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DOE OSTI2025

Offshore lightning more active and intense in U.S. East Coast wind areas than in North Sea

Fast winds over open areas off the U.S. East Coast resulted in many offshore areas being opened for wind power deployment. Yet lightning is active in this same general area. Lightning damage has been noted as the leading cause of unplanned downtime of wind farms. Here we use lightning observations from 2020–2022 to quantify lightning activity—U.S. East Coast Wind Lease and Wind Planning areas received about 14-times the lightning strokes and about 18-times the energy transfer compared to the operational wind farms in the North Sea. A strong north-south U.S. gradient of lightning exists : low off the coast of Maine, growing higher off Chesapeake Bay, and high south of Virginia. Very high lightning activity also occurs about 250 km from the U.S. coastline, which is 50–100 km outside currently designated Wind Areas but likely relevant to planning floating wind farms further from shore. International lightning protection standards, such as IEC 614200-24, use lightning stroke density and turbine top-height to estimate damage to wind turbines, but it remains unclear if such standards could protect U.S. offshore wind turbines in active lightning areas where the turbine top-height can be 100 m taller than the next tallest object for tens to hundreds of kilometers. This analysis suggests U.S. East Coast offshore wind farms will be subjected to much higher lightning stroke and lightning energy densities than the North Sea counterparts. Preparing the turbine designs in anticipation of these more active U.S. lightning risks will help foster wind’s growing contribution to a reliable and resilient low-carbon energy future.

lightning↗

NASA NTRS1984

Wind and solar powered turbine

A power generating station having a generator driven by solar heat assisted ambient wind is described. A first plurality of radially extendng air passages direct ambient wind to a radial flow wind turbine disposed in a centrally located opening in a substantially disc-shaped structure. A solar radiation collecting surface having black bodies is disposed above the fist plurality of air passages and in communication with a second plurality of radial air passages. A cover plate enclosing the second plurality of radial air passages is transparent so as to permit solar radiation to effectively reach the black bodies. The second plurality of air passages direct ambient wind and thermal updrafts generated by the black bodies to an axial flow turbine. The rotating shaft of the turbines drive the generator. The solar and wind drien power generating system operates in electrical cogeneration mode with a fuel powered prime mover.

Wells, I. D.↗

DOE OSTI2023

Data and code repository for "How do the weather regimes drive wind speed and power production at the sub-seasonal to seasonal timescales over the CONUS?"

There has been an increasing need for forecasting power generation at the sub-seasonal to seasonal (S2S) timescales to support the operation, management, and planning of the wind-energy system. At the S2S timescales, atmospheric variability is largely related to recurrent and persistent weather patterns, referred to as weather regimes (WRs). In the study "How do the weather regimes drive wind speed and power production at the sub-seasonal to seasonal timescales over the CONUS?", we identify four WRs that influence wind resources over North America using a self-organizing map (SOM) algorithm. These WRs are responsible for large-scale wind and power production anomalies over the CONUS at the S2S timescales. The WR-based reconstruction explains up to 50% of the monthly variance of power production over the western United States, and the explanatory power generally increases with the increase of timescales. The identified relationship between WRs and power production reveals the potential and limitations of the regional WR-based wind resource assessment over different regions of the CONUS across multiple timescales. This repository includes all the data and codes we use for analyses in this study. Users may use them to reproduce the results of this study on their end.

17 WIND ENERGY↗

DOE OSTI2016

Impacts of Federal Tax Credit Extensions on Renewable Deployment and Power Sector Emissions

