Performance of Gradient-Based Optimizer on Charging Station Placement Problem

Essam H. Houssein, Sanchari Deb, Diego Oliva, Hegazy Rezk, Hesham Alhumade and Mokhtar Said
Additional contact information
Essam H. Houssein: Faculty of Computers and Information, Minia University, Minia 61519, Egypt
Sanchari Deb: School of Engineering, University of Warwick, Coventry CV4 7AL, UK
Diego Oliva: División de Electrónica y Computación, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara 44430, Mexico
Hegazy Rezk: College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al-Kharj 11911, Saudi Arabia
Hesham Alhumade: Chemical and Materials Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Mokhtar Said: Electrical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum 43518, Egypt

Mathematics, 2021, vol. 9, issue 21, 1-16

Abstract: The electrification of transportation is necessary due to the expanded fuel cost and change in climate. The management of charging stations and their easy accessibility are the main concerns for receipting and accepting Electric Vehicles (EVs). The distribution network reliability, voltage stability and power loss are the main factors in designing the optimum placement and management strategy of a charging station. The planning of a charging stations is a complicated problem involving roads and power grids. The Gradient-based optimizer (GBO) used for solving the charger placement problem is tested in this work. A good balance between exploitation and exploration is achieved by the GBO. Furthermore, the likelihood of becoming stuck in premature convergence and local optima is rare in a GBO. Simulation results establish the efficacy and robustness of the GBO in solving the charger placement problem as compared to other metaheuristics such as a genetic algorithm, differential evaluation and practical swarm optimizer.

Keywords: gradient-based optimizer (GBO); charging station placement problem; electric vehicles (EVs); metaheuristic algorithms (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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