Accurate and scalable representation of electric vehicles in energy system models: A virtual storage-based aggregation approach

Jarusch Muessel; Oliver Ruhnau; Reinhard Madlener

doi:10.1016/j.isci.2023.107816

Accurate and scalable representation of electric vehicles in energy system models: A virtual storage-based aggregation approach

iScience. 2023 Sep 1;26(10):107816. doi: 10.1016/j.isci.2023.107816. eCollection 2023 Oct 20.

Authors

Jarusch Muessel^{1

2}, Oliver Ruhnau^{3

4}, Reinhard Madlener^{5

6}

Affiliations

¹ Potsdam Institute for Climate Impact Research, Potsdam, Germany.
² Global Energy Systems Analysis, Technical University Berlin, Germany.
³ Department of Economics and Institute of Energy Economics, University of Cologne, Germany.
⁴ Hertie School, Berlin, Germany.
⁵ Institute for Future Energy Consumer Needs and Behavior (FCN), School of Business and Economics/E.ON Energy Research Center, RWTH Aachen University, Germany.
⁶ Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.

Abstract

The growing number of electric vehicles (EVs) will challenge the power system, but EVs may also support system balancing via smart charging. Modeling EVs' system-level impact while respecting computational constraints requires the aggregation of individual profiles. We show that studies typically rely on too few profiles to accurately model EVs' system-level impact and that a naïve aggregation of individual profiles leads to an overestimation of the fleet's flexibility potential. To overcome this problem, we introduce a scalable and accurate aggregation approach based on the idea of modeling deviations from an uncontrolled charging strategy as virtual energy storage. We apply this to a German case study and estimate an average flexibility potential of 6.2 kWh/EV, only 10% of the result of a naïve aggregation. We conclude that our approach allows for a more realistic representation of EVs in energy system models and suggest applying it to other flexible assets.

Keywords: Energy Modelling; Energy resources; Energy systems.