Efficient Optimization of Large Wind Farms for Real-Time Control

Jennifer Annoni, Christopher Bay, Timothy Taylor, Lucy Pao, Paul Fleming, Kathryn Johnson

Research output: Contribution to conferencePaperpeer-review

44 Scopus Citations

Abstract

Wind turbines in a wind farm typically operate individually to maximize their own performance regardless of the impact of aerodynamic interactions on neighboring turbines. Properly coordinating turbines, by operating some turbines suboptimally, within a wind farm has the potential to improve overall wind farm performance. Computing the optimal control strategy under varying atmospheric conditions can be computationally intense for large wind farms. As wind power farms increase in size and related models become more complex, computationally efficient algorithms are needed to perform real-time optimization and control. This study proposes a distributed optimization framework and computationally efficient wake steering wind farm control strategy that uses the yaw angle of a turbine to alter the behavior of a turbine wake and minimize turbine interactions. This computational efficiency allows the strategy to be feasible for real-time control.

Original languageAmerican English
Pages6200-6205
Number of pages6
DOIs
StatePublished - 9 Aug 2018
Event2018 Annual American Control Conference, ACC 2018 - Milwauke, United States
Duration: 27 Jun 201829 Jun 2018

Conference

Conference2018 Annual American Control Conference, ACC 2018
Country/TerritoryUnited States
CityMilwauke
Period27/06/1829/06/18

Bibliographical note

See NREL/CP-5000-70937 for preprint

NREL Publication Number

  • NREL/CP-5000-73416

Keywords

  • distributed optimization
  • real time
  • wind energy
  • wind farm control

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