Periodic Time Series Model with Annual Component Applied to Operation Planning of Hydrothermal Systems

In countries that present a high share of hydropower, as is the case of Brazil, the operation planning is based on optimization models that require the generation of synthetic hydrological inflow scenarios by models capable of representing the associated natural periodic behavior. For example, in Brazil, the PAR(p) model is employed in the computational models officially used by the National Electrical System Operator for the long- and medium-term operation planning. Usually, the average of the synthetic monthly inflow scenarios generated by the PAR(p) model presents the usual prognostic of returning to the historical average roughly in some months even when the actual regime is presenting very dry or wet partner. This paper presents an extended memory approach for the PAR(p) model to overcome this drawback by including a new term in the periodic autoregressive regression given by the average of the 12 previous inflows. A case study of the monthly long-term operation program conducted by ONS with a real configuration of the Brazilian large scale interconnected hydrothermal system is presented and discussed.