Development of an Optimization/Simulation Model for Real-Time Flood-Control Operation of River-Reservoirs Systems

Che, Daniel; Mays, Larry W.

doi:10.1007/s11269-015-1041-8

Cited by 64 publications

(29 citation statements)

References 32 publications

(22 reference statements)

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“…Focusing more on operational aspects (not using forecasts), Che and Mays (2015) developed and tested a methodology for determining reservoir release schedules before, during, and after an extreme flood event in real time. The problem was formulated as a real-time optimal control problem in which reservoir releases represent the decision variables, similar to the methodology adopted in the present work.…”

mentioning

confidence: 99%

Performance of Deterministic and Probabilistic Hydrological Forecasts for the Short-Term Optimization of a Tropical Hydropower Reservoir

Fan

Schwanenberg

Alvarado

et al. 2016

Water Resour Manage

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Hydropower is the most important source of electricity in Brazil. It is subject to the natural variability of water yield. One building block of the proper management of hydropower assets is the short-term forecast of reservoir inflows as input for an online, event-based optimization of its release strategy. While deterministic forecasts and optimization schemes are the established techniques for short-term reservoir management, the use of probabilistic ensemble forecasts and multi-stage stochastic optimization techniques is receiving growing attention. The present work introduces a novel, mass conservative scenario tree reduction in combination with a detailed hindcasting and closed-loop control experiments for a multipurpose hydropower reservoir in a tropical region in Brazil. The case study is the hydropower project Três Marias, which is operated with two main objectives: (i) hydroelectricity generation and (ii) flood control downstream. In the experiments, precipitation forecasts based on observed data, deterministic and probabilistic forecasts are used to generate streamflow forecasts in a hydrological model over a period of 2 years. Results for a perfect forecast show the potential benefit of the online optimization and indicate a desired forecast lead time of 30 days. In comparison, the use of actual forecasts of up to 15 days shows the practical benefit of Water Resour Manage (2016) operational forecasts, where stochastic optimization (15 days lead time) outperforms the deterministic version (10 days lead time) significantly. The range of the energy production rate between the different approaches is relatively small, between 78% and 80%, suggesting that the use of stochastic optimization combined with ensemble forecasts leads to a significantly higher level of flood protection without compromising the energy production.

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confidence: 99%

Performance of Deterministic and Probabilistic Hydrological Forecasts for the Short-Term Optimization of a Tropical Hydropower Reservoir

Fan

Schwanenberg

Alvarado

et al. 2016

Water Resour Manage

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“…Chou and Wu (2015) developed a framework of reservoir release rules for managing flood risk based on dividing flood events into three stages. Che and Mays (2015) also investigate a modeling system that supports real-time reservoir operations before, during, and after an extreme flood event. Chen et al (2014) explore the many sources of uncertainties in real-time reservoir flood control operations.…”

Section: Reservoir System Operationsmentioning

confidence: 99%

Assessment of Flood Control Capabilities for Alternative Reservoir Storage Allocations

Demirel

Wurbs

2017

Turkish Journal of Water Science and Management

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Multiple-purpose reservoir system operations are based on the conflicting objections of maximizing storage contents to assure high water supply reliability and maximizing empty storage space to mitigate flood risk. Reallocation of storage capacity between conservation and flood control purposes provides a strategy for optimizing limited available storage capacity in response to growing demands and changing objectives. A modeling and analysis methodology is presented in the article for assessing alternative reservoir storage allocations. Flood control capabilities are evaluated in terms of the probabilities of overtopping the storage capacities of the reservoirs in the system. Water supply capabilities are quantified in terms of reliability metrics. Flood control analysis capabilities are implemented in a modeling system originally created for detailed assessments of water supply capabilities. The methodology is applied to a system of eight multiple-purpose reservoirs in the Dallas and Fort Worth metropolitan area in the Trinity River Basin of Texas in the United States. The generalized modeling system and analysis methods are applicable to reservoir systems located anywhere including systems that may be very complex.

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“…() presented a DSS for early flood warning and disaster management. Che and Mays () integrated a methodology and algorithm that determines the reservoir release schedule for an extreme flood event in a real‐time operation by combining HEC‐HMS and HEC‐RAS with genetic optimizer (GO).…”

Section: Introductionmentioning

confidence: 99%

Developing a decision support framework for real‐time flood management using integrated models

Şensoy

Uysal

Şorman

2016

J Flood Risk Management

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Flood risk mitigation during real‐time flood events comprising snowmelt under hydrological uncertainty is particularly challenging. Giving this due consideration, the aim is to develop a Decision Support System (DSS) for the real‐time operation of a reservoir and the channel downstream. This was accomplished with continuous catchment monitoring, runoff forecasting, optimal reservoir decisions, river, and flood inundation analysis. The proposed DSS consists of four major components: (1) Supervisory Control and Data Acquisition (SCADA) system for real‐time monitoring; (2) Hydrologic Engineering Center (HEC) – Hydrological Modelling System (HMS) to simulate rainfall/snowmelt runoff process; (3) HEC – Reservoir Simulation System (ResSim) for operation of the controlled reservoir; and (4) HEC – River Analysis System (RAS) for two‐dimensional unsteady river analysis and flood inundation mapping. After the DSS is set up, a real‐time operation for an extreme case scenario was simulated to test the performance, and flood maps were generated. Yuvacık Dam Basin in Turkey has a robust gauge network but is rather limited in reservoir storage and downstream channel capacity. This is the basis for selection of the area in the study. Results show that the developed DSS is useful for forecasting and reservoir operations, in addition to serving as an early warning system.

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Development of an Optimization/Simulation Model for Real-Time Flood-Control Operation of River-Reservoirs Systems

Cited by 64 publications

References 32 publications

Performance of Deterministic and Probabilistic Hydrological Forecasts for the Short-Term Optimization of a Tropical Hydropower Reservoir

Performance of Deterministic and Probabilistic Hydrological Forecasts for the Short-Term Optimization of a Tropical Hydropower Reservoir

Assessment of Flood Control Capabilities for Alternative Reservoir Storage Allocations

Developing a decision support framework for real‐time flood management using integrated models

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