Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/53360
Título: | An optimization approach to select portfolios of electricity generation projects with renewable energies |
Autor(es): | Matos, Eduardo Monteiro, M. Teresa T. Ferreira, Paula Varandas Cunha, Jorge Garca-Rubio, Raquel |
Palavras-chave: | Electricity planning Optimization Renewable energy Risk-Return |
Data: | 2015 |
Editora: | Natural Sciences Publishing |
Revista: | Applied Mathematics and Information Sciences: An International Journal |
Resumo(s): | The traditional approach to electricity planning has been the least cost methodology, which focus on finding the stand-alone cost of each technology in order to minimize power system cost. However, the increasing liberalization path of electricity markets as well as the growing inclusion of renewable energy sources in the power generation mix increased the complexity of the power system planning. To overcome these difficulties an alternative methodology has been proposed in the literature: the mean-variance approach, which has the advantage of explicitly taking into account risk measures as well as the potential correlation between technologies and fuels in power system planning. In this work seven technologies for electricity generation are considered to study the Portuguese case in 2009-2011. Five from these seven technologies are renewable ones. A multiobjective optimization approach is used to identify the optimal solutions, considering two conflicting objectives - risk and return. The computational results are obtained using the routine fgoalattain from the MATLAB optimization toolbox. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/53360 |
DOI: | 10.12785/amis/092L10 |
ISSN: | 1935-0090 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito autor |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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Matos_et_al_2015.pdf Acesso restrito! | 245,75 kB | Adobe PDF | Ver/Abrir |