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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Long-Term Planning for the Tamil Nadu Power System

Increased deployment of wind and solar raises new questions for power system planners regarding the future mix of generation technologies, optimal siting of generation capacity, trade-offs between generation and transmission investments, and system flexibility needs. The National Renewable Energy Laboratory (NREL) is working with Tamil Nadu Generation and Distribution Company (TANGEDCO) to answer the following questions: How much—and what type—of generation and transmission investments are needed to serve future demand at least cost? How can variable resources such as wind and solar be considered in the planning process? What are the key drivers (e.g., policies, technology costs, fuel constraints) for investments?

Children's Investment Fund Foundation↗
DOE OSTI2021

The curled wake model: a three-dimensional and extremely fast steady-state wake solver for wind plant flows

Abstract. Wind turbine wake models typically require approximations, such as wake superposition and deflection models, to accurately describe wake physics. However, capturing the phenomena of interest, such as the curled wake and interaction of multiple wakes, in wind power plant flows comes with an increased computational cost. To address this, we propose a new hybrid method that uses analytical solutions with an approximate form of the Reynolds-averaged Navier–Stokes equations to solve the time-averaged flow over a wind plant. We compare results from the solver to supervisory control and data acquisition data from the Lillgrund wind plant obtaining wake model predictions which are generally within 1 standard deviation of the mean power data. We perform simulations of flow over the Columbia River Gorge to demonstrate the capabilities of the model in complex terrain. We also apply the solver to a case with wake steering, which agreed well with large-eddy simulations. This new solver reduces the time – and therefore the related cost – it takes to simulate a steady-state wind plant flow (on the order of seconds using one core). Because the model is computationally efficient, it can also be used for different applications including wake steering for wind power plants and layout optimization.

17 WIND ENERGY↗
NASA NTRS2002

Prediction of Aerodynamic Coefficient using Genetic Algorithm Optimized Neural Network for Sparse Data

Wind tunnels use scale models to characterize aerodynamic coefficients, Wind tunnel testing can be slow and costly due to high personnel overhead and intensive power utilization. Although manual curve fitting can be done, it is highly efficient to use a neural network to define the complex relationship between variables. Numerical simulation of complex vehicles on the wide range of conditions required for flight simulation requires static and dynamic data. Static data at low Mach numbers and angles of attack may be obtained with simpler Euler codes. Static data of stalled vehicles where zones of flow separation are usually present at higher angles of attack require Navier-Stokes simulations which are costly due to the large processing time required to attain convergence. Preliminary dynamic data may be obtained with simpler methods based on correlations and vortex methods; however, accurate prediction of the dynamic coefficients requires complex and costly numerical simulations. A reliable and fast method of predicting complex aerodynamic coefficients for flight simulation I'S presented using a neural network. The training data for the neural network are derived from numerical simulations and wind-tunnel experiments. The aerodynamic coefficients are modeled as functions of the flow characteristics and the control surfaces of the vehicle. The basic coefficients of lift, drag and pitching moment are expressed as functions of angles of attack and Mach number. The modeled and training aerodynamic coefficients show good agreement. This method shows excellent potential for rapid development of aerodynamic models for flight simulation. Genetic Algorithms (GA) are used to optimize a previously built Artificial Neural Network (ANN) that reliably predicts aerodynamic coefficients. Results indicate that the GA provided an efficient method of optimizing the ANN model to predict aerodynamic coefficients. The reliability of the ANN using the GA includes prediction of aerodynamic coefficients to an accuracy of 110% . In our problem, we would like to get an optimized neural network architecture and minimum data set. This has been accomplished within 500 training cycles of a neural network. After removing training pairs (outliers), the GA has produced much better results. The neural network constructed is a feed forward neural network with a back propagation learning mechanism. The main goal has been to free the network design process from constraints of human biases, and to discover better forms of neural network architectures. The automation of the network architecture search by genetic algorithms seems to have been the best way to achieve this goal.

