Abstract
The increasing demand for new energy sources in China, coupled with the abundant waste, has prompted the exploration of converting excess renewable energy into hydrogen through electrolyzers. However, the uncertainty surrounding renewable energy supply, its remote distribution, and regional imbalance with demand pose significant challenges for designing and planning an integrated hydrogen supply chain. This paper addresses this challenge by proposing a two-stage robust optimization model based on ellipsoidal uncertainty sets. We derive a robust approximation model and develop an algorithm using generalized Benders decomposition to solve the resulting model. Extensive numerical experiments demonstrate the superior performance of the proposed algorithm compared to CPLEX. Additionally, a case study utilizing real data from China is presented to showcase the practicality and effectiveness of the proposed model. Finally, we draw conclusions and highlight potential avenues for future research.
