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DOE OSTI2025

Contrasting Trends in Colorado Fire Weather Index from Reanalysis and Observations

Recent wildfires in Colorado raise the question of whether rising global temperatures have increased fire weather occurrences in Colorado. The U.S. National Weather Service defines fire weather as when “forecast weather conditions will result in a significant threat for the ignition and/or spread of wildfires.” We use two datasets to address the question: “How has the occurrence of fire weather changed in Colorado?” Using 22 years of observed weather conditions from a meteorological tower at the National Renewable Energy Laboratory and 67 years of ERA5 reanalysis data, we assess changing trends in Colorado fire weather as defined by hot, dry, and windy conditions. Additionally, we explore if the difference in recorded wind speeds between observational data and reanalysis data can be explained by differences in spatial and temporal resolution and what are the implications in the context of quantifying fire weather occurrences. The observational data are limited in temporal extent and spatial representativeness, but they capture exact real-world conditions at a location in complex terrain. The reanalysis data are available for an extended period of time and for the entire state, but the data are of relatively coarse spatial and temporal resolution and may fail to capture extremes. To quantify fire risk, we calculate the hot–dry–windy index (HDWI), which relies on wind speed and vapor pressure deficit. No statistically significant trend in the HDWI appears in the observational dataset. However, according to the reanalysis data, strong increasing trends in HDWI values emerge across all of Colorado. This apparent conflict between observational and reanalysis data suggests that reanalysis data may not be representative. Further, more long-term observational datasets are required to assess fire risk.

17 WIND ENERGY↗
DOE OSTI2024

WTK-LED: The WIND Toolkit Long-Term Ensemble Dataset

To satisfy a wide group of stakeholders across various wind energy disciplines, including but not limited to stakeholders in the distributed and utility scale wind industry, the new emerging airborne wind energy field, grid integration, power systems modeling, environmental modeling, and researchers in academia, and to close some of the gaps that current public datasets have, we aimed at developing an updated version of the meteorological WIND Toolkit, named WIND Toolkit Long-term Ensemble Dataset (WTK-LED), which is a meteorological dataset providing time series every 5 min and 2 km, including model uncertainty of wind speed at every modeling grid point so that users are provided with a range of possible wind speeds every 2 km. The data were produced using the Weather Research and Forecasting Model (WRF). The vertical grid used in WTK-LED includes many vertical layers in the atmospheric boundary layer to provide information of atmospheric quantities across the rotor layer of utility scale and distributed wind turbines. The WTK-LED includes: 1) Numerical simulations covering the continental United States, Alaska, and Hawaii, with high-resolution data being available for 3 years (2018-2020). 2) Climate simulations from Argonne National Laboratories covering the North American continent, including Alaska, Canada, and most of Mexico and the Caribbean Islands. These simulations complement the new WTK-LED to offer a 4-km dataset covering 20 years, from 2001-2020. 3) Specific long-term,high-resolution offshore simulations have been conducted separately for the US coasts, Hawaii, and the Great Lakes, leading to the 2023 National Offshore Wind data set. This report focuses on a description of the land-based WTK-LED for CONUS, Hawaii, and Alaska, for the 3-year 2-km/5-min dataset and the 20-year 4-km/hourly dataset, as well as the uncertainty quantification method. We also provide limited validation results. Based on our results to date, we suggest use cases and applications for each dataset of the WTK-LED.

17 WIND ENERGY↗
DOE OSTI2021

Machine Learning for Gearbox Fault Prediction by Using Both Scada and Modeled Data

This presentation outlines the work in the paper titled "Prognostics of Wind Turbine Gearbox Bearing Failures Using SCADA and Modeled Data" published by the PHM Society and presented at its 2020 annual conference. It is accessible at https://papers.phmsociety.org/index.php/phmconf/article/download/1292/862. The technical work is on machine learning approaches for prognostics for gearbox faults. The methodology combines SCADA time series data and physics domain modeling data, derived from the models developed by the NREL team, as inputs to machine learning models to predict gearbox bearing failures with one month lead time. Based on SCADA data, modeled data, and bearing failure log data from an actual wind plant, the performances of different machine-learning models on unseen data are then evaluated using industry-standard metrics such as precision, recall, and F1 score, and AUC (area under receiver operating characteristic curve). Results show the overall system performance enhancement in predicting bearing failure when modeled data are included with SCADA data. The reduction in terms of false alarms is about 50%, and improvement in terms of precision, and F1 score, and AUC is about 33%, and 12%, and 6% respectively, based on the best modeling case in this study.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2021

Acoustic Exposure to Turbine Operation Quantifies Risk to Bats at Commercial Wind Energy Facilities

