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Utility-scale hydrogen production via alkaline electrolysis (AEL) is a promising pathway toward the decarbonization of the power, transportation, and chemical industries. The efficiency, load flexibility, and operational safety of the AEL system are subject to electrochemical, thermal, and mass transfer dynamics, and the corresponding parameters, including overvoltage coefficients, heat capacities and resistances of the stack and lye-gas separators, thickness and permeability of the diaphragm, etc. The community has developed many models to depict these dynamic behaviors. However, due to the lack of a comprehensive parameter estimation method, these models are generally tuned manually in industrial applications, which can be inaccurate and cannot fit their time-varying nature. To fill this gap, we present a fast and accurate parameter estimation method for the AEL system. Specifically, to address the difficulties of strong nonlinearity of the dynamic electrolyzer models and correlation between different parameters, a Bayesian inference-based Markov chain Monte Carlo method is proposed. To reduce the computing time for online estimation, data-driven adaptive polynomial surrogate models are established to replace repeated time-domain simulations of the electrolyzer model so that estimation can be finished within a few minutes. Experiments on a 5 Nm 3 /hr-rated AEL system validate the proposed method. Compared with the existing Kalman filter variants, the estimation error is reduced by at most 71.1% in terms of RMSE and NRMSE. In addition, the proposed method provides approaches to fault diagnosis and global sensitivity analysis for operating and designing AEL systems. • Dynamic parameters of the AEL system are estimated accurately and fast. • Strong nonlinearity and correlation of the parameters are overcome. • The proposed method is validated by experiments on a 5 Nm 3 /hr-rated electrolyzer. • Extended applications like fault diagnosis and sensitivity analysis are enabled. [ABSTRACT FROM AUTHOR]

Electrolysis systems use proportional–integral–derivative (PID) temperature controllers to maintain stack temperatures around set points. However, because of heat transfer delays in electrolysis systems, the manual tuning of PID temperature controllers is time-consuming, and temperature oscillations often occur. This paper focuses on the design of the PID temperature controller for an alkaline electrolysis system to achieve fast and stable temperature control. A thermal dynamic model of an electrolysis system is established in the frequency domain for controller designs. Based on this model, the temperature stability is analysed by the root distribution, and the PID parameters are optimized considering the temperature overshoot and the settling time. The performance of the optimal PID controllers is experimentally verified. Furthermore, the simulation results show that the before-stack temperature should be used as the feedback variable for small lab-scale systems to suppress stack temperature fluctuations, and the after-stack temperature should be used for larger systems to improve the economy. This study helps ensure the temperature stability and control of electrolysis systems. [ABSTRACT FROM AUTHOR]

Alkaline electrolysis (AEL) is the most mature electrolysis technology and is widely used in large-scale power-to-gas scenarios. However, the loading range of AEL system is restricted by the minimum load, 10%–40% in general, which prevents system operation during low-load periods. This minimum load is caused by the flammable mixture formed by the impurity crossover and is related to operation conditions such as current and pressure. In this paper, we propose a novel pressure control strategy to extend the loading range of an AEL system by reducing the system pressure at low-load periods while also maximizing the system efficiency. A dynamic impurity accumulation model is established illustrating the flexibility limitation mechanism of the AEL system. Two pressure controllers are designed based on the steady-state and dynamic impurity accumulation model, including an operation curve tracking controller (OCT) and a model predictive controller (MPC). In the case study, both controllers achieve satisfying results under the peak shaving scenario, with an extension of the AEL system's minimum load from 27.5% by the traditional constant pressure controller to 10.5% by the OCT controller and 10% by the MPC controller. This pressure control strategy reduces the electricity abandonment and improves the system economy significantly. • Establish a dynamic impurity accumulation model for gas crossover. • Propose a novel pressure control strategy to extend the loading range. • Design two pressure controllers: an OCT controller and an MPC controller. • Improve the system economy by reducing the electricity abandonment. [ABSTRACT FROM AUTHOR]

Thermal management is vital for the efficient and safe operation of alkaline electrolysis systems. Traditional alkaline electrolysis systems use simple proportional–integral–differentiation (PID) controllers to maintain the stack temperature near the rated value. However, in renewable-to-hydrogen scenarios, the stack temperature is disturbed by load fluctuations, and the temperature overshoot phenomenon occurs which can exceed the upper limit and harm the stack. This paper focuses on the thermal modeling and controller design of an alkaline electrolysis system under dynamic operating conditions. A control-oriented thermal model is established in the form of a third-order time-delay process, which is used for simulation and controller design. Based on this model, we propose two novel controllers to reduce temperature overshoot: one is a current feed-forward PID controller (PID-I), the other is a model predictive controller (MPC). Their performances are tested on a lab-scale system and the experimental results are satisfying: the temperature overshoot is reduced by 2.2 °C with the PID-I controller, and no obvious overshoot is observed with the MPC controller. Furthermore, the thermal dynamic performance of an MW-scale alkaline electrolysis system is analyzed by simulation, which shows that the temperature overshoot phenomenon is more general in large systems. The proposed method allows for higher temperature set points which can improve system efficiency by 1%. • Establish a control-oriented thermal model with multiple temperature nodes and time-delay terms. • Propose novel temperature control strategies to reduce temperature fluctuation under dynamic operating conditions. • Achieve stable temperature control under violent load fluctuation. • Higher temperature set points are available to improve system efficiency. [ABSTRACT FROM AUTHOR]