Probabilistic modeling of nodal electric vehicle load due to fast charging stations

Difei Tang, Peng Wang, Qiuwei Wu

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

In order to reduce greenhouse gas emission and fossil fuel dependence, Electric Vehicle (EV) has drawn increasing attention due to its zero emission and high efficiency. However, new problems such as range anxiety, long charging duration and high charging power may threaten the safe and efficient operation of both traffic and power systems. This paper proposes a probabilistic approach to model the nodal EV load at fast charging stations in integrated power and transport systems. Following the introduction of the spatial-temporal model of moving EV loads, we extended the model by taking fast charging station into consideration. Fuzzy logic inference system is applied to simulate the charging decision of EV drivers at fast charging station. Due to increasing EV loads in power system, the potential traffic congestion in fast charging stations is modeled and evaluated by queuing theory with spatial-temporal varying arrival and service rates. The time-varying nodal EV loads are obtained by the number of operating fast chargers at each node of the power system. System studies demonstrate that the combination of AC normal and DC charging may share the EV charging demand and alleviate the impact to power system due to fast charging with high power.
Original languageEnglish
Title of host publicationProceedings of 2016 International Conference on Probabilistic Methods Applied to Power Systems
Number of pages7
PublisherIEEE
Publication date2016
Pages1-7
ISBN (Print)9781509019700
DOIs
Publication statusPublished - 2016
EventInternational Conference on Probabilistic Methods Applied to Power Systems - Beijing, China
Duration: 16 Oct 201620 Oct 2016

Conference

ConferenceInternational Conference on Probabilistic Methods Applied to Power Systems
CountryChina
CityBeijing
Period16/10/201620/10/2016
Series2016 International Conference on Probabilistic Methods Applied To Power Systems (pmaps)

Keywords

  • Charging stations
  • Estimation
  • Decision support systems
  • Electric vehicles
  • Load modeling
  • fast charging station
  • Probabilistic model
  • power system
  • electric vehicle

Cite this

Tang, D., Wang, P., & Wu, Q. (2016). Probabilistic modeling of nodal electric vehicle load due to fast charging stations. In Proceedings of 2016 International Conference on Probabilistic Methods Applied to Power Systems (pp. 1-7). IEEE. 2016 International Conference on Probabilistic Methods Applied To Power Systems (pmaps) https://doi.org/10.1109/PMAPS.2016.7764219
Tang, Difei ; Wang, Peng ; Wu, Qiuwei. / Probabilistic modeling of nodal electric vehicle load due to fast charging stations. Proceedings of 2016 International Conference on Probabilistic Methods Applied to Power Systems. IEEE, 2016. pp. 1-7 (2016 International Conference on Probabilistic Methods Applied To Power Systems (pmaps)).
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title = "Probabilistic modeling of nodal electric vehicle load due to fast charging stations",
abstract = "In order to reduce greenhouse gas emission and fossil fuel dependence, Electric Vehicle (EV) has drawn increasing attention due to its zero emission and high efficiency. However, new problems such as range anxiety, long charging duration and high charging power may threaten the safe and efficient operation of both traffic and power systems. This paper proposes a probabilistic approach to model the nodal EV load at fast charging stations in integrated power and transport systems. Following the introduction of the spatial-temporal model of moving EV loads, we extended the model by taking fast charging station into consideration. Fuzzy logic inference system is applied to simulate the charging decision of EV drivers at fast charging station. Due to increasing EV loads in power system, the potential traffic congestion in fast charging stations is modeled and evaluated by queuing theory with spatial-temporal varying arrival and service rates. The time-varying nodal EV loads are obtained by the number of operating fast chargers at each node of the power system. System studies demonstrate that the combination of AC normal and DC charging may share the EV charging demand and alleviate the impact to power system due to fast charging with high power.",
keywords = "Charging stations, Estimation, Decision support systems, Electric vehicles, Load modeling, fast charging station, Probabilistic model, power system, electric vehicle",
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Tang, D, Wang, P & Wu, Q 2016, Probabilistic modeling of nodal electric vehicle load due to fast charging stations. in Proceedings of 2016 International Conference on Probabilistic Methods Applied to Power Systems. IEEE, 2016 International Conference on Probabilistic Methods Applied To Power Systems (pmaps), pp. 1-7, International Conference on Probabilistic Methods Applied to Power Systems, Beijing, China, 16/10/2016. https://doi.org/10.1109/PMAPS.2016.7764219

Probabilistic modeling of nodal electric vehicle load due to fast charging stations. / Tang, Difei; Wang, Peng; Wu, Qiuwei.

Proceedings of 2016 International Conference on Probabilistic Methods Applied to Power Systems. IEEE, 2016. p. 1-7 (2016 International Conference on Probabilistic Methods Applied To Power Systems (pmaps)).

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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N2 - In order to reduce greenhouse gas emission and fossil fuel dependence, Electric Vehicle (EV) has drawn increasing attention due to its zero emission and high efficiency. However, new problems such as range anxiety, long charging duration and high charging power may threaten the safe and efficient operation of both traffic and power systems. This paper proposes a probabilistic approach to model the nodal EV load at fast charging stations in integrated power and transport systems. Following the introduction of the spatial-temporal model of moving EV loads, we extended the model by taking fast charging station into consideration. Fuzzy logic inference system is applied to simulate the charging decision of EV drivers at fast charging station. Due to increasing EV loads in power system, the potential traffic congestion in fast charging stations is modeled and evaluated by queuing theory with spatial-temporal varying arrival and service rates. The time-varying nodal EV loads are obtained by the number of operating fast chargers at each node of the power system. System studies demonstrate that the combination of AC normal and DC charging may share the EV charging demand and alleviate the impact to power system due to fast charging with high power.

AB - In order to reduce greenhouse gas emission and fossil fuel dependence, Electric Vehicle (EV) has drawn increasing attention due to its zero emission and high efficiency. However, new problems such as range anxiety, long charging duration and high charging power may threaten the safe and efficient operation of both traffic and power systems. This paper proposes a probabilistic approach to model the nodal EV load at fast charging stations in integrated power and transport systems. Following the introduction of the spatial-temporal model of moving EV loads, we extended the model by taking fast charging station into consideration. Fuzzy logic inference system is applied to simulate the charging decision of EV drivers at fast charging station. Due to increasing EV loads in power system, the potential traffic congestion in fast charging stations is modeled and evaluated by queuing theory with spatial-temporal varying arrival and service rates. The time-varying nodal EV loads are obtained by the number of operating fast chargers at each node of the power system. System studies demonstrate that the combination of AC normal and DC charging may share the EV charging demand and alleviate the impact to power system due to fast charging with high power.

KW - Charging stations

KW - Estimation

KW - Decision support systems

KW - Electric vehicles

KW - Load modeling

KW - fast charging station

KW - Probabilistic model

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KW - electric vehicle

U2 - 10.1109/PMAPS.2016.7764219

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Tang D, Wang P, Wu Q. Probabilistic modeling of nodal electric vehicle load due to fast charging stations. In Proceedings of 2016 International Conference on Probabilistic Methods Applied to Power Systems. IEEE. 2016. p. 1-7. (2016 International Conference on Probabilistic Methods Applied To Power Systems (pmaps)). https://doi.org/10.1109/PMAPS.2016.7764219