This research aimed to increase the power captured from photovoltaic (PV) systems by continuously adjusting the PV systems to work at the maximum power point under climate changes such as solar irradiance change and temperature change and by tracking the global maximum power under partial shading conditions (PSCs). Under the effect of partial shading (PS), the PV curve has many local maximum peaks (LMPs) and one global maximum peak (GMP) which is dynamic because it changes with time when the shading pattern (SP) changes. The traditional maximum power point tracking (MPPT) methods are unable to track the Dynamic GMP and may fall into one of the LMPs. Many modern MPPT methods have been introduced that can track the Dynamic GMP, but their effectiveness can be improved. In this respect, this work introduces a new optimal MPPT technique to enhance the performance of the maximum power point tracking of solar cells under environmental changes and partial shading conditions. The proposed technique combines three well-known and important MPPT techniques, which are the Artificial Neural Network (ANN), Variable Step Perturb and Observe (VSP&O), and Fuzzy Logic Controller (FLC). Artificial Neural Network gives a voltage near the optimum voltage, Variable Step Perturb and Observe updates the voltage to get close to the optimum voltage, and Fuzzy Logic Controller updates the step size of the (P&O) technique. The proposed hybrid ANN-VSP&O-FLC technique showed its ability to track the Dynamic GMP accurately and quickly under the variation in the shading patterns with time and its ability to follow maximum power efficiently and quickly under climate changes. The proposed hybrid ANN-VSP&O-FLC technique also showed very low distortions in waveforms and very low oscillations around the steady state. The proposed hybrid ANN-VSP&O-FLC technique was compared to the most recent and effective MPPT techniques in terms of steady-state behavior, tracking speed, tracking efficiency, and distortions in waveforms, and the comparison showed that it is superior to them, with lower distortions in waveforms, a faster tracking speed (less than 0.1 s), higher tracking efficiency (greater than 99.65%), and lower oscillations around the steady state (less than 2 Watts).

Energy is a vital component of life today, and providing reliable power access remains a significant global challenge, particularly in remote areas. In Egypt, several isolated regions, including parts of South Sinai, suffer from limited electricity access. This study presents an enhanced environmental and techno-economic modeling of an off-grid hybrid renewable energy microgrid (HREM) tailored for such regions. A proposed configuration combining photovoltaic (PV) panels, wind turbines (WTs), a converter (CONV), and battery energy storage is evaluated to meet the residential energy demand of an isolated community in South Sinai. Four feasible system models, PV/CONV/BAT, WT/CONV/BAT, PV/WT/CONV/BAT, and a standalone diesel generator, were simulated using hybrid optimization of multiple energy resources. The cost of energy was analyzed under different scenarios. The results show that the combination of a 1109 kW PV system, 16 × 25 kW WTs, 439 kW converter, and 353 batteries is the best configuration that leads to the lowest values of Net Present Cost (NPC) and Levelized Cost of Energy (COE), and zero unmet load, making it the most economically and environmentally viable configuration.