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At least 55 records · Page 3

Design Load Basis Guidance for Distributed Wind Turbines

Aeroelastic modeling (AM) is the primary methodology for structural and performance assessment of any wind turbine. Nonetheless, the use of AM in the distributed wind (DW) industry sector is limited due to several challenges (Damiani, Davis, & Summerville, 2022). One of these challenges lies in the perceived complexity of generating a proper set of numerical simulations to extract and process the key outputs for component design and verification, and, ultimately, achieve certification. This makes it difficult to reliably predict the structural and performance response of small wind turbines. From the investigation carried out in (Damiani & Davis, 2022), it is apparent that many stakeholders in this sector believe that a comprehensive guide for developing a design load basis (DLB) for distributed wind turbines (DWTs) is necessary.

17 WIND ENERGY↗

Halau Uma: Kahikinui Community Cultural, Educational, and Resilience Center Preliminary Design Technical Assistance Summary Report

The Kahikinui Homestead Community in Maui faces energy resilience challenges due to its remote, off-grid location. This report summarizes preliminary technical assistance to design a microgrid for six modular homes using solar PV, wind, and battery storage. Results indicate that a hybrid PV and battery system, supplemented by small wind turbines, offers a cost-effective, resilient solution supporting energy independence and disaster preparedness.

Quiroz, Jimmy Edward [Sandia National Laboratories↗

Kauhale 'O Kipapa: Kahikinui Community Cultural, Educational, and Resilience Center Preliminary Design Technical Assistance Summary Report

The Kahikinui Homestead Community in Maui faces energy resilience challenges due to its remote, off-grid location. This report summarizes preliminary technical assistance to design a microgrid for six modular homes using solar PV, wind, and battery storage. Findings show hybrid PV and battery systems, supplemented by small wind turbines, offer cost-effective, resilient solutions. This work supports enhanced energy independence and disaster preparedness for the community.

Quiroz, Jimmy Edward [Sandia National Laboratories↗

2022 Product Commercialization and Market Development Awardee: Bergey Windpower

In 2023, consumers will finance more than 80% of the residential solar energy systems they purchase, which they use as collateral (called "non-recourse loans"). Similar loans are not yet offered to fund distributed wind energy systems. To accelerate deployment of small wind systems, which power individual rural homes and farms, the United States will need to make consumer loans available at reasonable rates with modest downpayments and collateral requirements. A team led by Bergey Windpower Co. is creating new consumer financing options to reduce or eliminate the upfront cash needed to buy distributed wind energy systems. This new financing structure would decrease purchase costs for small wind turbines produced by Bergey Windpower and possibly other manufacturers. Product financing is instrumental in growing wind power market share, clean energy manufacturing, and installation jobs while reducing greenhouse gas emissions. Bergey Windpower's previous Competitiveness Improvement Project (CIP) awards have led to the development of affordable, high-performance wind turbines, microgrids, and components.

CIP↗

2021 Small Turbine Certification Awardee: Intergrid LLC

Currently, there are no U.S., fully certified inverters for wind turbines in the 10-15-kilowatt (kW) range. The federal government's Competitiveness Improvement Project (CIP) and Small Business Innovation Research programs present the most viable paths for manufacturers in the distributed wind energy industry to conduct research, develop their products, and, ultimately, commercialize them. Through CIP, Intergrid LLC is reducing the cost, size, and weight of its legacy inverter for distributed wind turbines while increasing efficiency, reliability, and serviceability. To accomplish this, Intergrid will address three areas related to power electronics for distributed wind turbines: (1) Software verification and functional safety certification; (2) Simulation for grid-interconnecting testing; (3) Component alternatives that reduce cost and improve reliability and serviceability. The first two areas relate to long-term management of inverter software, which, for grid-connected inverters, must comply with three standards - one issued by Underwriters Laboratories (UL) and two issued by the Institute of Electrical and Electronics Engineers.

