Laser Disdrometer / Standardized Data
This dataset contains raw data from the laser disdrometer at Nantucket. The disdrometer measures droplet size distribution, hydrometer type, and precipitation rate.
17 WIND ENERGY↗
SEARCH · Engineering Papers
Results for “distributed wind energy”
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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Laser Disdrometer / Daily Standardized Files
This dataset contains standardized data from the laser disdrometer at ASIT. The disdrometer measures droplet size distribution, hydrometer type, and precipitation rate.
17 WIND ENERGY↗
Laser Disdrometer / Daily Standardized Files
This dataset contains standardized data from the laser disdrometer at ASIT. The disdrometer measures droplet size distribution, hydrometer type, and precipitation rate.
17 WIND ENERGY↗
ASIT Laser Disdrometer / Raw Data
This dataset contains raw data from the laser disdrometer at ASIT. The disdrometer measures droplet size distribution, hydrometer type, and precipitation rate.
17 WIND ENERGY↗
ASIT Laser Disdrometer 2 / Raw Data
This dataset contains raw data from the laser disdrometer at ASIT. The disdrometer measures droplet size distribution, hydrometer type, and precipitation rate.
17 WIND ENERGY↗
Cooperative fault management for resilient integration of renewable energy
Cooperative fault management (CFM) is designed herein to control different types of renewable energy resources cooperatively during electrical faults. This paper studies systems with a high penetration of photovoltaic (PV) energy and wind energy. First, CFM leverages power converters of PV farms to boost the ride-through capability of nearby doubly-fed induction generators (DFIGs). By controlling PV farms’ output voltages to change smoothly during both fault initiation and fault clearance, the widely used crowbar in DFIGs is less likely to be activated. Crowbar activation adversely makes DFIGs lose controllability and absorb reactive power. The second contribution is the development of a software-defined CFM controller and a controller in-the-loop demonstration of the real-time performance of this optimization-based CFM. CFM capitalizes on distributed optimization formulation to enable flexibility, plug-and-play, and privacy-preserving. Computation time, however, is a major concern for optimization-based dynamics control. Here, real-time controller-in-the-loop simulation results show optimization-based CFM can output reference values around 60 ms and is quick enough for dynamic control.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Long-run Marginal Emission Rates for Electricity - Workbooks for 2022 Cambium Data
These workbooks contain modeled estimates of long-run marginal emission rates (LRMER) for the contiguous United States. A LRMER is an estimate of the rate of emissions that would be either induced or avoided by a change in electric demand, taking into account how the change could influence both the operation as well as the structure of the grid (i.e., the building and retiring of capital assets, such as generators and transmission lines). It is therefore distinct from the more-commonly-known short-run marginal, which treat grid assets as fixed. Long-run marginal emissions rates are generally appropriate to use when trying to comprehensively estimate the impact of a long-lived (i.e., more than several years) intervention. There are two workbooks that supply the data at two different geographic resolutions: states and GEA regions (20 regions that are similar to, but not exactly the same as, the US EPA's eGRID regions). For more data underlying these emissions factors, see the Cambium 2022 project at https://scenarioviewer.nrel.gov/. For more details on input assumptions and methodology see the associated report (Cambium 2022 Scenario Descriptions and Documentation, https://www.nrel.gov/docs/fy23osti/84916.pdf). This data is planned to be updated annually. Information on the latest versions can be found at https://www.nrel.gov/analysis/cambium.html.
01 COAL, LIGNITE, AND PEAT↗
Autonomous Energy Systems
Energy systems are increasingly complicated by the proliferation of clean energy technologies such as solar, wind, storage, electric vehicles, and building automations. Future energy systems will require secure, autonomous, and reliable communications, control, and interoperability among millions of distributed generation points and billions of buildings, vehicles, and more. To enable effective management of the anticipated growth of distributed devices and the deluge of data and extensive metering that will follow, the National Renewable Energy Laboratory (NREL) has developed the concept of autonomous energy systems (AES).
autonomous↗
Investigating the impact of atmospheric conditions on wake-steering performance at a commercial wind plant
Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to deflect their wakes away from downstream turbines, thereby increasing net energy production. But research suggests that the effectiveness of wake steering strongly depends on atmospheric conditions such as stability. In this paper, we investigate results from a two-turbine wake-steering experiment at a commercial wind plant to assess the impact of stability and five other atmospheric variables on wake-steering performance. Specifically, for different atmospheric condition bins we compare the ability of the controller to achieve the intended yaw offsets, the power gain from wake steering, and the reduction in wake losses. Further, we analyze wake-steering performance as a function of wind speed to eliminate the confounding impact of different wind speed distributions in different atmospheric conditions. Overall, we find that wind direction standard deviation is the best predictor of wake-steering performance, followed by turbulence intensity and turbulent kinetic energy. The results suggest the importance of adapting wake-steering control strategies to different atmospheric conditions.
