Wind Energy Deployment Research Articles

Offshore wind and wave energy can reduce total installed capacity required in zero-emissions grids

As the world races to decarbonize power systems to mitigate climate change, the body of research analyzing paths to zero emissions electricity grids has substantially grown. Although studies typically include commercially available technologies, few of them consider offshore wind and wave energy as contenders in future zero-emissions grids. Here, we model with high geographic resolution both offshore wind and wave energy as independent technologies with the possibility of collocation in a power system capacity expansion model of the Western Interconnection with zero emissions by 2050. In this work, we identify cost targets for offshore wind and wave energy to become cost effective, calculate a 17% reduction in total installed capacity by 2050 when offshore wind and wave energy are fully deployed, and show how curtailment, generation, and transmission change as offshore wind and wave energy deployment increase.

Revealing technological entanglements in uncertain decarbonisation pathways using bayesian networks

To effectively meet the ambitious objectives set by the Paris Agreement, gaining a deeper understanding of the relationships between the key technologies involved in mitigation activities is pivotal. This research uses Bayesian Network (BN) methodology on a large ensemble of energy system model runs, aiming to shed light on the complex interdependencies, and related uncertainties, among the various technologies within the pathways. We specifically focus on tracking the evolution and interconnectedness of technology portfolios over time, enabling dynamic assessments of the impacts linked to specific deployment strategies. The results suggest that prioritizing early-stage transitions within the building sector is imperative and the consistent deployment of district heating emerges as a pivotal element in the long-term plans for decarbonisation. In the power sector, the rising trends in electrification and the substantial growth in low-carbon power plants and wind energy deployment, underscore the urgency for adaptable strategies within the power sector. Notably, the integration of bioenergy with carbon capture and storage (BECCS) also emerges as a crucial technology, offering a means to counterbalance emissions from carbon-intensive industries. The BN-based approach provides decision makers a powerful tool for comprehensive, informed, and systematic planning as they navigate towards a carbon-neutral future, but it is also crucial to acknowledge the reliance of our analysis on assumptions inherent in energy system models. Studies using different assumptions and model structures are needed to confirm the generalizability of our findings.

Assessment of Wind Energy Potential at Three Prime Locations in Saudi Arabia: Analysis of Sharma, Qurayyat and Sakaka Sites

Purpose: The research aims to assess the wind energy potential at three chosen sites in Saudi Arabia Sharma, Al Qurayyat, and Sakaka over a five-year period from 2019 to 2023. The main goal is to determine the most favorable locations for wind farm development based on wind speed characteristics, Weibull distribution parameters, and wind power density (WPD). Materials and Methods: The wind speed data for the chosen locations was obtained from NASA's wind website and analyzed using the Weibull distribution to determine the shape (k) and scale (c) parameters, which are indicative of wind speed distribution. Critical metrics such as mean wind speed, WPD, and the most probable wind speed were computed to evaluate the wind energy potential. The Vestas V150-4.2 MW turbine was utilized to estimate potential energy output, considering the variability and reliability of wind resources at the specified locations. The study utilized tables, figures, and wind rose diagrams to highlight important wind parameters like mean wind speed, Weibull parameters, WPD, and Vmax for each location. Flowcharts were also used to summarize the methodology and conclusions, ensuring a clear and comprehensive presentation of the data. Findings: The analysis showed that Qurayyat consistently had the highest mean wind speed and WPD, indicating excellent wind energy potential. Sharma and Sakaka also displayed significant wind resources, with high mean wind speeds and substantial WPD values. The Weibull parameters indicated that all three sites have favorable wind characteristics for energy production. The estimated energy output using the Vestas V150-4.2 MW turbine ranged from 12,809 to 17,807 MWh/yr at Site 18, 14,095 to 15,170 MWh/yr at Site 24, and 14,013 to 15,832 MWh/yr at Site 31. Implications to Theory, Practice and Policy: The study presents a thorough evaluation of the wind energy potential in the Al-Jawf and Tabuk regions of Saudi Arabia. It contributes to a deeper understanding of wind resource distribution and its significance for the development of renewable energy. The study provides valuable insights for wind farm developers and policymakers by identifying the most promising locations for investment in wind energy infrastructure. Furthermore, it emphasizes the need for detailed site-specific analyses to optimize wind energy deployment, aligning with Saudi Arabia's broader renewable energy objectives.

