Offshore Wind

2023 ATB data for offshore wind are shown above. Wind Resource Class 3 is displayed by default, as it is most representative of near-term U.S. fixed-bottom offshore wind projects. Details about the wind resource classes are provided in the Resource Categorization section of this page. 

The following chart shows the levelized cost of energy (LCOE) scenario results presented above normalized for a comparison with literature projections. Values are normalized by the base year estimates from each respective source to represent the percentage reduction over time.

We obtain offshore wind costs by first modeling costs in the base year (2021) and then applying derived cost reduction trajectories for each ATB technology innovation scenario. Base year costs are modeled with the National Renewable Energy Laboratory's (NREL's) Offshore Regional Cost Analyzer tool for commercial-scale projects of 1,000 MW (Beiter et al., 2016). Separate capital expenditure (CAPEX) and operating expenditure (OPEX) reduction trajectories are derived for each technology innovation scenario based on the assumed turbine technology and level of global offshore wind deployment by 2030. Annual energy production benefits from technology innovation trajectories driven by improved controls at the turbine and plant level, improved plant availability with less downtime for operation and maintenance (O&M), and reduced wake losses stemming from higher turbine ratings in future years. Costs beyond 2030 are obtained by extrapolating the cost trends estimated for 2021–2030 (commercial operation date, or COD).

The three technology innovation scenarios focus on turbine technology assumptions and globally installed offshore wind capacity in 2030:

• Conservative Technology Innovation Scenario (Conservative Scenario): Turbine size remains 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. Globally installed wind capacity in 2030 is assumed to be 60% less than under the Moderate Scenario, or 108 GW for fixed-bottom technology and 6.6 GW for floating technology.
• Moderate Technology Innovation Scenario (Moderate Scenario): Turbine size increases 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. Global offshore wind deployment in 2030 is assumed to be 270 GW for fixed-bottom and 16.5 GW for floating (GWEC, 2021).
• Advanced Technology Innovation Scenario (Advanced Scenario): Turbine size increases at a rate that is considerably higher than in recent years; accelerated technology innovation enables large turbine systems and fundamentally changes the manufacturing, installation, operation, and performance of a wind plant. Globally installed wind capacity in 2030 is assumed to be 60% more than under the Moderate Scenario, or 432 GW for fixed-bottom technology and 26.4 GW for floating technology.

On this page, the offshore wind ATB estimates are explored in detail. First, the offshore wind resource in the United States is categorized and split into wind classes. Then, the cost reduction pathways are outlined for each technology innovation scenario before the representative turbine technologies are detailed. Finally, the cost modeling methodology is presented via its main components: CAPEX, OPEX, and annual energy production.

Resource Categorization

The U.S. offshore wind technical resource potential exceeds 2,000 gigawatts (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.

In the 2023 ATB, each of thousands of potential wind sites across U.S. offshore regions represented by this technical resource potential is binned into one of 14 wind resource classes, which are then organized by substructure technology type, wind speed, and costs. Wind Resource Classes 1–7 represent fixed-bottom offshore wind technology, and Wind Resource Classes 8–14 represent floating offshore wind technology (see the wind resource class tables below). Sites are assigned to either fixed-bottom resource if they have water depths shallower than 60 meters (m) or to floating resource if the water depth exceeds 60 m.

For each substructure type, the break points for the wind resource classes occur at selected percentiles of the total wind resource technical potential. This specification is applied separately for each substructure type. For example, the most favorable resource class (Wind Resource 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 NREL's Regional Energy Deployment System (ReEDS) model has higher-resolution representation for the most favorable sites because those classes 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 using 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 100-m hub height wind speed ranges that define each class for fixed-bottom (first table) and floating offshore (second table) technologies in ReEDS. The annual mean wind speeds, averaged for all years between 2007 and 2013 and by wind resource class, range 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 resource class represents (e.g., Wind Resource Class 1 represents nearly 2% of the total resource potential whereas Wind Resource Class 7 represents 36% of the total), there is not necessarily a consistent trend in average cost and site parameters from Wind Resource Classes 1–7 (fixed-bottom) to Wind Resource Classes 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 resource class represents. These wind class categories are consistent with those used to represent the full wind resource in ReEDS  (Brown et al., 2020). Before the 2020 ATB, these wind resource classes were referred to as techno-resource groups (TRGs) and the total wind resource technical potential was divided into 5 fixed-bottom and 10 floating classes. TRG break points in previous ATB editions are different from the representation of the wind resource classes in the 2023 ATB. The TRG methodology is described in Appendix H of the Wind Vision Study  (DOE, 2015) and in earlier editions of the ATB.