ABSTRACT Turbine‐related bat mortality at commercial wind energy facilities may threaten populations of migratory tree‐roosting bat species in North America. Industry stakeholders and regulatory agencies alike are investigating strategies to reduce risk of population‐level consequences as the wind energy industry grows. Bats collide with turbines only when turbine rotors are spinning and curtailing turbine operation at low wind speeds can effectively reduce bat fatality rates. Nonetheless, few quantitative data exist to determine appropriate threshold wind speeds below which turbine operations should be curtailed. Carcass monitoring is labor‐intensive and does not provide information on factors linked to bat fatality rates on any scale finer than nightly. We tested whether acoustic bat data recorded at turbine nacelles could provide a more precise and sensitive measure of fatality risk to bats by analyzing acoustics, weather, turbine operation, and carcass data collected at 2 commercial wind energy facilities in West Virginia over 7 years. Each wind facility implemented several distinct curtailment treatments during our study, allowing us to compare fatality rates and acoustic bat activity across multiple operational strategies. We found that bat passes exposed to turbine operation explained close to 80% of the variation in carcass‐based estimates of bat fatality rates and accounted for significant variation in raw carcass counts per turbine and probability of finding bat carcasses during individual turbine searches. Conversely, bat activity occurring when turbines were not operating had little or no relationship to fatality rates. We also found that patterns in bat activity exposure could be predicted accurately among turbines and years. Our results demonstrate that measuring exposure of acoustic bat activity provides a quantitative basis for designing, evaluating, and adaptively managing curtailment strategies. This is an important advance towards using curtailment to reduce bat fatality rates strategically while allowing for increased generation of renewable energy. © 2021 The Authors. Wildlife Society Bulletin published by Wiley Periodicals LLC on behalf of The Wildlife Society.

17 WIND ENERGY↗
DOE OSTI2021

SSRS (Stochastic Soaring Raptor Simulator)

SSRS (Stochastic Soaring Raptor Simulator) is a generalizable, probabilistic, and predictive tool for wind energy developers, ecologists, wildlife managers and industry consultants to estimate the potential for soaring raptors to interact with operating wind turbines, without the need for site-specific data collection. Rapid expansion of wind energy development across the world has exposed the risk of turbine collisions for birds and bats. The risk to obligate soaring raptors such as golden eagles is of particular concern due to their small population and influence on ecological balance. Golden eagles rely heavily on updrafts to subsidize their flight, putting them in direct conflict with operational wind turbines that utilize the same wind resource. Understanding the behavior of soaring raptors with varying atmospheric conditions is crucial for predicting and mitigating the risk of turbine collision. This software contains a predictive movement model that simulates individual flight paths of golden eagles during updraft-subsidized long-distance flight, including migration. For a given set of atmospheric conditions, the model simulates thousands of eagles at turbine-scale spatial resolution (50m) to produce a relative presence density map. The simulated eagles rely on updrafts to pursue uninterrupted directional flight with minimal energy expenditure, following fluid-flow principles. The simulator includes a stochastic model of eagle behavior and a systematic method of accounting for spatiotemporal variations in atmospheric conditions. This framework only requires publicly available atmospheric data to estimate orographic and thermal updrafts, ensuring general usability.

Sandhu, Rimple↗
DOE OSTI2022

A Comparison of Preconstruction and Operational Wake Loss Estimates for Land-Based Wind Plants

Recent studies suggest that biases between wind plant pre-construction energy yield estimates and actual energy production are decreasing over time. However, variability in energy yield prediction accuracy across different projects and wind energy consultants remains high. Wake effects are one of the largest categories comprising the pre-construction energy yield assessment process. To assess the accuracy of wake loss predictions, we compare pre-construction wake loss estimates provided by 8 consultants to the estimated operational wake losses for 10 North American wind plants, as part of the Wind Plant Performance Prediction (WP3) Benchmark project. We estimate operational wake losses using supervisory control and data acquisition (SCADA) data by comparing total wind plant energy production to the potential energy production based on the power produced by freestream wind turbines. In the presentation, we will discuss the overall wake loss prediction bias as well as the project-to-project variability in the prediction accuracy. Further, we will highlight challenges encountered when estimating operational wake losses, including the impact of complex terrain and the presence of neighboring wind plants.

