Optimizing planning and operation of renewable energy communities with genetic algorithms

Renewable 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,...

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Detalles Bibliográficos
Autores: Lazzari, Florencia, Mor Martínez, Gerad, Cipriano, Jordi, Solsona, Francesc, Chemisana, Daniel, Guericke, Daniela
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Consejo General de la Arquitectura Técnica de España (CGATE)
Repositorio:RIARTE
OAI Identifier:oai:www.riarte.es:20.500.12251/3385
Acceso en línea:http://hdl.handle.net/20.500.12251/3385
https://doi.org/10.1016/j.apenergy.2023.120906
Access Level:acceso abierto
Palabra clave:Energías renovables
Electricidad
Ahorro energético
Algoritmos
Energía solar
Comunidad de Energía Renovable (CER)
Autosuficiencia energética
3322.05 Fuentes no Convencionales de Energía
3322.02 Generación de Energía
3305.14 Viviendas
5306.02 Innovación Tecnológica
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spelling Optimizing planning and operation of renewable energy communities with genetic algorithmsLazzari, FlorenciaMor Martínez, GeradCipriano, JordiSolsona, FrancescChemisana, DanielGuericke, DanielaEnergías renovablesElectricidadAhorro energéticoAlgoritmosEnergía solarComunidad de Energía Renovable (CER)Autosuficiencia energética3322.05 Fuentes no Convencionales de Energía3322.02 Generación de Energía3305.14 Viviendas5306.02 Innovación TecnológicaRenewable 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 Ge- netic 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. © 2023 The AuthorsElsevier B.V.2023info:eu-repo/semantics/articlehttp://hdl.handle.net/20.500.12251/3385https://doi.org/10.1016/j.apenergy.2023.120906reponame:RIARTEinstname:Consejo General de la Arquitectura Técnica de España (CGATE)Ingléshttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:www.riarte.es:20.500.12251/33852026-06-02T12:44:41Z
dc.title.none.fl_str_mv Optimizing planning and operation of renewable energy communities with genetic algorithms
title Optimizing planning and operation of renewable energy communities with genetic algorithms
spellingShingle Optimizing planning and operation of renewable energy communities with genetic algorithms
Lazzari, Florencia
Energías renovables
Electricidad
Ahorro energético
Algoritmos
Energía solar
Comunidad de Energía Renovable (CER)
Autosuficiencia energética
3322.05 Fuentes no Convencionales de Energía
3322.02 Generación de Energía
3305.14 Viviendas
5306.02 Innovación Tecnológica
title_short Optimizing planning and operation of renewable energy communities with genetic algorithms
title_full Optimizing planning and operation of renewable energy communities with genetic algorithms
title_fullStr Optimizing planning and operation of renewable energy communities with genetic algorithms
title_full_unstemmed Optimizing planning and operation of renewable energy communities with genetic algorithms
title_sort Optimizing planning and operation of renewable energy communities with genetic algorithms
dc.creator.none.fl_str_mv Lazzari, Florencia
Mor Martínez, Gerad
Cipriano, Jordi
Solsona, Francesc
Chemisana, Daniel
Guericke, Daniela
author Lazzari, Florencia
author_facet Lazzari, Florencia
Mor Martínez, Gerad
Cipriano, Jordi
Solsona, Francesc
Chemisana, Daniel
Guericke, Daniela
author_role author
author2 Mor Martínez, Gerad
Cipriano, Jordi
Solsona, Francesc
Chemisana, Daniel
Guericke, Daniela
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Energías renovables
Electricidad
Ahorro energético
Algoritmos
Energía solar
Comunidad de Energía Renovable (CER)
Autosuficiencia energética
3322.05 Fuentes no Convencionales de Energía
3322.02 Generación de Energía
3305.14 Viviendas
5306.02 Innovación Tecnológica
topic Energías renovables
Electricidad
Ahorro energético
Algoritmos
Energía solar
Comunidad de Energía Renovable (CER)
Autosuficiencia energética
3322.05 Fuentes no Convencionales de Energía
3322.02 Generación de Energía
3305.14 Viviendas
5306.02 Innovación Tecnológica
description Renewable 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 Ge- netic 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. © 2023 The Authors
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12251/3385
https://doi.org/10.1016/j.apenergy.2023.120906
url http://hdl.handle.net/20.500.12251/3385
https://doi.org/10.1016/j.apenergy.2023.120906
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier B.V.
publisher.none.fl_str_mv Elsevier B.V.
dc.source.none.fl_str_mv reponame:RIARTE
instname:Consejo General de la Arquitectura Técnica de España (CGATE)
instname_str Consejo General de la Arquitectura Técnica de España (CGATE)
reponame_str RIARTE
collection RIARTE
repository.name.fl_str_mv
repository.mail.fl_str_mv
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