CIP↗

Performance of wind assessment datasets in United States coastal areas

The atmospheric dynamics that occur near the intersection of land and water offer exciting and challenging opportunities for wind energy deployment in coastal locations. New models and tools are continually being developed in support of wind resource assessment, and three recent products are explored in this work for their performance in representing characteristics of the wind resource at coastal locations: the Global Wind Atlas 3 (GWA3), the 2023 National Offshore Wind dataset (NOW-23), and the wind climate simulations that are a component of the Wind Integration National Dataset (WIND) Toolkit Long-Term Ensemble Dataset (WTK-LED Climate). These relatively new products are freely available and user-friendly so that anyone – from a utility-scale developer to a resident or business owner – can evaluate the potential for wind energy generation at their location of interest. The validations in this work provide guidance on the accuracy of wind resource assessments for coastal customers interested in installing small or midsize wind turbines (≤ 1 MW in capacity) to support energy needs at the residential, business, or community scale, such as the island and remotely located participants of the U.S. Department of Energy's Energy Transitions Initiative Partnership Project. At 23 coastal locations across the United States, dataset performance varies according to different evaluation metrics. All three recent datasets tend to overestimate the observed coastal wind resource. GWA3 produces the smallest annual average wind speed relative errors, whereas WTK-LED Climate is in best agreement in terms of representing diurnal wind speed cycles. NOW-23 is the highest performing of the datasets for representing seasonal and interannual trends in the coastal wind resource. While GWA3 and WTK-LED Climate are relatively insensitive to the dataset output heights selected for wind resource assessment at small and midsize wind turbine hub heights (20–60 m), significant variation in the NOW-23 representation of wind shear across the wind profile in the lowest 100 m of the atmosphere leads to notable differences in wind speed estimates according to the dataset output heights selected for evaluation. GWA3 exhibits challenges in the representation of observed wind speed diurnal cycles at small and midsize turbine hub heights, likely due to the dataset's consistent treatment of hourly wind speed trends regardless of altitude.

17 WIND ENERGY↗

Validation of FAST.Farm Against Full-Scale Turbine SCADA Data for a Small Wind Farm

FAST.Farm is a new midfidelity engineering tool developed by the National Renewable Energy Laboratory targeted at accurately and efficiently predicting wind turbine power production and structural loading in wind farm settings, including wake interactions between turbines. FAST.Farm is based on some of the principles of the Dynamic Wake Meandering model—including passive tracer modeling of wake meandering—but addresses many of the limitations of previous Dynamic Wake Meandering (DWM) implementations. Previous FAST.Farm verification studies show the similarities and differences between FAST.Farm and large-eddy simulations for rigid and flexible turbines. In this validation study, FAST.Farm turbine responses are compared to multiturbine measurements from a subset of a full-scale wind farm. FAST.Farm predictions of turbine generator power, rotor speed, and blade pitch for five-turbine simulations are compared to supervisory control and data acquisition results. Results reveal that FAST.Farm generator power mean and standard deviation results reasonably match measured data for upstream and downstream turbines, as well as the mean rotor speed and blade pitch above rated wind speeds. However, FAST.Farm generally underpredicts the mean rotor speed and overpredicts the mean blade pitch below rated operation. These errors are likely related to inaccuracies in the generic controller simulated. Despite controller differences, FAST.Farm predicts the same overall relative rotor power trends for all waked turbines at all wind speeds.

17 WIND ENERGY↗

Competitiveness Improvement Project (CIP)

Through the Competitiveness Improvement Project (CIP), the U.S. DOE supports small businesses who design and manufacture small or medium wind turbine technology through cost-shared subcontracts awarded via a competitive process. The goals of the CIP are to: improve cost competitiveness with other distributed generation technology through optimized designs and advanced manufacturing processes; increase the number of certified small and medium wind turbine designs across a wide range of sizes; and accelerate deployment of improved, certified distributed wind energy technology. NREL manages the projects through a defined period of performance subcontract, providing technical monitoring and coordinating technical assistance from DOE labs.

advanced manufacturing↗

Comparison of pressure distributions on model and full-scale NACA 64-621 airfoils with ailerons for wind turbine application

