Performance of Gradient-Based Optimizer on Charging Station Placement Problem

Essam H Houssein,Mokhtar Said,Sanchari Deb,Diego Oliva,Hesham Alhumade,Hegazy Rezk

doi:10.3390/math9212821

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.

Highlights

The charger placement problem was solved by the Gradient-based optimizer (GBO) for the superimposed network of 33 distribution bus and 25 road nodes as shown in Figure 1; the two routes were assumed for following the pass of Electric Vehicles (EVs):
The general specific parameters of the algorithm were selected by fine tuning that was achieved by trial and error
The GBO method achieved a better accuracy than all competitor algorithms

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The distribution network voltage deviation, power losses and charging service have been used as a multi-objective function in the optimization placement of charging stations This problem has been tested on a road network of 25 nodes and an IEEE 33 bus network by using a cross-entropy method and data envelopment analysis [30]. The optimal design of a charging station has been performed by using the TLBO and CSO algorithms with the objective function of minimizing the cost and the behaviour improvement of the distribution network was taken into consideration. A hierarchical genetic algorithm was applied in the estimation of an optimal charging station design based on cost as an objective function and the constraints of the maximum capacity ofa charging station and limits of power loss.

Charger Placement Problem

Gradient-Based Optimizer

The Initialization Process

Experimental Results and Numerical Analysis

Conclusions