CFD wind farm evaluation in complex terrain under free and wake induced flow conditions

Massive deployment of wind energy is critical to achieving the renewable energy production targets. This requires the development and improvement of models and tools for the optimal exploitation of high altitude and complex terrain sites for wind energy installations. Predicting the wind resource assessment of these sites is very challenging, as is predicting the interaction of wind farms in complex terrain with neighbouring installations, which is necessary to maximise the efficiency of wind energy. To address these challenges, the use of high-fidelity Computational Fluid Dynamics (CFD) models is recommended. In this study, the wind resource at the complex terrain site of the CENER experimental wind farm (Alaiz) is evaluated using steady-state RANS CFD simulations performed with OpenFOAM v2212, taking into account the effects of terrain topography and vegetation. Furthermore, a virtual wind farm located at Alaiz is modelled with the Actuator Disk (AD) method to analyse the effect of topography on the the wake evolution.

Offshore Wind Energy Validation Experiment Hierarchy

This paper provides a summary of planning work for experiments that will be necessary to address the long-term model validation needs required to meet offshore wind energy deployment goals. Conceptual experiments are identified and laid out in a validation hierarchy for both wind turbine and wind plant applications. Instrumentation needs that will be required for the offshore validation experiments to be impactful are then listed. The document concludes with a nominal vision for how these experiments can be accomplished.

Quantifying land impacts of wind energy: a regional-scale assessment in Finland

AbstractLarge-scale deployment of wind energy raises ecological concerns about land use changes. Here, we conducted a quantitative assessment of land impacts of 42 wind farms (513 turbines) from Ostrobothnia, Finland. Located on the Baltic Sea coast, Ostrobothnia is a strategic region for national wind energy targets. Our main objectives were (1) to calculate landscape metrics (e.g., habitat composition, loss and fragmentation) within wind farm project areas, (2) compare the amount of building-related deforestation between project areas and simulation-based control areas and (3) compute the overlap between project areas and regional land use plans. According to CORINE Land Cover datasets, the predominant habitats within project areas were forests (371.7 km2; 81%) and transitional forests (52.7 km2; 11%). The remaining 8% were agricultural areas (17.6 km2; 4%), waters (12.5 km2; 3%) and artificial surfaces (2.9 km2; 1%). Wind farm infrastructure corresponded to 1.4-6.0% of project areas and caused similar degrees of habitat fragmentation. Deforestation levels in project areas were higher (~ 6 times on average) than in control areas, and the micro-siting of turbines did not avoid mature forests by favouring e.g., transitional forests (which are ecologically less valuable). We found some spatial incongruence between project areas and regional land use plans, with 78 turbines falling outside the established boundaries. Our results highlight the need for careful ecological considerations in wind energy spatial planning in Finland (and countries with similar landscapes and land uses) to limit forest loss and its detrimental impacts on forest-dependent species, but also on carbon storage and sequestration.

Machine learning methods to improve spatial predictions of coastal wind speed profiles and low-level jets using single-level ERA5 data

