  Active yaw control (AYC) of wind turbines has been widely applied to increase
the annual energy production (AEP) of a wind farm. AYC efficiency depends on
the wind direction and the wind farm layout because an AYC method utilizes wake
deflection by yawing wind turbines. Conventional optimization of a wind farm
layout assumed that the swept areas of all wind turbines are aligned
perpendicular to the wind direction, thereby allowing non-optimal utilization
of an AYC method. Higher AEP can be obtained by joint optimization which
considers an AYC method in the layout design stage. Joint optimization of the
farm layout and AYC has been difficult due to the non-convexity of the problem
and the computational inefficiency. In the present study, a particle swarm
optimization based method is developed for joint optimization. The layout is
optimized with simultaneous consideration for yaw angles for all wind
velocities to obtain a globally optimal layout. A number of random initial
particles consisting of the layout and yaw angles of wind turbines reduce the
initial layout dependency on the optimized layout. To deal with the challenge
of large-scale optimization, the adaptive granularity learning distributed
particle swarm optimization algorithm is implemented. The improvement in AEP
when using a jointly optimized layout compared to a conventionally optimized
layout in a real wind farm is demonstrated using the present method.
