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The space flight testing of a large, flat, flexible panel solar array is examined. The experiment objectives are: to demonstrate the functional operational of the wind deployment and packaging system; Electrical performance; Thermal performance; and dynamic performance. A complete description of the experiment and the flight results are given.
The controversial role of the dry Saharan Air Layer (SAL) on tropical storm intensification in the Atlantic will be addressed. The SAL has been argued in previous studies to have potential positive influences on storm development, but most recent studies have argued for a strong suppressing influence on storm intensification as a result of dry air, high stability, increased vertical wind shear, and microphysical impacts of dust. Here, we focus on observations of Hurricane Helene (2006), which occurred during the NASA African Monsoon Multidisciplinary Activities (NAMMA) experiment. Satellite and airborne observations, combined with global meteorological analyses depict the initial environment of Helene as being dominated by the SAL, although with minimal evidence that the SAL air actually penetrated to the core of the disturbance. Over the next several days, the SAL air quickly moved westward and was gradually replaced by a very dry, dust-free layer associated with subsidence. Despite the wrapping of this very dry air around the storm, Helene intensified steadily to a Category 3 hurricane suggesting that the dry air was unable to significantly slow storm intensification. Several uncertainties remain about the role of the SAL in Helene (and in tropical cyclones in general). To better address these uncertainties, NASA will be conducting a three year airborne campaign called the Hurricane and Severe Storm Sentinel (HS3). The HS3 objectives are: To obtain critical measurements in the hurricane environment in order to identify the role of key factors such as large-scale wind systems (troughs, jet streams), Saharan air masses, African Easterly Waves and their embedded critical layers (that help to isolate tropical disturbances from hostile environments). To observe and understand the three-dimensional mesoscale and convective-scale internal structures of tropical disturbances and cyclones and their role in intensity change. The mission objectives will be achieved using two Global Hawk (GH) Unmanned Airborne Systems (UASs) with separate comprehensive environmental and over-storm payloads. The GH flight altitudes (>16.8 km) allow overflights of most convection and sampling of upper-tropospheric winds. Deployments from Goddard s Wallops Flight Facility and ~26-hour flight durations will provide coverage of the entire Atlantic Ocean basin, and on-station times up to 5-22 h depending on storm location. Deployments will be in September of 2012 and from late-August to late- September 2013-2014, with up to eleven 26-h flights per deployment.
Summary As deployment of wind energy continues to expand, computationally efficient tools for predicting wind plant performance over a wide range of layout designs, technology innovations, and spatial locations are increasingly important for policy and investment decisions. We demonstrate two approaches to training a surrogate model to predict annual energy production (AEP) of parameterized wind plant layouts: one using a Gaussian process (GP) and the other using a fully convolutional neural network (FCNN). We leverage the powerful FCNN architecture by encoding wind plant design parameters and output response surface as an image. The FCNN produces more accurate results than the GP with mean absolute errors equivalent to 1% and 1.9% of plant rated power, respectively, although the GP performs well under limited training data and provides useful uncertainty information. We also evaluate a surrogate model for wake steering, enabling a nationwide assessment of the impact of plant control strategies and plant layout decisions. Across two million locations, we find that wake steering strategies boost AEP with relative gains upwards of 3%. Gains are most pronounced at sites without a dominant wind direction and where layout optimization is less fruitful. Additionally, we perform a nationwide sensitivity analysis showing that wake steering can mitigate wake losses from higher density plant layouts. Our results suggest that regions which have not been previously viable for wind deployment due to moderate wind resources are especially well enhanced by wake steering strategies that could help overcome land constraints and inflexible layout options, potentially identifying new deployment opportunities.