Abstract. Observations of the wind speed at heights relevant for wind power are sparse, especially offshore, but with emerging aid from advanced statistical methods, it may be possible to derive information regarding wind profiles using surface observations. In this study, two machine learning (ML) methods are developed for predictions of (1) coastal wind speed profiles and (2) low-level jets (LLJs) at three locations of high relevance to offshore wind energy deployment: the US Northeastern Atlantic Coastal Zone, the North Sea, and the Baltic Sea. The ML models are trained on multiple years of lidar profiles and utilize single-level ERA5 variables as input. The models output spatial predictions of coastal wind speed profiles and LLJ occurrence. A suite of nine ERA5 variables are considered for use in the study due to their physics-based relevance in coastal wind speed profile genesis and the possibility to observe these variables in real-time via measurements. The wind speed at 10 ma.s.l. and the surface sensible heat flux are shown to have the highest importance for both wind speed profile and LLJ predictions. Wind speed profile predictions output by the ML models exhibit similar root mean squared error (RMSE) with respect to observations as is found for ERA5 output. At typical hub heights, the ML models show lower RMSE than ERA5 indicating approximately 5 % RMSE reduction. LLJ identification scores are evaluated using the symmetric extremal dependence index (SEDI). LLJ predictions from the ML models outperform predictions from ERA5, demonstrating markedly higher SEDIs. However, optimization utilizing the SEDI results in a higher number of false alarms when compared to ERA5.

Critical material requirements and recycling opportunities for US wind and solar power generation

AbstractThe deployment of renewable energy generation technologies, driven primarily by concerns over catastrophic climate change, is expected to increase rapidly in the United States. Rapid increases in the deployment of wind and solar energy will translate to increases in critical material requirements, causing concern that demand could outstrip supply, leading to mineral price volatility and potentially slowing the energy transition. This study presents a detailed demand‐side model for wind and solar in the United States using dynamic material flow analysis to calculate the requirements for 15 elements: Cr, Zn, Ga, Se, Mo, Ag, Cd, In, Sn, Te, Pr, Nd, Tb, Dy, and Pb. Results show that transitioning to a completely decarbonized US energy system by 2050 could require a five‐to‐sevenfold increase in critical material flow‐into‐use compared with business as usual (BAU), with some materials requiring much larger increases. Rare earth elements (REEs) could require 60–300 times greater material flows into the US power sector in 2050 than in 2021, representing 13%–49% of the total global REE supply. Te requirements for reaching net zero by 2050 could exceed current supply, posing challenges for widespread deployment of cadmium‐telluride solar. We also investigate several strategies for reducing material requirements, including closed‐loop recycling, material intensity reduction, and changing market share of subtechnologies (e.g., using crystalline silicon solar panels instead of cadmium telluride). Although these strategies can significantly reduce critical material requirements by up to 40% on average, aggressive decarbonization will still require a substantial amount of critical material.

Forecasting End-of-Life Wind Turbine Material Flows in Australia under Various Wind Energy Deployment Scenarios

A circular economy involves managing and reducing the environmental and social impacts of products and materials throughout their entire lifecycle, from production to end of life, including clean energy technologies. The remarkable growth of wind turbine (WT) deployment in Australia, as a clean energy source, is promising, with over 10 gigawatts (GW) installed by 2023. Responsible management of wind turbines throughout the entire supply chain, including their end of life, is crucial to prevent potential environmental issues caused by significant waste volumes and to identify opportunities for resource recovery. This study offers a comprehensive overview of current and future WT waste through material flow analysis (MFA) under five national wind energy deployment scenarios, considering various wind turbine technologies. The results indicate that the projected cumulative WT installation capacity will range from 13 to 38 GW by 2041. Consequently, the cumulative WT waste volume is expected to range between 6.69 and 19.76 million tonnes in 2060, depending on the scenario, with the “slow change” scenario producing the least waste and the “step change” scenario generating the most. The estimated waste stream will see a rapid increase from about 2028, encompassing a variety of materials, primarily concrete at 10.20 million tonnes, followed by 3.21 million tonnes of steel and 35.41 kt of copper by 2060. Additionally, valuable materials such as rare earth elements (REEs) and composites, despite their smaller quantities, have significant environmental, economic, and supply chain security implications. This substantial waste material presents an opportunity for resource recovery and underscores the importance of adopting a circular economy approach for wind energy systems.

