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Charging Strategies for Battery Electric Vehicles: Economic Benchmark and V2G Potential

descriptionPublicationkeyboard_double_arrow_right Article , Journal 01 Sep 2014 Germany Publisher:Institute of Electrical and Electronics Engineers (IEEE)Journal:IEEE Transactions on Power Systems, volume 29, pages 2,014-2,022 (issn: 0885-8950, eissn: 1558-0679,

Authors: Schuller, Alexander; Dietz, Benjamin; Flath, Christoph M.; Weinhardt, Christof;

doi: 10.1109/tpwrs.2014.2301024

Charging Strategies for Battery Electric Vehicles: Economic Benchmark and V2G Potential

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Abstract

This paper benchmarks the economic benefits of a smart charging and a vehicle-to-grid (V2G) operating strategy of an electric vehicle (EV) against a zero-intelligence charging strategy in a simulation-based analysis. Smart charging optimizes the timing of EV charging, while V2G additionally allows for selling electricity to the energy market. The strategies internalize the wear on energy storage equipment. The simulation builds on over 11 400 empirical driving profiles from a German mobility panel and applies technical specifications of three currently available EVs. This allows for a realistic analysis of the applicability of smart charging and V2G as well as the extent to which EVs are capable to fulfill current driving needs. Results show that smart charging strategies will reduce charging cost on average by more than 32% in all analyzed cases. V2G could provide additional revenue for EV owners with shorter trip patterns while reducing electricity cost for others.

Country

Germany

Related Organizations

Roland Berger Strategy Consultants
Germany
Roland Berger Strategy Consultants
Germany
Karlsruhe Institute of Technology
Germany

Keywords

info:eu-repo/classification/ddc/330, 330, ddc:330, Economics

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	92
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 1%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%