Federal tax credits for renewable energy (RE) have served as one of the primary financial incentives for RE deployment over the last two decades in the United States. In December 2015, the wind power production tax credit and solar investment tax credits were extended for five years as part of the Consolidated Appropriations Act of 2016. This report explores the impact that these tax credit extensions might have on future RE capacity deployment and power sector carbon dioxide (CO2) emissions. The analysis examines the impacts of the tax credit extensions under two distinct natural gas price futures as natural gas prices have been key factors in influencing the economic competitiveness of new RE development. The analysis finds that, in both natural gas price futures, RE tax credit extensions can spur RE capacity investments at least through the early 2020s and can help lower emissions from the U.S. electricity system. More specifically, the RE tax credit extensions are estimated to drive a net peak increase of 48-53 GW in installed RE capacity in the early 2020s -- longer term impacts are less certain. In the longer term after the tax credits ramp down, greater RE capacity is driven by a combination of assumed RE cost declines, rising fossil fuel prices, and other clean energy policies such as the Clean Power Plan. The tax credit extension-driven acceleration in RE capacity development can reduce fossil fuel-based generation and lower electric sector CO2 emissions. Cumulative emissions reductions over a 15-year period (spanning 2016-2030) as a result of the tax credit extensions are estimated to range from 540 to 1420 million metric tonnes CO2. These findings suggest that tax credit extensions can have a measurable impact on future RE deployment and electric sector CO2 emissions under a range of natural gas price futures.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2018

The future of wind energy in California: Future projections with the Variable-Resolution CESM

Shifting wind patterns are an expected consequence of global climate change, with direct implications for wind energy production. However, wind is notoriously difficult to predict, and significant uncertainty remains in our understanding of climate change impacts on existing wind generation capacity. Historical and future wind climatology and associated capacity factors at five wind turbine sites in California are examined. Historical (1980–2000) and mid-century (2030–2050) simulations were produced using the Variable-Resolution Community Earth System Model (VR-CESM) to understand how these wind generation sites are expected to be impacted by climate change. A high-resolution statistically downscaled WRF product provided by DNV GL, reanalysis datasets MERRA-2, CFSR, NARR, and observational data were used for model validation and comparison. These projections suggest that wind power generation capacity throughout the state is expected to increase during the summer, and decrease during fall and winter, based on significant changes at several wind farm sites. Finally, this study improves the characterization of uncertainty around the magnitude and variability in space and time of California's wind resources in the near future, and also enhances our understanding of the physical mechanisms related to the trends in wind resource variability.

17 WIND ENERGY↗

DOE OSTI2020

Validating simulated mountain wave impacts on hub-height wind speed using SoDAR observations

The ascent of stably stratified air over a mountain barrier can trigger the generation of mountain waves. Mountain waves occur frequently over the Columbia River Gorge in western North America and can impact wind power generation over the area. Therefore, predicting the details of mountain waves events (e.g., dominant wavelength, timing, and duration) can be very valuable for the wind energy community. In this study, the ability of the Weather Research and Forecasting (WRF) model to simulate mountain waves and their impact on hub-height wind speed is investigated. Our results suggest that the WRF model has moderate skill in simulating observed mountain wave. Further, given WRF predictions of wavelength range and wave period, the Fast Fourier Transform can calculate the simulated mountain wave impact on hub-height wind speed. The resulting wind speeds agree well with SoDAR observations in terms of both magnitude and pattern. Finally, for the simulated cases, WRF consistently predicts impacts of significant mountain wave events about an hour earlier than the actual observations. Overall, in this work, the sensitivities as well as uncertainties associated with our methodology are discussed in detail.

17 WIND ENERGY↗

DOE OSTI2014

Optimization of Installation, Operation and Maintenance at Offshore Wind Projects in the U.S.: Review and Modeling of Existing and Emerging Approaches