A synergistic analysis of solar and wind energy deployment in Europe

Recent energy tensions have added to the pressure that global warming has been exerting for an energy transition towards low-carbon energy sources such as renewable energy sources (RES). This study proposes a novel methodology to inspect the interactions between wind and solar energy development relative to other RES and a wide range of socio-economic and environmental variables in 21 European countries during the period 2007–2021. First, countries are ordered according to their average level during the evaluated period. The ordinal position of each country is used as input for a multivariate analysis that avoids problems of multicollinearity and inefficiency inherent to model estimation with a large number of intertwined covariates. In a second stage, by means of Categorical Principal Component Analysis (CATPCA) all the information from the rankings is synthesized into two factors. This makes it possible to graphically evaluate the interplay between the different variables and the relative positioning of the countries in relation to the average level observed for the different factors during the sample period. Overall, the obtained results suggest a decoupling between the development of wind energy and solar energy sources. High levels of wind penetration in the RES mix seem to be positively associated with energy consumption and economic factors while solar energy seems to have experienced greater development in countries with greater energy dependence. The finding that energy, economic and environmental variables affect the development of wind and solar energy very differently highlights the need to analyze each of the renewable energy sources independently. Likewise, it has important implications in terms of the design of energy policies, highlighting the importance of implementing sector-specific measures.

Analysis of the main socio-economic impacts of wind energy in Europe

The large increase in renewable energy sources (RES-E) penetration in the European Union (EU) has raised the concern of policy makers about the costs of public promotion of RES-E, in spite of the commitment of the European Commission to the deployment of RES-E in the last years. This paper is focused on wind energy, because it is the renewable technology with the highest contribution to electricity mix in Europe (excluding hydro). An economic analysis of wind energy contribution should not only take into account the policy costs of the deployment, which are finally paid by electricity consumers, but also its benefits, particularly those related to climate change mitigation and the reduction of fossil fuel dependence distributed among citizens. The aim of this paper is to fill this gap and evaluate the policy costs and the most relevant benefits of wind energy deployment in the EU28 (28 Member States) using 2013 data. For this purpose, an innovated methodology internationally validated has been used. The results show that the benefits considered are relevant and should be taken into account when support costs are assessed and in the future development of energy policies in Europe.

Potentials and opportunities of solar PV and wind energy sources in Saudi Arabia: Land suitability, techno-socio-economic feasibility, and future variability

Solar and wind energy sources hold significant potential to meet the escalating energy demand in Saudi Arabia sustainably. This research aims to assess the feasibility and prospects of deploying solar photovoltaic (PV) and wind energy systems in Saudi Arabia (SA). The study adopts a comprehensive approach, encompassing spatial analysis of land suitability, techno-socio-economic feasibility studies, and prediction of renewable energy (RE) resource variability and trends using machine learning techniques. Geographical Information System (GIS) spatial analysis is employed to identify suitable areas for establishing solar PV and wind farms based on multi-criteria evaluation methods. Techno-socio-economic feasibility analysis of solar PV and wind energy systems, under various configurations and scenarios, is conducted using the System Advisor Model (SAM) tool. Furthermore, a Support Vector Machine (SVM) machine learning algorithm is developed and deployed to forecast Global Horizontal Irradiation (GHI), wind speed, and influential weather parameters at different locations in SA. The projections of these variables derived from global climate models are utilized to analyze prospects and variabilities. The land suitability analysis identified four optimal locations for large-scale solar PV fields in Tabuk, Al Madinah, Makkah, and Riyadh provinces. Additionally, four promising wind farms were found in Al Madinah, Makkah, Riyadh, and Eastern provinces. The analysis also identified one location in Al Jouf province suitable for hybrid systems combining solar PV and wind energy. The techno-economic assessment revealed that wind farms performed best overall, achieving a capacity factor of 42.6 % in Al Madinah province. Although current tariffs render projects economically unviable, solar PV, wind energy, and hybrid solar PV-wind technologies are economically feasible in SA at Power Purchase Agreement (PPA) rates above $32.8/MWh, $26.1/MWh, and $50.6/MWh, respectively. The social development analysis estimated potential job creation from solar PV and wind energy deployment under different scenarios from 2020 to 2060, indicating that more ambitious climate targets could translate to millions of renewable energy jobs. The SVM model predicted solar irradiance with an R-squared value of 0.893. The CMIP6 model was then used to project the GHI for SA in 2049, suggesting an increase of approximately 19 %. Wind speed is also expected to rise roughly 5 % over the same period. The integration of GIS spatial analysis, techno-socio-economic modeling, and machine learning-based forecasting provides comprehensive insights into harnessing solar and wind energy in SA. This study facilitates evidence-based planning and risk assessment, crucial for a sustainable energy transition. The insights and roadmap derived from this research can inform policy frameworks, support the United Nations Sustainable Development Goals (UNSDGs), and attract private investments for RE development in SA. Consequently, this study establishes solar and wind energy as viable and promising solutions for meeting SA's growing energy demands sustainably, while minimizing the associated environmental impact.

