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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Renewable Electricity: Insights for the Coming Decade

A sophisticated set of renewable electricity (RE) generation technologies is now commercially available. Globally, RE captured approximately half of all capacity additions since 2011. The cost of RE is already competitive with fossil fuels in some areas around the world, and prices are anticipated to continue to decline over the next decade. RE options, led by wind and solar, are part of a suite of technologies and business solutions that are transforming electricity sectors around the world. Renewable deployment is expected to continue due to: increasingly competitive economics; favorable environmental characteristics such as low water use, and minimal local air pollution and greenhouse gas (GHG) emissions; complementary risk profiles when paired with natural gas generators; strong support from stakeholders. Despite this positive outlook for renewables, the collapse in global oil prices since mid-2014 and continued growth in natural gas supply in the United States--due to the development of low-cost shale gas--raise questions about the potential impacts of fossil fuel prices on RE. Today, oil plays a very minor role in the electricity sectors of most countries, so direct impacts on RE are likely to be minimal (except where natural gas prices are indexed on oil). Natural gas and RE generating options appear to be more serious competitors than oil and renewables. Low gas prices raise the hurdle for RE to be cost competitive. Additionally, although RE emits far less GHG than natural gas, both natural gas and RE offer the benefits of reducing carbon relative to coal and oil (see Section 4.1 for more detail on the GHG intensity of electricity technologies). However, many investors and decision makers are becoming aware of the complementary benefits of pairing natural gas and renewables to minimize risk of unstable fuel prices and maintain the reliability of electricity to the grid.

14 SOLAR ENERGY↗
DOE OSTI2015

Reactive power planning under high penetration of wind energy using Benders decomposition

This study addresses the optimal allocation of reactive power volt-ampere reactive (VAR) sources under the paradigm of high penetration of wind energy. Reactive power planning (RPP) in this particular condition involves a high level of uncertainty because of wind power characteristic. To properly model wind generation uncertainty, a multi-scenario framework optimal power flow that considers the voltage stability constraint under the worst wind scenario and transmission N 1 contingency is developed. The objective of RPP in this study is to minimise the total cost including the VAR investment cost and the expected generation cost. Therefore RPP under this condition is modelled as a two-stage stochastic programming problem to optimise the VAR location and size in one stage, then to minimise the fuel cost in the other stage, and eventually, to find the global optimal RPP results iteratively. Benders decomposition is used to solve this model with an upper level problem (master problem) for VAR allocation optimisation and a lower problem (sub-problem) for generation cost minimisation. Impact of the potential reactive power support from doubly-fed induction generator (DFIG) is also analysed. Lastly, case studies on the IEEE 14-bus and 118-bus systems are provided to verify the proposed method.

17 WIND ENERGY↗
DOE OSTI2021

The Area Localized Coupled Model for Analytical Mean Flow Prediction in Arbitrary Wind Farm Geometries

This work introduces the area localized coupled (ALC) model, which extends the applicability of approaches that couple classical wake superposition models and atmospheric boundary layer models to wind farms with arbitrary layouts. Coupling wake and top–down boundary layer models is particularly challenging since the latter requires averaging over planform areas associated with turbine-specific regions of the flow that need to be specified. The ALC model uses Voronoi tessellation to define this local area around each turbine. A top–down description of a developing internal boundary layer is then applied over Voronoi cells upstream of each turbine to estimate the local mean velocity profile. Coupling between the velocity at hub-height based on this localized top–down model and a wake model is achieved by enforcing a minimum least-square-error in mean velocity in each cell. The wake model in the present implementation takes into account variations in wind farm inflow velocity and represents the wake profile behind each turbine as a super-Gaussian function that smoothly transitions between a top-hat shape in the region immediately following the turbine to a Gaussian profile downstream. Detailed comparisons to large-eddy simulation (LES) data from two different wind farms demonstrate the efficacy of the model in accurately predicting both wind farm power output and local turbine hub-height velocity for different wind farm geometries. These validations using data generated from two different LES codes demonstrate the model's versatility with respect to capturing results from different simulation setups and wind farm configurations.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2024

Scalable Risk Assessment of Rare Events in Power Systems With Uncertain Wind Generation and Loads

