Optimal Sizing for Large-Scale In-Situ Direct Seawater Electrolysis System to Minimize Levelized Cost of Hydrogen

In-situ direct seawater electrolysis (ISDSE) systems are a promising solution for large-scale hydrogen production, leveraging seawater as a readily available resource. This paper proposes a mixed-integer linear fractional programming (MILFP) model for optimizing the size of large-scale ISDSE systems powered by offshore wind farms. The model considers key system components, including novel seawater desalination, dynamic electrolysis, battery energy storage, and hydrogen storage, to minimize the levelized cost of hydrogen (LCOH). Further, to address the computational challenges of the proposed MILFP model, a model equivalent decomposition (MED) method is employed to reduce problem size, followed by a bounded Dinkelbach algorithm (BDA) to formulate and solve the resulting mixed-integer linear programming (MILP) subproblems. Finally, three real-life cases in China are studied to verify the effectiveness of both the model and solution method. This work highlights the importance of sizing optimization in enhancing the economic feasibility and production potential of large-scale ISDSE systems.