Abstract Energy losses due to wind farm clustering and wind farm interaction are rarely well represented in the wind farm design process because of the lack of fast models that can accurately account for neighboring wind farm wakes. A recently developed solution is the actuator wind farm (AWF) model, which is a Reynolds-averaged Navier-stokes (RANS) based wind farm parametrization that models a wind farm as a distributed thrust force and applies a global wind farm thrust coefficient controller. We propose an improved version of the AWF model, where each turbine employs a local thrust force controller and uses turbine thrust and power coefficients as input to better handle inhomogeneous inflow conditions. The proposed AWF model shows improved performance compared to the original AWF model in terms of predicted wind turbine power of a downstream wind farm that operates in a partial wake of an upstream wind farm, without significantly increasing the computational effort. However, the annual energy production (AEP) wake losses of a large wind farm cluster are nearly unaffected by using local or global control and input because the largest impact is found near the cut-in wind speed, which does not contribute much to the AEP wake losses.