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Demonstration of ALBidS: Adaptive Learning Strategic Bidding System

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dc.contributor.authorPinto, Tiago
dc.contributor.authorVale, Zita
dc.contributor.authorPraça, Isabel
dc.contributor.authorSantos, Gabriel
dc.date.accessioned2021-03-09T15:13:38Z
dc.date.available2021-03-09T15:13:38Z
dc.date.issued2016
dc.descriptionInternational Conference on Practical Applications of Agents and Multi-Agent Systemspt_PT
dc.description.abstractCurrent worldwide electricity markets are strongly affected by the increasing use of renewable energy sources [1]. This increase has been stimulated by new energy policies that result from the growing concerns regarding the scarcity of fossil fuels and their impact in the environment. This has also led to an unavoidable restructuring of the power and energy sector, which was forced to adapt to the new paradigm [2]. The restructuring process resulted in a deep change in the operation of competitive electricity markets. The restructuring made the market more competitive, but also more complex, placing new challenges to the participants, which increases the difficulty of decision making. This is exacerbated by the increasing number of new market types that are being implemented to deal with the new challenges. Therefore, the intervenient entities are relentlessly forced to rethink their behaviour and market strategies in order to cope with such a constantly changing environment [2].pt_PT
dc.description.sponsorshipThis project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1007/978-3-319-39324-7_31pt_PT
dc.identifier.isbn978-3-319-39324-7
dc.identifier.urihttp://hdl.handle.net/10400.22/17342
dc.language.isoengpt_PT
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://link.springer.com/chapter/10.1007%2F978-3-319-39324-7_31pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectElectricity Marketpt_PT
dc.subjectRealistic Simulation Conditionspt_PT
dc.subjectGlobal State Graphpt_PT
dc.subjectContext Awareness Capabilitiespt_PT
dc.subjectRequire Decision Supportpt_PT
dc.titleDemonstration of ALBidS: Adaptive Learning Strategic Bidding Systempt_PT
dc.typeconference object
dspace.entity.typePublication
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/H2020/641794/EU
oaire.citation.endPage285pt_PT
oaire.citation.startPage281pt_PT
oaire.citation.titleAdvances in Practical Applications of Scalable Multi-agent Systemspt_PT
oaire.citation.volume9662pt_PT
oaire.fundingStreamH2020
person.familyNamePinto
person.familyNameVale
person.familyNamePraça
person.familyNameSantos
person.givenNameTiago
person.givenNameZita
person.givenNameIsabel
person.givenNameGabriel
person.identifierR-000-T7J
person.identifier632184
person.identifier299522
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person.identifier.orcid0000-0002-2519-9859
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person.identifier.ridT-2245-2018
person.identifier.ridA-5824-2012
person.identifier.ridK-8430-2014
person.identifier.ridH-7012-2018
person.identifier.scopus-author-id35219107600
person.identifier.scopus-author-id7004115775
person.identifier.scopus-author-id22734900800
person.identifier.scopus-author-id48761868500
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.nameEuropean Commission
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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