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Plug-in-vehicle mobility and charging flexibility Markov model based on driving behavior
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
2012 (English)In: 9th International Conference on the European Energy Market, EEM 12, IEEE , 2012Conference paper (Refereed)
Abstract [en]

Climate targets around the globe are enforcing new strategies for reducing climate impacts, which encourage automobile and electricity companies to consider an electrified vehicle market. Furthermore, the variable electricity production in the electric power system is increasing, with higher levels of wind and solar power. Due to the increased variability in the system, the need to meet fluctuations with flexible consumption is intensified. Electric vehicles with rechargeable batteries seem to become an increasingly common feature in the car fleet. Plugin vehicles (PIVs), may therefore become valuable as flexible consumers. If so, flexible PIV owners could, if they are flexible enough, increase the value of owning an electric vehicle. This paper introduces a new PIV Mobility and Charging Flexibility Markov Model, based on driving behavior for private cars. By using the new model, it is possible to simulate the potential flexibility in a future system with many PIVs. The results from a case study indicate a potential need for usage of the batteries as flexible loads to reduce the grid power fluctuations and load peaks.

Place, publisher, year, edition, pages
IEEE , 2012.
, International Conference on the European Energy Market, ISSN 2165-4077
Keyword [en]
Electric power systems, Electric vehicles, Markov processes, Solar energy
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Energy Systems
URN: urn:nbn:se:kth:diva-101383DOI: 10.1109/EEM.2012.6254711ISI: 000321504700066ScopusID: 2-s2.0-84866792046ISBN: 978-146730832-8OAI: diva2:547245
9th International Conference on the European Energy Market, EEM 12; Florence; 10 May 2012 through 12 May 2012

QC 20121121

Available from: 2012-08-27 Created: 2012-08-27 Last updated: 2016-04-21Bibliographically 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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