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Open AccessArticle

Classical-Equivalent Bayesian Portfolio Optimization for Electricity Generation Planning

1
Quantitative Research, Itaú Asset Management, São Paulo 04538-132, Brazil
2
Institute of Mathematics and Statistics, University of São Paulo, São Paulo 05508-090, Brazil
3
Polytechnic School, University of São Paulo, São Paulo 05508-010, Brazil
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in 37th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Jarinu, Brazil, 9–14 July 2017.
Entropy 2018, 20(1), 42; https://doi.org/10.3390/e20010042
Received: 12 November 2017 / Revised: 24 December 2017 / Accepted: 8 January 2018 / Published: 10 January 2018
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. View Full-Text
Keywords: statistics; inference methods; energy analysis; policy issues statistics; inference methods; energy analysis; policy issues
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Takada, H.H.; Stern, J.M.; Costa, O.L.V.; Ribeiro, C.O. Classical-Equivalent Bayesian Portfolio Optimization for Electricity Generation Planning. Entropy 2018, 20, 42.

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