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The Impact of Electric Vehicle Fleets on the European Electricity Markets: Evidences from the German Passenger Car Fleet and Power Generation Sector

Author

Listed:
  • Maria Juliana Suarrez Foréro

    (IFPEN - IFP Energies nouvelles, IFP School, Technocentre Renault [Guyancourt] - RENAULT)

  • Frédéric Lantz

    (IFPEN - IFP Energies nouvelles, IFP School)

  • Pierre Nicolas

    (Technocentre Renault [Guyancourt] - RENAULT)

  • Patrice Geoffron

    (LEDa - Laboratoire d'Economie de Dauphine - IRD - Institut de Recherche pour le Développement - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

Abstract

The rapidly increasing participation of renewable energies (REn) into the electric mix, clearly traces the trends for the decarbonization goals in the European Union. Under the priority sale conditions established by governments, the commercialization of REn plays an important role in the consolidation of market prices, which are on a decreasing trend with large fluctuations that reduce the profit in the power sector and therefore, the interest of potential investors. The incorporation of small power capacities, available with a considerable fleet of electric vehicles (EV) disposed to support the bulk power system through an intelligent, and possibly bidirectional recharging system (the vehicle grid integration VGI), could have a positive impact on the electricity market as well as in CO2 emissions. In this context, our purpose is to simulate the impact of a large development of EV on the electricity market andthe economic surplus of the power sector. Through a VGI tool that includes an algorithm of smart charging, we simulate the behavior of a fleet composed by some millions of EV as follows: a decentralized VGI algorithm of smart charging included in each EV estimates the energy consumption in time of the EV fleet. For a specific number of EV, we simulate the aggregated charge on the power grid, and anticipate the total expected load curve for one day. We use the estimated load curve as input in an electricity market model for calculating the producer's surplus over one year. We show that the increasing EV fleet significantly decreases the fluctuation of the residual electricity demand as well as the electricity price. Consequently, this has a positive impact on the surplus of the sector.

Suggested Citation

  • Maria Juliana Suarrez Foréro & Frédéric Lantz & Pierre Nicolas & Patrice Geoffron, 2022. "The Impact of Electric Vehicle Fleets on the European Electricity Markets: Evidences from the German Passenger Car Fleet and Power Generation Sector," Working Papers hal-03898558, HAL.
    • Handle: RePEc:hal:wpaper:hal-03898558
    Note: View the original document on HAL open archive server: https://ifp.hal.science/hal-03898558
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    References listed on IDEAS

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    1. Richardson, David B., 2013. "Electric vehicles and the electric grid: A review of modeling approaches, Impacts, and renewable energy integration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 247-254.
    2. Jan Smolen & Branislav Dudic, 2017. "The Role of Residual Demand in Electricity Price Analysis and Forecasting: Case of Czech Electricity Market," International Journal of Energy Economics and Policy, Econjournals, vol. 7(5), pages 152-158.
    3. Jeannie Oliver, Benjamin Sovacool, 2017. "The Energy Trilemma and the Smart Grid: Implications Beyond the United States," Asia and the Pacific Policy Studies 201705, Crawford School of Public Policy, The Australian National University.
    4. Nicolosi, Marco, 2010. "Wind power integration and power system flexibility-An empirical analysis of extreme events in Germany under the new negative price regime," Energy Policy, Elsevier, vol. 38(11), pages 7257-7268, November.
    5. Do, Linh Phuong Catherine & Lyócsa, Štefan & Molnár, Peter, 2021. "Residual electricity demand: An empirical investigation," Applied Energy, Elsevier, vol. 283(C).
    6. Andreas Wagner, 2014. "Residual Demand Modeling and Application to Electricity Pricing," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    7. Noel, Lance & Papu Carrone, Andrea & Jensen, Anders Fjendbo & Zarazua de Rubens, Gerardo & Kester, Johannes & Sovacool, Benjamin K., 2019. "Willingness to pay for electric vehicles and vehicle-to-grid applications: A Nordic choice experiment," Energy Economics, Elsevier, vol. 78(C), pages 525-534.
    8. Li, Wenbo & Long, Ruyin & Chen, Hong & Geng, Jichao, 2017. "A review of factors influencing consumer intentions to adopt battery electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 318-328.
    9. Percebois, Jacques & Pommeret, Stanislas, 2019. "Storage cost induced by a large substitution of nuclear by intermittent renewable energies: The French case," Energy Policy, Elsevier, vol. 135(C).
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    Keywords

    Energy Transition; Electricity Markets; Merit order Effect; Vehicle Grid Integration;
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