Dynamic optimization of green hydrogen production using weather forecasts

Integrating intermittent renewable energy with hydrogen production for downstream chemical processes remains challenging due to forecast uncertainty and strict operational constraints. To address this problem, a real-time dynamic optimization strategy is introduced, based on a receding-horizon framework that uses imperfect weather forecasts and compensates forecast–actual deviations through adaptive reference production adjustments. The methodology minimizes grid dependence, equipment stress, and constraint violations while ensuring hydrogen productivity targets. A one-year simulation with CAISO renewable profiles satisfies all operational constraints and achieves 99.997% of the annual target (8760 t H ₂ /y), whereas systematic violations arise without the proposed scheduling and buffer strategy. The economic analysis yields a levelized cost of 3.53 USD/kg H ₂ , competitive with literature values (1.50-7.50 USD/kg H ₂ ) and below current industrial benchmarks (4-12 USD/kg H ₂ ). CO 2 emissions remain below 1 kg CO ₂ /kg H ₂ , representing over 90% reduction compared to steam reforming. The methodology is readily applicable to existing industrial facilities with minimal adaptation.