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At least 181 records · Page 10

Aeroelastic Modeling for Distributed Wind Turbines: March 11, 2021 - November 10, 2021

Aeroelastic modeling (AM) is the primary methodology for structural and performance assessment of any wind turbine whereby providing an understanding of the impact of design parameters on its loading and power response before witnessing it in the field. Despite these advantages, the use of AM in the Distributed Wind Technology (DWT) sector is limited, especially within the less established manufacturers. This project represents an in-depth assessment of the status of AM and its role within the Standards for the DWT industry. The study gathered input and feedback from a large number of national and international stakeholders, reviewed technical strengths and weaknesses of the current edition of the design standards, analyzed recent industry workshops' and meetings' minutes, collected publicly available AM templates, and provided an evaluation of the existing AM codes. The study achieved several goals including providing strategies for the load assessment categorization of turbines based on rotor swept area and archetype, and guidance for AM verification and validation (V&V), which includes discussions of measurement requirements and a sample test-plan useful for future V&V campaigns and design standard development. This document summarizes the different tasks conducted in the course of the project and highlights the steps required to improve the AM adoption based on a multifaceted approach that encompasses: 1) augmenting AM software capabilities, 2) publishing AM best-practices and design-basis, 3) creating new model templates, 4) providing guidance for V&V of codes and specific turbine models leveraging field testing best-practice, and 5) addressing weaknesses in the current standards. Many of the future objectives identified in this study could leverage NREL's upcoming testing campaigns of three modern distributed wind turbines. Recommendations within this study will advance the value and the ease-of-use of AM, thereby allowing the industry to better capitalize this underutilized tool resulting in a more efficient design process, an easier path to certification, and overall better and more distributed reliable wind turbine products.

17 WIND ENERGY↗

Setting the Baseline: The Current Understanding of Equity in Land-Based Wind Energy Development and Operation

As discussions about economic equity and environmental justice have become more prevalent in recent years, the related concepts of "energy justice" or "energy equity" have received increasing attention from policymakers, industry, nonprofits, and academics. According to the Initiative for Energy Justice (2019), energy justice is defined as "The goal of achieving equity in both the social and economic participation in the energy system, while also remediating social, economic, and health burdens on those disproportionately harmed by the energy system" The state of equity as it applies specifically to wind energy, however, remains relatively unexplored and isolated to academia. As a result, the National Renewable Energy Laboratory's Wind Energy Equity Engagement Series aims to better understand equity in wind energy through engagement with experts and communities, including representation in decision-making around new developments, potential impacts to communities near wind energy installations, and community-level distribution of the benefits and burdens of wind energy. This report covers the first three phases of the series.

17 WIND ENERGY↗

Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL) Research: Controls

Advanced Turbine Control for Distributed Wind Deployments: The Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL) research initiative is funded by the U.S. Department of Energy’s Wind Energy Technologies Office and led by researchers at the National Renewable Energy Laboratory (NREL) and Sandia National Laboratories (Sandia). The initiative’s controls research seeks to expand the benefits from distributed wind energy assets beyond solely providing low-cost power directly to consumers. To make these turbines operate more effectively, there is a need for more advanced ways to control them—allowing power companies, businesses, and energy consumers to take advantage of some of the unique technical characteristics of wind energy.

wind, Microgrids, Infrastructure, Resilience, Adva↗

2021 Prototype Manufacture and Installation Awardee: Pecos Wind Power, Inc.

Through the 2021 Competitiveness Improvement Project (CIP), Pecos Wind Power will manufacture a prototype of its 85-kilowatt (kW) horizontal-axis distributed wind turbine, the PW85, a new wind turbine that began development in 2017 when the company was founded. The PW85 wind turbine includes an industry-leading rotor diameter (30 meters) and full-span variable pitch blades to target a levelized cost of energy (LCOE) of $0.103/kilowatt-hour in low annual wind speeds (6 meters per second). This is 55% lower than the average small wind turbine project installed in 2018. The goal of this project is to spur the development of increasingly lower-cost, high-capacity-factor distributed wind turbines. As a result, Pecos Wind Power will manufacture and install wind turbines that increase the geographic area in which distributed wind power is cost competitive with retail-priced electricity and other distributed energy resources - primarily solar energy.

