Land-Based Wind

2024 ATB data for land-based wind are shown above. These projections use bottom-up engineering models in combination with representative 2030 wind turbine and plant technologies. The predicted future technology pathways are based on a series of innovations to overcome transportation challenges, advance wind turbine controls, and apply science-based modeling for next-generation wind turbines. These technology advancements enable economies of scale, balance-of-plant efficiencies, and more-efficient energy extraction for various turbine configurations in different wind resource regimes. Details on the representative 2030 wind turbines characteristics are presented in the Representative Technology section of this page. 

The following chart shows the levelized cost of energy (LCOE) scenario results presented above normalized for comparison with literature projections. 

As a result of intense competition from within as well as among several technologies, including solar photovoltaics (PV) and natural-gas-fired generation, there is substantial focus throughout the global wind industry on driving down costs and increasing performance.

Representative Technology

The four representative wind turbine technologies in the 2024 ATB allow for technology configurations that are more appropriate for each wind resource class (details on wind resource class are provided in the Resource Categorization section of this page). We selected these four wind turbine technologies in consultation with industry to represent the range of technology we expect to be available in 2030, including a higher-specific-power machine that would be more suitable for land-constrained sites. The representative near-future (2030) wind turbine characteristics presented in the 2024 ATB are derived from industry expert interviews and literature expectations. Turbine ratings range from 3.3 to 8.3 megawatts (MW), with rotor diameters of 148–196 m, hub heights of 100–140 m, and specific power ratings of 192–275 watts per square meter (W/m²). Notably, turbines nearly of this scale are commercially available and are expected to be installed at select U.S. sites in the 2020s. 

See the following table for details on each technology configuration (T1–T4) and how they compare to a 2022 industry-average turbine (Wiser and Bolinger, 2023).

Representative Wind Turbine and Plant Characteristics

We assume the characteristics for each technology configuration remain constant from 2022 to 2050, and we calculate cost and performance trajectories for each configuration using all three sets of cost and performance assumptions (see the Scenario Descriptions section of this page). The technology configuration that results in the minimum LCOE for a given range of annual average wind speeds (specified using a resource class; see the Resource Categorization section of this page) is the technology configuration reported in the ATB for that wind resource class (e.g., at strong wind resource sites, T1 results in the lowest LCOE of any technology; at a low-resource site, T4 results in lower LCOE values because of the taller hub height and lower specific power rating; see the Resource Categorization section of this page for details). The wind turbine technology assumptions are intended to allow for turbine configurations that are more appropriate for each wind resource class. 

Scenario Descriptions

The 2024 ATB scenarios for land-based wind assume wind turbine technology characterizations and projected innovations to overcome transportation challenges, advance wind turbine controls, and increase adoption of science-based modeling. In the 2023 and 2024 ATBs, multiple wind turbine technology configurations are developed separately, which allows for different technologies to be used within each scenario. 

The scenarios consider four technology configurations (see the Representative Technology section of this page for details), each with three cost and performance projections:

• Conservative Cost and Performance Scenario (Conservative Scenario): Cost and performance trajectories for each technology are based on conservative historical learning rates. Learning rates for capital expenditures vary by technology based on the Moderate Scenario's bottom-up, engineering-based cost modeling of each technology in 2030. 
• Moderate Cost and Performance Scenario (Moderate Scenario): From 2022 to 2030, cost and performance trajectories for each technology are based on bottom-up scaling relationships and process-based balance of system (BOS) and turbine component cost models for each technology in 2022 and 2030. From 2030 to 2050, cost and performance trajectories for each technology are based on moderate historical learning rates. 
• Advanced Cost and Performance Scenario (Advanced Scenario): Cost and performance trajectories for each technology are based on aggressive historical learning rates. Learning rates for capital expenditures vary by technology based on the Moderate Scenario's bottom-up, engineering-based cost modeling of each technology in 2030. 

The three scenarios are defined by combining bottom-up, engineering-based modeling with literature data and learning rates. Calculated learning rates are based on global deployment projections (DNV, 2023), and literature projections are used to inform the Conservative and Advanced Scenarios. The scenario definitions are used to estimate current and future cost and performance values over four representative wind turbine configurations. Details on the wind turbine's characteristics are presented in the Representative Technology section of this page.

All three scenarios start with the same cost and performance characteristics in the Base Year, and the projections are generated by linearly interpolating between the Base Year and updated cost and performance assumptions in two future years: 2030 and 2050. See the table below for a summary of how the cost and performance assumptions vary by scenario in future years; base year assumptions are documented in the Methodology section of this page. 

