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Behavior-oriented Modeling of Electric Vehicle Load Profiles: A Stochastic Simulation Model Considering Different Household Characteristics, Charging Decisions and Locations

Harbrecht, Alexander; McKenna, Russell; Fischer, David ORCID iD icon; Fichtner, Wolf ORCID iD icon

Abstract:

This paper presents a stochastic bottom-up model to assess electric vehicles' (EV) impact on load profiles at different parking locations as well as their load management potential assuming different charging strategies. The central innovation lies in theconsideration of socio-economic, technical and spatial factors, all of which influence charging behavior and location. Based on a detailed statistical analysis of a large dataset on German mobility, the most statistically significant influencing factors on residential charging behavior could be identified. Whilst household type and economic status are the most important factors for the number of cars per household, the driver's occupation has the strongest influence on the first departure time and parking time whilst at work. An inhomogeneous Markov-chain is used to sample a sequence of destinations of each car trip, depending (amongst other factors) on the occupation of the driver, the weekday and the time of the day. Probability distributions for the driven kilometres, driving durations and parking durations are used to derive times and electricity demand. The probability distributions are retrieved from a national mobility dataset of 70,000 car trips and filtered for a set of socio-economic and demographic factors. ... mehr


Volltext §
DOI: 10.5445/IR/1000082537
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Industriebetriebslehre und Industrielle Produktion (IIP)
Publikationstyp Forschungsbericht/Preprint
Publikationsjahr 2018
Sprache Englisch
Identifikator ISSN: 2196-7296
urn:nbn:de:swb:90-825375
KITopen-ID: 1000082537
Verlag Karlsruher Institut für Technologie (KIT)
Umfang XI, 241 S.
Serie Working Paper Series in Production and Energy ; 29
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