A two-stage distributionally robust optimization approach for integrated energy systems: Coordinated configuration of CCUS and hydrogen utilization

Facing global energy shortages and carbon emission reduction requirements, the optimal configuration of integrated energy systems (IESs) has become a critical research direction. This study proposes a two-stage distributionally robust optimization (DRO) method for IES configuration that considers carbon capture, utilization, and storage as well as comprehensive hydrogen energy utilization. First, an IES architecture incorporating a hydrogen-carbon coordinated power system (HCPS) module is constructed. Specifically, this module achieves carbon-hydrogen coupling by directing captured CO2 and green hydrogen into a methane reactor for synthetic natural gas production, establishing a ‘capture-conversion-reutilization’ closed loop. Crucially, a two-stage DRO model based on the Wasserstein distance is developed to coordinate capacity configuration and operational scheduling under renewable scenarios generated via improved K-means clustering. The model is then transformed into a mixed-integer linear programming problem and solved using the Big-M method and strong duality theory. Finally, the proposed method is validated through a case study using data from an IES in the coastal region of Zhejiang, China. The results show that, driven by this coupling mechanism, the IES integrated with HCPS reduces wind and photovoltaic curtailment costs by 82.8%, carbon emission costs by 43%, and the total daily cost by 2.3%, achieving synergistic improvement in both economic efficiency and low-carbon performance. Compared to the classical robust optimization model, the data-driven DRO model based on the Wasserstein ambiguity set achieves a lower total cost. When the sample size increases to 2000, the computation time is only 17.53s, significantly enhancing computational efficiency.