The United States Department of Energy (DOE) awarded a grant to GL Garrad Hassan (GL GH) to investigate the logistics, opportunities, and costs associated with existing and emerging installation and operation and maintenance (O&M) activities at offshore wind projects as part of the DOE’s program to reduce barriers facing offshore wind project development in the United States (U.S.). This report (the Report) forms part of Subtopic 5.3 “Optimized Installation, Operation and Maintenance Strategies Study” which in turn is part of the “Removing Market Barriers in U.S. Offshore Wind” set of projects for the DOE. The purpose of Subtopic 5.3 is to aid and facilitate informed decision-making regarding installation and O&M during the development, installation, and operation of offshore wind projects in order to increase efficiency and reduce the levelized cost of energy (LCoE). Given the large area of U.S. territorial waters, the generally higher mean wind speeds offshore, and the proximity to the coast of many large U.S. cities, offshore wind power has the potential to become a significant contributor of energy to U.S. markets. However, for the U.S. to ensure that the development of offshore wind energy projects is carried out in an efficient and cost-effective manner, it is important to be cognizant of the current and emerging practices in both the domestic and international offshore wind energy industries. The U.S. can harness the experience gained globally and combine this with the skills and assets of an already sizeable onshore wind industry, as well as the resources of a mature offshore oil and gas industry, to develop a strong offshore wind sector. The work detailed in this report is aimed at assisting with that learning curve, particularly in terms of offshore specific installation and O&M activities. This Report and the Installation and O&M LCoE Analysis Tool, which were developed together by GL GH as part of this study, allow readers to identify, model and probe the economic merits and sensitivities of various approaches to construction and O&M practices, using illustrative offshore projects across a wide range of alternative offshore development areas located in U.S. waters. The intention is to assist decision-makers in clearly understanding the relative economic benefits of both conventional and novel construction installation methodologies and maintenance techniques within the critical parameters of a Project’s LCoE.

17 WIND ENERGY↗

NASA NTRS2008

Energy and Mass Transport of Magnetospheric Plasmas during the November 2003 Magnetic Storm

Intensive energy and mass transport from the solar wind across the magnetosphere boundary is a trigger of magnetic storms. The storm on 20-21 November 2003 was elicited by a high-speed solar wind and strong southward component of interplanetary magnetic field. This storm attained a minimum Dst of -422 nT. During the storm, some of the solar wind particles enter the magnetosphere and eventually become part of the ring current. At the same time, the fierce solar wind powers strong outflow of H+ and O+ from the ionosphere, as well as from the plasmasphere. We examine the contribution of plasmas from the solar wind, ionosphere and plasmasphere to the storm-time ring current. Our simulation shows, for this particular storm, ionospheric O+ and solar wind ions are the major sources of the ring current particles. The polar wind and plasmaspheric H+ have only minor impacts. In the storm main phase, the strong penetration of solar wind electric field pushes ions from the geosynchronous orbit to L shells of 2 and below. Ring current is greatly intensified during the earthward transport and produces a large magnetic depression in the surface field. When the convection subsides, the deep penetrating ions experience strong charge exchange loss, causing rapid decay of the ring current and fast initial storm recovery. Our simulation reproduces very well the storm development indicated by the Dst index.

Fok, Mei-Chging↗

DOE OSTI2022

Grand Challenges in the Science of Making Torque from Wind: Then and Now

Shifting the electricity supply to renewable sources within the next two decades is the first step in decarbonizing the energy sector, and wind energy will play a central role in enabling this rapid energy transition to clean energy. The international wind energy research community - including NREL researchers - has determined that for wind power to fulfill its expected role as a major global supplier of carbon-free energy, critical challenges around the design, development, and deployment of wind energy must be addressed - quickly. Paul Veers and other NREL researchers joined 100 worldwide wind energy experts in writing a series of 10 articles for publication in Wind Energy Science. With the support and guidance of the European Academy of Wind Energy Publications Committee, the articles synthesize and clarify the most critical wind energy science needs. This presentation introduces the concept of the Grand Challenges and its 10 articles.

decarbonization↗

DOE OSTI2026

An Analysis of Future Wind Energy Resources and Cost Uncertainties Across the United States

Wind power is a growing source of energy generation that relies on complex global atmospheric and earth system processes. There has been evidence of reductions in average wind speeds over land in North America since the 1980s, and several models project that average wind speeds will continue to decrease. Concurrently, the cost of wind energy systems in the United States has been decreasing since around 2010, a trend also projected to continue. There is considerable uncertainty in these future projections, with quantitative estimates of future wind resource and system costs varying widely. To study this, we run wind energy models with possible future system costs, turbine designs, and meteorological inputs from multiple downscaled earth system models over the contiguous United States. Changes in mean annual energy production from 2000-2019 to 2040-2059 can be as high as +10% in South Texas or as low as -20% in Iowa. Larger turbines and moderate reductions in system costs can offset the largest projected decreases in wind resource, but much uncertainty remains in the extent to which wind resources will change and to what extent system costs can be reduced. An analysis of variance shows, in several states in the Midwest, uncertainty in future wind resources can influence the cost of wind energy nearly as much as uncertainty in future system costs.

