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Renewable Energy and Power Management in Smart Transportation

  • Junghoon Lee
  • Hye-Jin Kim
  • Gyung-Leen Park
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7198)

Abstract

This paper designs a heuristic-based charging scheduler capable of integrating renewable energy for electric vehicles, aiming at reducing power load induced from the large deployment of electric vehicles. Based on the power consumption profile as well as the preemptive charging task model which includes the time constraint on the completion time, a charging schedule is generated as a M ×N allocation table, where M is the number of time slots and N is the number of tasks. Basically, it assigns the task operation to those slots having the smallest power load until the last task allocation, further taking different allocation orders according to slack, operation length, and per-slot power demand. Finally, the peaking task of the peaking slot is iteratively picked to supply renewable energy stored in the battery device. The performance measurement result shows that our scheme can reduce the peak load by up to 37.3 % compared with the Earliest allocation scheme for the given amount of available renewable energy.

Keywords

smart grid charging scheduler preemptive task model renewable energy peak load reduction 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Junghoon Lee
    • 1
  • Hye-Jin Kim
    • 1
  • Gyung-Leen Park
    • 1
  1. 1.Dept. of Computer Science and StatisticsJeju National UniversityJeju CityRepublic of Korea

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