NREL researchers publish resources to press towards detailed wind plant models

Published on December 11, 2019 by Kevin Randolph

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National Renewable Energy Laboratory researchers and other experts recently released several resources that may eventually help enable the creation of detailed models for entire wind plants.

Paul Veers, the chief engineer at NREL’s National Wind Technology Center, worked with numerous other experts to develop a resource that explains the principal elements behind multiple subsystem models needed to create a full-system optimization framework. The book, titled Wind Energy Modeling and Simulation Volume 1: Atmosphere and plant and Volume 2: Turbine and system, addresses the challenges involved in creating predictive models for wind plant performance and the various disciplines required to solve it.

In a recent article in the journal Science, NREL researchers wrote that the need for a deeper understanding of atmospheric flow physics is one of three “grand challenges” facing the wind industry. The other two challenges are engineering large, dynamic rotating machines and integrating electricity generation seamlessly with the grid.

High-performance computing (HPC) developments are currently advancing models at the component, turbine, plant, and system levels. Last year, as part of the Department of Energy’s ExaWind Exascale Computing Project, NREL researchers helped complete their first blade-resolved model of a large modern wind turbine in the turbulent atmospheric flow. The simulation was performed on a 30-petaflop HPC system with multiple resolutions and included a model with 6 billion grid points.

“We have models that are capable of representing parts of the equation, but you lose some of the reality by simplifying the problem into pieces that are independently solvable,” Veers said. “In the future, people will be combining the areas and bridging the gaps between them.”

Future exascale systems will be capable of at least one-billion-billion calculations per second, which is approximately 50 to 100 times faster than the most powerful supercomputers in use today, according to NREL.

“With exascale computing capabilities on the horizon, we are starting to see new possibilities for combining scales and phenomena,” Veers said. “The pathway to including more of the relevant inputs requires a comprehensive description of how modeling is approached at each level.”