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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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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Benchmarking of three DWM-based wake models at below-rated wind speeds

Wind turbine wake models are essential tools for predicting power losses and structural loads in wind farms. Among these, the dynamic wake meandering (DWM) model, included as a recommended approach in the International Electrotechnical Commission design standard, is a widely used engineering-fidelity method that balances accuracy and computational cost. This study compares the performance of three DWM-based wake model implementations (from the Technical University of Denmark, the National Renewable Energy Laboratory, and the Institute for Energy Technology) under below-rated wind speed conditions. Model predictions of wake flow, power output, and structural loads for a four-turbine row are evaluated across different ambient turbulence levels and wind-direction misalignments and compared against high-fidelity large-eddy simulation results. All three models captured the overall wake evolution and mean turbine performance with reasonable accuracy; their predicted time-averaged thrust and power were typically within 5 %–10 % of the large-eddy simulation benchmark. However, notable differences emerged in wake structure and unsteady load predictions, with discrepancies increasing for turbines further downstream. These differences highlight the importance of modelling choices such as wake summation and turbulence treatment, which strongly influence power-deficit and fatigue-load predictions. Comparison with large-eddy simulations reveals each approach's strengths and weaknesses, indicating where improvements are needed. Overall, the findings point to specific refinements for DWM models to improve their fidelity, ultimately enabling more robust wake predictions for wind farm design and operation.

17 WIND ENERGY↗
DOE OSTI2021

MIRACL Co‐Simulation platform for control and operation of distributed wind in microgrid

Abstract This paper presents a co‐simulation platform (CSP) developed as a part of the “Microgrids, Infrastructure Resilience and Advanced Controls Launchpad (MIRACL)” project, hereafter called MIRACL‐CSP, to allow simulation‐based testing, demonstration, and evaluations of distributed wind under various grid operating conditions. MIRACL‐CSP provides modular interactions among the power distribution system, distributed wind, and utility decision‐making framework. A hierarchical engine for large‐scale infrastructure co‐simulations (HELICS) is used as the core engine of MIRACL‐CSP to establish time and information coordination among the MIRACL‐CSP modules. In this study, MIRACL‐CSP performance is demonstrated using the IEEE 123‐node test distribution grid modelled in GridLAB‐D and the utility decision support application modelled in Python. The functionality of MIRACL‐CSP is demonstrated through various grid operational scenarios in a microgrid and networked microgrid environment. A co‐simulation study is described that demonstrated MIRACL‐CSP capabilities for the microgrid operational scenario in the IEEE 123‐node test system. MIRACL‐CSP is a generic platform that facilitates distributed wind research for different test systems, applications, and valuation of distributed wind.

17 WIND ENERGY↗
DOE OSTI2021

Deep Reinforcement Learning for Automatic Generation Control of Wind Farms

This paper provides a model-free framework for real-time control of wind farms to accurately track a power reference signal. This problem requires tractable dynamical models for capturing the aerodynamic interaction between wind turbines and controllers that can make decisions in realtime given varying atmospheric conditions. In this paper, we propose a deep reinforcement learning framework to provide real-time yaw control of a wind farm. Modifications have been made to FLOw Redirection and Induction in Steady State (FLORIS), a modeling tool that incorporates transient wake behavior. The control problem is formulated to track a synthetic power reference signal based on historical atmospheric (wind speed and direction) information, price signals, and regulation deployment data from U.S. regional transmission operators. Results indicate that a wind farm, with this control paradigm, can achieve good tracking performance when tested with real atmospheric data.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2023

Turbulent entrainment in finite-length wind farms

In this article, we present an entrainment-based model for predicting the flow and power output of finite-length wind farms. The model is an extension of the three-layer approach of Luzzatto-Fegiz & Caulfield ( Phys. Rev. Fluids , vol. 3, 2018, 093802) for wind farms of infinite length, and assumes dependence of key flow quantities, such as the wind farm bulk velocity, on the streamwise distance from the farm entrance. To assist our analysis and validate the proposed model, we undertake a series of large-eddy simulations with different turbine spacing arrangements and layouts. Comparisons are also made with the top-down model with entrance effects of Meneveau ( J. Turbul. , vol. 13, 2012, N7) and data from the literature. The finite-length entrainment model is shown to be capable of capturing the power drop between contiguous rows of turbines as well as describing the advection and turbulent transport of kinetic energy in both the entrance and fully developed regions. The fully developed regime is approximated only deep in the wind farm, after approximately 15 rows of turbines. Our data suggest that for the cases considered in this study, the empirical coefficients that can be used to describe turbulent entrainment and transfers above the wind farm exhibit little dependence on the farm layout and may be considered constant for modelling purposes. However, the flow field within the wind farm layer can be strongly modulated by the turbine density (spacing) as well as the array layout, and to that extent it can be argued that they are both primary factors determining the wind farm power output.

