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DOE OSTI2024

Identification of Climatological Representative Days in the Mid-Atlantic for High-Fidelity Offshore Wind Energy Modeling

The goal of reaching 30 GW of offshore wind energy by 2030 becomes more realistic with the continued approval of offshore wind energy areas by the Biden Administration. In the Mid-Atlantic, where wind energy projects are in the most advanced stages of development, there is increased research focus on the eventual interaction of these wind farms. These interactions, in the form of wakes and cluster wakes, or wakes from multiple wind farms, could have detrimental effects on power production and forecastability for downwind wind farms (Pryor et al. 2022, Golbazi et al. 2022, Rosencrans et al. 2023). To help alleviate these issues, numerical simulations in the form of numerical weather prediction (NWP) and large eddy simulations (LES) can provide insight into when cluster wake situations may occur, but running such simulations can be expensive and difficult to run for multiple years. In this study, we leverage and build upon existing techniques in the literature (Fischereit et al. 2022) to identify climatologically representative days for wind energy areas in the Mid-Atlantic where conditions would promote cluster wake situations. We select meteorological variables (wind speed, wind direction, atmospheric stability, boundary-layer height, TKE) critical to understanding wind energy production and wake propagation. We then consider two different NWP datasets of varying spatial and temporal resolution: ERA5 provides data at hourly intervals from 1940 to present at 0.25 deg (31 km) spatial resolution (Hersbach et al. 2020), and the NOW-23 dataset provides data at 5-minute resolution for 21 years at 2-km spatial resolution (Bodini et al. 2020). Our first step is to compare these two datasets for an overlapping 21-year time period. Initial results show that the required number of days to represent the long-term climate increases with each additional variable considered. In their study of the German Bight, Fischereit et al. (2022) found that they could represent the long-term wind and wave climate in a "near-perfect" way with -180 days, by reaching a Perkins Skill Score (PSS) of 0.9; our investigation of the mid-Atlantic wind resource region with ERA5 and NOW-23 data suggests that we will need -100 days to reach a PSS of 0.9. As we expand our parameter space to include multiple variables, the number of required days will likely grow. These results will ultimately be used to select case studies to best represent cluster wake conditions that apply to this region for the lifetime of likely wind farms in this mid-Atlantic region.

clusterwakes↗

DOE OSTI2022

Eagle Behavior and Risk Modeling for Wind Energy Webinar

NREL presents recent progress in the development and validation of new eagle behavioral models, highlighting applications for wind-plant siting and operations. This webinar features talks focused on three areas: atmospheric modeling across the spatiotemporal scales of interest, facility-scale behavioral modeling, and turbine-scale data-driven behavioral modeling. A final presentation demonstrates micrositing as an application of our completely open-source analysis framework.

agent-based movement model↗

DOE OSTI2022

Process-based climate change assessment for European winds using EURO-CORDEX and global models

Wind energy is an important pillar of decarbonization strategies and potentially vulnerable to climate change. Existing wind climate change assessments rely on climate models but a systematic investigation of the global-to-regional climate modeling chain is missing. In this study, I highlight key limitations, namely (a) the differing representation of land use change in global and regional climate models which compromises comparability, and (b) the consistency of large-scale features along the global-to-regional climate modeling chain. To this end, I analyze the large European Coordinated Downscaling Experiment (EURO-CORDEX) ensemble (rcp85: N = 49; rcp45: N= 18; rcp26: N = 22) along with the driving global models (rcp85: N = 7; rcp45: N = 5; rcp26: N = 7), finding evidence that climate change reduces mean wind speeds by up to -0.8 ms -1 (offshore) and -0.3 ms -1 (onshore). I provide physical explanations for these changes by identifying two key drivers. First, onshore wind speeds drop in the driving global models in regions and scenarios with strong land use change but show no drop in EURO-CORDEX where land use is held constant. Second, offshore wind reductions follow decreases in the equator-to-pole temperature gradient remarkably well with correlations reaching around 0.9 in resource-rich European countries like Ireland, the United Kingdom and Norway, implying that arctic amplification is a severe risk for European offshore wind energy. My results suggest that earlier conclusions of negligible climate change impacts on wind energy might be premature if either land use changes strongly or polar amplification is at or above the range sampled in global climate models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Advancements on Multi-Fidelity Random Fourier Neural Networks: Application to Hurricane Modeling for Wind Energy

