FG Thermische Energietechnik
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Traditional hydrogen production methods contribute significantly to greenhouse gas emissions, making technological change essential. Water electrolysis powered by renewable energy offers a promising route for sustainable hydrogen production. However, the variability of renewables challenges efficient electrolyzer operation, often requiring detailed simulations with long time series and high computational effort. We present a dynamic model of a low-temperature electrolyzer system optimized for computational efficiency. The model increases computational speed by a factor of 1000 compared to full-balance models while maintaining accuracy above 99% in hydrogen prediction. The simplified model is used to investigate the benefits of modularization in large-scale hydrogen plants. Results show that modularization has a minor effect on overall output but significantly increases operational time. This extended production window can reduce the need for large-scale hydrogen storage, especially when integrated with a continuous hydrogen consumer.
Performance evaluation of reanalysis models for upsampling of solar irradiance and wind speed data
(2026)
The transition to renewable energy sources is a cornerstone of modern energy system transformation. Yet, the integration of solar and wind power remains challenging due to limited availability of high-resolution time series data. Accurate modeling and forecasting of renewable energy generation are essential for grid stability, and the effective deployment of technologies such as water electrolysis. However, most available datasets are provided at coarse temporal resolutions, which restricts their utility for dynamic system analysis and hinders the development of robust operational strategies. This study addresses this critical gap by evaluating the performance of reanalysis models and introducing a statistical upsampling approach that generates data with 1-min resolution from reanalysis data with 1-h resolution using a first-order Markov chain Monte Carlo method. The method is validated against measured data. The normalized root-mean-square error of the simulated data is less than 10% and the mean intra-hour standard deviation differs from 4.8% to 9.1% compared to measured values for solar irradiance and wind speed. The generated data facilitates the optimization of system operation, and enhances the planning of sector-coupled infrastructures. Ultimately, this approach enables reliable integration of renewable energy despite limited data availability and thus contributes to the advancement of the sustainable energy transition.
Hydrogen is expected to play a key role in future energy systems, with PEM electrolysis being particularly suitable for producing hydrogen from renewable sources. However, a significant amount of heat is released during operation. Utilizing this heat in heating networks or industrial processes could improve economic efficiency and help decarbonize the heating sector. Since PEM electrolyzers operate at relatively low temperatures, many applications need heat pumps to increase the temperature of the waste heat. Such a coupled system requires a controller that can handle the thermal management of the electrolysis stack with the heat pump. However, the rapid load changes of a PEM electrolyzer coupled with fluctuating renewable energy sources pose a challenge to the controller, as it must stabilize both the electrolyzer’s cooling cycle and the heat pump’s refrigeration cycle simultaneously.
Therefore, this paper presents a model predictive controller (MPC) based on neural networks that efficiently controls waste heat recovery while meeting the temperature requirements of the electrolyzer and the heat sink. We investigated the performance of the control strategy in numerical case studies and compared it with conventional control using proportional-integral (PI) controllers. We could demonstrate that our method provides significant improvements in terms of minimizing temperature fluctuations and maximizing heat recovery efficiency. Under the influence of volatile power input, the MPC could increase the average efficiency of the heat pump by up to 7 %, reduce the use of auxiliary heating energy by up to 52 %, and reduce the average and maximum deviation from the temperature setpoints by up to 1.4 K and 10.4 K, respectively, compared to PI control.
Operating alkaline water electrolysis (AWE) at enhanced temperatures (at or above 100 °C) and higher pressures can improve process efficiency due to a reduction of overvoltages. However, experimental research at these advanced AWE operation conditions is scarce. This study aims to fill this gap by presenting comprehensive experimental results using an AWE cell equipped with state-of-the-art electrodes from De Nora and separators from Agfa-Gevaert that were evaluated in a test rig with a wide range of the operational parameters, including temperatures from 80 to 130 °C, pressures from 2 to 24 bar and flow rates from 1.5 to 2.85 l/min. The temperature increase yielded the strongest improvement in cell performance, followed by the pressure with a moderate and the flow rate with a minimal impact on the reduction of the cell voltage. The best cell performance was achieved at 130 °C, 15 bar and 1.5 l/min, reaching 1.7 A/cm2 at a cell voltage of 1.99 V.
Dynamic operation of low-temperature electrolyzer systems in modular large-scale chemical plants
(2025)
As one of the main contributors to the greenhouse gas emissions worldwide, the reduction of emissions in chemical industry is indispensable. The production of hydrogen from renewable energy sources using water electrolysis can contribute to this goal. However, the dynamic character of renewable energy sources leads to major challenges regarding the dynamic operation of the production process. Here, a model of a modular low-temperature electrolyzer plant is presented. The effect of available time resolution of the source electricity as well as the impact of the integration time step is studied. With the presented model the positive effect of load-dependent operation of modular electrolyzer plants with fluctuating availability of source electricity on the total hydrogen output is demonstrated.
Operating alkaline water electrolysers above 100 °C improves electrolyte conductivity and reaction kinetics significantly. To examine alkaline water electrolysis in this area in more detail a high-temperature pressurized test rig is designed and constructed. Nickel (Ni) foam electrodes coated with Raney nickel (Raney Ni) or nickel manganese (NiMn) and expanded Ni mesh electrodes coated with nickel(II) oxide (NiO) are utilized and the effect of electrolyte flow rate, electrode structure, pressure and temperature variation on the cell performance at temperatures up to 130 °C and pressures up to 16 bar are investigated. At the maximum current density of 1.67 A/cm2, 110 °C and 16 bar the electrode combination of Raney Ni coated Ni foam as cathode and NiO coated expanded Ni mesh electrode as anode attain the best outcome with a cell voltage of 2.29 V. In a 70 h duration test three-dimensional (3D) Ni foam electrodes with Raney Ni cathode and NiMn anode catalyst show a moderate cell degradation of 9.26 μV/h.