Deployment of wind energy is an essential renewable energy source that mitigates climate change and reduces air pollution. Over the last several decades, wind energy development has increased worldwide, expanding from ~20 to ~900 GW (gigawatt) during 2001-2022. Nonetheless, researchers have identified unintended consequences of wind energy on microclimate via turbine-altered surface-atmosphere exchanges of energy, momentum, mass, and trace gases. Based on multi-source observations and models, researchers also have drawn some conclusions that wind farms could warm the land surface, especially at night, at regional and continental scales. Consequently, altered microclimates at wind farms may affect vegetation productivity and carbon sequestration, two critically important ecosystem services related to carbon dynamics; however, such potential impacts and driving mechanisms remain poorly understood. Wind energy deployment is increasing globally to meet carbon neutrality goals, with upscaling of onshore wind power capacity projected to grow from 542 GW in 2018 to 1787 and 5044 GW by 2030 and 2050, respectively. Furthermore, increased demand for wind energy deployment may lead to much larger wind farms in open, expansive landscapes. In turn, a large array of geographically clustered wind turbines could collectively modify local microclimate and amplify turbine-atmosphere interactions, which, if large enough, may produce detectable impacts on ecosystem dynamics. Thus, identifying and quantifying the potential impacts of wind farms on carbon-related ecosystem services may facilitate sustainable wind energy development globally.
The work performed in this Oregon study assessed the present and future costs of floating offshore wind technology deployment in the state of Oregon at commercial scale. It is widely recognized that floating offshore wind energy technology will be needed if significant offshore wind deployment in the Pacific region is to occur. The study was performed by the National Renewable Energy Laboratory (NREL) and its subcontractors and funded by the Bureau of Ocean Energy Management (BOEM). It was based, in part, on assumptions and analysis from an NREL report titled 'A Spatial-Economic Cost-Reduction Pathway Analysis for U.S. Offshore Wind Energy Development from 2015-2030' (Beiter et al. 2016), which supported the 'National Offshore Wind Strategy' (Gilman et al. 2016). The strategy builds on the previous DOE Wind Vision Study Scenario of 86 gigawatts (GW) of offshore wind deployed by 2050 in the United States. Under the Wind Vision scenario, 20% (17.2 GW installed capacity) of the nation's total offshore wind in 2050 comes from the Pacific coastal states (DOE 2015), which will require the development of floating wind technologies. Although most offshore development to date has been in depths of 50 meters (m) or less, 97% of Oregon's offshore wind resource is in water depths greater than 60 m, where floating wind is assumed to be needed. Floating offshore wind technology is still in its nascent stage of development but is advancing toward commercialization in both Europe and Asia. In this report, we use available floating prototype costs, pilot-scale costs, and commercial-scale costs from fixed-bottom offshore wind projects to model and analyze the cost of floating wind for five hypothetical wind sites in Oregon using specific geographical and utility grid information.
WINDExchange is the platform for the U.S. Department of Energy's (DOE's) Wind Energy Technologies Office to disseminate credible wind energy information on a national level. Stakeholder engagement and outreach activities are designed to enable well-informed decisions about appropriate wind energy deployment. WINDExchange focuses on wind energy outreach at the national level while managing and supporting similar regional efforts through the implementation of DOE Regional Resource Centers (RRCs). This fact sheet provides an overview of DOE's WINDExchange initiative and the RRCs. Examples of RRC activities are provided.
Volume II of the Final Report for the DeepCwind Consortium National Research Program funded by US Department of Energy Award Number: DE-EE0003278.001 summarizes the design, construction, deployment, testing, numerical model validation, retrieval, and post-deployment inspection of the VolturnUS 1:8-scale floating wind turbine prototype deployed off Castine, Maine on June 2nd, 2013. The 1:8 scale VolturnUS design served as a de-risking exercise for a commercial multi-MW VolturnUS design. The American Bureau of Shipping Guide for Building and Classing Floating Offshore Wind Turbine Installations was used to design the prototype. The same analysis methods, design methods, construction techniques, deployment methods, mooring, and anchoring planned for full-scale were used. A commercial 20kW grid-connected turbine was used and was the first offshore wind turbine in the US.