17 WIND ENERGY↗
FLOWERS AEP: An Analytical Model for Wind Farm Layout Optimization
Annual energy production (AEP) is commonly used in objective functions for wind farm layout optimization. AEP is proportional to wind farm power production integrated over an annual distribution of free-stream wind conditions. Physics-based estimates of wind farm power production typically rely on low-fidelity engineering wake models that approximate the steady-state wind farm flow field. AEP estimates are then obtained by performing independent simulations for discrete wind conditions and using rectangular quadrature to account for each condition's expected frequency of occurrence. Depending on the number of simulated discrete wind conditions, this numerical integral could be hampered by poor accuracy or high computational costs. The FLOWERS AEP model instead poses an analytical integral of the engineering wake model over the variable wind conditions, yielding a closed-form, analytical function for wind farm AEP. This paper derives the analytical functions for FLOWERS AEP and its derivatives with respect to turbine position, which are useful for gradient-based wind farm layout optimization, in nondimensional form. We then analyze the benefits of the FLOWERS AEP model over conventional reference models, focusing on its low cost, adequate wake loss predictions, and smooth design space. Although the FLOWERS approach is found to predict the exact value of AEP with some error relative to the reference model (within 14% on average), it dramatically reduces computation time by an order of magnitude, produces a qualitatively similar design space at relatively low resolution, and yields comparable optimal layouts. This significant speed improvement is critical in layout optimization applications, where determining an optimal layout in an efficient manner is more important than precise AEP prediction.
17 WIND ENERGY↗
Grid Value Proposition of Marine Energy
Marine energy technologies convert the energy of ocean waves, and tidal, river, and ocean currents into electricity and other forms of usable energy. The marine energy resource potential in the United States is significant and geographically diverse, with a study commissioned by the U.S. Department of Energy estimating that the nation’s annual marine energy potential is approximately 2,300 TWh/year across the 50 states, or greater than 57 percent of U.S. electricity generation in 2019. However, the marine energy industry still faces hurdles to commercialization. While high costs relative to wind and solar remain a key challenge, other hurdles relate to marine energy’s value streams not being well characterized and not captured by traditional energy comparison metrics like the levelized cost of energy. To address this challenge, this project undertakes several types of analyses to identify and illustrate value propositions for marine energy resources. It provides a fresh framework for considering electric system benefits based on unique marine energy attributes, and provides analyses illustrating and quantifying those benefits. The authors find many opportunities for the deployment of marine energy technologies both in the near term and within typical utility planning timeframes (i.e. up to 20 years). From a resource and technology perspective, marine energy resources can deliver distinct and valuable benefits to different configurations of the grid, whether the bulk system, isolated distribution systems, or remote communities, islands, and microgrids. Marine energy resources can be valuable in increasing technology diversity in a generation portfolio, providing energy where it is otherwise difficult to come by, supporting local resiliency, complementing and being complemented by other resources including solar, wind, and energy storage, and avoiding land constraints.
16 TIDAL AND WAVE POWER↗
Specification Document for OC6 Phase II: Verification of an Advanced Soil-Structure Interaction Model for Offshore Wind Turbines
The Norwegian Geotechnical Institute (NGI) has developed a new macro-element model that accounts for the soil-structure interaction in offshore wind turbines. This development was done as part of the REDWIN (REDucing cost of offshore WINd by integrated structural and geotechnical design) project. The focus of Phase II of the OC6 project was to integrate REDWIN’s new soil-structure modeling capability into the coupled modeling tools that are used to design fixed-bottom offshore wind systems. The integration was then verified through a series of load cases using an example monopile-based offshore wind system examined within the WAS-XL project. This document provides the details needed to model the system examined in OC6 Phase II, using the new REDWIN modeling approach, as well as other standard modeling approaches, including apparent fixity, distributed springs, and coupled springs.
