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Quantifying the Flexibility of Electric Vehicles in Germany and California—A Case Study

Author

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  • Michel Zade

    (Chair of Energy Economy and Application Technology, TUM Department of Electrical and Computer Engineering, Technical University of Munich, 80333 Munich, Germany)

  • Zhengjie You

    (Chair of Energy Economy and Application Technology, TUM Department of Electrical and Computer Engineering, Technical University of Munich, 80333 Munich, Germany)

  • Babu Kumaran Nalini

    (Chair of Energy Economy and Application Technology, TUM Department of Electrical and Computer Engineering, Technical University of Munich, 80333 Munich, Germany)

  • Peter Tzscheutschler

    (Chair of Energy Economy and Application Technology, TUM Department of Electrical and Computer Engineering, Technical University of Munich, 80333 Munich, Germany)

  • Ulrich Wagner

    (Chair of Energy Economy and Application Technology, TUM Department of Electrical and Computer Engineering, Technical University of Munich, 80333 Munich, Germany)

Abstract

The adoption of electric vehicles is incentivized by governments around the world to decarbonize the mobility sector. Simultaneously, the continuously increasing amount of renewable energy sources and electric devices such as heat pumps and electric vehicles leads to congested grids. To meet this challenge, several forms of flexibility markets are currently being researched. So far, no analysis has calculated the actual flexibility potential of electric vehicles with different operating strategies, electricity tariffs and charging power levels while taking into account realistic user behavior. Therefore, this paper presents a detailed case study of the flexibility potential of electric vehicles for fixed and dynamic prices, for three charging power levels in consideration of Californian and German user behavior. The model developed uses vehicle and mobility data that is publicly available from field trials in the USA and Germany, cost-optimizes the charging process of the vehicles, and then calculates the flexibility of each electric vehicle for every 15 min. The results show that positive flexibility is mostly available during either the evening or early morning hours. Negative flexibility follows the periodic vehicle availability at home if the user chooses to charge the vehicle as late as possible. Increased charging power levels lead to increased amounts of flexibility. Future research will focus on the integration of stochastic forecasts for vehicle availability and electricity tariffs.

Suggested Citation

  • Michel Zade & Zhengjie You & Babu Kumaran Nalini & Peter Tzscheutschler & Ulrich Wagner, 2020. "Quantifying the Flexibility of Electric Vehicles in Germany and California—A Case Study," Energies, MDPI, vol. 13(21), pages 1-21, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5617-:d:435447
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    References listed on IDEAS

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    1. Beaudin, Marc & Zareipour, Hamidreza, 2015. "Home energy management systems: A review of modelling and complexity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 318-335.
    2. Atabay, Dennis, 2017. "An open-source model for optimal design and operation of industrial energy systems," Energy, Elsevier, vol. 121(C), pages 803-821.
    3. Radecke, Julia & Hefele, Joseph & Hirth, Lion, 2019. "Markets for Local Flexibility in Distribution Networks," EconStor Preprints 204559, ZBW - Leibniz Information Centre for Economics.
    4. Strbac, Goran, 2008. "Demand side management: Benefits and challenges," Energy Policy, Elsevier, vol. 36(12), pages 4419-4426, December.
    5. Yan, Xing & Ozturk, Yusuf & Hu, Zechun & Song, Yonghua, 2018. "A review on price-driven residential demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 411-419.
    6. Paulus, Moritz & Borggrefe, Frieder, 2011. "The potential of demand-side management in energy-intensive industries for electricity markets in Germany," Applied Energy, Elsevier, vol. 88(2), pages 432-441, February.
    7. Steffen Limmer, 2019. "Dynamic Pricing for Electric Vehicle Charging—A Literature Review," Energies, MDPI, vol. 12(18), pages 1-24, September.
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    Cited by:

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    5. Kumar, Gokula Manikandan Senthil & Cao, Sunliang, 2023. "Leveraging energy flexibilities for enhancing the cost-effectiveness and grid-responsiveness of net-zero-energy metro railway and station systems," Applied Energy, Elsevier, vol. 333(C).

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