity factors with varying estimates of up-front installed costs, as well as fixed assumptions about operating expenses and financing terms, to yield countrywide estimates of the LCOE of each turbine configuration under three different cost scenarios. The purpose is to evaluate the cost conditions under which higher or lower SP turbines might prevail in terms of LCOE, given their relative capacity factors.

In the end, although the continuation of this trend toward lower SP is in no way assured, this analysis suggests that under reasonable cost scenarios, low-SP turbines could continue to be an important part of the United States fleet going forward, particularly as wind penetration increases (thereby eroding the market value of wind) and in lower wind-speed regions of the country.

Historical trends, drivers, and impacts of low-SP turbines in the United States

This section documents the historical trend toward the deployment of lower SP turbines in the United States, as well as the drivers and impacts of this trend. Figure 1 shows the annual averages and distributions of nameplate capacity, hub height, rotor diameter, and SP of utility-scale (i.e. >100 kW) wind turbines installed in the United States over the past decade. The trend toward greater capacity turbines with larger rotors and lower SP ratings is clear: the average nameplate capacity increased from 1.74 MW in 2009 to 2.43 MW in 2018, while the average rotor diameter grew from 81.5 to 115.6 m (pushing the average swept area from 5200 to 10,500 m²) and reducing the average SP rating from 329 to 230 W/m².

Figure 1.

Trends in turbine capacity, hub height (HH), rotor diameter (RD), and specific power (SP).

Figure 2 highlights the comparative differences in the growth of these four parameters, along with the swept area of the rotor. Growing at the square of blade length, the average swept area has doubled since 2009, greatly outpacing the 40% increase in nameplate capacity. As a result, average SP has declined by roughly 30%.

Figure 2.

Cumulative percentage change in key turbine parameters since 2009.

Although initially targeted at lower wind-speed sites, low-SP turbines have since been deployed more broadly in the United States—in some cases even at high wind-speed sites. For example, Figure 3 shows that by the end of 2018, projects with SP ratings of less than 250 W/m² (yellow dots) were widespread throughout the United States, with a number of projects under 200 W/m² (red dots) operating in the high wind-speed areas of Texas, New Mexico, Oklahoma, and Iowa (among other places). In many of these cases, a combination of relatively high site elevation (i.e. with lower air density), relatively low wind turbulence, and the capabilities of sophisticated control systems provides sufficient comfort to turbine engineers and manufacturers, as well as project developers and sponsors, that these low-SP turbines can withstand higher wind speeds than might have been intended by the initial turbine design.

Figure 3.

Map of wind project location and specific power overlaid on long-term average wind speed as estimated based on typical meteorological year conditions for the period of 1997–2010.

Although the United States has been a market leader in terms of deploying low-SP turbines, it is not alone. China and, more recently, India and Brazil, have increasingly deployed turbines with similarly low-SP ratings. This stands in contrast to most European countries, where the average SP remains higher (while similarly trending downward in recent years, but from a higher level). Although it is difficult to generalize, the distinction between these two camps seems to be those countries with ample land area but constrained transmission (i.e. the United States, China, India, Brazil) and those with stronger grids but constrained land area (i.e. Europe).³ The “capacity-constrained” camp has an incentive to maximize the MW h generated per (constrained) MW installed—for example, by reducing SP—while the “land-constrained” camp has an incentive to maximize the amount of capacity installed and MW h generated per (constrained) land area, which often results in higher capacity, higher SP turbines (in light of manufacturing and transportation constraints on blade length).⁴

As such, in capacity-constrained markets like the United States, the trend toward lower SP turbines has been driven by a desire to maximize MW h (and therefore revenue) per constrained MW through a higher capacity factor, leading to a lower LCOE. The industry’s ability to achieve higher capacity factors through lower SP is demonstrated in Figure 4, which shows project-level capacity factors in 2018 from 614 projects totaling 63.2 GW that were installed in the United States from 2009 to 2017. Clearly, the quality of the site (as denoted by the long-term average wind speed along the x-axis) matters for capacity factor, but so too does SP: for any given wind speed, those projects using the lowest SP turbines tend to have the highest capacity factors, and vice versa.

Figure 4.

Capacity factor in 2018 as a function of long-term average wind speed and specific power.

This relationship is even more evident after binning the empirical data from Figure 4 for both wind speed and SP, to reduce some of the inherent noise in the project-level data. In Figure 5, not surprisingly, the average capacity factor of virtually every SP bin increases when moving from a lower to a higher wind-speed bin along the x-axis. More notable, though, is that within any of the four wind-speed bins, moving from higher to lower SP turbines provides a similar or greater increase in capacity factor.

Figure 5.

Capacity factor in 2018 as a function of binned wind speed and specific power.

The time trend is also instructive. Drawing upon the same wind project sample as in Figures 4 and 5, Figure 6 shows the average SP of turbines deployed each year in the United States since 2009 (red line)—in this case, plotted on an inverse scale so that SP moves directionally with capacity factor (the blue line). Although averages mask geospatial variations in both SP and capacity factor over time, in general there is a strong correlation between the decline in average SP and the increase in the average capacity factor in 2018 among more recent project vintages.

