DocumentCode
666068
Title
Optimal electric vehicle charging stations placement in distribution systems
Author
Chun-Lien Su ; Rong-Ceng Leou ; Jun-Chang Yang ; Chan-Nan Lu
Author_Institution
Dept. of Marine Eng., Nat. Kaohsiung Marine Univ., Kaohsiung, Taiwan
fYear
2013
fDate
10-13 Nov. 2013
Firstpage
2121
Lastpage
2126
Abstract
This paper presents an algorithm dedicated to the electric vehicles (EVs) charging stations placement optimization in a given distribution system using genetic algorithms (GA), where daily time varying loads are considered together with random EVs charging patterns including starting time, duration, and power of charging. The problem is formulated as a non-differential combinational optimization problem, where the system losses to be minimized subject to capacity and system operation constraints. The placement alternatives considered are the installation of Level 2 single-phase slow chargers. In the GA evolutionary process, all individuals´ fitness is analyzed and for each feasible solution, a non-linear three phase power flow problem is solved and the system losses are calculated. A practical distribution system composed of 20 buses was used to validate the algorithm and demonstrate its applicability to large systems.
Keywords
combinatorial mathematics; distribution networks; electric vehicles; genetic algorithms; load flow; 20 buses; EV charging stations placement optimization; distribution systems; genetic algorithms; level 2 single-phase slow chargers; nondifferential combinational optimization problem; nonlinear three phase power flow problem; optimal electric vehicle charging stations placement; random EV charging patterns; system operation constraints; the GA evolutionary process; Biological cells; Electric vehicles; Genetic algorithms; Load flow; Mathematical model; Optimization; Time measurement; Charging stations; Electric vehicles; Impact study; Placement;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial Electronics Society, IECON 2013 - 39th Annual Conference of the IEEE
Conference_Location
Vienna
ISSN
1553-572X
Type
conf
DOI
10.1109/IECON.2013.6699459
Filename
6699459
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