Power fluctuation adaptive control strategy of alkaline water electrolyzer based on renewable energy

In the context of the global climate crisis and the urgent need for carbon reduction, hydrogen has emerged as a pivotal clean energy source in the ongoing energy transition. However, the inherent volatility of renewable power generation poses a significant challenge, particularly for electrolytic hydrogen production systems. Therefore, it is essential to develop adaptive control strategies and algorithms capable of accommodating fluctuations in the output power of renewable energy sources to ensure the stable operation of electrolytic hydrogen production systems. In this study, a multi-objective optimization model is developed based on the key parameters of the hydrogen electrolysis system, utilizing a genetic algorithm. Building on this foundation, a control system is constructed, integrating a 10 MW renewable energy generation system with four distinct types of alkaline electrolyzers. The system's input consists of the fluctuating power curve derived from wind power generation at a specific location. Additionally, an adaptive control strategy for power fluctuations is proposed. The results demonstrate that the proposed control strategy effectively meets the dynamic tracking requirements of the electrolytic hydrogen production system, accommodating input power fluctuations caused by wind power with a forward error of approximately 5%. Furthermore, the strategy exhibits a degree of self-adjustment capability in response to small-scale fluctuations at the input power sampling point, thereby enhancing both the stability and economic viability of the system. This study proposes an effective control strategy for integrating electrolytic hydrogen production technology with renewable energy sources, offering significant implications for the commercialization of hydrogen energy and the efficient utilization of renewable resources.