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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Hyperspectral Imagery Data for Remote Sensing

In order for remotely sensed data to be useful in a practical application for agriculture, an information product must be made available to the land management decision maker within 24 to 48 hours of data acquisition. Hyperspectral imagery data is proving useful in differentiation of plant species potentially allowing identification of non-healthy areas and pest infestations within crop fields that may require the farm managers attention. Currently however, extracting the needed site-specific feature information from the vast spectral content of large hyperspectral image files is a labor intensive and time consuming task prohibiting the necessary fast turnaround from raw data to final product. We illustrate the methods, techniques and technologies necessary to produce field-level information products from imagery and other related spatial data that are useful to the farm manager for specific decisions that must be made throughout the growing season. We also propose to demonstrate the cost effectiveness of an integrated system, from acquisition to final product distribution, to utilize imagery for decisions on a working farm in conjunction with a commercial agricultural services company and their crop scouts. The demonstration farm is Chesapeake Farms, a 3000 acre research farm in Chestertown, Maryland on the Eastern Shore and is owned by the DuPont Corporation.

Garegnani, Jerry↗
DOE OSTI2021

Quantifying Turbine-Level Risk to Golden Eagles Using a High-Fidelity Updraft Model and a Stochastic Behavioral Model

To minimize the effects of wind farms on Golden Eagle (Aquila chrysaetos) populations while enabling sustainable development of renewable energy resources, it is important to understand how eagles interact with atmospheric flows, terrain features, and anthropogenic structures. Models that predict migratory flight paths provide one tool that helps us grasp how the location of wind farms may influence interactions and impacts on migrating Golden Eagles. The current state-of-the-art in predicting migratory flight paths uses a deterministic fluid-flow analogy to predict eagle trajectory using only an orographic updraft potential computed from topographical features. This model does not take into account variables, such as thermal updrafts and time varying atmospheric conditions that are known to influence migratory behavior. In this work, we improve on the model with the objective of developing tools that advance our understanding of how atmospheric flows and terrain features affect migratory eagle behavior and their interactions with wind farms. Specifically, we 1) incorporate both orographic and thermal updraft information in simulating eagle flight paths; 2) incorporate stochasticity into eagle travel patterns to better capture the influence of exogenous factors on, and the inherent stochasticity of eagle behavior; 3) consider spatio-temporal atmospheric data at wind-farm-scale when computing updraft potential; and 4) account for how atmospheric conditions and the direction of migration change seasonally and how these changes affect eagle migratory flight behavior. We tested the model using a 50km by 50km region with 50 m resolution in the western United States. We simulated 900 independent, probabilistic eagle tracks during southerly and northerly migration, assuming eagles solely rely on orographic updrafts. The preliminary results indicate that the inclusion of finer resolution atmospheric data allows for the inclusion of realistic conditions that an eagle experiences. The stochasticity in eagle tracks provides a platform to include uncertainty in eagle decision making and help produce robust eagle presence maps. We will deploy updraft and downdraft velocities computed using a high-fidelity, wind farm scale, computational fluid dynamics solver under development at National Renewable Energy Laboratory. This work is a first step in the development of a predictive and generalizable eagle behavior model at the wind farm scale that does not rely on empirical data collection. Although the current model is intended for migratory eagles, we will extend and refine this model to inform the development of additional behavioral modes, including resident eagle behavior. This modeling approach improves our ability to understand eagle use of the landscape at a fine scale, and it is our hope that this work will ultimately help advance strategies that minimize the impact of wind development on Golden Eagle populations.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2024

Recommendations on setup in simulating atmospheric gravity waves under conventionally neutral boundary layer conditions

Wind farm-induced atmospheric gravity waves have been the subject of recent research as they can impact wind farm performance. Pressure variations associated with gravity waves can contribute to the global blockage effect and wind farm wake recovery. Therefore, accurate numerical simulation of flow fields, where wind-farm-induced gravity waves may be produced, is important. Three main considerations in such simulations are the overall domain size, the use of Rayleigh damping near domain boundaries to dampen gravity waves, and advection damping at the inlet to prevent spurious oscillations. Often these considerations are treated ad hoc rather than systematically. This work aims to test and extend the systematic modelling of internal gravity waves proposed in a preliminary investigation to modelling of both internal and trapped gravity waves. The preliminary study identifies the length scales to set the domain and damping layer sizes and the time scale to configure the Rayleigh damping coefficient but under linearly stratified conditions. Large eddy simulations of flow through a wind farm canopy are performed under conventionally neutral boundary layer (CNBL) conditions to test the validity of proposed setups for CNBL conditions. Background atmospheric parameters, such as Froude number (Fr), inversion height (H i ), and inversion layer Froude number (Fr i ) control most of the atmospheric gravity wave characteristics. We validated for CBNL conditions that the effective wavelengths of the internal gravity waves are the correct length scale to configure the domain size and damping layer thickness. Likewise, the optimum damping coefficient to dampen the internal gravity waves relates to the free atmosphere's buoyancy frequency or buoyant perturbations' time scale. We infer that the damping coefficient in the inversion layer may relate to the inversion buoyancy frequency to effectively dampen the trapped gravity waves. Moreover, the advection damping length is linked to the horizontal wavelength of the trapped gravity waves in the inversion layer to prevent spurious waves at the inlet by retaining wave energy accumulation.

