Genetic Algorithm-Driven Design of CCUS and Hydrogen Pipeline Networks: Decentralised Expansion with Complex Geographical Constraints

The development of Carbon Capture, Transport, and Storage (CCTS) and hydrogen pipeline networks is crucial for achieving deep decarbonisation in industrial sectors. However, existing network design models often assume perfect foresight, limiting their applicability to real-world infrastructure planning, which is inherently uncertain and iterative. This study introduces a novel rolling-horizon methodology for pipeline network expansion, leveraging a genetic algorithm-based approach that allows for adaptive routing and incremental infrastructure development. By comparing rolling-horizon designs to 2050-optimised networks in a case study of the Humber region in the UK, the analysis highlights the trade-offs between adaptability and cost efficiency. Results indicate that while rolling-horizon approaches better reflect real-world decision-making, they also introduce inefficiencies, increasing capital expenditures by approximately 8% for both hydrogen and CCTS infrastructure. Additionally, the study examines the economic risks associated with transitioning from blue to green hydrogen production, revealing a 20-fold increase in CCTS costs per tonne of CO2 when hydrogen production shifts to green. The findings underscore the importance of integrating uncertainty-aware strategies in infrastructure planning to mitigate inefficiencies while maintaining adaptability to technological and policy changes.