Rajkumar, T.↗
DOE OSTI2022

Hybrid RANS-LES of the Atmospheric Boundary Layer for Wind Farm Simulations: Preprint

Wind farm simulations often do not accurately represent wake-atmospheric boundary layer (ABL) interactions, blade boundary layer (BL) dynamics, and turbine-turbine interactions. In this work, we use Active Model Split (AMS), a new hybrid Reynolds-Averaged Navier Stokes (RANS)-large eddy simulation (LES) model, which is well suited to capture these effects because the model can (i) accurately simulate the ABL with the Coriolis effect, (ii) is accurate in adverse pressure gradients such as those near wind turbine blades, and (iii) has sufficiently low computational cost to simulate multiple turbines while resolving the blade BL. For simplicity and consistency we develop AMS to be used throughout the domain rather than in a zonal method. We implement our work in the massively parallel flow solver, Nalu-Wind, so that our model can access the compute resources needed for blade-resolved simulations of multiple wind turbines. To accomplish these aims, we modify the baseline AMS by changing the RANS contribution to SST k - omega with a length scale limiter, adding the Coriolis effect, and developing an appropriate wall treatment. We show that AMS of the ABL with the Coriolis effect matches LES reference results better than those obtained with RANS. We describe our plans to add buoyancy effects and wind turbines to our AMS simulations.

atmospheric boundary layer↗
DOE OSTI2022

Wind Energy Accomplishments & Midyear Performance Report: Fiscal Year 2022

2022 marks 43 years of innovation through the National Wind Technology Center at the U.S. Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) Flatirons Campus. Scientists, engineers, analysts, and researchers continue to build on NREL's legacy of innovation at this world-class hub for wind energy technology research and development. Today, the nation's goals to tackle climate change and revitalize the U.S. economy through increased investment in clean energy - particularly in land-based, offshore, and distributed wind energy - are more ambitious than ever. From advancing wind energy science, technologies, and materials to demonstrating thought leadership on a worldwide scale, NREL's Wind Energy Program played a powerful role in maximizing the impact of wind energy in the first half of Fiscal Year (FY) 2022. NREL's technical expertise, research capabilities, and industry understanding continued to support DOE's efforts to champion clean energy, addressing market and deployment barriers and driving down costs with more efficient, reliable, and predictable wind energy systems. This report provides an overview of the achievements NREL made on behalf of DOE's Wind Energy Technologies Office (WETO) and other partners between October 1, 2021, and March 31, 2022.

accomplishments↗
DOE OSTI2022

Data-driven wind turbine wake modeling via probabilistic machine learning

Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions and the interaction between wakes. Physics-based models that capture the wake flow field with high-fidelity are computationally very expensive to perform layout optimization of wind farms, and, thus, data-driven reduced-order models can represent an efficient alternative for simulating wind farms. In this work, we use real-world light detection and ranging (LiDAR) measurements of wind-turbine wakes to construct predictive surrogate models using machine learning. Specifically, we first demonstrate the use of deep autoencoders to find a low-dimensional latent space that gives a computationally tractable approximation of the wake LiDAR measurements. Then, we learn the mapping between the parameter space and the (latent space) wake flow fields using a deep neural network. Additionally, we also demonstrate the use of a probabilistic machine learning technique, namely, Gaussian process modeling, to learn the parameter-space-latent-space mapping in addition to the epistemic and aleatoric uncertainty in the data. Finally, to cope with training large datasets, we demonstrate the use of variational Gaussian process models that provide a tractable alternative to the conventional Gaussian process models for large datasets. Furthermore, we introduce the use of active learning to adaptively build and improve a conventional Gaussian process model predictive capability. Overall, we find that our approach provides accurate approximations of the wind-turbine wake flow field that can be queried at an orders-of-magnitude cheaper cost than those generated with high-fidelity physics-based simulations.

Deep neural networks↗
NASA NTRS1985

Development of large, horizontal-axis wind turbines

A program to develop large, horizontal-axis wind turbines is discussed. The program is directed toward developing the technology for safe, reliable, environmentally acceptable large wind turbines that can generate a significant amount of electricity at costs competitive with those of conventional electricity-generating systems. In addition, these large wind turbines must be fully compatible with electric utility operations and interface requirements. Several ongoing projects in large-wind-turbine development are directed toward meeting the technology requirements for utility applications. The machines based on first-generation technology (Mod-OA and Mod-1) successfully completed their planned periods of experimental operation in June, 1982. The second-generation machines (Mod-2) are in operation at selected utility sites. A third-generation machine (Mod-5) is under contract. Erection and initial operation of the Mod-5 in Hawaii should take place in 1986. Each successive generation of technology increased reliability and energy capture while reducing the cost of electricity. These advances are being made by gaining a better understanding of the system-design drivers, improving the analytical design tools, verifying design methods with operating field data, and incorporating new technology and innovative designs. Information is given on the results from the first- and second-generation machines (Mod-OA, - 1, and -2), the status of the Department of Interior, and the status of the third-generation wind turbine (Mod-5).

