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Planning Under Uncertainty for Aggregated Electric Vehicle Charging with Renewable Energy Supply. / Walraven, Erwin; Spaan, Matthijs T. J.

ECAI 2016 - 22nd European Conference on Artificial Intelligence. ed. / Gal A. Kaminka; Maria Fox; Paolo Bouquet; Eyke Hüllermeier; Virginia Dignum; Frank Dignum; Frank van Harmelen. IOS Press, 2016. p. 904-912 (Frontiers in Artificial Intelligence and Applications; Vol. 285).

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

Harvard

Walraven, E & Spaan, MTJ 2016, Planning Under Uncertainty for Aggregated Electric Vehicle Charging with Renewable Energy Supply. in GA Kaminka, M Fox, P Bouquet, E Hüllermeier, V Dignum, F Dignum & F van Harmelen (eds), ECAI 2016 - 22nd European Conference on Artificial Intelligence. Frontiers in Artificial Intelligence and Applications, vol. 285, IOS Press, pp. 904-912, ECAI 2016, The Hague, Netherlands, 29/08/16. https://doi.org/10.3233/978-1-61499-672-9-904

APA

Walraven, E., & Spaan, M. T. J. (2016). Planning Under Uncertainty for Aggregated Electric Vehicle Charging with Renewable Energy Supply. In G. A. Kaminka, M. Fox, P. Bouquet, E. Hüllermeier, V. Dignum, F. Dignum, & F. van Harmelen (Eds.), ECAI 2016 - 22nd European Conference on Artificial Intelligence (pp. 904-912). (Frontiers in Artificial Intelligence and Applications; Vol. 285). IOS Press. https://doi.org/10.3233/978-1-61499-672-9-904

Vancouver

Walraven E, Spaan MTJ. Planning Under Uncertainty for Aggregated Electric Vehicle Charging with Renewable Energy Supply. In Kaminka GA, Fox M, Bouquet P, Hüllermeier E, Dignum V, Dignum F, van Harmelen F, editors, ECAI 2016 - 22nd European Conference on Artificial Intelligence. IOS Press. 2016. p. 904-912. (Frontiers in Artificial Intelligence and Applications). https://doi.org/10.3233/978-1-61499-672-9-904

Author

Walraven, Erwin ; Spaan, Matthijs T. J. / Planning Under Uncertainty for Aggregated Electric Vehicle Charging with Renewable Energy Supply. ECAI 2016 - 22nd European Conference on Artificial Intelligence. editor / Gal A. Kaminka ; Maria Fox ; Paolo Bouquet ; Eyke Hüllermeier ; Virginia Dignum ; Frank Dignum ; Frank van Harmelen. IOS Press, 2016. pp. 904-912 (Frontiers in Artificial Intelligence and Applications).

BibTeX

@inproceedings{1cd47604e5ad468b995b3a8be155ac0b,
title = "Planning Under Uncertainty for Aggregated Electric Vehicle Charging with Renewable Energy Supply",
abstract = "Renewable energy sources introduce uncertainty regarding generated power in smart grids. For instance, power that is generated by wind turbines is time-varying and dependent on the weather. Electric vehicles will become increasingly important in the development of smart grids with a high penetration of renewables, because their flexibility makes it possible to charge their batteries when renewable supply is available. Charging of electric vehicles can be challenging, however, because of uncertainty in renewable supply and the potentially large number of vehicles involved. In this paper we propose a vehicle aggregation framework which uses Markov Decision Processes to control electric vehicles and deals with uncertainty in renewable supply. We present a grouping technique to address the scalability aspects of our framework. In experiments we show that the aggregation framework maximizes the profit of the aggregator, reduces cost of customers and reduces consumption of conventionally-generated power.",
keywords = "smart grids, electric vehicles, EV charging, markov decision processes, planning under uncertainty",
author = "Erwin Walraven and Spaan, {Matthijs T. J.}",
year = "2016",
month = "8",
day = "26",
doi = "10.3233/978-1-61499-672-9-904",
language = "English",
isbn = "978-1-61499-671-2",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press",
pages = "904--912",
editor = "Kaminka, {Gal A.} and Maria Fox and Paolo Bouquet and Eyke H{\"u}llermeier and Virginia Dignum and Frank Dignum and {van Harmelen}, Frank",
booktitle = "ECAI 2016 - 22nd European Conference on Artificial Intelligence",
address = "Netherlands",

}

RIS

TY - GEN

T1 - Planning Under Uncertainty for Aggregated Electric Vehicle Charging with Renewable Energy Supply

AU - Walraven, Erwin

AU - Spaan, Matthijs T. J.

PY - 2016/8/26

Y1 - 2016/8/26

N2 - Renewable energy sources introduce uncertainty regarding generated power in smart grids. For instance, power that is generated by wind turbines is time-varying and dependent on the weather. Electric vehicles will become increasingly important in the development of smart grids with a high penetration of renewables, because their flexibility makes it possible to charge their batteries when renewable supply is available. Charging of electric vehicles can be challenging, however, because of uncertainty in renewable supply and the potentially large number of vehicles involved. In this paper we propose a vehicle aggregation framework which uses Markov Decision Processes to control electric vehicles and deals with uncertainty in renewable supply. We present a grouping technique to address the scalability aspects of our framework. In experiments we show that the aggregation framework maximizes the profit of the aggregator, reduces cost of customers and reduces consumption of conventionally-generated power.

AB - Renewable energy sources introduce uncertainty regarding generated power in smart grids. For instance, power that is generated by wind turbines is time-varying and dependent on the weather. Electric vehicles will become increasingly important in the development of smart grids with a high penetration of renewables, because their flexibility makes it possible to charge their batteries when renewable supply is available. Charging of electric vehicles can be challenging, however, because of uncertainty in renewable supply and the potentially large number of vehicles involved. In this paper we propose a vehicle aggregation framework which uses Markov Decision Processes to control electric vehicles and deals with uncertainty in renewable supply. We present a grouping technique to address the scalability aspects of our framework. In experiments we show that the aggregation framework maximizes the profit of the aggregator, reduces cost of customers and reduces consumption of conventionally-generated power.

KW - smart grids

KW - electric vehicles

KW - EV charging

KW - markov decision processes

KW - planning under uncertainty

UR - http://resolver.tudelft.nl/uuid:1cd47604-e5ad-468b-995b-3a8be155ac0b

U2 - 10.3233/978-1-61499-672-9-904

DO - 10.3233/978-1-61499-672-9-904

M3 - Conference contribution

SN - 978-1-61499-671-2

T3 - Frontiers in Artificial Intelligence and Applications

SP - 904

EP - 912

BT - ECAI 2016 - 22nd European Conference on Artificial Intelligence

A2 - Kaminka, Gal A.

A2 - Fox, Maria

A2 - Bouquet, Paolo

A2 - Hüllermeier, Eyke

A2 - Dignum, Virginia

A2 - Dignum, Frank

A2 - van Harmelen, Frank

PB - IOS Press

ER -

ID: 5616996