Offshore Wind

Note the 2024 ATB floating offshore wind energy cost estimates (Offshore Wind Classes 8-14) are developed with an updated methodology to capture the cost reduction impacts of maturing from a nascent industry to the first wave of commercial projects. Prior ATB efforts modeled N^th of a kind commercial-scale floating wind projects assuming mature supply chains in all years. The 2024 ATB presents floating offshore wind energy costs in 2030 and beyond, when the first commercial-scale floating wind projects in the United States could feasibly come online. The cost trajectories for floating offshore wind energy are informed by global deployment projections, and regional costs will depend significantly on the investment in enabling infrastructure (such as ports) that will allow projects to be built more economically. Infrastructure costs are not directly included in levelized cost of energy (LCOE) but are expected to be key enablers of cost reductions.

2024 ATB data for offshore wind are shown above in the visualization tool. Wind Resource Classes 3 and 12 are displayed by default because they are most representative of near-term U.S. fixed-bottom and floating offshore wind projects, respectively. Floating costs are shown in 2030 and beyond. Details about the wind resource classes are provided in the Resource Categorization section of this page. Comparisons can be made by toggling settings to explore resulting costs (such as the impacts of subsidies in the Market financing case). 

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.

To estimate how offshore wind energy costs evolve over time, we first model bottom-up costs for the Base Year (2022) and then apply derived cost trajectories for each ATB technology innovation scenario through 2050. 

Base Year costs are calculated with a combination of the National Renewable Energy Laboratory's (NREL's) bottom-up cost models for gigawatt-scale fixed-bottom, but we only present floating offshore wind energy costs in 2030 and beyond when the first gigawatt-scale projects could feasibly come online in the United States. Specifically, the Renewable Energy Potential Model (reV) and NREL Wind Analysis Library (NRWAL) are used to assess offshore wind plant costs across U.S. waters as a function of site-specific parameters including wind resource, water depth, and distances to critical infrastructure (Maclaurin et al., 2019); (Nunemaker et al., 2023). Those site-specific cost estimates are informed by the Offshore Renewables Balance of System and Installation Tool (ORBIT) for capital expenditures (CAPEX), the Windfarm Operations and Maintenance cost-Benefit Analysis Tool (WOMBAT) for operating expenditures (OPEX), and the FLOw Redirection and Induction in Steady State (FLORIS) tool for annual energy production and capacity factors (Nunemaker et al., 2020);(Hammond and Cooperman, 2022);(National Renewable Energy Laboratory (NREL), 2021).

Separately, cost trajectories are derived for each technology innovation scenario in two parts: a long-term cost projection based on global industry experience and a near-term CAPEX adjustment to account for macroeconomic conditions faced by early U.S. offshore wind energy projects not captured in the learning curves. For the long-term CAPEX projections, we follow the approach outlined in (Shields et al., 2022) to derive learning curves for each scenario from historical offshore wind project CAPEX data and projected global offshore wind deployment. Floating CAPEX projections include effects from improving plant economies of scale as the floating offshore wind industry matures, whereas fixed-bottom CAPEX projections are reflective of gigawatt-scale projects in all years. This treatment of floating offshore wind represents a new approach for the 2024 ATB to better represent the cost reduction impacts from the technology's maturation. A range of short-term CAPEX adjustments is derived from literature and industry publications to highlight near-term macroeconomic uncertainty. The long-term projections for OPEX and Capacity Factor improvements are derived from (Wiser et al., 2021). 

Each step in this approach is outlined in greater detail in the following sections exploring the offshore wind ATB estimates. First, we categorize the offshore wind resource in the United States and bin sites into wind classes. Then, we define three possible scenarios for offshore wind energy deployment and costs in the United states and discuss how each is enabled. Finally, the cost modeling methodology is presented via its main components: CAPEX, OPEX, and capacity factor.

Resource Categorization

The U.S. offshore wind technical resource potential likely exceeds 2,000 gigawatts (GW) (Musial et al., 2016); (Zuckerman et al., 2023), after accounting for exclusions because of water depth, limits to floating technology in freshwater surface ice (Great Lakes region only), competing uses and environmental exclusions, marine protected areas, shipping lanes, pipelines, and many other factors. This analysis utilizes data from NREL's most recent offshore wind resource assessment for the United States: the 2023 National Offshore Wind dataset (NOW-23) (Bodini et al., 2023).

