Existing wind-storage dispatch strategies often overlook the optimization of energy storage utilization and the impact of fluctuations in tie-line power. To address these issues, a two-stage wind-storage dispatch strategy is proposed. In the day-ahead scheduling stage, a multi-objective optimization model is formulated to minimize system operating costs, wind curtailment, and maximize energy storage utilization. The model is solved using a multi-objective particle swarm optimization (MOPSO) algorithm. The model fully accounts for the volatility of renewable energy sources such as wind power and photovoltaic power, and improves energy storage utilization efficiency and dispatch economy by optimizing the charge-discharge schedule of energy storage. In the intra-day scheduling stage, model predictive control (MPC) is employed to dynamically adjust the output of energy storage and dispatchable resources, minimizing scheduling errors and enhancing system stability. Simulation results demonstrate that the proposed strategy significantly improves system performance. Specifically, MPC reduces scheduling errors by 50%, limits exceedance by 57%, improves tie-line stability, increases wind power utilization by 15.6%, boosts energy storage efficiency by 12%, and lowers operating costs by 10.5%. These findings validate that the proposed strategy optimizes energy storage utilization, reduces scheduling errors, and enhances the reliability and economic efficiency of the wind-storage system.