Abstract
The extensive use of renewable energy to supply hydrogen production for chemical processes is hindered by the uncertainty in power generation and by strict operational limits.These challenges are addressed through a real-time dynamic optimization approach based on a receding-horizon strategy that provides optimal decision variables. The framework explicitly relies on imperfect weather forecasts and dynamically adapts the hydrogen reference production to guarantee the final productivity target. The optimization methodology focuses on minimizing grid electricity imports, limiting excessive equipment stress, and preventing constraint violations, while ensuring that the hydrogen production target is satisfied.The proposed approach yields competitive economic and environmental performance, with a levelized cost of hydrogen of 3.31 USD/kgH2, well within literature values (1.50-7.50 USD/kgH2) and below typical industrial costs (4-12 USD/kgH2). At the same time, carbon dioxide emissions are reduced by more than 90% compared to steam methane reforming, resulting in specific emissions below 1 kgCO2/kgH2. These results are supported by a year-long simulation based on CAISO renewable energy data. The optimized system achieves the annual hydrogen production target while respecting all operational constraints, whereas the absence of operational strategy and buffering systems leads to systematic constraint violations. The strategy can be implemented in existing industrial facilities with limited modifications.