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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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DOE OSTI2026

Develop and verify soil/structure interaction for pile/foundation interaction

Phase II of the Offshore Code Comparison Collaboration, Continued, with Correlation and unCertainty (OC6) project was used to verify the implementation of a new soil-structure interaction (SSI) model for use within offshore wind turbine modeling software. The REDWIN Macro-element model implemented and verified in this study enables a computationally efficient way to model the linear and nonlinear SSI problem, including hysteretic damping, of a monopile structure. The modeling approach was integrated into several modeling tools and a series of increasingly complex simulations was conducted using the IEA 10MW reference turbine mounted on a monopile support structure to verify the coupling between the tools and the REDWIN Macro-element SSI model. This campaign includes only numerical verification between various software and modeling approaches so no experimental measurements are available. The load cases (LC) considered include: LC1 – static response of the tower and substructure LC2 – frequency and mode-shape analysis of the tower and substructure LC3 – response of the tower and substructure due to wind-only loading LC4 – response of the tower and substructure due to wave-only loading LC5 – response of the tower and substructure due to wind and wave loading. Detailed properties of the modeled system are found in the following reference, “Bergua, Roger, Amy Robertson, Jason Jonkman, and Andy Platt. 2021. "Specification Document for OC6 Phase II: Verification of an Advanced Soil-Structure Interaction Model for Offshore Wind Turbines.” Golden, CO: National Renewable Energy Laboratory. NREL/TP-5000-79938. https://www.nlr.gov/docs/fy21osti/79938.pdf. Details on the results from the OC6 Phase II project can be found in the following reference, “Bergua R, Robertson A, Jonkman J, et al. OC6 Phase II: Integration and verification of a new soil–structure interaction model for offshore wind design.” Wind Energy. 2022;25(5):793-810. doi:10.1002/we.2698

17 WIND ENERGY↗
DOE OSTI2020

Atmospheric condition identification in multivariate data through a metric for total variation

Identification of atmospheric conditions within a multivariable atmospheric data set is a necessary step in the validation of emerging and existing high-fidelity models used to simulate wind plant flows and operation.Atmospheric conditions relevant for wind energy research include stationary conditions, given the need for well-converged statistics for model validation, as well as conditions observed less frequently, such as extreme atmospheric events, which are used in wind turbine and wind plant design.Aggregation of observations without regard to covariance between time series discounts the dynamical nature of the atmosphere and is not sufficiently representative of atmospheric conditions.Identification and characterization of continuous time periods with atmospheric conditions that have a high value for analysis or simulation set the stage for more advanced model validation and the development of real-time control and operational strategies.The current work explores a single metric for variation in a multivariate data sample that quantifies variability within each channel as well as covariance between channels.The total variation is used to identify conditions of interest that conform to desired objective functions, such as stationary conditions, ramps or waves of wind speed, and changes in wind direction.Total variation is somewhat sensitive to the presence of outliers in the input data, and the method is best complemented by quality-control procedures to ensure reliable results.The direct detection and classification of events or conditions of interest within atmospheric data sets is vital to developing our understanding of wind plant response and to the formulation of forecasting and control models.

17 WIND ENERGY↗
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↗
DOE OSTI2021

INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: - Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate - Direct design using computational fluid dynamics (CFD): cost-prohibitive - Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this pr...

aerodynamics↗
DOE OSTI2025

Stochastic Model Predictive Control With Gaussian Wind Direction Preview for Wake Steering

This article addresses the problem of wake steering control for wind farms that explicitly consider the tradeoff between farm-level power generation and yaw duty cycle under variable and uncertain wind conditions. A novel stochastic model predictive control (MPC) algorithm is presented, which utilizes a stochastic model of the freestream wind field components in a receding horizon framework to compute optimal yaw set points that maximize the expected value of the farm power while constraining the yaw actuation. Different configurations of the algorithm are evaluated using a steady-state wind farm simulator. The proposed stochastic MPC algorithm can plan control actions over a future prediction horizon based on probabilistic estimates of the incoming wind magnitude and direction.

17 WIND ENERGY↗
DOE OSTI2019

The Power Curve Working Group's Assessment of Wind Turbine Power Performance Prediction Methods

A wind turbine power curve is often only strictly valid for a subset of all atmospheric conditions (i.e., the inner range), while wind turbines also operate in other scenarios (i.e., the outer range). Hence, modeling the power output in real-world conditions is a fundamental challenge. For example, the power deviation matrix (PDM) in Figure 1 displays an overprediction of power production using a reference power curve when wind speed (WS) and turbulence intensity (TI) are both low. Many experts use the PDM approach to observe any systematic bias in power curves and correct this in energy yield models. The mission of the Power Curve Working Group (PCWG) is to bring together wind industry stakeholders to help identify, validate, and develop ways to improve modeling of wind turbine performance in all atmospheric conditions.

