Documents

DOI

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.
Original languageEnglish
Title of host publicationECAI 2016 - 22nd European Conference on Artificial Intelligence
EditorsGal A. Kaminka, Maria Fox, Paolo Bouquet, Eyke Hüllermeier, Virginia Dignum, Frank Dignum, Frank van Harmelen
PublisherIOS Press
Pages904-912
Number of pages9
ISBN (Electronic)978-1-61499-672-9
ISBN (Print)978-1-61499-671-2
DOIs
StatePublished - 26 Aug 2016
EventECAI 2016 - World Forum, The Hague, Netherlands
Duration: 29 Aug 20162 Sep 2016
Conference number: 22
http://www.ecai2016.org/

Publication series

NameFrontiers in Artificial Intelligence and Applications
PublisherIOS press
Volume285
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314

Conference

ConferenceECAI 2016
Abbreviated titleECAI 2016
CountryNetherlands
CityThe Hague
Period29/08/162/09/16
OtherIncluding Prestigious Applications of Artificial Intelligence, PAIS 2016
Internet address

    Research areas

  • smart grids, electric vehicles, EV charging, markov decision processes, planning under uncertainty

ID: 5616996