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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2024

Distributed Wind and Impacts of FERC Order No. 2222 Implementation

In September of 2020, FERC issued Order No. 2222, directing ISOs to adjust their long-standing tariffs and participation models to enable the operation of distributed energy resource (DER) aggregators in wholesale energy markets. The rule sought to bring wholesale markets under its jurisdiction up to speed with existing expansion of DERs across the United States and to capture the potential benefits that these technologies can provide. This report describes the implementation of FERC Order No. 2222 and the compliance plans that have been submitted so far, attempt to understand the potential impact the rule may have on distributed wind, and provide opportunities for future work to analyze and encourage deployment under these policy conditions. There is an information gap for the type of market interactions distributed wind may have or how it could be best deployed in DER aggregations under future market conditions. There is significant potential for profitable deployment of distributed wind in states that are served by ISOs and covered under Order No. 2222. Distributed wind and other DERs provide local energy that does not need to travel those distances and avoids the losses typically associated with long-distance energy transmission. Deployment of distributed wind can benefit communities that exist away from large load centers by providing local, clean, and affordable energy. Aggregating DERs that include distributed wind could provide these benefits across multiple far-ranging communities if they have access to participate in wholesale markets. A new baseline valuation of distributed wind in areas covered by Order No. 2222 is required to accurately gauge where it is profitable and how it can compete or complement existing or future DER deployment, including as part of an aggregate.

17 WIND ENERGY↗
DOE OSTI2024

Reference Site Conditions for Floating Wind Arrays in the United States

Floating offshore wind farm design is highly site-specific, requiring detailed information about the specific conditions of a project area for realistic design studies. Unfortunately, publicly available site condition data for potential floating offshore wind project sites in the United States is scarce. To support U.S. offshore wind research, we developed reference site condition datasets, including metocean and seabed information, for four potential floating wind project areas in the U.S.: Humboldt Bay, Morro Bay, the Gulf of Maine, and the Gulf of Mexico. These datasets were compiled using publicly available data. Our metocean analysis, covering wind, waves, and surface currents, utilized measurement data from 2000 to 2020. Sources included the National Renewable Energy Laboratory’s National Offshore Wind Dataset for wind data, National Data Buoy Center buoys for wave data, and the High Frequency Radar Network for surface currents. These data were integrated into hourly time series used to compute extreme return periods up to 500 years, monthly statistics, and joint probability clusters for fatigue analysis. Soil conditions were evaluated using the usSEABED database and bathymetry grids were interpolated from the NCEI Digital Elevation Model Global Mosaic. In addition to providing curated reference site condition datasets for four U.S. areas, our assessment highlights the need for more publicly available metocean and soil condition data.

17 WIND ENERGY↗
DOE OSTI2025

Wind Supply Chain Security: Hardware Enumeration and Analysis

This project, undertaken by Idaho National Laboratory (INL) for the Department of Energy (DOE) Wind Energy Technologies Office (WETO), focused on the enumeration and analysis of six key devices important to wind technologies. The devices analyzed included Beckhoff Bus Terminal Controllers (BK1120 and BC9000), a Beckhoff Economy Built-in Panel PC (CP6231), an N-Tron Managed Industrial Ethernet Switch (711FX3), a Bachmann M1 Gateway, and a Bachmann Smart Power Plant Controller. Device selection was driven by availability and budget constraints, with several components sourced from existing wind farms and others procured through a co-agreement with another WETO-funded project. The project's primary objective was to create a hardware bill of materials (HBOM) for each device, identifying and documenting all components to assess potential security and supply chain risks. A detailed analysis revealed over 750 unique components across the six devices, with 80% successfully identified and accompanied by datasheets. Notably, Texas Instruments emerged as the leading supplier, providing over 16% of the components, followed by ON Semiconductor at 11.3%, Analog Devices at 5.3%, and Renesas Electronics Corp at 4.1%. Other notable vendors included Toshiba Corporation, iC-Haus Corporation, Atmel, Vishay, and STMicroelectronics. The enumeration process involved thorough documentation of each component, including its designation, quantity, identifiers, pin package, description, vendor, model, and country of origin. This process provided valuable insights into the complexity and diversity of the electronic systems within these wind devices. It also highlighted the distinct separation of components between vendors, suggesting a trend of vendor-specific component usage. Key findings from the project emphasized the importance of broadening the scope of vendor analysis in future research to gain a comprehensive understanding of component distribution and commonality. The identification of vendor-specific component usage patterns offers new avenues for research and underscores the significance of continued investigation in this field. Overall, this project provides critical insights into the component composition of wind devices, aiding in the development of improved supply chain management and component sourcing strategies. The results contribute valuable knowledge to the wind technology sector, laying the groundwork for enhanced security and resilience in wind energy systems.