17 WIND ENERGY↗
DOE OSTI2023

Fast Frequency Response using Reinforcement Learning-Controlled Wind Turbines

To fulfill the auxiliary grid services such as load regulation, spin and non-spin reserve, and frequency support during emergencies, power system operators often require certain wind farms to operate in de-loaded modes. By leveraging the fast response capability of wind farms, the reserved power in deloaded modes can significantly enhance the stability and reliability of power grids. This paper presents a novel methodology that incorporates wind turbines into reinforcement learning-based solutions for frequency response. The proposed approach employs the state-of-the-art reinforcement learning algorithm, surrogategradient- based evolution strategy (GSES), for continuous control of the wind farm output. Our methodology is tested on a modified IEEE-39 bus system, and simulation outcomes demonstrate that the proposed approach can reliably support the frequency of the power system and prevent unnecessary load shedding.

Gao, Wei↗
DOE OSTI2025

Flux-Switching Machine Based All-Electric Power Train for Future Aircraft

This research investigates a flux-switching motor with superconducting and cryogenically cooled windings, aimed at achieving exceptionally high power densities. In addition to the motor’s topology and superconducting windings, it was found that power density could be further enhanced by incorporating superconducting shields on the rotor at the interpole locations. The resulting publications and patented technology outline the design process. A motor power density of 64 kW/kg—including housing materials—was achieved for a 1 MW design, surpassing the performance of existing motors. An electronic drive was also developed, achieving a power density of 47 kW/kg. This includes the mechanical structure of the drivetrain, for which a detailed CAD model was created, resulting in an overall drivetrain power density of 27 kW/kg.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Using Machine Learning Tools to Predict Compressor Stall

Clean energy has become an increasingly important consideration in today’s power systems. As the push for clean energy continues, many coal-fired power plants are being decommissioned in favor of renewable power sources such as wind and solar. However, the intermittent nature of renewables means that dynamic load following traditional power systems is crucial to grid stability. With high flexibility and fast response at a wide range of operating conditions, gas turbine systems are poised to become the main load following component in the power grid. Yet, rapid changes in load can lead to fluid flow instabilities in gas turbine power systems. These instabilities often lead to compressor surge and stall, which are some of the most critical problems facing the safe and efficient operation of compressors in turbomachinery today. Although the topic of compressor surge and stall has been extensively researched, no methods for early prediction have been proven effective. This study explores the utilization of machine learning tools to predict compressor stall. The long short-term memory (LSTM) model, a form of recurrent neural network (RNN), was trained using real compressor stall datasets from a 100 kW recuperated gas turbine power system designed for hybrid configuration. Two variations of the LSTM model, classification and regression, were tested to determine optimal performance. The regression scheme was determined to be the most accurate approach, and a tool for predicting compressor stall was developed using this configuration. Overall, results show that the tool is capable of predicting stalls 5–20 ms before they occur. With a high-speed controller capable of 5 ms time-steps, mitigating action could be taken to prevent compressor stall before it occurs.

42 ENGINEERING↗
DOE OSTI2022

DigiFloat (Final Scientific/Technical Report)

This purpose of this report is to document the development of a floating offshore wind platform Digital Twin for the ARPA-E-funded DigiFloat project led by Principle Power, Inc. In Offshore Wind, Digital Twins are aimed at reducing farm-wide OPEX expenditures through reduced-basis inspection, assessing life extension methods, and obtaining better knowledge of the deployed assets for future product optimization. There is a high level of technical effectiveness and economic feasibility for the technology investigated, as it uses commercial off-the-shelf sensors, and a commercial software package. A substantial portion of the project goals are improvement of existing technology for use with floating wind turbines, and new application of an existing technology to a rapidly-growing sector. A few sensors applied to a small number of units bring benefit to the entire fleet of floating wind turbines in a given farm, and in other future wind farms. The benefits to the public of this project are further optimization and maturation of floating wind technology, to bring it to the U.S. market faster and cheaper.

