Transformer-based predictive energy management in hydrogen-integrated renewable systems

Hydrogen, once produced from renewable energy via water electrolysis, can power zero-carbon transportation with the additional benefits of reducing combustion-generated air pollutants including particulates and greenhouse gases. Meanwhile, storage of hydrogen, in either its gaseous or liquid form, does not require rare-earth materials and hence it becomes more economic and environmentally friendly. Incorporating hydrogen and battery storage technologies into an advanced predictive energy management approach to electrolysis based systems can significantly enhance renewable energy utilization, optimize power use from grid, improve system adaptability, and increase overall energy efficiency. This study develops a dynamic energy management algorithm by employing transformer-based time series models for accurate demand forecasting, which enables rolling window optimization using model predictive control. Simulation results indicate that this approach improves demand forecasting accuracy by 41.21% and increases the adjusted green hydrogen production rate from 29.54% to 54.3% compared to the conventional model. It is further demonstrated that the renewable-based hybrid energy storage system can achieve a renewable energy production ratio of 71% with an effective 95% renewable energy utilization rate, while the grid dependency is reduced from 57% to 43%. This study proposes a novel dynamic energy management approach in a hydrogen-integrated Renewable Energy System, using transformer-based models for time series forecasting and a model predictive control framework for rolling window optimization. • Preformed transformer-based rolling window optimization for dynamic energy management • Developed a predictive model for hydrogen-integrated renewable energy systems • Revealed the strategy to improve green hydrogen production while reducing the grid dependency