Investigation of the user behaviour of EV drivers and consequent grid impacts
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F24%3A43974879" target="_blank" >RIV/49777513:23520/24:43974879 - isvavai.cz</a>
Result on the web
<a href="https://digital-library.theiet.org/doi/10.1049/icp.2024.2123" target="_blank" >https://digital-library.theiet.org/doi/10.1049/icp.2024.2123</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1049/icp.2024.2123" target="_blank" >10.1049/icp.2024.2123</a>
Alternative languages
Result language
angličtina
Original language name
Investigation of the user behaviour of EV drivers and consequent grid impacts
Original language description
This paper presents the methodology adopted within the EU-project "XL-Connect" for investigating the future Electric Vehicle (EV) user behaviour and consequent impacts for the energy system. The user behaviour has been investigated through literature research, a survey and expert interviews. The findings from the user behaviour analysis have been used to formulate the "XL-Connect" framework evaluating grid impacts of EVs. A probabilistic calculation component stands for the cornerstone of the framework, which calculates probabilistic power network state and grid security indicators based on two categories of inputs resulting from data-driven models. At first, a "Grid generator" defines grid operational or planning scenarios, which embrace grid structure properties, including network topology, electrical properties of network elements or settings of controllable assets. Secondly, a "Scenario generator" composes power injection profiles and operational uncertainty (i.e. confidence bounds) related to consumption, production, energy storage or EV (dis)charging patterns. Third, an "EV pattern generator" provides (dis)charging power profiles and spatiotemporal probability of their occurrence in a power system. The scenario parametrisation of data-driven models is based on real-world datasets and data science approaches (e.g. cluster analysis).
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
R - Projekt Ramcoveho programu EK
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
IET Conference Proceedings
ISBN
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ISSN
2732-4494
e-ISSN
2732-4494
Number of pages
4
Pages from-to
657-660
Publisher name
The Institution of Engineering and Technology
Place of publication
Neuveden
Event location
Vienna
Event date
Jun 19, 2024
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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