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DOE OSTI2016

Wind Energy Workforce Development & Jobs

The United States needs a skilled and qualified wind energy workforce to produce domestic clean power. To assist with wind energy workforce development, the U.S. Department of Energy (DOE) and National Renewable Energy Laboratory are engaged with several efforts.This presentation by Suzanne Tegen describes these efforts, including a wind industry survey, DOE's Wind Career Map, the DOE Wind Vision report, and an in-depth discussion of the Jobs & Economic Development Impacts Model.

17 WIND ENERGY↗
DOE OSTI2017

Deployment of Wind Turbines in the Built Environment: Risks, Lessons, and Recommended Practices

Built-environment wind turbine (BEWT) projects are wind energy projects that are constructed on, in, or near buildings, as shown below. These projects present an opportunity for distributed, low-carbon generation combined with highly visible statements on sustainability, but the BEWT niche of the wind industry is still developing and is relatively less mature than the utility-scale wind or conventional ground-based distributed wind sectors. This poster investigates the current state of the BEWT industry by reviewing available literature on BEWT projects as well as interviewing project owners on their experiences deploying and operating the technology.

17 WIND ENERGY↗
DOE OSTI2019

Wind Energy Modeling and Simulation, Volume 2: Turbine and System

In order to optimise the yield of wind power from existing and future wind plants, the entire breadth of the system of a plant, from the wind field to the turbine components, needs to be modelled in the design process. The modelling and simulation approaches used in each subsystem as well as the system-wide solution methods to optimize across subsystem boundaries are described in this reference. Chapters are written by technical experts in each field, describing the current state of the art in modelling and simulation for wind plant design. This comprehensive, two-volume research reference will provide long-lasting insight into the methods that will need to be developed for the technology to advance into its next generation. Volume 2 covers turbine level aerodynamics, aeroelasticity, rotors drivetrains and electrical systems, wind turbine control, offshore foundations, system optimization, and grid modelling.

17 WIND ENERGY↗
DOE OSTI2023

Wind as a Distributed Energy Resource

Distributed wind can be installed in a wide range of locations and wind conditions, supporting millions of systems and thousands of gigawatts of power production capacity. As a result, utilities, communities, and nations are looking to distributed generation as an effective way to meet future energy needs. This fact sheet provides an overview of distributed wind, including where distributed wind projects can be located, and how U.S. and international research supports distributed wind applications. This fact sheet was produced as a resource for the International Energy Agency Task 41 members to use as an educational resource.

behind-the-meter wind energy↗
DOE OSTI2025

A perspective on lessons learned and future needs for wind energy field campaigns

This perspective paper provides motivation and guidance to the wind energy community for suggested future investments and a long-term strategy for wind-energy-related field campaigns that will provide much-needed observations for improving wind energy science and model validation. We synthesize key lessons learned from past field campaigns, identify critical science gaps that we think should drive future field efforts, and provide a suggested pipeline on how future endeavors should be developed. When considering future grand field campaigns, we stress the need for international cooperation across funding bodies, research institutes, and industry partners to collect observations to overcome current and future grand challenges for wind energy.

17 WIND ENERGY↗
DOE OSTI2016

Wind Plant Power Optimization through Yaw Control using a Parametric Model for Wake Effects -- A CFD Simulation Study

This article presents a wind plant control strategy that optimizes the yaw settings of wind turbines for improved energy production of the whole wind plant by taking into account wake effects. The optimization controller is based on a novel internal parametric model for wake effects, called the FLOw Redirection and Induction in Steady-state (FLORIS) model. The FLORIS model predicts the steady-state wake locations and the effective flow velocities at each turbine, and the resulting turbine electrical energy production levels, as a function of the axial induction and the yaw angle of the different rotors. The FLORIS model has a limited number of parameters that are estimated based on turbine electrical power production data. In high-fidelity computational fluid dynamics simulations of a small wind plant, we demonstrate that the optimization control based on the FLORIS model increases the energy production of the wind plant, with a reduction of loads on the turbines as an additional effect.