Figure 6.

Average specific power and capacity factor by project vintage.

Of course, a higher capacity factor is not necessarily an end goal itself, particularly when there is a cost to achieving it—in this case, the cost of mounting a larger rotor on a given turbine. Moreover, due to the “square-cube law,” the cost of a larger rotor extends beyond just the immediate first-order effects. The square-cube law states that as the diameter of a wind turbine’s rotor increases, theoretical energy output increases by the square of the rotor diameter, but the volume and mass of material required to scale the rotor increases as the cube of the rotor diameter, all else being equal (Burton et al., 2001). Consequently, at some size, the cost of a larger turbine will increase faster than the resulting energy output and revenue, making further size increases uneconomical (Sieros et al., 2010).

To date, the wind industry has been able to avoid uneconomical scaling-related cost increases by streamlining manufacturing operations, optimizing turbine design, and using fewer, lighter, and stronger materials (Garrett and Rønde, 2011a, 2011b; Razdan and Garrett, 2015a, 2015b, 2018a, 2018b; Wiser et al., 2011). Figure 7 employs mass data sourced from Vestas’ life-cycle analyses of its 2.0 MW platform in order to plot how mass intensity (expressed in three ways, from left to right, in kg/kW, kg/m² of swept area, and kg/MW h) has changed with SP over time.⁵ Expressed in kg/kW, mass intensity has generally (and not surprisingly, given the static 2.0 MW turbine capacity) increased with longer blades. To the extent that mass can be considered as a loose proxy for cost, this increase in kg/kW as SP declines should push turbine costs higher on a US$/kW basis. When expressed in either kg/m² or kg/MW h, however, mass intensity within each turbine class declines with SP, enabling lower US$/MW h costs. In this way, scaling-related cost increases can still be economical, by enabling lower LCOE and power purchase agreement (PPA) prices.

Figure 7.

Mass intensity of Vestas 2.0 MW turbines as a function of specific power.

Progressing from mass intensity to cost, analysis by DOE (2015b), Moné et al. (2015), Stehly et al. (2017), and Wiser et al. (2012) suggests that, at least historically, a reduction in SP of 100−125 W/m² (i.e. between International Electrotechnical Commission (IEC) certified Class I and Class III turbines) can plausibly push turbine costs higher by US$200−US$300/kW. Similarly, Bloomberg New Energy Finance (2018) estimates that for a smaller SP differential (i.e. approximately 40−50 W/m²) between IEC certified Class II and Class III turbines, an approximately US$100/kW cost differential could be observed in pricing data through 2018. Finally, analysis of the project-level installed cost data for projects in the United States from Wiser and Bolinger (2019) suggests a somewhat smaller premium, ranging from US$90 to US$170 for a 100 W/m² reduction in SP.

Although the precise cost differentials between low- and high-SP turbines remain both uncertain (given the ranges noted in the previous paragraph) and perhaps also variable (e.g. based on supply and demand for specific turbine platforms), the scaling-related cost increases experienced to date have seemingly not been enough to outweigh the LCOE benefit derived from the corresponding increase in generation. This is evident in not only the deployment trends shown earlier, but also when running the respective cost and capacity factor differentials through a simple LCOE calculator. For example, using the assumptions for operational expenditures (OpEx) and financing terms described later, a 100-W/m² reduction in SP that increases capital expenditures (CapEx) from US$1400 to US$1600/kW (i.e. within the range of incremental costs from the previous paragraph) will still yield a lower LCOE with just a 4 percentage-point boost in capacity factor (i.e. from 36% to 40%—conservative based on the range of empirical capacity factor increases shown earlier in Figures 4 to 6).

By enabling a lower LCOE—even with a higher up-front cost—the trend toward lower SP turbines has been one important driver of the broader trend toward lower PPA prices and LCOE in the United States over time. Figure 8 shows that these two metrics have declined on average by 60%−70% since 2009.

Figure 8.

Trends in PPA prices and LCOE by project vintage (bubble size corresponds to PPA capacity).

Of course, cost is only one side of the coin, with market value being the other. Here again, lower SP turbines appear to offer meaningful benefits, generally providing greater wholesale market value (i.e. energy and capacity value) than higher SP turbines by shifting generation from high wind hours—when local wholesale power prices are more likely to be depressed by an inrush of wind generation—to lower wind hours, when there is generally less wind generation on the system and wholesale power prices are, therefore, likely to be higher.