Fixed-Bottom Offshore Wind Resource Classes

Floating Offshore Wind Resource Classes

Wind Resource Class 3 best represents the resource characteristics of near-term deployment for fixed-bottom technology along the U.S. East Coast such as at Vineyard Wind; Wind Resource Class 12 most closely represents our most recent assessment of the resource characteristics of mid-term deployment for floating technology in the California Wind Energy Areas defined by the Bureau of Ocean Energy Management (Beiter et al., 2020). These wind speed classes are determined to be most comparable to the site conditions and cost characteristics of commercial-scale 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 2023 ATB scenarios are attributed to changes in turbine size, size-agnostic innovations, and increased supply chain efficiencies and industry learning. The technology innovations listed in the following table are structured around the assumed turbine trajectory for the 2023 ATB. The effects of turbine size increases are estimated from techno-economic cost models. Changes in the supply chain and from the introduction of size-agnostic innovations are captured in the learning curve effects derived from offshore wind project data.

Summary of Technology Innovation by Scenario (2030)

Scenario Assumptions

The scenarios for offshore wind described above have the following deployment assumptions underlying the CAPEX learning curves. We retain the same learning curves derived in the previous ATB. The process for deriving CAPEX learning rates is described above.

Capacity Assumptions by Year 

 The cost curves are derived using the learning rates for CAPEX in the following table.

Learning Rate Assumptions by Scenario

Representative Technology

Cost and performance estimates in the Base Year are intended to represent a commercial-scale project of 1,000 MW in capacity. The Base Year technology reflects a machine rating of 8 MW with a rotor diameter of 159 m and hub height of 102 m, which is typical of European projects installed in 2021 (Stehly and Duffy, 2022)(Musial et al., 2022), (Musial et al., 2022). For the 2023 ATB cost projections, this representative technology evolves to a 15-MW turbine (Moderate Scenario), 12-MW turbine (Conservative Scenario), and 18-MW turbine (Advanced Scenario) by 2030. Four substructure types are represented in the cost estimates: jacket and monopile (fixed-bottom) and semi-submersible and spar (floating technology). The following table summarizes key technology details for each technology innovation scenario.

Turbine Technology Details by Scenario

Methodology

This section describes the methodology to develop assumptions and cost reduction/innovation trajectories for CAPEX, O&M, and capacity factor. For standardized assumptions, see labor cost, regional cost variation, materials cost index, scale of industry, policies and regulations, and inflation.

First, we estimate baseline costs in 2021 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 considers various spatial parameters, 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 substructure appropriate for the water depth from four options: monopile (fixed-bottom), jacket (fixed-bottom), semi-submersible (floating), and spar (floating) (Beiter et al., 2016).

Next, cost projections between the Base Year and 2030 (COD) are derived from a regression of offshore wind turbine CAPEX data and assessment of trends in turbine technology. Separate cost reductions are derived for CAPEX and OPEX, as well as an innovation trajectory representing performance improvements over time (capacity factor). To quantify future costs in the Moderate Scenario between 2021 and 2030, the cost modeling focuses on the impact of:

• Turbine and Plant Upsizing: NREL's Offshore Renewable Balance-of-system Installation Tool (available on GitHub) is used to assess the impacts of turbine and plant upsizing (Shields et al., 2021). The impact from greater turbine size is reflected in lower CAPEX and O&M costs and higher annual energy production because of reduced wake losses (when turbine spacing is held constant).
• Supply Chain Efficiencies and Learning: An analysis is conducted to assess learning rates of the supply chain using empirical project data from (Musial et al., 2022) and NREL's Forecasting Offshore Wind Reductions in Cost of Energy, or FORCE, model described by (Shields et al., 2022) (also available on GitHub). The FORCE model is a regression-based tool used to calculate the learning rate for offshore wind CAPEX while controlling for exogenous variables such as water depth, distance to shore, project country, plant capacity, and rated turbine capacity. This learning rate is then translated into cost reductions using the methodology outlined by (Beiter et al., 2020) and (Shields et al., 2022) based on assumed levels of global offshore wind deployment in 2030 COD associated with the different ATB technology and innovation scenarios. 
• Technology Innovation 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 1,000 MW in all future years considered, which is consistent with the Base Year assumptions. Tabular power curve data and additional documentation are available on GitHub.

