Optimized scheduling of wind-solar energy storage system using adaptive variable step size SMPC

Due to the volatility and uncertainty of renewable energy, a significant amount of wind and solar power is wasted. With the increasing maturity of battery manufacturing, the promotion of the “new energy + energy storage” model allows for the absorption of wind and solar energy through battery charging and discharging, which can be coordinated with peak shaving in the power system. To address the issue of the rolling optimization step affecting prediction accuracy in the Stochastic Model Predictive Control (SMPC) algorithm, this paper establishes a model for wind-solar energy storage systems and innovatively proposes an adaptive variable step size SMPC algorithm. Through comparison with simulation data, the proposed variable-weight adaptive SMPC algorithm improves prediction accuracy and disturbance resistance, effectively reducing the impact of wind and solar power generation uncertainty on system scheduling and increasing the utilization rate of energy storage stations. The research findings will provide theoretical support for the optimization scheduling and capacity configuration of wind-solar energy storage.