Optimizing EV charging deployment in megacities: A Cairo case study using clustering and load analysis

• Urbanization challenges equitable deployment of EV charging infrastructure. • Data-driven framework optimizes EV charging station siting in Cairo. • Socket per density and travel burden indices assess infrastructure equity. • Central districts over-served; residential areas suffer under-provision. • Vehicle-to-Grid simulation predicts peak-load reduction without extra infrastructure. The accelerating urbanization of Global South megacities presents considerable challenges to the equitable and technically efficient deployment of Electric vehicle charging infrastructure. This paper presents a data-driven planning framework applied to Cairo, integrating. K-Means spatial clustering, district-level demographic projections (2020–2025), and national electricity load analysis to optimize the siting of vehicle charging stations. A total of 85 public EV stations comprising 209 sockets were georeferenced and analyzed. Two novel indices were introduced to assess infrastructure equity: the socket per density index and the socket travel burden index. Results show that while central business districts are over-served, high-density residential areas such as Ain Shams and Dar Al Salam suffer from significant under-provision. Type 2 connectors dominate the network (77.5 %), leading to functional exclusion for users of CHAdeMO, CCS2, and GB/T vehicles. Vehicle-to-Grid simulation with 40 % vehicle charging participation, representing 5,078 vehicles, demonstrated a potential peak-load reduction of 25.4 MW without requiring additional infrastructure. The proposed framework offers a scalable and transferable model for equitable, resilient, and technically inclusive EV infrastructure planning in rapidly urbanizing regions of the Global South.