This paper suggests an optimal maximum power point tracking (MPPT) control scheme for a grid-connected photovoltaic (PV) system using the arithmetic optimization algorithm (AOA). The parameters of the proportional-integral (PI) controller-based incremental conductance (IC) MPPT are optimally selected using AOA. To accomplish this study, a 100-kW benchmark PV system connected to a medium distribution utility is constructed and analyzed employing MATLAB/SIMULINK. The optimization framework seeks to minimize four standard benchmark performance indices, then select the best of the best among them. To verify the efficacy of the recommended methodology, a comprehensive comparison is conducted between AOA-based PI-IC-MPPT, modified incremental conductance MPPT (MIC), grey wolf optimization (GWO), genetic algorithm (GA), and particle swarm optimization (PSO)-based MPPT. The proposed control approach has achieved a reduction of 61, 3, 4.5, and 26.9% in the rise time and a decrease of 94, 84.7, 86.6, and 79.3% in the settling time compared with MIC, GWO, GA, and PSO in extracting MPPT of the proposed system, respectively.

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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 (π-EEIA). The main concept behind the proposed π-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 TM 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.

The importance of using renewable energy systems (RESs) worldwide has been consolidated. Moreover, connecting more RESs to the utility grid will lead to more technical problems. Photovoltaic (PV) and wind turbine (WT) based power plants are the most nonlinear sources of renewable energies contributing to the energy mix Electronic ballast and switching mode power supply in energy conservation of the PV and WT have caused power quality problems and less reliable output voltage. PV power plants are becoming increasingly integrated with the utility grid by onboarding certain superior power quality features. This grid integration drastically reduces the use of fossil fuels and prevents environmental hazards. This article presents the design of a 26 MWp grid-connected PV power plant, which is already tied to the Egyptian electrical network in Fares City, Kom Ombo Center, Aswan Governorate, Egypt The 26 MWp PV power plant consists of (11) blocks and the utility grid, which are simulated using Matlab/Simulink. Every block contains 2,376 kWp PV arrays connected directly to DC-DC boost converters to regulate the output DC power generated by each PV array. This output DC power is fed into a particular type of inverter called a “central inverter”, which converts it to AC power. In some cases, higher harmonic distortion at the grid and a greater negative impact on the power system performance occur when using this type of inverter. To optimize the gains of the proportional-integral (PI) controller for both the voltage and current regulators of this central inverter, meta-heuristic optimization techniques (MOTs) are used. During this article, Gray Wolf Optimization (GWO), Harris Hawks Optimization (HHO), and Arithmetic Optimization Algorithm (AOA) are applied as MOTs to enhance the quality of the power and voltage in addition to limiting the total harmonic distortions (THD) under the effect of different sunlight conditions and partial shading. As a result, the AOA-based controllers are found to show outstanding results and superior performance compared to GWO and HHO regarding solution quality and computational efficiency. Finally, MOTs are the best solution to most electrical problems regarding controlling nonlinear and high-penetration systems, such as PV power plants connected to the utility grid.

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