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Reservation-Based Charging Service for Electric Vehicles

  • Junghoon Lee
  • Gyung-Leen Park
  • Hye-Jin Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7017)

Abstract

This paper designs a telematics service capable of providing electric vehicles with a reservation-based charging mechanism, aiming at improving acceptance ratio. By the telematics network, each vehicle retrieves the current reservation status of charging stations of interest and then sends a reservation request specifying its requirement on charging amount and time constraint. Receiving the request, the charging station checks if it can meet the requirement of the new request without violating the constraints of already admitted requests. In this admission test, the charging scheduler, which may work in the charging station or a remote data center, implements a genetic algorithm to respond promptly to the fast moving vehicle. The performance measurement result, obtained from a prototype implementation, shows that the proposed scheme can significantly improve the acceptance ratio for all range of the number of tasks and permissible peak load, compared with a conventional scheduling strategy.

Keywords

Smart transportation electric vehicle telematics charging schedule reservation service acceptance ratio 

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References

  1. 1.
    Guille, C., Gross, G.: A Conceptual Framework for the Vehicle-to-grid (V2G) Implementation. Energy Policy 37, 4379–4390 (2009)CrossRefGoogle Scholar
  2. 2.
    Markel, T., Simpson, A.: Plug-in Hybrid Electric Vehicle Energy Storage System Design. In: Advanced Automotive Battery Conference (2006)Google Scholar
  3. 3.
    Spees, K., Lave, L.: Demand Response and Electricity Market Efficiency. The Electricity Journal, 69–85 (2007)Google Scholar
  4. 4.
    Katsigiannis, Y., Georgilakis, P., Karapidakis, E.: Multiobjective Genetic Algorithm Solution to the Optimum Economic and Environmental Eerformance Problem of Small Autonomous Hybrid Power Systems with Renewables. In: IET Renewable Power Generation, pp. 404–419 (2010)Google Scholar
  5. 5.
    Gellings, C.W.: The Smart Grid: Enabling Energy Efficiency and Demand Response. CRC Press, Boca Raton (2009)Google Scholar
  6. 6.
    Morrow, K., Karner, D., Francfort, J.: Plug-in Hybrid Electric Vehicle Charging Infrastructure Review. Battelle Energy Alliance (2008)Google Scholar
  7. 7.
    Kaplan, S.M., Sissine, F.: Smart Grid: Modernizing Electric Power Transmission and Distribution; Energy Independence, Storage and Security. TheCapitol.Net (2009)Google Scholar
  8. 8.
    Schweppe, H., Zimmermann, A., Grill, D.: Flexible In-vehicle Stream Processing with Distributed Automotive Control Units for Engineering and Diagnosis. In: IEEE 3rd International Symposium on Industrial Embedded Systems, pp. 74–81 (2008)Google Scholar
  9. 9.
    Ipakchi, A., Albuyeh, F.: Grid of the Future. IEEE Power & Energy Magazine, 52–62 (2009)Google Scholar
  10. 10.
    Frost & Sullivan: Strategic Market and Technology Assessment of Telematics Applications for Electric Vehicles. In: 10th Annual Conference of Detroit Telematics (2010)Google Scholar
  11. 11.
    Lee, J., Park, G., Kim, S., Kim, H., Sung, C.: Power Consumption Scheduling for Peak Load Reduction in Smart Grid Homes. In: ACM Symposium on Applied Computing, pp. 584–588 (2011)Google Scholar
  12. 12.
    Derin, O., Ferrante, A.: Scheduling Energy Consumption with Local Renewable Micro-Generation and Dynamic Electricity Prices. In: First Workshop on Green and Smart Embedded System Technology: Infrastructures, Methods, and Tools (2010)Google Scholar
  13. 13.
    Mohsenian-Rad, A., Wong, V., Jatskevich, J., Leon-Garcia, A.: Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Transactions on Smart Grid 1, 320–331 (2010)CrossRefGoogle Scholar
  14. 14.
    Caron, S., Kesidis, G.: Incentive-based energy consumption scheduling algorithms for the smart grid. In: IEEE SmartGridComm (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

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

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