Online Dynamic Parameter Estimation of an Alkaline Electrolysis System Based on Bayesian Inference
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.
Cite
@article{arxiv.2203.03883,
title = {Online Dynamic Parameter Estimation of an Alkaline Electrolysis System Based on Bayesian Inference},
author = {Xiaoyan Qiu and Hang Zhang and Yiwei Qiu and Buxiang Zhou and Tianlei Zang and Ruomei Qi and Jin Lin and Jiepeng Wang},
journal= {arXiv preprint arXiv:2203.03883},
year = {2022}
}
Comments
Accepted by 2022 IEEE 5th International Electrical and Energy Conference