The following chart shows the LCOE scenario results presented above normalized for a comparison with literature projections.
The three scenarios for technology innovation are:
- Conservative Technology Innovation Scenario (Conservative Scenario): wind technology scale increasing in the near term but leveling off soon afterward, with limited advancement in turbine controls and science-based modeling to inform the next generation of wind technology
- Moderate Technology Innovation Scenario (Moderate Scenario): scale continuing to increase rapidly with innovations overcoming transportation challenges; advancements occurring in turbine controls; science-based modeling informing the next generation of wind technology
- Advanced Technology Innovation Scenario (Advanced Scenario): enabling large-scale increases in turbine technology size and
scope,new transportation solutions, fully integrated wind plant advanced control systems, and high-fidelity science-based modeling to inform multiple aspects of turbine design.
In the 2021 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 NREL Regional Energy Deployment System (ReEDS) model (Brown et al., 2020). In ATB editions before 2020, these classes were referred to as techno-resource groups (TRGs) and they were designed based on site-specific levelized cost of energy 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 (U.S. Department of Energy, 2015). Starting with the 2020 ATB, the TRG-based classification was replaced with a simpler set of resource "wind speed classes" that are defined based on only annual mean wind speed.
For land-based wind, each of the potential wind sites represented in the ReEDS model is associated with one of 10 wind speed classes. The range of annual mean wind speeds, averaged for all years between 2007 and 2013, ranges from 1.72 m/s to 12.89 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 that 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 referred to as the "open access" scenario (Lopez et al., 2021) and based on analysis from the Renewable Energy Potential (reV) model (Maclaurin et al., 2019). Although the ReEDS model and other
The average wind speed varies from project to project across the United States.
|Wind Speed Class||Min. Wind Speed (m/s)||Max. Wind Speed (m/s)||Wind Speed Range (m/s)||Percentile Range|
- Values reported in table are for wind speeds 110 meters above the ground.
|Scenario||Rotor, Nacelle Assembly||Tower||Science-Based|
Technology Description: Nacelles and m
Justification: Current wind turbine blades are fabricated as a single piece and are typically transported from the manufacturer to the project site by truck or rail, which means that without introducing new innovations (e.g., blade segmentation), they are limited by the infrastructure constraints on the transportation route (e.g., overhead bridge heights and tunnel openings).
Technology Description: Steel towers are transportable, road limitations are similar to current ones, and hub heights are
Justification: Turbine tower fabricators currently
Technology Description: The scenario is limited to no integration of high-fidelity modeling or advanced controls, and plant optimization does not change.
Justification: Legacy wind turbine and plant control strategies remain in place, limiting introduction of new lightweight turbine designs and turbine wake loss impacts
Technology Description: Segmentation of the lower tower enables large-diameter towers, increased hub heights, and larger turbines
Justification: Several advanced steel construction and concrete/steel hybrid tower designs from various design and manufacturing firms are available on the market, enabling cost-effective taller towers exceeding 140 m. One example of an advanced steel construction designed tower is the large-diameter steel tower (LDST) launched by Vestas in 201
Technology Description: Increased integration of high-fidelity modeling and advanced controls enables lower CAPEX and higher capacity factors.
Justification: Widespread application of advanced wind plant prognostic systems by several w
Technology Description: Larger nameplate turbines with segmented blades or partial pitch rotors are transported by truck or rail, which enables significantly larger blades. There is increased adoption of active aerodynamic controls and partial pitch blades.
Technology Description: On-site manufacturing of advanced steel tower (spiral welding) or h
Justification: The ability to enable onsite fabrication of continuous spiral-welded towers to be optimized to system requirements without needing to constrain the tower base has been demonstrated by Keystone Tower Systems, which has also designed optimal high hub-height towers up to 180 m (see Keystone Tower Systems).
Technology Description: All levels of high-fidelity modeling and advanced controls are achieved.
|Impacts by Technology Innovation|
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
such 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 allows for a common blade base to be married to a number of segmented blade tips, aiding in reducing 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 O&M procedures combined with more reliable components to reduce annual average fixed operation and maintenance (FOM) costs Continued manufacturing and design efficiencies such 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.
To obtain current and future cost and performance estimates, the technology representations for the Base Year (2019) and 2030 are defined. The representative technology for land-based wind in the Base Year consists of a 2.6-MW nameplate rating, a rotor diameter of 121 m, and a hub height of 90 m (Wiser et al., 2020). The representative technology for 2030, in the Moderate Scenario, assumes a 5.5-MW turbine with a rotor diameter of 175 m and a hub height of 120 m. Notably, turbines that are nearly of this scale are commercially available today and are expected to be installed at select sites in the United States in the 2020s.
Capital Expenditures (CAPEX)
Base Year: Capital expenditures (CAPEX) associated with wind plants installed in the interior of the country are used to characterize CAPEX for hypothetical wind plants with average annual wind speeds that correspond with the median conditions for land-based wind projects installed in 2019 (Stehly et al., 2020).
Future Years: To reduce the vast number of combinations of future pathways, NREL analysts define three future turbine configuration in 2030 to estimate cost and performance for the Conservative, Moderate, and Advanced Scenarios.
The defined turbine characteristics are used to estimate the total system CAPEX of a theoretical commercial scale (e.g., 200-MW) project. In the 2021 ATB, this site-specific design optimization process, which is often reflected in different CAPEX values across wind speed classes, is simplified. The CAPEX estimates for the Base Year (2019) is the same across the 10 wind speed classes. In 2030, the CAPEX changes for each of the scenarios (i.e., Conservative, Moderate, and Advanced) since each scenario assumes a different turbine technology.
Operation and Maintenance (O&M) Costs
Source: (Wiser et al., 2019)
Future Years: Future FOM is assumed to decline by approximately 10% by 2030 in the Moderate Scenario and 20% in the Advanced Scenario. These values are informed by recent benchmarking work for wind power operating costs in the United States (Wiser et al., 2019). The ATB does not consider differences in regional FOM costs associated with labor, materials or differences in O&M strategies—for example, operating the wind plant to maximize tax credits by deferring maintenance activities.
Base Year: 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 as well as 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.
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.
To calculate the Base Year and future capacity factors, the 2019 and 2030 turbine characteristics defined in the Representative Technology section are input into the System Advisor Model (SAM) to develop an idealized power curve, and SAM is run for each of the weighted average wind speeds in each wind speed class. The capacity factors are calculated at the representative turbine hub height by extrapolating the wind speed up or down from the referenced 110-m, above-ground-level, long-term average hourly wind resource data from the Wind Integration National Dataset (WIND) Toolkit.
The following references are specific to this page; for all references in this ATB, see References.