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How Do Contract Parameters Influence the Economics of Vehicle-to-Grid?

Author

Listed:
  • Gerald Broneske

    (TUM School of Management, Technical University of Munich, 80333 Munich, Germany)

  • David Wozabal

    (TUM School of Management, Technical University of Munich, 80333 Munich, Germany)

Abstract

By modifying the charging patterns of a pool of electric vehicles (EVs), aggregators are able to provide services to the electric grid. The parameters that shape the agreement between aggregators and EV owners, such as plug-in duration and guaranteed driving range, affect the appeal of signing up with an aggregator. Our study examines how these contract parameters influence the profitability of vehicle pools. We use a bottom-up model that encompasses the entire planning problem faced by an aggregator. The model is applied to the German secondary reserve market using actual driving patterns and market data. Our results indicate that contract parameters influence EVs’ value to aggregators in surprising ways, partially contradicting findings in previous literature. In essence, high levels of plug-in durations or low levels of guaranteed driving for planned trips do not necessarily provide the highest profit. Furthermore, we find that realistic nonanticipative strategies lead to annual profits in the range of €167 to €125 per vehicle, which is in the lower range of previous studies. Our results are a starting point for a discussion about optimal contract parameters and the design of corresponding marketing strategies.

Suggested Citation

  • Gerald Broneske & David Wozabal, 2017. "How Do Contract Parameters Influence the Economics of Vehicle-to-Grid?," Manufacturing & Service Operations Management, INFORMS, vol. 19(1), pages 150-164, February.
    • Handle: RePEc:inm:ormsom:v:19:y:2017:i:2:p:150-164
    DOI: 10.1287/msom.2016.0601
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    References listed on IDEAS

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    Cited by:

    1. Esther H. Park Lee & Zofia Lukszo & Paulien Herder, 2018. "Conceptualization of Vehicle-to-Grid Contract Types and Their Formalization in Agent-Based Models," Complexity, Hindawi, vol. 2018, pages 1-11, March.
    2. Jiao, Zihao & Ran, Lun & Zhang, Yanzi & Ren, Yaping, 2021. "Robust vehicle-to-grid power dispatching operations amid sociotechnical complexities," Applied Energy, Elsevier, vol. 281(C).
    3. Heilmann, Christoph & Wozabal, David, 2021. "How much smart charging is smart?," Applied Energy, Elsevier, vol. 291(C).
    4. Ekaterina Abramova & Derek Bunn, 2021. "Optimal Daily Trading of Battery Operations Using Arbitrage Spreads," Energies, MDPI, vol. 14(16), pages 1-23, August.
    5. Heilmann, C. & Friedl, G., 2021. "Factors influencing the economic success of grid-to-vehicle and vehicle-to-grid applications—A review and meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).

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