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Offshore Wind

ATB data for offshore wind are shown above. These projections are derived by combining techno-economic modeling with an assessment of future technology and innovation pathways. Baseline offshore wind costs in 2018 (COD—commercial operation date) are modeled in NREL's Offshore Regional Cost Analyzer (ORCA) tool (Beiter et al. 2016) for thousands of potential offshore wind sites across major U.S. coastal areas. The representation of ORCA used for the 2020 ATB includes assumptions that were updated to correspond to the latest technology and market developments. Future cost scenarios up to 2032 (COD) are determined using an assessment of technology and innovation pathways and increased efficiencies and learning in the supply chain. These technology and innovation pathways are structured around increasing turbine nameplate capacity through 2032, which induce economies of scale and yield cost reductions in the capital (CAPEX) and operation and maintenance (OPEX) expenditures. Higher turbine ratings also reduce wake losses and thereby increase the annual energy production of an offshore wind plant. Costs beyond 2032 are obtained by extrapolating the cost trends estimated for 2018–2032 (COD).

The three scenarios for technology innovation are:

  • Conservative Technology Innovation Scenario (Conservative Scenario): turbine size remaining at a level consistent with the technology solutions available in today's markets;limited advancements in technology innovation are characteristic of this scenario. Logistical and manufacturing constraints are similar to those today, and they limit turbine size growth.
  • Moderate Technology Innovation Scenario (Moderate Scenario): turbine size increasing at a rate commensurate with growth in recent years;logistical, manufacturing, operating and performance constraints are addressed by technology innovation in turbine, substructure, port and vessel capabilities to enable the next generation of offshore wind technology. These increases in turbine size are accompanied by continued increases in supply chain efficiencies.
  • Advanced Technology Innovation Scenario (Advanced Scenario): turbine size increasing at a rate that is considerably higher than in recent years; accelerated technology innovation enables large turbine systems and fundamentally change the manufacturing, installation, operation, and performance of a wind farm.

Resource Categorization

Continental U.S. offshore wind technical resource potential exceeds 2,000 GW (Musial et al. 2016), after accounting for exclusions that are due to water depth, minimum wind speed, limits to floating technology in freshwater surface ice, competing uses and environmental exclusions, marine protected areas, shipping lanes, pipelines, and other factors.

Each of the potential wind sites represented by this technical resource potential is binned into 14 wind speed classes, which are organized by substructure technology type and wind speed. Wind speed classes 1–7 represent fixed-bottom offshore wind technology, and wind speed classes 8–14 represent floating offshore wind technology (see the tables below). Sites are assigned to either fixed-bottom resource if they are located in water depths < 60 meters (m) or to floating resource if the water depth exceeds 60 m.

For each substructure type, the breakpoints for the wind speed classes occur at selected percentiles of the total wind resource technical potential. This specification is conducted separately for each substructure type. For example, the most favorable resource class (Wind speed class 1) is defined to represent the top 2% of all potential wind capacity offshore of the contiguous United States in terms of wind speed and costs. We specify a narrower percentile range for the top classes so that the NREL Regional Energy Deployment System (ReEDS) model has higher resolution representation for the most favorable sites because they are more likely to be developed in the near-to medium term. In ReEDS, these percentiles are applied to a representation of the wind resource using only the most basic exclusions and based on analysis from the Renewable Energy Potential (reV) model (Maclaurin et al. 2019). The following tables show the percentile ranges assumed for each resource class and the resulting mean wind speed ranges that define each class for fixed-bottom (first table) and floating offshore (second table) technologies in ReEDS (Lopez et al. forthcoming). The ranges of annual mean wind speeds, averaged for all years between 2007 and 2013 and by wind speed class, ranges from 5.9–10.0 m/s for fixed-bottom technology to 6.0–11.3 m/s for floating technology. Because of the different resource potential that each wind speed class represents (e.g., wind speed class 1 represents nearly 2% whereas wind speed class 7 represents 36% of the total resource potential), there is not necessarily a consistent trend in average cost and site parameters from wind speed class 1 through 7 (fixed-bottom) and 8-14 (floating). For instance, the average water depth for floating sites does not increase consistently because of the variation in resource potential each wind speed class represents. These wind class categories are consistent with those used to represent the full wind resource in ReEDS (Brown et al. 2020). In prior editions of the ATB, these wind speed classes were referred to as techno-resource groups (TRGs) with the total wind resource technical potential divided into 5 fixed-bottom and 10 floating classes. TRG breakpoints in earlier ATB versions are different from the representation of the wind speed classes in this year's ATB. The TRG methodology is described in Appendix H of the Wind Vision Study (DOE 2015) and in earlier version of the ATB documentation.