17 WIND ENERGY↗
DOE OSTI2021

Event-Based Analysis of Solar Power Distribution Feeder Using Micro-PMU Measurements

Solar distribution feeders are commonly used in solar farms that are integrated into distribution substations. In this paper, we focus on a real-world solar distribution feeder and conduct an event-based analysis by using micro-PMU measurements. The solar distribution feeder of interest is a behind-the-meter solar farm with a generation capacity of over 4 MW that has about 200 low-voltage distributed photovoltaic (PV) inverters. The event-based analysis in this study seeks to address the following practical matters. First, we conduct event detection by using an unsupervised machine learning approach. For each event, we determine the event’s source region by an impedancebased analysis, coupled with a descriptive analytic method. We segregate the events that are caused by the solar farm, i.e., locallyinduced events, versus the events that are initiated in the grid, i.e., grid-induced events, which caused a response by the solar farm. Second, for the locally-induced events, we examine the impact of solar production level and other significant parameters to make statistical conclusions. Third, for the grid-induced events, we characterize the response of the solar farm; and make comparisons with the response of an auxiliary neighboring feeder to the same events. Fourth, we scrutinize multiple specific events; such as by revealing the dynamics to the control system of the solar distribution feeder. The results and discoveries in this study are informative to utilities and solar power industry.

14 SOLAR ENERGY↗
DOE OSTI2021

Develop Wake Mitigation Strategy (CRADA CRD-17-00693 Final Report)

Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. In an autonomous wind farm, enabling cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving an autonomous wind farm is to develop algorithms that provide necessary information to ensure reliable, robust, and efficient operation of wind turbines in a wind plant using local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. In this work consensus control is applied in a hybrid analysis to data from an existing wind farm to demonstrate the benefit of consensus control.

17 WIND ENERGY↗
DOE OSTI2025

CRADA Final Report: CRADA Number NFE-22-09311 with Agriwater Tech

Livestock wastewater management is a critical concern in the United States, with an annual production of approximately 1.37 billion tons of waste, surpassing human waste by three to twenty times. The mismanagement of manure wastewater poses significant threats to freshwater sources, ecosystems, and public health. Through this project, we proposed an innovative solution using electrocoagulation (EC) treatment. The EC technique is an electrochemical process involving the intentional corrosion of aluminum and iron electrodes to introduce trivalent ions into the solution, facilitating the co-precipitation and coagulation of contaminants and making the removal of water from sludge easier. The project's primary objective is to use EC to convert liquid animal manure into clean water for farm irrigation, drinking, and maintenance. This solution is vital for various farms including those facing drought, pursuing zero-discharge, and seeking Environmental Protection Agency (EPA) permits for livestock farm manure discharge into rivers. Preliminary research shows EC's potential to significantly reduce turbidity and phosphate levels in livestock wastewater, forming the basis for scalable onsite treatment. The goal of this proposed project is to develop an innovative farm-wastewater-treatment process to achieve clean water, fertilizer, and reduced greenhouse gases through electrification of current processes such as coagulation, dewatering, inactivation of viruses and bacteria, and filtration for recycling surface water from farm lagoons.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Differences in cluster and internal wake effects from mesoscale and large-eddy simulations off the US East Coast

