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DOE OSTI2023

NOW-23: The 2023 National Offshore Wind Data Set

In this report, we present the latest wind resource data specifically tailored for offshore regions in the United States. The data set, known as the 2023 National Offshore Wind data set (NOW-23), has been developed by the National Renewable Energy Laboratory (NREL) and its partners, and surpasses the previous resource data set, the Wind Integration National Dataset (WIND) Toolkit, which was released approximately ten years ago, in its offshore component. The WIND Toolkit has been widely utilized by stakeholders involved in wind resource assessments across the continental United States. However, with significant advancements in numerical weather prediction modeling over the past decade, the NOW-23 data set incorporates the latest research and development progress to provide stakeholders with an updated and cutting-edge resource for offshore wind analysis.

17 WIND ENERGY↗
DOE OSTI2023

Capacity Density Considerations for Offshore Wind Plants in the United States

The United States is a rapidly emerging market for offshore wind energy, with a project pipeline estimated at over 52 GW as of May 31, 2023 (Musial et al. 2023). The capacity density, measured in megawatts per square kilometer (MW/km 2 ), is a crucial parameter for estimating the magnitude of the development pipeline and the nameplate potential of existing lease areas. The offshore wind energy industry is comprised of diversity of participants, including developers, governmental bodies, investors, environmental advocacy groups, and researchers. These various stakeholders use capacity density in different ways as a key metric for evaluating the potential of individual offshore wind lease area or even a section of ocean space. This report presents our assessment of capacity density values in the current pipeline of emerging U.S. offshore wind farms and a detailed list of the main factors that influence capacity density. This understanding is critical for planning of future lease areas, for estimating the technical resource potential for offshore wind on the U.S. outer continental shelf (OCS), and for estimating ocean space requirements needed for meeting state and national goals for a carbon neutral energy transition.

17 WIND ENERGY↗
DOE OSTI2026

Evaluation of a high-resolution regional climate simulation for surface and hub-height wind climatology over North America

Assessing the availability of key wind resources requires augmenting observations to support the implementation of wind energy infrastructure. However, observations are limited, necessitating the development of high-resolution, long-term gridded datasets. This study presents a robust, dynamically downscaled climatological dataset, offering 20 years of hourly wind data at a 4 km spatial resolution across North America, and evaluates its performance against observations, including meteorological towers and automated surface-observing system (ASOS) stations, as well as coarse-resolution reanalysis data (the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5)). Results demonstrate that the downscaled high-resolution wind data outperform ERA5 in regions of complex terrain and coastal areas, with improved overlap coefficients for wind data distributions and reduced root mean square errors (RMSEs) for hub-height and near-surface diurnal wind patterns. The downscaled simulation also captures the synoptic drivers of seasonal wind direction patterns reasonably well, indicated by high wind rose similarity indices. This study also provides an analysis of interannual variability, utilizing the dataset's full 20-year period, and model uncertainty, generated by varying model initial conditions and physics parameterizations across 1-year ensemble members, which are key considerations for wind resource assessment in wind farm development.

17 WIND ENERGY↗
DOE OSTI2024

Identification of Climatological Representative Days in the Mid-Atlantic for High-Fidelity Offshore Wind Energy Modeling

The goal of reaching 30 GW of offshore wind energy by 2030 becomes more realistic with the continued approval of offshore wind energy areas by the Biden Administration. In the Mid-Atlantic, where wind energy projects are in the most advanced stages of development, there is increased research focus on the eventual interaction of these wind farms. These interactions, in the form of wakes and cluster wakes, or wakes from multiple wind farms, could have detrimental effects on power production and forecastability for downwind wind farms (Pryor et al. 2022, Golbazi et al. 2022, Rosencrans et al. 2023). To help alleviate these issues, numerical simulations in the form of numerical weather prediction (NWP) and large eddy simulations (LES) can provide insight into when cluster wake situations may occur, but running such simulations can be expensive and difficult to run for multiple years. In this study, we leverage and build upon existing techniques in the literature (Fischereit et al. 2022) to identify climatologically representative days for wind energy areas in the Mid-Atlantic where conditions would promote cluster wake situations. We select meteorological variables (wind speed, wind direction, atmospheric stability, boundary-layer height, TKE) critical to understanding wind energy production and wake propagation. We then consider two different NWP datasets of varying spatial and temporal resolution: ERA5 provides data at hourly intervals from 1940 to present at 0.25 deg (31 km) spatial resolution (Hersbach et al. 2020), and the NOW-23 dataset provides data at 5-minute resolution for 21 years at 2-km spatial resolution (Bodini et al. 2020). Our first step is to compare these two datasets for an overlapping 21-year time period. Initial results show that the required number of days to represent the long-term climate increases with each additional variable considered. In their study of the German Bight, Fischereit et al. (2022) found that they could represent the long-term wind and wave climate in a "near-perfect" way with -180 days, by reaching a Perkins Skill Score (PSS) of 0.9; our investigation of the mid-Atlantic wind resource region with ERA5 and NOW-23 data suggests that we will need -100 days to reach a PSS of 0.9. As we expand our parameter space to include multiple variables, the number of required days will likely grow. These results will ultimately be used to select case studies to best represent cluster wake conditions that apply to this region for the lifetime of likely wind farms in this mid-Atlantic region.

clusterwakes↗
DOE OSTI2023

Radar Wind Profiler at McKinleyville, CA

These data are collected as part of an observational database developed to support the floating offshore wind energy research under the ORACLE project funded by DOE Wind Energy Technologies Office (WETO). The radar wind profiler network data are collected by NOAA (https://psl.noaa.gov/data/obs/datadisplay), and only data within the state of California are part of the database.

