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Hydropower Generation Programming with Application of the Stochastic Reservoir Theory and Streamflow Prediction Ensemble Alexandre K. Guetter, Federal.

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Presentation on theme: "Hydropower Generation Programming with Application of the Stochastic Reservoir Theory and Streamflow Prediction Ensemble Alexandre K. Guetter, Federal."— Presentation transcript:

1 Hydropower Generation Programming with Application of the Stochastic Reservoir Theory and Streamflow Prediction Ensemble Alexandre K. Guetter, Federal University of Paraná (UFPR) Overview of the Brazilian Hydropower System Streamflow and Teleconnections Stochastic Reservoir Theory 1st EUROBRISA Workshop, Paraty, 17/03/2008

2 Objective: end to end application for flood control and hydropower generation Climate prediction-application assumption: if the climate anomalies are predicted, then application actions will be taken to mitigate risks (flood control) and maximize benefits (meeting energy demands) Today´s data requirements for Hydropower Programming: reservoir storage (state of the system) and naturalized streamflow time series for each reservoir (Reservoir Stochastic Theory – ensemble of synthetic time series). Hydropower and Flood Control Sectors do not use Precipitation (monitoring and forecasting) and Climate Prediction as input data for the operational models, but use such information for guidance procedures

3 Overview of the Brazilian Hydropower System

4 Brazilian Hydropower System: Regional Interconnection Capacidade instalada Energia armazenada máxima Installed Capacity Energy Storage

5 Basins relevant for Hydropower Production and Flood Control

6 Parana São Francisco Tocantins

7 (1)Different regional climates grouped on a continental scale – complementary sub-systems, when one is dry the other is wet (2)Determination of the amount of guaranteed energy (which is as function of current storage and future inflows) that the system can supply at a given risk level. (3)Distribution of the guaranteed energy production among the hydropower units.

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10 Streamflow and Teleconnections -Composite of SST´s anomalies conditioned on the extremes of the monthly streamflow distribution for each basin (15% highest and 15% lowest) -Scope: 12 large basins (90% of Brazil´s hydropower generation) -Naturalized monthly streamflow series: Reynolds SST´s datasets

11 Southern Region – Iguaçu Basin

12 SST´s OND composites conditioned on the 15% highest streamflows in January

13 Southern Region – Iguaçu Basin SST´s OND composites conditioned on the 15% lowest streamflows in January

14 Southern Region – Iguaçu Basin SST´s MJJ composites conditioned on the 15% highest streamflows in August

15 Southern Region – Iguaçu Basin SST´s MJJ composites conditioned on the 15% highest streamflows in August

16 Sorting streamflow series conditioned on teleconnections Paraná Basin: Reach between Porto Primavera e Itaipu

17 Streamflow Diagnostics : Paraná Basin (Itaipu) CHEIA SECA CHEIA SECA

18 Sorting streamflow series conditioned on teleconnections umidoseco Streamflow: AprilStreamflow Sequence: Padrões TSM: janeiro – abril úmido Padrões TSM: janeiro – abril seco umido seco

19 Streamflow sequences conditioned on teleconnections Input data: Streamflow and SST´s Teleconnection/Streamflow associations

20 Stochastic Reservoir Theory

21 Conceptual Framework Input data sets: updated storages (state variable) and naturalized streamflow series Synthetic series: the spatially distributed seasonal correlation structure (sample attribute) is used to build large sets of synthetic series State (updated reservoir storage) + streamflow synthetic sequences are used to distribute energy production among the sub-systems Climate prediction statistics may be assimilated to censor the sample used to build the synthetic series


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