Baldwin, D. H.↗
DOE OSTI2022

Cumulative Effects Analysis for Wind Energy Development: Current Practices, Challenges, and Opportunities (IEA Wind White Paper)

The increasing global deployment of wind energy has given rise to concerns about potential adverse effects on certain wildlife species and habitats. The United States and European nations use environmental impact assessments (EIAs) to evaluate the environmental effects of wind energy and inform wind energy planning, siting, and operational processes. A key component of the EIA is the cumulative effects analysis/assessment (CEA). CEAs consider the effects of a proposed development in the context of past, present, and future developments, as well as other (non-wind) activities. However, practitioners worldwide have struggled to implement cost-effective and consistent processes for CEAs. Further, there is no widely accepted scientific methodology to assess cumulative effects. As wind energy deployment continues to expand, developing a consistent and scientifically based approach to CEAs may provide a more comparable across assessments and cost-effective means of reducing risk during siting, operations, and decommissioning/repowering, while minimizing regulatory hurdles. This technical report evaluates the current state of CEA practices, covering both land-based and offshore wind energy development. It focuses on impacts from the preconstruction, construction, and operational phases of the wind farm, which are the phases where most research currently exists. Emerging research addresses impacts from the perspective of life cycle assessments (LCAs), including the impacts of manufacturing and preconstruction (May et al. 2020). The technical report also summarizes CEA processes and guidelines, analysis approaches, and current challenges. Finally, it highlights opportunities for further research and coordination, and includes a geographically organized CEA information resource bank.

17 WIND ENERGY↗
DOE OSTI2022

Techno‐economic, environmental, and social measurement of clean energy technology supply chains

Abstract In addition to the criteria of reliability and cost, clean energy technologies, such as wind, solar, and batteries, need to strive to a higher standard of environmental and societal benefit along their entire supply chain. This means additional performance metrics for these technologies should be considered, such as embodied energy, embodied carbon, recycled content and recyclability, environmental impact of material sourcing, impact on land and ecosystems, materials recovery at end of life, and production through quality nonexploitive jobs with community benefit. Many commercial and emerging energy technologies have not yet been explicitly evaluated based on these environmental and social performance metrics, which presents multiple opportunities for researchers and analysts. In this paper, we review the importance and current limitations of techno‐economic and life‐cycle assessment models for research design and manufacturing decisions. We explore emerging manufacturing modeling options that could improve environmental and social performance and how they could be used to help guide research. Even with the deployment of low‐carbon energy‐generation technologies, the future of a successful clean energy transition requires collaboration between researchers, advanced manufacturers, independent standards and tracking organizations, local communities, and national governments, to ensure the financial, environmental, and social sustainability of the entire supply and manufacturing process of energy technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Policy implications of net-zero emissions: A multi-model analysis of United States emissions and energy system impacts

Many countries, subnational jurisdictions, and companies are setting net-zero emissions goals; however, questions remain about strategies to reach these targets, policy measures, technology gaps, and economic impacts. Here, we investigate the potential policy implications of reaching economy-wide net-zero CO 2 emissions across the United States by 2050 using results from a multi-model comparison with 14 energy-economic models. Model results suggest that achieving net-zero CO 2 targets depends on policies that accelerate deployment of zero- and low-emitting technologies that have seen rapid cost reductions in recent years (including wind, solar, battery storage, and electric vehicles) as well as relatively nascent options (including carbon capture and storage, advanced biofuels, low-carbon hydrogen, advanced nuclear, and long-duration energy storage). While net-zero policies are likely to lower fossil fuel consumption, including considerable coal and petroleum reductions, achieving net-zero emissions does not necessarily mean phasing out all fossil fuels. Model results indicate that the Inflation Reduction Act’s energy and climate provisions amplify near-term decarbonization but that net-zero policies have larger impacts on long-run outcomes. Stringent climate policy can have large fiscal impacts on tax revenue and government spending—revenues from carbon pricing and subsidies for carbon removal range from 0.1 % to 3.7 % of GDP in 2050 across models. Each dollar per metric ton carbon price leads to a 0.06 % to 0.31 % reduction in economy-wide CO 2 emissions relative to a reference scenario with current policies. Spending on energy across the economy decreases relative to today for many models under reference and net-zero policies, especially as a share of GDP, due primarily to end-use electrification and energy efficiency.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1973