Recent research out of Europe, summarized in Table 1, corroborates the boost in market value provided by turbines with lower SP ratings (and higher hub heights). At low levels of market penetration, with not enough wind on the system to depress wholesale power prices during windy periods, most of these studies find little or no incremental market value provided by taller, low-SP turbines. But above 5%−15% wind penetration—that is, the range in which the United States currently finds itself—these turbines begin to provide incremental market value that grows commensurate with market penetration. At penetration levels of 30%−50%, these studies find that, by shifting generation from higher to lower wind-speed hours (via a power curve that cuts in at lower wind speeds), taller turbines with lower SP ratings can boost market value by 8%−30% (US$3−US$15/MW h), depending on the scenario.

Table 1.

Studies find that lower specific power can boost market value at high wind penetrations.

Report citation	Wind value increase		Specific power (W/m²)		Hub height (m)		Wind penetration analyzed
	%	US$/MW h	Start	End	Start	End
Dalla Riva et al. (2017)	8	2.9	325	250	100	125	Varies by country
Dalla Riva et al. (2017)	13	4.8	325	175	100	150	Varies by country
Hirth and Müller (2016)	15	9	472	211	90	120	30%
Hirth and Müller (2016)	23	14	472	100	90	120	30%
Hirth (2016)	12−14	6.5	568	289	90	120	30%
May (2017)	14%	11	630	200	80	140	50% (all renewable energy)
Johansson et al. (2017)	20%	11	200	100	100	100	40%
Johansson et al. (2017)	30%	15	300	100	100	100	40%

This body of research suggests that, in the future, the boost in market value provided by lower SP turbines could become an increasingly significant selling point, presuming that wind penetration continues to increase.

Looking ahead

Wind turbines with lower SP have provided tangible benefits to date—primarily via a higher capacity factor achieved at a cost that has enabled LCOE to decline, but also increasingly in the form of enhanced market value. However, questions persist with respect to whether this trend will continue—or potentially even reverse—going forward.

Simple linear extrapolation of the historical trends shown in Figure 1 suggests that, by 2025 under a “business-as-usual” scenario, the average nameplate capacity of land-based wind turbines installed in the United States could increase to 3.0 MW (up from 2.43 MW in 2018), while the average rotor diameter could grow to 139 m (up from 116 m in 2018), resulting in an average SP rating of 197 W/m² (down from 231 W/m² in 2018). However, while the trends in these parameters have been essentially linear in the past, one might reasonably argue that linear extrapolation is not appropriate going forward, given some of the significant challenges noted earlier—most notably, the square-cube law and the logistical and transportation-related constraints imposed by ever-larger blades (DNV GL, 2019). These potentially binding constraints, which could restrict further increases in blade length, might suggest that the next concerted move in turbine scaling is more likely to come from the generator than the blades.

A similar view has recently been put forth by analysts such as Wood Mackenzie (2018), who expect the increasing adoption of competitive tenders for renewable energy worldwide, as well as the phase-out of the production tax credit (PTC) in the United States, to squeeze profit margins all along the value chain and drive the need for further turbine cost reductions in order for wind to remain competitive. Such cost reductions can potentially be achieved through additional economies of scale brought about by greater consolidation and standardization of global production lines toward the higher capacity, higher SP turbines favored in many other countries.⁶ As a result of this view, Wood Mackenzie projects that the average capacity of wind turbines installed in the United States in 2025 will be 4.2 MW (significantly higher than the 3.0 MW derived from linear extrapolation), while the average rotor diameter will increase to 155 m, resulting in an average SP rating of 224 W/m²—that is, not significantly different from the 231 W/m² seen in 2018.

Recent product announcements from major wind turbine manufacturers also support this view. Over the past year, GE, Vestas, and Siemens Gamesa have all announced “next-generation” 5-MW platforms that feature SP ratings of 255−270 W/m²—that is, significantly higher than 2018’s average of 230 W/m².⁷ This notable step-up in SP is driven entirely by the sharp increase in turbine capacity, as these turbines are generally using the longest blades that are currently available on the market (at least for land-based turbines).⁸ As blade technology continues to advance, making even longer blades possible in the future, one can envision the SP rating of these new platforms declining over time—just as it has in the past for earlier, smaller platforms, following what seems to be the normal product development cycle of initially boosting capacity and then letting rotors catch up over time. To that end, one of the three new product lines—GE’s Cypress platform—is expected to mark the first widespread commercial use of segmented blades in order to achieve a 158-m rotor. This use of segmented blades is perhaps a harbinger of things to come, as transportation constraints for one-piece molded blades could become more and more binding (DNV GL, 2019).

Finally, at the other end of the spectrum, the US Department of Energy’s (DOE) Wind Energy Technologies Office has its Big Adaptive Rotor (BAR) initiative, which seeks to realize the benefits of very low-SP turbines even among the next generation of higher capacity turbines. Through a series of interrelated tasks (including one task that funded the research presented in this article), the BAR initiative is in the process of modeling innovative large blade design concepts that will enable a 5.0 MW reference turbine with a 206-m rotor and a SP rating of 150 W/m²—that is, considerably below 2018’s average SP rating of 230 W/m², let alone the 255−270 W/m² range exhibited by the “next generation” 5-MW product launches mentioned above.

