Two modern methods of the energy management system (EMS) based on a modified cost function are addressed in this paper. Fuzzy logic (FL) and Harris Hawks Optimization (HHO) is implemented to achieve the optimal performance of seawater desalination plants (SWDP) within the minimum feed-in tariff (FiT). The technical difficulties involved in the variation of energy price from one time to another and the system parameters uncertainties. For example, the price of energy is higher at the peak time and the price is lower at normal times. Also, the peak time can change from one day to another day. The proposed management system can deal with these variations and uncertainty cases. The suggested EMS is achieved through a bidirectional electrical energy interchange approach ( $\pi $ -EEIA). The main concept behind the proposed $\pi $ -EEIA is how and when to inject the excess generated energy of renewable energy into the utility grid or charge the battery depending on the minimum dynamic cost criterion and vice versa. To accomplish this study a 700 m ³ /day SWDP located in Egypt fed on solar energy and a utility network has been constructed and analyzed. The system includes SWDP fed from a photovoltaic (PV) array as well as the utility grid in addition to a battery energy storage system (BESS). The main objective of this study is the management and coordination between the energy exchange process from the solar energy, the utility network, and BESS to provide sufficient electrical energy for SWDP within the minimum FiT. The system is constructed and validated using the MATLAB/SIMULINK™ software package. The proposed FL and HHO-based EMSs are investigated in the presence of the system uncertainties such as the change in the energy (excess or shortage) as well as the change in the energy price in the utility network (high or low) with (normal or peak) time. The attained results demonstrate that the proposed FL and HHO-based EMSs provide high dynamic performance and accurate coordination between various energy resources and BESS. The results show that FL-based EMS achieves a profit of 10.28 $\$ $ but the HHO-based EMS achieves a profit of 10.11 $\$ $ in the same period.

No one denies the importance of renewable energy sources in modern power systems in terms of sustainability and environmental conservation. However, due to their reliance on environmental change, they are unreliable systems. This paper uses a Unified Power Flow Controller (UPFC) to enhance the reliability and performance of grid-tied renewable energy systems. This system consists of two renewable sources, namely photovoltaic cells (PV) and wind turbines (WTs). The UPFC was selected for its unique advantage in both active and reactive power control. The UPFC is controlled with an optimized Fractional Order Proportional–Integral–Derivative (FOPID) controller. The parameters of this controller were tuned using an Atomic Search Optimization (ASO) algorithm. Simulation results confirm the efficiency of the suggested controller in supporting the reliability and performance of the hybrid power system during some disturbance events including voltage sag, swell, and unbalanced loading. In addition, power quality can be improved through reducing the total harmonic distortion. It is worth mentioning that two maximum point tracking techniques had been included for the PV and WT systems separately. MATLAB/SIMULINK 2021a software was used to model the system.

Designing control systems for islanded microgrids poses significant challenges due to the absence of inertia and parameter uncertainties. These factors increase the complexity of traditional methods when applied to highly nonlinear and interdependent systems. To address this issue, a novel Electric Eel Foraging Optimization (EEFO) technique is proposed for tuning control parameters within a hierarchical structure of primary and secondary control levels. The control system employs proportional resonant (PR) controllers for voltage and current regulation, alongside a synchronization loop to enable seamless grid reconnection. Comparative analysis with Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) confirms EEFO’s superior convergence speed and solution quality performance. Simulation results using MATLAB/SIMULINK demonstrate effective active power sharing, minimal overshoot and settling times for voltage (2.4%, 0.25 s) and frequency (0.42%, 0.53 s), and seamless grid reconnection. Experimental validation using hardware-in-the-loop (HIL) real-time emulation further verifies the feasibility and robustness of the proposed approach for practical microgrid applications.

Siwa Oasis, a remote desert region in Egypt, depends heavily on diesel generators, leading to high fuel costs, logistical challenges, and significant CO₂ emissions. This study evaluates hybrid renewable energy systems for the region using real-world data, considering three configurations: Case 1 (PV, BESS, Diesel), Case 2 (PV, Ternary Pumped Hydro Storage (T-PSH), Diesel), and Case 3 (PV, Wind, T-PSH, Diesel). T-PSH enhances system flexibility by enabling three operational modes—pumping, generation, and simultaneous pumping and generating—optimizing the management of surplus renewable energy.A novel Hybrid Cheetah Optimization with Particle Swarm Optimization (HYCHOPSO) algorithm, along with CHO, PSO, GWO, and SSIA, is used to optimize system performance. Results show that Case 1 achieves a COE of $0.03Fih/kWh with 210.6 tons/year emissions and a 61.9% renewable share. Case 2 has the COE ($0.592/kWh) but the highest emissions (525.3 tons/year) due to 91% diesel dependency, while Case 3 attains the lowest emissions (123.6 tons/year) with 69.8% renewable energy at a COE of $0.0935/kWh.These findings highlight the trade-offs between cost and environmental performance and demonstrate that hybrid systems with advanced storage and diversified renewables provide a viable pathway for sustainable energy management in isolated microgrids.