Mesoscale simulations are increasingly used to estimate wake effects within and between large wind farms, despite limited validation for large-scale wake effects. This study evaluates the capabilities and limitations of mesoscale simulations in capturing wake-induced impacts on wind turbine power production through a direct comparison with large-domain large-eddy simulations (LESs) for three planned offshore wind farms under realistic atmospheric conditions and a range of atmospheric stabilities. We assess mesoscale performance in replicating wake characteristics behind single and multiple turbine clusters and quantify the resulting variability in mean turbine power. Results show that mesoscale Weather Research and Forecasting simulations with the Fitch wind farm parameterization capture key features of the velocity deficit downstream of both single and multiple wind farms, with mean root-mean-square errors near 5 % and good agreement with stability-driven wake behavior. However, in these simulations, the mesoscale Fitch parameterization underestimates power losses from internal wake effects, particularly when turbines align with the prevailing wind direction or under stable stratification. In these conditions, individual wakes persist and dominate downstream power deficits. The coarse resolution of the mesoscale simulations limits their ability to resolve individual wind turbine wakes that drive power fluctuations within wind farms. Nonetheless, mesoscale simulations can yield accurate estimates of combined wake losses from internal and cluster effects across some wind direction sectors, where errors in wake representation may cancel each other out. These findings underscore the strengths of mesoscale simulations for capturing broader wake patterns while highlighting their limitations for modeling turbine-level power losses. Future work should explore hybrid modeling approaches to capture both long-range cluster wake propagation and localized internal wake dynamics.

17 WIND ENERGY↗
DOE OSTI2026

Impacts of Dual-Use Solar (Agrivoltaics) on Crop Productivity and the Agricultural Economy in Massachusetts and Beyond

This project contributes applied research to better understand the impacts of agrivoltaics on farm microclimates, crop productivity, and economics at the farm and sector level. The site trial research is divided into two sets of site trials integrated into commercial farm and agrivoltaic operations – one set on annual vegetable and hay crops and one on perennial cranberry bog operations. The economic research developed methods for recording and evaluating changes in farm operations and costs, and on the public perception of and willingness-to-accept agrivoltaics.

14 SOLAR ENERGY↗
DOE OSTI2023

Integration of a physics-based direct normal irradiance ($\mathrm{DNI}$) model to enhance the National Solar Radiation Database ($\mathrm{NSRDB}$)

The National Solar Radiation Database (NSRDB) is an extensively used dataset that furnishes satellite-retrieved solar resource data across the United States and an expanding list of other countries. Although the NSRDB uses a physical model to compute global horizontal irradiance (GHI), it currently employs an empirical approach based on surface observations to estimate cloudy-sky direct normal irradiance (DNI). Recently, a new physics-based approach, known as the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI), was developed to improve DNI forecasting. FARMS-DNI integrates direct and scattered solar radiances within the circumsolar region, resulting in improved day-ahead forecasting of DNI by incorporating it into the Weather Research and Forecasting model with Solar extensions (WRF-Solar). This study incorporates FARMS-DNI into the NSRDB algorithm to produce high-spatiotemporal-resolution DNI data from satellite data. The accuracy of the NSRDB based on FARMS-DNI is analyzed using surface observations from 19 sites situated within the National Oceanic and Atmospheric Administration (NOAA) Surface Radiation Budget (SURFRAD) and Solar Radiation (SOLRAD) networks, the University of Oregon (UO) network, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) network, and at the National Renewable Energy Laboratory (NREL). The results demonstrate that FARMS-DNI reduces the significant overestimation of DNI in the conventional NSRDB at all surface sites, particularly in cloud overcast conditions classified using both satellite retrievals and surface observations. Consequently, this new model can effectively improve the overall accuracy of the NSRDB. The results also suggest that further improvement of DNI estimates at individual time steps, however, requires advanced satellite techniques and precise identification of clouds and retrieval of cloud properties.

14 SOLAR ENERGY↗
DOE OSTI2026

Hybrid geothermal-wind integration in the US energy grid

Here, we combine US wind generation—a cheap yet intermittent source of electricity—with the latest geothermal resource estimates to understand the technoeconomic possibilities of pairing enhanced geothermal systems (EGSs) with existing wind farms to develop a hybrid energy system. Using observed generation data from 583 wind farms, generation gaps are quantified and geographically paired with the latest EGS estimates. Results demonstrate that EGS potential within a 1 km 2 footprint can supplement wind generation at 56% of onshore wind farms. Each wind farm can be supplemented by EGSs when 10% of its surface-occupying footprint is available. The cost of EGSs at wind farms is lowest in the western US and southern Texas border and highest in the central US. While further experiments are warranted, a wind + EGS hybrid system offers an opportunity to increase power output from the same land footprint while maximizing the use of existing electrical infrastructure.

