Real-Time Bidding Strategies from Micro-Grids Using Reinforcement Learning

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dc.contributor.affiliationUniversity of Liège
dc.contributor.affiliationUniversity of Liège
dc.contributor.affiliationUniversity of Liège
dc.contributor.authorBoukas, Ioannis
dc.contributor.authorErnst, Damien
dc.contributor.authorCornélusse, Bertrand
dc.contributor.countryBelgium
dc.contributor.countryBelgium
dc.contributor.countryBelgium
dc.contributor.detailedauthorBoukas, Ioannis, University of Liège, Belgium
dc.contributor.detailedauthorErnst, Damien, University of Liège, Belgium
dc.contributor.detailedauthorCornélusse, Bertrand, University of Liège, Belgium
dc.date.accessioned2019-12-19T18:20:10Z
dc.date.available2019-12-19T18:20:10Z
dc.date.conferencedate7 - 8 June 2018
dc.date.issued2018-06-07
dc.description.abstractWe address the problem faced by the operator of a microgrid participating in a continuous real-time market. Themicrogrid consists of distributed generation, flexible loadsand a storage device. The goal of the microgrid operatoris the maximization of the profits over the entire tradinghorizon, while taking into account operational constraints.The variability of the Renewable Energy Sources (RES) isconsidered and the energy trading is modeled as a MarkovDecision Process. The problem is solved using reinforcement learning (RL). The resulting optimal real time bidding strategy of a microgrid is discussed.
dc.description.conferencelocationLjubljana, Slovenia
dc.description.conferencenameCIRED 2018 Ljubljana Workshop
dc.description.openaccessYes
dc.description.peerreviewedYes
dc.description.sessionBusiness models, roles, responsibilities and regulatory aspects
dc.description.sessionid1
dc.identifier.isbn978-2-9602415-1-8
dc.identifier.issn2032-9628
dc.identifier.urihttps://www.cired-repository.org/handle/20.500.12455/1129
dc.identifier.urihttp://dx.doi.org/10.34890/163
dc.language.isoen
dc.publisherAIM
dc.relation.ispartProc. of CIRED 2018 Ljubljana Workshop
dc.relation.ispartofseriesCIRED Workshop Proceedings
dc.titleReal-Time Bidding Strategies from Micro-Grids Using Reinforcement Learning
dc.title.number0440
dc.typeConference Proceedings
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