Fixed-Bottom Offshore Wind Resource Classes

Resource Class

Min. Wind Speed (m/s)

Max. Wind Speed (m/s)

Average Wind Speed (m/s)

Wind Speed Range (m/s)

Percentile range of total resource potential (%)

1

9.98

10.33

10.24

0.35

<2%

2

9.31

9.98

9.82

0.67

2%–4%

3

9.13

9.31

9.20

0.18

4%–8%

4

8.85

9.13

9.00

0.28

8%–16%

5

7.94

8.85

8.40

0.91

16%–32%

6

7.07

7.93

7.44

0.86

32%–64%

7

6.00

7.07

6.56

1.07

64%–100%

Floating Offshore Wind Resource Classes

Resource Class

Min. Wind Speed (m/s)

Max Wind Speed (m/s)

Average Wind Speed (m/s)

Wind Speed Range (m/s)

Percentile range of total resource potential (%)

8

10.30

11.47

10.57

1.17

<2%

9

10.18

10.30

10.23

0.12

2%–4%

10

10.01

10.18

10.09

0.17

4%–8%

11

9.60

10.01

9.81

0.41

8%–16%

12

8.84

9.60

9.23

0.76

16%–32%

13

7.43

8.84

8.04

1.41

32%–64%

14

5.98

7.43

6.95

1.45

64%–100%

Wind speed class 3 best represents the resource characteristics of near-term deployment for fixed-bottom technology; wind speed class 13 best represents the resource characteristics of near-term deployment for floating technology. These wind speed classes were determined to be most comparable to the site conditions and cost characteristics of projects with an anticipated COD before 2030.

Scenario Descriptions

Various technology innovations and cost drivers are considered in the ATB cost projection scenarios. Future cost changes represented in the 2020 ATB scenarios are attributed to changes in turbine size, size-agnostic innovations and increased supply chain efficiencies and learning. The technology innovations listed in the following table are structured around the assumed turbine trajectory for the 2020 ATB.

Summary of Technology Innovation by Scenario (2030)

Scenario

Turbine Size

Supply Chain

Size-Agnostic Innovations

Conservative

Technology Description: Turbines are rated at 12 MW. Moderate retooling of blade manufacturing facilities is needed.

Justification: This turbine size is currently tested as a prototype (Haliade-X) and these turbines are available for purchase in global markets. The technological and logistical challenges associated with a 12-MW turbine and strategies to overcome them are relatively well understood. Adjustments to U.S. port and vessel infrastructure are underway to accommodate this turbine size. U.S. project developers have ordered 12-MW turbine ratings and it is expected they will be deployed in the early- to mid-2020s.

Technology Description: This scenario assumes a supply chain that generates efficiency gains below the levels of the past few years. Under this scenario, the 12-MW turbine would be mounted on a monopile or jacket substructure using existing technology/materials. The turbine system is installed and operated using moderately enhanced port infrastructure and vessel capabilities.

Justification: The U.S. port infrastructure and vessel fleet is currently developed with 12-MW turbine designated as the anticipated near-term technology by most OEMs, manufacturers and project developers.

Technology Description: Limited integration of high-fidelity modeling and advanced controls.

Justification: Sustained wind plant technology that would be in operation today with incremental improvements only.

Moderate

Technology Description: Turbines are rated at 15 MW. Major retooling of blade manufacturing facilities is needed and major adjustments must be made to drivetrain and control technologies. A 15-MW turbine is also expected to require large generator bearings and pitch/yaw drive actuators.

Justification: 15-MW turbines are reportedly under development by major turbine OEMs. The turbine technological and logistical challenges are somewhat understood. It is expected that 15-MW turbines might be paired with floating substructures to overcome some of the logistical and port constraints in installation and maintenance. Although this turbine rating is expected by the early 2030s, there is some uncertainty whether they will widely be operating in U.S. markets by 2030. This uncertainty stems in part from limitations in current blade design and lifting requirements (in terms of both weight and height) of cranes needed during the installation process.

Technology Description: This scenario assumes a supply chain that generates efficiency gains commensurate with the levels of the past few years. Under this scenario, the 15-MW turbine would be mounted on a fixed-bottom or floating substructure using improved and highly tailored technology/materials. The turbine system is installed and operated using greatly enhanced port infrastructure and vessel capabilities.

Justification: This scenario would necessitate a growth rate in installed global offshore wind capacity commensurate with the rate in the last few years. Efficiency gains are achieved through standardization, economies of scale and increased competition.