CIP↗

Hybrid Power Plants for Energy Resilience: A Case Study

As renewable energy technologies are increasingly adopted, they pose an opportunity to improve the sustainability and resilience of distributed grids, especially when their design and operation is coordinated as a hybrid power plant. When included in hybrid power plants, distributed wind turbines in particular have the potential to enhance the resilience of distributed grids in areas with good wind resource, due to their ability to provide more consistent generation and ancillary services as compared to photo-voltaic (PV) solar panels. Despite this benefit, U.S. distributed wind adoption is lower than other comparable renewable energy technologies. In this study, we seek to demonstrate how hybrid power plants that include distributed wind turbines can contribute to distribution grid resilience by meeting loads (especially critical loads) more consistently, increasing reserve capacity, and providing value to customers during outages. To demonstrate these contributions, we integrate three separate frameworks and apply them to a case study in a rural electric cooperative in Iowa. Through this case study, we simulate and compare hybrid power plant design and operation during two hazard events: a tornado that causes a 48-hour distribution outage and a winter weather event that causes a 6-hour generation outage. The inclusion of a hybrid power plant that leverages 1) increased battery duration and 2) advanced forecasting and dispatch strategies that reserve capacity leading up to a hazard event best reduce lost loads as well as diesel consumption that would otherwise be used to meet those loads during short- and long-duration hazard events. Depending on the hybrid power plant capacity and operation, we find that the outage mitigation value of a hybrid power plant (measured in value to customers to avoid an outage and avoided lost revenues for the utility) is significant in both hazard events; adding wind, solar, and battery assets to the existing system adds about $50-$100M in avoided lost load and at least $4-$8k in utility value in the tornado hazard event, and $570k-$2.2M in avoided lost load and at least $220-$650 in utility value in the winter hazard scenario. In both the tornado and winter hazard scenarios, optimizing the operation of the hybrid system for resilience can lend similar value as increasing battery duration by 5 MWh for the lower capacity systems considered.

17 WIND ENERGY↗

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↗

Cybersecurity for Distributed Wind: What Integrators Need to Know

Few resources exist to address a growing need to secure distributed wind systems. Idaho National Laboratory recently published the Cybersecurity Guide for Distributed Wind, a richly detailed resource outlining a distributed wind system's possible architecture, relevant standards, risk management strategies, and key recommendations for stakeholders. This document highlights key actionable insights from the Guide that integrators can use to execute an effective cybersecurity strategy.

17 WIND ENERGY↗

Cybersecurity for Distributed Wind: What Manufacturers Need to Know

Few resources exist to address a growing need to secure distributed wind systems. Idaho National Laboratory recently published the Cybersecurity Guide for Distributed Wind, a richly detailed resource outlining a distributed wind system's possible architecture, relevant standards, risk management strategies, and key recommendations for stakeholders. This document highlights key actionable insights from the Guide that manufacturers can use to execute an effective cybersecurity strategy.

17 WIND ENERGY↗

Cybersecurity for Distributed Wind: What Operators Need to Know

Few resources exist to address a growing need to secure distributed wind systems. Idaho National Laboratory recently published the Cybersecurity Guide for Distributed Wind, a richly detailed resource outlining a distributed wind system's possible architecture, relevant standards, risk management strategies, and key recommendations for stakeholders. This document highlights key actionable insights from the Guide that operators can use to execute an effective cybersecurity strategy.

17 WIND ENERGY↗

Evaluation of obstacle modelling approaches for resource assessment and small wind turbine siting: case study in the northern Netherlands

Abstract. Growth in adoption of distributed wind turbines for energy generation is significantly impacted by challenges associated with siting and accurate estimation of the wind resource. Small turbines, at hub heights of 40 m or less, are greatly impacted by terrestrial obstacles such as built structures and vegetation that can cause complex wake effects. While some progress in high-fidelity complex fluid dynamics (CFD) models has increased the potential accuracy for modelling the impacts of obstacles on turbulent wind flow, these models are too computationally expensive for practical siting and resource assessment applications. To understand the efficacy of available models in situ, this study evaluates classic and commonly used methods alongside new state-of-the-art lower-order models derived from CFD simulations and machine learning approaches. This evaluation is conducted using a subset of an extensive original dataset of measurements from more than 300 operational wind turbines in the northern Netherlands. The results show that data-driven methods (e.g. machine learning and statistical modelling) are most effective at predicting production at real sites with an average error in annual energy production of 2.5 %. When sufficient data may not be available de novo to support these data-driven approaches, models derived from high-fidelity simulations show promise and reliably outperform classic methods. On average these models have 6.3 %–11.5 % error compared with 26 % for classic methods and 27 % baseline error for reanalysis data without obstacle correction. While more performant on average, these methods are also sensitive to the quality of obstacle descriptions and reanalysis inputs.

