research-article

Defining a synthetic data generator for realistic electric vehicle charging sessions

Authors:

Manu Lahariya,

Dries Benoit,

Chris DevelderAuthors Info & Claims

e-Energy '20: Proceedings of the Eleventh ACM International Conference on Future Energy Systems

Pages 406 - 407

https://doi.org/10.1145/3396851.3403509

Published: 18 June 2020 Publication History

Get Access

Abstract

Electric vehicle (EV) charging stations have become prominent in electricity grids in the past years. Analysis of EV charging sessions is useful for flexibility analysis, load balancing, offering incentives to customers, etc. Yet, limited availability of such EV sessions' data hinders further development in these fields. Addressing this need for publicly available and realistic data, we develop a synthetic data generator (SDG) for EV charging sessions. Our SDG assumes the EV inter-arrival time to follow an exponential distribution. Departure times are modeled by defining a conditional probability density function (pdf) for connection times. This pdf for connection time and required energy is fitted by Gaussian mixture models. Since we train our SDG using a large real-world dataset, its output is realistic.

References

[1]

Chris Develder, Nasrin Sadeghianpourhamami, Matthias Strobbe, and Nazir Refa. 2016. Quantifying flexibility in EV charging as DR potential: Analysis of two real-world data sets. In 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm). IEEE, 600--605.

Crossref

Google Scholar

[2]

Dario Pevec, Jurica Babic, and Vedran Podobnik. 2019. Electric Vehicles: A Data Science Perspective Review. Electronics 8 (10 2019), 1190. https://doi.org/10.3390/electronics8101190

Crossref

Google Scholar

Cited By

View all

Elhattab MKhabbaz MAl-Dahabreh NAtallah RAssi C(2024)Leveraging Real-World Data Sets for QoE Enhancement in Public Electric Vehicles Charging NetworksIEEE Transactions on Network and Service Management10.1109/TNSM.2023.329346021:1(217-231)Online publication date: Feb-2024
https://doi.org/10.1109/TNSM.2023.3293460
Van Kriekinge GDe Cauwer CSapountzoglou NCoosemans TMessagie M(2023)Electric Vehicle Charging Sessions Generator Based on Clustered Driver BehaviorsWorld Electric Vehicle Journal10.3390/wevj1402003714:2(37)Online publication date: 2-Feb-2023
https://doi.org/10.3390/wevj14020037
Leijon JBoström C(2022)Charging Electric Vehicles Today and in the FutureWorld Electric Vehicle Journal10.3390/wevj1308013913:8(139)Online publication date: 29-Jul-2022
https://doi.org/10.3390/wevj13080139
Show More Cited By

Index Terms

Defining a synthetic data generator for realistic electric vehicle charging sessions
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
2. Mathematics of computing
  1. Probability and statistics
    1. Probabilistic algorithms

Recommendations

Electric Vehicle Optimized Charge and Drive Management

Electric vehicles (EVs) have been considered as a solution to the environmental issues caused by transportation, such as air pollution and greenhouse gas emission. However, limited energy capacity, scarce EV supercharging stations, and long recharging ...
A Decision Function Based Smart Charging and Discharging Strategy for Electric Vehicle in Smart Grid
Abstract
As the number of Electric Vehicle (EV) increases, the uncontrolled EV charging behaviors may cause the grid load fluctuations and other passive effects. In order to balance the EV charging load in the smart grid, an Electric Vehicle Charging and ...
Investigating Plug-in Electric Vehicle Charging Stations in Microgrid
ICECC '12: Proceedings of the 2012 International Conference on Electronics, Communications and Control

PHEVs/PEVs have received increasing attention because of their low pollution emissions, low energy dependence, and high fuel economy. In the near future, most PHEV/PEV enabled parking decks are expected to be powered by small-scale and onsite ...

