The current study uses the Chernobyl disaster optimizer (CDO), a new metaheuristic optimizer, to identify the seven unknown parameters of solid oxide fuel cells (SOFCs). The procedures of the CDO is based on physical behavior of the elaborated radiations from the well-known Chernobyl disaster according to their mass, speed, frequency, and degree of ionization. The sum of square errors (SMSE) among the estimated and the real measured output voltage datasets of SOFCs is minimized employing the CDO. Set of boundaries of the SOFC’s process is taken into consideration with the problem formulation. SOFCs stack’s model is examined at 800οC and 900οC and its performance is confirmed. The CDO extracts more precise SOFCs’ parameters compared to other competitors. The CDO’s convergence patterns and the SOFCs unit’s performance are studied and proved at steady-state by comparing its results to a number of recognized algorithms under varied operating scenarios. A significant SMSE’s values of 3.46 µV2 and 7.38 µV2 are attained at 800οC and 900οC, respectively by the CDO. As a result, the polarization principal curves of the measured and estimated voltage datasets are checked and verified with very close matching. The dynamic behavior of the SOFCs stack is examined in relation to direct load, electric networks, and superconducting magnetic energy storage devices (SMES) for additional validation and illustration. The role of the SOFCs stack in controlling the active and reactive power delivered to the network and direct load is investigated using two controllers: one to control the inverter, which converts the SOFC’s dc output to the main network, and the other to control the SMES. The Simulink/MATLAB environment is used to indicate the validity of the proposed framework under both steady-state and dynamical conditions. The comprehensive assessments show that the CDO capabilities are very effective when used with microgrids.