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International Conference on Data Mining and Big Data

DMBD 2017: Data Mining and Big Data pp 199–208Cite as

Design of a Dynamic Electricity Trade Scheduler Based on Genetic Algorithms

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10387))

Abstract

This paper presents a design and measures the performance of a dynamic electricity trade scheduler employing genetic algorithms for the convenient application of vehicle-to-grid services. Arriving at and being plugged-in to a microgrid, each electric vehicle specifies its stay time and sales amount, while the scheduler, invoked before each time slot, creates a connection schedule considering the microgrid-side demand and available electricity from vehicles for the given scheduling window. For the application of genetic operations, each schedule is encoded to an integer-valued vector with the complementary definition of C-space, which orderly lists all combinatory allocation maps for a task. Then, each integer element indexes a map entry in its C-space. The performance measurement result, obtained from a prototype implementation, reveals that our scheduler can stably work even when the number of sellers exceeds 100 as well as improves demand meet ratio by up to 6.3% compared with the conventional scheduler for the given parameter set.

This research was supported by Korea Electric Power Corporation through Korea Electrical Engineering & Science Research Institute. (Grant number: R15XA03-62).

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Author information

Authors and Affiliations

  1. Department of Computer Science and Statistics, Jeju National University, Jeju City, Republic of Korea

    Junghoon Lee & Gyung-Leen Park

Authors
  1. Junghoon Lee
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  2. Gyung-Leen Park
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Correspondence to Gyung-Leen Park .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Lee, J., Park, GL. (2017). Design of a Dynamic Electricity Trade Scheduler Based on Genetic Algorithms. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_21

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  • DOI: https://doi.org/10.1007/978-3-319-61845-6_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61844-9

  • Online ISBN: 978-3-319-61845-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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