Hydropower Forecasting in Brazil

Tucci, Carlos Eduardo Morelli; Collischonn, Walter; Fan, Fernando Mainardi; Schwanenberg, Dirk

doi:10.1007/978-3-642-40457-3_55-1

Cited by 1 publication

(1 citation statement)

References 25 publications

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“…Regarding the flow forecasting process, Tucci et al (2015) presented an assessment of ensemble inflow forecasts for a hydropower reservoir in Brazil, the Três Marias dam. Inflow forecasts with lead times of 15 days were generated twice a day using a 14-members ensemble obtained from the global numerical weather prediction, run by the Brazilian Weather Forecasting Center (CPTEC), and a large-scale hydrological model.…”

Section: Introductionmentioning

confidence: 99%

Prediction of monthly flows for Três Marias reservoir (São Francisco river basin) using the CFS climate forecast model

Paiva

Montenegro

Cataldi

2020

RBRH

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Despite the water crisis in 2016, 76% of the energy in Brazil was generated by hydroelectric plants, which shows that the Brazilian system is still strongly dependent on the hydrological conditions of basins. Therefore, the flow forecasts for these plants subsidize the decision making within the scope of the Electric Sector, since they allow the evaluation of the operational conditions of the hydroelectric and thermoelectric plants through the use of energy optimization models, providing gains in the operations of SIN (Sistema Interligado Nacional – the Brazilian National Interconnected System). The precipitation forecast is of fundamental importance for the elaboration of these hydroelectric flow forecasts. For energy evaluations, the DECOMP and NEWAVE models are used, with the GEVAZP model being applied to generate scenarios through an AR (p) (autoregressive) model. Accordingly, this study shows the impact of precipitation forecast on flow predictions in the climate horizon. For this, a statistical correction was made in the rain predicted by the CFS (Climate Forecast System) model, which tends to overestimate the predicted rain, with rainfall-flow models being calibrated. Tests were performed with this new modeling system and the results, in the form of scenarios, were compared with the scenarios generated by the GEVAZP model, showing the possibility of reducing the generated range by the latter, consequently causing the DECOMP model to not consider ranges with little or no probability of occurrence, which can improve the optimization of the SIN operation planning. This work also shows that the SMAP model exhibited better performance when compared to the Neural Networks model, in terms of the average flow range predicted in relation to the observed flow. There was a clear improvement in the flow predictions with the incorporation of the rain observed one month ahead in the simulations, mainly in the forecast of high flows. Finally, the climate indices had a good relationship with the flow and rain variables.

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Section: Introductionmentioning

confidence: 99%