Multi-fidelity approaches are emerging as effective strategies in computational science to handle otherwise intractable tasks like Uncertainty Quantification (UQ), training of Machine Learning (ML) models, and optimization, for expensive high-fidelity applications in which the amount of available simulations or data is limited. The main idea is simple: large datasets generated for low-fidelity approximations of the problem at hand are fused with a much sparser dataset for the target (high-fidelity) system. In this paper, we build on our recent success in designing random Fourier Neural Networks (rFNNs) [1] to target problems arising in wind energy applications and in particular problems of interest for hurricane modeling. In this context, data for the high-fidelity models are limited and lower fidelity alternatives are needed. In this work, we introduce a novel multi-fidelity training approach for our rFNNs and demonstrate its use on a simple verification problem and on a hurricane modeling problem in which high-fidelity data are generated via Large-Eddy Simulations (LES), while low-fidelity data are given by a mesoscale model. Initial results demonstrate how the multi-fidelity training approach can improve the quality of the resulting surrogate.

Fourier Neural Networks↗

DOE OSTI2021

Coupled Ocean Wave-Atmosphere Models for Offshore Wind Energy Applications

Coupling ocean wave models to mesoscale atmospheric models is necessary to represent the effect of waves on wind turbine hub-height winds. In this report we provide a review of the most widely used ocean waves models and the phased-averaged spectral wave modeling paradigm that they are based on. Methodologies used to couple these wave models to mesoscale atmospheric models are described along with details of existing coupled modeling systems. We summarize impacts on offshore wind resource assessments that have been investigated with such coupled modeling systems to-date. Specifically, coupling in the North and Baltic Seas was shown to have a small negative effect on the offshore wind energy resource at weak-to-moderate wind speeds, with little impact at higher wind speeds. Finally, limitations of these existing coupled modeling systems and impact assessments are discussed, including examples of and the potential for improved parameterizations of the air-sea fluxes, and the use of fine-scaled simulations using phased-resolved wave models.

17 WIND ENERGY↗

DOE OSTI2025

Future Wind Energy Resources and Cost Uncertainties Across the United States

This dataset contains results estimating projections of change of annual capacity factors and levelized cost of energy for several turbine technologies in the 2024 Annual Technology Baseline (ATB). Projections of change are based on downscaled earth system model (ESM) data from Sup3rCC. There has been evidence of reductions in average wind speeds over land in North America since the 1980s, and several models project that average wind speeds will continue to decrease. Concurrently, the cost of wind energy systems in the United States has been decreasing since around 2010, a trend also projected to continue. There is considerable uncertainty in these future projections, with quantitative estimates of future wind resource and system costs varying widely. To study this, we run land-based wind energy models with a range of possible future system costs, turbine designs, and meteorological inputs from multiple downscaled earth system models over the contiguous United States to estimate critical system performance metrics such as annual energy production (AEP) and levelized cost of energy. Where multiple earth system models agree, changes in mean AEP from the time period 2000-2019 to 2040-2059 can be as high as +10% in South Texas or as low as -20% in Iowa. Several additional states in the Midwest that currently have considerable wind generation capacity show the possibility of substantial decreases in AEP by mid-century. Larger turbines and moderate reductions in system costs can offset even the largest projected decreases in wind resource, but much uncertainty remains in the extent to which wind resources will actually change into the future and to what extent wind energy systems can drive down future costs. An analysis of variance shows, in several states in the Midwest, the uncertainty in future wind resource can be almost as important for future changes in the cost of wind energy as the uncertainty in future system costs.

