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

Martijn H.H. Schoot Uiterkamp; Marco E.T. Gerards; Johann L. Hurink

doi:10.1109/ISGTEurope.2018.8571605

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

Martijn H.H. Schoot Uiterkamp, Marco E.T. Gerards, Johann L. Hurink

Discrete Mathematics and Mathematical Programming

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

7 Citations (Scopus)

330 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 language	English
Title of host publication	Proceedings - 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2018
Place of Publication	Piscataway, NJ
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	978-1-5386-4505-5, 978-1-5386-4504-8
ISBN (Print)	978-1-5386-4506-2
DOIs	https://doi.org/10.1109/ISGTEurope.2018.8571605
Publication status	Published - 13 Dec 2018
Event	8th IEEE PES Innovative Smart Grid Technologies Conference Europe 2018 - Hotel Holiday, Sarajevo, Bosnia and Herzegovina Duration: 21 Oct 2018 → 25 Oct 2018 Conference number: 8 http://sites.ieee.org/isgt-europe-2018/

Conference

Conference	8th IEEE PES Innovative Smart Grid Technologies Conference Europe 2018
Abbreviated title	ISGT Europe 2018
Country/Territory	Bosnia and Herzegovina
City	Sarajevo
Period	21/10/18 → 25/10/18
Internet address	http://sites.ieee.org/isgt-europe-2018/

Keywords

2019 OA procedure
Online algorithm
Prediction
Valley-filling
Electric vehicle charging

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ISGTEurope.2018.8571605

Schoot2018fill-levelFinal published version, 255 KBLicence: Taverne

Cite this

@inproceedings{39d8e47532f7470a9fe58e8c3451e875,

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 = "2019 OA procedure, Online algorithm, Prediction, Valley-filling, Electric vehicle charging",

author = "{Schoot Uiterkamp}, {Martijn H.H.} and Gerards, {Marco E.T.} and Hurink, {Johann L.}",

year = "2018",

month = dec,

day = "13",

doi = "10.1109/ISGTEurope.2018.8571605",

language = "English",

isbn = "978-1-5386-4506-2",

booktitle = "Proceedings - 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2018",

publisher = "IEEE",

address = "United States",

note = "8th IEEE PES Innovative Smart Grid Technologies Conference Europe 2018, ISGT Europe 2018 ; Conference date: 21-10-2018 Through 25-10-2018",

url = "http://sites.ieee.org/isgt-europe-2018/",

}

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, Piscataway, NJ, 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. Piscataway, NJ: IEEE, 2018. 8571605.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-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.

N1 - Conference code: 8

PY - 2018/12/13

Y1 - 2018/12/13

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.

KW - 2019 OA procedure

KW - Online algorithm

KW - Prediction

KW - Valley-filling

KW - Electric vehicle charging

U2 - 10.1109/ISGTEurope.2018.8571605

DO - 10.1109/ISGTEurope.2018.8571605

M3 - Conference contribution

SN - 978-1-5386-4506-2

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

PB - IEEE

CY - Piscataway, NJ

T2 - 8th IEEE PES Innovative Smart Grid Technologies Conference Europe 2018

Y2 - 21 October 2018 through 25 October 2018

ER -

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

Abstract

Conference

Keywords

UN SDGs

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