Comments

Information & Contributors

Information

Published In

e-Energy '20: Proceedings of the Eleventh ACM International Conference on Future Energy Systems

June 2020

601 pages

ISBN:9781450380096

DOI:10.1145/3396851

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 June 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

Flemish Government

Conference

e-Energy '20

Sponsor:

SIGEnergy

e-Energy '20: The Eleventh ACM International Conference on Future Energy Systems

June 22 - 26, 2020

Virtual Event, Australia

Acceptance Rates

e-Energy '20 Paper Acceptance Rate 77 of 173 submissions, 45%;

Overall Acceptance Rate 160 of 446 submissions, 36%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
162
Total Downloads

Downloads (Last 12 months)15
Downloads (Last 6 weeks)3

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Elhattab MKhabbaz MAl-Dahabreh NAtallah RAssi C(2024)Leveraging Real-World Data Sets for QoE Enhancement in Public Electric Vehicles Charging NetworksIEEE Transactions on Network and Service Management10.1109/TNSM.2023.329346021:1(217-231)Online publication date: Feb-2024
https://doi.org/10.1109/TNSM.2023.3293460
Van Kriekinge GDe Cauwer CSapountzoglou NCoosemans TMessagie M(2023)Electric Vehicle Charging Sessions Generator Based on Clustered Driver BehaviorsWorld Electric Vehicle Journal10.3390/wevj1402003714:2(37)Online publication date: 2-Feb-2023
https://doi.org/10.3390/wevj14020037
Leijon JBoström C(2022)Charging Electric Vehicles Today and in the FutureWorld Electric Vehicle Journal10.3390/wevj1308013913:8(139)Online publication date: 29-Jul-2022
https://doi.org/10.3390/wevj13080139
Alvarez Guerrero JBhattarai BShrestha RAcker TCastro R(2021)Integrating Electric Vehicles into Power System Operation Production Cost ModelsWorld Electric Vehicle Journal10.3390/wevj1204026312:4(263)Online publication date: 15-Dec-2021
https://doi.org/10.3390/wevj12040263
Akil MDokur EBayindir R(2021)The SOC Based Dynamic Charging Coordination of EVs in the PV-Penetrated Distribution Network Using Real-World DataEnergies10.3390/en1424850814:24(8508)Online publication date: 17-Dec-2021
https://doi.org/10.3390/en14248508
Reinhardt APereira L(2021)Special Issue: “Energy Data Analytics for Smart Meter Data”Energies10.3390/en1417537614:17(5376)Online publication date: 30-Aug-2021
https://doi.org/10.3390/en14175376
Barthel VSchlund JLandes PBrandmeier VPruckner M(2021)Analyzing the Charging Flexibility Potential of Different Electric Vehicle Fleets Using Real-World Charging DataEnergies10.3390/en1416496114:16(4961)Online publication date: 13-Aug-2021
https://doi.org/10.3390/en14164961
Amara-Ouali YGoude YMassart PPoggi JYan H(2021)A Review of Electric Vehicle Load Open Data and ModelsEnergies10.3390/en1408223314:8(2233)Online publication date: 16-Apr-2021
https://doi.org/10.3390/en14082233
Akil MKilic EBayindir RSebati AMalek R(2021)Uncoordinated Charging Profile of EVs Based on An Actual Charging Session Data2021 10th International Conference on Renewable Energy Research and Application (ICRERA)10.1109/ICRERA52334.2021.9598554(459-462)Online publication date: 26-Sep-2021
https://doi.org/10.1109/ICRERA52334.2021.9598554
Akil MDokur EBayindir R(2021)A Systematic Data-driven Analysis of Electric Vehicle Electricity Consumption with Wind Power Integration2021 10th International Conference on Renewable Energy Research and Application (ICRERA)10.1109/ICRERA52334.2021.9598483(397-401)Online publication date: 26-Sep-2021
https://doi.org/10.1109/ICRERA52334.2021.9598483

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Electric Vehicle Optimized Charge and Drive Management

A Decision Function Based Smart Charging and Discharging Strategy for Electric Vehicle in Smart Grid

Investigating Plug-in Electric Vehicle Charging Stations in Microgrid

Comments

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Acceptance Rates

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Electric Vehicle Optimized Charge and Drive Management

A Decision Function Based Smart Charging and Discharging Strategy for Electric Vehicle in Smart Grid

Investigating Plug-in Electric Vehicle Charging Stations in Microgrid

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Get Access

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations