Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Comparing Power-System- and User-Oriented Battery Electric Vehicle Charging Representation and its Implications on Energy System Modeling

Version 1 : Received: 18 January 2020 / Approved: 20 January 2020 / Online: 20 January 2020 (10:08:13 CET)

A peer-reviewed article of this Preprint also exists.

Wulff, N.; Steck, F.; Gils, H.C.; Hoyer-Klick, C.; van den Adel, B.; Anderson, J.E. Comparing Power-System and User-Oriented Battery Electric Vehicle Charging Representation and Its Implications on Energy System Modeling. Energies 2020, 13, 1093. Wulff, N.; Steck, F.; Gils, H.C.; Hoyer-Klick, C.; van den Adel, B.; Anderson, J.E. Comparing Power-System and User-Oriented Battery Electric Vehicle Charging Representation and Its Implications on Energy System Modeling. Energies 2020, 13, 1093.

Journal reference: Energies 2020, 13, 1093
DOI: 10.3390/en13051093

Abstract

Battery electric vehicles provide an opportunity to balance supply and demand in future power systems with high shares of fluctuating renewable energy. Compared to other storage systems such as pumped-storage hydroelectricity, electric vehicle energy demand is highly dependent on charging and connection choices of vehicle users. We present a model framework of a utility-based stock and flow model, a utility-based microsimulation of charging decisions, and an energy system model including respective interfaces to assess how the representation of battery electric vehicle charging affects energy system optimization results. We then apply the framework to a scenario study for controlled charging of nine million electric vehicles in Germany in 2030. Assuming a respective fleet power demand of 27 TWh, we analyze the difference between power-system-based and vehicle user-based charging decisions in two respective scenarios. Our results show that taking into account vehicle users’ charging and connection decisions significantly decreases the load shifting potential of controlled charging. The analysis of marginal values of equations and variables of the optimization problem yields valuable insights on the importance of specific constraints and optimization variables. In particular, state-of-charge assumptions and representing fast charging drive curtailment of renewable energy feed-in and required gas power plant flexibility. A detailed representation of fleet charge connection is less important. Peak load can be significantly reduced by 5% and 3% in both scenarios, respectively. Shifted load is very robust across sensitivity analyses while other model results such as curtailment are more sensitive to factors such as underlying data years. Analyzing the importance of increased BEV fleet battery availability for power systems with different weather and electricity demand characteristics should be further scrutinized.

Subject Areas

electric vehicles; sector coupling; energy system optimization; renewable energy integration; REMix; charging behavior; marginal values

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