Mechanisms of Low-Level Jet Formation in the U.S. Mid-Atlantic Offshore

Abstract Low-level jets (LLJs), in which the wind speed attains a local maximum at low altitudes, have been found to occur in the U.S. mid-Atlantic offshore, a region of active wind energy deployment as of 2023. In contrast to widely studied regions such as the U.S. southern Great Plains and the California coastline, the mechanisms that underlie LLJs in the U.S. mid-Atlantic are poorly understood. This work analyzes floating lidar data from buoys deployed in the New York Bight to understand the characteristics and causes of coastal LLJs in the region. Application of the Hilbert–Huang transform, a frequency analysis technique, to LLJ case studies reveals that mid-Atlantic jets frequently occur during times of adjustment in synoptic-scale motions, such as large-scale temperature and pressure gradients or frontal passages, and that they do not coincide with motions at the native inertial oscillation frequency. Subsequent analysis with theoretical models of inertial oscillation and thermal winds further reveals that these jets can form in the stationary geostrophic wind profile from horizontal temperature gradients alone—in contrast to canonical LLJs, which arise from low-level inertial motions. Here, inertial oscillation can further modulate the intensity and altitude of the wind speed maximum. Statistical evidence indicates that these oscillations arise from stable stratification and the associated frictional decoupling due to warmer air flowing over a cold sea surface during the springtime land–sea breeze. These results improve our conceptual understanding of mid-Atlantic jets and may be used to better predict low-level wind speed maxima. Significance Statement The purpose of this work is to identify and characterize the atmospheric mechanisms that result in an occasional low-level maximum in the wind speed off the U.S. mid-Atlantic coastline. Our findings show that these low-level jets form due to horizontal temperature gradients arising from fronts and synoptic systems, as well as from the land–sea breeze that forces warmer air over the cold ocean surface. This work aids predictability of such jets, improves our understanding of this coastal environment, and has implications for future deployment of offshore wind energy in this region.

Control Co-Design of Wind Turbines

Wind energy is recognized worldwide as cost-effective and environmentally friendly, and it is among the fastest-growing sources of electrical energy. To further decrease the cost of wind energy, wind turbines are being designed at ever-larger scales. To expand the deployment of wind energy, wind turbines are also being designed on floating platforms for placement in deep-water locations offshore. Both larger-scale and floating wind turbines pose challenges because of their greater structural loads and deflections. Complex, large-scale systems such as modern wind turbines increasingly require a control co-design approach, whereby the system design and control design are performed in a more integrated fashion. This article reviews recent developments in control co-design of wind turbines. We provide an overview of wind turbine design objectives and constraints, issues in the design of key wind turbine components, modeling of the wind turbine and environment, and controller coupling issues. Wind turbine control functions and the integration of control design in co-design are detailed with a focus on co-design compatible control approaches.

