Model Predictive Control Method of Multi-Energy Flow System Considering Wind Power Consumption

In recent years, global energy shortages and environmental pollution have intensified. With the large-scale development of new energy wind energy, there is also a major problem-insufficient absorption capacity. In order to promote large-scale wind power consumption, a model predictive control method based on wind hydrogen coupled power generation system is proposed. Firstly, the mathematical models of equivalent state of charge in wind power, hydrogen energy storage systems (HESS), and gas storage tanks are analyzed. Secondly, the optimization objective function is to maximize the local wind energy consumption and minimize the energy interaction between the main grid. Establish an SSM prediction model based on the MPC strategy. Genetic optimization algorithm is used for rolling solution. Aiming at the interference and prediction error generated during the operation of the system, a feedback mechanism is introduced to embed it into the MPC framework. Then, the rolling time domain method is used to compensate for system interference. Finally, a case study was conducted based on actual measurement data from a certain area in the Netherlands. By comparing the wind power dissipation effects of the system during operation, it is verified that the proposed method can effectively reduce interactive power consumption. Maximize the local consumption of wind power.