Two-stage distributionally robust optimization-based configuration for a wind-PV- storage energy system incorporating seasonal hydrogen storage

Under the backdrop of global energy transition, the high-penetration wind and solar integration is facing the dual challenges of output uncertainty and seasonal fluctuation. Against this backdrop, this study presents a wind-PV-storage energy system incorporating seasonal hydrogen storage to facilitate large-scale cross-seasonal renewable energy utilization. To handle multiple uncertainties in wind-PV output and electricity load, an uncertainty set based on composite norms is constructed. A two-stage distributionally robust optimization model based on probability distribution is designed. The first-stage minimizes the system's average annual investment cost to optimize equipment capacities. The second-stage minimizes operation and maintenance costs considering power curtailment penalty in the worst scenario probability distribution of uncertain parameters, to optimize equipment output and the start-stop states of energy storage devices. The resolution of the model is achieved efficiently via the column and constraint generation algorithm. Case studies demonstrate that the system with seasonal hydrogen storage significantly enhances wind-solar complementarity utilization and ensures a stable power supply across seasons compared to non-hydrogen configurations, bringing the system curtailment rate down from 37.68% to 5.00% more cost-effectively. Comparative analysis confirms that the two-stage distributionally robust optimization model based on composite norms achieves a more optimal balance between robustness and economic efficiency.