Risk assessment of rare events has become increasingly important in power system planning and operation with the increasing integration of renewable energy and the presence of system uncertainties. However, quantifying the risk posed by rare events via the traditional method, i.e., Monte Carlo sampling (MCS), incurs substantial computational expense stemming from the vast ensemble of power flow simulations. To accelerate the assessment, this paper proposes a Deep Neural Network (DNN)-kernelized vector-valued Gaussian Process (VVGP) approach with excellent computational efficiency while maintaining high accuracy. Consequently, serving as a surrogate model for the power flow solver, the DNN-kernelized VVGP enables significantly faster but accurate risk assessment compared to the power flow solver. The developed surrogate model evaluates low-order N - k events that contain more than 90% instances by adeptly capturing the topological features while the high-order N - k events are assessed via a power flow solver, thereby striking a balance between computational efficiency and uncertainty quantification accuracy. Moreover, the model incorporates a Support Vector Machine (SVM) classifier to resample concerning low-probability tail events to counteract the biases potentially introduced during the DNN-kernelized VVGP evaluations. Simulations conducted on the modified IEEE 24-bus, 118-bus, and European 1354-bus systems demonstrate that the proposed method maintains the accuracy benchmark set by MCS while significantly reducing computational demands in large-scale power systems as compared to other state-of-the-art methods.

17 WIND ENERGY↗
NASA NTRS1982

Free yaw performance of the Mod-0 large horizontal axis 100 kW wind turbine

The NASA Mod-0 Large Horizontal Axis 100 kW Wind Turbine was operated in free yaw with an unconed teetered, downwind rotor mounted on a nacelle having 8-1/2 deg tilt. Two series of tests were run, the first series with 19 meter twisted aluminum blades and the second series with 19 meter untwisted steel spar blades with tip control. Rotor speed were nominally 20, 26 and 31 rpm. It was found the nacelle stabilized in free yaw at a yaw angle of between -55 deg to -45 deg was relatively independent of wind speed and was well damped to short term variations in wind direction. Power output of the wind turbine in free yaw, aligned at a large yaw angle, was considerably less than that if the wind turbine were aligned with the wind. For the Mod-0 wind turbine at 26 rpm, the MOSTAB computer code calculations of the free yaw alignment angle and power output compare reasonably well with experimental data. MOSTAB calculations indicate that elimination of tilt and adding coning will improve wind turbine alignment with the wind and that wind shear has a slight detrimental effect on the free yaw alignment angle.

Corrigan, R. D.↗
DOE OSTI2023

21-Year Modeled Spatiotemporal Low-Level Jet Climatology Along the U.S. Mid-Atlantic Coast

Offshore wind farms are influenced by a variety of meteorological phenomena, ranging in spatial and temporal extent. One of these phenomena, the low-level jet (LLJ), is defined as a local maximum in wind speed with height in the planetary boundary layer, exhibiting a "nose-like" structure usually in the lowest few hundred meters above the surface. This vertical extent of LLJs may coincide with the rotor-swept area of a wind turbine, thus having implications for potential power production and mechanical loading. Previous studies in this area based on observations have confirmed the prevalence of LLJs here, with implications on power generation, wind turbine loads, and wake recovery depending on the LLJ characteristics, such as nose height and what part of the wind turbine power curve is being impacted.

LLJ↗
DOE OSTI2012

Wind Turbine Engineering R&D at Los Alamos National Laboratory [Slides]

There are enough wind resources in the US to provide more than 10 times the electric power we currently use, however wind power only accounts for 3% of our total electricity production. DOE’s goal is to increase wind power capacity to 20% of the total by the year 2030. LANL’s Intelligent Wind Turbine Project is applying leading-edge engineering expertise in modeling and simulation, experimental validation, and advanced sensing technologies to challenges faced in the design and operation of modern wind turbines. One of the main limitations to wind use is cost. Wind power currently costs about 8 cents per kilowatt-hour, which is more than 1.4 times the cost of electricity generated by burning natural gas. DOE desires to reduce wind power costs through technological innovation and reliability improvements. LANL’s engineering expertise, which is well-suited to the wind energy application, is born out of the Laboratory’s core national security mission. Our ability to visualize detailed flowfields around operating wind turbines is founded upon experimental turbulence measurement techniques developed for the nuclear weapons program. The capability to model multiple wind turbines on any terrain is built upon past work in modeling urban dispersion, wildland fires, and hurricane intensification. In addition, research using embedded sensing for the structural assessment of civil structures has led to the ability to detect incipient damage in operating wind turbine blades. This presentation will provide an overview of the innovative wind turbine research ongoing at LANL and its connection to LANL’s core mission.

