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Power System Impacts of Electric Vehicles in Germany: Charging with Coal or Renewables


  • Schill, Wolf-Peter
  • Gerbaulet, Clemens


We analyze the impacts of future scenarios of electric vehicles (EVs) on the German power system, drawing on different assumptions on the charging mode. We find that the impact on the load duration curve strongly differs between charging modes. In a fully user-driven mode, charging largely occurs during daytime and in the evening, when power demand is already high. User-driven charging may thus have to be restricted because of generation adequacy concerns. In contrast, cost-driven charging is carried out during night-time and at times of high PV availability. Using a novel model formulation that allows for simulating intermediate charging modes, we show that even a slight relaxation of fully user-driven charging results in much smoother load profiles. Further, cost-driven EV charging strongly increases the utilization of hard coal and lignite plants in 2030, whereas additional power in the user-driven mode is predominantly generated from natural gas and hard coal. Specific CO2 emissions of EVs are substantially higher than those of the overall power system, and highest under cost-driven charging. Only in additional model runs, in which we link the introduction of EVs to a respective deployment of additional renewables, electric vehicles become largely CO2-neutral.

Suggested Citation

  • Schill, Wolf-Peter & Gerbaulet, Clemens, 2015. "Power System Impacts of Electric Vehicles in Germany: Charging with Coal or Renewables," EconStor Open Access Articles, ZBW - German National Library of Economics, pages 185-196.
  • Handle: RePEc:zbw:espost:121250

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    1. Kostevšek, Anja & Klemeš, Jiří Jaromír & Varbanov, Petar Sabev & Papa, Gregor & Petek, Janez, 2016. "The concept of an ecosystem model to support the transformation to sustainable energy systems," Applied Energy, Elsevier, vol. 184(C), pages 1460-1469.
    2. Paterakis, Nikolaos G. & Gibescu, Madeleine, 2016. "A methodology to generate power profiles of electric vehicle parking lots under different operational strategies," Applied Energy, Elsevier, vol. 173(C), pages 111-123.
    3. Xydas, Erotokritos & Marmaras, Charalampos & Cipcigan, Liana M., 2016. "A multi-agent based scheduling algorithm for adaptive electric vehicles charging," Applied Energy, Elsevier, vol. 177(C), pages 354-365.
    4. Nienhueser, Ian Andrew & Qiu, Yueming, 2016. "Economic and environmental impacts of providing renewable energy for electric vehicle charging – A choice experiment study," Applied Energy, Elsevier, vol. 180(C), pages 256-268.
    5. repec:gam:jeners:v:11:y:2018:i:7:p:1728-:d:155686 is not listed on IDEAS
    6. repec:gam:jeners:v:9:y:2016:i:3:p:157:d:64969 is not listed on IDEAS
    7. Li, Ying & Davis, Chris & Lukszo, Zofia & Weijnen, Margot, 2016. "Electric vehicle charging in China’s power system: Energy, economic and environmental trade-offs and policy implications," Applied Energy, Elsevier, vol. 173(C), pages 535-554.
    8. repec:eee:appene:v:226:y:2018:i:c:p:582-594 is not listed on IDEAS
    9. Yunna Wu & Meng Yang & Haobo Zhang & Kaifeng Chen & Yang Wang, 2016. "Optimal Site Selection of Electric Vehicle Charging Stations Based on a Cloud Model and the PROMETHEE Method," Energies, MDPI, Open Access Journal, vol. 9(3), pages 1-20, March.
    10. Hofmann, Jana & Guan, Dabo & Chalvatzis, Konstantinos & Huo, Hong, 2016. "Assessment of electrical vehicles as a successful driver for reducing CO2 emissions in China," Applied Energy, Elsevier, vol. 184(C), pages 995-1003.
    11. Falcão, Eduardo Aparecido Moreira & Teixeira, Ana Carolina Rodrigues & Sodré, José Ricardo, 2017. "Analysis of CO2 emissions and techno-economic feasibility of an electric commercial vehicle," Applied Energy, Elsevier, vol. 193(C), pages 297-307.
    12. Francisco J. Bahamonde-Birke & Tibor Hanappi, 2015. "The Potential of Electromobility in Austria: An Analysis Based on Hybrid Choice Models," Discussion Papers of DIW Berlin 1472, DIW Berlin, German Institute for Economic Research.
    13. Craig, Christopher A. & Feng, Song, 2017. "Exploring utility organization electricity generation, residential electricity consumption, and energy efficiency: A climatic approach," Applied Energy, Elsevier, vol. 185(P1), pages 779-790.
    14. Brady, John & O’Mahony, Margaret, 2016. "Development of a driving cycle to evaluate the energy economy of electric vehicles in urban areas," Applied Energy, Elsevier, vol. 177(C), pages 165-178.
    15. repec:eee:appene:v:203:y:2017:i:c:p:608-622 is not listed on IDEAS
    16. repec:eee:appene:v:204:y:2017:i:c:p:1347-1362 is not listed on IDEAS
    17. repec:zbw:espost:165995 is not listed on IDEAS
    18. Riesz, Jenny & Sotiriadis, Claire & Ambach, Daisy & Donovan, Stuart, 2016. "Quantifying the costs of a rapid transition to electric vehicles," Applied Energy, Elsevier, vol. 180(C), pages 287-300.
    19. Craig, Christopher A., 2016. "Energy consumption, energy efficiency, and consumer perceptions: A case study for the Southeast United States," Applied Energy, Elsevier, vol. 165(C), pages 660-669.
    20. Craig, Christopher A. & Feng, Song, 2016. "An examination of electricity generation by utility organizations in the Southeast United States," Energy, Elsevier, vol. 116(P1), pages 601-608.
    21. Fernandes, A. & Woudstra, T. & van Wijk, A. & Verhoef, L. & Aravind, P.V., 2016. "Fuel cell electric vehicle as a power plant and SOFC as a natural gas reformer: An exergy analysis of different system designs," Applied Energy, Elsevier, vol. 173(C), pages 13-28.

    More about this item


    Electric vehicles; Power system; Dispatch model; Renewable energy;

    JEL classification:

    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming


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