The aerodynamic similarity between a small (4-inch chord) wind tunnel model and a full-scale wind turbine blade (24-foot tip section with a 36-inch chord) was evaluated by comparing selected pressure distributions around the geometrically similar cross sections. The airfoils were NACA 64-621 sections, including trailing-edge ailerons with a width equal to 38 percent of the airfoil chord. The model airfoil was tested in the OSU 6- by 12-inch High Reynolds Number Wind Tunnel; the full-scale blade section was tested in the NASA Langley Research Center 30- by 60-foot Subsonic Wind Tunnel. The model airfoil contained 61 pressure taps connected by embedded tubes to pressure transducers. A belt containing 29 pressure taps was fixed to the full-scale section at midspan to obtain surface pressure data. Lift coefficients were obtained by integrating pressures, and corrections were made for the 3-D effects of blade twist and downwash in the blade tip section. The results of the two different experimental methods correlated well for angles of attack from minus 4 to 36 degrees and aileron reflections from 0 to 90 degrees.

Gregorek, G. M.↗

FAST.Farm Development and Validation of Structural Load Prediction Against Large Eddy Simulations

FAST.Farm is a mid-fidelity engineering tool developed by the National Renewable Energy Laboratory targeted at accurately and efficiently predicting wind turbine power production and structural loading in wind farm settings, including wake interactions between turbines. FAST.Farm is based on several principles of the dynamic wake meandering (DWM) model, but also addresses limitations of previous DWM implementations. Previous FAST.Farm studies have shown the similarities and differences between FAST.Farm and large eddy simulations for rigid turbine cases. The objective of this work is to quantify the ability of FAST.Farm to accurately predict turbine structural response in a small wind farm. This is done by comparing FAST.Farm structural response to SOWFA-OpenFAST results for three laterally-aligned turbines. The purpose of this study is to characterize the similarities and differences between FAST.Farm and a higher fidelity model for predicting turbine structural response in a wind farm for differing atmospheric inflows. Strong statistical agreement was found between FAST.Farm and SOWFA-OpenFAST structural response for the non-waked upstream turbine, and good agreement was found for the downstream turbines for most structural quantities. Higher differences were seen for downstream turbines with low ambient turbulence intensity or yawed turbines, suggesting areas for FAST.Farm wake dynamics modeling improvements. For all cases and turbines, small statistical differences were seen between blade deflections and bending moments, with larger differences for tower-top and tower-base bending moments. Overall, the results establish confidence for applying FAST.Farm to wind farm power and loads analyses and identify areas where further model validations and model improvements should be targeted.

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

High Efficiency 2-Bladed Utility Wind Turbine Enhanced by CoFlow Jet Active Flow Control

The aerodynamic performance and power output of a high efficiency Co-Flow Jet (CFJ) 2-bladed Wind Turbine is studied in this paper. CFJ is a Zero-Net-Mass-Flux (ZNMF) active flow control method that dramatically increases airfoil lift coefficient and suppresses flow separation at a low energy expenditure. The 3D Reynolds Averaged Navier-Stokes (RANS) equations with one-equation Spalart-Allmaras (SA) turbulence model are solved to simulate the 3D flows of the wind turbines. The CFJ-Wind Turbine in this paper is modified from the NREL 3-bladed 3.4 MW reference wind turbine with the same RPM, blade length, distribution of chord, twist, and bending. The CFJ 2-bladed wind turbine utilizes the CFJ-NACA 64 series airfoils at most of the blade span from 31.88\% to the tip. The CFJ injection and suction slots are implemented along the full blade span to enhance the effectiveness and power output. The study shows that the 2-bladed CFJ turbine significantly increases power output compared with the 3-bladed baseline turbine at all wind speeds by 15.8\% at the wind speed of 9.8 m/s and by 25.9\% at 4 m/s respectively. The efficiency enhancement is substantially more at the low wind speed than at high wind speed. This would be very beneficial to increase the annual power output or capacity factor as wind turbines work at the speeds lower than the rated speed for most of the time. The results show that the flow field around the blade surface presents a significant enhanced suction peak near the blade leading edge, which results in a very high tangential force and thus increased wind turbine net power coefficient. Consistent with the previous study for the small NREL Phase VI research wind turbine, this paper demonstrates that CFJ 2-bladed wind turbine is an effective technology to extract more power from the wind at all speeds. This study is just an initial effort to demonstrate the advantage of the CFJ blade with no design optimization. It is believed that there is more potential to further enhance the efficiency improvement.

