Application of the Kriging method in energy matrix portfolio optimization

The remarkable growth of discussions about energy matrix in the last decades and the recognition of the necessity of the adoption of renewable energies led to the questioning of which would be the ideal energy matrix for a country, in terms of costs, benefits, and risks to the population. Hence, with the purpose of supporting politicians in hers decisions, this study proposed to create a method to define the optimal portfolio of energy matrix, which considers not only the generation costs of each technology, but also its risks. In order to do so, not just deviation risk measures (e.g. variance) were taken into consideration: tail measures were also used, for example the Value at Risk (VaR) and the Conditional Value at Risk (CVaR), capturing as well extreme events, which are very important to the analysis. Therefore, data on seven technologies of the United States was analyzed, Monte Carlo simulations were carried out, and with the support of the Kriging Method, the Paretto`s efficient frontier and the compositions of the optimal portfolio were finally obtained for the years of 2030, 2035, and 2040. The results, besides of assuring that tail risk measures are the most applicable in this kind of analysis, also pointed out a greater allocation in the future of renewable energies, such as wind and biomass technologies, revealing, hence, that environment aggressive technologies (e.g. coal and gas) should play a minimal role in future energy matrix.