With such a wide range of options to consider—for example, from the BAR initiative’s 150 W/m² target to the 270 W/m² currently available in the 5-MW capacity range—we turned to geospatial modeling to quantitatively weigh the various tradeoffs involved in such different design paths. Specifically, this section of the article relies on national supply curve modeling and CapEx sensitivities to further inform the economic opportunities associated with higher and lower SP wind turbines. The purpose is to illuminate those conditions under which lower or higher SP turbines may prevail in the future.

To quantify the change in capacity factor and LCOE associated with the turbine configurations studied, the analysis relies on hourly time series wind-speed data from NREL’s WIND Toolkit.⁹ The toolkit data can be briefly characterized as a national mesoscale wind-resource data set that includes meteorological data for more than 1.85 million locations in the contiguous United States. Each pixel in the data set reflects a 2 × 2 km² grid cell. The toolkit provides 7 years of time series wind-speed data derived from model simulations of the weather patterns from the historical period of 2007−2013. These model simulations use real-world historical data to create synthetic representations of the mesoscale meteorological phenomena for the period of time from which the model input data are drawn. Although there are multiple hub heights available, we focus primarily on the data for 140 m (based on our turbine and hub height selections described below). Calculated capacity factors reported here reflect the multiyear mean capacity factor based on all 7 years of available WIND Toolkit data and include all pixels within the contiguous United States. This particular analysis does not consider exclusions of any particular location, even though there are areas where wind development is either unlawful (such as national parks) or impractical (such as very steep slopes and urban city centers).

As with all mesoscale data sets, there is uncertainty in the wind-speed data of the toolkit. This uncertainty is generally believed to increase at greater above-ground-level heights, where there has been less validation of the model output data. Accordingly, it is the relative differences in capacity factors and LCOE across the different turbine configurations, as opposed to the absolute capacity factor or LCOE values that are of principal interest and focus in this analysis. Moreover, the geospatial results mapped in Figures 13 and 14 should be viewed for the general trends they illuminate as opposed to the precise spatial results that are associated with any single data pixel.

To estimate annual energy generation and gross capacity factors, the WIND Toolkit’s hourly wind-speed data were applied to wind turbine power curves derived from the turbine configurations detailed in Table 2. Net capacity factors were estimated based on the application of a simple 16.7% loss adjustment. This adjustment has been used extensively in national supply curve characterizations by DOE and NREL (Cole et al., 2018; DOE, 2008, 2015b; Lantz et al., 2019; Stehly et al., 2017) and is intended to reflect a combination of array and electrical losses, as well as turbine downtime.

Table 2.

Turbine configurations applied in national supply curve modeling.

	2018 average	Constant SP	Low SP	High SP
Nameplate capacity (MW)	2.43	5.0	5.0	5.0
Rotor diameter (m)	115.6	165.8	206	153.5
Hub height (m)	88	140	140	140
SP (W/m²)	231.5	231.5	150	270

SP: specific power.

The turbine configurations shown in Table 2 are intended to be illustrative of current as well as potential future turbine SP configurations. The 2018 Average turbine is derived from the average statistics of turbines installed in the United States in 2018; it has an SP of approximately 231 W/m². The Constant SP turbine scales the 2018 Average to the same 5.0-MW capacity rating as the Low SP and High SP turbines, while holding SP constant at the 2018 average of 231 W/m². The Low SP turbine has an SP of 150 W/m², which is generally below the low end of the range of commercially available machines today (and well below that range if only considering larger, 5-MW platforms), but is consistent with the DOE BAR target. The High SP turbine reflects a 5.0-MW platform with an SP of 270 W/m², which is generally commensurate with recent commercial offerings in this size class. With the exception of the 2018 Average turbine being run at 88 m (to enable comparison against current technology), a single 140-m hub height was assumed for all analysis results.¹⁰

Figure 9 shows the absolute net capacity factor distributions for each of these four turbines across the United States, while Figure 10 shows the relative differences in these distributions, in this case, focusing on just the Low SP and High SP turbines relative to the Constant SP turbine.

Figure 9.

Net capacity factor distributions for each of the four turbines analyzed.

Figure 10.

Net capacity factor of Low SP and High SP turbines relative to the Constant SP turbine.

These data illustrate the potential gains in capacity factor that could be achieved relative to the 2018 Average turbine (Figure 9) as well as differences in capacity factor among the 5-MW turbines due to having different SP ratings (Figure 10). Not surprisingly, the Low SP turbine observes both the highest absolute capacity factors and the largest relative gains. Although the High SP turbine sees a modest increase in median capacity factor (3 percentage points) relative to the 2018 Average turbine, a small number of sites actually have lower capacity factors with the High SP turbine than with the 2018 Average turbine. The median capacity factors for the Low SP and High SP turbines are approximately 7 percentage points greater than and 2 percentage points lower than the median value for the Constant SP turbine, respectively (Figure 10). These data show that low-SP turbines could continue to provide substantial capacity factor and annual energy production gains into the future. Whether such turbines will become commonplace in the industry, however, will depend on their relative costs and value.

