Title: Genetic algorithm-based demand response scheme for electric vehicle charging

Authors: Junghoon Lee; Gyung-Leen Park

Addresses: Department of Computer Science and Statistics, Jeju National University, Jejudaehakno 66, Jeju City, Jeju-Do, Republic of Korea ' Department of Computer Science and Statistics, Jeju National University, Jejudaehakno 66, Jeju City, Jeju-Do, Republic of Korea

Abstract: 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. Our 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 our scheme can reduce the peak load for the given charging task sets by up to 4.9%, compared with conventional schemes.

Keywords: smart grid; electric vehicles; vehicle charging; battery charging; genetic algorithms; initial population; peak load reduction; demand response; task scheduling; service ratio.

DOI: 10.1504/IJIIDS.2013.057420

International Journal of Intelligent Information and Database Systems, 2013 Vol.7 No.6, pp.535 - 549

Received: 01 Mar 2013
Accepted: 23 Jun 2013
Published online: 31 Mar 2014 *

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