Optimization of wind farms for communities

Optimization of wind farms for communities

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Energy supplies are moving away from environmentally damaging, finite, and expensive fossil fuels to renewable energy resources through technological innovations. Wind energy is one of the most advanced renewable energy resources due to the extensive research that has been ongoing over the last decades to optimize aerodynamic performance of wind turbines, structural design of wind turbines, control strategies, site selection, and the layout of wind farms. This chapter outlines fundamental elements of wind-farm-layout optimization including optimization parameters, objective functions, wake loss models, and search methods. Optimization parameters include base location, number, rotor diameter, hub height, rotational direction, and yaw angle of wind turbines, as well as shape of wind farm area. In the wake loss models section, all existing wake-loss models including large eddy simulation, nonlinear and linearized Reynolds-averaged Navier-Stokes models, stochastic models, kinematic models, and empirical models are discussed. In addition, different search methods, from simple greedy search algorithms to advanced genetic algorithms (GAs), are briefly reviewed and compared.

Chapter Contents:

  • Abstract
  • 3.1 Introduction
  • 3.2 Objective functions and optimization variables
  • 3.2.1 Objective functions
  • 3.2.2 Optimization variables
  • 3.3 Wake-loss models
  • 3.3.1 Large eddy simulations
  • Governing equations
  • The actuator line model
  • 3.3.2 Nonlinear Reynolds-averaged Navier–Stokes (RANS) models
  • Governing equations
  • The actuator disk model
  • 3.3.3 Stochastic models
  • 3.3.4 Linearized RANS models
  • Ainslie model
  • Fuga model
  • 3.3.5 Empirical wake models
  • 3.3.6 Kinematic (analytical) models
  • PARK
  • Xie and Archer (XA) model
  • Bastankah and Porté-Agel (BPA) model
  • Larsen model
  • Frandsen model
  • Geometric model
  • Wake overlapping
  • 3.4 Search algorithms
  • 3.5 Practice your knowledge
  • 3.5.1 Case I: Shape of the wind farm
  • 3.5.2 Case II: Wake of wind turbines
  • 3.5.3 Case III: Wind speed deficit in wind farms
  • 3.5.4 Case IV: Yaw angle of wind turbines
  • 3.5.5 Case V: Variation of power production with wind direction
  • 3.5.6 Case VI: Surface roughness
  • 3.5.7 Case VII: Inner turbines versus outer turbines
  • 3.5.8 Case VIII: Wind farm noise production
  • 3.5.9 Case IX: Hub height optimization
  • 3.5.10 Case X: Fatigue loads
  • 3.5.11 Case XI: Turbine type
  • 3.5.12 Case XII: Atmospheric stability
  • 3.5.13 Case XIII: Wind farms and hurricanes
  • References

Inspec keywords: aerodynamics; search problems; genetic algorithms; wakes; stochastic processes; wind turbines; wind power plants

Other keywords: control strategies; greedy search algorithms; aerodynamic performance optimization; large eddy simulation; site selection; optimization parameters; structural design; rotor diameter; objective functions; linearized Reynolds-averaged Navier-Stokes models; wind turbines; kinematic models; technological innovations; wind energy; wind farm optimization; rotational direction; expensive fossil fuels; advanced renewable energy resources; yaw angle; energy supplies; advanced genetic algorithms; hub height; nonlinear Reynolds-averaged Navier-Stokes models; search methods; wind-farm-layout optimization; base location; stochastic models; empirical models; wake loss models; wake-loss models

Subjects: Wind power plants; Combinatorial mathematics; Other topics in statistics; Optimisation techniques

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