Technology Description: High levels of integration of high-fidelity modeling, measurement and advanced control strategies. These are used for better site understanding and optimization of O&M strategies at the turbine, plant and fleet level.

Justification: Increasing digitization and analysis of operational data is already a trend in the offshore wind industry. It is expected that these tools will be widely adopted in combination with a 15-MW turbine by 2030.

Advanced

Technology Description: Turbines are rated at 18 MW. Next-generation drivetrain and blade materials need to be combined with new turbine installation and maintenance methods.

Justification: Although an 18-MW prototype might be available by the early 2030s, it remains questionable whether turbines of this size will be commercially operating widely by 2030. New testing capacity would have to be developed and next-generation materials and installation/operation regimes would be required to enable this turbine. These enhanced capabilities are not yet well understood.

Technology Description: This scenario assumes a supply chain that generates efficiency gains above the level of the past few years.

Justification: This scenario would necessitate a growth rate in installed global offshore wind capacity above the rate in the last few years. Under this scenario, the 18-MW turbine would be mounted on a fixed-bottom or floating substructure using next-generation technology/materials, and port infrastructure and vessel capabilities. Efficiency gains are achieved through accelerated standardization, large economies of scale and fiercely increased competition.

Technology Description: Next-generation farm optimization, high-fidelity modeling and advanced controls are available. These capabilities are necessary to better understand atmospheric sciences and forecasting and make use of novel sensing technologies and measurement techniques, computational sciences, digitalization, big data, and information/data science.

Justification: Though next generation tools and methods are not yet well understood, they are explored in research settings and might be available in combination with an 18-MW turbine rating by 2030.

Impact

CAPEX and OPEX reductions from economies of scale

Capacity factor increases through reduced wake losses with higher rated turbine capacity

CAPEX and OPEX reductions from lower fabrication and maintenance costs.

CAPEX and OPEX reductions from lower fabrication and maintenance costs; CF increases through improved understanding, measurement and response of wind regimes

References

Gaertner et al. (2020); Musial et al. (2019)

Musial et al. (2019)

Valpy et al. (2017); Hundleby et al. (2017)

Representative Technology

Cost and performance estimates in the Base Year are intended to represent a commercial-scale project of 600 MW in capacity. The ATB Base Year offshore wind plant technology reflects a machine rating of 6 MW with a rotor diameter of 155 m and hub height of 100 m, which is typical of European projects installed in 2016 and 2017 and the turbine rating installed at the Block Island Wind Farm (GE Haliade 150-6MW). For the 2020 ATB cost projections, this representative technology changes to a 15-MW turbine (Moderate case), 12-MW (Conservative case), and 18-MW (Advanced case) turbine rating by 2030. Four substructure types are represented in the cost estimates, namely jacket and monopile (fixed-bottom) and semi-submersible and spar (floating technology). See below for details on changes to parameters in ATB projections.

Methodology

This section describes methodology to develop assumptions for CAPEX, O&M, and capacity factor. Click on these links for standardized assumptions for labor cost, regional cost variation, materials cost index, scale of industry, policies and regulations, and inflation.

In a first step, baseline costs are estimated at more than 7,000 potential offshore wind sites using an updated version of NREL's Offshore Regional Cost Analyzer (ORCA) (Beiter et al. 2016). ORCA is calibrated for the 2020 ATB with the latest cost and technology trends observed in the U.S. and European offshore wind markets. For this assessment, various spatial parameters are considered, including wind speeds, water depth, distance from shore, distance to ports, and wave height. CAPEX, O&M, and capacity factor are calculated for each location using (bottom-up) techno-economic models, hourly wind resource profiles, and representative sea states. Only sites that exceed a distance to cable landfall of 30 kilometers (km) and a water depth of 10 meters (m) are included in this spatial assessment because these are more likely to be developed in the near-to-medium term. ORCA determines the least-cost choice among four substructure types (Beiter et al. 2016): Monopile (fixed-bottom), Jacket (fixed-bottom), Semi-submersible (floating), Spar (floating).

