Designing electricity generation portfolios using the mean-variance approach

Detalhes bibliográficos
Autor(a) principal: Cunha, Jorge
Data de Publicação: 2014
Outros Autores: Ferreira, Paula Varandas
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/1822/36914
Resumo: The mean-variance approach (MVA) is commonly used in the financial literature for the optimal design of financial asset portfolios. The electricity sector portfolios are also guided by similar objectives, namely maximising return and minimising risk. As such, this paper proposes two possible MVAs for the design of optimal renewable electricity production portfolios. The first approach is directed at portfolio output maximisation and the second one is directed at portfolio cost optimisation. The model was implemented on data compiled from the Portuguese electricity system collected for each quarter of an hour, for a period close to four years. Three renewable energy sources (RES) portfolios were used, namely hydropower, wind power and photovoltaic. This highlighted the resource seasonality demonstrating that hydropower output positively correlates with wind power and that photovoltaic correlates negatively with both hydro and wind power. The results show that for both models the least risky solutions are characterised by a mix of RES technologies, taking advantage of the diversification benefits. As for the highest return solutions, as expected, these are the ones associated with higher risk but the portfolio composition largely depends on the assumed costs of each technology.
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spelling Designing electricity generation portfolios using the mean-variance approachRenewable energy sourcesElectricity generationPortfolio selectionMean-variance approachInvestment riskEngenharia e Tecnologia::Outras Engenharias e TecnologiasThe mean-variance approach (MVA) is commonly used in the financial literature for the optimal design of financial asset portfolios. The electricity sector portfolios are also guided by similar objectives, namely maximising return and minimising risk. As such, this paper proposes two possible MVAs for the design of optimal renewable electricity production portfolios. The first approach is directed at portfolio output maximisation and the second one is directed at portfolio cost optimisation. The model was implemented on data compiled from the Portuguese electricity system collected for each quarter of an hour, for a period close to four years. Three renewable energy sources (RES) portfolios were used, namely hydropower, wind power and photovoltaic. This highlighted the resource seasonality demonstrating that hydropower output positively correlates with wind power and that photovoltaic correlates negatively with both hydro and wind power. The results show that for both models the least risky solutions are characterised by a mix of RES technologies, taking advantage of the diversification benefits. As for the highest return solutions, as expected, these are the ones associated with higher risk but the portfolio composition largely depends on the assumed costs of each technology.(undefined)Aalborg University PressUniversidade do MinhoCunha, JorgeFerreira, Paula Varandas20142014-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/36914eng2246‐292910.5278/ijsepm.2014.4.3info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:35:03Zoai:repositorium.sdum.uminho.pt:1822/36914Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:30:51.908120Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Designing electricity generation portfolios using the mean-variance approach
title Designing electricity generation portfolios using the mean-variance approach
spellingShingle Designing electricity generation portfolios using the mean-variance approach
Cunha, Jorge
Renewable energy sources
Electricity generation
Portfolio selection
Mean-variance approach
Investment risk
Engenharia e Tecnologia::Outras Engenharias e Tecnologias
title_short Designing electricity generation portfolios using the mean-variance approach
title_full Designing electricity generation portfolios using the mean-variance approach
title_fullStr Designing electricity generation portfolios using the mean-variance approach
title_full_unstemmed Designing electricity generation portfolios using the mean-variance approach
title_sort Designing electricity generation portfolios using the mean-variance approach
author Cunha, Jorge
author_facet Cunha, Jorge
Ferreira, Paula Varandas
author_role author
author2 Ferreira, Paula Varandas
author2_role author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Cunha, Jorge
Ferreira, Paula Varandas
dc.subject.por.fl_str_mv Renewable energy sources
Electricity generation
Portfolio selection
Mean-variance approach
Investment risk
Engenharia e Tecnologia::Outras Engenharias e Tecnologias
topic Renewable energy sources
Electricity generation
Portfolio selection
Mean-variance approach
Investment risk
Engenharia e Tecnologia::Outras Engenharias e Tecnologias
description The mean-variance approach (MVA) is commonly used in the financial literature for the optimal design of financial asset portfolios. The electricity sector portfolios are also guided by similar objectives, namely maximising return and minimising risk. As such, this paper proposes two possible MVAs for the design of optimal renewable electricity production portfolios. The first approach is directed at portfolio output maximisation and the second one is directed at portfolio cost optimisation. The model was implemented on data compiled from the Portuguese electricity system collected for each quarter of an hour, for a period close to four years. Three renewable energy sources (RES) portfolios were used, namely hydropower, wind power and photovoltaic. This highlighted the resource seasonality demonstrating that hydropower output positively correlates with wind power and that photovoltaic correlates negatively with both hydro and wind power. The results show that for both models the least risky solutions are characterised by a mix of RES technologies, taking advantage of the diversification benefits. As for the highest return solutions, as expected, these are the ones associated with higher risk but the portfolio composition largely depends on the assumed costs of each technology.
publishDate 2014
dc.date.none.fl_str_mv 2014
2014-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1822/36914
url http://hdl.handle.net/1822/36914
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2246‐2929
10.5278/ijsepm.2014.4.3
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Aalborg University Press
publisher.none.fl_str_mv Aalborg University Press
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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