Modeling of Solid Oxide -Electrolyzer and -Fuel Cell for Nuclear-Renewable Integrated Energy Systems

Abstract

The popularity and recognition of hydrogen (H2) as a clean energy source is gaining momentum. Producing H2 using green energy such as nuclear and renewable sources is important to meet carbon emission goals. This paper demonstrates the modeling of solid oxide electrolyzer cell (SOEC) and solid oxide fuel cell (SOFC) systems which are coupled with nuclear-renewable sources, energy storage systems, and power conversion systems to form a nuclear-renewable integrated energy system (NR-IES). The design and development of the solid oxide cell (SOC) systems are performed using MATLAB/Simulink. A functional mock-up unit (FMU) of these models is generated to integrate them with a Python-based co-simulation NR-IES framework. These behavioral models are developed with the objective to facilitate the simulation of hydrogen sub-systems with integrated energy systems locally in MATLAB/Simulink or in FMU-based co-simulation frameworks. Hydrogen production during low electricity prices can augment the flexibility and profitability of baseload nuclear power plants (NPP). Similarly, during high electricity prices, the produced hydrogen could be used by the SOFC system to generate grid-compatible electricity to further increase its flexibility. Deep reinforcement learning (DRL) technique has been utilized in this paper to control and coordinate the energy-dispatching decisions in the NR-IES framework. The simulation results reveal that coupling hydrogen sub-systems and NPP in an IES can have beneficial economic effects on energy power markets.