Modeling and robust economic optimization of electricity-hydrogen-carbon integrated energy systems under uncertainty

Amid escalating energy crises and carbon emissions, sustainable integrated energy systems (IES) are imperative. However, uncertainties in renewable generation and load demand expose IES to operational risks and unexpected costs. To address these challenges, a robust low-carbon economic dispatch model integrating multiple uncertainties and electricity-hydrogen-carbon coupling (EHCC) is proposed. First, an EHCC model formulates energy flows and equipment interactions. Second, a two-stage robust optimization (RO) model using multi-interval uncertainty sets is constructed to minimize costs and enhance system robustness against source-load variations. Third, an outer parametric layer adaptively adjusts ladder carbon trading mechanism (LCTM) parameters to further reduce emissions. The inner RO model is solved via column-and-constraint generation (C&CG), while particle swarm optimization (PSO) solves the outer layer. Case studies validate the model’s effectiveness: (1) Compared to the baseline, the proposed approach reduces carbon emissions by 44.01% and operating costs by 22.34%. (2) It effectively alleviates the conservatism of traditional RO and stochastic methods. (3) Adaptive LCTM parameter tuning is crucial for balancing emissions and costs.