Model-Based Optimization of Solid Oxide Electrolysis Cells and Stacks for Power-to-Gas Applications

Abstract

The temporarily fluctuating power supply produced by renewable energy sources (RES) forces the integration of energy storage technologies in our energy network at a massive scale, in order to make future scenarios of a sector-coupling and distributed clean energy supply feasible. Power-to-Methane (PtM) chemical storage concepts are very attractive in this regard, since the production of synthetic CH4 has the advantages of a high-energy density carrier that is easily transportable within the existing pipeline infrastructure and that is a feedstock for various applications in the form of compressed or liquefied natural gas (CNG/LNG). However, in all these scenarios, the levelized cost of the synthetic fuel is a major obstacle due the relatively inefficient conversion chain, i.e. energy losses associated with the fuel production plus consumption. Thermal integration of high-temperature (HT) steam electrolysis based on solid oxide electrolysis cells (SOECs) with the exothermic catalytic methanation is a very promising concept to boost the system’s efficiency by making use of the intrinsically favored thermodynamics and kinetics of HT-electrolysis for H2 generation. On stack level, electrical efficiencies (based on the lower heating value (LHV)) of 96% and specific energy consumptions of 3.3 kWh per Nm3 of H2 were reported for HT-steam electrolysis [1], whilst net system PtM efficiencies of ~80% could be demonstrated in a pilot project [2]. Still, there exists the fundamental question on how to optimize the cell design and the operating conditions in terms of balancing area-specific power density, efficiency, degradation rates, and costs, i.e., how to use this synergy optimally in a future real-world application. Here, we use a detailed multi-physics and multi-scale SOEC simulation tool alongside initial and long-term experiments performed on commercial electrolyte- (ESC) and anode-supported cells (ASCs) to make further strikes towards this question. Firstly, a planar single cell model coupling 1D+1D gas transport and spatially-resolved charge transfer across the electrode thickness is calibrated based on polarization curves recorded at different temperatures (600-900 °C) and inlet compositions (80-95% H2O, balance H2). By accounting for realistic microstructural cell data acquired from Scanning Electron Microscopy (SEM), a nearly quantitative accordance with the experimental data could be achieved. Subsequently, by incorporating a 2D heat transport model, adiabatic simulations are performed to identify optimal operation conditions in a parametric study, which are used as a basis for the long-term degradation experiments. It is demonstrated how the selection of the cell design affects the selection of optimal operation points, and how the creation of reactant starvation zones within the cell can severely impact the electrical efficiency. A scale-up to the stack level is performed afterwards by applying a 3D stack model [3], which considers heat loss to the surroundings across a thermal insulation layer. This enables to realize system-oriented simulations with conditions experienced by the stack that are close to the real-world application. In this way, the implications of scaling-up on (i) the SOEC performance depending on the cell design, and (ii) the selection of optimal operation conditions from an electrolyzer point-of-view in the context of PtM are illustrated on the basis of industrially-scaled stacks. Acknowledgements Financial support by the federal ministry for economic affairs and energy (Bundesministerium für Wirtschaft und Energie, BMWi) under Grant Numbers 03EIV041D and 03EIV041E in the “MethFuel” group of the collaborative research project “MethQuest” is gratefully acknowledged. References [1] sunfire GmbH, Gasanstaltstraße 2, 01237 Dresden, Germany. [2] M. Gruber, P. Weinbrecht, L. Biffar, S. Harth, D. Trimis, J. Brabandt, O. Posdziech and R. Blumentritt, Fuel Process. Technol., 181, 61 (2018). [3] A. Banerjee, Y. Wang, J. Diercks and O. Deutschmann, Appl. Energy, 230, 996 (2018).