Scipediacontent: Scipediacontent moved page Draft Content 795241520 to Godde et al 2015a

2021-02-08T08:43:09Z

Scipediacontent moved page Draft Content 795241520 to Godde et al 2015a

Scipediacontent: Created page with " == 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 a..."

2021-02-08T08:43:06Z

Created page with " == 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 a..."

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== 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 multivari-ate 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 com-pared to Monte Carlo-like simulations.

== Original document ==

The different versions of the original document can be found in:

* [https://research.tue.nl/nl/publications/modelling-the-charging-probability-of-electric-vehicles-as-a-gaussian-mixture-model-for-a-convolution-based-power-flow-analysis(faa0bc5e-1974-4d9a-b3cf-2feb98c09af7).html https://research.tue.nl/nl/publications/modelling-the-charging-probability-of-electric-vehicles-as-a-gaussian-mixture-model-for-a-convolution-based-power-flow-analysis(faa0bc5e-1974-4d9a-b3cf-2feb98c09af7).html]

* [http://dx.doi.org/10.1109/ptc.2015.7232376 http://dx.doi.org/10.1109/ptc.2015.7232376],
: [http://www.scopus.com/inward/record.url?scp=84951335223&partnerID=8YFLogxK http://www.scopus.com/inward/record.url?scp=84951335223&partnerID=8YFLogxK]

* [http://xplorestaging.ieee.org/ielx7/7210291/7232233/07232376.pdf?arnumber=7232376 http://xplorestaging.ieee.org/ielx7/7210291/7232233/07232376.pdf?arnumber=7232376],
: [http://dx.doi.org/10.1109/ptc.2015.7232376 http://dx.doi.org/10.1109/ptc.2015.7232376]

* [https://ieeexplore.ieee.org/document/7232376 https://ieeexplore.ieee.org/document/7232376],
: [https://research.tue.nl/en/publications/modelling-the-charging-probability-of-electric-vehicles-as-a-gaus https://research.tue.nl/en/publications/modelling-the-charging-probability-of-electric-vehicles-as-a-gaus],
: [https://www.narcis.nl/publication/RecordID/oai%3Apure.tue.nl%3Apublications%2Ffaa0bc5e-1974-4d9a-b3cf-2feb98c09af7 https://www.narcis.nl/publication/RecordID/oai%3Apure.tue.nl%3Apublications%2Ffaa0bc5e-1974-4d9a-b3cf-2feb98c09af7],
: [https://publications.rwth-aachen.de/record/465656 https://publications.rwth-aachen.de/record/465656],
: [https://academic.microsoft.com/#/detail/1768520252 https://academic.microsoft.com/#/detail/1768520252]

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Godde et al 2015a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 795241520 to Godde et al 2015a

Scipediacontent: Created page with " == 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 a..."