Integrated location-allocation-inventory optimization for offshore wind-to-hydrogen systems

Offshore wind-to-hydrogen systems are a novel class of renewable energy infrastructures to produce zero-emission hydrogen. This paper investigates the integrated planning of such complex systems and proposes a two-stage mixed-integer linear programming model to jointly optimize all interrelated siting, assignment, transportation, and inventory decisions related to the wind turbines, electrolyzer platforms, and storage devices. The model minimizes total system cost over a multi-period horizon under uncertainties in wind generation and hydrogen demand. To apply the model in a realistic context, we develop a novel exact algorithm based on Benders decomposition to solve the model. The algorithm integrates three acceleration strategies to accelerate convergence, i.e., warm-start initialization, Pareto-optimal cuts, and lower-bound lifting inequalities. Based on real data and synthetic data, we conduct computational experiments to demonstrate the robustness of our proposed model and the optimality of our proposed algorithm. The computational results also demonstrate that the accelerating strategies significantly reduce the computation time. Moreover, sensitivity analyses are conducted and managerial insights are derived that are useful for the offshore wind-to-hydrogen system managers. For example, the total cost exhibits a non-monotonic relationship with the number of electrolyzer platforms, initially decreasing and subsequently increasing; shorter decision period durations improve responsiveness but may increase total cost; evenly distributed electricity generation across periods improves system efficiency by decreasing storage and shortage risks; far-offshore settings are particularly well-suited for our wind-to-hydrogen system. Our proposed methodology could support the managers to find the proper settings on electrolyzer platforms’ amounts, as well as the transportation period durations for wind-to-hydrogen systems.