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

被引：16

作者：

Palanca, Javier ^{[1
]}

Jordan, Jaume ^{[1
]}

Bajo, Javier ^{[2
]}

Botti, Vicent ^{[1
]}

机构：

[1] Univ Politecn Valencia, Inst Valencia Invest Intelligencia Artificial VRA, Camino Vera S-N, E-46022 Valencia, Spain

[2] Univ Politecn Madrid, Dept Inteligencia Artificial, Campus Montegancedo, E-28660 Madrid, Spain

来源：

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE | 2020年 / 108卷

关键词：

Electric vehicle; Charging station; Genetic algorithm; Energy; Smart city; Multi-objective; Evolutionary algorithm; Deap; Peru;

D O I：

10.1016/j.future.2020.03.001

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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. (C) 2020 Elsevier B.V. All rights reserved.

引用

页码：454 / 466

页数：13

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