Multi-objective Optimization Approach for the Allocation of Fast Charging Stations and Distributed Energy Resources

Rafael S. F. Ferraz; Renato S. F. Ferraz; Augusto C. Rueda-Medina; Jussara F. Fardin

doi:10.20906/sbse.v2i1.2966

Rafael S. F. Ferraz Programa de Pós-Graduação em Engenharia Elétrica. Departamento de Engenharia Elétrica. Universidade Federal do Espírito Santo, ES
Renato S. F. Ferraz Programa de Pós-Graduação em Engenharia Elétrica. Departamento de Engenharia Elétrica. Universidade Federal do Espírito Santo, ES
Augusto C. Rueda-Medina Programa de Pós-Graduação em Engenharia Elétrica. Departamento de Engenharia Elétrica. Universidade Federal do Espírito Santo, ES
Jussara F. Fardin Programa de Pós-Graduação em Engenharia Elétrica. Departamento de Engenharia Elétrica. Universidade Federal do Espírito Santo, ES

DOI: https://doi.org/10.20906/sbse.v2i1.2966

Keywords: charging station, distributed energy resource, electric vehicle, multi-objective algorithm

Abstract

The increasing inclusion of Electric Vehicles (EVs) in distribution systems is a global trend due to their several advantages, such as increased autonomy and reduced price. However, the high amount of EVs requires the installation of sufficient EV Charging Stations (EVCSs) to recharge them. If there is no adequate planning for the EVCSs allocation, it can result in a reduction in the power quality indices such as increased power losses in the system and voltage variation outside the limits established in IEEE Std 1547-2018. Therefore, this paper aims to define the best locations for the installation of EVCSs in the system, in addition to performing the optimal allocation and sizing of Distributed Energy Resources (DERs) in order to mitigate the problem related to voltage levels. Moreover, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was used with the purpose of minimizing the investment and operation costs and voltage deviation in the system. The validation of this methodology was performed using the IEEE 13 and 34 node test feeders, and it was possible to compare optimized solutions based on the Pareto curve for both systems, which made it possible to minimize the objective functions.