Data-driven robust optimisation of hydrogen infrastructure planning under demand uncertainty using a hybrid decomposition method

In the race towards “Net-zero”, hydrogen has emerged as one of the key alternatives to carbon-based fossil fuels for a sustainable decarbonisation. This work studies the spatially explicit multi-period hydrogen infrastructure planning under demand uncertainty that contributes to the heat decarbonisation in Great Britain. Demand uncertainty surrounding future hydrogen supply chains poses challenges to cost optimisation and system security, so uncertainty-resilient policies are required to ensure robust operations. In this work, we employ data-driven robust optimisation to develop a framework for uncertainty-aware representative days explicitly characterised by polyhedral uncertainty sets. The proposed framework is applied on a multi-period mixed-integer linear model with dual temporal resolution which aims to determine the optimal yearly investment decisions and hourly operational decisions for the hydrogen infrastructure planning under demand uncertainty. To efficiently solve the large-scale two-stage adaptive robust optimisation problem, a hybrid decomposition algorithm is developed based on a two-step hierarchical procedure and the column-and-constraint generation method, which can significantly reduce the computational complexity. The optimisation results highlight how uncertainty can result in the total cost increase, and verify the advantages on controlling solution conservatism in the adaptive robust optimisation compared to the static robust optimisation.