Modeling coordinated operation of multiple hydropower reservoirs at a continental scale using artificial neural network: the case of Brazilian hydropower system
ABSTRACT Reservoirs considerably affect river streamflow and need to be accurately represented in environmental impact studies. Modeling reservoir outflow represents a challenge to hydrological studies since reservoir operations vary with flood risk, economic and demand aspects. The Brazilian Interc...
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Associação Brasileira de Recursos Hídricos
2021-06-01
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| Series: | Revista Brasileira de Recursos Hídricos |
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| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312021000100212&tlng=en |
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| author | João Paulo Lyra Fialho Brêda Rodrigo Cauduro Dias de Paiva Olavo Corrêa Pedrollo Otávio Augusto Passaia Walter Collischonn |
| author_facet | João Paulo Lyra Fialho Brêda Rodrigo Cauduro Dias de Paiva Olavo Corrêa Pedrollo Otávio Augusto Passaia Walter Collischonn |
| author_sort | João Paulo Lyra Fialho Brêda |
| collection | DOAJ |
| description | ABSTRACT Reservoirs considerably affect river streamflow and need to be accurately represented in environmental impact studies. Modeling reservoir outflow represents a challenge to hydrological studies since reservoir operations vary with flood risk, economic and demand aspects. The Brazilian Interconnected Energy System (SIN) is an example of a unique and complex system of coordinated operation composed by more than 160 large reservoirs. We proposed and evaluated an integrated approach to simulate daily outflows from most of the SIN reservoirs (138) using an Artificial Neural Network (ANN) model, distinguishing run-of-the-river and storage reservoirs and testing cases whether outflow and level data were available as input. Also, we investigated the influence of the proposed input features (14) on the simulated outflow, related to reservoir water balance, seasonality, and demand. As a result, we verified that the outputs of the ANN model were mainly influenced by local water balance variables, such as the reservoir inflow of the present day and outflow of the day before. However, other features such as the water level of 4 large reservoirs that represent different regions of the country, which infers about hydropower demand through water availability, seemed to influence to some extent reservoirs outflow estimates. This result indicates advantages in using an integrated approach rather than looking at each reservoir individually. In terms of data availability, it was tested scenarios with (WITH_Qout) and without (NO_Qout and SIM_Qout) observed outflow and water level as input features to the ANN model. The NO_Qout model is trained without outflow and water level while the SIM_Qout model is trained with all input features, but it is fed with simulated outflows and water levels rather than observations. These 3 ANN models were compared with two simple benchmarks: outflow is equal to the outflow of the day before (STEADY) and the outflow is equal to the inflow of the same day (INFLOW). For run-of-the-river reservoirs, an ANN model is not necessary as outflow is virtually equal to inflow. For storage reservoirs, the ANN estimates reached median Nash-Sutcliffe efficiencies (NSE) of 0.91, 0.77 and 0.68 for WITH_, NO_ and SIM_Qout respectively, compared to a median NSE of 0.81 and 0.29 for the STEADY and INFLOW benchmarks respectively. In conclusion, the ANN models presented satisfactory performances: when outflow observations are available, WITH_Qout model outperforms STEADY; otherwise, NO_Qout and SIM_Qout models outperform INFLOW. |
| first_indexed | 2024-12-17T20:15:11Z |
| format | Article |
| id | doaj.art-01b512959a2740de8500e7941ba6f391 |
| institution | Directory Open Access Journal |
| issn | 2318-0331 |
| language | English |
| last_indexed | 2024-12-17T20:15:11Z |
| publishDate | 2021-06-01 |
| publisher | Associação Brasileira de Recursos Hídricos |
| record_format | Article |
| series | Revista Brasileira de Recursos Hídricos |
