Planejamento energético da operação de médio prazo conjugando as técnicas de PDDE, PAR(p) e Bootstrap

Abstract

Aiming to match the long term load demand with a low cost in power generation, it is very important to improve more and more the operation planning of the Brazilian electric sector. The operation planning of medium/long term takes into account the water inflows, which are strongly stochastic, and it must be evaluated using the series of Natural Energy Inflows (NEI). In the current computational model applied to Brazilian operation planning of medium/long term, the study of ENA has been done by Box and Jenkins methodology, which determines the periodic autoregressive model (PAR (p)), as well as its order p. A lognormal distribution with three parameters is applied on the residues that are created by the PAR (p) model, as a way to generate synthetic hydrologic series similar to the original series. However, this lognormal transformation brings nonlinearities which can disturb the stability and convergence of Stochastic Dual Dynamic Programming (SDDP). This thesis incorporates the bootstrap technique to create synthetic scenarios which will be taken into account as a basis for the SDDP implementation. This statistical technique, called bootstrap, is an alternative method used to determine both the order (p) of the model PAR (p), and, after that, to produce synthetic hydrological series. Thus, the objective of this thesis is to analyze the impact of the Bootstrap technique compared to the current methodology. The results showed that the bootstrap technique is suitable to obtain adherent hydrological series. So, it was created reliable scenarios regarding the planning of the operation of hydrothermal systems. Finally, this new methodology can be used as an alternative technique to long term hydrothermal planning problems.