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

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.

Emissions of electric vehicle charging in future scenarios: The effects of time of charging

“…In addition, increased use of fast chargers in combination with new electric car sharing systems may contribute to more daytime charging in the future (Biondi et al, 2016). While we have quantified emissions associated with predefined, stylized charging patterns, low emission intensities of daytime electricity can in practice also be exploited through controlled ("smart") charging (Coignard et al, 2018;Xu et al, 2020;Wulff et al, 2020) and/or grid-level energy storage (Garcia et al, 2018;Jafari et al, 2020), which are two avenues policy makers can consider. The uncertainties and limitations of the current study-as discussed in the following paragraphs-should be borne in mind when interpreting the results.…”

“…Some studies explore hypothetical charging regimes (e.g., Coignard et al, 2018;Tamayao et al, 2015), others derive charging patterns from detailed tests for a small number of vehicles (Rangaraju et al, 2015), from travel surveys covering a larger number of vehicles (Crossin & Doherty, 2016;Jochem et al, 2015) or from data from websites and fleet tests (Plötz et al, 2017). A number of studies examine charging time in the context of electricity supply and demand balancing, but do not quantify emissions (e.g., Babrowski et al, 2014;Coignard et al, 2018;Khoo et al, 2014;Sadeghianpourhamami et al, 2018;Wulff et al, 2020). Other analyses quantify only direct emissions of electricity generation (i.e., emissions occurring in the energy conversion process itself) (Donateo et al, 2015;Ensslen et al, 2017;Jochem et al, 2015), while yet others also consider indirect emissions of electricity (such as emissions occurring in transport of fuels to power plant or manufacturing of power plant infrastructure) (Garcia et al, 2018).…”