Modeling and Observations of North Atlantic Cyclones: Implications for U.S. Offshore Wind Energy: Article No. 052702

Jiali Wang, Eric Hendricks, Christopher Rozoff, Matt Churchfield, Longhuan Zhu, Sha Feng, William Pringle, Mrinal Biswas, Sue Ellen Haupt, Georgios Deskos, Chunyong Jung, Pengfei Xue, Larry Berg, George Bryan, Branko Kosovic, Rao Kotamarthi

Research output: Contribution to journalArticlepeer-review

Abstract

To meet the Biden-Harris administration's goal of deploying 30 GW of offshore wind power by 2030 and 110 GW by 2050, expansion of wind energy into U.S. territorial waters prone to tropical cyclones (TCs) and extratropical cyclones (ETCs) is essential. This requires a deeper understanding of cyclone-related risks and the development of robust, resilient offshore wind energy systems. This paper provides a comprehensive review of state-of-the-science measurement and modeling capabilities for studying TCs and ETCs, and their impacts across various spatial and temporal scales. We explore measurement capabilities for environments influenced by TCs and ETCs, including near-surface and vertical profiles of critical variables that characterize these cyclones. The capabilities and limitations of Earth system and mesoscale models are assessed for their effectiveness in capturing atmosphere-ocean-wave interactions that influence TC/ETC-induced risks under a changing climate. Additionally, we discuss microscale modeling capabilities designed to bridge scale gaps from the weather scale (a few kilometers) to the turbine scale (dozens to a few meters). We also review machine learning (ML)-based, data-driven models for simulating TC/ETC events at both weather and wind turbine scales. Special attention is given to extreme metocean conditions like extreme wind gusts, rapid wind direction changes, and high waves, which pose threats to offshore wind energy infrastructure. Finally, the paper outlines the research challenges and future directions needed to enhance the resilience and design of next-generation offshore wind turbines against extreme weather conditions.
Original languageAmerican English
Number of pages25
JournalJournal of Renewable and Sustainable Energy
Volume16
Issue number5
DOIs
StatePublished - 2024

NREL Publication Number

  • NREL/JA-5000-90126

Keywords

  • energy system
  • machine learning
  • oceanography
  • surface waves
  • turbulence simulations
  • weather hazard
  • wind energy
  • wind turbines

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