The increasing global deployment of wind energy has given rise to concerns about potential adverse effects on certain wildlife species and habitats. The United States and European nations use environmental impact assessments (EIAs) to evaluate the environmental effects of wind energy and inform wind energy planning, siting, and operational processes. A key component of the EIA is the cumulative effects analysis/assessment (CEA). CEAs consider the effects of a proposed development in the context of past, present, and future developments, as well as other (non-wind) activities. However, practitioners worldwide have struggled to implement cost-effective and consistent processes for CEAs. Further, there is no widely accepted scientific methodology to assess cumulative effects. As wind energy deployment continues to expand, developing a consistent and scientifically based approach to CEAs may provide a more comparable across assessments and cost-effective means of reducing risk during siting, operations, and decommissioning/repowering, while minimizing regulatory hurdles. This technical report evaluates the current state of CEA practices, covering both land-based and offshore wind energy development. It focuses on impacts from the preconstruction, construction, and operational phases of the wind farm, which are the phases where most research currently exists. Emerging research addresses impacts from the perspective of life cycle assessments (LCAs), including the impacts of manufacturing and preconstruction (May et al. 2020). The technical report also summarizes CEA processes and guidelines, analysis approaches, and current challenges. Finally, it highlights opportunities for further research and coordination, and includes a geographically organized CEA information resource bank.
Improvements to wind technologies have, in part, led to substantial deployment of U.S. wind power in recent years. The degree to which technology innovation will continue is highly uncertain adding to uncertainties in future wind deployment. We apply electric sector modeling to estimate the potential wind deployment opportunities across a range of technology advancement projections. The suite of projections considered span a wide range of possible cost and technology innovation trajectories, including those from a recent expert elicitation of wind energy experts, a projection based on the broader literature, and one reflecting estimates based on a U.S. DOE research initiative. In addition, we explore how these deployment pathways may impact the electricity system, electricity consumers, the environment, and the wind-related workforce. Overall, our analysis finds that wind technology innovation can have consequential implications for future wind power development throughout the United States, impact the broader electricity system, lower electric system and consumer costs, provide potential environmental benefits, and grow the U.S. wind workforce.
Offshore wind energy development along the East Coast of the US is proceeding quickly as a result of large areas with an excellent wind resource, low water depths and proximity to large electricity markets. Careful planning of wind turbine deployments in these offshore wind energy lease areas (LA) is required to maximize power output and to minimize wake losses between neighboring wind farms as well as those internal to each wind farm. Here, we used microscale wind modeling with two wake parameterizations to evaluate the potential annual energy production (AEP) and wake losses in the different LA areas, and we developed and applied a levelized cost of energy (LCoE) model to quantify the impact of different wind turbine layouts on LCoE. The modeling illustrated that if the current suite of LA is subject to deployment of 15 MW wind turbines at a spacing of 1.85 km, they will generate 4 to 4.6% of total national electricity demand. The LCoE ranged from $68 to $102/MWh depending on the precise layout selected, which is cost competitive with many other generation sources. The scale of the wind farms that will be deployed greatly exceed those currently operating and mean that wake-induced power losses are considerable but still relatively poorly constrained. AEP and LCoE exhibited significant dependence on the precise wake model applied. For the largest LA, the AEP differed by over 10% depending on the wake model used, leading to a $10/MWh difference in LCoE for the wind turbine layout with 1.85 km spacing.
This document summarizes the status and drivers for U.S. wind energy development during 2017. Regional Resource Center (RRC) leaders provided a report of wind energy development in their regions, which was combined with findings from National Renewable Energy Laboratory (NREL) researchers to provide an account of the state of the regions, as well as updates on developments in individual states. NREL researchers and state partners added updates for all states that are not directly supported by an RRC. Accounts for each region include updates on renewable portfolio standards, workforce development, manufacturing and economic development, and individual state updates for installed wind capacity, ongoing policy developments, planned projects and their status, transmission progress reports, etc. This report also highlights the efforts of the RRCs to engage stakeholders in their individual regions.
