With the growing concern about climate change and the rapid advancement of technology, integrating electric vehicles (EVs) into power systems, particularly within low voltage (LV) distribution networks, is becoming increasingly vital to ensure reliable and sustainable grid operation. This significant paradigm shift necessitates careful consideration of the impact of home charging on the grid, including potential issues such as power quality degradation, voltage deviation, and the overloading of distribution transformers. Under-voltages, especially at the far end of the feeder, can significantly undermine the network's hosting capacity. An active voltage constraint management approach for EVs has been implemented to effectively manage the clustering of EVs within residential power distribution networks, aimed at enhancing the grid's hosting capacity while accounting for realistic household electricity demand. Additionally, the substantial impact on transformer health and power losses is carefully analyzed. The results indicate that the hosting capacity of the studied network could be notably increased through the proposed EV charging demand-shifting strategy. Moreover, the negative impacts on transformer health could be significantly mitigated, leading to a 34.57% improvement in life expectancy.

Power quality phenomena, especially voltage sags, characterized by short-duration reductions in RMS voltage, pose significant challenges to power systems. This study investigates and validates different faults-induced balanced and imbalanced voltage sags at comparative magnitudes of 50% and 30% with a frequency of 50Hz in a multilevel industrial distribution system. The sag magnitudes and phase angle jumps are calculated and further validated through simulations based on phase and RMS voltage drops, sag-type transformations, and sequence component propagation. Fast Fourier transform (FFT) analysis is employed to investigate the magnitudes and propagation of sequence components across different distribution levels. Based on fault induction, the results reveal the transformation of different types of sags or the conversion to a new distorted version of the original type of sag with a unique matrix. The zero-sequence component for each type of sag is removed after passing through a ∆Y configured transformer with a Y-connected load. Moreover, a comparison shows the critical role of different sag magnitudes on phase angle jumps. At both 50% and 30% magnitudes, the system becomes more vulnerable during a phase-to-phase fault. Based solely on phase angle jumps, the sag types D and F are more susceptible at 30% magnitude, while the types C and G are more susceptible at 50%.