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Cross Entropy Covariance Matrix Adaptation Evolution Strategy for Solving the Bi-Level Bidding Optimization Problem in Local Energy Markets

2022Journal article Open access
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dc.contributor.authorDabhi, Dharmesh
dc.contributor.authorPandya, Kartik
dc.contributor.authorSoares, João
dc.contributor.authorLezama, Fernando
dc.contributor.authorVale, Zita
dc.date.accessioned2023-02-02T10:06:54Z
dc.date.available2023-02-02T10:06:54Z
dc.date.issued2022
dc.description.abstractThe increased penetration of renewables in power distribution networks has motivated significant interest in local energy systems. One of the main goals of local energy markets is to promote the participation of small consumers in energy transactions. Such transactions in local energy markets can be modeled as a bi-level optimization problem in which players (e.g., consumers, prosumers, or producers) at the upper level try to maximize their profits, whereas a market mechanism at the lower level maximizes the energy transacted. However, the strategic bidding in local energy markets is a complex NP-hard problem, due to its inherently nonlinear and discontinued characteristics. Thus, this article proposes the application of a hybridized Cross Entropy Covariance Matrix Adaptation Evolution Strategy (CE-CMAES) to tackle such a complex bi-level problem. The proposed CE-CMAES uses cross entropy for global exploration of search space and covariance matrix adaptation evolution strategy for local exploitation. The CE-CMAES prevents premature convergence while efficiently exploring the search space, thanks to its adaptive step-size mechanism. The performance of the algorithm is tested through simulation in a practical distribution system with renewable energy penetration. The comparative analysis shows that CE-CMAES achieves superior results concerning overall cost, mean fitness, and Ranking Index (i.e., a metric used in the competition for evaluation) compared with state-of-the-art algorithms. Wilcoxon Signed-Rank Statistical test is also applied, demonstrating that CE-CMAES results are statistically different and superior from the other tested algorithms.pt_PT
dc.description.sponsorshipThis work has received funding from the EU Horizon 2020 research and innovation program under project TradeRES (grant agreement No 864276). The authors acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020 and UIDP/00760/2020) and grant CEECIND/02814/2017.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/en15134838pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/22091
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationTools for the Design and modelling of new markets and negotiation mechanisms for a ~100% Renewable European Power Systems
dc.relationResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
dc.relationResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
dc.relationNot Available
dc.relation.publisherversionhttps://www.mdpi.com/1996-1073/15/13/4838pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectBi-level problempt_PT
dc.subjectCovariance matrixpt_PT
dc.subjectCross-Entropy Methodpt_PT
dc.subjectLocal energy marketpt_PT
dc.subjectOptimal biddingpt_PT
dc.titleCross Entropy Covariance Matrix Adaptation Evolution Strategy for Solving the Bi-Level Bidding Optimization Problem in Local Energy Marketspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleTools for the Design and modelling of new markets and negotiation mechanisms for a ~100% Renewable European Power Systems
oaire.awardTitleResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
oaire.awardTitleResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
oaire.awardTitleNot Available
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/864276/EU
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00760%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00760%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/CEEC IND 2017/CEECIND%2F02814%2F2017%2FCP1417%2FCT0002/PT
oaire.citation.issue13pt_PT
oaire.citation.startPage4838pt_PT
oaire.citation.titleEnergiespt_PT
oaire.citation.volume15pt_PT
oaire.fundingStreamH2020
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStreamCEEC IND 2017
person.familyNameSoares
person.familyNameLezama
person.familyNameVale
person.givenNameJoão
person.givenNameFernando
person.givenNameZita
person.identifier1043580
person.identifier632184
person.identifier.ciencia-id1612-8EA8-D0E8
person.identifier.ciencia-idE31F-56D6-1E0F
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0002-4172-4502
person.identifier.orcid0000-0001-8638-8373
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridA-6945-2017
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id35436109600
person.identifier.scopus-author-id36810077500
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/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameEuropean Commission
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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