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Artificial intelligence-aided wind plant optimization for nationwide evaluation of land use and economic benefits of wake steering

Nature Energy (2024)Cite this article

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Abstract

If clean energy pathways are to harness massive increases in wind power, innovations with broad geographic viability will be needed to support buildout in diverse locations. However, geodiversity in impact potential is seldom captured in technology assessment. Here we propose a scalable approach to plant-level optimization using artificial intelligence to evaluate land sparing and economic benefits of wake steering at more than 6,800 plausible onshore wind locations in the USA. This emerging controls strategy optimizes plant energy production by directing turbine wakes. On the basis of estimates from our artificial intelligence model trained on engineering wind flow simulations, co-optimizing plant layouts with wake steering can reduce land requirements by an average of 18% per plant (site-specific benefits range from 2% to 34%), subject to errors and uncertainties in the flow model, wind resource estimates, buildout scenario and geographic factors. According to model estimates, wake steering is predicted to increase power production during high-value (relatively low wind) periods, boosting the annual revenue of individual plants by up to US$3.7 million (equivalent to US$13,000 MW−1 yr−1) but producing negligible gains in some settings. Consideration of wake steering’s geographic potential reveals divergent nationwide prospects for improved economics and siting flexibility.

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Fig. 1: Nationwide modelling of wind power innovation impact potential.
Fig. 2: Nationwide benefits of wake steering.
Fig. 3: National land-use reductions and energy density benefits of wake steering.
Fig. 4: Drivers behind reduced cost of energy.
Fig. 5: Dynamic energy pricing and increased value capture through wake steering.

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Data availability

Wind plant simulation data from FLORIS, which were used for training and testing the WPGNN model, have been made publicly available through the Open Energy Data Intiative at https://doi.org/10.25984/2222588 (ref. 105). Data regarding the projected buildouts can found at nrel.gov/analysis/reeds/. Site-specific atmospheric data are obtained from NREL’s WIND Toolkit, which is available at nrel.gov/grid/wind-toolkit.html. Lastly, pricing data is from NREL’s Cambium database, available at nrel.gov/analysis/cambium.html. Additional data supporting the results are available upon request. Source data are provided with this paper.

Code availability

The code for the trained WPGNN model and the wind plant layout generator is available on GitHub at github.com/NREL/WPGNN (ref. 54).

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Acknowledgements

We are grateful to P. Brown, T. Mai, P. Veers, O. Roberts, A. Lopez, P. Fleming and M. Sprague for feedback on earlier drafts of this work. This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the US Department of Energy (DOE) under contract number DE-AC36-08GO28308. Funding provided by the US Department of Energy Office of Energy Efficiency and Renewable Energy Wind Energy Technologies Office. The research was performed using computational resources sponsored by the Department of Energy’s Office of Energy Efficiency and Renewable Energy and located at the National Renewable Energy Laboratory. The views expressed in the article do not necessarily represent the views of the DOE or the US Government. The US Government retains and the publisher, by accepting the article for publication, acknowledges that the US Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for US Government purposes.

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  1. These authors contributed equally: Dylan Harrison-Atlas, Andrew Glaws.

Authors and Affiliations

  1. Strategic Energy Analysis Center, National Renewable Energy Laboratory, Golden, CO, USA

    Dylan Harrison-Atlas

  2. Computational Science Center, National Renewable Energy Laboratory, Golden, CO, USA

    Andrew Glaws & Ryan N. King

  3. National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO, USA

    Eric Lantz

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Contributions

D.H.-A. contributed to conceptualization, methodology, software, formal analysis, investigation, visualization, project administration, supervision, writing (original draft, review and editing) and funding acquisition. A.G. contributed to methodology, software, data curation, formal analysis, validation, investigation, visualization and writing (original draft, review and editing). R.N.K. contributed to methodology, investigation, supervision and writing (original draft, review and editing). E.L. contributed to conceptualization, writing (original draft, review and editing) and funding acquisition.

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Extended data

Extended Data Fig. 1 Potential expansion of wind energy under a decarbonized power sector and associated buildout characteristics.

Spatial distribution of onshore wind plant buildouts (n=6,862, capacity=720 GW) projected under our focal scenario5, which captures plausible changes to the bulk power system with a goal of reducing 95% of carbon emissions from the energy sector by 2050 (a). Under energy scenarios that rely heavily on wind power, like the one considered here, technologies must be viable in diverse settings. As captured by the breakdown of buildouts for this scenario, important characteristics of future buildouts include plant size (b), quality of wind resource (c), and land constraint (d). Using the projected plant sites as meaningful growth targets that reflect the scope and magnitude of possible future wind energy deployment, we perform a series of plant layout optimization studies to evaluate impacts of wake steering on land use and economic objectives. State boundaries from US Census Bureau104. Locations of water bodies from NaturalEarth (https://www.naturalearthdata.com/).

Extended Data Fig. 2 Randomly selected examples from test set A showing WPGNN predictive error for turbine and plant power output.