17 WIND ENERGY↗

DOE OSTI2022

Probabilistic Modeling of Climate Change Impacts on Renewable Energy and Storage Requirements for NM's Energy Transition Act (SAND Report)

This report provides a study of the potential impacts of climate change on intermittent renewable energy resources, battery storage, and resource adequacy in Public Service Company of New Mexico’s Integrated Resource Plan for 2020 – 2040. Climate change models and available data were first evaluated to determine uncertainty and potential changes in solar irradiance, temperature, and wind speed in NM in the coming decades. These changes were then implemented in solar and wind energy models to determine impacts on renewable energy resources in NM. Results for the extreme climate-change scenario show that the projected wind power may decrease by ~13% due to projected decreases in wind speed. Projected solar power may decrease by ~4% due to decreases in irradiance and increases in temperature in NM. Uncertainty in these climate-induced changes in wind and solar resources was accommodated in probabilistic models assuming uniform distributions in the annual reductions in solar and wind resources. Uncertainty in battery storage performance was also evaluated based on increased temperature, capacity fade, and degradation in round-trip efficiency. The hourly energy balance was determined throughout the year given uncertainties in the renewable energy resources and energy storage. The loss of load expectation (LOLE) was evaluated for the 2040 No New Combustion portfolio and found to increase from 0 days/year to a median value of ~2 days/year due to potential reductions in renewable energy resources and battery storage performance and capacity. A rank-regression analyses revealed that battery round-trip efficiency was the most significant parameter that impacted LOLE, followed by solar resource, wind resource, and battery fade. An increase in battery storage capacity to ~25,000 – 30,000 MWh from a baseline value of ~14,000 MWh was required to reduce the median value of LOLE to ~0.2 days/year with consideration of potential climate impacts and battery degradation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

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

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

17 WIND ENERGY↗

DOE OSTI2023

Modelos de riesgo de colision: una herramienta para evaluar los riesgos para las aves rapaces en instalaciones de energia eolica (Spanish)

In January 2022, the International Energy Agency Wind Task 34 - Working Together to Resolve the Environmental Effects of Wind Energy (WREN) - organized a forum to discuss aspects of raptor collision risk with wind turbines. The forum included experts in raptor biology and physiology, collision risk modeling, wind energy development, and atmospheric scientists from seven countries. They represented a range of international stakeholder groups including academia, government agencies, national laboratories, and wildlife consultants. This educational brief summarizes the discussion during the forum and written comments from those who could not attend. Relevant literature was used to provide additional context when needed. For several species of raptors, such as golden eagles (Aquila chrysaetos), griffon vultures (Gyps fulvus) and white-tailed eagles (Haliaatus albicilla), collision risk with wind turbines continues to be a concern among stakeholders. These concerns include the potential population-level impact related to collisions, compliance with regulatory mechanisms for protected species, and the ability to generate renewable energy. To make siting and operational decisions, stakeholders require some level of certainty of the risk associated with a proposed project. Understanding this risk, in part, requires species-specific data on raptors and how they perceive and interact with wind farms or individual wind turbines. Collision risk models (CRMs) are a tool, often used in environmental impact assessments, that can provide estimates of risk relative to specific turbines or an entire wind farm. However, questions associated with the uncertainty in CRM estimates remain. This is the Spanish translation of NREL/FS-5000-84747, "Collision Risk Modeling - A Tool for Assessing Risks to Raptors at Wind Energy Facilities."

collision risk↗

DOE OSTI2022

The Determinants of Offshore Wind's Role in a Future U.S. Energy System: A Preliminary Modeling Sensitivity Analysis [Slides]

Offshore wind is an emerging industry in the with rapidly changing technology advancements and an accelerating global deployment. Policy demands and interest in the United States has grown, but the long-term future for offshore wind remains uncertain. This study is designed to evaluate offshore wind's potential role in the future U.S. energy system. The analysis uses long-term power system models to assess a wide-range of power system possibilities. Specific objectives of the analysis are to: (1) identify the determinants of offshore wind deployment under different scenarios and (2) understand the impacts of substantial levels of offshore wind deployment on the power system. We also highlight future research needs for more robust understanding the opportunities and challenges to U.S. offshore wind development.

