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Optimal Charging of Electric Vehicles with Trading on the Intraday Electricity Market

Author

Listed:
  • Ilham Naharudinsyah

    (Department of Systems and Control, Graduate School of Engineering, Tokyo Institute of Technology, Tokyo 152-8552, Japan)

  • Steffen Limmer

    (Honda Research Institute Europe GmbH, Offenbach/Main 63073, Germany)

Abstract

Trading on the energy market is a possible way to reduce the electricity costs of charging electric vehicles at public charging stations. In many European countries, it is possible to trade electricity until shortly before the period of delivery on so called intraday electricity markets. In the present work, the potential for reducing the electricity costs by trading on the intraday market is investigated using the example of the German market. Based on simulations, the authors reveal that by optimizing the charging schedule together with the trading on the intraday electricity market, the costs can be reduced by around 8% compared to purchasing all the required energy from the energy supplier. By allowing the charging station operator to resell the energy to the intraday electricity market, an additional cost reduction of around 1% can be achieved. Besides the potential cost savings, the impacts of the trading unit and of the lead time of the intraday electricity market on the costs are investigated. The authors reveal that the achievable electricity costs can be strongly affected by the lead time, while the trading unit has only a minor effect on the costs.

Suggested Citation

  • Ilham Naharudinsyah & Steffen Limmer, 2018. "Optimal Charging of Electric Vehicles with Trading on the Intraday Electricity Market," Energies, MDPI, vol. 11(6), pages 1-12, June.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1416-:d:150053
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    References listed on IDEAS

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    1. Monica Alonso & Hortensia Amaris & Jean Gardy Germain & Juan Manuel Galan, 2014. "Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms," Energies, MDPI, vol. 7(4), pages 1-27, April.
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    Cited by:

    1. Isaias Gomes & Rui Melicio & Victor Mendes, 2020. "Comparison between Inflexible and Flexible Charging of Electric Vehicles—A Study from the Perspective of an Aggregator," Energies, MDPI, vol. 13(20), pages 1-13, October.
    2. Cesar Diaz-Londono & Luigi Colangelo & Fredy Ruiz & Diego Patino & Carlo Novara & Gianfranco Chicco, 2019. "Optimal Strategy to Exploit the Flexibility of an Electric Vehicle Charging Station," Energies, MDPI, vol. 12(20), pages 1-29, October.
    3. Steffen Limmer & Nils Einecke, 2022. "An Efficient Approach for Peak-Load-Aware Scheduling of Energy-Intensive Tasks in the Context of a Public IEEE Challenge," Energies, MDPI, vol. 15(10), pages 1-23, May.
    4. Steffen Limmer, 2019. "Dynamic Pricing for Electric Vehicle Charging—A Literature Review," Energies, MDPI, vol. 12(18), pages 1-24, September.
    5. Benjamin Schaden & Thomas Jatschka & Steffen Limmer & Günther Robert Raidl, 2021. "Smart Charging of Electric Vehicles Considering SOC-Dependent Maximum Charging Powers," Energies, MDPI, vol. 14(22), pages 1-33, November.
    6. Felix Heider & Amra Jahic & Maik Plenz & Detlef Schulz, 2022. "Extended Residential Power Management Interface for Flexibility Communication and Uncertainty Reduction for Flexibility System Operators," Energies, MDPI, vol. 15(4), pages 1-23, February.
    7. Steffen Limmer & Johannes Varga & Günther Robert Raidl, 2023. "Large Neighborhood Search for Electric Vehicle Fleet Scheduling," Energies, MDPI, vol. 16(12), pages 1-14, June.

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