Distributionally Robust Optimization for integrated energy system accounting for refinement utilization of hydrogen and ladder-type carbon trading mechanism

This study presents a hydrogen-IES with hydrogen refinement utilization under the framework of ladder-type carbon trading mechanism (LCTM). To navigate the uncertainties associated with RE in the proposed hydrogen-IES, we introduce a data-driven Distributionally Robust Optimization (DRO) method based on an innovative ambiguity set construction approach. This method leverages nonparametric Kernel Density Estimation (KDE) to fit the error probability distribution function and determine the predicted interval for wind turbine (WT) and photovoltaic (PV) output under a given confidence level. Subsequently, Latin hypercube sampling (LHS) and a reduction method based on probability distance are employed to generate original and typical scenarios, respectively. Ultimately, the construction of ambiguity sets imposes constraints on typical scenarios and probabilities using 1-norm and ∞-norm. Case study is conducted to validate the effectiveness of the proposed innovations. The results underscore the significant advantages of hydrogen refinement utilization within the proposed framework. Specifically, it demonstrates notable economic benefits, with costs lower by 11.98%, 2.46%, and 1.53% compared to scenarios omitting gas synthesis, hydrogen blending combustion in CHP and GB, and hydrogen storage, respectively. Moreover, hydrogen refinement exhibits superior performance in accommodating WT and PV, with ratios 25.29%, 0.43%, and 1.91% higher than alternative methods, with lower carbon emissions of 42.23%, 11.91%, and 6.13%. The LCTM can reduce more carbon emissions by 7.60% and 0.145% compared to scenarios without carbon trading mechanisms and with fixed-price trading mechanisms. The probability distribution derived from nonparametric KDE aligns closely with the true distribution, fostering a more objective analysis and mitigating the conservatism of the ambiguity set and optimization scheduling. Finally, sensitivity analyses are conducted in detail, including hydrogen blending ratio, basic price and growth ratio of LCTM.