17 WIND ENERGY↗
DOE OSTI2024

Impact Study of Thunderstorms on the US Power Grid Using Publicly Available Datasets

This work analyzes the impact of thunderstorms on the US power grid based on publicly available data. Since thunderstorms can bring lightning, heavy precipitation, and wind storms, analyzing their impact on the power system provides a combined correlation of lightning strikes, floods, and wind storms on power outages. This paper leverages publicly available thunderstorm datasets from the National Weather Service (NWS) and power outage datasets from Oak Ridge National Laboratory’s Environment for Analysis of Geo-Located Energy Information (EAGLE-I) to study the correlation between thunderstorms and power outages. This work is analyzing the patterns of thunderstorms from 2013-2022, which shows that the thunderstorms are not slowing down and will seem to continue their impact on human life in the future. This work also analyzes the monthly and yearly pattern of the impact of thunderstorms on power systems at the national, state, and county level.

Bhusal, Narayan↗
DOE OSTI2021

Implication of production tax credit on economic dispatch for electricity merchants with storage and wind farms

The production tax credit (PTC) promotes wind energy development, reduces power generation costs, and can affect merchants’ joint economic dispatch, particularly for electricity merchants with both energy storage and wind farms. Two common PTC policies are studied – in the first policy, a wind farm receives PTC by selling wind generation to the market and its storage can be used to store energy from the wind generation and energy purchased from the grid but the energy released from the storage cannot receive PTC; in the second policy, the energy released from the storage can also qualify for PTC but purchasing energy from the grid is not allowed. We then employ dynamic programming to study merchants' optimal decision-making while considering PTC and the physical characteristics of storage systems. We analytically show that the state of charge (SOC) range can be segmented into different regions by SOC reference points under two PTC policies. The merchant's optimal action can be conveniently and uniquely determined based on the region within which the current SOC falls. Moreover, this study illustrates that PTC could substantially alter the optimal scheduling policy structures by affecting reference points and their relationships. The results showed that the frequencies for charging and discharging storage decisions decreased with an increase in PTC subsidy. Last, we confirm that, although the first policy allows merchants to buy electricity from the market, the second policy can bring more profits when the PTC is large at the current PTC rates. Furthermore, the findings can provide multistage decision-making guidance to electricity merchants in the wholesale power market.

17 WIND ENERGY↗
DOE OSTI2024

Fish Friendly Water Rules' Impact On the Power Grid with High VRE Levels

Columbia River basin multipurpose reservoirs are operated for hydropower production and many other purposes considering the aquatic habitat of the river basin. Specifically, the river basin fish population is a vital element for the tribal community of the river basin. When conventional thermal power plants are retired, hydropower flexibility balances the wind and solar variability of the power grid and provides grid services to maximize the high renewable power absorption. On the other hand, over-reliance on hydropower could harm the river basin fish habitat. For example, water release pulses during peak electricity demand hours and shorter-term water release variation could harm the fish population. Power grid planners, environmentalists, Columbia Basin tribes, hydro regulators, and water resource planners work together to understand the impacts of Columbia River basin operation in a fish-friendly way in the current power grid and power grid with higher renewable power share. We examine different levels of renewable share in the Western Interconnection to understand the multiple weather years and future climate projection and operating scenario impact on the power grid and water system. We measure power grid impacts for various water resources planning scenarios in terms of total system operating cost, system reliability indicators, changes in wind and solar generation and curtailments, local marginal prices, and revenue for hydropower producers. The study results inform reservoir operating rules decisions from hydropower power producers, system operators, other water users, tribes, environmentalists, and other stakeholders.