17 WIND ENERGY↗
DOE OSTI2024

The value of wake steering wind farm flow control in US energy markets

Abstract. Wind farm flow control represents a category of control strategies for achieving wind-plant-level objectives, such as increasing wind plant power production and/or reducing structural loads, by mitigating the impact of wake interactions between wind turbines. Wake steering is a wind farm flow control technology in which specific turbines are misaligned with the wind to deflect their wakes away from downstream turbines, thus increasing overall wind plant power production. In addition to promising results from simulation studies, wake steering has been shown to successfully increase energy production through several recent field trials. However, to better understand the benefits of wind farm flow control strategies such as wake steering, the value of the additional energy to the electrical grid should be evaluated – for example, by considering the price of electricity when the additional energy is produced. In this study, we investigate the potential for wake steering to increase the value of wind plant energy production by combining model predictions of power gains using the FLOw Redirection and Induction in Steady State (FLORIS) engineering wind farm flow control tool with historical electricity price data for 15 existing US wind plants in four different electricity market regions. Specifically, for each wind plant, we use FLORIS to estimate power gains from wake steering for a time series of hourly wind speeds and wind directions spanning the years 2018–2020, obtained from the ERA5 reanalysis dataset. The modeled power gains are then correlated with hourly electricity prices for the nearest transmission node. Through this process we find that wake steering increases annual energy production (AEP) between 0.4 % and 1.7 %, depending on the wind plant, with average increases in potential annual revenue (i.e., annual revenue of production, ARP) 4 % higher than the AEP gains. For most wind plants, ARP gain was found to exceed AEP gain. But the ratio between ARP gain and AEP gain is greater for wind plants in regions with high wind penetration because electricity prices tend to be relatively higher during periods with below-rated wind plant power production, when wake losses occur and wake steering is active; for wind plants in the Southwest Power Pool – the region with the highest wind penetration analyzed (31 %) – the increase in ARP from wake steering is 11 % higher than the AEP gain. Consequently, we expect the value of wake steering, and other types of wind farm flow control, to increase as wind penetration continues to grow.

17 WIND ENERGY↗
DOE OSTI2023

Effects of land-based wind turbine upsizing on community sound levels and power and energy density

Multiple technological, social, and market factors of wind power are evolving rapidly. Most notably, significant wind turbine scaling is occurring and is forecasted to continue. While the larger turbines expected to be deployed in the future are more powerful and efficient, they are also expected to operate at higher sound levels and require larger setbacks than those installed in the last decade. These sometimes-competing deployment trends and impacts cannot be understood via simple extrapolations of past trends. This study analyzes the effect of these future larger turbines on wind turbine micro-siting, project-level power and energy density, and community noise impacts. Due to their taller heights, larger rotors, and higher sound power levels, future wind turbines will require larger setbacks from homes and greater inter-turbine spacing, resulting in fewer turbines deployed for a given land area. This research finds these changes more than offset the effect of the higher turbine sound emissions, significantly decreasing the average sound levels that wind plant hosting communities experience. Yet, simultaneously, plant layouts using future turbine designs also result in projects with higher installed capacities and annual energy output for a given land area. These increases will likely lead to increased tax benefits and local income in the community. The deployment of fewer turbines on a smaller number of parcels could have implications on siting flexibility and landowner payments.

17 WIND ENERGY↗
DOE OSTI2019

Physics-informed Machine Learning Method for Forecasting and Uncertainty Quantification of Partially Observed and Unobserved States in Power Grids

We present a physics-informed Gaussian Process Regression (GPR) model to predict the phase angle, angular speed, and wind mechanical power from a limited number of measurements. In the traditional data-driven GPR method, the form of the Gaussian Process covariance matrix is assumed and its parameters are found from measurements. In the physics-informed GPR, we treat unknown variables (including wind speed and mechanical power) as a random process and compute the covariance matrix from the resulting stochastic power grid equations. We demonstrate that the physics-informed GPR method is significantly more accurate than the standard data-driven one for immediate forecasting of generators' angular velocity and phase angle. We also show that the physics-informed GPR provides accurate predictions of the unobserved wind mechanical power, phase angle, or angular velocity when measurements from only one of these variables are available. The immediate forecast of observed variables and predictions of unobserved variables can be used for effectively managing power grids (electricity market clearing, regulation actions) and early detection of abnormal behavior and faults. The physics-based GPR forecast time horizon depends on the combination of input (wind power, load, etc.) correlation time and characteristic (relaxation) time of the power grid and can be extended to short and medium-range times.

machine learning, power grid, forecasting, Gaussia↗