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The Leading Edge: March 2020 Wind Energy Newsletter

In this edition of The Leading Edge, researchers combine two modeling approaches to get the best of both worlds in their most recent update to FLOw Redirection and Induction in Steady State, and a new Competitiveness Improvement Project funding cycle for distributed wind has launched.

News Stories

NREL Boosts Speed and Accuracy of Wind Plant Optimization Model

The National Renewable Energy Laboratory (NREL) has released a new version of its FLOw Redirection and Induction in Steady State (FLORIS) model for wind plant performance optimization. The latest update, which combines curl model physics with Gaussian computational efficiency, enhances the ability of FLORIS to accurately design and analyze wind farm control strategies for larger arrays of turbines.

Woman smiling

Peak performance. Jennifer King (NREL) and her colleagues advance control strategies to improve the performance of wind farms. Models like the Gaussian-curl hybrid model better capture the effects of turbines working together to maximize the power of an entire wind farm, rather than one individual turbine. Photo by Dennis Schroeder, NREL

NREL Issues Request for Proposals To Enhance Distributed Wind

A new U.S. Department of Energy Competitiveness Improvement Project funding cycle for distributed wind is underway. NREL issued a request for proposals that will remain open through the end of March. Proposals, which require a cost-share component and should be focused on projects nearing market readiness, will be evaluated based on technical merit and the ability to reduce the levelized cost of distributed wind energy.

Photo of six different types of distributed wind turbines.
The Competitiveness Improvement Project has just announced a new funding solicitation to support manufacturers of distributed wind turbines. Photo courtesy of Sonsight Wind 

Downwind – In Case You Missed It

Exploring Opportunities for Recycling Wind Turbine Blades

recent article from The Colorado Sun highlights NREL's efforts to reduce waste by developing recyclable turbine blades as part of a circular economy. NREL researcher Derek Berry, who is quoted throughout the article, notes that research conducted by the lab in collaboration with industry partners through the Institute for Advanced Composites Manufacturing Innovation works toward the goal of recycling material from old blades to make new composite structures. Institute members include Arkema, Colorado School of Mines, Johns Manville, Oak Ridge National Laboratory, Purdue University, TPI Composites, University of Tennessee, and Vanderbilt University.

This research includes investigating thermoplastic composite materials, which:

  • Are recyclable at the end of a blade's life
  • Could reduce wind blade manufacturing costs
  • Enable thermal joining and shaping, which are potentially lighter and more reliable manufacturing processes.

Learn more about NREL and partner efforts to advance the commercialization and industry use of thermoplastic resins in wind blades.


Hurricane Eyewall Winds and Structural Response of Wind Turbines

This journal article describes the analysis of a wind turbine and support structure subject to simulated Category 5 hurricane wind fields. Hurricanes contain wind field characteristics (high mean wind speeds and shifts in direction) that are not currently considered in design and that may exacerbate turbine loads in unexpected ways. The challenges posed by this analysis may indicate a need to revisit offshore design standards pending validation of a range of other hurricane and tropical storm simulations.

Opportunities for and Challenges to Further Reductions in the "Specific Power" Rating of Wind Turbines Installed in the United States

NREL researchers Robert Hammond and Eric Lantz co-authored an article that summarizes the economics of large-rotor, low-specific-power turbines—machines that use large blades relative to generator capacity to enhance energy capture—in land-rich and transmission-capacity-constrained wind power regions. Levelized cost of energy analysis modeling across the United States shows that low-specific-power turbines could continue to be in demand moving forward if transportation challenges associated with larger turbine blades can be resolved.

Turbulent Kinetic Energy over Large Offshore Wind Farms Observed and Simulated by the Mesoscale Model WRF (3.8.1)

This research examines whether additional turbulent kinetic energy sources other than drag effects should be included in simulations of wind farm impacts on local weather and microclimates. With offshore wind becoming more prominent, it is important to understand how large offshore wind turbines impact the surrounding atmosphere and the efficiency of downwind offshore turbines or wind plants. Based on comparisons to aircraft data in an offshore wind farm wake, researchers conclude that the additional turbulence generated by turbines should be included in simulations of wind farm impacts.

The Power Curve Working Group's Assessment of Wind Turbine Power Performance Prediction Methods

This study summarizes the findings from an intelligence-sharing initiative of the Power Curve Working Group. Designed to identify modeling methods that reduce error and uncertainty in power prediction when wind shear and turbulence digress from design condition, the study analyzed 55 power performance assessments with modern wind turbines from 9 contributing organizations. Findings reveal that new trial methods involving more comprehensive power deviation matrices lead to more accurate power prediction.

Validation of RU-WRF, the Custom Atmospheric Mesoscale Model of the Rutgers Center for Ocean Observing Leadership

The Rutgers University Center for Ocean Observing Leadership (RU-COOL) and NREL evaluated RU-COOL's mesoscale model as well as its observation and validation capabilities for New Jersey offshore wind resource characterization. This report provides the results of that evaluation, which is important because an accurate characterization of the wind resources off the coast of New Jersey and neighboring states helps ensure confidence in analyses—such as energy prediction, grid integration, extreme events, and capacity expansion applications—that require these data. Among fifteen recommendations resulting from the work were various upgrades to the numerical weather prediction model and the use of optimal coastal lidar configurations for offshore wind resource characterization, which can provide a valuable data for model validation and wind resource characterization.