benchmark↗
DOE OSTI2024

The value of wake steering wind farm flow control in US energy markets

Abstract. Wind farm flow control represents a category of control strategies for achieving wind-plant-level objectives, such as increasing wind plant power production and/or reducing structural loads, by mitigating the impact of wake interactions between wind turbines. Wake steering is a wind farm flow control technology in which specific turbines are misaligned with the wind to deflect their wakes away from downstream turbines, thus increasing overall wind plant power production. In addition to promising results from simulation studies, wake steering has been shown to successfully increase energy production through several recent field trials. However, to better understand the benefits of wind farm flow control strategies such as wake steering, the value of the additional energy to the electrical grid should be evaluated – for example, by considering the price of electricity when the additional energy is produced. In this study, we investigate the potential for wake steering to increase the value of wind plant energy production by combining model predictions of power gains using the FLOw Redirection and Induction in Steady State (FLORIS) engineering wind farm flow control tool with historical electricity price data for 15 existing US wind plants in four different electricity market regions. Specifically, for each wind plant, we use FLORIS to estimate power gains from wake steering for a time series of hourly wind speeds and wind directions spanning the years 2018–2020, obtained from the ERA5 reanalysis dataset. The modeled power gains are then correlated with hourly electricity prices for the nearest transmission node. Through this process we find that wake steering increases annual energy production (AEP) between 0.4 % and 1.7 %, depending on the wind plant, with average increases in potential annual revenue (i.e., annual revenue of production, ARP) 4 % higher than the AEP gains. For most wind plants, ARP gain was found to exceed AEP gain. But the ratio between ARP gain and AEP gain is greater for wind plants in regions with high wind penetration because electricity prices tend to be relatively higher during periods with below-rated wind plant power production, when wake losses occur and wake steering is active; for wind plants in the Southwest Power Pool – the region with the highest wind penetration analyzed (31 %) – the increase in ARP from wake steering is 11 % higher than the AEP gain. Consequently, we expect the value of wake steering, and other types of wind farm flow control, to increase as wind penetration continues to grow.

17 WIND ENERGY↗
DOE OSTI2024

High-Resolution Meteorology with Climate Change Impacts from Global Climate Model Data Using Generative Machine Learning

As renewable energy generation increases, the impacts of weather and climate on energy generation and demand become critical to the reliability of the energy system. However, these impacts are often overlooked. Global climate models (GCMs) can be used to understand possible changes to our climate, but their coarse resolution makes them difficult to use in energy system modelling. Here we present open-source generative machine learning methods that produce meteorological data at a nominal spatial resolution of 4 km at an hourly frequency based on inputs from 100 km daily-average GCM data. These methods run 40 times faster than traditional downscaling methods and produce data that have high-resolution spatial and temporal attributes similar to historical datasets. We demonstrate that these methods can be used to downscale projected changes in wind, solar and temperature variables across multiple GCMs including projections for more frequent low-wind and high-temperature events in the Eastern United States.

climate change↗
DOE OSTI2023

A Machine Learning–Based Approach to Time-series Wave Identification in the Solar Wind

The Wind spacecraft has yielded several decades of high-resolution magnetic field data, a large fraction of which displays small-scale structures. In particular, the solar wind is full of wavelike fluctuations that appear in both the field magnitude and its components. The nature of these fluctuations can be tied to the properties of other structures in the solar wind, such as shocks, that have implications for the time evolution of the solar wind. As such, having a large collection of wave events would facilitate further study of the effects that these fluctuations have on solar wind evolution. Given the large volume of magnetic field data available, machine learning is the most practical approach to classifying the myriad small-scale structures observed. To this end, a subset of Wind data is labeled and used as a training set for a multibranch 1D convolutional neural network aimed at classifying circularly polarized wave modes. Using this algorithm, a preliminary statistical study of 1 yr of data is performed, yielding about 300,000 wave intervals out of about 5,000,000 solar wind intervals. The wave intervals come about more often in the fast solar wind and at higher temperatures, and the number of waves per day is highly periodic. This machine learning–based approach to wave detection has the potential to be a powerful, inexpensive way to catalog waves throughout decades of spacecraft data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Power production from the U.S. east coast offshore wind lease areas

The accompanying file include information regarding the set up of WRF simulations of power production and wake extents from offshore wind lease areas along the U.S. east coast, and also data presented in figures in the "Wind power production from the U.S. east coast offshore lease areas" paper and the associated MATLAB data processing code.

