Intelligent Digital Twin Modelling for Hybrid PV-SOFC Power Generation System

Hydrogen (H2) energy is an ideal non-polluting renewable and can achieve long-term storage, which effectively regulate the intermittence seasonal fluctuation of solar energy. Solid oxide fuel cells (SOFC) generate electricity from H2 with only outputs water, waste heat, almost no pollution. To solve power generation instability discontinuity photovoltaic (PV) systems, a hybrid PV-SOFC system has become one feasible solution. The “digital twin”, integrates physical systems information technology, offers new view to deal current problems encountered during smart development. In particular, accurate reliable model basis for achieving this vision. As core components, modelling PV (FCs) crucial whole system’s optimal operation, based on identification unknown parameters. Hence, in study, artificial rabbits optimization (ARO)-based parameter strategy was proposed SOFCs, then validated double diode (DDM) SOFC electrochemical under various operation scenarios. simulation results demonstrated that ARO shows more desirable performance accuracy stability compared other algorithms. For instance, root mean square error (RMSE) obtained by are 1.81% 13.11% smaller than ABC WOA algorithms DDM cell. Meanwhile, 5 kW cell stack dataset, RMSE 2.72% 4.88% PSO (1 atm, 1173 K) (3 1273 conditions, respectively. By establishing digital twin intelligent management both be further achieved.