MS-PAR(p): generation of synthetic flow scenarios using a Markov-switching periodic auto-regressive model

Abstract

ABSTRACT The operation planning of the National Interconnected System (NIS) is based on optimization models that use synthetic inflow scenarios to represent the periodic behavior observed in historical data. Currently, the PAR(p)-A model (Periodic Autoregressive with Annual Component) is officially employed in computational models by the responsible organizations for short and medium-term operation planning. This paper has the aim of presenting an experiment using an alternative model that takes into consideration information regarding climatic variables, which can influence the hydrological regime of river basins and therefore the entire energy planning. The evaluated model employs the ONI index as a measure of the El Niño-Southern Oscillation (ENSO) phenomenon, in addition to a Markovian switching process. The results of the experiment demonstrate that the methodology is able to capture the influence of this phenomenon on inflows and generate scenarios closer to observed flow values.

Keywords

• Synthetic streamflow scenario generation
• Markov chain
• Autoregressive models
• Operation planning
• ENSO