17 - WIND ENERGY↗
DOE OSTI2023

Sink or Swim: A Tutorial on the Control of Floating Wind Turbines: Preprint

Within the rapidly growing wind energy sector, floating offshore wind turbines are expected to be the fastest growing portion. This is largely driven by the immense offshore wind resources that are mostly over deep water, where fixed-bottom concepts become cost prohibitive. However, compared to fixed-bottom wind turbines, floating wind turbines are more dynamic and exhibit potential instabilities, which requires advanced control technologies to ensure a safe and efficient operation. Beyond their existing objectives of maximizing power production while minimizing structural loads, floating wind turbine controllers must also avoid large platform oscillations and accommodate wave disturbances. This paper provides an overview of the challenges and opportunities in the control of floating offshore wind energy systems.

control↗
DOE OSTI2019

Contributing to Long-term Wind Resource Characterization through Buoy-based Observations of Meteorological and Oceanographic Conditions

The U.S. Department of Energy (DOE) supports development of renewable power generation within the U.S. with increasing emphasis on offshore wind energy. This development is currently impeded by a lack of long-term wind observations above the ocean surface at wind turbine rotor plane heights. Pacific Northwest National Laboratory (PNNL) procured two AXYS WindSentinelTM buoys on behalf of DOE to support offshore wind resource assessment. The buoys are available for loan to qualified users according to agency priorities. The Lidar Buoy Loan Program represents an opportunity for organizations with an interest in offshore wind energy to work together with DOE to provide valuable meteorological and oceanographic data to the offshore community that is needed for offshore wind resource characterization. The buoys have already begun to fill observational gaps in offshore winds during long-term deployments off the coasts of Virginia and New Jersey and are scheduled for deployment off the coast of California in 2020. This paper provides an overview of the Lidar Buoy Loan Program, along with highlights of previous deployments, a summary of the upgrades to the lidar systems, and a discussion of the planned activities related to the buoys and the Lidar Buoy Loan Program.

floating lidar, offshore wind energy, wind resourc↗
DOE OSTI2014

Wind Turbine Gearbox Condition Monitoring Vibration Analysis Benchmarking Datasets

Wind turbine condition monitoring (CM) can potentially help the wind industry reduce turbine downtime and operation and maintenance (O&M) cost. NREL CM research has investigated various condition-monitoring techniques such as acoustic emission (AE specifically stress wave), vibration, electrical signature, lubricant and debris monitoring based on the Gearbox Reliability Collaborative dynamometer and field tests, and other test turbines and resources accessible by NREL. During the past several years, NREL CM research has shown that there are very few validation and verification efforts on commercial wind turbine CM systems. One of the reasons might be limited benchmarking datasets accessible by stakeholders. To fill this gap, NREL executed a data collection effort. The targeted users of these datasets include those investigating vibration-based wind turbine CM research, evaluating commercially available vibration-based CM systems, or testing prototyped vibration-based CM systems. NREL collected data from a healthy and a damaged gearbox of the same design tested by the GRC. Vibration data were collected by accelerometers along with high-speed shaft RPM signals during the dynamometer testing. The healthy gearbox was only tested in the dynamometer. The damaged gearbox was first tested in the dynamometer and later sent to a wind farm close to NREL for field testing. In the field test, it experienced two loss-of-oil events that damaged its internal bearings and gear elements. The gearbox was brought back to NREL and it was retested in the dynamometer with CM systems deployed under controlled loading conditions that would not cause catastrophic failure of the gearbox. The objective of releasing these datasets to the public along with information about the real damage that occurred to the damaged gearbox is to provide the wind industry with some benchmarking datasets. These datasets will benefit research, development, validation, verification, and advancement of vibration-based wind condition-monitoring techniques. By accessing this data you acknowledge the terms outlined in the "License Information" document. Please contract Shawn Sheng (NREL) if you have any questions on the data or would like to collaborate on publications based on the datasets.

