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This paper presents a predictive control approach for long-term generation scheduling of hydro-thermal power systems. The approach is based on an open-loop feedback control scheme that uses a neural fuzzy network forecasting model, for handling the stochastic nature of inflows, and a deterministic nonlinear optimization model, to determine the discharge decisions to be implemented. As a consequence,...
The optimization of the water resource usage in hydrothermal electric energy systems is crucial to assure an economic and reliable load supply. The long term hydrothermal scheduling is a complex problem mainly due to the randomness of the inflows and the nonlinearity of hydro production and thermal cost functions. Some optimization approaches have been proposed, including the Stochastic Dynamic Programming...
This paper is concerned with the performance of stochastic dynamic programming for long term hydrothermal scheduling. Different streamflow models progressively more complex have been considered in order to identify the benefits of increasing sophistication of streamflow modeling on the performance of stochastic dynamic programming. The first and simplest model considers the inflows given by their...
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