Summary of Cost and Performance Assumptions and Justifications by Scenario (2030 and 2050)

In general, differences among the technology cost cases reflect different levels of adoption of innovations. Reductions in technology costs reflect the following cost reduction opportunities:

• Continued turbine scaling to higher-megawatt turbines with larger rotors so that the megawatt capacity/swept area decreases, resulting in higher capacity factors for a given location
• Continued diversification of turbine technology whereby the largest rotor diameter turbines tend to be located in lower-wind-speed sites, but the number of turbine options for higher-wind-speed sites increases
• Introduction of segmented blades, which allows a common blade base to be married to several segmented blade tips, helping reduce blade production costs
• Taller towers that result in higher capacity factors for a given site that are due to the wind speed increase with elevation above ground level
• Improved plant siting and operation to reduce plant-level energy losses, resulting in higher capacity factors
• Wind turbine technology and plants that are increasingly tailored to and optimized for local site-specific conditions
• More-efficient operation and maintenance (O&M) procedures combined with more reliable components to reduce annual average fixed O&M (FOM) costs
• Continued manufacturing and design efficiencies so that the capital cost per kilowatt decreases with larger turbine components
• Adoption of a wide range of innovative control, design, and material concepts that facilitate the above high-level trends.

Scenario Assumptions

The cost reduction estimates for the Conservative and Advanced Scenarios are derived from the calculated number of doublings for projected global land-based wind deployment projections (DNV, 2023), and they assume an applied learning rate. These assumed values and the calculated percentage reduction for 2030 are summarized in the following tables. The Moderate Scenario estimates are produced from bottom-up, engineering-based models and literature estimates. 

Scenario Assumptions for CAPEX (2030)

Scenario Assumptions for OPEX (2030)

Resource Categorization

In the 2024 ATB, the cost and performance data for wind technologies are specified for different resource categories that are consistent with those used to represent the full wind resource in the National Renewable Energy Laboratory (NREL) Regional Energy Deployment System (ReEDS) model (Brown et al., 2020). These specified resource classes are no longer used in ReEDS but are reported in ATB to illustrate resource differences. In ATB editions before 2020, these classes were referred to as techno-resource groups (TRGs) and were designed based on site-specific LCOE by considering, in combination, the wind resource quality (e.g., wind speed) and turbine configuration (e.g., specific power). The TRG methodology is described in Appendix H of the Wind Vision study (DOE, 2015). Starting with the 2020 ATB, the TRG-based classification was replaced with a simpler set of resource wind speed classes defined based only on annual mean wind speed. 

For land-based wind, each of the potential wind sites represented in the ReEDS model is associated with 1 of 10 wind speed classes. Annual mean wind speeds, averaged for all years from 2007 through 2013, range from 1.72 to 12.89 meters per second (m/s). To identify the break points that define the 10 wind speed classes within this wind speed range, we specify the percentile of the total wind resource technical potential in capacity terms associated with each class. For example, the top wind speed class (Wind Speed Class 1) is defined based on the mean wind speed range of the top 1% of all potential wind capacity in the contiguous United States. We specify a narrower percentile range for the top classes so ReEDS has higher-resolution representation for the best sites.

The following table shows the percentile ranges assumed for each resource class as well as the resulting mean wind speed ranges that define each class. We apply these percentiles to a representation of the wind resource using only the most basic exclusions, which are referred to as the "open access" scenario (Lopez et al., 2021) and based on analysis using the Renewable Energy Potential (reV) model (Maclaurin et al., 2019). Although the ReEDS-based analysis and other analyses can and do rely on different resource representations with various exclusion assumptions, the same mean wind speed break points are used for the 10 wind speed classes shown in the table.

The average wind speed varies by project across the United States. Wind Speed Class 4 is indicative of a moderate-quality wind regime and is intended to be a representative wind resource for most wind projects installed in the United States. Wind Speed Class 1 suggests a resource-rich wind resource that is most attractive for wind project development, and Wind Speed Class 10 represents a less favorable wind resource site.

In the 2024 ATB, one technology configuration (see the Representative Technology section of this page) is assigned to each wind speed class selected by the technology configuration with the lowest LCOE within each wind speed class. This method results in technology configuration T1 being selected for Wind Speed Classes 1–7, T2 for Wind Speed Class 8, T3 for Wind Speed Class 9, and T4 for Wind Speed Class 10. Assigning specific wind turbine technologies to different wind classes is expected to represent a more accurate supply curve.  

Land-Based Wind Resource and Technology Classes

Methodology

This section describes the methodology to estimate base year and future CAPEX, OPEX, and NCF. The base year and future cost and performance estimates assume a 200-MW wind plant, which is consistent with recently installed project sizes (Wiser and Bolinger, 2023). For standardized assumptions, see labor cost, regional cost variation, materials cost index, scale of industry, policies and regulations, and inflation.

Capital Expenditures (CAPEX)

Definition: Wind plant capital expenditures are defined to include items noted in the Components of CAPEX table below.

Base Year: CAPEX associated with the four representative technologies are estimated using bottom-up engineering models for hypothetical wind plants installed in 2022. The base year value for each wind speed class depends on the selected representative technology (see the Land-Based Wind Resource and Technology Classes table in the Resource Categorization section of this page). In 2023, CAPEX increases from 2022 because of supply chain stress, high commodity prices, and increased logistics costs. These near-term factors are accounted for by applying an average CAPEX multiplier of 10% in 2023 and 5% in 2024, relative to 2022 costs. These inflationary impacts are assumed to recover around 2024 ((IEA, 2022), (Vestas, 2022)). The following chart shows historical CAPEX for land-based wind.

