Expanding the charging infrastructure is essential for the widespread adoption of electric vehicles (EVs). A promising and effective solution is integrating EV charging points into existing fuel stations, thus optimizing land use while enhancing accessibility. Hence, the optimal placement of EV chargers within fuel station networks is a critical challenge. Traditional approaches rely on the so-called Maximal Covering Location Problem (MCLP), which assumes binary coverage and overlooks capacity constraints. This paper extends the MCLP framework by introducing a novel distance-based scalable coverage function and incorporating capacity limitations to prevent stations overload. The proposed model enhances the accuracy of demand distribution and offers a realistic, scalable approach to planning EV charging infrastructure. To validate its effectiveness, the proposed model is tested on a real-world case study involving the city of Bari, Italy.
Optimal Positioning of Electric Vehicle Chargers for Efficient Land Use in Smart Cities: Integration with Fuel Stations / Signorile, Federico; Mastromarino, Fabio; Scarabaggio, Paolo; Gialò, Valeria; Carli, Raffaele; Dotoli, Mariagrazia. - ELETTRONICO. - 59:9(2025), pp. 315-320. ( 1st IFAC Workshop on Smart Energy System for Efficient and Sustainable Smart Grids and Smart Cities, SENSYS 2025 Bari, Italy June 18-20, 2025) [10.1016/j.ifacol.2025.08.156].
Optimal Positioning of Electric Vehicle Chargers for Efficient Land Use in Smart Cities: Integration with Fuel Stations
Signorile, Federico;Mastromarino, Fabio;Scarabaggio, Paolo;Gialò, Valeria;Carli, Raffaele;Dotoli, Mariagrazia
2025
Abstract
Expanding the charging infrastructure is essential for the widespread adoption of electric vehicles (EVs). A promising and effective solution is integrating EV charging points into existing fuel stations, thus optimizing land use while enhancing accessibility. Hence, the optimal placement of EV chargers within fuel station networks is a critical challenge. Traditional approaches rely on the so-called Maximal Covering Location Problem (MCLP), which assumes binary coverage and overlooks capacity constraints. This paper extends the MCLP framework by introducing a novel distance-based scalable coverage function and incorporating capacity limitations to prevent stations overload. The proposed model enhances the accuracy of demand distribution and offers a realistic, scalable approach to planning EV charging infrastructure. To validate its effectiveness, the proposed model is tested on a real-world case study involving the city of Bari, Italy.| File | Dimensione | Formato | |
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