To evaluate the cost conditions under which higher or lower SP turbines might prevail, we constructed three simple cost-sensitivity scenarios—Reference, Favor Low SP, and Favor High SP. These scenarios are intended to highlight conceptual cost differences that could play out in the future (Table 3). Given that the focus of this analysis is on understanding the opportunity space for relatively lower and higher SP turbines, the Constant SP turbine maintains the same project-level CapEx value of US$1500/kW—comparable to the average project-level CapEx estimate for 2018 as reported in Wiser and Bolinger (2019)—across all three scenarios.

Table 3.

CapEx assumptions in each sensitivity scenario.

	Low SP (2018 US$/kW)	Constant SP (2018 US$/kW)	High SP (2018 US$/kW)
Reference	1620	1500	1380
Favor low SP	1500	1500	1500
Favor high SP	1740	1500	1260

CapEx: capital expenditures; SP: specific power.

The Reference scenario generally aligns with traditional scaling theory—assuming equivalent site and design requirements—as well as the analytical and empirical cost estimates cited earlier (Bloomberg New Energy Finance, 2018; DOE, 2015b; Moné et al., 2015; Stehly et al., 2017; Wiser and Bolinger, 2019; Wiser et al., 2012), which hold that lower SP turbines will have higher up-front costs (all else being equal). Informed by these prior estimates, this scenario pegs the Low SP and High SP CapEx at 8% above and below, respectively, the Constant SP CapEx. This results in a CapEx span of US$240/kW between Low SP and High SP, which falls within the range of cost premiums suggested by prior analysis of blade scaling.

The Favor Low SP scenario holds CapEx constant at US$1500/kW for all three turbine configurations, and therefore highlights how the differences in capacity factor shown in Figures 9 and 10 would translate to LCOE, all else being equal. Although somewhat in conflict with traditional scaling theory (i.e. the square-cube law), this scenario is considered plausible for two primary reasons. First, turbine manufacturers can potentially influence and reduce costs through supply chain optimization, including high-volume purchases. Second, higher SP turbines designed to extract maximum energy from very strong and turbulent wind conditions could potentially require greater structural strength and, hence, ultimately more materials and mass, increasing their overall cost. The Favor Low SP scenario assumes that the Low SP turbine benefits substantially from supply chain optimization and volume, and therefore costs the same as the Constant SP turbine. Conversely, it also assumes that the High SP turbine that would be commercially available is penalized by increased strength requirements and low volume, and therefore loses any inherent cost advantage it might otherwise have (in theory) relative to the Constant SP turbine.

The final sensitivity, Favor High SP, was created for three specific reasons. First, we wanted to identify those conditions under which low- and high-SP technology prevail, and we know that to date, even with some empirical data points indicating modestly higher CapEx for low-SP technology (as reflected in the Reference scenario), these low-SP turbines have still tended to rapidly gain market share, particularly in markets with relatively fewer land constraints (e.g. United States, China, Brazil). Second, because the physics of scaling should tend to drive costs higher (all else being equal) for low-SP turbines and because there are additional potential system costs (e.g. in transport, towers, and foundations) that could be significant, we anticipate potentially more risk of cost escalation associated with lower SP technology. Third, it is possible that turbine manufacturers operating in markets that are less capacity-constrained could develop the means to boost generator capacity at relatively low incremental costs (e.g. through software upgrades in the controls or power electronics) resulting in circumstances whereby the cost spread between lower and higher SP turbines, when denominated in US$/kW, is relatively large. As such, in this scenario, we characterize the High SP turbine at a cost that is 16% less than the Constant SP turbine, while the Low SP turbine costs 16% more.

The next step in the analysis is to combine the CapEx estimates from Table 3 with the estimated capacity factors from Figure 9, along with fixed assumptions for total OpEx and financing terms, to estimate the LCOE. For total OpEx, we assume an estimated US$41/kW-year, as informed by Wiser et al. (2019). For financing, we assume a real fixed charge rate of 8%,¹¹ commensurate with an implied nominal, after-tax weighted-average cost of capital of approximately 6.4%, and an implied real, after-tax weighted-average cost of capital of approximately 3.9%. Given our simplifying assumptions that OpEx and financing costs are constant across each turbine and scenario, these values affect the absolute, but not the relative, LCOE results.

Figures 11 and 12 present the distribution of LCOE and comparisons across turbine concepts and cost scenarios. Figure 11 holds CapEx constant across the three turbine configurations (i.e. reflective of the Favor Low SP scenario) in order to isolate the impact of capacity factor differences on LCOE, while Figure 12 highlights the LCOE differences relative to the Constant SP turbine under all three CapEx scenarios.

Figure 11.

LCOE distributions among the turbines considered in the Favor Low SP scenario (i.e. assuming all three turbines have the same CapEx of US$1500/kW).

Figure 12.