Future Years: The technology configurations are used to estimate the total system CAPEX of a theoretical commercial-scale (e.g., 200-MW) project. The site-specific design optimization process, which is often reflected in different CAPEX values across wind speed classes, is simplified. In 2030, the CAPEX changes for each scenario (i.e., Conservative, Moderate, and Advanced) and for each turbine. The Conservative Scenario estimates are derived assuming an 8% base learning rate, and the Advanced Scenario assumes a 30% base learning rate. Learning rates vary by technology in the Conservative Scenario and the Advanced Scenario based on the Moderate Scenario's bottom-up, engineering-based costs for each technology. The base learning rates are adjusted using a multiplier to account for changes in learning by technology. The multiplier is based on the change in CAPEX from the Base Year to 2030 for each technology and is normalized by the technology with the highest change in CAPEX, which is T4. The learning rate multipliers for each technology are captured in the table below. In 2050, CAPEX is back-calculated based on an LCOE learning rate of 10%, 14%, and 20% from 2030 to 2050 for the Conservative, Moderate, and Advanced Scenarios, respectively.

The relatively low observed sensitivity to significantly different turbine configurations for a single reference site indicates uncertainty and a need for wind turbine tailoring for varied site conditions. It is generally expected over the long term, wind turbine designs will be optimized for project-specific site conditions. In the ATB, CAPEX reflects typical wind plants and does not include differences in regional costs associated with labor, materials, taxes, or system requirements. Starting in 2024, Grid Connection Costs (i.e., tie line, new or upgraded substation, and interconnection costs) are included in CAPEX. The related NREL Standard Scenarios use regional CAPEX adjustments. The range of CAPEX demonstrates variation with wind resources in the contiguous United States.

Use the following table to view the components of CAPEX and how they change with the scenarios.

Components of CAPEX

Cost Details by Representative Wind Turbine Technology (2030)

Operation and Maintenance (O&M) Costs

Definition: OPEX represent the all-in fixed and variable expenditures required to operate and maintain a wind plant, including items noted in the Components of O&M Costs table below. For land-based wind, the all-in O&M expenditures are reported in FOM. 

Base Year: The all-in OPEX cost for each representative technology is informed by recent literature (Liu and Garcia da Fonseca, 2021) and (Wiser et al., 2019)) and is captured in the FOM costs for the ATB. The base year cost is different for each representative technology because O&M costs are expected to vary by wind turbine rating with projections showing lower FOM costs as turbine rating increases. FOM will vary by technology, again because of the differences in turbine rating. The following chart shows OPEX based on wind plant commissioning date (Wiser and Bolinger, 2023).

Future Years: Future FOM is assumed to decline by approximately 10% by 2030 in the Moderate Scenario, 5% in the Conservative Scenario (assuming a 5% learning rate), and 19% in the Advanced Scenario (assuming a 20% learning rate). These values vary by wind turbine rating, with greater OPEX reductions resulting from larger machine ratings. The ATB does not consider differences in regional FOM costs associated with labor, materials, or differences in O&M strategies (e.g., operating the wind plant to maximize tax credits by deferring maintenance activities). 

Use the following table to view the components of O&M costs.

Components of O&M Costs

Cost Details by Representative Wind Turbine Technology (2030)

Capacity Factor

Definition: The capacity factor is influenced by the wind plant's generation profile, expected downtime, and energy losses within the plant. The specific power (i.e., ratio of machine rating to rotor-swept area) and hub height are design choices that influence the capacity factor. Most installed U.S. wind plants generally align with ATB estimates for performance in Wind Speed Classes 2–7. High wind resource sites associated with Wind Speed Class 1 and very low wind resource sites associated with Wind Speed Classes 8–10 are not as common in the historical data, but the range of observed data encompasses ATB estimates.

Base Year: The base year capacity factors are calculated by generating a power curve for each wind turbine defined in the Representative Technology section of this page and using the Weibull distribution with average wind speeds in each of the appropriate wind speed classes (see the Resource Categorization section of this page) to produce the annual energy production. The hub height of each representative wind turbine is considered by extrapolating the wind speed up or down from the referenced 110-m, above ground level and assumes a power law shear exponent of 0.2.

The following chart shows a range of capacity factors based on variation in the resource for wind plants in the contiguous United States and the future capacity factor estimates for the Conservative, Moderate, and Advanced Scenarios, which vary by scenario and technology.   

Future Years: The methods for calculating future year net capacity factors are like the methods for the Base Year. However, technology innovations are assumed to increase wind plant energy capture through advanced controls and to reduce total system losses that increase capacity factor for all wind speed classes (Dykes et al., 2017). Turbine rotor diameter, specific power, and hub height can each be traded off to achieve a given capacity factor, depending on site conditions and costs for pursuing one approach or the other; wind plant layout and operating strategies that impact losses may also be used to achieve a given capacity factor. The 2024 ATB presents one of many capacity factor improvement pathways for LCOE reduction. 

Performance Details by Representative Wind Turbine Technology (2030)

References

The following references are specific to this page; for all references in this ATB, see References.