17 WIND ENERGY↗

DOE OSTI2023

Developing a Vorticity-Velocity-Based Off-Body Solver to Perform Multifidelity Simulations of Wind Farms

Wind power has become a key player in satisfying the global energy needs. With increased market penetration, unanticipated unsteady loading induced failures, installation related reductions in power generation, and significant maintenance costs have underscored the need to predict the unsteady fluid-structure interactions related to turbine layout and off-design wind conditions. Contemporary turbine design tools are incapable of accounting for such loadings. As a result, researchers have started utilizing high-Performance-Computing (HPC) based Computational Fluid Dynamics (CFD) solvers, such as the U.S. Department of Energy sponsored ExaWind software package, to investigate these phenomena. Unfortunately, such HPC tools are computationally expensive for routine industrial use, often because of the sheer number of cells required to resolve the wake flowfield. This paper describes a preliminary effort to address this issue by developing a vorticity-velocity based CFD off-body solver, VorTran-M2-AMReX, that integrates directly with DOE's ExaWind wind turbine analysis system to perform accurate and reliable simulations of wind turbine/farm at a lower computational cost than ExaWind alone. This article summarizes work undertaken to date concerning the assembly of the proposed analysis tool, and provides preliminary validation and verification of the VorTran-M2-AMReX off-body solver.

adaptive mesh refinement↗

DOE OSTI2015

Social Acceptance of Wind: A Brief Overview (Presentation)

This presentation discusses concepts and trends in social acceptance of wind energy, profiles recent research findings, and discussions mitigation strategies intended to resolve wind power social acceptance challenges as informed by published research and the experiences of individuals participating in the International Energy Agencies Working Group on Social Acceptance of Wind Energy

17 WIND ENERGY↗

DOE OSTI2016

Transforming Wind Turbine Blade Mold Manufacturing with 3D Printing

Innovation in the design and manufacturing of wind power generation components continues to be critical to achieving our national renewable energy goals. As a result of this challenge, the U.S. Department of Energy's Wind Program and Advanced Manufacturing Office are partnering with public and private organizations to apply additive manufacturing, commonly known as 3D printing, to the production of wind turbine blade molds.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Quantum Stochastic Programming [SWR-26-040]

The Quantum Stochastic Programming tool contains quantum computing algorithms for two-stage stochastic optimization, with a focus on the Unit Commitment (UC) problem in power systems. The algorithms combine Discrete Quantum Annealing (DQA) with Quantum Amplitude Estimation (QAE) to compute expected-value objective functions over a probability distribution of wind-power scenarios. Based on: arXiv 2402.15029 - "Quantum algorithms for the two-stage stochastic unit commitment problem"

Maack, Jonathan [National Laboratory of the Rockie↗

DOE OSTI2013

Wind Turbine Drivetrain Condition Monitoring - An Overview (Presentation)

High operation and maintenance costs still hamper the development of the wind industry despite its quick growth worldwide. To reduce unscheduled downtime and avoid catastrophic failures of wind turbines and their components have been and will be crucial to further raise the competitiveness of wind power. Condition monitoring is one of the key tools for achieving such a goal. To enhance the research and development of advanced condition monitoring techniques dedicated to wind turbines, we present an overview of wind turbine condition monitoring, discuss current practices, point out existing challenges, and suggest possible solutions.