Second, cost projections between the Base Year and 2032 (COD) are derived from a combination of (bottom-up) techno-economic models and an assessment of future technology and innovation pathways and supply chain evolution. For quantifying future costs in the Moderate Scenario between 2018 and 2030, the cost modeling focused on the impact of:

  • Turbine and Plant Upsizing: NREL's Offshore Renewable Balance-of-system Installation Tool (ORBIT) is used to assess the impact from turbine and plant upsizing. The impact from greater turbine size reflects in lower CAPEX and O&M costs and higher annual energy production because of reduce wake losses (when turbine spacing is held constant).
  • Supply Chain Efficiencies and Learning: An analysis is conducted to assess the learning rate of the supply chain using empirical project data from Musial et al. (2019). To estimate the impact of supply chain efficiencies and learning, future trajectories for fixed-bottom and floating offshore wind installed capacity are derived from literature sources. The impact of these supply chain effects are reflected in lower CAPEX and OPEX.
  • Size-Agnostic Technology Innovations: The impact from size-agnostic technology innovations is assessed using a version of the 50 technology innovations elicited in Valpy et al. (2017) and Hundleby et al. (2017) that are modified to only represent size-agnostic technology innovations. The impact from these size-agnostic technology innovations are reflected in CAPEX, OPEX, and AEP components. A multiplier that varies by scenario changes the impact of the size-agnostic technology innovations. The Moderate Scenario used a multiplier of 1.0, while the Conservative Scenario used a multiplier of 0.5 and the Advanced Scenario a multiplier of 1.2. These multipliers are meant to account for different magnitudes of impact that these technology innovations might have on CAPEX, OPEX, and AEP under the different scenarios.
  • The technology innovation scenarios are distinguished using different assumptions about turbine ratings in 2030. The Moderate Scenario reflects a turbine rating of 15 MW, the Advanced Scenario an 18-MW turbine rating, and the Conservative Scenario a 12-MW turbine rating. The wind plant size is held constant at 600 MW in all future years considered, consistent with the Base Year assumptions.

The resulting percentage reduction in LCOE is applied equally across all wind classes. The cost reductions for all scenarios between 2030 and 2050 are extrapolated (i.e., logarithmic fit) based on the estimated trend between 2018 and 2030 (COD). The resulting cost and performance assumptions are validated using an assessment of the first commercial-scale offshore wind projects announced in the U.S. (see e.g., Beiter et al. 2019). For instance, wind speed class 3 is assessed to represent the cost and performance characteristics of near-term project deployments of fixed-bottom technology, such as Vineyard Wind.

Capital Expenditures (CAPEX)

Definitions: Capital expenditures (CAPEX) are expenditures required to achieve commercial operation in a given year. In the ATB, CAPEX reflects typical plants and does not include differences in regional costs associated with labor, materials, taxes, or system requirements. The range of CAPEX demonstrates variation with spatial site parameters in the contiguous United States.

Based on Moné at al. (2015) and Beiter et al. (2016), the CAPEX of the 2020 ATB for the wind plant envelope is defined to include items noted in the Summary of Technology Innovation by Scenario table above.

CAPEX within the ATB represents the capacity-weighted average values of all potential wind plant areas within a wind speed class and varies with water depth, metocean conditions and distance from shore.

Base Year: Base Year estimates for CAPEX are derived using an updated version of NREL's Offshore Regional Cost Analyzer (ORCA) (Beiter et al. 2016). The following chart shows historical CAPEX for offshore wind.

To estimate CAPEX, various spatial parameters are considered, including water depth, distance from shore, distance to ports, and wave height. CAPEX estimates are calibrated to correspond to the latest cost and technology trends observed in the U.S. and European offshore wind markets, including:

  • Turbine CAPEX: CAPEX of $1,300/kW are assumed for the Base Year to account for decreases in turbine CAPEX that are observed in global offshore wind markets.
  • Turbine Rating: Turbine technology trends of 6 MW (Base Year), 8 MW (2022 COD), 12 MW (2027 COD), and 15 MW (2030 COD) are assumed to correspond to recent technology trends in the Moderate Scenario.
  • Cost Reduction Trajectory: Recent literature is surveyed to identify the most up-to-date cost reduction trends expected for U.S. and European offshore wind projects; cost reduction trajectories are estimated from bottom-up modeling of the turbine and plant upsizing impact, increased supply chain efficiencies and learning, and size-agnostic technology innovation.

Future Years: CAPEX improvements are estimated to be driven by an increase in turbine rating, enhanced supply chain efficiencies and size-agnostic technology innovations. The following chart shows the scenarios in comparison to literature projections.

Use the following table to view the components of CAPEX.

Operation and Maintenance (O&M) Costs

Definition: O&M costs represent the annual fixed expenditures required to operate and maintain a wind plant over its lifetime, including items noted in the Summary of Technology Innovation by Scenario table above.

Base Year: FOM costs vary by distance from shore and metocean conditions.

Future Years: OPEX are estimated to be driven by an increase in turbine rating, enhanced supply chain efficiencies and size-agnostic technology innovations.

