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Bilateral contract prices estimation using a Q-learning based approach

2017Conference object Open access
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dc.contributor.authorRodriguez-Fernandez, Jaime
dc.contributor.authorPinto, Tiago
dc.contributor.authorSilva, Francisco
dc.contributor.authorPraça, Isabel
dc.contributor.authorVale, Zita
dc.contributor.authorCorchado, Juan Manuel
dc.date.accessioned2021-03-09T14:17:14Z
dc.date.available2021-03-09T14:17:14Z
dc.date.issued2017
dc.description.abstractThe electricity markets restructuring process encouraged the use of computational tools in order to allow the study of different market mechanisms and the relationships between the participating entities. Automated negotiation plays a crucial role in the decision support for energy transactions due to the constant need for players to engage in bilateral negotiations. This paper proposes a methodology to estimate bilateral contract prices, which is essential to support market players in their decisions, enabling adequate risk management of the negotiation process. The proposed approach uses an adaptation of the Q-Learning reinforcement learning algorithm to choose the best from a set of possible contract prices forecasts that are determined using several methods, such as artificial neural networks (ANN), support vector machines (SVM), among others. The learning process assesses the probability of success of each forecasting method, by comparing the expected negotiation price with the historic data contracts of competitor players. The negotiation scenario identified as the most probable scenario that the player will face during the negotiation process is the one that presents the higher expected utility value. This approach allows the supported player to be prepared for the negotiation scenario that is the most likely to represent a reliable approximation of the actual negotiation environment.pt_PT
dc.description.sponsorshipThis work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 703689 (project ADAPT) and No 641794 (project DREAM-GO); NetEfficity Project (P2020 − 18015); and UID/EEA/00760/2013 funded by FEDER Funds through COMPETE pro-gram and by National Funds through FCT.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/SSCI.2017.8285198pt_PT
dc.identifier.isbn978-1-5386-2726-6
dc.identifier.urihttp://hdl.handle.net/10400.22/17331
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relationAdaptive Decision support for Agents negotiation in electricity market and smart grid Power Transactions
dc.relationEnabling Demand Response for short and real-time Efficient And Market Based smart Grid Operation - An intelligent and real-time simulation approach
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8285198pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectBilateral contractspt_PT
dc.subjectDecision supportpt_PT
dc.subjectEnergy marketspt_PT
dc.subjectLearning algorithmpt_PT
dc.subjectNegotiation processpt_PT
dc.subjectElectricity marketpt_PT
dc.titleBilateral contract prices estimation using a Q-learning based approachpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleAdaptive Decision support for Agents negotiation in electricity market and smart grid Power Transactions
oaire.awardTitleEnabling Demand Response for short and real-time Efficient And Market Based smart Grid Operation - An intelligent and real-time simulation approach
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/703689/EU
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FEEA%2F00760%2F2013/PT
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/641794/EU
oaire.citation.conferencePlaceHonolulu, HI, USApt_PT
oaire.citation.endPage6pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleIEEE Symposium Series on Computational Intelligence (SSCI 2017)pt_PT
oaire.fundingStreamH2020
oaire.fundingStream5876
oaire.fundingStreamH2020
person.familyNamePinto
person.familyNameSilva
person.familyNamePraça
person.familyNameVale
person.givenNameTiago
person.givenNameFrancisco
person.givenNameIsabel
person.givenNameZita
person.identifierR-000-T7J
person.identifier299522
person.identifier632184
person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.ciencia-idC710-4218-1BFF
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0003-4551-6732
person.identifier.orcid0000-0002-2519-9859
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridT-2245-2018
person.identifier.ridK-8430-2014
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id35219107600
person.identifier.scopus-author-id56234082600
person.identifier.scopus-author-id22734900800
person.identifier.scopus-author-id7004115775
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.nameEuropean Commission
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameEuropean Commission
rcaap.rightsopenAccesspt_PT
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