The (de-)construction of technology legitimacy: Contending storylines surrounding wind energy in Austria and Switzerland

Why do some countries assign a major role to wind energy in decarbonizing their electricity systems, while others are much less committed to this technology? We argue that processes of (de-)legitimation, driven by discourse coalitions who strategically employ certain storylines in public debates, provide part of the answer. To illustrate our approach, we comparatively investigate public discourses surrounding wind energy in Austria and Switzerland, two countries that differ strongly in wind energy deployment. By combining a qualitative content analysis and a discourse network analysis of 808 newspaper articles published 2010–2020, we identify four distinct sets of storylines used to either delegitimize or legitimize the technology. Our study indicates that low deployment rates in Switzerland can be related to the prominence of delegitimizing storylines in the public discourse, which result in a rather low socio-political acceptance of wind energy. In Austria, by contrast, there is more consistent support for wind energy by discourse coalitions using a broad set of legitimizing storylines. By bridging the related but separate literatures of technology legitimacy and social acceptance, our study contributes to a better understanding of socio-political conflict and divergence in low-carbon technological pathways.

Development of wind turbine blade recycling baselines in the United States

Over the past several years, the wind energy industry has received scrutiny regarding wind turbine blade (WTB) recycling due to the landfilling of WTBs caused by a lack of industrially viable recycling solutions. The amount of WTBs that will need to be recycled is set to increase in the United States as the deployment of wind energy is expected to rapidly grow to meet the nation’s energy goals by 2035. While significant progress has been made worldwide, it is still unclear which WTB recycling solutions would be the most cost and energy effective within the United States for the existing fleet of wind turbines. To guide researchers and industry with a clear path forward, a range of options for WTB recycling in the United States are modeled through development of baseline scenarios and the use of formal life cycle assessment (LCA). Model data have been collected through literature review, industry engagement, and expert opinion regarding current end of life practices and considerations surrounding equipment, labor, and logistics. A detailed baseline for WTB decommissioning processes has been developed and used to assess alternative approaches, such as on-site shredding to compare the impacts on greenhouse gas (GHG) emissions. The developed LCA model and baseline scenarios for WTB recycling is used to assess the current WTB decommissioning practices in the United States along with emerging recycling pathways, including cement kiln co-processing and pyrolysis. Initial findings indicate that there are different approaches to decommissioning WTBs in the United States, each of which has unique implications for recycling. In light of this finding, additional results from the modeling will be used to better understand decommissioning practices and assist in making educated decisions on recycling pathways for the future. Throughout the analysis, focus was given to where international efforts might differ from the United States. WTB recycling is occurring worldwide, and different countries have different drivers for creating markets for recycled WTB materials. The contrasts and similarities between the United States and other countries offer insight to areas of opportunity that the United States could investigate and areas that can be readily transferred from existing solutions. By modeling and characterizing the current decommissioning practices and potential recycling solutions for the United States, a clearer vision will be created for pathways forward as to how to handle end of life WTBs to enable more efficient and cost-effective opportunities for material recovery from end-of-life WTBs.

Study of Potential Impact of Wind Energy on Electricity Price Using Regression Techniques

This paper seeks to investigate the impact analysis of wind energy on electricity prices in an integrated renewable energy market, using regression models. This is especially important as wind energy is hard to predict and its integration into electricity markets is still in an early stage. Price forecasting has been performed with consideration of wind energy generation to optimize energy portfolio investment and create an efficient energy-trading landscape. It provides an insight into future market trends which allow traders to price their products competitively and manage their risks within the volatile market. Through the analysis of an available dataset from the Austrian electricity market, it was found that the Decision Tree (DT) regression model performed better than the Linear Regression (LR), Random Forest (RF), and Least Absolute Shrinkage Selector Operator (LASSO) models. The accuracy of the model was evaluated using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The MAE values considering wind energy generation and without wind energy generation for the Decision Tree model were found to be lowest (2.08 and 2.20, respectively) among all proposed models for the available dataset. The increasing deployment of wind energy in the European grid has led to a drop in prices and helped in achieving energy security and sustainability.