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

Capacity Factor

Definition: The capacity factor is influenced by the rotor swept area/generator capacity, hub height, hourly wind profile, expected downtime, and energy losses within the wind plant. It is referenced to 100-m above-water-surface, long-term average hourly wind resource data from Musial et al. (2016).

Base Year: The capacity factor for the Base Year is determined using a representative power curve for a generic NREL-modeled 6-MW offshore wind turbine (Beiter et al. 2016) and includes geospatial estimates of gross capacity factors for the entire resource area (Walt Musial et al. 2016). The net capacity factor considers spatial variation in wake losses, electrical losses, turbine availability, and other system losses. Each wind speed class represents the capacity weighted average of its resource potential.

In the following chart, preconstruction annual energy estimates from publicly available global operating wind capacity in 2018 (Walter Musial et al. 2019) are shown in a box-and-whiskers format for comparison. The range of capacity factors is estimated based on variation in the wind resource for offshore wind plants in the contiguous United States. The range of Base Year estimates illustrates the effect of locating an offshore wind plant in a variety of wind resource.

Future Years: Projections of capacity factors for plants installed in future years are determined based on increasing turbine size and size-agnostic innovations. Under constant turbine spacing, these yield reduced wake losses and thereby, higher annual energy production.

References

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

Beiter, Philipp, Musial, Walter, Smith, Aaron, Kilcher, Levi, Damiani, Rick, Maness, Michael, Sirnivas, Senu, Stehly, Tyler, Gevorgian, Vahan, Mooney, Meghan, & Scott, George. (2016). A Spatial-Economic Cost-Reduction Pathway Analysis for U.S. Offshore Wind Energy Development from 2015-2030. (No. NREL/TP-6A20-66579). National Renewable Energy Laboratory. https://doi.org/10.2172/1324526

Brown, Maxwell, Cole, Wesley, Eurek, Kelly, Becker, Jon, Bielen, David, Chernyakhovskiy, Ilya, Cohen, Stuart, Frazier, Will, Gagnon, Pieter, Gates, Nathaniel, Greer, Daniel, Gudladona, Sai Sameera, Ho, Jonathan, Jadun, Paige, Lamb, Katherine, Mai, Trieu, Mowers, Matthes, Murphy, Caitlin, Rose, Amy, Schleifer, Anna, Steinberg, Daniel, Sun, Yinong, Vincent, Nina, Zhou, Ella, & Zwerling, Matthew. (2020). Regional Energy Deployment System (ReEDS) Model Documentation: Version 2019. (No. NREL/TP-6A20-74111). National Renewable Energy Laboratory. https://www.nrel.gov/docs/fy20osti/74111.pdf

Gaertner, Evan, Rinker, Jennifer, Sethuraman, Latha, Zahle, Frederik, Anderson, Benjamin, Barter, Garrett, Abbas, Nikhar, Meng, Fanzhong, Bortolotti, Pietro, Skrzypinski, Witold, Scott, George, Feil, Roland, Bredmose, Henrik, Dykes, Katherine, Shields, Matt, Allen, Christopher, & Viselli, Anthony. (2020). IEA Wind TCP Task 37: Definition of the IEA 15-Megawatt Offshore Reference Wind. (No. NREL/TP-5000-75698). National Renewable Energy Laboratory. https://www.nrel.gov/docs/fy20osti/75698.pdf

Maclaurin, Galen, Grue, Nick, Lopez, Anthony, & Heimiller, Donna. (2019). The Renewable Energy Potential (reV) Model: A Geospatial Platform for Technical Potential and Supply Curve Modeling. (No. NREL/TP-6A20-73067). National Renewable Energy Laboratory. https://www.nrel.gov/docs/fy19osti/73067.pdf

Moné, C., Smith, A., Maples, B., & Hand, M. (2015). 2013 Cost of Wind Energy Review. (No. NREL/TP-5000-63267). National Renewable Energy Laboratory. https://www.nrel.gov/docs/fy15osti/63267.pdf

Musial, Walt, Heimiller, Donna, Beiter, Philipp, Scott, George, & Draxl, Caroline. (2016). 2016 Offshore Wind Energy Resource Assessment for the United States. (No. NREL/TP-5000-66599). National Renewable Energy Laboratory. https://www.nrel.gov/docs/fy16osti/66599.pdf

Musial, Walter, Beiter, Philipp, Spitsen, Paul, & Nunemaker, Jake. (2019). 2018 Offshore Wind Technologies Market Report. National Renewable Energy Laboratory. https://www.nrel.gov/docs/fy20osti/74598.pdf