A novel framework for interconnected hybrid power system design using hybridization of metaheuristic algorithms and fuzzy inference

In this study, load frequency control (LFC) of an interconnected isolated hybrid power system (IHPS) with different power generating units (PUGs) like a diesel engine generator (DEG), a combined cycle gas turbine (CCGT), and a wind turbine generator (WTG) are integrated for more sustainable energy. Proportional-integral-derivative (PID) and fuzzy PID (FPID) controllers are employed for governor speed control of DEG and CCGT and pitch control of WTG to enhance system stability. The study is focused on optimizing the controller parameters to match power output with load demand, ensuring stable system frequency under various load and wind perturbations. Two optimization algorithms, the student psychology-based optimization algorithm (SPBOA) and the quasi-oppositional based whale optimization algorithm (QOWOA), are utilized to optimize controller gains. The integral of time absolute error served as the objective function to assess the effectiveness of the proposed SPBOA (for PID and FPID-based) and QOWOA (for PID and FPID-based) models. Comparative analysis revealed that the SPBOA-based IHPS model demonstrated superior response in terms of objective function, settling time, and peak, particularly outperforming the QOWOA-based model across different scenarios. Notably, the SPBOA-FPID controller exhibited enhanced dynamic responses and overall performance compared to SPBOA-based PID, fuzzy proportional-integral, and fuzzy proportional-derivative controllers.