Optimal active power control of a wind farm equipped with energy storage system based on distributed model predictive control

Abstract

This study presents the distributed model predictive control (D-MPC) of a wind farm equipped with fast and short-term energy storage system (ESS) for optimal active power control using the fast gradient method via dual decomposition. The primary objective of the D-MPC control of the wind farm is power reference tracking from system operators. Besides, by optimal distribution of the power references to individual wind turbines (WTs) and the ESS unit, the WT mechanical loads are alleviated. With the fast gradient method, the convergence rate of the D-MPC is significantly improved which leads to a reduction of the iteration number. Accordingly, the communication burden is reduced. Case studies demonstrate that the additional ESS unit can lead to a larger WT load reduction, compared with the conventional wind farm control without ESS. Moreover, the efficiency of the developed D-MPC algorithm is independent from the wind farm size and is suitable for the real-time control of the wind farm with ESS.