17 WIND ENERGY↗
Distributed Optical Sensing Platform for Subsea Cable Monitoring and Fault Detection (Final Report)
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17 WIND ENERGY↗
Fermi-type particle acceleration from magnetic reconnection at the termination shock of a relativistic striped wind
An oblique-rotating pulsar generates a relativistic striped wind in a pulsar wind nebula (PWN). The termination shock of the PWN compresses the Poynting-flux-dominated flow and drives magnetic reconnection. By carrying out particle-in-cell simulations of the termination shock of the PWN, we study the shock structure as well as the energy conversion processes and particle acceleration mechanisms. With the recent advances in the numerical methods, we extend the simulations to the ultrarelativistic regime with a bulk Lorentz factor of up to γ 0 = 10 6 . Magnetic reconnection at the termination shock is highly efficient at converting magnetic energy to particle kinetic energy and accelerating particles to high energies. Similar to earlier studies, we find that the resulting energy spectra crucially depend on λ/d e (λ is the wavelength of the striped wind and de is the relativistic plasma skin depth). When λ/d e is large (λ ≳ 40d e ), the downstream particle spectra form a power-law distribution in the magnetically dominated relativistic wind regime. By analyzing particle trajectories and statistical quantities relevant to particle energization, we find that Fermi-type mechanism dominates the particle acceleration and power-law formation. We find that the results for particle acceleration are scalable as γ 0 and σ 0 increase to large values. The maximum energy for electrons and positrons can reach hundreds of TeV if the wind has a bulk Lorentz factor of γ0 ≈ 106 and a magnetization parameter of σ 0 = 10, which can explain the recent observations of high-energy gamma rays from PWNe
79 ASTRONOMY AND ASTROPHYSICS↗
Composited structure of non-precipitating shallow cumulus clouds
The normalized distributions of thermodynamic and dynamical variables both within and outside shallow clouds are investigated through a composite algorithm using large-eddy simulations of oceanic and continental cases. The normalized magnitude is maximum near the cloud centre and decreases outwards. While relative humidity (RH) and cloud liquid water ( q l ) decrease smoothly to match the environment, the vertical velocity, virtual potential temperature (θ v ), and potential temperature (θ) perturbations have more complicated behaviour towards the cloud boundary. Below the inversion layer, θ' v becomes negative before the vertical velocity has turned from an updraft to a subsiding shell outside the cloud, indicating the presence of a transition zone where the updraft is negatively buoyant. Due to the downdraft outside the cloud and enhanced horizontal turbulent mixing across the edge, the normalized turbulent kinetic energy (TKE) and horizontal turbulent kinetic energy (HTKE) decrease more slowly from the cloud centre outwards than the thermodynamic variables. The distributions all present asymmetric structures in response to the vertical wind shear, with more negatively buoyant air, stronger downdrafts, and larger TKE on the downshear side. We discuss several implications of the distributions for theoretical models and parameterizations. Positive buoyancy near the cloud base is mostly due to the virtual effect of water vapour, emphasizing the role of moisture in triggering. The mean vertical velocity is found to be approximately half the maximum vertical velocity within each cloud, providing a constraint to achieve possible power-law distributions for some models. Finally, the normalized distributions for different variables are used to estimate the vertical heat and moisture fluxes within clouds. The results suggest that distributions near the cloud edge and variability of maximum perturbations need careful treatment. The fluxes are underestimated in the inversion layer because cloud-top downdrafts cannot be captured well.
54 ENVIRONMENTAL SCIENCES↗
Distributed Generation Market Demand (dGen) model
The Distributed Generation Market Demand (dGen) model simulates customer adoption of distributed energy resources (DERs) for residential, commercial, and industrial entities in the United States or other countries through 2050. The dGen model can be used for identifying the sectors, locations, and customers for whom adopting DERs would have a high economic value, for generating forecasts as an input to estimate distribution hosting capacity analysis, integrated resource planning, and load forecasting, and for understanding the economic or policy conditions in which DER adoption becomes viable, and for illustrating sensitivity to market and policy changes such as retail electricity rate structures, net energy metering, and technology costs.
Array↗
Design and Operation of Energy Systems with Large Amounts of Variable Generation: IEA Wind TCP Task 25 (Final Summary Report)
This report summarizes findings on wind integration from the 17 countries or sponsors participating in the International Energy Agency Wind Technology Collaboration Program (IEA Wind TCP) Task 25 from 2006-2020. Both real experience and studies are reported. Many wind integration studies incorporate solar energy, and most of the results discussed here are valid for other variable renewables in addition to wind. The national case studies address several impacts of wind power on electric power systems. In this report, they are grouped under long-term planning issues and short-term operational impacts. Long-term planning issues include grid planning and capacity adequacy. Short-term operational impacts include reliability, stability, reserves, and maximizing the value of wind in operational timescales (balancing related issues). The first section presents the variability and uncertainty of power system-wide wind power, and the last section presents recent studies toward 100% shares of renewables. The appendix provides a summary of ongoing research in the national projects contributing to Task 25 for 2021-2024. The design and operation of power and energy systems is an evolving field. As ambitious targets toward net-zero carbon energy systems are announced globally, many scenarios are being made regarding how to reach these future decarbonized energy systems, most of them involving large amounts of variable renewables, mainly wind and solar energy. The secure operation of power systems is increasingly challenging, and the impacts of variable renewables, new electrification loads together with increased distribution system resources will lead to somewhat different challenges for different systems. Tools and methods to study future power and energy systems also need to evolve, and both short-term operational aspects (such as power system stability) and long-term aspects (such as resource adequacy) will probably see new paradigms of operation and design. The experience of operating and planning systems with large amounts of variable generation is accumulating, and research to tackle the challenges of inverter-based, nonsynchronous generation is on the way. Energy transition and digitalization also bring new flexibility opportunities, both short and long term.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Fiber Optic based Borehole Multi Sensor & Source System for Characterization and Monitoring of Carbon Storage Sites
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15 GEOTHERMAL ENERGY↗