This work embodies a numerical model development and simulation of a metal hydride-based thermal energy storage system with integrated cooling as well heat upgradation. The concept of this system is unique as it operates on thermal drive during the energy storage cycle while during the energy release cycle it is compressor-operated. The utility of the mechanical compressor in the system is two-fold i.e., one operation mode would harness cooling, and another operation mode would fetch heat upgradation. The numerical model devised is a lumped parameter model which solves for hydrogen concentration and temperature of the linked metal hydride reactors. The proposed thermal energy storage system is of 5 MJ capacity which engages tube bundle reactor settings for both high-temperature and low-temperature hydride alloys. Each energy storage and release cycles are simulated for a fixed duration of 10000 s. For a fixed heat supply and heat recovery temperatures of 623 K and 573 K, the system offered cooling coefficient of performance in the range of 1.43–1.58 at 283–293 K. Also, approximately 77% of the energy stored at 623 K could be retrieved at 573 K. In heat upgradation operation mode, 10–30 K temperature lift was accomplished.
Anion Exchange Membrane Water Electrolysis (AEMWE) has emerged as a promising technology for generating green hydrogen, particularly using intermittent renewable energy sources. AEMWE offers a compelling opportunity for cost-effective and sustainable hydrogen production by combining the advantages of traditional alkaline water electrolysis, such as the use of abundant and low-cost catalysts, with the benefits of Proton Exchange Membrane (PEM) electrolysis, which includes membrane separation with pure or low-concentration alkaline water and operation at high current densities. However, the widespread commercialization of AEMWE remains in its early stages, with small-scale active cell areas (less than 300 cm²) and ongoing challenges related to performance and durability. Consequently, it is vital to develop high-performance, durable AEMWE cells with larger active areas to facilitate the broader adoption of this technology. This contribution presents our recent progress in AEMWE, focusing on the development of cells with an active area of up to 360 cm². In particular, we are advancing AEMWE technology to Technology Readiness Level (TRL) 5/6, incorporating precious metal-free catalysts and advanced membranes to enhance the flexibility and efficiency of hydrogen production. As part of this effort, we have designed and experimentally tested a 100 cm² AEMWE cell and conceptualized and designed a 4-kW short-stack with a total active cell area of 1800 cm², as shown in Figure. 1, for high-pressure operation at 30 bar. Our ongoing efforts includes the laboratory testing of this newly designed stack at high pressure operation and evaluating its electrochemical performance and durability up to 1,000 h continuous operation using precious metal-free electrocatalysts.
- Novel dynamic PEM stack model with focus on thermal behavior is presented.
- Model incorporates gas-liquid heat transfer and pressure drop phenomena.
- Electrochemical and dynamic thermal behavior is validated with experimental data.
- Validation shows higher gas fraction in cooling water increases heat transfer.
Maintaining a constant stack temperature and minimizing temperature gradients within the stack during operation is critical to the efficiency and durability of proton exchange membrane (PEM) electrolyzers. At the same time, the significant amount of heat released during operation provides opportunities to reuse this heat, potentially improving economic efficiency and contributing to the decarbonization of the heating sector. The design of stack temperature and heat recovery control systems requires accurate and effective models that describe the dynamic thermal behavior of the stack during load changes without being overly computationally intensive. Therefore, this paper presents a dynamic quasi-2D model of a PEM electrolysis stack that rapidly calculates these dynamic processes in a generally applicable manner. The model also incorporates phenomena such as gas and liquid crossover and pressure drop. We validated the performance of the model with experimental data from a PEM electrolysis test bench and demonstrated its accuracy in describing the electrochemical and dynamic thermal behavior. The stack voltage was simulated with a mean absolute deviation (MAD) of 0.0895 V and the anode outlet temperature with an MAD of 0.127 K, while the simulated time constants of the thermal step responses showed an MAD of 4.06 %.
Aqueous redox flow batteries (ARFBs) are considered a potential technology for large-scale energy storage owing to their eco-friendliness, high current density, and tuneable capacity. Polymer electrolyte membranes (PEMs) are vital components of ARFB, but they suffer from significant capacity/efficiency deterioration due to a lack of advancement in structural framing. To enhance the effectiveness of ARFBs, various membrane types have been optimized. However, the development of an efficient PEM remains a significant problem. Metal–organic frameworks (MOFs) made up of metal sites and organic linkers have gained significant scientific interest. As a result of their large surface area, adjustable pore diameters, and customized functionality, MOF-based PEMs are regarded as effective separators for ARFB. This review covers the state of the art MOF-based PEMs as ARFB separators. In recent years, significant efforts have been made to utilize the unique characteristics of MOFs as they not only improve the conductivity (attributed to their 3D channel structure) and gradient distribution in the polymer framework but also stabilize and mitigate active species cross-over by regulating the effective pore size. The aforementioned tactics can stimulate more research on energy storage technologies and provide future insight into the development and design of size-sieving separators for ARFB.
