Macro Level Modeling of a Tubular Solid Oxide Fuel Cell

Abstract

This paper presents a macro-level model of a solid oxide fuel cell (SOFC) stack implemented in Aspen Plus® for the simulation of SOFC system. The model is 0-dimensional and accepts hydrocarbon fuels such as reformed natural gas, with user inputs of current density, fuel and air composition, flow rates, temperature, pressure, and fuel utilization factor. The model outputs the composition of the exhaust, work produced, heat available for the fuel reformer, and electrochemical properties of SOFC for model validation. It was developed considering the activation, concentration, and ohmic losses to be the main over-potentials within the SOFC, and mathematical expressions for these were chosen based on available studies in the literature. The model also considered the water shift reaction of CO and the methane reforming reaction. The model results were validated using experimental data from Siemens Westinghouse. The results showed that the model could capture the operating pressure and temperature dependency of the SOFC performance successfully in an operating range of 1–15 atm for pressure and 900 °C–1,000 °C for temperature. Furthermore, a sensitivity analysis was performed to identify the model constants and input parameters that impacted the over-potentials.

A Review of the Optimization and Control Strategies for Fuel Cell Power Plants in a Microgrid Environment

“…Controlling the output voltage is crucial in ensuring the power quality of SOFCs, but challenging because of the electrochemical nonlinearity, modelling uncertainties, load disturbances, and actuator constrains. Moreover, throughout the brief voltage regulation, the fuel utilization rate should adhere to limitations within a safety range [85]. Recent research has been attractive to model predictive control (MPC) by constructing the challenges into a constrained optimization problem, however, due to its complex computations, this method has proven troublesome for real-time applications [85].…”

“…Moreover, throughout the brief voltage regulation, the fuel utilization rate should adhere to limitations within a safety range [85]. Recent research has been attractive to model predictive control (MPC) by constructing the challenges into a constrained optimization problem, however, due to its complex computations, this method has proven troublesome for real-time applications [85]. L. Sun et al [30] propose a combined control structure containing fundamental building blocks, with the ability to achieve main objectives with less computation.…”

“…The fundamental control design of the second order ADRC is shown in [30] with the parameters explained. After the derivation, the research scholars of [30] concluded that the upgraded plant can be said to resemble cascaded integrators and that dependent on the improved plant, configuring the external-loop control using the proportional-derivative (PD) structure is possible [85]. By parameter turning and verification, L. Sun et al [30] concludes that for comparison purposes, ADRC and PID have comparative performances under nominal conditions.…”