EGS↗
DOE OSTI2022

Parameterization of cloud transmittance for expeditious assessment and forecasting of all-sky DNI

Radiative transfer models require vast computing resources to solve cloud transmittance and reflectance from the radiative transfer equation. As a result, models offering precise simulation in operations often acquire individual cloud transmittance or reflectance from a lookup table precomputed for practicable scenarios. To further expedite the computation of global horizontal irradiance and to reduce the storage requirements, the Fast All-sky Radiation Model for Solar applications (FARMS) parameterized the lookup table using elementary functions with specified coefficients. This study extends FARMS direct normal irradiance (DNI) computation by utilizing hyperbolic tangent functions and various polynomial functions to parameterize the cloud transmittance for scattered solar radiation in the circumsolar region. The parameterization is implemented in FARMS with DNI (FARMS-DNI) and accounts for the circumsolar radiation when assessing or forecasting DNI. The evaluation, with long-term observations at the National Renewable Energy Laboratory's, Solar Radiation Research Laboratory, and the Atmospheric Radiation Measurement, Southern Great Plains, Central Facility, shows that the parameterized DNIs are virtually identical with those computed by coupling FARMS-DNI to a lookup table of cloud transmittance. This parameterization has diverse applications in radiative transfer models and numerical weather prediction models used to assess or forecast direct solar radiation.

14 SOLAR ENERGY↗
DOE OSTI2024

Wake Effects in Lower Carbon Future Scenarios

In August 2022, the U.S. Congress passed the Inflation Reduction Act (IRA), which intended to accelerate U.S. decarbonization, clean energy manufacturing, and deployment of new power and end-use technologies. The National Renewable Energy Laboratory has examined possible scenarios for growth by 2050 resulting from the IRA and other emissions reduction drivers and defined several possible scenarios for large-scale wind deployment. These scenarios incorporate large clusters of turbines operating as wind farms grouped around existing or likely transmission lines which will result in wind farm wakes. Using a numerical weather prediction (NWP) model, we assess these wake effects in a domain in the U. S. Southern Great Plains for a representative year with four scenarios in order to validate the simulations, estimate the internal wake impact, and quantify the cluster wake effect. Herein, we present a validation of the ”no wind farm” scenario and quantify the internal waking effect for the ”ONE” wind farm scenario. Future work will use the “MID” scenario (more than 8000 turbines) and the “HI” scenario (more than 16,000 turbines) to quantify the effect of cluster wakes or inter-farm wakes on power production.

17 WIND ENERGY↗
DOE OSTI2025

Deep Reinforcement Learning Based Control of Wind Turbines for Fast Frequency Response

In order to fulfill vital auxiliary grid services, such as load regulation, spin and non-spin reserve provision, and frequency support during emergencies, there is often a requirement for certain wind farms to operate in de-loaded modes. Leveraging the swift response capabilities of wind farms, this study demonstrates that reserving power in de-loaded modes can significantly enhance power grid stability and reliability during system contingencies. Controlling wind farms optimally for frequency support is intricate due to the nonlinearity of models and controllers and the complexity of wind farm interactions with power systems. Here, to address this challenge, this paper introduces a novel approach that integrates wind turbines into reinforcement learning-based solutions for frequency response. This innovative methodology utilizes the state-of-the-art reinforcement learning algorithm known as the surrogate-gradient-based evolutionary strategy. The proposed learning-based algorithm provides continuous control of wind farm output to rapidly stabilize system frequency and prevent unnecessary trips of under-frequency load shedding relays. To facilitate efficient training, parallel computing techniques are employed. The proposed methodology is evaluated on a modified IEEE-39 bus system, and simulation results reveal its efficacy in reliably supporting power system frequency and preventing the need for unnecessary load shedding.

Gao, Wei [Argonne National Laboratory (ANL), Argon↗
DOE OSTI2024

A multisensory approach to understanding bat responses to wind energy developments

Abstract Millions of bats are killed at wind energy facilities worldwide, yet the behavioural mechanisms underlying why bats are vulnerable to wind turbines remain unclear. Anthropogenic stimuli that alter perceptions of the environment, known as sensory pollution, could create ecological traps and cause bat mortality at wind farms. We review the sensory abilities of bats to evaluate potential stimuli associated with wind farms and examine the role of spatial scale on the perceptual mechanisms of sensory pollutants associated with wind energy facilities. Audition, vision, somatosensation and olfaction are sensory modalities that bats use to perceive their environment, including wind farms and turbine structures, but they will not all be useful at the same spatial scales. Bats most likely use vision to perceive wind farms on the landscape, and obstruction lighting may be the first sensory cue to attract bats to wind farms from kilometres away. Research that assesses the risks posed by specific sensory pollutants, when conducted at the appropriate scale, can help identify solutions to reduce bat mortality, such as determining the attractiveness of obstruction lighting to bats at a landscape scale.

