The integration of electric vehicles (EVs) into low-voltage power distribution networks (LVDNs) introduces a new class of mobile, high-power, and behaviorally uncertain loads. This review article systematically analyzes the existing literature to assess how EV charging affects transformer and cable loading, voltage deviation, phase unbalance, power losses, and harmonics. Unlike existing reviews that assess the impact of EVs alongside other distributed technologies, or those that focus primarily on medium-voltage networks, this paper provides a targeted synthesis of EV impacts specifically within LVDNs. The analysis shows that the types and severity of impacts are influenced by grid topology: rural networks are more prone to voltage deviations due to long feeders, urban networks face transformer overloading from dense residential demand, and suburban networks are vulnerable to both. Moreover, user-centric charging strategies, especially when applied uniformly across many EVs, can aggravate grid issues by synchronizing demand. Conversely, EVs hold significant potential as flexible grid assets, provided that local flexibility solutions and coordination mechanisms are in place. To this end, the review identifies key opportunities to strengthen research and practice, including clearer methodological reporting, standardized baseline scenarios, and more realistic modeling approaches that reflect the heterogeneity of charging behaviors and EV types. While simulation remains a powerful tool, expanding empirical validation through field studies is essential to ground findings in operational reality. Such studies can also demonstrate how improved observability in LVDNs is critical for unlocking EV flexibility and enabling proactive grid integration. • EVs influence component loading and power quality in low-voltage networks. • Impact severity depends on charging coincidence, EV models, and grid topology. • EVs have untapped potential to actively support distribution grid operation. • Future research needs more heterogeneous modeling of EVs and charging behavior.
Zunino et al. (Wed,) studied this question.