LCOE of Low SP and High SP turbines relative to the Constant SP turbine across the three CapEx scenarios shown in Table 3.

The absolute LCOE data from the Favor Low SP scenario (Figure 11) highlight potentially significant differences in LCOE solely as a function of the observed capacity factor differences. The Low SP turbine has the lowest overall LCOE value, with the highest quality resource sites achieving LCOE values that are less than US$35/MW h; moreover, the shape of the Low SP distribution results in greater clustering of geospatial pixels at the low end of the LCOE distribution. This drives a median value for the Low SP turbine of US$43/MW h compared to US$52/MW h for the Constant SP turbine and US$56/MW h for the High SP turbine. Clearly, if CapEx were equal across these turbine options, as is assumed in the Favor Low SP scenario, preferences for low-SP technology would be high.

The relative outcomes across all the three scenarios (Figure 12) suggest that the LCOE results and preferences for lower or higher SP technology are indeed sensitive to the cost conditions assumed. Under the Favor Low SP scenario, the preference for low-SP technology is likely to be strong in all locations, as described above. Under the Reference scenario, the distributions in Figure 12 are much closer together and the High SP turbine appears to be increasingly competitive with the Constant SP turbine. However, even under the Reference scenario, the Low SP turbine appears to almost universally have the lowest LCOE, indicating that for any given site, the Low SP turbine is likely to be preferred, all else being equal.¹² In this scenario, the median values for Low SP and High SP turbines are separated by about US$7/MW h. Under the Favor High SP scenario, the results are more even, with the High SP turbine having a lower LCOE than the Constant SP turbine in all locations, while also overlapping significantly with the Low SP turbine. In this scenario, the median LCOE values of the High SP and Low SP turbines are separated by only about US$2/MW h. Under these conditions, preferences for both turbines are likely to be meaningful, with the lowest LCOE turbine determined largely by the site-specific characteristics of a given project.

While the absolute and relative LCOE distributions shown in Figures 11 and 12 provide a sense of the opportunities and challenges facing each turbine type under the three cost scenarios, Figures 13 and 14 illustrate where in the United States each turbine type is likely to find favor under two of these three scenarios,¹³ by showing which turbine type has the lowest LCOE for a given geospatial pixel. In the Reference scenario (Figure 13), it is only the very highest wind resource sites—for example, along the mountain ridgetops in the West and East, where the capacity factor advantage of the Low SP turbine is partially eroded by increased time spent operating at rated power—that the High SP turbine prevails on the basis of LCOE. However, as we transition to the Favor High SP scenario (Figure 14), a CapEx tipping point occurs, whereby the High SP turbine wins out across much of the high-quality wind resource locations of the Central Plains and Upper Midwest.

Figure 13.

Lowest LCOE turbine by location, Reference scenario.

Figure 14.

Lowest LCOE turbine by location, Favor High SP scenario.

Overall, Figures 11 to 14 suggest that there are conditions under which either low- or high-SP turbines could penetrate the market going forward. However, as long as the CapEx advantage for higher SP turbines is approximately US$240/kW or less, it is likely that lower SP turbines will continue to support the lowest LCOE in virtually all locations in which they can be suitably deployed. Conversely, if the cost premium for low-SP turbines exceeds US$240/kW, higher SP turbines could become increasingly competitive, initially in very high wind resource sites but increasingly in moderate wind-speed sites as well, particularly if the cost differential is at or in excess of US$480/kW.

Within the context of the observed empirical trends described earlier, these modeling results suggest that real-world market conditions are perhaps most comparable to the Reference scenario, whereby there is meaningful competition between lower and higher SP turbines, but low-SP technology often wins. In this context, a competitive marketplace could be pushed further toward low-SP technology in places where there are capacity constraints, or alternatively toward higher SP turbines where there are land constraints (and fewer capacity constraints). This observation could provide some insight into the overall global trends described earlier—that is, toward lower SP in general, but with certain land-rich but capacity-constrained markets like the United States and China pushing SP ratings even lower.

For a final perspective on the relative economics of lower versus higher SP turbines, we turn to a metric known as the “breakeven cost,” described by Lantz et al. (2019). In short, the breakeven cost equals the incremental CapEx premium or savings that a given turbine configuration (in this case, the Low SP and High SP turbines) would need to achieve at a given site in order to be competitive with a reference turbine (in this case, the Constant SP turbine) in terms of LCOE. Breakeven costs are a useful way to evaluate the magnitude of the potential challenge or opportunity associated with a given turbine configuration, without requiring a bottom-up cost estimate to compute actual competitiveness. In this case, the differences in capacity factors between the Low SP and High SP turbines relative to the Constant SP turbine (from Figure 10) are used to calculate the difference in CapEx—that is, the breakeven cost—required for the Low SP and High SP turbines to achieve an equivalent LCOE as the Constant SP turbine.

