风光-氢-储耦合一体化系统是提高新能源消纳和促进氢经济发展的重要手段之一，但风光氢负荷的高随机性和系统模型求解速度慢的问题给系统优化调度带来了困难，为此提出了一种基于鲁棒模型预测控制（model predict control，MPC）-粒子群（particle swarm optimization，PSO）的多时间尺度优化调度模型。首先，为更精准地剖析风光负荷中的不确定性因素，采用变分模态分解（variational mode decomposition，VMD）技术，并采用动态时间规整（dynamic time warping，DTW）算法与K-medoids聚类方法结合，得到风光负荷的不确定性以及系统运行场景；而后，构建风光-氢-储耦合系统模型和基于鲁棒MPC-PSO的多时间尺度优化调度模型；继而，采用鲁棒MPC和PSO算法结合，实现系统运行滚动优化的快速解耦求解；最后，建立风光-氢-储耦合系统仿真算例进行分析。结果表明，所提方法提升了制氢的经济性和风光消纳率，与单一鲁棒MPC相比所提方法提高了求解效率。

The integrated wind-solar-hydrogen storage coupling system is one of the important means to improve the consumption of new energy and promote the development of hydrogen economy, but the high randomness of wind-solar-hydrogen load and the slow solution speed of the system model bring difficulties to the optimal scheduling of the system. Firstly, in order to analyze the uncertainty factors in the wind and solar load more accurately, the variational mode decomposition （VMD） technology is adopted, and the dynamic time warping （DTW） algorithm is combined with the K-medoids clustering method to obtain the uncertainty of the wind and solar load and the system operation scenario. Then, a wind-solar-hydrogen storage coupling system model and a multi-time-scale optimal scheduling model based on robust MPC-PSO are constructed. Then, the combination of robust MPC and PSO algorithm is used to realize the fast decoupling solution of rolling optimization of system operation. Finally, a simulation example of wind-solar-hydrogen-storage coupling system is established for analysis. The results show that the proposed method improves the economy of hydrogen production and the wind and solar absorption rate, and improves the solution efficiency compared with a single robust MPC.