FloatingOffshore wind energy OffshoreOffshore Wind TurbinesWind turbine (FOWTs) presentOffshore a prospective solution capable ofDynamic layout optimization significantly increasing the share of renewable energy in the global energy mix. However, their relatively high levelized cost of energy hinders their widespread deployment. Wake effect, arising from aerodynamic interactions among turbines, plays a crucial role as it diminishes overall wind farms performance while increasing maintenance costs. In this study, an innovative approach is presented and analyzed to mitigate wake losses through dynamic repositioning of FOWTs, leveraging the additional degrees of freedom inherent in this technology. This strategy is applied to a 1 × 3 array of 5-MW FOWTs, where optimal positions are determined using a pattern search optimization algorithm. The repositioning is achieved using a model predictive controlModel predictive control-based controller (MPC) that manipulates the aerodynamic thrust force to reach the position targets. Numerical simulations, based on a floating wind farm model under fluctuating wind conditionsWind conditions, demonstrate a substantial 25% increase in energy production over a one-hour operational period compared to a wind farm lacking a repositioning mechanism.

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Floating Offshore Wind Farm Power Maximization Through Dynamic Layout Optimization Control

  • Timothé Jard,
  • Reda Snaiki

摘要

FloatingOffshore wind energy OffshoreOffshore Wind TurbinesWind turbine (FOWTs) presentOffshore a prospective solution capable ofDynamic layout optimization significantly increasing the share of renewable energy in the global energy mix. However, their relatively high levelized cost of energy hinders their widespread deployment. Wake effect, arising from aerodynamic interactions among turbines, plays a crucial role as it diminishes overall wind farms performance while increasing maintenance costs. In this study, an innovative approach is presented and analyzed to mitigate wake losses through dynamic repositioning of FOWTs, leveraging the additional degrees of freedom inherent in this technology. This strategy is applied to a 1 × 3 array of 5-MW FOWTs, where optimal positions are determined using a pattern search optimization algorithm. The repositioning is achieved using a model predictive controlModel predictive control-based controller (MPC) that manipulates the aerodynamic thrust force to reach the position targets. Numerical simulations, based on a floating wind farm model under fluctuating wind conditionsWind conditions, demonstrate a substantial 25% increase in energy production over a one-hour operational period compared to a wind farm lacking a repositioning mechanism.