Doaa Khalil Ibrahim

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 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.

The extracted power from wind is clean, plentiful, and completely renewable. All over the world, researchers keep looking for the best layouts of wind parks to maximize captured energy. To design wind farms suitably, forecast their performance, and understand the strain loads of wind turbines, there is a persistent need to catch a perfect wake model. Wind turbine wakes are one of the most vital factors in the meteorology of wind power due to reducing the power production and the necessity to raise the downstream capacity of wind turbines. This study is divided into two main aspects: firstly, enhancing the optimal layout for the wind turbines at a farm using Marine Predators Algorithm (MPA). The Jensen wake model is applied to get the extracted power for each turbine, which is one of the mutual analytic models used to reach the optimized layout. By comparing the performance of the proposed algorithm with the previous studies achieved by several techniques, the obtained results revealed that the MPA achieves promising results. Secondly, the proposed algorithm is applied for four sites in Egypt as the fraction of occurrence for the selected locations has been adequately calculated using wind speed over five years.

Converting from a complete fossil fuel energy system to a decarbonized one is crucial to mitigating climate
change and protecting human health. Hybrid energy sources are better than producing energy from a single
technology. The combination of renewable energy and fuel generators allows users to cover seasonal fluctuations of resources and protects them from the unpredictability of fuel prices and supply. Nonetheless, the large-scale industrial demand presents a real challenge due to its consistency throughout the day and the intermittent nature of renewable sources. This research proposes a novel approach for optimal hybrid energy decarbonization for any demand type in general and industrial demand in particular. The proposed framework is developed by integrating the Hybrid Optimization of Multiple Energy Resources (HOMER) for the simulations of Hybrid Configurations (HCs), the Electrical Transient Analysis Program (ETAP) for the stability studies, and the Potentially All Pairwise RanKings of all possible Alternatives (PAPRIKA) method to rank the resulting configurations.
An industrial oil and gas complex with high wind and solar resource availability is adopted as a study case. It is
located in Abu Rudeis, Egypt, and currently utilizes only 11 Gas Turbine Generators (GTGs) to generate electricity. Five different HCs are investigated, including PV and wind systems. The proposed approach considers technical, environmental, economic, and socio-political criteria, with a total of 21 sub-criteria, and reveals that incorporating a wind farm of nine 2-MW wind turbines with the GTGs is the Optimal Hybrid Configuration (OHC).