Figure 15 shows the breakeven cost results. The capacity factor improvement offered by the Low SP turbine provides a significant margin—that is, with a median exceeding US$400/kW of incremental CapEx—for maintaining competitiveness with the Constant SP turbine. This breakeven cost is higher than even the upper end of the range of cost premiums incurred by low-SP turbines noted earlier, providing some justification for the historical deployment of such turbines.¹⁴ Conversely, with a median breakeven cost approaching negative US$150/kW, the High SP turbine must—due to its lower capacity factor—demonstrate sizable cost savings per unit capacity in order to be competitive with the Constant SP turbine on an LCOE basis. Of course, depending on the degree of innovation realized and potential scaling effects, costs for low-SP technology that exceed these breakeven levels are certainly possible, as are pathways that could enable relatively lower cost per kW values for higher SP technology.

Figure 15.

Relative change in CapEx that would result in an equivalent LCOE as the Constant SP turbine.

Conclusion

The emergence and subsequent prevalence in many markets around the world of low-SP wind turbine technology over the past decade is a notable development and indicative of the rapid pace of technological advancement within the wind industry. In relatively capacity-constrained markets, lower SP has been the most direct way to boost energy and revenue per invested dollar. In addition, lower SP has supported large reductions in LCOE as innovation in blade design and materials has enabled turbine manufacturers to minimize scaling-related cost increases associated with larger rotors, thereby enabling the higher capacity factors of low-SP technology to translate into lower LCOE. Looking ahead, however, the future of low-SP technology is not altogether clear, as new commercial offerings with relatively higher SP demonstrate that turbine design is a system optimization with competing objectives that may or may not drive toward lower SP.

In this context, the geospatial analysis conducted herein suggests that under reasonable cost scenarios, low-SP turbines could continue to play an important role in the US and global markets going forward. Even under conditions that strongly favor high SP (e.g. by penalizing low SP by US$480/kW), low-SP turbines could still be preferred (on economic grounds) in many moderate to low wind-speed niches. At the same time, there are conditions under which a shift toward higher SP could be anticipated, depending on the degree of cost savings per unit capacity offered by higher nameplate capacity turbines. Nevertheless, so long as the relative cost premium for low-SP technology can be restrained, and costs to boost generator capacity remain at or near current levels, the future for low-SP technology and large-rotor turbines more generally could be bright, especially as existing transmission capacity is increasingly consumed by wind plants or other generators, including solar photovoltaics. Of course, a key risk associated with lower SP technology is the need to keep costs in check, particularly as turbine and blade designers must consider increasingly binding constraints in blade manufacture and transport.

Beyond these LCOE-based considerations, the boost in market value provided by low-SP turbines could also become increasingly important as wind penetration continues to grow. Further work examining this value boost from low-SP turbines at wind project sites across the United States is underway and is expected to provide greater insights into the relative economics of both large rotor turbines generally, and low-SP technology, specifically. Other future work that could provide insights into optimal SP is bottom-up techno-economic cost modeling focusing on the incremental system costs associated with rotor and generator scaling and considering the enhanced operational sophistication of modern turbine control systems. Such efforts could help to inform estimates of actual cost differences between lower and higher SP technology, the value of specific innovations that could affect SP, and the potential tradeoffs between turbines optimized for capacity- or land-constrained markets versus unconstrained turbines optimized to minimize LCOE.

Footnotes

Acknowledgements

This work was authored by the Lawrence Berkeley National Laboratory (under Contract No. DE-AC02-05CH11231 with the US Department of Energy (DOE)) and the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the DOE under Contract No. DE-AC36-08GO28308.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the US Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy Wind Energy Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the US Government. The US Government retains and the publisher, by accepting the article for publication, acknowledges that the US Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for US Government purposes.

ORCID iDs

Mark Bolinger

Robert Hammond

Notes

References

Bloomberg New Energy Finance (2018) 2H 2018 Wind Turbine Price Index. Report, Bloomberg New Energy Finance, 12 December, London.

Burt

Firestone

Madsen

, et al. (2017) Tall towers, long blades and manifest destiny: The migration of land-based wind from the Great Plains to the thirteen colonies. Applied Energy 206: 487–497.

Burton

Sharpe

Jenkins

, et al. (2001) Wind Energy: Handbook. Chichester; New York: John Wiley.

Capps

Hall

Hughes

(2012) Sensitivity of Southern California wind energy to turbine characteristics. Wind Energy 17(1): 141–159.

Cole

Frazier

Donohoo-Vallett

, et al. (2018) 2018 Standard Scenarios Report: A U.S. Electricity Sector Outlook. Report, National Renewable Energy Laboratory, Golden, CO.

Cotrell

Stehly

Johnson

, et al. (2014) Analysis of transportation and logistics challenges affecting the deployment of larger wind turbines: Summary of results. Report, National Renewable Energy Laboratory, Golden, CO.

Dalla Riva

Hethey

Vītiņa

(2017) Impacts of wind turbine technology on the system value of wind in Europe. Report, Ea Energy Analyses for IEA Wind Task 26, Paris.