17 WIND ENERGY↗

DOE OSTI2021

Verification of induction zone models for wind farm annual energy production estimation

Fast engineering wake models are the backbone of wind farm annual energy production (AEP) estimators, whereas the addition of induction zone models in existing tools is a more recent response to rising concerns over wind farm blockage associated losses. Here, "blockage" describes the combined induction fields of all wind turbines inside a farm. Unlike the term might suggest, blockage not only reduces flow speeds, but also increases them; for instance along the outer edges of wind farms. Evaluating the overall impact on AEP of these gains and losses necessitates accurate wind farm induction models. Whilst engineering wake models are tuned to predict wind farm performance, existing induction zone models are all derived for accurately predicting the near field induction not the far field value—important for wind farm simulations. This paper presents the induction models implemented in the wind farm simulation tool PyWake, as well as results from novel analytical models and compares their far field predictions to RANS-AD simulations of different turbines. We demonstrate that when including blockage in AEP simulations, the downstream speed-ups need to be included to avoid an unrealistic bias toward AEP loss and that wake expansion significantly impacts induction at rated thrust levels.

17 WIND ENERGY↗

DOE OSTI2020

ExaWind: A multifidelity modeling and simulation environment for wind energy

We introduce the open-source ExaWind modeling and simulation environment for wind energy. The primary physics codes of ExaWind are Nalu-Wind and OpenFAST. Nalu-Wind is a wind-focused computational fluid dynamics (CFD) code that is coupled to the whole-turbine simulation code OpenFAST. The ExaWind environment was created under U.S. Department of Energy funding to achieve the highest-fidelity simulations of wind turbines and wind farms to date, with the goal of enabling disruptive changes to turbine and plant design and operation. Innovation will be gleaned through better understanding of the complex flow dynamics in wind farms, including wake evolution and the impact of wakes on downstream turbines and turbulent flow from complex terrain. High-fidelity predictive simulations employ hybrid turbulence models, geometry/boundary-layer-resolving CFD meshes, atmospheric turbulence, nonlinear structural dynamics, and fluid-structure interaction. While there is an emphasis on very high-fidelity simulations (e.g., blade resolved with full fluid-structure coupling), the ExaWind environment supports lower-fidelity modeling capabilities including actuator-line and -disk methods. Important in the development of ExaWind codes is that the codes scale well on today's largest petascale supercomputers and on the next-generation platforms that will enable exascale computing.

17 WIND ENERGY↗

DOE OSTI2020

A simple method for modelling fatigue spectra of small wind turbine blades

Small-scale wind turbines have market opportunities in distributed energy generation applications but face future challenges in remaining cost competitive compared with solar photovoltaic systems. High unit costs can be attributed to design conservatism when calculating fatigue loads of key structural components such as the blades. In this study, we use the aeroelastic software FAST to highlight limitations of the International Electrotechnical Commission 61400-2:2013 small wind turbine design standard for calculating fatigue life using the simplified load model. We present a modified method for calculating the fatigue spectra of small wind turbine blades. An advantage of this method is that it does not require complex aeroelastic simulations or field measurements. This modified method is intended to be implemented early in the blade design stage, such as during rotor optimization simulations, allowing for multiple rotor configurations to be rapidly compared.

17 WIND ENERGY↗

DOE OSTI2024

Incorporating Wind Turbine Choice in High-Resolution Geospatial Supply Curve and Capacity Expansion Models

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.

17 WIND ENERGY↗

DOE OSTI2022