An energy-aware algorithm for electric vehicle infrastructures in smart cities

The deployment of a charging infrastructure to cover the increasing demand of electric vehicles (EVs) has become a crucial problem in smart cities. Additionally, the penetration of the EV will increase once the users can have enough charging stations. In this work, we tackle the problem of locating...

Full description

Saved in:
Bibliographic Details
Published in:Future generation computer systems 2020-07, Vol.108, p.454-466
Main Authors: Palanca, Javier, Jordán, Jaume, Bajo, Javier, Botti, Vicent
Format: Article
Language:English
Subjects:
Charging station
Deap
Electric vehicle
Energy
Evolutionary algorithm
Genetic algorithm
Multi-objective
Peru
Smart city
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The deployment of a charging infrastructure to cover the increasing demand of electric vehicles (EVs) has become a crucial problem in smart cities. Additionally, the penetration of the EV will increase once the users can have enough charging stations. In this work, we tackle the problem of locating a set of charging stations in a smart city considering heterogeneous data sources such as open data city portals, geo-located social network data, and energy transformer substations. We use a multi-objective genetic algorithm to optimize the charging station locations by maximizing the utility and minimizing the cost. Our proposal is validated through a case study and several experimental results. •A multi-objective genetic algorithm to measure utility and cost.•Inclusion of power substations to consider the distance to the charging stations.•Geo-located social networks data using a geohashing algorithm.•Case of study and experimental evaluation in the city of Lima (Peru).
ISSN:0167-739X
1872-7115
DOI:10.1016/j.future.2020.03.001