A methodological approach to the deployment of electric vehicle charging infrastructure in urban areas

RELEVANCE. The rapid growth of electric vehicle (EV) fleets worldwide and in Russia outpaces the deployment of charging infrastructure, causing uneven distribution of charging stations (EVCS) and reducing operational efficiency. A comprehensive methodology is required to account for technical, urban planning, and behavioral factors influencing EVCS siting decisions. THE PURPOSE. To develop and test a multifactor model for assessing the suitability of urban sites for EVCS deployment using the Analytic Hierarchy Process (AHP). The model integrates expert and user survey results and applies correction coefficients for urban zone type and existing EVCS density. METHODS. A set of factors was identified from a survey of two target groups: power engineering experts and EV owners. Pairwise comparisons of factors were performed using T. Saaty’s nine-point scale to determine weight coefficients, followed by a consistency check. The resulting weights were incorporated into an integral suitability formula adjusted for functional zoning and infrastructure saturation. The model was tested on five sites in Novosibirsk and Moscow with different functional zoning types (business, residential, transit, industrial, and highdensity EVCS areas). RESULTS. The model identified sites with high and low suitability scores, demonstrating sensitivity to both factor structure and correction coefficients. High-scoring sites showed balanced factor contributions, while low-scoring sites revealed critical constraints such as low traffic, poor connectivity, or over-saturation with charging infrastructure. The model successfully detected zones with excessive infrastructure, thus preventing resource duplication. CONCLUSIONS. The proposed methodology provides a practical decision-support tool for urban planners, investors, and developers to optimize EVCS siting. It enables early-stage planning, reduces risks of inefficient investment, and is adaptable to other cities and contexts. The integration of technical, spatial, and behavioral parameters improves infrastructure efficiency and supports sustainable urban mobility strategies.

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