Classical-Equivalent Bayesian Portfolio Optimization for Electricity Generation Planning
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...
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MDPI AG
2018-01-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/20/1/42 |
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| author | Hellinton H. Takada Julio M. Stern Oswaldo L. V. Costa Celma O. Ribeiro |
| author_facet | Hellinton H. Takada Julio M. Stern Oswaldo L. V. Costa Celma O. Ribeiro |
| author_sort | Hellinton H. Takada |
| collection | DOAJ |
| description | 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. |
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| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-13T06:18:04Z |
| publishDate | 2018-01-01 |
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| spelling | doaj.art-6cbdbf1e034a4631bcab99e8dd86a2ae2022-12-22T02:58:45ZengMDPI AGEntropy1099-43002018-01-012014210.3390/e20010042e20010042Classical-Equivalent Bayesian Portfolio Optimization for Electricity Generation PlanningHellinton H. Takada0Julio M. Stern1Oswaldo L. V. Costa2Celma O. Ribeiro3Quantitative Research, Itaú Asset Management, São Paulo 04538-132, BrazilInstitute of Mathematics and Statistics, University of São Paulo, São Paulo 05508-090, BrazilPolytechnic School, University of São Paulo, São Paulo 05508-010, BrazilPolytechnic School, University of São Paulo, São Paulo 05508-010, BrazilThere 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.http://www.mdpi.com/1099-4300/20/1/42statisticsinference methodsenergy analysispolicy issues |
| spellingShingle | Hellinton H. Takada Julio M. Stern Oswaldo L. V. Costa Celma O. Ribeiro Classical-Equivalent Bayesian Portfolio Optimization for Electricity Generation Planning Entropy statistics inference methods energy analysis policy issues |
| title | Classical-Equivalent Bayesian Portfolio Optimization for Electricity Generation Planning |
| title_full | Classical-Equivalent Bayesian Portfolio Optimization for Electricity Generation Planning |
| title_fullStr | Classical-Equivalent Bayesian Portfolio Optimization for Electricity Generation Planning |
| title_full_unstemmed | Classical-Equivalent Bayesian Portfolio Optimization for Electricity Generation Planning |
| title_short | Classical-Equivalent Bayesian Portfolio Optimization for Electricity Generation Planning |
| title_sort | classical equivalent bayesian portfolio optimization for electricity generation planning |
| topic | statistics inference methods energy analysis policy issues |
| url | http://www.mdpi.com/1099-4300/20/1/42 |
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