Remotely sensed evidence of the divergent climate impacts of wind farms on croplands and grasslands

To mitigate climate change, the utilization of wind energy has rapidly expanded over the last two decades. However, when producing clean electricity, wind farms (WFs) may in turn alter the local climate by interfering in land surface-atmosphere interactions. Currently, China and the United States have the highest wind energy capacities globally. Thus, quantitatively analyzing the impacts of WFs on land surface temperature (LST) between the two countries is valuable to deeply understand the climate impact of WF. In this study, we use the moderate-resolution imaging spectroradiometer (MODIS) time series from 2001 to 2018 to reveal the impacts of 186 WFs (76 in China and 110 in the US) on local LSTs. The remote sensing observations reveal that WFs generally lead to warming impacts in both countries, with stronger effects in the US compared to China. During the daytime, WFs in the US exhibit a significant warming effect of 0.08 °C (p < 0.05), while the impact in China is nonsignificant (0.06 °C, p = 0.15). At night, the warming impacts in the US are approximately 1.7 times greater than in China (0.19 °C vs. 0.11 °C). Differences in the LST impacts between the two countries are primarily driven by cropland WFs, which cause more significant cooling effects in China (−0.34 °C in the daytime and − 0.19 °C at night, p < 0.01) compared to the US. However, these differences are not significant for grassland WFs. Moreover, the impacts of WFs on croplands' LSTs are strongly correlated with their evapotranspiration impacts, likely influenced by irrigation practices. In addition to evapotranspiration, a machine learning model suggests that background climate and terrain factors can alter the LST impacts. Our observations in the two largest WF-deployment countries provide a new understanding of the climate impacts of WFs, which should be considered in the fields of wind and renewable energy deployment.

Wind Fields in Category 1–3 Tropical Cyclones Are Not Fully Represented in Wind Turbine Design Standards

AbstractOffshore wind energy deployment in the U.S. is expected to increase in the years to come, with proposed wind farm sites located in regions with high risk for tropical cyclones. Yet, the wind turbine design criteria outlined by the International Electrotechnical Commission for extreme events may not account for wind field characteristics unique to tropical cyclones. To evaluate if current design standards capture the extreme conditions of these storms, we perform idealized large‐eddy simulations of three tropical cyclones (one Category 1, one Category 2, and one Category 3 storms) using the Weather Research and Forecasting model. Wind conditions near the eyewall of Cat‐1, Cat‐2, and Cat‐3 storms can exceed current design standards for offshore wind turbines. Winds at 90 m can be faster than outlined in the design criteria for Class I and Class T turbines for 50‐year recurrence periods. Wind speed shear across the turbine rotor layer is also larger than assumed in design specifications. Moreover, vertical variations in wind direction across the turbine rotor layer are large for tropical cyclones of all intensity levels, suggesting design standards should include veer, which can amplify loads in wind turbines. The existence of hurricane wind field characteristics that are not represented in design standards does not imply that damage or failure will certainly occur. Engineering safety factors incorporated in the design of the turbine's blades and structural components may prevent damage from occurring.

Lessons learned in coupling atmospheric models across scales for onshore and offshore wind energy

Abstract. The Mesoscale to Microscale Coupling team, part of the U.S. Department of Energy Atmosphere to Electrons (A2e) initiative, has studied various important challenges related to coupling mesoscale models to microscale models for the use case of wind energy development and operation. Several coupling methods and techniques for generating turbulence at the microscale that is subgrid to the mesoscale have been evaluated for a variety of cases. Case studies included flat-terrain, complex-terrain, and offshore environments. Methods were developed to bridge the terra incognita, which scales from about 100 m through the depth of the boundary layer. The team used wind-relevant metrics and archived code, case information, and assessment tools and is making those widely available. Lessons learned and discerned best practices are described in the context of the cases studied for the purpose of enabling further deployment of wind energy.