Finally, the cost reductions for all scenarios between 2030 and 2050 are extrapolated (i.e., logarithmic fit) based on the estimated trend between the base year 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 United States (e.g., (Beiter et al., 2019)). For instance, Wind Resource Class 3 is assessed to represent the cost and performance characteristics of near-term project deployments of fixed-bottom technology, such as at 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; it 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 et al., 2015) and (Beiter et al., 2016), the CAPEX of the 2023 ATB for the wind plant envelope is defined to include items noted in the Summary of Technology Innovation by Scenario table in the Scenario Descriptions section of this page. CAPEX within the ATB represents the capacity-weighted average values of all potential wind plant areas within a wind resource class and varies with water depth, metocean conditions, and distance from shore.

Base Year: 2023 ATB estimates for CAPEX in the Base Year are derived using an updated version of ORCA (Beiter et al., 2016). It calculates CAPEX based on various spatial parameters, including water depth, distance from shore, distance to ports, and wave height. Array cable, export cable, and an onshore spur line to the nearest grid feature are accounted for in the CAPEX costs. 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 for the turbine (rotor nacelle assembly and tower) of approximately $1,300/kW are assumed for the Base Year to account for decreases in turbine CAPEX that are observed in global offshore wind markets (Musial et al., 2022).
• Turbine Rating: Turbine technology trends of 8 MW (Base Year) and 15 MW (2030 COD) are assumed to correspond to recent technology trends in the Moderate Scenario. We acknowledge this trajectory is somewhat conservative by the definition of the Moderate Scenario, but it accounts for the delay between the release of a turbine platform and when the global market average installed turbine capacity reflects that technology.
• 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 using the learning curve methodology described above as well as turbine and plant upsizing impacts (Shields et al., 2021).

In 2022, CAPEX increases from 2021 due to supply chain stress, high commodity prices, and increased logistics costs. These near-term factors are accounted for by applying an average CAPEX multiplier of 15% in 2022 and 10% in 2023, relative to 2021 costs. These inflationary impacts are assumed to recover around 2024 ((IEA, 2022), (Vestas, 2022), (Wood Mackenzie, 2022)). Note that in contrast with land-based wind, offshore wind represents a range of CAPEX between the scenarios. This is a result of differences in the modeling methodologies between offshore wind and land-based wind.

Future Years: CAPEX improvements are driven by an increase in turbine rating, learning from enhanced supply chain efficiencies, and size-agnostic technology innovations.

The following chart illustrates the base year scenarios along with historical CAPEX data for offshore wind.

To better illustrate the cost categories included in CAPEX, the following table outlines line items unique to offshore wind as well as those included across with technologies.

CAPEX Breakdown

The following chart indicates how major CAPEX line items change with foundation technology in the Base Year scenarios. Costs are broken into broad categories. The balance of system (BOS) covers the up-front CAPEX for installing the wind plant and its components except the turbine. Soft costs include insurance, project financing, construction contingency costs, and decommissioning costs.

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 in the Scenario Descriptions section of this page.

Base Year: Fixed operation and maintenance (FOM) costs vary by distance from shore and metocean conditions.

Future Years: OPEX change from experiential learning and from an increase in turbine rating.

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

OPEX Breakdown

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 factors for the Base Year were originally developed using a representative power curve for a generic NREL-modeled 6-MW offshore wind turbine and then updated based on performance improvement trajectories captured in ORCA (Beiter et al., 2016) and  (Valpy et al., 2017). They include geospatial estimates of gross capacity factors for the entire resource area  (Musial et al., 2016). The net capacity factor considers spatial variation in wake losses, electrical losses, turbine availability, and other system losses. Each wind resource 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 (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 various wind resources.

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. Advanced controls are assumed to be partially responsible for improvements in future turbine and plant level performance. Improved O&M practices and digitalization improve plant availability via reduced downtime.

References

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