Multi-objective approach for optimized planning of electric vehicle charging stations and distributed energy resources

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, this growth requires a high investment in electric vehicle charging stations (EVCSs) infrastructure to satisfy the dem...

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Bibliographic Details
Published in:Electrical engineering 2023-12, Vol.105 (6), p.4105-4117
Main Authors: Ferraz, Rafael S. F., Ferraz, Renato S. F., Rueda Medina, Augusto C., Fardin, Jussara F.
Format: Article
Language:English
Subjects:
Distributed generation
Economics and Management
Electric potential
Electric power loss
Electric vehicle charging
Electric vehicle charging stations
Electric vehicles
Electrical Engineering
Electrical Machines and Networks
Energy costs
Energy Policy
Energy resources
Energy sources
Engineering
Genetic algorithms
Graph theory
Multiple objective analysis
Original Paper
Power Electronics
Sizing
Sorting algorithms
Spatial distribution
Voltage
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Summary: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, this growth requires a high investment in electric vehicle charging stations (EVCSs) infrastructure to satisfy the demand. Thus, in this paper, an adequate planning of the EVCSs allocation and sizing is carried out to ensure better power quality indices, in addition to reducing costs related to EVCSs installation and EV users’ recharging. Besides the optimal planning of EVCSs, this paper performs the optimal allocation and sizing of distributed energy resources (DERs) in order to mitigate the problem related to voltage levels and power losses. Additionally, a spatial distribution of EVs was performed for the 24 h, considering residential and commercial nodes of a distribution feeder test, from the closeness centrality of graph theory. 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 34-node test feeder. Even with the integration of EV loads, the optimal allocation and sizing of EVCSs and DERs promoted a reduction in voltage deviation and power losses of 11.576% and 56.683%, respectively, in addition to a low variation in the energy costs of 4.447%.
ISSN:0948-7921
1432-0487
DOI:10.1007/s00202-023-01942-z