Distributionally robust optimization of non-fossil fuels processing network under uncertainty

Under the ever more prominent climate issues concerning greenhouse gas emission reduction, governments have sped up the pace of Decarbonization while meeting demand, and traditional energy companies have also begun to advance strategy layout and transformation, of which gasoline substitute, diesel substitute, hydrogen and ammonia are the highly potential candidates in the future. This paper investigates the design of non-fossil fuels processing network with renewable feedstocks under various demand uncertainties in the framework of twostage distributionally robust optimization (TSDRO) model. To handle the difficulties of estimating the exact probability distribution of the considered uncertainties, 1-norm Wasserstein metric is applied to construct the ambiguity set, and the resulting tractability issue is recast in terms of sufficiently expensive recourse and strong duality theorem. Compared with two-stage stochastic programming and two-stage robust optimization, optimal design decisions of the Wasserstein TSDRO of base case and net-zero emission case exhibit the balance between the optimality and robustness, which lay a foundation to ensure an orderly transition towards sustainable and resilient network systems under various demand uncertainties, as well as a pathway to alleviate the abandon or redundancy power phenomena existing in renewable power through employing non-fossil fuels as the storage medium.

Automated summary

This paper investigates the design of non-fossil fuels processing network with renewable feedstocks under various demand uncertainties. Using the two-stage distributionally robust optimization model, the optimal design decisions balance optimality and robustness, laying a foundation for a transition towards sustainable and resilient network systems and addressing the issues of abandoned or redundant renewable power.