Robust Design of a Green Hydrogen Supply Chain Under Spatiotemporal Imbalance

To reduce substantial renewable energy spillage and satisfy the increase in green hydrogen demand in China, conversion of surplus renewable energy into hydrogen via electrolyzers has attracted increasing attention in recent years. Seasonal storages and interregional hydrogen supply chains are commonly employed to eliminate the inherent spatiotemporal imbalance between renewable energy supply and hydrogen demand. Moreover, the efficiency and reliability of the hydrogen supply chain must be ensured. This paper addresses a robust design and planning problem for an integrated hydrogen supply chain in which the uncertainty of hydrogen demand is considered. We propose a two-stage robust optimization model with a budget-based uncertainty set, and the corresponding robust approximation model is derived. We develop a Benders decomposition based algorithm to solve the resulting model. Extensive numerical experiments reveal that the proposed algorithm outperforms CPLEX. A case study using real data is presented to demonstrate the applicability of the proposed model. Finally, conclusions are drawn, and several future research directions are outlined.
Note to Practitioners
—This paper was motivated by the problem of renewable energy spillage in green manufacturing systems. The intermittent nature of renewable energy leads to serious spillage and mismatch of sources and demand. This paper proposes a novel method by converting surplus renewable energy into hydrogen and designing a green hydrogen supply chain to deal with the inherent spatiotemporal imbalance between renewable energy supply and hydrogen demand. In this paper, we characterize a more reliable way to handle to uncertainty in demand side. Practitioners can deploy the proposed model to the facility location and capacity problems in risk averse systems. We then design a mathematical algorithm incorporated with accelerating approaches significantly outperforms CPLEX. Practitioners can apply our algorithm to cope with facility location and capacity problems with inventory decisions in multiple levels. Real data experiments demonstrate the applicability of the proposed model and several managerial are explored. In future research, we will address the design of multi-uncertainties in green manufacturing systems.