Designing electricity generation portfolios using the mean-variance approach
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | |
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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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) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799132814501740544 |