Fill-level prediction in online valley-filling algorithms for electric vehicle charging

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)
8 Downloads (Pure)

Abstract

Due to the large increase in electric vehicles (EVs), smart charging strategies are required in order for the distribution grid to accommodate all these EVs. Many charging strategies either assume that future loads are known in advance, or use predictions of these loads as input. However, accurate prediction of uncontrollable load is very difficult. Online valley-filling algorithms circumvent this problem by determining the charging profile based on a prediction of the fill-level: a single parameter that characterizes the optimal EV schedule. This paper presents a simple, but accurate, method to predict this fill-level. We show that near-optimal charging profiles with an optimality gap of less than 1% can be realized when our method is used to predict the input level for the online valley-filling approach. Furthermore, our method is very fast and thus suitable for use in decentralized energy management systems that employ the online valley-filling approach.
Original languageEnglish
Title of host publicationProceedings - 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2018
PublisherIEEE
Number of pages6
ISBN (Electronic)9781538645055
DOIs
Publication statusE-pub ahead of print/First online - 13 Dec 2018
Event8th IEEE PES Innovative Smart Grid Technologies Conference Europe 2018 - Hotel Holiday, Sarajevo, Bosnia and Herzegovina
Duration: 21 Oct 201825 Oct 2018
Conference number: 8
http://sites.ieee.org/isgt-europe-2018/

Conference

Conference8th IEEE PES Innovative Smart Grid Technologies Conference Europe 2018
Abbreviated titleISGT Europe 2018
CountryBosnia and Herzegovina
CitySarajevo
Period21/10/1825/10/18
Internet address

Fingerprint

Charging (furnace)
Electric Vehicle
Electric vehicles
Prediction
Predict
Energy management systems
Energy Management
Decentralized
Optimality
Schedule
Grid
Strategy
Profile

Keywords

  • electric vehicle charging
  • online algorithm
  • prediction
  • valley-filling

Cite this

Schoot Uiterkamp, Martijn H. H. ; Gerards, Marco E. T. ; Hurink, Johann L. / Fill-level prediction in online valley-filling algorithms for electric vehicle charging. Proceedings - 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2018. IEEE, 2018.
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title = "Fill-level prediction in online valley-filling algorithms for electric vehicle charging",
abstract = "Due to the large increase in electric vehicles (EVs), smart charging strategies are required in order for the distribution grid to accommodate all these EVs. Many charging strategies either assume that future loads are known in advance, or use predictions of these loads as input. However, accurate prediction of uncontrollable load is very difficult. Online valley-filling algorithms circumvent this problem by determining the charging profile based on a prediction of the fill-level: a single parameter that characterizes the optimal EV schedule. This paper presents a simple, but accurate, method to predict this fill-level. We show that near-optimal charging profiles with an optimality gap of less than 1{\%} can be realized when our method is used to predict the input level for the online valley-filling approach. Furthermore, our method is very fast and thus suitable for use in decentralized energy management systems that employ the online valley-filling approach.",
keywords = "electric vehicle charging, online algorithm, prediction, valley-filling",
author = "{Schoot Uiterkamp}, {Martijn H. H.} and Gerards, {Marco E. T.} and Hurink, {Johann L.}",
year = "2018",
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}

Schoot Uiterkamp, MHH, Gerards, MET & Hurink, JL 2018, Fill-level prediction in online valley-filling algorithms for electric vehicle charging. in Proceedings - 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2018., 8571605, IEEE, 8th IEEE PES Innovative Smart Grid Technologies Conference Europe 2018, Sarajevo, Bosnia and Herzegovina, 21/10/18. https://doi.org/10.1109/ISGTEurope.2018.8571605

Fill-level prediction in online valley-filling algorithms for electric vehicle charging. / Schoot Uiterkamp, Martijn H. H.; Gerards, Marco E. T.; Hurink, Johann L.

Proceedings - 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2018. IEEE, 2018. 8571605.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

TY - GEN

T1 - Fill-level prediction in online valley-filling algorithms for electric vehicle charging

AU - Schoot Uiterkamp, Martijn H. H.

AU - Gerards, Marco E. T.

AU - Hurink, Johann L.

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N2 - Due to the large increase in electric vehicles (EVs), smart charging strategies are required in order for the distribution grid to accommodate all these EVs. Many charging strategies either assume that future loads are known in advance, or use predictions of these loads as input. However, accurate prediction of uncontrollable load is very difficult. Online valley-filling algorithms circumvent this problem by determining the charging profile based on a prediction of the fill-level: a single parameter that characterizes the optimal EV schedule. This paper presents a simple, but accurate, method to predict this fill-level. We show that near-optimal charging profiles with an optimality gap of less than 1% can be realized when our method is used to predict the input level for the online valley-filling approach. Furthermore, our method is very fast and thus suitable for use in decentralized energy management systems that employ the online valley-filling approach.

AB - Due to the large increase in electric vehicles (EVs), smart charging strategies are required in order for the distribution grid to accommodate all these EVs. Many charging strategies either assume that future loads are known in advance, or use predictions of these loads as input. However, accurate prediction of uncontrollable load is very difficult. Online valley-filling algorithms circumvent this problem by determining the charging profile based on a prediction of the fill-level: a single parameter that characterizes the optimal EV schedule. This paper presents a simple, but accurate, method to predict this fill-level. We show that near-optimal charging profiles with an optimality gap of less than 1% can be realized when our method is used to predict the input level for the online valley-filling approach. Furthermore, our method is very fast and thus suitable for use in decentralized energy management systems that employ the online valley-filling approach.

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Schoot Uiterkamp MHH, Gerards MET, Hurink JL. Fill-level prediction in online valley-filling algorithms for electric vehicle charging. In Proceedings - 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2018. IEEE. 2018. 8571605 https://doi.org/10.1109/ISGTEurope.2018.8571605