Genetic algorithm–based demand response scheme for electric vehicle charging

This paper presents a design and evaluates the performance of a charging task scheduler for electric vehicles, aiming at reducing the peak load and improving the service ratio in charging stations. Based on a consumption profile and the real–time task model consisting of actuation time, operation length, and deadline, the proposed scheduler fills the time table, by which the power controller turns on or off the electric connection switch to the vehicle on each time slot boundary. Genetic evolutions yield better results by making the initial population include both heuristic–generated schedules for fast convergence and randomly generated schedules for diversity loss compensation. The authors' heuristic scheme sequentially fills the time slots having lowest load for different orders such as deadline and operation length. The performance measurement result obtained from a prototype implementation reveals that the authors' scheme can reduce the peak load for the given charging task sets by up to 4.9%, compared with conventional schemes.

• Record URL:
  https://doi.org/10.1504/IJIIDS.2013.057420
• Record URL:
  http://inderscience.metapress.com/link.asp?target=contribution&id=3696N117633N371U
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/17515866
• Supplemental Notes:
  • Copyright © 2013 Inderscience Enterprises Ltd.
• Authors:
  • Lee, Junghoon
  • Park, Gyung–Leen
• Publication Date: 2013

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References;
• Pagination: pp 535-549
• Serial:
  • International Journal of Intelligent Information and Database Systems
  • Volume: 7
  • Issue Number: 6
  • Publisher: Inderscience Enterprises Limited
  • ISSN: 1751-5866
  • EISSN: 1751-5858
  • Serial URL: http://www.inderscience.com/jhome.php?jcode=ijiids

Subject/Index Terms

• TRT Terms: Electric vehicle charging; Electric vehicles; Energy consumption; Genetic algorithms; Peak periods; Performance measurement; Prototypes
• Candidate Terms: Smart grid
• Subject Areas: Energy; Highways; Planning and Forecasting; Vehicles and Equipment; I15: Environment; I72: Traffic and Transport Planning; I91: Vehicle Design and Safety;

Filing Info

• Accession Number: 01498786
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 21 2013 9:07AM