Optimizing Operational Policies of a Korean Multireservoir System Using Sampling Stochastic Dynamic Programming with Ensemble Streamflow Prediction

Kim, Young-Oh; Eum, Hyung‐Il; Lee, Eun-Goo; Ko, Ick HwanDir.

doi:10.1061/(asce)0733-9496(2007)133:1(4)

Cited by 81 publications

(48 citation statements)

References 22 publications

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“…For instance, Pianosi and Ravazzani (2010) apply an MPC-like approach based on Stochastic Dynamic Programming to a single reservoir system, and show the benefits of considering predictive uncertainty of rainfall-runoff models, both empirical and physical-based, for flood control. Kim et al (2007) use Sampling Stochastic Dynamic Programming with ensemble streamflow predictions for the operation of a multi-reservoir system and show the improvements in system performance by explicitly including inflow uncertainty via ensemble forecasts. Roulin (2007) shows the improvement of a flood early warning system by using ensemble forecasts instead of deterministic ones in a simple static cost-loss decision model for two test catchments.…”

Section: Introductionmentioning

confidence: 99%

Optimal Operation of the Multireservoir System in the Seine River Basin Using Deterministic and Ensemble Forecasts

Ficchì

Raso

Dorchies

et al. 2016

J. Water Resour. Plann. Manage.

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms Correspondence to: andrea.ficchi@irstea.fr 2 AbstractWe investigate the improvement of the operation of a four-reservoir system in the Seine River basin, France, by use of deterministic and ensemble weather forecasts and real-time control. In the current management, each reservoir is operated independently from the others and following prescribed rule-curves, designed to reduce floods and sustain low-flows under the historical hydrological conditions. However, this management system is inefficient when inflows are significantly different from their seasonal average and may become even more inadequate to cope with the predicted increase in extreme events induced by climate change.In this work, we develop and test a centralized real-time control system to improve reservoirs operation by exploiting numerical weather forecasts that are becoming increasingly available.The proposed management system implements a well-established optimization technique, Model Predictive Control (MPC) and its recently modified version that can incorporate uncertainties, Tree-Based Model Predictive Control (TB-MPC), to account for deterministic and ensemble forecasts respectively. The management system is assessed by simulation over historical events and compared to the "no-forecasts" strategy based on rule-curves.Simulation results show that the proposed real-time control system largely outperforms the "no-forecasts" management strategy, and that explicitly considering forecasts uncertainty through ensembles can compensate for the loss in performance due to forecasts inaccuracy.

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Section: Introductionmentioning

confidence: 99%

Optimal Operation of the Multireservoir System in the Seine River Basin Using Deterministic and Ensemble Forecasts

Ficchì

Raso

Dorchies

et al. 2016

J. Water Resour. Plann. Manage.

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“…The model has advantage of updating its optimal release each time a new set of ESP forecasts is available. Recently, Kim et al (2007) presented state-of-the-art optimization models using SSDP with ESP.…”

Section: Dynamic Programmingmentioning

confidence: 99%

Simulation–Optimization Modeling: A Survey and Potential Application in Reservoir Systems Operation

Rani

Moreira

2009

Water Resour Manage

393

199

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This paper presents a survey of simulation and optimization modeling approaches used in reservoir systems operation problems. Optimization methods have been proved of much importance when used with simulation modeling and the two approaches when combined give the best results. The main objective of this review article is to discuss simulation, optimization and combined simulationoptimization modeling approach and to provide an overview of their applications reported in literature. In addition to classical optimization techniques, application and scope of computational intelligence techniques, such as, evolutionary computations, fuzzy set theory and artificial neural networks, in reservoir system operation studies are reviewed. Conclusions and suggestive remarks based on this survey are outlined, which could be helpful for future research and for system managers to decide appropriate methodology for application to their systems.

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“…We assumed that the monthly target storage can be assessed by a stochastic monthly operating model in the higher hierarchy of the decision process. Either two-stage SLP model (Lee et al 2006) or the sampling SDP model (Kim et al 2007) based on ESP can be used to provide the monthly storage targets. For successful daily operation, the trade-off analysis between storage maximization and release for hydroelectric energy generation has to be made in multiobjective analysis as demonstrated by Kim et al (2005).…”

Section: Objective Functionmentioning

confidence: 99%

Multistage stochastic linear programming model for daily coordinated multi-reservoir operation

Lee

Kim

2008

Journal of Hydroinformatics

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Operation planning for a coordinated multi-reservoir is a complex and challenging task due to the inherent uncertainty in inflow. In this study, we suggest the use of a new, multi-stage and scenariobased stochastic linear program with a recourse model incorporating the meteorological weather prediction information for daily, coordinated, multi-reservoir operation planning. Stages are defined as prediction lead-time spans of the weather prediction system. The multi-stage scenarios of the stochastic model are formed considering the reliability of rainfall prediction for each lead-time span.Future inflow scenarios are generated by a rainfall-runoff model based on the rainfall forecast. For short-term stage (2 days) scenarios, the regional data assimilation and prediction system (RDAPS) information is employed, and for mid-term stage (more than 2 days) scenarios, precipitation from the global data assimilation and prediction system (GDAPS) is used as an input for the rainfall-runoff model. After the 10th day (third stage), the daily historical rainfall data are used following the ensemble streamflow prediction (ESP) procedure. The model is applied to simulate the daily reservoir operation of the Nakdong River basin in Korea in a real-time operational environment. The expected benefit of the stochastic model is markedly superior to that of the deterministic model with average rainfall information. Our study results confirm the effectiveness of the stochastic model in real-time operation with meteorological forecasts and the presence of inflow uncertainty.

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Optimizing Operational Policies of a Korean Multireservoir System Using Sampling Stochastic Dynamic Programming with Ensemble Streamflow Prediction

Cited by 81 publications

References 22 publications

Optimal Operation of the Multireservoir System in the Seine River Basin Using Deterministic and Ensemble Forecasts

Optimal Operation of the Multireservoir System in the Seine River Basin Using Deterministic and Ensemble Forecasts

Simulation–Optimization Modeling: A Survey and Potential Application in Reservoir Systems Operation

Multistage stochastic linear programming model for daily coordinated multi-reservoir operation

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