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

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

Overview of the Brazilian Hydropower System

Brazilian Hydropower System: Regional Interconnection

SE63%

SU17%

NE14%

NO6%

Capacidade instaladaCapacidade instalada

SE68%

SU6%

NE21%

NO5%

Energia armazenada máximaEnergia armazenada máxima

NO-Norte

NE-Nordeste

SE - Sudeste/Centro-oeste

SU-Sul

Installed Capacity

Energy Storage

Basins relevant for Hydropower Production and Flood Control

Parana

São FranciscoTocantins

(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.

January - Streamflow Climatology

15.746 m3/s (S+SE)

Barra Bonita 4%

Três Irmãos 4%

Capivara 9%

Rosana 1%

Foz do Areia 4%

Salto Caxias 3%

Ilha Solteira 7%

Porto Primavera 9%

Itaipu 8% Furnas 11%

Agua Vermelha 13%

Emborcação 6%

São Simão 21%

July - Streamflow Climatology

8.884 m3/s (S+SE)

Furnas6%

Agua Vermelha8%

Emborcação3%

São Simão12%

Barra Bonita3%

Três Irmãos3%

Capivara11%Rosana

2%Foz do Areia

9%

Salto Caxias9%

Ilha Solteira6%

Porto Primavera11%

Itaipu17%

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: 1941-2000

- Reynolds SST´s datasets

Southern Region – Iguaçu Basin

Southern Region – Iguaçu Basin

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

Southern Region – Iguaçu Basin

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

Southern Region – Iguaçu Basin

SST´s MJJ composites conditioned on the 15% highest streamflows in August

Southern Region – Iguaçu Basin

SST´s MJJ composites conditioned on the 15% highest streamflows in August

Sorting streamflow series conditioned on teleconnections

Paraná Basin: Reach between Porto Primavera e Itaipu

Streamflow Diagnostics : Paraná Basin (Itaipu)

PARANÁ - ITAIPU - CLIMATOLOGIA

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PARANÁ - ITAIPUHISTOGRAMA: MARÇO

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CHEIASECA CHEIASECA

Sorting streamflow series conditioned on teleconnections

PARANÁ - ITAIPUHISTOGRAMA: ABRIL

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VAZÃO INCREMENTAL (m3/s)

NO

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OS

(T

OT

=60

) umido

seco

Streamflow: April Streamflow Sequence:

Padrões TSM: janeiro – abril úmido

Padrões TSM: janeiro – abril seco

PARANÁ - ITAIPU - ABRIL

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Streamflow sequences conditioned on teleconnections

Input data: Streamflow and SST´s Teleconnection/Streamflow associations

Stochastic Reservoir Theory

Conceptual FrameworkInput 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