, vol. 82, pp. 110606, 2024.
Load frequency control (LFC) is vital for isolated microgrids (IMGs), especially when uncertain renewable energy sources (RESs) are present. Enhancing LFC schemes relies mainly on three tracks. Adding new resources to IMG structures is the first track, designing novel controller structures for LFC schemes is the second, and the third is improving controller tuning procedures in the LFC schemes. This research suggests an innovative multi-objective formula (MOF) for controller tuning that combines a novel error criterion termed the integral-square time absolute error of frequency change with the integral-square of IMG controllers' signals. Tested IMG includes multi-sources of diesel engine generators, fuel cells, battery energy storage technologies, and RESs (like photovoltaic and wind turbines). The proposed MOF tuning is evaluated compared to four different objective functions, which are the integral-absolute error (IAE), integral-time absolute error (ITAE), integral-square error (ISE), and integral-time square error (ITSE). The proportional-integral-derivative (PID), fractional-order, and cascade PID controllers are implemented to appraise the proposed MOF extensively against all these single objectives. Statistical analyses are accomplished comprehensively to verify the effectiveness of the artificial rabbits' optimization algorithm (ARO) in competition with other recent optimization algorithms to tune different controllers utilized in the LFC schemes of the examined IMG based on tested objectives. Therefore, ARO is applied to optimize controller settings by combining system nonlinearities with IMG sources' maximal generation rate constraints. The comparative analysis considers settling times, overshoots, IAE, ITAE, ISE, and ITSE performance indices. MATLAB/Simulink simulations confirmed the ability of the suggested MOF tuning to stabilize the system and keep improving performance indices, significantly attaining the minimum settling time even for massive three-type load fluctuations. The first type is step disturbances ranging between 0.1 and 0.25 p.u, the second is varying step disturbances every 5 s, and the third is severe dynamic random load shifts from −0.2 to 0.2 p.u. In addition, the MOF outperforms other competitors' tuning formulas while adding fluctuations of RESs with load disturbances. Furthermore, the robustness analysis is conducted for the applied controllers based on the proposed MOF tuning approach by changing the IMG nominal parameters with ±25 % and adding system nonlinearities. The analysis ensured its efficacy in preserving system stability. Finally, the stability test in the frequency domain using the MATLAB/control design tool verified system stability when different LFC controllers were tuned based on the proposed MOF tuning.
