Multi-scenario memory hybrid hydrogen production scheduling strategy based on dynamic time-scale and multi-objective coordinated power allocation

Hybrid hydrogen production systems are crucial for green hydrogen energy development. However, they face challenges like low power conversion efficiency and high costs, which limit large-scale use. To address the shortcomings of current scheduling strategies, this paper constructs and validates an integrated optimization framework. Firstly, we elucidate the coupling relationship between scheduling time scales and power fluctuations, establishing a self-adaptive dynamic time-scale scheduling mechanism. This mechanism achieves a 90.7% fluctuation suppression rate, representing an 11.6% improvement over conventional fixed-interval strategies. Secondly, we design a multi-objective coordinated power allocation mechanism with a price-sensitive dynamic weighting adaptation. This mechanism increases the net profit by an average of 3.18% compared to fixed-weight models under hydrogen price fluctuations. Finally, we design an intelligent optimization algorithm leveraging multi-scenario memory for precise pressure and flow rate control. The algorithm elevates the average hydrogen production efficiency to 68.22% for AEL and 83.64% for PEMEL. The proposed strategy demonstrates superior performance through a closed-loop workflow of fluctuation suppression, economic dispatch, and precise control, significantly enhancing the system's adaptability, economy, and efficiency for time-varying operations.