Abstract
Renewable power fluctuations and uncertain ammonia synthesis performance complicate operational coordination in green hydrogen–ammonia systems. This study develops a closed-loop prediction–scheduling framework using dual Gaussian processes to characterize renewable power and process performance. Calibrated lower and upper bounds enter material and energy constraints, while multi-objective particle swarm optimization balances operating cost and schedule variation. A joint-risk index selects plan retention, local repair, or global re-optimization, combined with plan refresh and conditional Pareto reuse in rolling horizon scheduling. The framework is evaluated with 24 h aggregated simulations using public meteorological data and published experimental data. Paired comparisons across seven fixed scenarios show that event-triggered and periodic scheduling both completed all 20 runs per scenario while satisfying all hard constraints. Under the reported serial timing protocol, event triggering reduced total controller runtime by 15.30–34.73%. Of 234 supplementary stress-test requests, 177 completed the day, including 176 that satisfied all hard constraints and one with a terminal audit failure; 57 stopped during the search. These results show that the framework coordinates probabilistic prediction, physical constraints, and scheduling responses under the specified simulation conditions, providing a reproducible approach to operational optimization of green hydrogen–ammonia systems.