17 WIND ENERGY↗
DOE OSTI2023

Drivers of Cloud Condensation Nuclei in the Eastern North Atlantic as Observed at the ARM Site

Abstract Seven years of data collected at the Atmospheric Radiation Measurement's Eastern North Atlantic (ENA) site are analyzed to understand the controls of Cloud Condensation Nuclei (CCN) concentrations in the region. Day‐night differences in the aerosol data as segregated by wind direction demonstrate the aerosol observations to be impacted by local emissions when the wind direction (wdir) is between 90° and 310° (measured clockwise from the North where air is coming from). Data collected during marine conditions (wdir <90° or wdir >310°) show the CCN concentrations to be higher in the summer months as compared to the winter months. CCN budget analysis revealed advection and precipitation scavenging being primarily responsible for modulating the CCN concentrations at the site on monthly timescales, with rain rates driving the precipitation scavenging term. High (greater than 75th percentile) and low (lower than 25th percentile) CCN events were identified for each month to characterize the sub‐monthly variability of CCN concentrations. Low CCN events had thicker clouds, stronger rain rates, and lower reanalysis reported free‐tropospheric aerosol pseudo number concentration at the ENA site as compared to the high CCN events. Analysis of satellite data of air‐parcels 48 hr prior to their arrival at the ENA site demonstrated the air parcels during low CCN events to encounter higher cloudiness, stronger rain rates, and higher cloud top heights as compared to the high CCN events. The results presented herein provide key constraints for model evaluation studies and climatological studies conducted at the ENA site.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Uncertainty Quantification for Capacity Expansion Planning

This report quantifies the uncertainty in output decisions from a Capacity Expansion Planning (CEP) model. The need to understand how uncertainties within CEP models and modeling assumptions affect Quantities of Interest (QoIs) such as expansion and operating costs, as well as expansion decisions remains an ongoing challenge in scientific research and industrial operations. This area of research is particularly important for models which seek to capture how large networks will evolve and operate under increased sources of variable generation, i.e., higher penetration of renewable technologies such as solar and wind generators. Uncertainty quantification (UQ) of CEP models which estimate expansion costs and decisions, and production cost models which estimate operating costs and dispatch decisions, is a key focus of research at NREL. The Regional Energy Deployment System (ReEDS) represents a state-of-the-art CEP model and considers a range of possible grid evolutions in an attempt to identify key drivers, ramifications, and decisions which contribute to better informed investment and policy decisions. However, research to quantify how uncertainties and model assumptions, such as unit commitment (UC), within ReEDS may be affecting its outputs remains challenging due to to size and complexity of the model

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Wind Energy Accomplishments and Year-End Performance Report: Fiscal Year 2022

Four decades ago, construction was just beginning on experimental turbines at the National Wind Technology Center (NWTC). Today, the U.S. Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) facility is the centerpiece of the laboratory's Flatirons Campus, a world-class hub for renewable energy research. The nation's shift to 100% clean electricity by 2035 will require a mix of renewable energy sources and strategies - and together, wind and solar energy could account for 60% to 80% of that clean energy resource. In Fiscal Year (FY) 2022, NREL scientists, engineers, and analysts contributed to these visionary goals through their wind energy research. As wind innovations push into new areas, NREL continues to play a vital role in advancing technology and addressing deployment barriers in pursuit of more efficient, reliable, and predictable wind energy systems. FY 2022 wind research and development explored the potential for dramatic growth in land-based systems, the launch of the nation's first commercial-scale offshore installations, and transmission infrastructure buildout. Land-based wind energy is one of the most cost-effective electricity supply options - but utility-scale deployment will require up to 10 times the current number of turbines. An NREL plan addressed this need to accelerate U.S. wind technology rollout at distributed and utility scales. Another project conducted by NREL and the Pacific Northwest National Laboratory (PNNL) helps position the nation's first major offshore wind corridor for success. The WETO-funded Atlantic Offshore Wind Transmission Study is evaluating options for balancing electricity supply and demand, while supporting resilience of the grid and marine industries. WETO, NREL, and other partners are working to enable the enormous supply chain and workforce changes the U.S. wind energy industry will need to meet net-zero-carbon-emissions targets. As part of a seminal series of DOE-funded supply chain studies, NREL analysts reported on the trade-offs involved in manufacturing large volumes of wind technologies, while addressing cost, workforce, and logistics issues. All of this research is supported by NREL's outstanding research teams, tools, data, and facilities. A WETO-funded international wind energy field campaign, the American WAKE experimeNt (AWAKEN), has brought together experts from NREL, PNNL, and Sandia National Laboratories to create the world's most comprehensive set of high-resolution data on wind energy atmospheric phenomenon. This study could lead to more accurate predictions of losses from turbine-to-turbine wake interactions, eventually helping wind plants capture more energy and operators save millions of dollars. In addition, NREL researchers developed testing, modeling, and analysis tools to improve the security of power grids by identifying wind power plant dynamic stability problems. A new Stochastic Soaring Raptor Simulator (SSRS) protects golden eagles from turbine encounters by predicting flight paths. This report provides more detail on these top achievements and other accomplishments made by NREL and its partners during FY 2022 (between October 1, 2021, and September 30, 2022).