In the 2024 ATB, each of thousands of potential wind sites across U.S. offshore regions represented by this technical resource potential is binned into 1 of 14 wind resource classes. These wind resource classes 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 fixed-bottom potential wind capacity offshore of the contiguous United States and Hawaii in terms of wind speed and costs. We specify a narrower percentile range for the top classes so 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. ReEDS uses the wind classes data in conjunction with spatial exclusions and transmission cost data from the reV model (Maclaurin et al., 2019) in the annual Standard Scenarios report (Gagnon et al., 2024).

The following tables illustrate the range of mean wind speeds taken at hub height (137-m) across all sites in each wind class, the average site mean wind speed for each wind class, and the percentage of the total resource potential assumed for each resource class. The first table presents the data for fixed-bottom wind classes and the second table for floating offshore technologies. Spatial parameters are averaged out in the binning of the resource into wind classes. Because of the different resource potential that each wind resource class represents (e.g., Wind Resource Class 1 represents nearly 2% of the total fixed-bottom 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 divided into 5 fixed-bottom and 10 floating classes. 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 midterm deployment for floating technology in the Wind Energy Lease Areas defined by the Bureau of Ocean Energy Management. These wind speed classes are determined to be most comparable to the site conditions and cost characteristics of commercial-scale projects with an anticipated commercial operations date (COD) in the early-to-mid 2030s.

Scenario Descriptions

The ATB defines Conservative, Moderate, and Advanced scenarios for all technologies on the Definitions page to illustrate a range of possibilities for how costs could evolve with technology. For offshore wind energy, cost projections derived for each ATB scenario are driven by global offshore wind deployment considering impacts from learning-by-doing, supply chain maturation and efficiencies, turbine (and floating plant) upsizing, and size-agnostic innovations. The Moderate scenario deployment trajectories (described in following section) for fixed-bottom and floating offshore wind technologies are derived from literature projections. Deployment trajectories in the Conservative and Advanced scenarios are assumed to be 60% less than and 60% more than under the Moderate scenario, respectively. 

Enabling offshore wind deployment through infrastructure investment allows the industry to achieve cost reductions from learning and technology innovations. Cost trajectories by scenario are explicitly tied to global deployment, but implicitly depend on different levels of enabling investments in and construction of ports, vessels, and transmission infrastructure as well as the domestic supply chain and technology research and development (Ury et al., 2024);(Shields et al., 2023); (Duffy et al., 2023); (Barter et al., 2020); (Gagnon et al., 2024). Earlier investment in key infrastructure would likely contribute to more rapid cost declines through accelerated deployment. The next section outlines the different deployment trajectories and derived learning rates by ATB scenario.

Scenario Assumptions

The ATB scenarios intend to capture the impacts of how costs, technology, and deployment may evolve over time. The scenarios for offshore wind described above have the following global offshore wind energy deployment assumptions which drive the CAPEX reductions (learning curves). The methodology for projecting CAPEX, OPEX, and future performance (capacity factors) is detailed in the Methodology section below.

Assumed Global Deployment Over Time by Scenario 

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

CAPEX Learning Rates by Scenario

Representative Technology

Cost and performance estimates in the Base Year are intended to represent commercial-scale fixed-bottom projects of 1,008 MW. The Base Year offshore turbine technology reflects the range of turbines installed at the first larger-scale U.S. projects (11-MW turbines at South Fork Wind and 13-MW turbines at Vineyard Wind I) with a rating of 12 MW, a rotor diameter of 216 m, and hub height of 137 m. The Base Year turbine parameters match the turbine modeled in the 2022 Cost of Wind Energy Review (Stehly et al., 2023) and is derived by downscaling the International Energy Agency (IEA) 15-MW Reference wind turbine (Gaertner et al., 2020). Two substructure types are represented in the cost estimates: monopile (fixed-bottom) and semisubmersible (floating technology). Floating costs are presented only in 2030 and beyond.  Note that we do not specify future turbine technology parameters since turbine upsizing effects are captured in the derivation of the learning rate. The following table summarizes key technology details for the Base Year. Tabular power curve data and additional documentation are available on GitHub.