17 WIND ENERGY↗
DOE OSTI2025

Evaluating the potential of short-term instrument deployment to improve distributed wind resource assessment

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.

17 WIND ENERGY↗
DOE OSTI2021

A Spatially Explicit Individual-Based Modeling Approach to Evaluate the Cumulative Effects of Wind Energy Development on the Greater Sage-Grouse: Sensitivity Analysis and Validation of Key Parameters

To address the need for an approach to evaluate cumulative ecological impacts of wind energy development, particularly those on critical wildlife habitats and species, the authors developed a proof-of-concept, landscape-based, spatially explicit individual-based modeling (IBM) framework for populations of the greater sage-grouse (Centrocercus urophasianus) in Albany County, Wyoming, based on published and other available information on the life history of the species. This sage-grouse IBM estimates the spatial and temporal movements of sage-grouse based on modeled effects of habitat characteristics and developments on sage-grouse condition, survivorship, and reproduction. After completing the initial version of the model, we conducted a series of sensitivity analyses of key model parameters and validation of lek occurrences and spatial distributions to better understand performance of the model relative to observed values of important life history characteristics. The parameter sensitivity analysis was conducted as a series of one-factor-at-a-time tests in which the values of selected parameters were individually varied to evaluate the corresponding effects on model output. We examined the model’s response to changes in parameter values to gain insight into the overall robustness of the model, identify key driver parameters, and initiate qualitative validation. Five functions selected for sensitivity analyses were (1) the rate of change in body condition in relationship to habitat suitability index (HSI) of occupied habitat (condition change rate), (2) the effect of competition between individuals with overlapping home ranges expressed as a percentage reduction in HSI (competition factor), (3) value of condition at which survivorship probability is equal to zero (zero survivorship condition), (4) the relationship between female body condition and clutch size, and (5) the relationship between female body condition and nest success. We varied the parameter values to be tested for the sensitivity analysis around the original value used in the model (i.e., tested values were higher and lower than the model’s original value) to examine the effect of this variation on the predicted output of the model. Model outputs evaluated in the sensitivity analysis included (1) predicted age-class distribution (as measured by the percentage of immature individuals within the population); (2) predicted sex ratio; (3) predicted age-sex class distribution (percent of population in each of six age-sex classes, i.e., male and female juveniles, yearlings, and adults); (4) lifespan; (5) population size; and (6) spatial distribution. Of the five parameters analyzed, condition change rate, survivorship, clutch size, and nest success did not substantially affect any of the evaluated model outputs. For the predicted age-class distribution, the percentage of immature individuals predicted by the model for these four parameters ranged from 61.9% to 65.0%, which was reasonable with respect to the reference values of 51.4 to 57.8% in published studies. The ratio of females to males ranged from 1.09 to 1.14, which was comparable to the reference values of 1.2 to 3.0 presented in an existing study. The predicted mean lifespan ranged from 1.62 to 1.74 years, which appears to be reasonable with respect to the reference values of 0.9 to 1.1 years for sharp-tail grouse and greater prairie chickens presented by published studies. The predicted population size ranged from 1,877 to 3,140. This prediction would be reasonable with respect to our estimate of 5,000 yearlings and adults, which was based on the USFWS estimate for the state of Wyoming scaled to the number of leks in the county. The predicted spatial distribution was not substantially affected by the parameter values examined. In our sensitivity analyses, the only tested parameter that had a noticeable effect on model output was competition factor. Over the range of values tested for this parameter, predicted population size exhibited the largest range (from 1,833 to 4,737) of all parameters tested. As the competition factor increased, the predicted population size decreased. This reflects the effect competition had on the number of individuals that could co occur in high-quality habitat patches. Despite this apparently significant effect, the range of values appears to be reasonable with respect to our estimate of 5,000 yearlings and adults, which was based on the USFWS estimate for the state of Wyoming scaled to the number of leks in the county.

17 WIND ENERGY↗
DOE OSTI2022

Windfarm Operations and Maintenance cost-Benefit Analysis Tool (WOMBAT)

This report provides technical documentation and background on the newly-developed Wind Operations and Maintenance cost-Benefit Analysis Tool (WOMBAT) software. WOMBAT is an open-source model that can be used to obtain cost estimates for operations and maintenance of land-based or offshore wind power plants. The software was designed to be flexible and modular to allow for implementation of new strategies and technological innovations for wind plant maintenance. WOMBAT uses a process-based simulation approach to model day-to-day operations, repairs, and weather conditions. High-level outputs from WOMBAT, including time-based availability and annual operating costs, are found to agree with published results from other models.

