Characterizing the Dynamic Response and Start-up Process of the Electrolyzer for Operation Optimization in Renewable Energy Applications

Abstract

The current water electrolyzer industry is experiencing rapid growth due to the increasing demand for renewable hydrogen as a clean energy. The application in renewable energy demands the dynamic operation of the electrolyzer, while the dynamic and start-up response of the electrolyzer is not comprehensively studied. This study focuses on characterizing the dynamic response, as well as the start-up process of electrolyzers. The dynamic response of the electrolyzer is analyzed using an alternating input current step, and the electrochemical response is correlated with the dynamic control of the electrolyzer. The overshoot voltage during the alternation process is taken as the indicative response under different baseline current, current change amplitude and frequency. The start-up process of the electrolyzer is also explored and analyzed with respect to the start-up time, energy cost, and the initial status of the electrolyzer as well as the liquid circulation rate. The overall energy cost, as well as the start-up time, across the start-up process possesses a strong linear relationship towards the initial temperature of the start-up. Besides, the liquid circulation rate has an impact on start-up power, because higher circulation rate brings more uniform temperature distribution of the electrolyzer. Tuning the liquid circulation rate can help to reduce the elapsed time, and hence accelerate the response speed. The findings provide a framework for testing the electrolyzer and optimizing the start-up process. This paper offers valuable insights that can be applied in the development and design of large-scale electrolyzers, thereby ensuring more efficient and sustainable production of hydrogen. Overall, this paper constitutes an important contribution to the study of electrolyzer and has the potential to shape future research in the area of electrolyzer development and operation optimization. Figure 1