Base Year Turbine Technology Details

Methodology

This section describes the methodology to develop cost trajectories for offshore wind energy project 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 costs in 2022 at thousands of potential offshore wind sites for the representative plants using the Renewable Energy Potential (reV) model and NREL Wind Analysis Library (NRWAL). CAPEX, O&M, and capacity factor are calculated with NRWAL for each location to account for site-specific parameters including wind profiles, water depth, wave height, and distances to critical infrastructure (ports and points of interconnection). Those site-specific cost estimates are informed by a suite of modeling tools including the Offshore Renewables Balance of System and Installation Tool (ORBIT) for CAPEX, the Windfarm Operations and Maintenance cost-Benefit Analysis Tool (WOMBAT) for OPEX, and the FLOw Redirection and Induction in Steady State (FLORIS) tool for annual energy production and capacity factor (Nunemaker et al., 2020);(Hammond and Cooperman, 2022);(National Renewable Energy Laboratory (NREL), 2021).

Next, cost projections are derived for each technology ATB scenario. For CAPEX, this consists of two pieces: a long-term cost projection based on global industry experience and a near-term CAPEX adjustment to account for macroeconomic conditions facing early U.S. offshore wind energy projects not captured in the learning curves. For the long-term CAPEX projections, we follow the approach outlined in (Shields et al., 2022) to derive learning curves for each scenario from historical offshore wind project CAPEX data and projected global offshore wind deployment. Floating CAPEX projections include effects from improving plant economies of scale as the floating offshore wind industry matures, whereas fixed-bottom CAPEX projections are reflective of gigawatt-scale projects in all years. A range of short-term CAPEX adjustments is derived from literature and industry publications to highlight near-term macroeconomic uncertainty. The long-term projections for OPEX and capacity factor improvements are derived from (Wiser et al., 2021). 

We have taken steps to align this methodology with a parallel assessment of offshore wind energy costs in the United States (Fuchs et al., forthcoming).

Capital Expenditures (CAPEX)

Definitions: Capital expenditures 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 2024 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: 2024 ATB estimates for CAPEX in the Base Year are derived using NRWAL to calculate CAPEX as a function of spatial parameters, including water depths, distances from shore, distances to ports, and significant wave heights. Array cable, export cable, and a spur line on land to the nearest grid feature are accounted for in the CAPEX costs, but no transmission system upgrade costs are modeled in the ATB estimates. Balance-of-system (BOS) costs obtained with NRWAL are informed by updates to NREL's ORBIT model. Plant capacities in the base year reflect the current maturity level of fixed-bottom (mature at 1,008 MW) and floating (nascent <100 MW) offshore wind energy projects, but we only present floating costs in 2030 and beyond when a gigawatt-scale project could feasibly be constructed in the United States to align with other nascent ATB technologies (Dominion Energy, 2024); (Equinor, 2023). Capital costs for demonstration-scale floating offshore wind energy projects before 2030 in the United States may exceed $10,000/kW due to supply chain immaturity and limited experience (Musial et al., 2019a)(Shields et al., 2022), but reducing risk and enabling offshore wind deployment through early projects and infrastructure investment is critical in allowing the industry to achieve cost reductions from learning and technology innovations. Note that in contrast with land-based wind, offshore wind represents a range of Base Year CAPEX between the scenarios. This is a result of differences in the modeling methodologies between offshore wind and land-based wind.

Future Years: CAPEX projections are derived in two parts: 

A long-term cost trajectory driven by a learning rate, which includes cost-reducing effects from learning-by-doing, supply chain maturation and efficiencies, turbine (and [floating] plant) upsizing, and size-agnostic technology innovations. Learning rates describe the percentage cost reductions associated with producing more of a particular good or service. They are observed empirically for any form of industrial production (Louwen and Lacerda, 2020); (Junginger and Louwen, 2020). 

We derive the CAPEX learning rates documented in the Scenario Assumptions section with NREL's Forecasting Offshore Wind Reductions in Cost of Energy (FORCE) model following the approach outlined in (Shields et al., 2022). These learning rates are calculated from historical fixed-bottom offshore wind project CAPEX data and combined with assumed global deployment trajectories to create learning curve forecasts of future costs. These learning curves do not capture cost impacts of macroeconomic conditions facing early U.S. offshore wind plants. 

