Abdelmonem Elsawy Abdelmonem Elsawy Khalil (A. Elsawy Khalil)

In fast-growing isolated microgrids (IMGs), load frequency control (LFC) ensures optimal power quality for end users. Stochastic grids, notably with renewable energy resources (RESs), require robust and intelligently designed LFC schemes. Thus, this research presents a novel cascade-loop controller combining a fractional order-proportional derivative with a filter and a fractional order-proportional tilt integral derivative (FPDN-FPTID) to improve LFC for single and multi-area IMGs. Recent dandelion optimization adjusts FPDN-FPTID controller settings. Anti-windup keeps the controller out of the non-linear zone for low inertia IMGs. It concerns various sources' maximum generating rates. The two-area IMG model shows its potential and scalability. Extensive MATLAB/Simulink simulations show that the FPDN-FPTID controller outperforms numerous published controllers, either single or cascade-loop, in minimum error criteria, undershoots/ overshoots/settling times, frequency, and tie-line power deviation following load and RES variations. Finally, the sensitivity study indicates the suggested controller stabilizes the system despite ±25 % parameter changes.

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.

In rapidly expanding isolated microgrids (IMGs), load frequency control (LFC) should ensure optimal power quality for end users. In particular, renewable energy sources (RES) require robust and intelligently designed LFC systems for their stochastic nature. This study presents a novel single-loop controller that combines a fractional order-proportional beside a tilt integral derivative with fractional order (FPTID) to improve the LFC of multi-source IMGs. Diesel generators, fuel cells, battery storage devices, and RES (solar and wind power generation) are included in the evaluated IMG. Recent geometric mean optimization adjusts FPTID controller parameters. Extensive MATLAB/Simulink simulations reveal that the FPTID controller outperforms numerous previously published controllers regarding the minimum error criteria, undershoots/overshoots/settling times, and frequency deviation in response to load and RES variations.

Isolated microgrids (IMGs) power remote areas. However, IMG may lower the frequency stability and increase frequency excursions with low system inertia. Load frequency management ensures system stability. Thus, the paper proposes a novel multi-objective tuning strategy to improve IMG’s load frequency control (LFC) and take the microgrid controller’s control signals into account. Diesel engine generator, fuel cell, battery energy storage system, and renewable energy sources (RESs) like photovoltaic and wind systems make up the IMG. Conventional controllers such as proportional-integral (PI) and proportional integral derivative (PID) are classically tuned based on the standard error criteria as a traditional single-objective tuning approach. Due to the low inertia of the system and the stochastic nature of RES, they cannot act as required under different operating scenarios. Therefore, the PI and PID controllers are tuned using the proposed multi-objective-based tuning approach to reduce the frequency deviations. In addition, anti-windup is applied to the enhanced classic controllers to keep them distant from the nonlinear zone and beyond the source’s physical constraints. The proposed tuning process also considers the maximum practical generation rates for different sources. The recent Artificial Rabbits Optimization (ARO) algorithm is applied to simultaneously adjust the controller parameters for several controlled sources in IMG. Extensive simulations in MATLAB and Simulink confirm the effectiveness of the proposed approach to keep the system stable even when facing high levels of disturbances. In addition, accomplishing sensitivity analysis, severe ±25% changes to the system’s parameters guarantee that the proposed tuning strategy keeps the system stable.