Nature-Inspired Optimization Algorithms Applied for Solving Charging Station Placement Problem: Overview and Comparison
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ORIGINAL PAPER
Nature‑Inspired Optimization Algorithms Applied for Solving Charging Station Placement Problem: Overview and Comparison Sanchari Deb1 · Xiao‑Zhi Gao2 · Kari Tammi3 · Karuna Kalita4 · Pinakeswar Mahanta4,5 Received: 29 March 2019 / Accepted: 11 November 2019 © CIMNE, Barcelona, Spain 2019
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. Nomenclature Abbreviations EV Electric vehicle NIO Natire inspired optimization GA Genetic algorithm DE Differential evolution ES Evolutionary strategy PSO Particle swarm optimization CSO Chicken swarm optimization BSA Bird swarm algorithm ACO Ant Colony optimization * Sanchari Deb [email protected] 1
Centre of Energy, Indian Institute of Technology, Guwahati, India
2
School of Computing, University of Eastern Finland, Kuopio, Finland
3
Department of Mechanical Engineering, Aalto University, Espoo, Finland
4
Department of Mechanical Engineering, Indian Institute of Technology, Guwahati, India
5
Department of Mechanical Engineering, National Institute of Technology, Yupia, India
EHO Elephant herding optimization FA Firefly algorithm GWA Grey wolf algorithm WOA Whale optimization algorithm CS Cuckoo search FPA Flower pollination algorithm SOS Symbiotic organisms search SA Simulated annealing LSA Lightning search algorithm GSA Gravitational search algorithm RWA Rain water algorithm HS Harmony search TLBO Teaching learning based optimization YYPA Ying Yang pair algorithm SHO Spotted Hyena optimizer NFL No free lunch theorem THD Total harmonic distortion Decision Variables b Bus number where charging station is to be placed NFb Number of fast charging station at bus b NSb Number of slow charging station at bu
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