environmental↗
DOE OSTI2020

The Cost of Floating Offshore Wind Energy in California Between 2019 and 2032

California’s energy planning is centered around meeting the emissions reduction and renewable energy requirements of Senate Bill 350 by 2030. However, state power system planning is expected to eventually address California’s requirement to achieve 100% of total retail electricity sales from renewable energy and zero-carbon resources by 2045, as mandated by Senate Bill 100. To comply with these directives, California needs to investigate the further development of energy efficiency, storage, and a diverse range of renewable energy, zero-carbon emission, and transmission resources, including offshore wind. Wind resources off the coast of California have the potential to generate a significant portion of the state’s electric energy as it moves toward a zero-carbon economy and can help diversify its energy mix. Floating offshore wind technology, which is suitable for the deep waters along the California coast, is currently in a precommercial phase, with approximately 84 megawatts (MW) installed worldwide at the end of 2019. Globally there are over 7,000 MW in planning and permitting phases of development, with the first commercial-scale projects expected to be operational in 2024. This study provides site-specific cost and performance data for floating offshore wind to inform California’s long-term energy planning. The identification of new resources to meet California’s policy goals at least cost is part of the Integrated Resource Planning (IRP) process, which is coordinated by the California Public Utilities Commission (CPUC). In 2019–2020 IRP modeling, offshore wind was included for the first time as a candidate resource in some sensitivity cases (CPUC 2019). The data and information presented in this report can be used to update offshore wind inputs in future IRP cycles. The authors conducted a geospatial cost analysis over portions of the offshore wind resource area of California. The analyzed spatial domain includes sites with a mean wind speed of at least 7 meters per second and water depths between 40 meters (m) and 1,300 m. Costs and energy production vary across this analysis domain. We calculated these parameters on a grid layout with over 750 sites, with each site representing a 1,000-MW commercial offshore wind power plant. Levelized cost of energy (LCOE) was calculated at each site over the analysis domain. The resulting variation in LCOE across the analysis domain is illustrated through heat maps in this report. Five study areas were selected within the analysis domain where more detailed cost analysis was conducted and cost parameters, such as annual energy production, capital cost expenditures (CapEx), operational cost expenditures (OpEx), and net capacity factors are reported. These five study areas include Morro Bay, Diablo Canyon, Humboldt, Cape Mendocino and Del Norte.

17 WIND ENERGY↗
DOE OSTI2021

Machine learning enables national assessment of wind plant controls with implications for land use

Summary As deployment of wind energy continues to expand, computationally efficient tools for predicting wind plant performance over a wide range of layout designs, technology innovations, and spatial locations are increasingly important for policy and investment decisions. We demonstrate two approaches to training a surrogate model to predict annual energy production (AEP) of parameterized wind plant layouts: one using a Gaussian process (GP) and the other using a fully convolutional neural network (FCNN). We leverage the powerful FCNN architecture by encoding wind plant design parameters and output response surface as an image. The FCNN produces more accurate results than the GP with mean absolute errors equivalent to 1% and 1.9% of plant rated power, respectively, although the GP performs well under limited training data and provides useful uncertainty information. We also evaluate a surrogate model for wake steering, enabling a nationwide assessment of the impact of plant control strategies and plant layout decisions. Across two million locations, we find that wake steering strategies boost AEP with relative gains upwards of 3%. Gains are most pronounced at sites without a dominant wind direction and where layout optimization is less fruitful. Additionally, we perform a nationwide sensitivity analysis showing that wake steering can mitigate wake losses from higher density plant layouts. Our results suggest that regions which have not been previously viable for wind deployment due to moderate wind resources are especially well enhanced by wake steering strategies that could help overcome land constraints and inflexible layout options, potentially identifying new deployment opportunities.

17 WIND ENERGY↗
DOE OSTI2022

Chapter 38: Aerodynamics of Wake Steering

This chapter discusses the mechanisms that enable wake steering within a wind farm with a focus on wake steering performed using yaw misaligned turbines as this is the most popular approach to wake steering, although there are others. Wake steering is a type of wind farm control in which wind turbines in a wind farm operate with an intentional yaw misalignment to mitigate the effects of its wake on downstream turbines in order to increase overall combined wind farm energy production. This chapter goes into detail regarding the dominant aerodynamic characteristics that are present when a turbine operates in yaw misaligned conditions and suggests analytical models that can capture these effects. A detailed analysis of large-scale flow structures generated in wind farm control through yaw misalignment is presented. A collection of counter-rotating vortices, produced from a misaligned turbine, deforms the shape of the wake and produces asymmetric effects with oppositely signed yaw angles. These vortices generated by an upstream misaligned turbine can also deflect wakes of downstream non-misaligned turbines. This chapter also addresses the importance of modeling these counter-rotating vortices in analytical models for wind farm control design and for accurately quantifying the impacts of wake steering on gains in power production in larger wind farms.

modeling↗
DOE OSTI2022

SIW21-95: Hybridizing Synchronous Condensers with Grid-Forming Battery Energy Storage Systems