17 WIND ENERGY↗
DOE OSTI2024

Droughts in Wind and Solar Power: Assessing Climate Model Simulations for a Net‐Zero Energy Future

Abstract Understanding and predicting “droughts” in wind and solar power availability can help the electric grid operator planning and operation toward deep renewable penetration. We assess climate models' ability to simulate these droughts at different horizontal resolutions, ∼100 and ∼25 km, over Western North America and Texas. We find that these power droughts are associated with the high/low pressure systems. The simulated wind and solar power variabilities and their corresponding droughts during historical periods are more sensitive to the model bias than to the model resolution. Future climate simulations reveal varied future change of these droughts across different regions. Although model resolution does not affect the simulation of historical droughts, it does impact the simulated future changes. This suggests that regional response to future warming can vary considerably in high‐ and low‐resolution models. These insights have important implications for adapting power system planning and operations to the changing climate.

Liu, Xue [Department of Oceanography Texas A&amp,M↗
DOE OSTI2023

Scenario Discovery Analysis of Drivers of Solar and Wind Energy Transitions Through 2050

Deep human-Earth system uncertainties and strong multi-sector dynamics make it difficult to anticipate which conditions are most likely to lead to higher or lower adoption of renewable energy, and models project a broad range of future solar and wind energy shares across future scenarios. To elucidate these dynamics, we explore a large data set of scenarios simulated from the Global Change Analysis Model (GCAM) and use scenario discovery to identify the most significant factors affecting solar and wind adoption by mid-century. We generated a data set of over 4,000 scenarios from GCAM by varying 12 different socioeconomic factors at high and low levels, including assumptions about future energy demand, resource costs, and fossil fuel emissions paths, as well as specific technology assumptions including wind and solar backup requirements and storage costs. Using scenario discovery, we assess the most important factors globally and regionally in creating high fractions of solar and wind energy and explore interconnected effects on other systems including water and non-CO 2 emissions. Globally and regionally, we found that solar and wind-related technology costs were the primary drivers of high wind and solar energy adoption, though a few regions depend heavily on other parameters like carbon capture and storage costs, population and gross domestic product trajectories, and fossil fuel costs. We also identify four key paths to high solar and wind energy by mid-century and discuss their tradeoffs in terms of other outcomes.

14 SOLAR ENERGY↗
DOE OSTI2022

FLOW Estimation and Rose Superposition (FLOWERS): an integral approach to engineering wake models

Abstract. Annual energy production (AEP) is often the objective function in wind plant layout optimization studies. The conventional method to compute AEP for a wind farm is to first evaluate power production for each discrete wind direction and speed using either computational fluid dynamics simulations or engineering wake models. The AEP is then calculated by weighted-averaging (based on the wind rose at the wind farm site) the power produced across all wind directions and speeds. We propose a novel formulation for time-averaged wake velocity that incorporates an analytical integral of a wake deficit model across every wind direction. This approach computes the average flow field more efficiently, and layout optimization is an obvious application to exploit this benefit. The clear advantage of this new approach is that the layout optimization produces solutions with comparable AEP performance yet is completed 2 orders of magnitude faster. The analytical integral and the use of a Fourier expansion to express the wind speed and wind direction frequency create a relatively smooth solution space for the gradient-based optimizer to excel in comparison to the existing weighted-averaging power calculation.

17 WIND ENERGY↗
DOE OSTI2022

Explainable Bayesian Neural Network for Probabilistic Transient Stability Analysis Considering Wind Energy

While several data-driven models have been developed for transient stability assessment, how to consider the uncertainties from load and renewable generations and provide interpretation of data-driven assessment results are still open. This paper proposes an explainable Bayesian Neural Network (BNN) for probabilistic transient stability assessment (TSA). By extracting the uncertainties from loads and wind farms, the BNN model can make a reliable prediction and quantify the prediction uncertainties. We also develop the Gradient Shap algorithm to make the global and local explanations for the probabilistic TSA model, a significant advantage over existing black-box data-driven methods. Numerical results on the modified IEEE 39-bus system show that the proposed method outperforms the existing methods in terms of prediction accuracy and uncertainty quantification capabilities. The explainability of the proposed method allows system operators to design preventive controls for enhancing system stability.

