Optimization Approach for Hydrogen Infrastructure Planning Under Uncertainty

Abstract

Toward the Net-Zero goal, deciphering trade-offs in strategic decisions for the role of hydrogen is vital for transitioning to low-carbon energy systems. This work proposes a two-stage stochastic optimization framework to provide insights for infrastructure investments in hydrogen production, storage, transmission, and CO₂ capture and storage. The mixed-integer linear programming (MILP) model aims to minimize total system cost with detailed spatiotemporal resolution to meet hydrogen demand in Great Britain. Uncertainty is considered in hydrogen demand, gas, and technology costs, as well as renewables and biomass availability. To address the resulting combinatorial complexity, scenarios are reduced using forward scenario reduction. Optimization results indicate that a combination of autothermal reforming and biomass gasification with carbon capture and storage (CCS) is the most cost-efficient strategy under uncertainty. A what-if analysis explores the impact of water electrolysis penetration on the production mix. The results demonstrate that considering uncertainties provides a risk-averse strategy for decision-making in low-carbon hydrogen pathways.