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PHEV Utilization Model Considering Type-of-Trip and Recharging Flexibility
KTH, School of Electrical Engineering (EES), Electric Power Systems.
KTH, School of Electrical Engineering (EES), Electric Power Systems.
KTH, School of Electrical Engineering (EES), Electric Power Systems.ORCID iD: 0000-0002-8189-2420
2014 (English)In: IEEE Transactions on Smart Grid, ISSN 1949-3053, Vol. 5, no 1, 139-148 p.Article in journal (Refereed) Published
Abstract [en]

Electric vehicles (EVs) may soon enter the vehicle market in large numbers and change the overall fuel usage within the passenger transport sector. With increased variable consumption from EVs together with anticipated increased production from variable sources, due to renewable wind and solar power, also the balancing of the electric power system incur increased attention. This emphasizes the importance of developing models to estimate and investigate the stochasticity of personal car travel behavior and induced EV charging load. Several studies have been made in order to model the stochasticity of passenger car travel behavior but none have captured the charging behavior dependence of the type-of-trip conducted. This paper proposes a new model for plug-in-hybrid electric vehicle (PHEV) utilization and recharging price sensitivity, to determine charging load profiles based on driving patterns due to the type-of-trip and corresponding charging need. This approach makes it possible to relate the type-of-trip with the consumption level, the parking location, and the charging opportunity. The proposed model is applied in a case study using Swedish car travel data. The results show the charging load impact and variation due to the stochastic PHEV type-of-trip mobility, allowing quantification of the PHEV charging impact on the system.

Place, publisher, year, edition, pages
2014. Vol. 5, no 1, 139-148 p.
Keyword [en]
Charging flexibility, electric vehicle charging (EVC) behavior, load profiles, Plug-in-hybrid electric vehicles (PHEVs), price sensitivity, type-of-trip
National Category
Engineering and Technology
URN: urn:nbn:se:kth:diva-136327DOI: 10.1109/TSG.2013.2279022ISI: 000329517300014ScopusID: 2-s2.0-84892616215OAI: diva2:675805

QC 20140211. Updated from accepted to published.

Available from: 2013-12-04 Created: 2013-12-04 Last updated: 2014-09-24Bibliographically approved
In thesis
1. Electric Vehicle Charging Modeling
Open this publication in new window or tab >>Electric Vehicle Charging Modeling
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

With an electrified passenger transportation fleet, carbon dioxide emissions could be reduced significantly depending on the electric power production mix. Increased electric power consumption due to electric vehicle charging demands of electric vehicle fleets may be met by increased amount of renewable power production in the electrical systems. With electric vehicle fleets in the transportation system there is a need for establishing an electric vehicle charging infrastructure that distributes this power to the electric vehicles. Depending on the amount of electric vehicles in the system and the charging patterns, electric vehicle integration creates new quantities in the overall load profile that may increase the load peaks. The electric vehicle charging patterns are stochastic since they are affected by the travel behavior of the driver and the charging opportunities which implies that an electric vehicle introduction also will affect load variations. Increased load variation and load peaks may create a need for upgrades in the grid infrastructure to reduce losses, risks for overloads or damaging of components. However, with well-designed incentives for electric vehicle users and electric vehicle charging, the electric vehicles may be used as flexible loads that can help mitigate load variations and load peaks in the power system.

The aim with this doctoral thesis is to investigate and quantify the impact of electric vehicle charging on load profiles and load variations. Three key factors are identified when considering the impact of electric vehicle charging on load profiles and load variations. The key factors are: The charging moment, the charging need and the charging location. One of the conclusions in this thesis is that the level of details and the approach to model these key factors impact the estimations of the load profiles. The models that take into account a high level of mobility details will be able to create a realistic estimation of a future uncontrolled charging behavior, enabling for more accurate estimates of the impact on load profiles and the potential of individual charging strategies and external charging strategies. The thesis reviews and categorizes electric vehicle charging models in previous research, and furthermore, introduces new electric vehicle charging models to estimate the charging impact based on charging patterns induced by passenger car travel behavior. The models mainly consider EVC related to individual car travel behavior and induced charging needs for plug-in-hybrid electric vehicles. Moreover, the thesis comments on dynamic electric vehicle charging along electrified roads and also on individual charging strategies.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2014. viii, 88 p.
TRITA-EE, ISSN 1653-5146 ; 2014:044
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
urn:nbn:se:kth:diva-152237 (URN)978-91-7595-255-0 (ISBN)
Public defence
2014-10-13, F3, Lindstedtsvägen 26, KTH, Stockholm, 10:00 (English)

QC 20140924

Available from: 2014-09-24 Created: 2014-09-24 Last updated: 2014-09-24Bibliographically approved

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Grahn, PiaAlvehag, KarinSöder, Lennart
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