Bayesian Neural Network↗
DOE OSTI2021

Quantifying the Impacts of Land Surface Modeling on Hub-Height Wind Speed under Different Soil Conditions

We investigate the impact of three land surface models (LSMs) on hub-height wind speed under three different soil regimes (dry, wet, and frozen) to understand and improve the physics of wind energy forecasts using the Weather Research and Forecasting (WRF) model. A six-day representative period is selected for each soil condition. The simulated wind speed, surface energy budget and soil properties are compared with the observations collected from the second Wind Forecast Improvement Project (WFIP2). For the selected cases, our simulation results suggest that, the impact of LSMs on hub-height wind speed are sensitive to the soil states but not so much to the choice of LSM. The simulated hub-height wind speed is in much better agreement with the observations for the dry soil case than the wet and frozen soil cases. Over the dry soil, there is a strong physical connection between the land surface and hub-height wind speed through near-surface turbulent mixing. Over the wet soil, the simulated hub-height wind speed is less impacted by land surface due to weaker surface fluxes and more dominated by large-scale synoptic disturbances. Over the frozen soil, the land surface model seems to have limited impact on hub-height wind speed variability due to the decoupling of the land surface with the overlying atmosphere. Two main sources of modeling uncertainties are proposed. The first are the insufficient model physics representing the surface energy budget, especially the ground heat flux, and the second are the inaccurate initial soil states such as soil temperature and soil moisture.

Xia, Geng↗
DOE OSTI2026

Modeling, Validation, and Control of the IEA‐15 MW Reference Wind Turbine and VolturnUS‐S Platform

This paper presents the acausal modeling, validation, and control of floating offshore wind turbines (FOWTs). The model simulates the IEA‐15 MW reference turbine and the semi‐submersible VolturnUS‐S platform utilizing a Control‐oriented, Reconfigurable, and Acausal Floating Turbine Simulator (CRAFTS), which integrates the key coupled aero‐hydro‐elasto‐servo dynamics and is being developed by authors at the University of Central Florida. Verification and validation are conducted using numerical data from the industry‐standard simulation platform OpenFAST and experimental data from the Floating Offshore‐wind and Controls Advanced Laboratory (FOCAL) project, in which the authors were involved. Numerical results demonstrate the model's ability to qualitatively capture loads and responses across various load cases, highlighting the impact of the control system under different wind and wave conditions and opening new opportunities for optimizing FOWT designs. This paper provides wind turbine researchers with valuable insights into system characteristics, system frequencies, damping effects, and internal reaction forces, serving as a reference for future studies in FOWT modeling and control.

17 WIND ENERGY↗
DOE OSTI2023

PR100 One-Year Progress Summary Report: Preliminary Modeling Results and High-Resolution Solar and Wind Data Sets

The Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100) is a 2-year study by the U.S. Department of Energy's (DOE's) Grid Deployment Office and six national laboratories to comprehensively analyze stakeholder-driven pathways to Puerto Rico's clean energy future. In Year 1 of the study, the PR100 team rigorously modeled and analyzed scenarios that meet Puerto Rico's renewable energy targets and achieve short-term recovery goals and long-term energy resilience. This report, which summarizes PR100 progress in Year 1, provides considerations that can inform potential funding and implementation decisions by key federal and local agencies and stakeholders. The summary report follows the publication in July 2022 of a PR100 Six-Month Progress Update (in English and Spanish), as well as public webinars in February 2022 to kick off the study and in July 2022 to present the 6-month update. A final written report and web-based visuals will be published in late 2023. All publications and public events associated with the study will be available in Spanish and English. This report is also available in Spanish https://www.nrel.gov/docs/fy23osti/85144.pdf.

100%↗
DOE OSTI2020

Distributed Wind Representation in Modeling and Simulation Tools: An Assessment of Existing Tools

PNNL evaluated the inclusion of wind in distributed energy resource modeling and simulation tools. Tools were assessed based on a survey of publicly available and readily accessible information. While the authors have made a good faith effort to understand these tools, PNNL does not know the intentions of the developers and acknowledges that some tool capabilities or features may not have been accurately captured in this assessment. This evaluation informs an ongoing effort by PNNL to modernize existing tools both directly by working with developers of some tools and indirectly by providing best practices on how different types of tools could enhance certain modeling features to improve distributed wind’s representation and valuation.

17 WIND ENERGY↗
DOE OSTI2025

Implementation of a turbine farm model into the Energy Exascale Earth System Model for investigation and quantification of global climate impacts

Although there has been widespread deployment of wind farms in the United States, and plans to continue deployment into the future, the complete effects of wind farms on Earth systems are not well understood. The work performed here has incorporated wind farm models into the Energy Exascale Earth System Model (E3SM) capable of simulating the effects of extracting momentum from the atmospheric flow field using power generating wind farms. This new capability will allow scientists to quantify the impacts of wind farm induced changes on Earth systems by exploiting E3SM’s ability to couple atmospheric, oceanic, and biogeochemical (BGC) models on a global scale and monitor precipitation levels, extreme weather events, soil moisture content and jet stream location over decades-long time periods. This tool will be used to inform decision making on wind farm citing and will contribute to the Lab’s ability to assess energy technology impacts on the environment and evaluate the trade-offs between energy infrastructure investments and their impacts on natural systems.

17 WIND ENERGY↗