Model predictive estimation of wind power for coordinated generation

To follow recently released grid code requirements and support grid frequency, Transmission system operators (TSOs) have to change the operation modes of their wind turbines (WTs) from maximum power point tracking (MPPT) to de-loading and, when possible, from grid following (GFL) to grid forming (GFM) mode. Thus, the available power feedback signal from each WT to TSO is pivotal. Inaccurate estimation of WT available power leads to wrong set-point assignment from TSO and a possible dramatic impact on grid stability. This paper suggests using a model predictive estimation (MPE) algorithm to estimate wind turbines’ available power. Among other online model learning methods, MPE can solve an optimization problem in a receding horizon and use the adaptation and prediction features to estimate time-varying parameters online. Receiving an accurate wind turbine available power will facilitate the TSO’s role in providing each WT with the optimal operation set-points based on the reserved power at each time step.