active flow control↗

LiDAR Measurements to Investigate Farm-to-Farm Interactions at the AWAKEN Experiment

The exponential growth of wind energy and the need to exploit wind resources over areas with higher energy potential have led to the construction of neighboring wind turbines and farms with relatively small separation distances. As a result, for specific wind and atmospheric conditions, the wakes generated by an upstream wind farm may affect wind resources available for a downstream wind farm resulting in detrimental impacts on energy harvesting and structural loads for the downwind wind turbines. Distances between neighboring wind farms are typically larger than those associated with intra-wind-farm wake interactions, generating cumulative wakes whose characteristics might differ from those predicted through classical engineering wake models. These phenomena are referred to as farm-to-farm interactions. A better understanding and characterization of farm-to-farm interactions is one of the science goals tackled by the ongoing American WAKE experimeNt (AWAKEN). The site under investigation for this field campaign comprises two large wind farms in northern Oklahoma, USA, which are spaced roughly 5km apart along the prevailing South-North wind direction. To investigate possible interactions between these two wind farms, the WindFluX mobile LiDAR station has been deployed mainly to perform volumetric scans over their gap region. In this paper, preliminary results from these LiDAR volumetric scans will be discussed, specifically for a case with multiple wind turbine wakes evolving during the occurrence of a low-level jet.

17 WIND ENERGY↗

Contextualizing Wind Turbine Blade Waste: Comparison to Other Global Waste Streams

Worldwide wind energy generation capacity has grown rapidly over the past several decades, and wind turbines installed at the beginning of this wave of growth are approaching the end of their design lifetimes. As an increasing number of wind power plants reach their end of life, both decommissioning and repowering (i.e., dismantling or refurbishing existing turbines and commissioning new ones) will produce waste material from the retired wind turbines, foundations, and balance of plant. However, the amount and type of waste, particularly for wind blades, is often mischaracterized. Although wind turbine components are largely recyclable, the blades are typically made of fiberglass composites, which can present challenges for material recovery and reuse. Within the USA, the accumulation of wind turbine blades in landfills has raised questions about whether the continued expansion of wind energy is sustainable if it results in substantial future waste. This study compares the mass and volume of potential global wind blade waste to other waste streams. It also discusses the materials used to manufacture wind turbine blades and summarizes current options for material redesign, recycling (recovery and reuse), repurposing, and disposal of used blades. The analysis indicates that, although wind turbine blades could represent 14% of the composite market by 2027, the potential future mass and volume of wind turbine blade waste is relatively small compared to other industries. These findings suggest that although the development of scalable, economically viable, and environmentally sustainable methods for wind turbine manufacturing, repurposing, and recycling is important, it may make sense to take advantage of synergies among multiple industries in recycling composite waste, rather than focusing solely on wind turbine blades. From a global perspective, larger sustainability, recycling, and waste stream reduction impacts can be made in other industries, such as transportation and construction.

17 WIND ENERGY↗

2022 Small Turbine Certification Awardee: NPS Solutions

Inverters convert the direct current power generated by wind turbines into the alternating current needed to serve local loads and feed onto the electricity grid. Although distributed wind systems typically supply electricity for homes and businesses near the turbines, these systems can also provide clean power to strengthen grid resilience. Certification to UL 1741 SA ensures that inverters will enable distributed wind turbines to provide grid support services and stay online when the grid experiences stability issues. Small companies often find the electrical listing process too complex and expensive to undertake on their own, so few distributed wind turbine inverters are certified to this standard. A U.S. Department of Energy (DOE) Competitiveness Improvement Project (CIP) award is helping NPS Solutions (NPS) secure this critical certification for the inverter in its 100-kilowatt NPS 100C wind turbine, which is optimized for on-site generation at farms, businesses, schools, hospitals, and remote locations.

CIP↗