Simple-Multi-Attribute-Rating-Technique Assisted Reduced Order Modeling of Fuel Cell Integrated Autonomous Microgrid Ensuring Stability and Zero Steady State Error

This paper introduces an efficient method for reduced-order modeling of a fuel cell-integrated autonomous microgrid (AMG) utilizing the simple multi-attribute rating technique (SMART). The higher-order system (HOS) of the AMG is reduced to a second-order model, ensuring computational efficiency while retaining the system's essential characteristics. Time moments (TMs) and Markov parameters (MPs), well-known for their ability to simplify complex systems, play a central role in this process. Their use not only decreases computational demands but also enhances understanding of the HOS dynamics, making them valuable for deriving low-order approximations.The proposed method constructs three sub-objective functions based on TMs and MPs, with weights determined through the SMART technique to refine both transient and steady-state responses. These sub-objective functions are then combined into a single objective function, which is minimized using the brown bear optimization algorithm (BBOA). Stability and steady-state performance are maintained by imposing constraints, including adherence to the Hurwitz criterion and ensuring zero steady-state error. To validate the approach, the reduced model is evaluated through Bode, step, impulse, and Nyquist response comparisons with the original system. The proposed reduced order model (ROM) is also benchmarked against existing techniques, highlighting its improved accuracy and efficiency. Additionally, error indices and time-domain parameters are provided to support the findings. This study underscores the importance of TMs and MPs in achieving computationally efficient and insightful ROMs for autonomous microgrids, offering a reliable framework for system analysis and optimization.