Participants of the COP 26 summit have agreed to limit global temperature rise to 1.5 K by 2050. Out of the
many strategies envisaged to meet the targets of COP 26, the βSector Couplingβ process aims to use renewable
electricity in residential heating, chemical industry, and transportation sectors. Several studies predict that
de-central energy systems will play a significant role in the future. Among the proposed sector coupling
strategies in de-central energy systems, the Power to Gas (PtG) process producing chemical energy carriers like
Substitute Natural Gas (SNG) from renewable power is gaining acceptance. Numerical models of de-central
energy systems are needed to analyse sector coupling under fluctuating renewable energy generation and
changing gas demand. This study introduces a numerical model of a decentral energy system that includes
a novel methanation concept developed at the Engler Bunte Institut of KIT called 3 Phase Methanation.
Here, H2 from electrolysis and CO2 from DAC or other biomass-based sources are passed through a slurry
bubble column reactor. The slurry is a suspension of the catalyst in a liquid heat transfer medium where
the heat of the reaction is dissipated. The 3-Phase methanation process is modelled in this study using the
axial dispersion method. Earlier studies describing experimental campaigns conducted on the pilot plant in KIT
have proven that the reactor core is nearly isothermal with stable product gas compositions even if the load
changes are instantaneous. In this study, it is shown that the numerical model can replicate the experimental
results. Following modelling and validation, the numerical model of the PtG plant is integrated with the other
components to simulate the de-central energy system. The simulation results demonstrate the dynamic output
of all the components and, in particular, the response provided by the PtG plant. This model can be adapted to
simulate sector coupling in future de-central energy systems and analyse aspects like long-term energy storage,
GHG minimisation and cost-optimal operation