Optimizing planning and operation of renewable energy communities with genetic algorithms

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dc.contributor.authorLazzari, Florencia
dc.contributor.authorMor Martínez, Gerard
dc.contributor.authorCipriano, Jordi
dc.contributor.authorSolsona Tehàs, Francesc
dc.contributor.authorChemisana Villegas, Daniel
dc.contributor.authorGuericke, Daniela
dc.date.accessioned2023-03-31T10:21:17Z
dc.date.available2023-03-31T10:21:17Z
dc.date.issued2023
dc.description.abstractRenewable Energy Communities (REC) have the potential to become a key agent for the energy transition. Since consumers have different consumption patterns depending on their habits, their grouping allows for a better use of the resource. REC provide both economic and environmental benefits. However, its potential drastically diminishes when grouping of prosumers and energy al- location is performed improperly, as the energy generated ends up not being consumed. Given the importance of extracting the maximum potential of REC, this study presents a tool to assist in both the planning and the operation phases. We present a combinatorial optimization method for participant selection and a multi-objective (MO) optimization of solar energy allocation. Specific Genetic Algorithms (GA) were developed including problem-specific approaches for reducing the search space, encoding, techniques for space ordering, fitness functions, special operators to replace duplicate individuals and decoding for equality constraints. The performance of the novel solution approach was exper- imentally proved with an electrical solar installation and electricity consumers from Northern east Spain. The results show that the developed tool achieves energy sharing in REC with low solar energy excess, high self-consumption and high avoided CO2 emissions while assuring low payback periods for all partic- ipants. This tool will be essential to increase revenues of REC schemes and boost their beneficial environmental impact.
dc.description.sponsorshipThis work was developed during the PhD thesis of F. Lazzari. D. Chemisana thanks ICREA for the ICREA Acad‘emia. This work emanated from research conducted with the financial support of the European Commission through the POCTEFA project EKATE, grant agreement EFA312/19 and the H2020 project ePLANET, grant agreement 101032450.
dc.identifier.doihttps://doi.org/10.1016/j.apenergy.2023.120906
dc.identifier.idgrec033129
dc.identifier.issn0306-2619
dc.identifier.urihttps://repositori.udl.cat/handle/10459.1/463145
dc.language.isoeng
dc.publisherElsevier
dc.relation.isformatofReproducció del document publicat a https://doi.org/10.1016/j.apenergy.2023.120906
dc.relation.ispartofApplied Energy, 2023, vol. 338, 120906
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/101032450/EU/ePLANET
dc.rightscc-by-nc (c) Florencia Lazzari et al., 2023
dc.rightsAttribution-NoDerivatives 4.0 International*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectRenewable Energy Communities
dc.subjectSolar energy
dc.subjectOptimization
dc.subjectGenetic Algorithm
dc.titleOptimizing planning and operation of renewable energy communities with genetic algorithms
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
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