17 WIND ENERGY↗
DOE OSTI2025

Efficient wind farm layout optimization with the FLOWERS AEP model and analytic gradients

Wind farm layout optimization (WFLO) studies often aim to maximize the annual energy production (AEP) of a wind farm by choosing an arrangement of turbines that minimizes wake interactions. One way to reduce the cost of WFLO studies is by using more computationally efficient AEP models. The cost of standard AEP modeling approaches, based on the numerical integration of low-fidelity engineering wake models, scales poorly with the number of simulated discrete wind conditions. A second way to reduce cost when using a gradient-based algorithm is to supply exact gradient information instead of finite-difference estimates. However, analytical functions for the derivatives of AEP with respect to turbine positions are not always available in the conventional modeling approach. FLOWERS is a computationally inexpensive, analytical model for wind farm AEP that is specifically developed for WFLO applications. In this paper, we analyze the performance of the FLOWERS AEP model with analytic gradients in a layout optimization study compared with a reference optimization framework across three wind farm case studies. We find that the FLOWERS-based approach reduces computation time by a factor of 50–4000 and improves optimal AEP by about 0.3% with less than half of the variability in AEP across instances with randomized initial conditions. We also find the optimal layouts to be insensitive to model parameter tuning, making FLOWERS-based layout optimization a streamlined, user-friendly approach.

17 WIND ENERGY↗
DOE OSTI2022

Grid-Forming Wind: Getting Ready for Prime Time With and Without Inverters

Variable wind power is one of the fastest-growing energy-generation technologies, harnessing the energy of wind, both on land and at sea. During the past decade, the global share of wind power has grown tremendously, and wind power is evolving into a major contributor to electricity supplies in many countries. In this journey, wind is also becoming a source of reliability services to the grid, which has required grid-supporting functions originally provided by synchronous generators, enabling very high levels of instantaneous penetration (ranging from 60 to 70% in some power systems). To get beyond this, a fundamental shift is required to address challenges associated with the transition to a grid with only a few remaining (or even without any) conventional synchronous generators while achieving a minimum acceptable level of stability.

energy consumption↗
DOE OSTI2016

Potential Offshore Wind Energy Areas in California: An Assessment of Locations, Technology, and Costs

This report summarizes a study of possible offshore wind energy locations, technologies, and levelized cost of energy in the state of California between 2015 and 2030. The study was funded by the U.S. Department of the Interior's Bureau of Ocean Energy Management (BOEM), the federal agency responsible for regulating renewable energy development on the Outer Continental Shelf. It is based on reference wind energy areas where representative technology and performance characteristics were evaluated. These reference areas were identified as sites that were suitable to represent offshore wind cost and technology based on physical site conditions, wind resource quality, known existing site use, and proximity to necessary infrastructure. The purpose of this study is to assist energy policy decision-making by state utilities, independent system operators, state government officials and policymakers, BOEM, and its key stakeholders. The report is not intended to serve as a prescreening exercise for possible future offshore wind development.

17 WIND ENERGY↗
DOE OSTI2017

Wind Energy Finance in the United States: Current Practice and Opportunities

In the United States, investment in wind energy has averaged nearly $13.6 billion annually since 2006 with more than $140 billion invested cumulatively over that period (BNEF 2017). This sizable investment activity demonstrates the persistent appeal of wind energy and its increasing role in the U.S electricity generation portfolio. Despite its steady investment levels over the last decade, some investors still consider wind energy as a specialized asset class. Limited familiarity with the asset class both limit the pool of potential investors and drive up costs for investors. This publication provides an overview of the wind project development process, capital sources and financing structures commonly used, and traditional and emerging procurement methods. It also provides a high-level demonstration of how financing rates impact a project's all-in cost of energy. The goal of the publication is to provide a representative and wide-ranging resource for the wind development and financing processes.

17 WIND ENERGY↗
DOE OSTI2024

Investigation of the Challenges of Offshore Wind in Ultradeep Water

Floating offshore wind technology allows offshore wind energy systems to be deployed in water depths that are inaccessible with conventional fixed bottom technology. Several floating offshore wind energy pilot projects have demonstrated reliable operation of the technology in water depths between 200 m and 300 m. Building on that experience, commercial-scale projects are being developed in areas out to 1,300 m depths. In some regions there is substantial resource potential for wind energy generation in even deeper waters, however, increasing depths may introduce new challenges for installation, maintenance, and repair. In this report, we consider technical, environmental, and economic challenges for floating offshore wind energy in ultradeep water, defined here as depths between 1,300 m and 3,000 m.

