A decomposition algorithm using multiple linear approximations to solve integrated design and scheduling optimization frameworks — case study of nuclear based co-production of electricity and hydrogen

This study introduces a modeling framework for optimizing integrated design and scheduling (IDS) of grid-interactive facilities with heat and mass integration and multiple co-products. Using a novel decomposition algorithm utilizing multiple linear approximations, the framework enables efficient optimization over 8760 h of annual operation while including a large number of nonlinear constraints, improving scalability and performance compared to previous methods. We apply the framework to optimize a nuclear power plant (NPP) and high-temperature steam electrolysis (HTSE) co-production system. The case study shows that co-production can be cost-competitive with standalone HTSE under realistic scenarios. We perform a sensitivity analysis of the cost of H₂ as a function of HTSE capital costs and current density limits, which suggests that early R&D should focus on increasing current density to 3–4 A/cm² before targeting cost reductions.