Green hydrogen is one of the most promising choices among hydrogen production methods due to its zero-emission, environmentally friendly, and sustainable characteristics. Choosing an appropriate model for green hydrogen production via photovoltaic electrolysis is critical to improving its efficiency and reliability. However, existing studies lack a comprehensive review and comparison of photovoltaic electrolysis for green hydrogen production. This gap makes it challenging for researchers to select suitable models for photovoltaic electrolysis in their investigations. To address this challenge, this article provides a comprehensive review and comparison of existing modeling research on photovoltaic hydrogen production systems. It classifies and analyzes photovoltaic panels, converters, and electrolyzers in photovoltaic electrolysis hydrogen production, summarizing general modeling approaches and criteria for selecting appropriate electrolyzer models for different applications. The article also highlights shortcomings in existing research and proposes directions for future investigations.

Off-grid wind-to-hydrogen systems are considered a promising solution for sustainable, large-scale green hydrogen production in remote areas. However, under the combined effects of highly fluctuating wind generation and stochastic load variations, existing energy management methods still face a challenge: in off-grid wind-to-hydrogen systems, intelligent energy management studies that jointly address economic performance and operational stability are still limited. To address these issues, this paper develops a mathematical model for an off-grid wind-to-hydrogen system to reveal the coupling characteristics of the wind–electricity–hydrogen conversion process. Building on this model, a deep-reinforcement-learning-based energy management strategy is proposed. By formulating objectives that simultaneously capture economic benefits and stability requirements, the proposed strategy enables adaptive power flow allocation and dynamic optimization under uncertainty. Case studies demonstrate that, while fully satisfying load demand, the proposed strategy can significantly improve renewable energy utilization and hydrogen production, thereby increasing profit and ensuring stable and sustainable system operation.