17 WIND ENERGY↗

The July climate and a comparison of the January and July climates simulated by the GISS general circulation model

Results are presented for a study directed to evaluate the ability of the global general circulation model of the Goddard Institute for Space Studies (GISS) in simulating seasonal differences as related to an experiment simulating the July climatology which parallels the January simulation presented by Somerville et al. (1974). The July and January simulations are compared with each other and with climatological data on seasonal changes, mainly for the Northern Hemisphere troposphere. The comparison shows that the model-generated energy cycle, distribution of winds, temperature, humidity and pressure, dynamical transports, diabatic heating, evaporation, precipitation and cloud cover are all realistic for the Northern Hemisphere troposphere in July. The model's simulation of seasonal differences is generally quite realistic since the systematic quantitative errors do not affect the simulation of relative changes, to first order. Defects that could seriously bias the model's performance in particular climate experiments are identified and discussed.

Stone, P. H.↗

HR 4511 - A probable Cepheid with a supergiant-like hot companion

IUE observations have shown HR 4511 to have an early B-type companion with a strong stellar wind. Fitting the energy distribution of the system from far-UV to middle-IR yields a difference of about 3.2 mag at the V passband. Since HR 4511 is in the small cluster Stock 14 and has M(V) = -7.8, M(V) for the secondary is about -4.6, which is only slightly more luminous than the known BO.5 III and B1 stars at the turnoff point. This implies an inconsistency between the deduced luminosity of the secondary and the nature of its UV spectrum, which mimics stars of considerably greater luminosity. It is suggested that the secondary may have been observed in a brief stage between core and shell H burning.

Parsons, S. B.↗

The ultraviolet spectra of the O and B stars in the young galactic cluster NGC 6530

The UV spectra between 1200 and 3000 A of stars in the young galactic cluster NGC 6530 and the surrounding association are studied. From the UBV colors and empirical as well as theoretical calibrations, the T(eff) and L for those stars which follow a sequence in the H-R diagram corresponding to the main sequence are determined. From a comparison with theoretical evolutionary tracks, the age of the cluster is estimated to be 5 + or - 2 x 10 to the 6th yr, with a very small scatter for the different stars. The UV extinction is determined for the stars from a comparison of theoretical model energy distributions for the stellar T(eff)s and the observed energy distributions. The stellar wind lines are studied, and strong stellar winds are found for bolometric magnetidues less than -8.

Boehm-Vitense, E.↗

Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL): Use Cases and Definitions

This resource document is designed to establish common use cases and definitions for U.S. Department of Energy national laboratories and partners participating in the Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL) effort. Use case definitions can be used to classify and connect research-and-development efforts and ultimately to organize project goals. Establishing use cases will also allow for the definition of operational benchmarks for various elements of the MIRACL project and broader distributed wind program as well as enable future alignment with other distributed energy resource research projects.

17 WIND ENERGY↗

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↗

Advanced Distributed Wind Turbine Controls Series: Part 1-Flatirons Campus Model Overview – Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL)

Wind turbines are typically deployed to provide energy, reduce diesel-fuel consumption, reduce carbon emissions, and reduce costs for energy and fuel transportation. However, in addition to solely providing energy to the power system, wind turbines contain rotating masses and inverter-based controls that can enable various reliability and resilience services through advance controls. As part of the Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL), it is demonstrated that advanced wind turbine controls can be employed to support higher contributions of wind, and to demonstrate ways that wind can play a role in supporting grid stability in islanded or grid-connected configurations. This paper documents models of various subsystem comprising a portion of NREL's Flatirons campus that will be used in three subsequent reports to demonstrate capabilities of advanced wind turbine controls. The series of reports will detail advanced capabilities of distributed wind turbines to provide support to isolated grids, distribution grids, and microgrids. We developed models to simulate a wind turbine (600 kW), solar PV (430 kW), battery energy storage system (1 MW/1MWh), a diesel generator (2 MW) and various types of loads (critical, dynamic). The model of the subsystems in MATLAB/Simulink are validated with available data from real-world components on NREL's Flatirons Campus. These validated models can be configured for various studies including four MIRACL use cases: 1) isolated grids, 2) microgrids, and 3) behind-the-meter, and 4) front-of-the-meter wind turbine deployments.

17 WIND ENERGY↗

Case Study: Resilience Benefits of Distributed Wind Against Fuel and Weather Hazards in Alaska

In this case study of St. Mary’s Village, Alaska, we present a resilience evaluation exercise. A resilience framework is employed to identify system characteristics, relevant metrics, and resilience hazards and to assess the performance against the hazards with and without a distributed wind system installed. The results show the resilience benefits provided by the distributed wind installation against fuel shortage hazards and cold weather hazards. The resilience benefits can be assigned monetary values, which will be highly dependent on actual circumstances of the hazard, but provide insight into value streams of distributed wind that are not usually considered. For example, the single 900 kW turbine was found to prevent an average of 14,643 kWh of load from being dropped during a two-day diesel fuel shortage event, which saved the community $447,592 from the prevented outages. This case study serves as an example for novel power system resilience analysis and builds understanding of resilience hazards that are common across many power systems.

Culler, Megan J.↗