Multi-objective Optimization Approach for Allocation of Electric Vehicles Parking Lots and Smart Charging with Distributed Energy Resource

• Published in: Journal of control, automation & electrical systems automation & electrical systems, 2023-10, Vol.34 (5), p.1070-1079
• Main Authors: Ferraz, Rafael S. F., Ferraz, Renato S. F., Rueda-Medina, Augusto C., Fardin, Jussara F.
• Format: Article
• Language: English
• Subjects: Control; Control and Systems Theory; Distributed generation; Electric potential; Electric power demand; Electric power loss; Electric vehicle charging; Electric vehicles; Electrical Engineering; Energy management; Energy sources; Engineering; Fossil fuels; Genetic algorithms; Graph theory; Mechatronics; Multiple objective analysis; Pareto optimization; Parking facilities; Robotics; Robotics and Automation; Sizing; Sorting algorithms; Spatial distribution; Time of use electricity pricing; Transportation networks; Voltage
• ISSN: 2195-3880; 2195-3899
• DOI: 10.1007/s40313-023-01027-z

Electric vehicles (EVs) are a promising solution to resolve the greenhouse emission issue as well as the fossil fuels scarcity problem in the future. However, as the number of EVs significantly increases, the excessive charging demand of EV parking lots (EVPLs) in the charging station may incur an instability problem to the electricity network. In this paper, the optimal allocation and sizing of EVPLs were carried out in order to minimize the costs of the EVPLs installation and EV users’ distance traveled and recharge. As a consequence, it encourages the participation of vehicle users in a demand response program related to smart charging and time of use tariffs. It is important to mention that the increasing inclusion of EVs negatively affects the voltage level and power losses of the distribution system. Therefore, this paper also performs the optimal allocation and sizing of distributed energy resources (DERs) in order to mitigate these problems. The non-dominated sorting genetic algorithm II (NSGA-II) was applied to obtain the Pareto curve, which made it possible to minimize the objective functions for the IEEE 33-node test feeder and 25-node transportation network. Furthermore, it was considered a spatial distribution of EVs for the 24-h from the closeness centrality of graph theory. The optimal planning of EVPLs and DERs promoted a 35%, 23.14% and 34.11% reduction of the travel distance, voltage deviation and power losses, respectively.