The Production Tax Credit (PTC) and the Investment Tax Credit (ITC) are currently set to phase down and it is unclear how wind and PV deployment will be impacted by the phase down. This work draws from 14 scenarios of the National Renewable Energy Laboratory's 2018 Standard Scenarios Report and 32 scenarios from the Energy Information Administration's Annual Energy Outlook 2018 to explore wind and PV deployment following the tax credit expiration.
To achieve national decarbonization goals, U.S. annual deployment of wind energy will need to increase by at least fivefold compared to the recent past. Modeling and analysis frameworks can help inform where and how wind energy deployment might occur and thereby help enable the achievement of decarbonization goals. However, most prior wind energy modeling and analysis studies rely on generalized representations of wind energy technologies. Since wind energy technology advancements are expected to increase the competitiveness of wind energy, it is important to incorporate more detailed representations of turbine technology into wind energy modeling. Here we present a new method that incorporates wind turbine choice into the technology representation of land-based wind energy in long-term planning models. Our method integrates three previously published modeling and analysis capabilities: 1) bottom-up cost modeling to estimate future technology costs, 2) geospatial modeling to represent siting decisions, and 3) power sector modeling to evaluate potential deployment. We refer to this approach as a "customized turbine choice" methodology because it creates a composite turbine scenario by choosing from multiple wind turbine technologies—using site-specific optimized turbine layout and selecting the least-cost technology at each location. We demonstrate the capabilities of this new modeling pipeline by examining how the selection of four different wind turbine configurations might evolve from 2021 through 2040. Our results show that using our customized turbine choice methodology could lead to higher estimates for wind future deployment, which indicates that more simplified modeling might underestimate the role that wind energy could play in meeting decarbonization goals. Future research is needed to further explore the implications of turbine choice and to better inform technology researchers, original equipment manufacturers, and other wind industry stakeholders about the market potential of different wind turbine technologies.
As wind energy deployment grows, interactions between wind plants and the surrounding environment become more prevalent. The current investigation seeks to understand these interactions by characterizing the impact of wind plants on the planetary boundary layer height (PBLH), utilizing observations from the American WAKE experimeNt (AWAKEN) campaign. Given the ambiguity of the definition of PBLH under stable atmospheric conditions, where the impact of wind plants is expected to be strongest, a comparison of different methods for identifying PBLH is first conducted using data collected by multiple types of instruments. The Heffter method is selected as the thermodynamic method because it generates the most consistent results for the radiosonde and infrared spectrometer. A minimum vertical velocity variance method is used for a turbulence-based definition. Using both of these methods, the values of PBLH measured at spatially distributed sites are compared under a range of atmospheric conditions. Both methods show a clear increase in PBLH downstream of a wind plant for stable conditions. These impacts are strongest when the upstream PBLH is shallow (less than 0.25 km), with the thermodynamic method showing a PBLH increase of 35 %–39 % and the turbulence-based method showing a 143 % increase. At a site 20 km downstream of the wind plant, these effects are no longer observed, suggesting PBLH is not influenced by the wind plant at this distance. The results of this investigation show that wind plants can modify PBLH in their vicinity. As PBLH is a key parameter for numerical models, this insight into wind plant–atmosphere interactions can be used to validate and improve the accuracy of wind energy forecasts.
Together, offshore wind (OSW) and marine hydrokinetic (MHK) technologies have vast potential to expand the world’s access to abundant energy resource. With more than 60 GW of offshore wind energy capacity and 527 MW of ocean energy deployed globally by 2023, there is a significant amount of available resources; however, technical and non-technical challenges prevent the combined large-scale deployment of these technologies. There is still a lack of research that provides a parallel review of both MHK and OSW technologies in order to better understand their synergistic working principles. This paper aimed to address that research gap by presenting a comprehensive side-by-side review of the worldwide technological landscape, global deployment trends, integration strategies, and modeling approaches for MHK and OSW. A particular focus has been given on analyzing existing modeling and simulation techniques, assessing integration and control strategies, and comparing technologies based on water depth. Furthermore, this study provides important insights into the readiness levels of both technologies by highlighting ongoing international projects. By addressing these issues, this review will give researchers and industry stakeholders an outline for assessing the maturity of OSW and MHK systems and facilitating their transition to large-scale, sustainable deployment.
