SOFCs integrated with SMES under dynamic power control using Chernobyl disaster optimizer.

Area of Science:

• Energy Systems Engineering
• Computational Intelligence
• Materials Science

Background:

• Solid Oxide Fuel Cells (SOFCs) are crucial for clean energy generation, but accurate parameter identification is vital for performance optimization.
• Existing methods for SOFC parameter estimation often face challenges in precision and computational efficiency.
• The Chernobyl disaster optimizer (CDO) is a novel metaheuristic algorithm inspired by radiation physics.

Purpose of the Study:

• To introduce and validate the Chernobyl disaster optimizer (CDO) for identifying the seven unknown parameters of solid oxide fuel cells (SOFCs).
• To minimize the sum of square errors (SMSE) between estimated and measured SOFC output voltage datasets.
• To evaluate the performance and dynamic behavior of SOFCs integrated with microgrid components.

Main Methods:

• The study employed the Chernobyl disaster optimizer (CDO), a metaheuristic algorithm, to estimate SOFC parameters.
• The CDO's procedure is based on the physical behavior of radiation from the Chernobyl disaster, considering mass, speed, frequency, and ionization.
• Simulink/MATLAB was used to simulate and validate the SOFC model under steady-state and dynamic conditions, including integration with loads and superconducting magnetic energy storage (SMES).

Main Results:

• The CDO achieved significant accuracy in parameter identification, yielding low SMSE values of 3.46 µV² at 800°C and 7.38 µV² at 900°C.
• Estimated SOFC voltage datasets closely matched measured data, validating the model's accuracy.
• The CDO demonstrated superior performance compared to other algorithms in parameter extraction and SOFC performance validation.

Conclusions:

• The Chernobyl disaster optimizer (CDO) is a highly effective tool for precise parameter identification in solid oxide fuel cells (SOFCs).
• The proposed framework accurately models SOFC behavior under various operating conditions and load scenarios within microgrids.
• The CDO's application enhances the control and integration of SOFCs in microgrid systems, improving active and reactive power management.