17 WIND ENERGY↗
DOE OSTI2023

Pacific Coast High-Frequency Radar 2 km

High frequency (HF) radar systems measure the speed and direction of ocean surface currents in near real time provided by Integrated Ocean Observing System (IOOS). The HFR data along the Pacific Coast are at a spatial resolution of 2 km. These data are collected as part of an observational database developed to support the floating offshore wind energy research within the ORACLE project funded by DOE WETO.

17 WIND ENERGY↗
DOE OSTI2023

Pacific Coast High Frequency Radar 6 km

High frequency (HF) radar systems measure the speed and direction of ocean surface currents in near real time provided by Integrated Ocean Observing System (IOOS). The HFR data along the Pacific Coast are at a spatial resolution of 6 km. These data are collected as part of an observational database developed to support the floating offshore wind energy research within the ORACLE project funded by DOE WETO.

17 WIND ENERGY↗
DOE OSTI2023

Radar Wind Profiler at Bodega Bay

These data are collected as part of an observational database developed to support the floating offshore wind energy research under the ORACLE project funded by DOE Wind Energy Technologies Office (WETO). The radar wind profiler network data are collected by NOAA (https://psl.noaa.gov/data/obs/datadisplay), and only data within the state of California are part of the database.

17 WIND ENERGY↗
DOE OSTI2023

Radar Wind Profiler at Point Sur

These data are collected as part of an observational database developed to support the floating offshore wind energy research under the ORACLE project funded by DOE Wind Energy Technologies Office (WETO). The radar wind profiler network data are collected by NOAA (https://psl.noaa.gov/data/obs/datadisplay), and only data within the state of California are part of the database.

17 WIND ENERGY↗
DOE OSTI2023

Radar Wind Profiler at Santa Barbara

These data are collected as part of an observational database developed to support the floating offshore wind energy research under the ORACLE project funded by DOE Wind Energy Technologies Office (WETO). The radar wind profiler network data are collected by NOAA (https://psl.noaa.gov/data/obs/datadisplay), and only data within the state of California are part of the database.

17 WIND ENERGY↗
DOE OSTI2023

Radar Wind Profiler at San Nicolas Island

These data are collected as part of an observational database developed to support the floating offshore wind energy research under the ORACLE project funded by DOE Wind Energy Technologies Office (WETO). The radar wind profiler network data are collected by NOAA (https://psl.noaa.gov/data/obs/datadisplay), and only data within the state of California are part of the database.

17 WIND ENERGY↗
DOE OSTI2020

Environmental Permitting and Compliance Cost Reduction Strategies for the MHK Industry: Lessons Learned from Other Industries

The marine and hydrokinetic (MHK) industry plays a vital role in the U.S. clean energy strategy by providing a renewable, domestic energy source that may offset the need for traditional energy sources. The first MHK deployments in the U.S. have incurred very high permitting costs and long timelines for deploying projects, which increases project risk and discourages investment. A key challenge to advancing an economically competitive U.S. MHK industry is reducing the time and cost required for environmental permitting and compliance with government regulations. Other industries such as offshore oil and gas, offshore wind energy, subsea power and data cables, onshore wind energy, and solar energy facilities have all developed more robust permitting and compliance pathways that provide lessons for the MHK industry in the U.S. and may help inform the global consenting process. Based on in-depth review and research into each of the other industries, we describe the environmental permitting pathways, the main environmental concerns and types of monitoring typically associated with them, and factors that appear to have eased environmental permitting and compliance issues.

other industries↗
DOE OSTI2023

Wind RD&T: On Site Wind for Rural Load Centers

This presentation will provide an overview of the "On Site Wind for Rural Load Center" project for an FY23 research, development, and technology review with other Wind Energy Technologies Office researchers and DOE program managers. This copy of slides is appropriate for public release.

