Nature-Inspired Optimization Algorithms Applied for Solving Charging Station Placement Problem: Overview and Comparison

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Nature-Inspired Optimization Algorithms Applied for Solving Charging Station Placement Problem : Overview and Comparison. / Deb, Sanchari; Gao, Xiao-Zhi; Tammi, Kari; Kalita, Karuna; Mahanta, Pinakeswar.

In: ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING, 19.11.2019.

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@article{3b1e65fd59de4f569b2a4b024cdcf43e,
title = "Nature-Inspired Optimization Algorithms Applied for Solving Charging Station Placement Problem: Overview and Comparison",
abstract = "The escalated energy demand in conjunction with the global warming and environmental degradation has paved the path of transportation electrification. Electric Vehicles (EVs) need to recharge their batteries after travelling certain distance. Thus, large scale deployment of EVs calls for development of sustainable charging infrastructure. The placement of charging stations is a complex optimization problem involving a number of decision variables, objective functions, and constraints. Placement of charging station mimics a non-convex and non- combinatorial problem involving both transport and distribution network. The complex and non-linear nature of the charging station placement problem has compelled researchers to apply Nature Inspired Optimization (NIO) algorithms for solving the problem. This study aims to review the NIO algorithms applied for solving the charging station placement problem. This work will endow the research community with a systematic review of NIO algorithms for solving charging station placement problem thereby revealing the key features, advantages, and disadvantages of each of these algorithms. Thus, this work will help the researchers in selecting suitable algorithm for solving the charging station placement problem and will serve as a guide for developing efficient algorithms to solve the charging station placement problem.",
keywords = "PARTICLE SWARM OPTIMIZATION, PLUG-IN HYBRID, SEARCH, PERFORMANCE, IMPACTS",
author = "Sanchari Deb and Xiao-Zhi Gao and Kari Tammi and Karuna Kalita and Pinakeswar Mahanta",
year = "2019",
month = "11",
day = "19",
doi = "10.1007/s11831-019-09374-4",
language = "English",
journal = "ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING",
issn = "1134-3060",
publisher = "Springer Netherlands",

}

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TY - JOUR

T1 - Nature-Inspired Optimization Algorithms Applied for Solving Charging Station Placement Problem

T2 - Overview and Comparison

AU - Deb, Sanchari

AU - Gao, Xiao-Zhi

AU - Tammi, Kari

AU - Kalita, Karuna

AU - Mahanta, Pinakeswar

PY - 2019/11/19

Y1 - 2019/11/19

N2 - The escalated energy demand in conjunction with the global warming and environmental degradation has paved the path of transportation electrification. Electric Vehicles (EVs) need to recharge their batteries after travelling certain distance. Thus, large scale deployment of EVs calls for development of sustainable charging infrastructure. The placement of charging stations is a complex optimization problem involving a number of decision variables, objective functions, and constraints. Placement of charging station mimics a non-convex and non- combinatorial problem involving both transport and distribution network. The complex and non-linear nature of the charging station placement problem has compelled researchers to apply Nature Inspired Optimization (NIO) algorithms for solving the problem. This study aims to review the NIO algorithms applied for solving the charging station placement problem. This work will endow the research community with a systematic review of NIO algorithms for solving charging station placement problem thereby revealing the key features, advantages, and disadvantages of each of these algorithms. Thus, this work will help the researchers in selecting suitable algorithm for solving the charging station placement problem and will serve as a guide for developing efficient algorithms to solve the charging station placement problem.

AB - The escalated energy demand in conjunction with the global warming and environmental degradation has paved the path of transportation electrification. Electric Vehicles (EVs) need to recharge their batteries after travelling certain distance. Thus, large scale deployment of EVs calls for development of sustainable charging infrastructure. The placement of charging stations is a complex optimization problem involving a number of decision variables, objective functions, and constraints. Placement of charging station mimics a non-convex and non- combinatorial problem involving both transport and distribution network. The complex and non-linear nature of the charging station placement problem has compelled researchers to apply Nature Inspired Optimization (NIO) algorithms for solving the problem. This study aims to review the NIO algorithms applied for solving the charging station placement problem. This work will endow the research community with a systematic review of NIO algorithms for solving charging station placement problem thereby revealing the key features, advantages, and disadvantages of each of these algorithms. Thus, this work will help the researchers in selecting suitable algorithm for solving the charging station placement problem and will serve as a guide for developing efficient algorithms to solve the charging station placement problem.

KW - PARTICLE SWARM OPTIMIZATION

KW - PLUG-IN HYBRID

KW - SEARCH

KW - PERFORMANCE

KW - IMPACTS

U2 - 10.1007/s11831-019-09374-4

DO - 10.1007/s11831-019-09374-4

M3 - Review Article

JO - ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING

JF - ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING

SN - 1134-3060

ER -

ID: 39225479