17 WIND ENERGY↗

DOE OSTI2022

Stochastic agent-based model for predicting turbine-scale raptor movements during updraft-subsidized directional flights

Rapid expansion of wind energy development across the world has highlighted the need to better understand turbine-caused avian mortality. The risk to golden eagles (Aquila chrysaetos) is of particular concern due to their small population size and conservation status. Golden eagles subsidize their flight in part by soaring in orographic updrafts, which can place them in conflict with wind turbines utilizing the same low-altitude wind resource. Understanding the behavior of soaring raptors in varying atmospheric conditions can therefore be relevant to predicting and mitigating their risk of collision. We present a predictive movement model that simulates individual paths of golden eagles during directional flight (such as migration) that is subsidized by orographic updraft. We modeled eagles in a 50 km by 50 km study area in Wyoming containing three wind power plants with documented golden eagle collisions with turbines. The movement model is applicable to any region where ground elevation is known at turbine scale (50 m) and wind conditions are known at facility scale (3 km). For a given set of atmospheric conditions, the model simulates movements of thousands of orographic soaring eagles to produce a density map quantifying the relative probability of eagle presence. We validated the simulated tracks with GPS telemetry data showing four directional tracks made by golden eagles transiting through the area in 2019 and 2020. For each eagle track, validation was performed using the ratio of the model-simulated eagle presence likelihood with uniform eagle presence and the presence computed using directed random-walk movements. We found that the predictive performance of the model was significantly better (likelihood ratio 1) for low-altitude movements than high-altitude movements that can involve thermal-soaring. We employed the model to produce seasonal presence maps for migrating golden eagles. We found significant turbine-level variations in eagle presence between northerly and southerly migration routes through the study area. Overall, the proposed model offers a generalizable, probabilistic, and predictive tool to assist wind energy developers, ecologists, wildlife managers, and industry consultants in estimating the potential for conflict between soaring birds and wind turbines, thereby reducing the need for site-specific data on golden eagle movements.

17 WIND ENERGY↗

DOE OSTI2023

Interpretable Data-Driven Probabilistic Power System Load Margin Assessment with Uncertain Renewable Energy and Loads

The increasing uncertainties caused by the high-penetration of stochastic renewable generation resources poses a significant threat to the power system voltage stability. To address this issue, this paper proposes a probabilistic deep kernel learning enabled surrogate model to extract the hidden relationship between uncertain sources, i.e., wind power and loads, and load margin for probabilistic load margin assessment (PLMA). Unlike other deep learning approaches, a kernel SHAP provides the sensitivity analysis as well as interpretability of the inputs to outputs influences. This allows identifying the critical factors that affect load margin so that corrective control can be initiated for stability enhancement. Numerical results carried out on the IEEE 118-bus power system demonstrate the accuracy and efficiency of the proposed data-driven PLMA scheme.

deep kernel learning↗

DOE OSTI2022

IACMI Project 4.10: Blade Finishing Automation (Final Report)

IACMI Project 4.10, the Innovative Wind Turbine Blade Finishing with Advanced Automated Technology project, performed research addressing the challenges and opportunities associated with automating wind turbine blade finishing operations, including blade trimming and grinding. These activities, developed and conducted in close cooperation with wind industry partners, included: • The development of new automation technology and systems; • The integration of automation technology and components; • The improvements of sensor technology and their deployment in automation systems • The elimination of extensive manual programing for the automation of wind blade finishing; • The rethinking of the approach to specific work tasks for automation systems; • The demonstration at scale of innovative wind turbine blade finishing automations systems. The IACMI Wind TA team worked with GE Renewable Energy and LM Wind Power to establish the metrics of the automated blade finishing system developed in the project. In addition, the project team helped to integrate the advanced automated systems into wind turbine blade production facilities. This project was divided into two phases. Phase I provided project management, preliminary techno-economic model development, early US wind turbine industry outreach, and early research into robotic solutions for automated wind blade finishing. This second phase continued with project management, as well as final techno-economic model development, advanced research into robotic solutions for automated wind blade finishing, as well as specifications and procurement of a robotic system for automated wind blade finishing. The 4.10 project leveraged the capabilities and facilities established through the Institute for Advanced Composites Manufacturing Innovation (IACMI), the Colorado Office of Economic Development and International Trade (OEDIT) and the National Renewable Energy Laboratory (NREL). The IACMI Wind Technology Area (TA) designed and prepared for deployment a robotic automated wind turbine blade finishing system at the NREL/IACMI Composites Manufacturing Education and Technology (CoMET) facility. The results of this project led to follow-on funding to build upon and expand research in automated wind turbine blade finishing.

17 WIND ENERGY↗