The Oregon State University wind studies

The economic feasibility of commercial use of wind generated power in selected areas of Oregon is assessed. A number of machines for generating power have been examined. These include the Savonius rotor, translators, conventional wind turbines, the circulation controlled rotor and the vertical axis winged turbine. Of these machines, the conventional wind turbine and the vertical axis winged turbine show the greatest promise on the basis of the power developed per unit of rotor blade area. Attention has been focused on the structural and fatigue analysis of rotors since the economics of rotary winged, wind generated power depends upon low cost, long lifetime rotors. Analysis of energy storage systems and tower design has also been undertaken. An economic means of energy storage has not been found to date. Tower design studies have produced cost estimates that are in general agreement with the cost of the updated Putnam 110-foot tower.

Wilson, R. E.↗
NASA NTRS1977

Low-profile heliostat design for solar central receiver systems

Heliostat designs intended to reduce costs and the effect of adverse wind loads on the devices were developed. Included was the low-profile heliostat consisting of a stiff frame with sectional focusing reflectors coupled together to turn as a unit. The entire frame is arranged to turn angularly about a center point. The ability of the heliostat to rotate about both the vertical and horizontal axes permits a central computer control system to continuously aim the sun's reflection onto a selected target. An engineering model of the basic device was built and is being tested. Control and mirror parameters, such as roughness and need for fine aiming, are being studied. The fabrication of these prototypes is in process. The model was also designed to test mirror focusing techniques, heliostat geometry, mechanical functioning, and tracking control. The model can be easily relocated to test mirror imaging on a tower from various directions. In addition to steering and aiming studies, the tests include the effects of temperature changes, wind gusting and weathering. The results of economic studies on this heliostat are also presented.

Fourakis, E.↗
NASA NTRS1979

Large horizontal axis wind turbine development

The paper presents an overview of the NASA activities in large horizontal axis wind turbine development. First generation technology large wind turbines (Mod-0A, Mod-1) have been designed and are in operation at selected utility sites. Second generation machines (Mod-2) are scheduled to begin operations on a utility site in 1980. These machines are estimated to generate electricity at less than 4 cents/kWh when manufactured in modest production rates. Meanwhile, plans are being made to continue developing wind turbines which can meet the cost goals of 2 to 3 cents/kWh.

Robbins, W. H.↗
NASA NTRS1988

Large-scale wind turbine structures

The purpose of this presentation is to show how structural technology was applied in the design of modern wind turbines, which were recently brought to an advanced stage of development as sources of renewable power. Wind turbine structures present many difficult problems because they are relatively slender and flexible; subject to vibration and aeroelastic instabilities; acted upon by loads which are often nondeterministic; operated continuously with little maintenance in all weather; and dominated by life-cycle cost considerations. Progress in horizontal-axis wind turbines (HAWT) development was paced by progress in the understanding of structural loads, modeling of structural dynamic response, and designing of innovative structural response. During the past 15 years a series of large HAWTs was developed. This has culminated in the recent completion of the world's largest operating wind turbine, the 3.2 MW Mod-5B power plane installed on the island of Oahu, Hawaii. Some of the applications of structures technology to wind turbine will be illustrated by referring to the Mod-5B design. First, a video overview will be presented to provide familiarization with the Mod-5B project and the important components of the wind turbine system. Next, the structural requirements for large-scale wind turbines will be discussed, emphasizing the difficult fatigue-life requirements. Finally, the procedures used to design the structure will be presented, including the use of the fracture mechanics approach for determining allowable fatigue stresses.

Spera, David A.↗
NASA NTRS2011

Base Flow Model Validation

A method was developed of obtaining propulsive base flow data in both hot and cold jet environments, at Mach numbers and altitude of relevance to NASA launcher designs. The base flow data was used to perform computational fluid dynamics (CFD) turbulence model assessments of base flow predictive capabilities in order to provide increased confidence in base thermal and pressure load predictions obtained from computational modeling efforts. Predictive CFD analyses were used in the design of the experiments, available propulsive models were used to reduce program costs and increase success, and a wind tunnel facility was used. The data obtained allowed assessment of CFD/turbulence models in a complex flow environment, working within a building-block procedure to validation, where cold, non-reacting test data was first used for validation, followed by more complex reacting base flow validation.

