Project overview:

Reducing LCoE from Offshore Wind by Multiscale Wake Modeling

Awardee:

Cornell University

Start Date:

Q2, 2020

Technical Challenge Area:

Wind Resource and Site Characterization

Completion Date:

Q3, 2022

Synopsis:

Cornell will be developing and applying multi-scale high-fidelity atmospheric and wake models to the US East Coast to improve accuracy of simulated high-resolution projected power output from wind farms, in an effort to optimize spacing of wind turbines within farms to maximize power production and reduce Levelized Cost of Energy (LCoE).

Target Outcomes:

Develop and apply multi-scale high-fidelity atmospheric and wake models to the US East Coast to significantly improve the accuracy of simulating high-resolution projected power output from offshore wind farms.

Use enhanced simulation accuracy for optimizing offshore wind turbine spacing to maximize power production and minimize fatigue loading on equipment. This aims to improve operational efficiency and lifespan of windfarms, potentially reducing LCoE.

Develop a report that includes a comparison tool detailing inputs, assumptions, and conclusions. The report highlights the potential for capacity improvement and a reduction in the LCoE through the application of improved wake modeling and turbine layout optimization.