Abstract
The growing penetration of renewable energy sources and power-to-hydrogen (P2H) systems demands high-fidelity, large-scale optimization frameworks that capture the nonlinear physics of both AC power flow and hydrogen thermodynamics. However, existing approaches rely on DC approximations and simplified electrolyzer models, neglecting critical operational constraints. As a result, accurately modeling such systems leads to large-scale nonlinear programs that are computationally intractable for conventional CPU-based solvers. This motivates the need for scalable optimization frameworks capable of handling both physical fidelity and computational complexity. This paper proposes a fully GPU-native framework for solving large-scale multi-period AC optimal power flow (AC-OPF) problems with integrated power-to-hydrogen systems. High-fidelity thermodynamic models of hydrogen production, compression, cooling, and storage are coupled with AC power flow constraints, resulting in large-scale nonlinear programs (NLPs) with up to 14.4 million variables and 22.4 million constraints in the largest benchmark case (9, 591-bus network with a 168-hour horizon). To enable scalable solutions, condensed-space KKT reformulations and GPU-accelerated sparse Cholesky factorization are employed within an end-to-end GPU optimization pipeline. Numerical results on benchmark networks up to 9, 591 buses demonstrate 10-600× speedups over CPU solvers, whereas CPU-based solvers fail to converge on the largest instances. Operational studies further highlight the importance of thermodynamic constraints in realistic hydrogen system scheduling.