DNV

(2019) Supersized wind turbine blade study: R&D pathways for supersized wind turbine blades. Report, DNV GL, Seattle, WA.

EWEA (2009) Wind Energy, the Facts (EWEA). Report, European Wind Energy Association, Brussels.

10.

Garrett

Rønde

(2011a) Life cycle assessment of electricity production from a V80–2.0 Gridstreamer wind plant. Report, Vestas Wind Systems A/S, Aarhus, December.

11.

Garrett

Rønde

(2011b) Life cycle assessment of electricity production from a V90–2.0 Gridstreamer wind plant. Report, Vestas Wind Systems A/S, Aarhus, December.

12.

Hirth

(2013) The market value of variable renewables: The effect of solar wind power variability on their relative price. Energy Economics 38: 218–236.

13.

Hirth

(2016) The benefits of flexibility: The value of wind energy with hydropower. Applied Energy 181: 2010–2223.

14.

Hirth

Müller

(2016) System-friendly wind power. Energy Economics 56: 51–63.

15.

Johansson

Thorson

Goop

, et al. (2017) Value of wind power—Implications from specific power. Energy 126: 352–360.

16.

Lantz

Roberts

Nunemaker

, et al. (2019) Increasing wind turbine tower heights: Opportunities and challenges. Report, National Renewable Energy Laboratory, Golden, CO.

17.

McKenna

Ostman von der Leye

Fichtner

(2016) Key challenges and prospects for large wind turbines. Renewable and Sustainable Energy Reviews 53: 1212–1221.

18.

May

(2017) The impact of wind power support schemes on technology choices. Energy Economics 65: 343–354.

19.

Mills

Wiser

(2014) Changes in the economic value of wind energy and flexible resources at increasing penetration levels in the Rocky Mountain Power Area. Wind Energy 17(11): 1711–1726.

20.

Molly

(2011) Rated power of wind turbines: What is best. DEWI Magazine 38: 49–57.

21.

Moné

Stehly

Maples

, et al. (2015) 2014 cost of wind energy review. Report, National Renewable Energy Laboratory, Golden, CO.

22.

Razdan

Garrett

(2015a) Life cycle assessment of electricity production from an onshore V100–2.0 wind plant. Report, Vestas Wind Systems A/S, Aarhus, December.

23.

Razdan

Garrett

(2015b) Life cycle assessment of electricity production from an onshore V110–2.0 wind plant. Report, Vestas Wind Systems A/S, Aarhus, December.

24.

Razdan

Garrett

(2018a) Life cycle assessment of electricity production from an onshore V116–2.0 wind plant. Report, Vestas Wind Systems A/S, Aarhus, July.

25.

Razdan

Garrett

(2018b) Life cycle assessment of electricity production from an onshore V120–2.0 wind plant. Report, Vestas Wind Systems A/S, Aarhus, July.

26.

Rinne

Holtinnen

Kiviluoma

, et al. (2018) Effects of turbine technology and land use on wind power resource potential. Nature Energy 3: 494–500.

27.

Sieros

Chaviaropoulos

Sørensen

, et al. (2010) Upscaling wind turbines: Theoretical and practical aspects and the impact on the cost of energy. Wind Energy 15: 3–17.

28.

Stehly

Heimiller

Scott

(2017) 2016 cost of wind energy review. Report, National Renewable Energy Laboratory, Golden, CO.

29.

U.S. Department of Energy (DOE) ( 2008) 20% wind energy by 2030: Increasing wind energy’s contribution to U.S. electricity supply. Report, U.S. Department of Energy, Washington, DC.

30.

U.S. Department of Energy (DOE) ( 2015a) Enabling wind power nationwide. Report, U.S. Department of Energy, Washington, DC.

31.

U.S. Department of Energy (DOE) ( 2015b) Wind vision: A new era for wind power in the United States. Report, U.S. Department of Energy, Washington, DC.

32.

Wiser

Bolinger

(2019) 2018 wind technologies market report. Report, U.S. Department of Energy, Washington, DC.

33.

Wiser

Bolinger

Lantz

(2019) Assessing wind power operating costs in the United States: Results from a survey of wind industry experts. Renewable Energy Focus 30: 46–57.

34.

Wiser

Jenni

Seel

, et al. (2016) Expert elicitation survey on future wind energy costs. Nature Energy 1: 16135.

35.

Wiser

Lantz

Bolinger

, et al. (2012) Recent developments in the levelized cost of energy from U.S. wind power projects. Report, Lawrence Berkeley National Laboratory, Berkeley, CA.

36.

Wiser

Yang

Hand

, et al. (2011) Wind energy. In: Edenhofer

Pichs-Madruga

Sokona

, et al (eds) IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation. Cambridge; New York: Cambridge University Press, pp. 535–607.

37.

Wood Mackenzie (2018) Global wind turbine technology market report 2018. Report, December. Available at: https://www.woodmac.com/reports/power-markets-global-wind-turbine-technology-market-report-2018-37070