One of the main challenges associated with deployment of high shares of inverter-based resources (IBRs) in power grid is not only reduced system inertia but also degrading system strength that may cause severe stability impacts. A minimum level of system strength is needed for the power system to remain stable under normal conditions and to return to a steady state condition following a system disturbance. Significant system strength reduction is expected in almost all planned areas for solar and wind generation deployment. Synchronous condensers (SC) have been considered as one main technology to address the system strength issues for the areas with high levels of IBRs. SCs can help improving reliability and resiliency of power system but they do not provide the full range of services needed by power systems for reliable and economic integration of high shares of inverter-coupled variable generation such as PV generation. Services related to active power controls cannot be provided by SCs due to lack of prime mover. Even for provision of reactive power SCs have certain constraints based on their thermal and stability limits. NREL has been conducting research on a hybridized concept that combines SCs with grid forming (GFM) battery energy storage systems (BESS). This super flexible AC transmission system (SuperFACTS) that combines these two technologies in a single plant under the same controller offers a unique scalable set of services to the power system at all levels (transmission, sub-transmission, distribution, islands and isolated microgrids). Depending on use cases, SuperFACTS can be controlled to provide fully dispatchable and flexible operation using energy storage component, provide a full range of existing and future ancillary and reliability services to the grid (similar or better than conventional sources), maintain adequate levels of grid strength and inertia, and provide fault current for proper operation of protection systems. Therefore, this economic and easy to commercialize solution has potential for significant impacts on certain segments of global energy sector. All types of gird services (market based, reliability and resiliency) can be provided by SuperFACTS plants. GFM BESS can act as a self-black start source for each SuperFACTS module, which in turn can act a black start resource for co-located PV and wind power plants, segments of transmission and distribution networks, for conventional power plants, etc. The issue of in-rush currents during black start is addressed by overcurrent capability of SCs. In this paper we describe the results of modeling for SuperFACTS concept.

grid forming↗
DOE OSTI2024

Identification of Climatological Representative Days in the Mid-Atlantic for High-Fidelity Offshore Wind Energy Modeling

The goal of reaching 30 GW of offshore wind energy by 2030 becomes more realistic with the continued approval of offshore wind energy areas by the Biden Administration. In the Mid-Atlantic, where wind energy projects are in the most advanced stages of development, there is increased research focus on the eventual interaction of these wind farms. These interactions, in the form of wakes and cluster wakes, or wakes from multiple wind farms, could have detrimental effects on power production and forecastability for downwind wind farms (Pryor et al. 2022, Golbazi et al. 2022, Rosencrans et al. 2023). To help alleviate these issues, numerical simulations in the form of numerical weather prediction (NWP) and large eddy simulations (LES) can provide insight into when cluster wake situations may occur, but running such simulations can be expensive and difficult to run for multiple years. In this study, we leverage and build upon existing techniques in the literature (Fischereit et al. 2022) to identify climatologically representative days for wind energy areas in the Mid-Atlantic where conditions would promote cluster wake situations. We select meteorological variables (wind speed, wind direction, atmospheric stability, boundary-layer height, TKE) critical to understanding wind energy production and wake propagation. We then consider two different NWP datasets of varying spatial and temporal resolution: ERA5 provides data at hourly intervals from 1940 to present at 0.25 deg (31 km) spatial resolution (Hersbach et al. 2020), and the NOW-23 dataset provides data at 5-minute resolution for 21 years at 2-km spatial resolution (Bodini et al. 2020). Our first step is to compare these two datasets for an overlapping 21-year time period. Initial results show that the required number of days to represent the long-term climate increases with each additional variable considered. In their study of the German Bight, Fischereit et al. (2022) found that they could represent the long-term wind and wave climate in a "near-perfect" way with -180 days, by reaching a Perkins Skill Score (PSS) of 0.9; our investigation of the mid-Atlantic wind resource region with ERA5 and NOW-23 data suggests that we will need -100 days to reach a PSS of 0.9. As we expand our parameter space to include multiple variables, the number of required days will likely grow. These results will ultimately be used to select case studies to best represent cluster wake conditions that apply to this region for the lifetime of likely wind farms in this mid-Atlantic region.

clusterwakes↗
DOE OSTI2020

Opportunities for Research and Development of Hybrid Power Plants

Hybrid power plants show promise to provide significant value to the electric grid system especially as shares of renewable energy in systems increase from 10% to 20% or more. Many manufacturers and developers are proactively developing hybrid power plant strategies to ensure plant profitability in markets where it’s important to provide energy that is more predictable and controllable, with higher capacity values, as well as various ancillary services to the grid system. However, there still are many questions around if and when hybrid power plants make sense versus leaving it to the larger grid system to ensure low cost and reliable supply by engaging directly with all individual assets. This report summarizes literature on the state-of-the-art in research concerning hybrid power plants from multiple perspectives including: 1) resource and market opportunities, 2) technology selection and sizing, 3) physical design, and 4) operation and control. The emphasis was placed on wind and solar hybrid power plants (with or without storage) but additional technologies were briefly considered and merit further investigation.

17 WIND ENERGY↗