Jonasson, Kristin A.↗
DOE OSTI2024

Ard [SWR-25-18]

A wind farm optimization suite for wind energy that is built for modular, gradient-enabled multi-disciplinary and multi-fidelity optimizations. Dig into wind farm design. An ard is a type of simple and lightweight plow, used through the single-digit centuries to prepare a farm for planting. The intent of Ard is to be a modular, full-stack multi-disciplinary optimization tool for wind farms. The problem with wind farms is that they are complicated, multi-disciplinary objects. They are aerodynamic machines, with complicated control systems, power electronic devices, social and political objects, and the core value (and cost) of complicated financial instruments. Moreover, the design of one of these aspects affects all the rest! Ard seeks to make plant-level design choices that can incorporate these different aspects and their interactions to make wind energy projects more successful.

Frontin, Cory [National Renewable Energy Laborator↗
DOE OSTI2021

AmeriFlux US-HWB USDA ARS Pasture Sytems and Watershed Management Research Unit- Hawbecker Site

This is the AmeriFlux version of the carbon flux data for the site US-HWB USDA ARS Pasture Sytems and Watershed Management Research Unit- Hawbecker Site. Site Description - Hawbecker farm is owned by Penn State University. The farming that took place was performed by their Farm Operations Division. The ground is rolling terrain, next to wooded areas, the Beef and Sheep Reasearch Farm, The University Airport, and other large fields maintained by Farm Operations. At the time of this collection period, the site housed another Meteorological Site, a Phenocam, and the GraceNet plots. Crop Rotation during period was 2 years Alfalfa, 1 year Corn for grain and 1 year Wheat.

Goslee, Sarah↗
DOE OSTI2022

AmeriFlux FLUXNET-1F US-HWB USDA ARS Pasture Sytems and Watershed Management Research Unit- Hawbecker Site

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-HWB USDA ARS Pasture Sytems and Watershed Management Research Unit- Hawbecker Site. This is the FLUXNET version of the carbon flux data for the site US-HWB USDA ARS Pasture Sytems and Watershed Management Research Unit- Hawbecker Site produced by applying the standard ONEFlux (1F) software. Site Description - Hawbecker farm is owned by Penn State University. The farming that took place was performed by their Farm Operations Division. The ground is rolling terrain, next to wooded areas, the Beef and Sheep Reasearch Farm, The University Airport, and other large fields maintained by Farm Operations. At the time of this collection period, the site housed another Meteorological Site, a Phenocam, and the GraceNet plots. Crop Rotation during period was 2 years Alfalfa, 1 year Corn for grain and 1 year Wheat.

Goslee, Sarah↗
DOE OSTI2021

Wind Energy Forecasting with the Weather Research and Forecasting Model

This was a collaborative effort between Lawrence Livermore National Security, LLC as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Siemens Energy, Inc. (Siemens) to develop a wind resource forecasting tool. LLNL was to develop an independent high-resolution mesoscale modeling capability forecasting tool that could be implemented in conjunction with existing wind farm control and monitoring software to provide forecasting of wind resources using local observations of winds and temperature. Research with LLNL’s state-of-the-art large-eddy simulation meteorological prediction model, based on the community WRF model and innovative turbulence parameterizations, would improve that model’s applicability to large wind farms offshore and in complex terrain. The modeling capability would include uncertainty quantification. Finally, the application of the modeling tool and existing global climate change predictions would enable the delineation of the likely effects of climate change on wind resources. Siemens was to provide high time resolution hub-height wind speed and other meteorological data streams, including temperature profiles from wind farms, for LLNL to incorporate into the modeling system, to validate and tune this forecasting model for their locations of interest. These data streams would also be used for longer-term studies of correlations of wind resources to climate oscillations to indicate how long-term climate change trends may affect the available wind resource. Siemens would also provide information and observations of turbine wakes for incorporation into the modeling tool. By implementing state-of-the-art turbulence parameterizations into a simulation model and/or ensembles of simulation models, and by integrating real-time hub height wind speed and other meteorological datastreams from wind farms into that model or ensemble of models, LLNL would develop a forecasting tool that could be implemented by Siemens as an add-on to existing wind farm control and monitoring software to provide owners with useful resource forecasting. The desired outcome was that the accuracy level of the output would be sufficient to substantiate power output commitments. The final deliverable for this work would consist of a document outlining the algorithms and software tools that could be integrated into Siemens Wind Park Supervisor.

17 WIND ENERGY↗