Array↗
DOE OSTI2020

Validation of Reanalysis-Based Offshore Wind Resource Characterization Using Lidar Buoy Observations

The offshore wind industry in the U.S. is gaining strong momentum to achieve sustainable energy goals, and the need for observations to provide resource characterization and model validation is greater than ever. Pacific Northwest National Laboratory (PNNL) operates two lidar buoys for the U.S. Department of Energy (DOE) in order to collect hub height wind data and associated meteorological and oceanographic information near the surface in areas of interest for offshore wind development. This work evaluates the performance of commonly used reanalysis products and spatial approximation techniques using lidar buoy observations off the coast of New Jersey and Virginia, USA. Reanalysis products are essential tools in order to set performance expectations and quantify the wind resource variability at a given site. Long-term accurate observations at typical wind turbine hub-heights have been lacking at offshore locations. Using wind speed observations from both lidar buoy deployments, biases and degrees of correspondence for the Modern Era Retrospective Analysis for Research and Applications-2 (MERRA-2), the North American Regional Reanalysis (NARR), and the analysis system of the Rapid Refresh (RAP) are examined both at hub height and near surface. Results provide insights on the performance and uncertainty of using reanalysis products for long-term wind resource characterization.

lidar buoy, model validation, offshore wind energy↗
DOE OSTI2017

Optimization of wind plant layouts using an adjoint approach

Abstract. Using adjoint optimization and three-dimensional steady-state Reynolds-averaged Navier–Stokes (RANS) simulations, we present a new gradient-based approach for optimally siting wind turbines within utility-scale wind plants. By solving the adjoint equations of the flow model, the gradients needed for optimization are found at a cost that is independent of the number of control variables, thereby permitting optimization of large wind plants with many turbine locations. Moreover, compared to the common approach of superimposing prescribed wake deficits onto linearized flow models, the computational efficiency of the adjoint approach allows the use of higher-fidelity RANS flow models which can capture nonlinear turbulent flow physics within a wind plant. The steady-state RANS flow model is implemented in the Python finite-element package FEniCS and the derivation and solution of the discrete adjoint equations are automated within the dolfin-adjoint framework. Gradient-based optimization of wind turbine locations is demonstrated for idealized test cases that reveal new optimization heuristics such as rotational symmetry, local speedups, and nonlinear wake curvature effects. Layout optimization is also demonstrated on more complex wind rose shapes, including a full annual energy production (AEP) layout optimization over 36 inflow directions and 5 wind speed bins.

17 WIND ENERGY↗
DOE OSTI2020

Lidar measurements of yawed-wind-turbine wakes: characterization and validation of analytical models

Abstract. Wake measurements of a scanning Doppler lidar mounted on the nacelle of a full-scale wind turbine during a wake-steering experiment were used for the characterization of the wake flow, the evaluation of the wake-steering set-up, and the validation of analytical wake models. Inflow-scanning Doppler lidars, a meteorological mast, and the supervisory control and data acquisition (SCADA) system of the wind turbine complemented the set-up. Results from the wake-scanning Doppler lidar showed an increase in the wake deflection with the yaw angle and that the wake deflection was not in all cases beneficial for the power output of a downstream turbine due to a bias of the inflow wind direction perceived by the yawed wind turbine and the wake-steering design implemented. Both observations could be reproduced with an analytical model that was initialized with the inflow measurements. Error propagation from the inflow measurements that were used as model input and the power coefficient of a waked wind turbine contributed significantly to the model uncertainty. Lastly, the span-wise cross section of the wake was strongly affected by wind veer, masking the effects of the yawed wind turbine on the wake cross sections.

17 WIND ENERGY↗
DOE OSTI2021

Objective and algorithm considerations when optimizing the number and placement of turbines in a wind power plant

Abstract. Optimizing turbine layout is a challenging problem that has been extensively researched in the literature. However, optimizing the number of turbines within a given boundary has not been studied as extensively and is a difficult problem because it introduces discrete design variables and a discontinuous design space. An essential step in performing wind power plant layout optimization is to define the objective function, or value, that is used to express what is valuable to a wind power plant developer, such as annual energy production, cost of energy, or profit. In this paper, we demonstrate the importance of selecting the appropriate objective function when optimizing a wind power plant in a land-constrained site. We optimized several different wind power plants with different wind resources and boundary sizes. Results show that the optimal number of turbines varies drastically depending on the objective function. For a simple, one-dimensional, land-based scenario, we found that a wind power plant optimized for minimal cost of energy produced just 72 % of the profit compared to the wind power plant optimized for maximum profit, which corresponded to a loss of about USD 2 million each year. This paper also compares the performance of several different optimization algorithms, including a novel repeated-sweep algorithm that we developed. We found that the performance of each algorithm depended on the number of design variables in the problem as well as the objective function.