Sinha, Neeraj↗
NASA NTRS2012

An Update on the Mechanical and EM Performance of the Composite Dish Verification Antenna (DVA-1) for the SKA

This paper will give an overview of the unique mechanical and optical design of the DVA-1 telescope. The rim supported carbon fibre reflector surfaces are designed to be both low cost and have high performance under wind, gravity, and thermal loads. The shaped offset Gregorian optics offer low and stable side lobes along with a large area at the secondary focus for multiple feeds with no aperture blockage. Telescope performance under ideal conditions as well as performance under gravity, wind, and thermal loads will be compared directly using calculated radiation patterns for each of these operating conditions.

composite dish↗
DOE OSTI2023

2022 Prototype Installation and Testing Awardee: Windward Engineering

The 60-kilowatt (kW) Zephyr 21/60 wind turbine from Windward Engineering uses a low-cost, full-span independent pitch system that can act as a redundant aerodynamic braking system. This design uses a relatively simple pneumatic rotary vane actuator, allowing each of the turbine's three blades to be pitched independently and any single blade to activate emergency braking for the entire turbine. A previous Competitiveness Improvement Project (CIP) funding award enabled Windward Engineering to design and develop the Zephyr 21/60 wind turbine's aeroelastic model and pitch system. At the end of this current Competitiveness Improvement Project award, a fully validated aeroelastic model will make it possible to certify Windward Engineering's Zephyr 21/60 design and quantify the technology's levelized cost of energy in preparation for certification and entry into the distributed wind market. The Zephyr 21/60 will feature an attractive levelized cost of energy, improved reliability, and a full-span pitch system for safe and redundant protection against rotor overspeed (which occurs when the rotor turns beyond its design limit).

CIP↗
DOE OSTI2021

Progress on Optimizing Wind Farms and Rotor Designs Using Adjoints

Modern wind plants are increasingly tasked with multiple performance objectives. In addition to designing plants that maximize power output and minimize the levelized cost of energy (LCOE), the design and operation of wind plants is increasingly influenced by challenges regarding grid integration of variable generation renewables. This places a growing emphasis on making wind plants more controllable and predictable. WindSE is a Reynolds-averaged Navier-Stokes (RANS) model designed around analytical gradient and adjoint methods, with the ability to capture terrain-induced effects, as shown in Figure 1. The recent addition of an unsteady solver with an actuator line method (ALM) and ongoing work to enable massively parallel optimizations gives it a unique niche to explore coupled plant-level controls and design problems. This code is an open source python package built on the FEniCS framework that utilizes fast, parallel PETSc solvers to model fluid flow throughout wind-farm scale domains. Two recent studies performed using WindSE demonstrate the capability to optimize under a wide variety of flow conditions and objective functions. In the first, we present an optimization focused on modifying the layout of a wind farm with a fixed number of turbines for maximum total power output [1]. This study highlights the ability to quickly perform simulations using the steady Navier-Stokes solver combined with rotors represented as actuator disks while also stressing the importance of capturing terrain-induced effects. Gradient-based optimization using the RANS equations is viable due to the inclusion of efficiently computed adjoint derivatives. We interpret the physical results of the optimal layout and also discuss the computational cost of scaling to larger problems. In the second study, we present the capabilities of the unsteady Navier-Stokes solver, where rotor-blade profiles represented by actuator lines are optimized to enhance wake steering effects and overall power production [2]. We quantify the wind plant performance gains obtained from this type of simultaneous control co-design optimization as compared to optimizing the blade design and yaw independently. Figure 2 shows the differences between a baseline two-turbine system and an optimized system where we fine-tune the blade chord profile. Results and challenges from each study are quickly summarized and used to motivate the current development efforts within WindSE. Current and future work is focused on enabling higher-resolution studies with more degrees of freedom through parallelization of both the simulation and optimization algorithms. We present benchmarking results to show that WindSE performs well in both weak- and strong-scaling tests and further demonstrate that the optimizer obtains the same convergence rates in both shared- and distributed-memory environments. Using larger wind farms, we can study deep-array effects within an optimization context, allowing the use of objective functions that have been previously unstudied. As an example, we present ongoing work on a blockage metric which characterizes the loss of available kinetic energy due to wake effects from multiple upstream turbines.

adjoint optimization↗