Optimal Locating of Electric Vehicle Charging Stations by Application of Genetic Algorithm

Milad Akbari, Morris Brenna and Michela Longo
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Milad Akbari: Politecnico di Milano, Department of Energy, Via La Masa, 34, 20156 Milano (MI), Italy
Morris Brenna: Politecnico di Milano, Department of Energy, Via La Masa, 34, 20156 Milano (MI), Italy
Michela Longo: Politecnico di Milano, Department of Energy, Via La Masa, 34, 20156 Milano (MI), Italy

Sustainability, 2018, vol. 10, issue 4, 1-14

Abstract: The advent of alternative vehicle technologies such as Electrical Vehicles (EVs) is an efficient effort to reduce the emission of carbon oxides and nitrogen oxides. Ironically, EVs poses concerns related to vehicle recharging and management. Due to the significance of charging station infrastructure, electric vehicles’ charging stations deployment is investigated in this work. Its aim is to consider several limitations such as the power of charging station, the average time needed for each recharge, and traveling distance per day. Initially, a mathematical formulation of the problem is framed. Then, this problem is optimized by application of Genetic Algorithm (GA), with the objective to calculate the necessary number of charging stations then finding the best positions to locate them to satisfy the clients demand.

Keywords: electric vehicles; genetic algorithm; charging stations; bass model; smart cities (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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