17 WIND ENERGY↗
DOE OSTI2023

Enabling control co-design of the next generation of wind power plants

Abstract. Layout design and wake steering through wind plant control are important and complex components in the design and operation of modern wind power plants. They are currently optimized separately, but with more and more computational and experimental studies demonstrating the gains possible through wake steering, there is a growing need from industry and regulating bodies to combine the layout and control optimization in a co-design process. However, combining these two optimization problems is currently infeasible due to the excessive number of design variables and large solution space. In this article, we present a method that enables the coupled optimization of wind power plant layout and wake steering with no additional computational expense than a traditional layout optimization. We developed a geometric relationship between wind turbines to find an approximate optimal yaw angle, bypassing the need for either a nested or coupled wind plant control optimization. It also provides a significant and immediate improvement to wind power plant design by enabling the co-design of turbine layout and yaw control for wake steering. A small co-designed plant shown in this article produces 0.8 % more energy than its sequentially designed counterpart. This additional energy production comes with no additional infrastructure, turbine hardware, or control software; it is simply the outcome of optimizing the turbine layout and yaw control together, resulting in millions of dollars of additional revenue for the wind power plants of the future.

17 WIND ENERGY↗
NASA NTRS2023

Slow Solar Wind Connection Science during Solar Orbiter's First Close Perihelion Passage

The Slow Solar Wind Connection Solar Orbiter Observing Plan (Slow Wind SOOP) was developed to utilize the extensive suite of remote-sensing and in situ instruments on board the ESA/NASA Solar Orbiter mission to answer significant outstanding questions regarding the origin and formation of the slow solar wind. The Slow Wind SOOP was designed to link remote-sensing and in situ measurements of slow wind originating at open–closed magnetic field boundaries. The SOOP ran just prior to Solar Orbiter's first close perihelion passage during two remote-sensing windows (RSW1 and RSW2) between 2022 March 3–6 and 2022 March 17–22, while Solar Orbiter was at respective heliocentric distances of 0.55–0.51 and 0.38–0.34 au from the Sun. Coordinated observation campaigns were also conducted by Hinode and IRIS. The magnetic connectivity tool was used, along with low-latency in situ data and full-disk remote-sensing observations, to guide the target pointing of Solar Orbiter. Solar Orbiter targeted an active region complex during RSW1, the boundary of a coronal hole, and the periphery of a decayed active region during RSW2. Postobservation analysis using the magnetic connectivity tool, along with in situ measurements from MAG and SWA/PAS, showed that slow solar wind originating from two out of three of the target regions arrived at the spacecraft with velocities between ∼210 and 600 km s −1 . The Slow Wind SOOP, despite presenting many challenges, was very successful, providing a blueprint for planning future observation campaigns that rely on the magnetic connectivity of Solar Orbiter.

Slow solar wind↗
DOE OSTI2013

Wind Plant Cost of Energy: Past and Future (Presentation)

This presentation examines trends in wind plant cost of energy over the last several decades and discusses methods and examples of projections for future cost trends. First, the presentation explores cost trends for wind energy from the 1980s, where there had been an overall downward trend in wind plant energy costs. Underlying factors that influenced these trends, including turbine technology innovation for lower wind speed sites, are explored. Next, the presentation looks at projections for the future development of wind energy costs and discusses a variety of methods for establishing these projections including the use of learning curves, qualitative assessment using expert elicitation, and engineering-based analysis. A comparison of the methods is provided to explore their relative merits. Finally, a brief introduction is provided for the U.S. Department of Energy program-wide shift towards an integrative use of qualitative and quantitative methods for assessing the potential impacts of wind plant technology innovations on reducing the wind plant cost of energy.

17 WIND ENERGY↗
DOE OSTI2013

Assessment of Vessel Requirements for the U.S. Offshore Wind Sector

Douglas-Westwood was commissioned by the US Department of Energy (DOE) to investigate the anticipated demand for various vessel types associated with offshore wind development in the United States through 2030, as well as to assess related market barriers and mitigating policy options. This Report contains our findings. It is intended to provide guidance on all vessel-related aspects of offshore wind installation to a wide range of audiences, including federal and state-level government agencies, research institutions, prospective project developers, installation companies, vessel operators and shipbuilders, as well as the general public. To develop scenarios of potential vessel demand, Douglas-Westwood, in conjunction with Navigant Consulting and the National Renewable Energy Laboratory (NREL) and in cooperation with the Department of Energy (DOE), established detailed rollout scenarios for four US offshore wind regions, respectively Atlantic Coast, Great Lakes, Gulf Coast and Pacific Coast. Rollout scenarios were based on DOE-provided offshore wind capacity targets by region in three sets of variations, reflecting several possible rates of wind farm installation and differing levels of technology advancement prior to 2030. The highest growth scenario reflects the potential for 54 GW of capacity installed by 2030 in alignment with the National Offshore Wind Strategy issued in 2011 by DOE and the Department of the Interior. The resulting scenarios used throughout this Report are referred to as the High Growth – High Technology (HH) scenario, the Medium Growth – High Technology (MH) scenario, and the Low Growth – Low Technology (LL) scenario. These scenarios inform our views of the vessel requirements in the US offshore wind sector through 2030. Each scenario falls into two phases: Phase 1 covers 2013 to 2020, while Phase 2 covers 2021 to 2030. The rollout scenarios are discussed in detail in Chapter 7 of this Report.

