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Model-Based Quantification of Load Shift Potentials and Optimized Charging of Electric Vehicles

Author

Listed:
  • Hahn, Tobias
  • Schönfelder, Martin
  • Jochem, Patrick
  • Heuveline, Vincent
  • Fichtner, Wolf

Abstract

Managing the charging process of a large number of electric vehicles to decrease the pressure on the local electricity grid is of high interest to the utilities. Using efficient mathematical optimization techniques, the charging behavior of electric vehicles shall be optimally controlled taking into account network, vehicle, and customer requirements. We developed an efficient algorithm for calculating load shift potentials defined as the range of all charging curves meeting the customer’s requirements and respecting all individual charging and discharging constraints over time. In addition, we formulated a mixed integer linear program (MIP) applying semi-continuous variables to find cost-optimal load curves for every vehicle participating in a load shift. This problem can be solved by e.g. branch-and-bound algorithms. Results of two scenarios of Germany in 2015 and 2030 based on mobility studies show that the load shifting potential of EV is significant and contribute to a necessary relaxation of the future grid. The maximum charging and discharging power and the average battery capacity are crucial to the overall load shift potential.

Suggested Citation

  • Hahn, Tobias & Schönfelder, Martin & Jochem, Patrick & Heuveline, Vincent & Fichtner, Wolf, 2013. "Model-Based Quantification of Load Shift Potentials and Optimized Charging of Electric Vehicles," MPRA Paper 91613, University Library of Munich, Germany, revised 04 Jul 2013.
    • Handle: RePEc:pra:mprapa:91613
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    References listed on IDEAS

    as
    1. Shin-ichi Inage, 2010. "Modelling Load Shifting Using Electric Vehicles in a Smart Grid Environment," IEA Energy Papers 2010/7, OECD Publishing.
    2. Weiller, Claire, 2011. "Plug-in hybrid electric vehicle impacts on hourly electricity demand in the United States," Energy Policy, Elsevier, vol. 39(6), pages 3766-3778, June.
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    Cited by:

    1. Jochem, Patrick & Babrowski, Sonja & Fichtner, Wolf, 2015. "Assessing CO2 emissions of electric vehicles in Germany in 2030," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 68-83.
    2. Schücking, Maximilian & Jochem, Patrick & Fichtner, Wolf & Wollersheim, Olaf & Stella, Kevin, 2017. "Charging strategies for economic operations of electric vehicles in commercial applications," MPRA Paper 91599, University Library of Munich, Germany.
    3. Heuberger, Clara F. & Bains, Praveen K. & Mac Dowell, Niall, 2020. "The EV-olution of the power system: A spatio-temporal optimisation model to investigate the impact of electric vehicle deployment," Applied Energy, Elsevier, vol. 257(C).
    4. Kaschub, Thomas & Jochem, Patrick & Fichtner, Wolf, 2016. "Solar energy storage in German households: profitability, load changes and flexibility," Energy Policy, Elsevier, vol. 98(C), pages 520-532.
    5. Dunnan Liu & Tingting Zhang & Weiye Wang & Xiaofeng Peng & Mingguang Liu & Heping Jia & Shu Su, 2021. "Two-Stage Physical Economic Adjustable Capacity Evaluation Model of Electric Vehicles for Peak Shaving and Valley Filling Auxiliary Services," Sustainability, MDPI, vol. 13(15), pages 1-22, July.

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    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning

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