1. Online Dynamic Parameter Estimation of an Alkaline Electrolysis System Based on Bayesian Inference
- Author
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Qiu, Xiaoyan, Zhang, Hang, Qiu, Yiwei, Zhou, Buxiang, Zang, Tianlei, Qi, Ruomei, Lin, Jin, and Wang, Jiepeng
- Subjects
Optimization and Control (math.OC) ,FOS: Electrical engineering, electronic engineering, information engineering ,FOS: Mathematics ,Systems and Control (eess.SY) ,Electrical Engineering and Systems Science - Systems and Control ,Mathematics - Optimization and Control - Abstract
When directly coupled with fluctuating energy sources such as wind and photovoltage power, the alkaline electrolysis (AEL) in a power-to-hydrogen (P2H) system is required to operate flexibly by dynamically adjusting its hydrogen production rate. The flex-ibility characteristics, e.g., loading range and ramping rate, of an AEL system are significantly influenced by some parameters re-lated to the dynamic processes of the AEL system. These parame-ters are usually difficult to measure directly and may even change with time. To accurately evaluate the flexibility of an AEL system in online operation, this paper presents a Bayesian Inference-based Markov Chain Monte Carlo (MCMC) method to estimate these parameters. Meanwhile, posterior joint probability distribu-tions of the estimated parameters are obtained as a byproduct, which provides valuable physical insight into the AEL systems. Experiments on a 25 kW electrolyzer validate the proposed pa-rameter estimation method., Accepted by 2022 IEEE 5th International Electrical and Energy Conference
- Published
- 2022