HYDROTHERMAL SCHEDULING POLICY STABILITY UNDER INFLOW UNCERTAINTY

Abstract Hydrothermal systems optimal scheduling requires the representation of uncertainties in future inflows in order to hedge against adverse future low inflows by committing thermal plants, and also to store water in reservoirs while avoiding spillage when high future inflows occur. An inflow scenario tree must be correctly dimensioned so as to provide a parsimonious yet representative sample of possible future inflows to reservoirs. We use a multivariate stochastic inflow model to generate an i.i.d sample of future inflows at each stage, followed by a clustering of similar inflow scenarios to reduce the dimension of the scenario tree. A stochastic scheduling optimization model is applied to different scenario tree sizes and the stability of results is analyzed