Optimizing electric vehicle charging infrastructure powered by photovoltaics: A geospatial and multi-criteria analysis for sustainable urban mobility

In response to the increasing need for sustainable urban mobility and the mitigation of greenhouse gas emissions, this study proposes an integrated methodology for planning electric vehicle (EV) charging infrastructure powered by photovoltaic (PV) systems. The approach combines an Analytic Hierarchy Process (AHP) based multi-criteria decision analysis with geospatial tools and land cover classification using a deep learning model (applied to Sentinel-2 imagery) to identify optimal locations for solar powered charging stations. The evaluation criteria include technical and environmental factors such as proximity to fuel stations, parking areas, commercial zones, public infrastructure, topography, urban density, and solar irradiance. A 22 Kw prototype solar charging station was developed in Benguerir, Morocco, incorporating PV panels, a 2.34 MWh battery storage system, and a backup grid connection. Simulations conducted using PVsyst estimate an annual energy production of 117,402 kWh, with a performance ratio of 68.3 % and a surface requirement of approximately 391 m². At the urban scale, the analysis identified 36 suitable areas totaling 58,107 m², enabling the installation of up to 148 charging stations far exceeding the 10 units required to meet the daily needs of 50 EVs. Environmental impact analysis indicates a net annual CO₂ emission reduction of 54.75 tonnes, representing a 44.4 % decrease compared to conventional internal combustion engine vehicles. These findings demonstrate the technical viability and deployment potential of PV-powered EV charging infrastructure in sun-rich urban environments. The proposed framework offers a scalable and transferable planning tool to support policy-makers in advancing clean mobility strategies and accelerating the transition toward low-carbon, resilient urban transport systems.