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

Research output: Contribution to journalReview ArticleScientificpeer-review

Researchers

  • Sanchari Deb
  • Xiao-Zhi Gao
  • Kari Tammi
  • Karuna Kalita
  • Pinakeswar Mahanta

Research units

  • Indian Institute of Technology
  • University of Eastern Finland
  • National Institute of Technology Yupia

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.

Details

Original languageEnglish
Number of pages16
JournalARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
Publication statusE-pub ahead of print - 19 Nov 2019
MoE publication typeA2 Review article in a scientific journal

    Research areas

  • PARTICLE SWARM OPTIMIZATION, PLUG-IN HYBRID, SEARCH, PERFORMANCE, IMPACTS

ID: 39225479