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A New Hybrid-Adaptive Differential Evolution for a Smart Grid Application Under Uncertainty

2018Conference object Open access
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dc.contributor.authorLezama, Fernando
dc.contributor.authorSoares, João
dc.contributor.authorFaia, Ricardo
dc.contributor.authorPinto, Tiago
dc.contributor.authorVale, Zita
dc.date.accessioned2021-02-24T15:06:53Z
dc.date.available2021-02-24T15:06:53Z
dc.date.issued2018
dc.description.abstractPower systems are showing a dynamic evolution in the last few years, caused in part by the adoption of smart grid technologies. The integration of new elements that represent a source of uncertainty, such as renewables generation, electric vehicles, variable loads and electricity markets, poses a higher degree of complexity causing that traditional mathematical formulations struggle in finding efficient solutions to problems in the smart grid context. In some situations, where traditional approaches fail, computational intelligence has demonstrated being a very powerful tool for solving optimization problems. In this paper, we analyze the application of Differential Evolution (DE) to address an energy resource management problem under uncertain environments. We perform a systematic parameter tuning to determine the best set of parameters of four state-of-the-art DE strategies. Having knowledge of the sensitivity of DE to the parameter selection, self-adaptive parameter control DE algorithms are also implemented, showing that competitive results can be achieved without the application of parameter tuning methodologies. Finally, a new hybrid-adaptive DE algorithm, HyDE, which uses a new “DE/target - to - perturbed_best/1” strategy and an adaptive control parameter mechanism, is proposed to solve the problem. Results show that DE strategies with fixed parameters, despite very sensitive to the setting, can find better solutions than some adaptive DE versions. Overall, our HyDE algorithm excelled all the other tested algorithms, proving its effectiveness solving a smart grid application under uncertainty.pt_PT
dc.description.sponsorshiphis work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 641794 (project DREAM-GO) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/CEC.2018.8477808pt_PT
dc.identifier.isbn978-1-5090-6017-7
dc.identifier.urihttp://hdl.handle.net/10400.22/17123
dc.language.isoengpt_PT
dc.publisherIEEEpt_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://ieeexplore.ieee.org/document/8477808pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectUncertaintypt_PT
dc.subjectSmart gridpt_PT
dc.subjectMathematical modelpt_PT
dc.subjectComplexity theorypt_PT
dc.subjectOptimizationpt_PT
dc.subjectEnergy resourcespt_PT
dc.subjectWeather forecastingpt_PT
dc.titleA New Hybrid-Adaptive Differential Evolution for a Smart Grid Application Under Uncertaintypt_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.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FEEA%2F00760%2F2013/PT
oaire.citation.conferencePlaceRio de Janeiro, Brazilpt_PT
oaire.citation.endPage8pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.title2018 IEEE Congress on Evolutionary Computation (CEC)pt_PT
oaire.fundingStreamH2020
oaire.fundingStream5876
person.familyNameLezama
person.familyNameSoares
person.familyNameFaia
person.familyNamePinto
person.familyNameVale
person.givenNameFernando
person.givenNameJoão
person.givenNameRicardo Francisco Marcos
person.givenNameTiago
person.givenNameZita
person.identifier1043580
person.identifier78FtZwIAAAAJ
person.identifierR-000-T7J
person.identifier632184
person.identifier.ciencia-idE31F-56D6-1E0F
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person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0001-8638-8373
person.identifier.orcid0000-0002-4172-4502
person.identifier.orcid0000-0002-1053-7720
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridA-6945-2017
person.identifier.ridT-2245-2018
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id36810077500
person.identifier.scopus-author-id35436109600
person.identifier.scopus-author-id35219107600
person.identifier.scopus-author-id7004115775
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameEuropean Commission
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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