Each example depicts a unique plant layout and inflow condition (arrows indicate the inflow direction). Differences between WPGNN predictions and FLORIS predictions for plant power production are quantified using the relative root mean squared error (RRMSE) metric. Examples contained in test set A are used exclusively for model validation. Note that all of the turbines shown in these specific cases have randomly sampled yaw angles. The high accuracy of WPGNN across the test set, as illustrated here for a few examples, demonstrates its ability to generalize well to new plant layouts and different inflow conditions.

Extended Data Fig. 3 Predictive error of the WPGNN for plant power against test set A visualized parametrically across key dimensions including wind speed, turbulence intensity, layout type, turbine spacing and plant capacity.

For the continuous plots, the solid lines represent the median error, the colored regions show the 25-75% range of errors, and the dotted lines show the 1.5x interquartile range. Similarly, the box plots display the median error line in each box, the 25-75% box limits, and the 1.5x interquartile range as whiskers. The error rates are plotted separately for two cases, one where turbines are randomly yawed and another where yawing has been optimized. Random yaws are used in training and testing data to ensure that WPGNN learns to generalize about how wake effects propagate through a wind plant via complex turbine interactions. Overall, the plant power relative errors are small, except as the wind speed approaches the lower cut-in limit (4 m/s). Here, relative errors are driven up by vanishingly small normalization values. Rug plots along the x-axes show the relative frequency of training data along these dimensions.

Source data

Extended Data Fig. 4 Predictive error of the WPGNN for turbine-level power against test set A visualized parametrically across key dimensions including wind speed, turbulence intensity, layout type and turbine spacing.

For the continuous plots, the solid lines represent the median error, the colored regions show the 25-75% range of errors, and the dotted lines show the 1.5x interquartile range. Similarly, the box plots display the median error line in each box, the 25-75% box limits, and the 1.5x interquartile range as whiskers. The error rates are plotted separately for two cases, one where turbines are randomly yawed and another where yawing has been optimized. Random yaws are used in training and testing data to ensure that WPGNN learns to generalize about how wake effects propagate through a wind plant via complex turbine interactions. The majority of the turbine relative errors are within a couple of percent. Rug plots along the x-axes show the relative frequency of training data along these dimensions.

Source data

Extended Data Fig. 5 Randomly selected examples, taken from test set B, of wind plant layouts and corresponding accuracy of the WPGNN predictions for complete response surfaces.

For a given layout (a) and (d), the response surfaces (b) and (e) visualize predicted plant power output for all possible combinations of inflow direction and wind speed (shown here above turbine cut-in speed of 4 m/s). The cases shown here assume reference controls (that is, no wake steering via yaw). Predictive error of the WPGNN (evaluated relative to FLORIS) across the response surfaces is captured in (c) and (f). As predicted, maximal waking occurs for the plant in (a) when inflows have greatest alignment with the orientation of the turbine strings, at approximately 30 and 200 degrees in this case. For both visualized scenarios, relative errors are within an acceptable range of a couple percent with the largest errors occurring as the plant approaches the lower bound cut-in threshold.

Extended Data Fig. 6 Predictive error for the relative improvements obtained from the WPGNN-based optimization using test set C across the three objectives.

The box plots display the means as scatter points, the median lines in each box, the 25-75% box limits, and the 1.5x interquartile range as whiskers. Note that for the validation of the land area minimization objective, we report errors in predictions of energy density as the more meaningful metric for evaluating WPGNN outputs since the plant footprint is not dependent on FLORIS power outputs. Errors are obtained by computing FLORIS power outputs for 10% of the output designs from the three optimization studies and comparing to the WPGNN reported improvements. The 10% of cases were chosen to span all of the geographic regions and a wide range of plant capacities. The small discrepancy between the WPGNN-predicted improvements and the target FLORIS quantities provide confidence in the scalable surrogate modeling approach.

Source data

Extended Data Fig. 7 Stylized examples of the four canonical wind plant layout types generated by the Plant Layout Generator (PLayGen).

For a given layout type and number of turbines, PLayGen is able to generate randomized plant layouts that are consistent with a specified average turbine spacing parameterization. Hypothetical wind plants developed by PLayGen are used as inputs to train and test the Wind Plant Graph Neural Network (WPGNN).

Supplementary information

Supplementary Information

Supplementary Figs. 1–4 and Table 1.

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Source Data Fig. 4

Statistical source data for Fig. 4b.

Source Data Extended Data Fig. 3

Statistical source data for Extended Data Fig. 3.

Source Data Extended Data Fig. 4

Statistical source data for Extended Data Fig. 4.

Source Data Extended Data Fig. 6

Statistical source data for Extended Data Fig. 6.

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Harrison-Atlas, D., Glaws, A., King, R.N. et al. Artificial intelligence-aided wind plant optimization for nationwide evaluation of land use and economic benefits of wake steering. Nat Energy (2024). https://doi.org/10.1038/s41560-024-01516-8

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