17 WIND ENERGY↗
DOE OSTI2021

Wind and Structural Loads on Parabolic Trough Solar Collectors at Nevada Solar One

Wind loading is a main contributor to structural design costs of Concentrating Solar Power (CSP) collectors, such as heliostats and parabolic troughs. These structures must resist the mechanical forces generated by turbulent wind. At the same time, the reflector surfaces must exhibit the necessary rigidity to maintain their optimal optical performance in windy conditions. Over two years, NREL conducted comprehensive field measurements of the atmospheric turbulent wind conditions and the resulting structural wind loads on parabolic troughs at the Nevada Solar One (NSO) plant. The measurement set-up included meteorological masts and structural load sensors on four trough rows. Additionally, we commissioned a lidar scanning the horizontal plane over the trough field. This data set catalogs the high-resolution data set characterizing the complex flow field and resulting structural loads on parabolic trough collectors. By providing this first-of-its-kind data set to the CSP community, we aim to enhance the community's understanding of wind-loading experienced by CSP collector structures. This data set will also help design next-generation solar collectors and photovoltaic trackers.

Array↗
DOE OSTI2024

Offshore Wind Composite Siting Exclusions Scenarios

This dataset consolidates criteria that restrict the siting of offshore wind energy installations, an update from Zuckerman et al. (2023). All scenarios exclude area from wind energy development due to legal or administrative restrictions (e.g., Marine Protected Areas), existing infrastructure, and technical constraints (e.g., 1,300 m bathymetry limit). A TIF data file and a PNG cartographic map of the data are provided for three scenarios ("Open", "Reference", and "Limited"), showing areas where offshore wind energy is restricted across the contiguous United States. The "Open" scenario only excludes areas with known preclusions for offshore wind deployment. The "Reference" scenario excludes all areas from the "Open" scenario, as well as additional best management practices such as known proposed protected areas, and state waters. The "Limited" scenario excludes all area from the "Reference" scenario. Additionally, it has more stringent siting criteria such as larger setbacks from shore and infrastructure, as well as shallower depth limits on floating technology. For further details and citation, please refer to the publication linked below: Zuckerman, Gabriel R., Anthony Lopez, Travis Williams, Rebecca Green, and Grant Buster. 2023. Impacts of Siting Considerations on Offshore Wind Technical Potential in the United States. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20- 85088.

Array↗
DOE OSTI2024

The 2023 National Offshore Wind data set (NOW-23)

Abstract. This article introduces the 2023 National Offshore Wind data set (NOW-23), which offers the latest wind resource information for offshore regions in the United States. NOW-23 supersedes, for its offshore component, the Wind Integration National Dataset (WIND) Toolkit, which was published a decade ago and is currently a primary resource for wind resource assessments and grid integration studies in the contiguous United States. By incorporating advancements in the Weather Research and Forecasting (WRF) model, NOW-23 delivers an updated and cutting-edge product to stakeholders. In this article, we present the new data set which underwent regional tuning and performance validation against available observations and has data available from 2000 through, depending on the region, 2019–2022. We also provide a summary of the uncertainty quantification in NOW-23, along with NOW-WAKES, a 1-year post-construction data set that quantifies expected offshore wake effects in the US Mid-Atlantic lease areas. Stakeholders can access the NOW-23 data set at https://doi.org/10.25984/1821404 (Bodini et al., 2020).

17 WIND ENERGY↗
DOE OSTI2023

On the characteristics of the wake of a wind turbine undergoing large motions caused by a floating structure: an insight based on experiments and multi-fidelity simulations from the OC6 project Phase III

