Classical-Equivalent Bayesian Portfolio Optimization for Electricity Generation Planning

Autor(es)

Takada, Hellinton H. ^{[1, 2]} ; Stern, Julio M. ^[1] ; Costa, Oswaldo L. V. ^[3] ; Ribeiro, Celma O. ^[3]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Math & Stat, BR-05508090 Sao Paulo - Brazil

^[2] Itau Asset Management, Quantitat Res, BR-04538132 Sao Paulo - Brazil

^[3] Univ Sao Paulo, Polytech Sch, BR-05508010 Sao Paulo - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Entropy; v. 20, n. 1 JAN 2018.
• Citações Web of Science: 3

Resumo

There are several electricity generation technologies based on different sources such as wind, biomass, gas, coal, and so on. The consideration of the uncertainties associated with the future costs of such technologies is crucial for planning purposes. In the literature, the allocation of resources in the available technologies has been solved as a mean-variance optimization problem assuming knowledge of the expected values and the covariance matrix of the costs. However, in practice, they are not exactly known parameters. Consequently, the obtained optimal allocations from the mean-variance optimization are not robust to possible estimation errors of such parameters. Additionally, it is usual to have electricity generation technology specialists participating in the planning processes and, obviously, the consideration of useful prior information based on their previous experience is of utmost importance. The Bayesian models consider not only the uncertainty in the parameters, but also the prior information from the specialists. In this paper, we introduce the classical-equivalent Bayesian mean-variance optimization to solve the electricity generation planning problem using both improper and proper prior distributions for the parameters. In order to illustrate our approach, we present an application comparing the classical-equivalent Bayesian with the naive mean-variance optimal portfolios. (AU)

• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 14/50279-4 - Brasil Research Centre for Gas Innovation
• Beneficiário: Julio Romano Meneghini
• Modalidade de apoio: Auxílio à Pesquisa - Programa Centros de Pesquisa em Engenharia