Abstract: An optimization method based on economic stochastic model prediction control is proposed to address the impact of uncertainty on both sides of the source and load of the wind-solar-hydrogen coupling system. Firstly, according to the characteristics of the equipment in the wind-solar-hydrogen coupling system, a state-space model considering the start-stop state of the equipment is established. Then, the scenario generation technology is used to process wind and solar output, as well as electrical load prediction data to generate a scenario set that describes the system uncertainty. Finally, based on the generated scenarios, a mixed-integer linear programming problem is formulated under the designed economic stochastic model predictive control framework, and then the system is economically optimized and controlled. A scenario generation mechanism based on nonparametric prediction is proposed, which provides a scenario set that accurately describes the system′s uncertainty for the economic stochastic model prediction control method.Simulation results demonstrate the effectiveness of the proposed method in addressing uncertainty in the wind-solar-hydrogen coupling system, achieving a 5.89% reduction in operating costs compared to conventional stochastic model predictive control method, and a 13.25% reduction compared to conventional model predictive control method.