Therefore, a near-term CAPEX adjustment is derived to account for cost impacts of rising interest rates, inflation, supply chain/labor shocks such as the COVID-19 pandemic, and Russia-Ukraine war based on impacts reported to U.S. projects. For each scenario, we define assumptions to bound several possible trajectories for how critical conditions may evolve over time, impacting the magnitude and duration of the near-term cost adjustment. The resulting short-term cost corrections vary by scenario and adjust the starting point of the cost trajectory to reflect the cost uncertainty within the industry.

Specifically, we impose a 70% capital cost increase factor in the ATB Conservative scenario, 40% in the ATB Moderate scenario, and 20% in the ATB Advanced scenario  (Jain et al., 2023), (Wood Mackenzie, 2022), (Vestas Wind Systems A/S, 2023), (Vestas Wind Systems A/S, 2021). These cost adjustments remain constant until 2029 and 2027 in the ATB Conservative and ATB Moderate scenarios. These added costs are assumed to phase out by 2035, 2032, and 2028 for the ATB Conservative, ATB Moderate, and ATB Advanced scenarios, respectively. 

The magnitudes of the short-term adjustment in the ATB Moderate scenario roughly aligns with industry-reported cost increases between 2020-2023 (Reed and Penn, 2023),(PUBLIC SERVICE COMMISSION STATE OF NEW YORK, 2023). Since the end of 2022, steel prices have stabilized or decreased and the U.S. Federal Reserve has stabilized interest rates. The Moderate scenario assumes that these trends incrementally reduce financing rates, pipeline uncertainty, and inflationary capital cost pressures on projects with a COD after 2027—and likely a final investment decision around 2025—until the macroeconomic factors recede in the early 2030s and cost evolution over time is exclusively driven by the learning curve.   

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 other technologies.

CAPEX Breakdown

The following chart provides bottom-up estimates of CAPEX line items from the 2022 Cost of Wind Energy Review for reference (Stehly et al., 2023). The 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 average 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 O&M (FOM) costs are estimated for the Base Year representative offshore wind technology described above using the Windfarm Operations and Maintenance cost-Benefit Analysis Tool (WOMBAT) (Hammond and Cooperman, 2022) and failure rate assumptions based on (Schwarzkopf et al., 2021) and (Carroll et al., 2016). OPEX costs are a function of distances to operations ports and metocean conditions.

Future Years: Projections of OPEX cost reductions are derived for each ATB scenario from the survey of wind energy industry experts (expert elicitation) detailed in (Wiser et al., 2021). We fit a learning curve to match the total cost reduction through 2035 expected by experts and account for the fact that respondents' expectations were reported relative to fixed-bottom offshore wind in real 2019 terms.

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

OPEX Breakdown

Capacity Factor

Definition: The capacity factor represents offshore wind plant performance as a fraction of time the plant would need to operate at full capacity to equal its total energy output over that period. It is driven by the turbine design (i.e., rotor diameter, generator capacity, blade design, controls, hub height) and the site-specific wind resource.

Base Year: The capacity factors for the Base Year are calculated with site-specific data from NREL's most recent offshore wind resource assessment for the United States: the 2023 National Offshore Wind dataset (NOW-23) (Bodini et al., 2023). Performance calculations are informed by hub-height resource data and the Base Year turbine technology described above using power and thrust curves downscaled from the IEA 15-MW reference wind turbine (Gaertner et al., 2020). The net capacity factor considers spatial variation in wake losses, electrical losses, turbine availability, and other system losses (see approach outlined in (Beiter et al., 2020)). Wake losses internal to the wind plant are calculated with the site-specific resource data and representative turbine technology using the gauss-curl-hybrid wake model in FLORIS and an assumed average offshore turbulence intensity of 6% but recognize turbulent conditions depend on site-specific atmospheric conditions. Although developers will optimize their wind plant layout based on numerous factors, we assume a constant layout at each site consisting of turbines arranged on a square grid spaced 7 rotor diameters apart in the north-south and east-west directions. No wake losses from neighboring wind plants are included, though these can be significant in some regions (Lundquist et al., 2019); (Pryor et al., 2021).

In the following chart, preconstruction annual energy estimates from publicly available global operating wind capacity in 2018 (Musial et al., 2019b) 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 factor improvements are derived for each ATB scenario from the survey of wind energy industry experts (expert elicitation) detailed in (Wiser et al., 2021). We account for the fact that respondents' expectations were reported relative to fixed-bottom offshore wind in real 2019 terms and assume the same total percentage improvement for both fixed-bottom and floating offshore wind performance over time.

References

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