Zhou, X; Efthymiadou, ME; Papageorgiou, LG; Charitopoulos, VM; (2024) Data-driven robust hydrogen infrastructure planning under demand uncertainty using a hierarchical-based decomposition method. Computer Aided Chemical Engineering , 53 pp. 3397-3402. 10.1016/B978-0-443-28824-1.50567-6.

Text ESCAPE_34_final_2711.pdf - Accepted Version Access restricted to UCL open access staff until 27 June 2025. Download (332kB)

Abstract

Strategic planning of national hydrogen infrastructure constitutes a prominent topic within the “Net-zero” agenda. Nevertheless, uncertainty surrounding future hydrogen supply chains could lead to significant economic loss and even jeopardise the security of energy systems. This work aims to provide an uncertainty-resilient scheme to alleviate these disadvantages. We propose a data-driven adaptive robust mixed-integer linear programming (MILP) optimisation framework with 5-year steps 2035-2050 and hourly resolution by explicitly accounting for demand uncertainty typically introduced in energy planning models through the introduction of representative days. To solve this complex MILP problem, we propose an enhanced column-and-constraint generation algorithm based on a hierarchical method that can significantly reduce the computational effort.

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