Distributed wind projects, which are connected at the distribution level of an electricity system or in off-grid applications to serve specific or local energy needs, often rely solely on wind resource models to establish wind speed and energy generation expectations. Historically, anemometer loan programs have provided an affordable avenue for more accurate onsite wind resource assessment, and the lowering cost of lidar systems has shown similar advantages for more recent assessments. While a full 12 months of onsite wind measurement is the standard for correcting model-based long-term wind speed estimates for utility-scale wind farms, the time and capital investment involved in gathering onsite measurements must be reconciled with the energy needs and funding opportunities that drive expedient deployment of distributed wind projects. Much literature exists to quantify the performance of correcting long-term wind speed estimates with 1 or more years of observational data, but few studies explore the impacts of correcting with months-long observational periods. This study aims to answer the question of how short you can go in terms of the observational time period needed to make impactful improvements to model-based long-term wind speed estimates. Three algorithms, multivariable linear regression, adaptive regression splines, and regression trees, are evaluated for their skill at correcting long-term wind resource estimates from the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) using months-long periods of observational data from 66 locations across the US. On average, correction with even 1 month of observations provides significant improvement over the baseline ERA5 wind speed estimates and produces median bias magnitudes and relative errors within 0.22 m s −1 and 4 percentage points of the median bias magnitudes and relative errors achieved using the standard 12 months of data for correction. However, in cases when the shortest observational periods (1 to 2 months) used for correction are not well correlated with the overlapping ERA5 reference, the resultant long-term wind speed errors are worse than those produced using ERA5 without correction. Summer months, which are characterized by weaker relative wind speeds and standard deviations for most of the evaluation sites, tend to produce the worst results for long-term correction using months-long observations. The three tested algorithms perform similarly for long-term wind speed bias; however, regression trees perform notably worse than multivariable linear regression and adaptive regression splines in terms of correlation when using 6 months or less of observational data for correction. Translating the analysis to wind energy, median relative errors in the capacity factor are on average within 10 % using 1 month of training. If the observation period used for correction is not well correlated with the reference data, however, misrepresentation of the observed capacity factor can be substantial. The risk associated with poor correlation between the observed and reference datasets decreases with increasing training period length. In the worst-correlation scenarios, the median capacity factor relative errors from using 1, 3, and 6 months are within 47 %, 26 %, and 16 %, respectively.
A wind tunnel test has been conducted on one-eighth scale models of the Space Shuttle Solid Rocket Booster drogue parachute system. The test included an investigation of four candidate drogue deployment concepts and a parametric steady state drag study of 20-degree conical ribbon parachutes. The results show that at least two of the four deployment concepts tested are viable candidates for the full scale deployment system. The interference free steady state drag results obtained show excellent agreement with available drop test results on large 20-degree conical ribbon parachutes.
A 2 micrometer pulsed Doppler lidar was deployed to the Juneau Airport in 1998 to measure turbulence and wind shear in and around the departure and arrival corridors. The primary objective of the measurement program was to demonstrate and evaluate the capability of a pulsed coherent lidar to remotely and unambiguously measure wind turbulence. Lidar measurements were coordinated with flights of an instrumented research aircraft operated by representatives of the University of North Dakota (UND) under the direction of the National Center for Atmospheric Research (NCAR). The data collected is expected to aid both turbulence characterization as well as airborne turbulence detection algorithm development activities within NASA and the FAA. This paper presents a summary of the deployment and results of analysis and simulation which address important issues regarding the measurement requirements for accurate turbulent wind statistics extraction.