Table 1.

Research studies optimizing electric vehicle charging stations.

References	Focus	Methodology	Key findings	Research implications
37	Placement of the CS, power losses, and voltage swings	TLBO algorithm	Optimizing CS placements	It explore integrating efficient , environmentally friendly charging features
43	EV charging station locations, grid reliability	Fuzzy decision-making with the hybrid CSO TLBO algorithm	Pareto ideal solutions for power loss, cost reduction
45	Distribution network stability and placement of solar-powered charging stations	Probabilistic modeling using feed-forward neural networks, modified CSO approach, and EV load prediction	lower power losses and better voltage profiles on a 33-bus system	Using hybrid algorithms to locate charging facilities in cities and in-depth models to analyze the behavior of EV drivers
47	Grid stability, cost savings, and efficient implementation of charging infrastructure	Stochastic modeling and PV generation integration with PSO	Significant cost reductions, a reduction in transformer congestion	adaptable solution that delays the need for new transformers in both residential and commercial settings
48	EV charging stations with solar panels strategically placed	Hybrid BFOA-PSO optimization algorithm	Effectiveness in maintaining voltage stability and reducing power losses	Make it easier for EVs to be integrated into modern distribution networks