Abstract Fast engineering wake models are the backbone of wind farm annual energy production (AEP) estimators, whereas the addition of induction zone models in existing tools is a more recent response to rising concerns over wind farm blockage associated losses. Here, “blockage” describes the combined induction fields of all wind turbines inside a farm. Unlike the term might suggest, blockage not only reduces flow speeds, but also increases them; for instance along the outer edges of wind farms. Evaluating the overall impact on AEP of these gains and losses necessitates accurate wind farm induction models. Whilst engineering wake models are tuned to predict wind farm performance, existing induction zone models are all derived for accurately predicting the near field induction not the far field value—important for wind farm simulations. This paper presents the induction models implemented in the wind farm simulation tool PyWake, as well as results from novel analytical models and compares their far field predictions to RANS-AD simulations of different turbines. We demonstrate that when including blockage in AEP simulations, the downstream speed-ups need to be included to avoid an unrealistic bias toward AEP loss and that wake expansion significantly impacts induction at rated thrust levels.