Constructing reliable clean energy facility network under ambiguous distribution information

In the context of the transition from clean energy to sustainable development, as an emerging low-carbon energy carrier, hydrogen is of crucial importance. The planning of a hydrogenation facility network (HFN) with a reliable hydrogen supply has thus become an important research topic in the field of hydrogen energy provision. This study investigates the planning of the HFN from the perspectives of economic cost and network reliability, under conditions of uncertain hydrogen supply capacity. To address the challenges posed by uncertainty, the renewable energy-powered hydrogen production process is categorized into multiple scenarios. We formulate an ambiguity set using the Kullback-Leibler (KL) divergence and establish ambiguous chance constraints, thereby proposing a novel bi-objective distributionally robust HFN optimization model (DR-HFN). Based on the decision-maker’s preferences in practice, an economic-oriented optimization model and a reliability-oriented optimization model are developed, respectively. Subsequently, a mixed-integer linear programming (MILP) representation is derived from the proposed model to ensure computational tractability. A customized branch-and-cut (BC) algorithm is further designed to enhance the model’s solvability and scalability. Finally, a practical case study conducted in Guangdong Province, China demonstrates that our proposed method can withstand uncertainties in hydrogen supply capacity with a probability of 90%. Moreover, the comparison with the sample average approximation (SAA) method shows that our method only generates a robustness cost of about 0.087% when addressing distributional uncertainty. This further demonstrates that our method is capable of generating high-quality decision solutions while also exhibiting strong robustness and applicability.