Machine learning enables national assessment of wind plant controls with implications for land use

Harrison‐Atlas, Dylan; King, Ryan N.; Glaws, Andrew

doi:10.1002/we.2689

Title: Machine learning enables national assessment of wind plant controls with implications for land use

Journal Article · Sun Oct 24 00:00:00 UTC 2021 · Wind Energy

DOI: https://doi.org/10.1002/we.2689 · OSTI ID:1827417

^[1]; King, Ryan N. ^[2]; Glaws, Andrew ^[2]

Strategic Energy Analysis Center National Renewable Energy Laboratory Golden Colorado USA
Computational Science Center National Renewable Energy Laboratory Golden Colorado USA

Summary As deployment of wind energy continues to expand, computationally efficient tools for predicting wind plant performance over a wide range of layout designs, technology innovations, and spatial locations are increasingly important for policy and investment decisions. We demonstrate two approaches to training a surrogate model to predict annual energy production (AEP) of parameterized wind plant layouts: one using a Gaussian process (GP) and the other using a fully convolutional neural network (FCNN). We leverage the powerful FCNN architecture by encoding wind plant design parameters and output response surface as an image. The FCNN produces more accurate results than the GP with mean absolute errors equivalent to 1% and 1.9% of plant rated power, respectively, although the GP performs well under limited training data and provides useful uncertainty information. We also evaluate a surrogate model for wake steering, enabling a nationwide assessment of the impact of plant control strategies and plant layout decisions. Across two million locations, we find that wake steering strategies boost AEP with relative gains upwards of 3%. Gains are most pronounced at sites without a dominant wind direction and where layout optimization is less fruitful. Additionally, we perform a nationwide sensitivity analysis showing that wake steering can mitigate wake losses from higher density plant layouts. Our results suggest that regions which have not been previously viable for wind deployment due to moderate wind resources are especially well enhanced by wake steering strategies that could help overcome land constraints and inflexible layout options, potentially identifying new deployment opportunities.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office

Grant/Contract Number:: AC36-08GO28308

OSTI ID:: 1827417

Report Number(s):: NREL/JA-6A20-79267

Journal Information:: Wind Energy, Journal Name: Wind Energy Journal Issue: 4 Vol. 25; ISSN 1095-4244

Publisher:: Wiley Blackwell (John Wiley & Sons)Copyright Statement

Country of Publication:: United Kingdom

Language:: English

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