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Long-term hydropower scheduling using model predictive control approach with hybrid monthly-annual inflow forecasting

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3 Author(s)
M. S. Zambelli ; School of Electrical and Computing Engineering, University of Campinas, SP, Brazil ; M. S. Lopes ; S. Soares

In this paper, a hybrid monthly-annual inflow forecasting approach is proposed and tested within a model predictive control framework for the long-term hydropower scheduling (LTHS). The inflow forecasts are provided on a monthly basis for a short horizon (close to present) and on an annual basis for the remaining optimization horizon, up to three years. The tests are conducted in a simulation environment with historical inflows for single reservoir hydrothermal systems. Results are compared with those using a monthly inflow forecasting approach and that from traditional stochastic dynamic programming approach, showing that the hybrid model is a promising approach to be used in the decision making process on LTHS problems.

Published in:

Transmission and Distribution: Latin America Conference and Exposition (T&D-LA), 2012 Sixth IEEE/PES

Date of Conference:

3-5 Sept. 2012