17 WIND ENERGY↗
DOE OSTI2014

Operational Impacts of Large Deployments of Offshore Wind (Poster)

The potential operational impact of deploying 54 GW of offshore wind in the United States was examined. The capacity was not evenly distributed; instead, it was concentrated in regions with better wind quality and close to load centers (Table 1). A statistical analysis of offshore wind power time series was used to assess the effect on the power system. The behavior of offshore wind resembled that of onshore wind, despite the former presenting higher capacity factors, more consistent power output across seasons, and higher variability levels. Thus, methods developed to manage onshore wind variability can be extended and applied to offshore wind.

17 WIND ENERGY↗
DOE OSTI2016

A Spatial-Economic Cost-Reduction Pathway Analysis for U.S. Offshore Wind Energy Development from 2015-2030

The potential for cost reduction and economic viability for offshore wind varies considerably within the United States. This analysis models the cost impact of a range of offshore wind locational cost variables across more than 7,000 potential coastal sites in the United States' offshore wind resource area. It also assesses the impact of over 50 technology innovations on potential future costs between 2015 and 2027 (Commercial Operation Date) for both fixed-bottom and floating wind systems. Comparing these costs to an initial assessment of local avoided generating costs, this analysis provides a framework for estimating the economic potential for offshore wind. Analyzing economic potential within this framework can help establish a refined understanding across industries of the technology and site-specific risks and opportunities associated with future offshore wind development. The findings from the original report indicate that under the modeled scenario, offshore wind can be expected to achieve significant cost reductions and may approach economic viability in some parts of the United States within the next 15 years.

17 WIND ENERGY↗
DOE OSTI2016

Comparison of Standard Wind Turbine Models with Vendor Models for Power System Stability Analysis: Preprint

The International Electrotechnical Commission Standard 61400-27-1 was published in February 2015. This standard deals with the development of generic terms and parameters to specify the electrical characteristics of wind turbines. Generic models of very complex technological systems, such as wind turbines, are thus defined based on the four common configurations available in the market. Due to its recent publication, the comparison of the response of generic models with specific vendor models plays a key role in ensuring the widespread use of this standard. This paper compares the response of a specific Gamesa dynamic wind turbine model to the corresponding generic IEC Type III wind turbine model response when the wind turbine is subjected to a three-phase voltage dip. This Type III model represents the doubly-fed induction generator wind turbine, which is not only one of the most commonly sold and installed technologies in the current market but also a complex variable-speed operation implementation. In fact, active and reactive power transients are observed due to the voltage reduction. Special attention is given to the reactive power injection provided by the wind turbine models because it is a requirement of current grid codes. Further, the boundaries of the generic models associated with transient events that cannot be represented exactly are included in the paper.

17 WIND ENERGY↗
DOE OSTI2017

Controllable Grid Interface for Testing Ancillary Service Controls and Fault Performance of Utility-Scale Wind Power Generation: Preprint

The rapid expansion of wind power has led many transmission system operators to demand modern wind power plants to comply with strict interconnection requirements. Such requirements involve various aspects of wind power plant operation, including fault ride-through and power quality performance as well as the provision of ancillary services to enhance grid reliability. During recent years, the National Renewable Energy Laboratory (NREL) of the U.S. Department of Energy has developed a new, groundbreaking testing apparatus and methodology to test and demonstrate many existing and future advanced controls for wind generation (and other renewable generation technologies) on the multimegawatt scale and medium-voltage levels. This paper describes the capabilities and control features of NREL's 7-MVA power electronic grid simulator (also called a controllable grid interface, or CGI) that enables testing many active and reactive power control features of modern wind turbine generators -- including inertial response, primary and secondary frequency responses, and voltage regulation -- under a controlled, medium-voltage grid environment. In particular, this paper focuses on the specifics of testing the balanced and unbalanced fault ride-through characteristics of wind turbine generators under simulated strong and weak medium-voltage grid conditions. In addition, this paper provides insights on the power hardware-in-the-loop feature implemented in the CGI to emulate (in real time) the conditions that might exist in various types of electric power systems under normal operations and/or contingency scenarios. Using actual test examples and simulation results, this paper describes the value of CGI as an ultimate modeling validation tool for all types of 'grid-friendly' controls by wind generation.

17 WIND ENERGY↗