Scaling Wind Power Innovation Assessment for Rapid Energy Transition with Artificial Intelligence

Andrew Glaws, Dylan Harrison-Atlas, Ryan King, Eric Lantz

Research output: NRELPresentation


Planning for energy system decarbonization requires new insights into the potential of renewable technologies, deployed at unprecedented scale, to meet urgent sustainability goals. However, limited scalability of current wind energy research tools restricts characterization of innovation impacts to isolated reference sites, challenging investment and decision making under rapid growth. We demonstrate the transformative potential of artificial intelligence (AI) to inform future technology advancement and energy systems design by leveraging a state-of-the-art surrogate model to conduct a series of fleet-wide wind plant layout optimizations for greater than 6,800 projected U.S. onshore buildout locations. We show how innovative wake steering technology can address an array of barriers to large-scale deployment and integration of wind power. Specifically, wake steering reduces required plant area by an average of 18% and could preserve upwards of 13,000 km2 for future greenfield deployment, potentially easing siting challenges associated with wind energy infrastructure. Further, by enabling reduced turbine spacing and increased energy production, flexible operations of wake steering improve levelized cost of energy, particularly for large plants and in land-constrained settings. Finally, optimizations that consider dynamic energy prices can deliver increased power production and revenue capture during high-value (often low-wind) periods, further bolstering plant economics. Our computationally efficient approach offers a pathway to accelerate nationwide geographic evaluation of innovative technologies.
Original languageAmerican English
Number of pages24
StatePublished - 2022

Publication series

NamePresented at the 39th USAEE/IAEE North American Conference, 23-26 October 2022, Houston, Texas

NREL Publication Number

  • NREL/PR-2C00-84403


  • graph neural networks
  • wake steering
  • wind energy


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