Optimal electric vehicle charging station allocation

Published: 18 October 2022| Version 2 | DOI: 10.17632/g3kp7yt2m5.2


Despite the environmental and economic benefits of Electric Vehicles (EVs), distribution network operators will need to understand the location where the charging infrastructure will be placed to ensure EV users’ needs are met. In this sense, this work proposes a methodology to define the optimal location of EV semi-fast charging stations (CS) at a neighborhood level, through a multi-objective approach. It applies a hierarchical clustering method to define CS service zones, considering technical and mobility aspects. Besides, it considers uncertainties related to the EV load profile to determine the CS capacity, based on the user’s charging behavior. A Pareto Frontier method is deployed to support the decision-making process on CS optimal location, considering utility and EV users’ preferences. The results indicate that the best CS locations for mid-term EV penetration can also fit into long-term planning, with higher EV charging demand. Thus, these locations would be good candidates for the power utility to make initial investments, in both planning horizons. A real distribution system case is used to demonstrate the applicability of the results.


Steps to reproduce

The codes are in MATLAB and OpenDSS. To run the program just go to the "Main.m" file and run it. The ".mat" files are not saved in the main folder but in the "DSS" folder. After obtaining the results, transfer the ".mat" files to the "Results' Treatment" folder. There is another code there, named "PF.mat", which generates the figures for analysis. Note that the name of the ".mat" to be loaded in the code must be changed according to the name of the ".mat" saved in the "Main.m" code. OPENDSS FILES NEED THE FILE PATH ON YOUR PC. DON'T FORGET TO CHANGE THEM IN THE CODE! Files that need file path on PC: "RunOpenDSS.m" "Results.m" "Fob.m" If you have any questions, feel free to send me an email: leonardo.bitencourt@engenharia.ufjf.br


Universidad de Costa Rica, Universidade Federal de Juiz de Fora, Universidade Federal Fluminense


Clustering, Multi-Objective Optimization, Long-Term Planning of Power System, Electric Vehicles