17 WIND ENERGY↗
DOE OSTI2020

Scalable Electrolytic Systems for Renewable Hydrogen Production (Cooperative Research and Development Final Report, CRADA Number CRD-18-747)

The project focuses on offshore wind energy which is an emergent renewable energy source in the United States. Onsite hydrogen production for this sector is not available. The overall goal of GTA's work is development of scalable multi-megawatt water electrolysis systems that exclude platinum group metals, are constructed from readily available commodity materials and are mass produced using advanced manufacturing and robotics. They fabricated and successfully tested a scalable TRL 4 laboratory prototype that demonstrated promising results for achieving the goal. They further performed calculations and prepared scale drawings for an envisioned onsite TRL 8 electrolytic hydrogen production system at an offshore 8 MW wind turbine. The work plan of the CRADA proposal between NREL and GTA focuses on validation of the current TRL 4 prototype with potential development and refinement for advancement to a TRL 5 system. GTA, Inc. proposes joining the H2@Scale consortium to leverage DOE funds, benefit from and contribute to the common knowledge-base and work with NREL as a CRADA partner.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

NOW-23: the 2023 National Offshore Wind Data Set

In this report, we present the latest wind resource data specifically tailored for offshore regions in the United States. The data set, known as the 2023 National Offshore Wind data set (NOW-23), has been developed by the National Renewable Energy Laboratory (NREL) and its partners, and surpasses the previous resource data set, the Wind Integration National Dataset (WIND) Toolkit, which was released approximately ten years ago, in its offshore component. The WIND Toolkit has been widely utilized by stakeholders involved in wind resource assessments across the continental United States. However, with significant advancements in numerical weather prediction modeling over the past decade, the NOW-23 data set incorporates the latest research and development progress to provide stakeholders with an updated and cutting-edge resource for offshore wind analysis. The NOW-23 data set is created using the Weather Research and Forecasting (WRF) model and its output is available, as for its predecessor, at 5-minute time resolution and 2-kilometer horizontal spatial resolution. However, the NOW-23 data set improves upon the WIND Toolkit through: 1. A modeling period of at least 20 years (and as long as 23 years in selected regions), starting in 2000 (compared to the 7-year 2007–2013 modeling period in the WIND Toolkit). 2. For several offshore regions, a region-specific sensitivity analysis, driven by an ensemble of WRF simulations, to assess the most adequate region-specific WRF setup. 3. An updated WRF model, from Version 3.4 used in the WIND Toolkit to Version 4.2.1 used for the NOW-23 data set, which incorporates significant research advancements. 4. The use of the state-of-the-art reanalysis product ERA5 (which supersedes the older ERA-Interim used in the WIND Toolkit) to provide atmospheric forcing at the WRF domain boundaries. Figure 1 shows the NOW-23 mean wind speed at 160 m above sea level (asl), which we use as proxy for hub-height of a commercial offshore wind turbine in this report across all regions.

17 WIND ENERGY↗
DOE OSTI2025

Design, Optimization, and Control of Floating Offshore Wind Farms for Optimal Energy Production (Final report)

The uncertainty and irregularity of ocean waves and the ocean environment is a major factor in the development of commercial scale floating wind turbines as the operation of floating structures in such an environment can lead to irregular and unpredictable loading, fatigue, and ultimately a reduction in the operational life of the turbine system which affects energy production over the lifetime of the turbine. Control solutions that can limit float motions and mitigate stressful events on the structure become essential for extending lifetime and limiting the operational uncertainty of a floating wind turbine. Digital twins are computational replicas of physical systems that operate in parallel with the operation of the physical system. Given advanced knowledge of a systems input, digital twins have the ability to predict the behavior of a system in advance, which can be valuable in the control of that system. In this project, we developed and assessed potential digital twin models developed in house and openly available (OpenFast) for use in the real time control of the six degree of freedom response motions of a floating wind turbine in ocean waves. Coupling these models with near-field real time irregular sea surface (wve) measurement/sensings and prediction models, we used the digital twin to predict how the floating turbine will respond to the incoming waves. Applying this information to a motion control system of the float, one can limit and control float motions to prevent undesirable loading events/large angular motions, thus increasing system life and ultimately contributing to optimizing energy production. Due to the computational intensity of operating a digital twin in real time, we investigated the use of artificial intelligence techniques to speed-up the processes of the digital twin, as well as the wave reconstruction/prediction models. Model tank testing at the University of Rhode Island and University of Maine both validated and demonstrated the developed techniques on simple float geometries and a scale model of the NREL 15 MW reference turbine.

17 WIND ENERGY↗
DOE OSTI2022

Wind turbine drivetrains: state-of-the-art technologies and future development trends

Abstract. This paper presents the state-of-the-art technologies and development trends of wind turbine drivetrains – the system that converts kinetic energy of the wind to electrical energy – in different stages of their life cycle: design, manufacturing, installation, operation, lifetime extension, decommissioning and recycling. Offshore development and digitalization are also a focal point in this study. Drivetrain in this context includes the whole power conversion system: main bearing, shafts, gearbox, generator and power converter. The main aim of this article is to review the drivetrain technology development as well as to identify future challenges and research gaps. The main challenges in drivetrain research identified in this paper include drivetrain dynamic responses in large or floating turbines, aerodynamic and farm control effects, use of rare-earth material in generators, improving reliability through prognostics, and use of advances in digitalization. These challenges illustrate the multidisciplinary aspect of wind turbine drivetrains, which emphasizes the need for more interdisciplinary research and collaboration.

17 WIND ENERGY↗