Data–model hybrid hierarchical scheduling for grid-connected PEM green hydrogen production in multi-energy systems with LLM-aided coordination

The integration of variable wind and photovoltaic generation with proton exchange membrane (PEM) water electrolysis is a key enabler for green hydrogen production in smart multi-energy systems, but it exposes coupled multi-physics dynamics, conflicting operational objectives, and strict safety constraints. This paper proposes a hierarchical data–model hybrid scheduling framework for grid-connected PEM green hydrogen production. At the modeling layer, a physics-informed representation with six governing equations in the main text and twenty-two supporting equations in Appendix A defines explicit “red-line” constraints for electrochemical efficiency, thermal safety, and equipment degradation. At the coordination layer, a high-level module—designed with the aid of a large language model during a 90 day design phase—maps heterogeneous operational context (renewable forecasts, electricity prices, stack health indicators) into adaptive multi-objective preference weights and operating bounds. At the execution layer, Takagi–Sugeno fuzzy controllers provide real-time set-point tracking for multiple PEM units. Numerical studies using European wind, photovoltaic, and day-ahead price data over a 7 day test period demonstrate that the proposed framework achieves ∼4.9% power tracking root mean square error [compared with 7.8% for proportional-integral-derivative (PID) control], thermal safety margins approximately 174% larger than conventional baselines, and roughly 11.8% faster fault recovery. These results indicate that the proposed physics-informed, data–model hybrid architecture effectively supports the integration of green hydrogen into smart multi-energy systems.