This study reports the results of the second round of analyses of the Offshore Code Comparison, Collaboration, Continued, with Correlation and unCertainty (OC6) project Phase III. While the first round investigated rotor aerodynamic loading, here, focus is given to the wake behavior of a floating wind turbine under large motion. Wind tunnel experimental data from the UNsteady Aerodynamics for FLOating Wind (UNAFLOW) project are compared with the results of simulations provided by participants with methods and codes of different levels of fidelity. The effect of platform motion on both the near and the far wake is investigated. More specifically, the behavior of tip vortices in the near wake is evaluated through multiple metrics, such as streamwise position, core radius, convection velocity, and circulation. Additionally, the onset of velocity oscillations in the far wake is analyzed because this can have a negative effect on stability and loading of downstream rotors. Results in the near wake for unsteady cases confirm that simulations and experiments tend to diverge from the expected linearized quasi-steady behavior when the rotor reduced frequency increases over 0.5. Additionally, differences across the simulations become significant, suggesting that further efforts are required to tune the currently available methodologies in order to correctly evaluate the aerodynamic response of a floating wind turbine in unsteady conditions. Regarding the far wake, it is seen that, in some conditions, numerical methods overpredict the impact of platform motion on the velocity fluctuations. Moreover, results suggest that the effect of platform motion on the far wake, differently from original expectations about a faster wake recovery in a floating wind turbine, seems to be limited or even oriented to the generation of a wake less prone to dissipation.

17 WIND ENERGY↗
DOE OSTI2021

ExaWind: Exascale Predictive Wind Plant Flow Physics Modeling

The scientific goal of the ExaWind project is to advance our fundamental understanding of the flow physics governing whole wind plant performance, including wake formation, complex terrain impacts, and turbine-turbine-interaction effects. The primary application codes in the ExaWind environment are Nalu-Wind, an unstructured-grid computational fluid dynamics (CFD) code, AMR-Wind, a structured-grid CFD code, and OpenFAST, a whole-turbine simulation code. In this poster we present our progress towards simulating the ExaWind challenge problem, which is a predictive simulation of a wind farm with tens of megawatt-scale wind turbines dispersed over an area of 50 square kilometers.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2022

Design Guidelines for Deployable Wind Turbines for Defense and Disaster Response Missions

Access to on-site electrical energy is critical to ensuring a successful military or humanitarian response to conflicts and disasters. These missions typically rely on access to liquid fuel that could be vulnerable to disruption or attack during transport. Generating power on location with wind technology can reduce this risk and enhance mission reach by diversifying energy sources. Common characteristics of these missions are short planning and execution time horizons and a global scope of potential locations. Compared to conventional wind turbine applications, defense and disaster response applications place a premium on rapid shipping and installation, short-duration operation (days to months), and quick teardown upon mission completion. These design drivers depart from features found in conventional distributed wind turbines, thus necessitating unique design guidance. The supporting information for this guidance comes from available relevant references, technical analyses, and input from industry and military stakeholders. This poster serves as a summary of project publications which presents the best currently available design guidance for deployable wind turbines to facilitate the effective development and acquisition of technology solutions to support mission success. This Defense and Disaster Deployable Turbine Project (D3T) is a multi-laboratory effort led by Sandia National Laboratories and funded by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Wind Energy Technologies Office.

deployable wind↗
DOE OSTI2016

Information theoretical approach to discovering solar wind drivers of the outer radiation belt

The solar wind-magnetosphere system is nonlinear. The solar wind drivers of geosynchronous electrons with energy range of 1.8–3.5 MeV are investigated using mutual information, conditional mutual information (CMI), and transfer entropy (TE). These information theoretical tools can establish linear and nonlinear relationships as well as information transfer. The information transfer from solar wind velocity ( V sw ) to geosynchronous MeV electron flux ( J e ) peaks with a lag time of 2 days. As previously reported, J e is anticorrelated with solar wind density ( n sw ) with a lag of 1 day. However, this lag time and anticorrelation can be attributed at least partly to the J e ( t + 2 days) correlation with V sw ( t ) and n sw ( t + 1 day) anticorrelation with V sw ( t ). Analyses of solar wind driving of the magnetosphere need to consider the large lag times, up to 3 days, in the ( V sw , n sw ) anticorrelation. Using CMI to remove the effects of V sw , the response of J e to n sw is 30% smaller and has a lag time < 24 h, suggesting that the MeV electron loss mechanism due to n sw or solar wind dynamic pressure has to start operating in < 24 h. n sw transfers about 36% as much information as V sw (the primary driver) to J e . Nonstationarity in the system dynamics is investigated using windowed TE. Here, when the data are ordered according to transfer entropy value, it is possible to understand details of the triangle distribution that has been identified between J e ( t + 2 days) versus V sw ( t ).

58 GEOSCIENCES↗

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