Distributionally robust optimization-based scheduling for a hydrogen-coupled integrated energy system considering carbon trading and demand response

Addressing climate change and facilitating the large-scale integration of renewable energy sources (RESs) have driven the development of hydrogen-coupled integrated energy systems (HIES), which enhance energy sustainability through coordinated electricity, thermal, natural gas, and hydrogen utilization. This study proposes a two-stage distributionally robust optimization (DRO)-based scheduling method to improve the economic efficiency and reduce carbon emissions of HIES. The framework incorporates a ladder-type carbon trading mechanism to regulate emissions and implements a demand response (DR) program to adjust flexible multi-energy loads, thereby prioritizing RES consumption. Uncertainties from RES generation and load demand are addressed through an ambiguity set, enabling robust decision-making. The column-and-constraint generation (C&CG) algorithm efficiently solves the two-stage DRO model. Case studies demonstrate that the proposed method reduces operational costs by 3.56%, increases photovoltaic consumption rates by 5.44%, and significantly lowers carbon emissions compared to conventional approaches. Furthermore, the DRO framework achieves a superior balance between conservativeness and robustness over conventional stochastic and robust optimization methods, highlighting its potential to advance cost-effective, low-carbon energy systems while ensuring grid stability under uncertainty.