| spelling | doaj.art-01b512959a2740de8500e7941ba6f3912022-12-21T21:34:07ZengAssociação Brasileira de Recursos HídricosRevista Brasileira de Recursos Hídricos2318-03312021-06-012610.1590/2318-0331.262120210011Modeling coordinated operation of multiple hydropower reservoirs at a continental scale using artificial neural network: the case of Brazilian hydropower systemJoão Paulo Lyra Fialho Brêdahttps://orcid.org/0000-0002-8360-1308Rodrigo Cauduro Dias de Paivahttps://orcid.org/0000-0003-2918-6681Olavo Corrêa Pedrollohttps://orcid.org/0000-0001-7264-0259Otávio Augusto Passaiahttps://orcid.org/0000-0001-8744-228XWalter Collischonnhttps://orcid.org/0000-0002-7630-396XABSTRACT Reservoirs considerably affect river streamflow and need to be accurately represented in environmental impact studies. Modeling reservoir outflow represents a challenge to hydrological studies since reservoir operations vary with flood risk, economic and demand aspects. The Brazilian Interconnected Energy System (SIN) is an example of a unique and complex system of coordinated operation composed by more than 160 large reservoirs. We proposed and evaluated an integrated approach to simulate daily outflows from most of the SIN reservoirs (138) using an Artificial Neural Network (ANN) model, distinguishing run-of-the-river and storage reservoirs and testing cases whether outflow and level data were available as input. Also, we investigated the influence of the proposed input features (14) on the simulated outflow, related to reservoir water balance, seasonality, and demand. As a result, we verified that the outputs of the ANN model were mainly influenced by local water balance variables, such as the reservoir inflow of the present day and outflow of the day before. However, other features such as the water level of 4 large reservoirs that represent different regions of the country, which infers about hydropower demand through water availability, seemed to influence to some extent reservoirs outflow estimates. This result indicates advantages in using an integrated approach rather than looking at each reservoir individually. In terms of data availability, it was tested scenarios with (WITH_Qout) and without (NO_Qout and SIM_Qout) observed outflow and water level as input features to the ANN model. The NO_Qout model is trained without outflow and water level while the SIM_Qout model is trained with all input features, but it is fed with simulated outflows and water levels rather than observations. These 3 ANN models were compared with two simple benchmarks: outflow is equal to the outflow of the day before (STEADY) and the outflow is equal to the inflow of the same day (INFLOW). For run-of-the-river reservoirs, an ANN model is not necessary as outflow is virtually equal to inflow. For storage reservoirs, the ANN estimates reached median Nash-Sutcliffe efficiencies (NSE) of 0.91, 0.77 and 0.68 for WITH_, NO_ and SIM_Qout respectively, compared to a median NSE of 0.81 and 0.29 for the STEADY and INFLOW benchmarks respectively. In conclusion, the ANN models presented satisfactory performances: when outflow observations are available, WITH_Qout model outperforms STEADY; otherwise, NO_Qout and SIM_Qout models outperform INFLOW.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312021000100212&tlng=enReservoir outflow estimateMachine learning |
| spellingShingle | João Paulo Lyra Fialho Brêda Rodrigo Cauduro Dias de Paiva Olavo Corrêa Pedrollo Otávio Augusto Passaia Walter Collischonn Modeling coordinated operation of multiple hydropower reservoirs at a continental scale using artificial neural network: the case of Brazilian hydropower system Revista Brasileira de Recursos Hídricos Reservoir outflow estimate Machine learning |
| title | Modeling coordinated operation of multiple hydropower reservoirs at a continental scale using artificial neural network: the case of Brazilian hydropower system |
| title_full | Modeling coordinated operation of multiple hydropower reservoirs at a continental scale using artificial neural network: the case of Brazilian hydropower system |
| title_fullStr | Modeling coordinated operation of multiple hydropower reservoirs at a continental scale using artificial neural network: the case of Brazilian hydropower system |
| title_full_unstemmed | Modeling coordinated operation of multiple hydropower reservoirs at a continental scale using artificial neural network: the case of Brazilian hydropower system |
| title_short | Modeling coordinated operation of multiple hydropower reservoirs at a continental scale using artificial neural network: the case of Brazilian hydropower system |
| title_sort | modeling coordinated operation of multiple hydropower reservoirs at a continental scale using artificial neural network the case of brazilian hydropower system |
| topic | Reservoir outflow estimate Machine learning |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312021000100212&tlng=en |
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