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| Content Provider | ACM Digital Library |
|---|---|
| Author | Kim, Hye-Jin Lee, Junghoon Park, Gyung-Leen |
| Abstract | This paper presents a design and evaluates the performance of a genetic scheduler for electric vehicles, aiming at reducing the peak load of a fast charging station while meeting the time constraint of all charging requests. Upon the preemptive task model consisting of actuation time, operation length, deadline, and a consumption profile, the proposed scheduler fills the allocation table, by which charging tasks can be started, suspended, and resume at each time slot. To obtain a better initial population, our heuristic approach selects time slots having the lowest power load until the previous task allocation. Then, the regular genetic operations further improve the schedule, additionally creating new chromosomes capable of satisfying all time constraints. The performance measurement result obtained from a prototype implementation shows that our scheme can reduce the peak load for the given charging task sets by up to 11.0 %, compared with a conventional scheme. |
| Starting Page | 1843 |
| Ending Page | 1844 |
| Page Count | 2 |
| File Format | |
| ISBN | 9781450308571 |
| DOI | 10.1145/2245276.2232076 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-03-26 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Genetic algorithm Initial population Peak load reduction Electric vehicle charging Smart grid |
| Content Type | Text |
| Resource Type | Article |
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