Online Dynamic Parameter Estimation of an Alkaline Electrolysis System Based on Bayesian Inference

TL;DR

This paper introduces a Bayesian inference-based method to dynamically estimate key parameters of an alkaline electrolysis system, enhancing real-time flexibility assessment in fluctuating energy environments.

Contribution

It presents a novel online parameter estimation approach using MCMC, providing real-time insights into AEL system dynamics and their variability.

Findings

Validated on a 25 kW electrolyzer

Accurately estimates dynamic parameters in real-time

Provides joint probability distributions for system insights

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.