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Content Provider | IEEE Xplore Digital Library |
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Author | Godde, M. Findeisen, T. Sowa, T. Nguyen, P.H. |
Copyright Year | 2015 |
Description | Author affiliation: Inst. for High Voltage Technol., RWTH Aachen Univ., Aachen, Germany (Godde, M.; Findeisen, T.; Sowa, T.) || Electr. Energy Syst., Univ. of Technol. Eindhoven, Eindhoven, Netherlands (Nguyen, P.H.) |
Abstract | This paper presents an approach for modelling the charging probability of electric vehicles as a Gaussian mixture model. The model is built up by assembling adapted multivariate normal probability density functions. This is done because the expectation maximization algorithm fails finding maximum likelihood estimates in respect of the charging power of the generated charging profiles. This Gaussian mixture model enables for capturing the charging profiles comprehensively with a few parameters and therefore it enables for calculating the charging probability dynamically for individual parameter intervals. The underlying assumptions about battery capacity, consumption, charging infrastructure, type of weekday and settlement structure determine the generation of the charging profiles. The proposed approach makes these parameters available for the density. Thereby, the provision of the charging profiles gets obsolete. This density can be used for a convolution based power flow analysis which offers benefits regarding the computational effort and random access memory usage compared to Monte Carlo-like simulations. |
Starting Page | 1 |
Ending Page | 6 |
File Size | 884014 |
Page Count | 6 |
File Format | |
e-ISBN | 9781479976935 |
DOI | 10.1109/PTC.2015.7232376 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2015-06-29 |
Publisher Place | Netherlands |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Convolution Batteries Load flow analysis Electric vehicles Analytical models Gaussian mixture model Load flow |
Content Type | Text |
Resource Type | Article |
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