Frequency Optimization Control Strategy for Offshore Wind Power Based on RL-MPC Algorithm

To address the frequency fluctuations in the power system caused by the integration of offshore wind power, a frequency control strategy for offshore wind power based on reinforcement learning model predictive control (RL-MPC) algorithm is proposed. Firstly, considering the dynamic parameters of the model caused by changes in wind speed output, a frequency control model for the receiving end power grid with offshore wind power was constructed; then, reinforcement learning is used to optimize the parameters of model predictive control in the system in real-time, reducing the dependence of control schemes on accurate models and ensuring that the controller can adaptively adjust to changes in system parameters in the case of uncertain wind power output, in order to ensure the optimal frequency control effect; finally, the effectiveness of the proposed control strategy was verified by comparing the frequency modulation performance under different control strategies through simulation examples.