Full text
| |
| Author(s): |
Danilo Alvares da Silva
Total Authors: 1
|
| Document type: | Master's Dissertation |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) |
| Defense date: | 2013-05-20 |
| Examining board members: |
Marinho Gomes de Andrade Filho;
Adriano Alber de Franca Mendes Carneiro;
Eduardo Fontoura Costa
|
| Advisor: | Marinho Gomes de Andrade Filho |
| Abstract | |
Some approaches for problem of Optimal Operation Planning of Hydrothermal Systems (OOPHS) use stochastic models to represent the inflows in the reservoirs that compose the system. These approaches typically use the Stochastic Dynamic Programming (SDP) to solve the OOPHS. On the other hand, many authors defend the use of deterministic models and, particularly, the Deterministic Dynamic Programming (DDP) since it individually represents the interaction between the hydroelectric plants. In this context, this dissertation aims to compare the performance of the OOPHS solution obtained via DDP with the one given by SDP, which employs a periodic Markovian model with conditional Truncated Log-Normal distribution to represent the inflows. Furthermore, it is performed a bayesian approach analysis, in the inflow model, for estimating the parameters and forecasting the inflows. We have compared the performances of the DDP and SDP solutions by simulating the hydroelectric plants of Furnas and Sobradinho, employing artificially generated series (AU) | |
| FAPESP's process: | 11/14585-5 - Analysis of stochastic models used in planning the operation of hydrothermal systems. |
| Grantee: | Danilo Alvares da Silva |
| Support Opportunities: | Scholarships in Brazil - Master |