Distributionally Robust Optimization Scheduling for Off-grid Hydrogen Systems Considering Wind and Solar Uncertainty

The surge in renewable energy penetration rates has unveiled the limitations of off-grid power systems in effectively integrating and accommodating renew able energy resources, as well as in reliably meeting load demands. This challenge is primarily attributed to the absence of active regulation equipment. This paper addresses the challenge of insufficient flexibility in existing off-grid grids by proposing an optimization strategy for an off-grid DC hydrogen energy system that incorporates the uncertainty of wind and solar energy. The strategy commences with the establishment of a hydrogen energy unit, comprising electrolyzers, hydrogen fuel cells, and hydrogen storage, based on historical wind power generation data. The objective is to minimize the expected values of start-stop costs for the electro-hydrogen system units and operational costs under various scenarios. It employs a data -driven distributionally robust optimization approach with norm-1 and norm- infinity constraints on the probabilistic distribution of typical wind and solar power output scenarios. The resulting optimization model is solved using a column -and -constraint generation (CCG) algorithm. Finally, simulation case studies are presented to validate the effectiveness of the proposed method in enhancing the operational flexibility of the system.