Electric Vehicle Charging/Discharging Models for Estimation of Load Profile in Grid Environments
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU146806" target="_blank" >RIV/00216305:26220/22:PU146806 - isvavai.cz</a>
Result on the web
<a href="https://www.tandfonline.com/doi/full/10.1080/15325008.2022.2146811" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/15325008.2022.2146811</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/15325008.2022.2146811" target="_blank" >10.1080/15325008.2022.2146811</a>
Alternative languages
Result language
angličtina
Original language name
Electric Vehicle Charging/Discharging Models for Estimation of Load Profile in Grid Environments
Original language description
The increasing electric vehicles (EVs) and their integration with the electrical networks to achieve the charging demands has highly affected the operation of the power system. These effects are due to the increased and variable amount of electricity consumption caused by the irregular charging patterns of EVs. As the EV penetration level increases with such irregular charging patterns, the overall load profile in a network will increase resulting in load peaks. To overcome this, a well-planned charging-discharging algorithm is needed for the EV users and the power suppliers. In light of the above issues and requirements, this paper proposes a Markov Chain Monte Carlo (MCMC) simulation-based EV charging (EVC) behavior models and charging-discharging power prediction models. The proposed methods analyze the traveling behavior of EVs, and their charging patterns to make EV a flexible load. This helps in understanding the variable electricity consumption and mitigating the peak loads and load variations. Further, to assess the operation of the proposed methods, the charging-discharging power prediction is carried out for a large EV fleet based on the historical data. The power predictions are carried out for 100 days and the convergence of the proposed algorithm is targeted to be kept below 0.5% to achieve optimal power prediction. The results showed the credibility of the proposed approach in identifying the daily average charging-discharging load curves.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
ELECTRIC POWER COMPONENTS AND SYSTEMS
ISSN
1532-5008
e-ISSN
1532-5016
Volume of the periodical
2022
Issue of the periodical within the volume
12
Country of publishing house
US - UNITED STATES
Number of pages
17
Pages from-to
1-17
UT code for WoS article
000899058000001
EID of the result in the Scopus database
2-s2.0-85144149758