accomplishments↗
DOE OSTI2024

An international benchmark for wind plant wakes from the American WAKE ExperimeNt (AWAKEN)

This article introduces the first benchmark study within the International Energy Agency Wind Task 57 framework, focusing on wind plant wakes. Leveraging data from the American WAKE ExperimeNt (AWAKEN), the benchmark aims to assess the accuracy of simulation tools in modeling wind plant wakes and their impact on the downstream flow under diverse inflow conditions. The AWAKEN field campaign, conducted in Oklahoma from 2022 to 2024, provides unprecedented observations of wind plant-atmosphere interactions, thus offering a large dataset to validate numerical models of different complexity. The benchmark will include three phases—code calibration, blind comparison, and iteration—allowing participants to refine their numerical models based on the feedback from the benchmark team. This article describes the benchmark case study selected from observations providing details on atmospheric conditions, wake evidence, and wind turbine operation. The benchmark’s structure and timeline, along with the expected publication of results, are discussed as well. This collaborative effort aims to enhance the accuracy of wind plant wake simulations, thus contributing to the improvement of wind energy production estimates.

17 WIND ENERGY↗
DOE OSTI2024

Exploring Wholesale Energy Price Trends: The Renewables and Wholesale Electricity Prices (ReWEP) tool (Ver. 2024.1)

The Renewables and Wholesale Electricity Prices (ReWEP) visualization tool from Berkeley Lab has been updated with nodal electricity pricing and wind and solar generation data through the end of 2023. ReWEP users can explore trends in wholesale electricity prices and their relationship to wind and solar generation. ReWEP includes nodal pricing trends across locations, regions, and different timeframes. The tool consists of maps, time series, and other interactive figures that provide: (1) a general overview of how average pricing, negative price frequency, and extreme high prices vary over time, and (2) a summary of how pricing patterns are related to wind and solar generation. Interactive functionality allows investigation by year, season, time of day, and region, where region is defined as the Independent System Operators (ISO) or Regional Transmission Organizations (RTO) region. ReWEP also contains prices throughout much of the western United States from the Western Energy Imbalance Market and the Western Energy Imbalance Service Market.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Exploring Wholesale Energy Price Trends: The Renewables and Wholesale Electricity Prices (ReWEP) tool (Ver. 2024.1)

The Renewables and Wholesale Electricity Prices (ReWEP) visualization tool from Berkeley Lab has been updated with nodal electricity pricing and wind and solar generation data through the end of 2023. ReWEP users can explore trends in wholesale electricity prices and their relationship to wind and solar generation. ReWEP includes nodal pricing trends across locations, regions, and different timeframes. The tool consists of maps, time series, and other interactive figures that provide: (1) a general overview of how average pricing, negative price frequency, and extreme high prices vary over time, and (2) a summary of how pricing patterns are related to wind and solar generation. Interactive functionality allows investigation by year, season, time of day, and region, where region is defined as the Independent System Operators (ISO) or Regional Transmission Organizations (RTO) region. ReWEP also contains prices throughout much of the western United States from the Western Energy Imbalance Market and the Western Energy Imbalance Service Market.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Bayesian State-Space Modeling Framework for Understanding and Predicting Golden Eagle Movements Using Telemetry Data

Predicting raptor movements through a wind power plant under given atmospheric and topographical conditions is a crucial first step in the overall goal of quantifying the risk of turbine-related collisions and mortalities. Extracting behavioral traits of golden eagles (Aquila chrysaetos) from telemetry data requires the fusion of noisy and sparse movement data (location, heading, velocity) with a stochastic mathematical representation of the eagles' decision-making processes. In this study, we framed this problem in a Bayesian state-space framework where both observations and decision-making are assumed to be stochastic processes connected through hidden states (mode of flight, intent), and the unknown model parameters are assumed to be random variables that are calibrated using the available telemetry data. This framework allowed for rigorous consideration of underlying uncertainties while allowing for both data and prior biological knowledge to contribute to a probabilistic and predictive agent-based movement model. We implemented and applied the Bayesian framework to understand movement behavior of 23 GPS-tagged golden eagles travelling in the western US for years 2019 and 2020. Our preliminary findings show that the Bayesian state-space framework provides a robust inverse modeling apparatus to decode eagle behavioral characteristics from telemetry data. This study was primarily aimed at verifying and validating the framework with selected golden eagle tracks (both long- and short-ranged), with future research aimed at extending the framework to include multi-mode flight, consideration of atmospheric data and uplift mechanisms, eagle-to-eagle interaction, and eagle-to-turbine interaction.

Bayesian modeling↗