Synthetic inertial control of wind farm with BESS based on model predictive control

Abstract

Wind farms (WFs) can provide controlled inertia through synthetic inertial control (SIC) to support system frequency recovery after disturbances. This study proposes a model predictive control (MPC)-based SIC for a WF consisting of wind turbines (WTs) and a battery storage energy system (BESS). In the proposed MPC-SIC, the active power output of the WTs and BESS during the SIC are optimally coordinated in order to avoid over-deceleration of the WTs' rotor, and minimise the loss of extracted wind energy during the SIC and degradation cost of the BESS. The IEEE 39-bus system with a WF consisting of 100 WTs and a BESS is used to validate the performance of the proposed MPC-SIC. Case studies show that, compared with the conventional SIC, the minimum rotor speed among all WTs with MPC-SIC can be improved by 0.08–0.11 p.u., the loss of captured wind energy of WF with MPC-SIC can be reduced by 12–64% and the degradation cost of the BESS with MPC-SIC can be reduced by 72–83%. The results proves that with the proposed MPC-SIC, the WF can avoid the over-deceleration of the WTs' rotor and reduce the operation cost of the WF by improving the efficiency of wind energy usage and lifetime of the BESS.