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

Abstract

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.