Goal Programming Decision Support System for Multiobjective Operation of Reservoir Systems

Eschenbach, Elizabeth A.; Magee, Timothy; Zagona, Edith; Goranflo, Morgan; Shane, Richard M.

doi:10.1061/(asce)0733-9496(2001)127:2(108)

Cited by 71 publications

(41 citation statements)

References 6 publications

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“…In rule-based simulation, certain information is generated by prioritized policy rules specified by the model-user. Preemptive goal programming considers multiple prioritized objectives based on multiple LP solutions (Eschenbach et al, 2001). As additional goals are considered, the optimal solution of a higher priority goal is not sacrificed in order to optimize a lower priority goal.…”

Section: Riverware Reservoir and River Operation Modeling Systemmentioning

confidence: 99%

Generalized Models of River System Development and Management

Wurbs¹

2011

Current Issues of Water Management

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Section: Riverware Reservoir and River Operation Modeling Systemmentioning

confidence: 99%

Generalized Models of River System Development and Management

Wurbs¹

2011

Current Issues of Water Management

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“…The used pre-emptive goal programming optimization algorithm is not presented in this contribution. Further information can be found in literature (Yeh and Becker 1982, Yeh 1985, Eschenbach et al 2001, Labadie 2004.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the flood protection effect of a hydropower multi-reservoir system

Jordan

Boillat

Schleiss

2012

International Journal of River Basin Management

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The use of existing hydroelectricity multi-reservoir systems for flood protection may be an efficient approach in many catchment areas. The assessment of the protection potential offered by the hydropower plants during floods requires a comprehensive analysis of the catchment area, including the simulation of flood scenarios. A methodology for the optimization of turbine and bottom outlet operations of multi-reservoir systems during floods is presented. Based on a theoretical catchment configuration, the most relevant parameters in view of reducing of the peak flows in rivers located downstream of the reservoirs are analysed in a systematic way with the help of hydrological forecasts. The influence of the drained areas, the installed turbine capacities, the emergency rules and the location of the reservoirs on the flood control in such a complex catchment area is highlighted. The optimization approach is applied in the Upper Rhone River basin in Switzerland, where 10 major hydropower schemes with large reservoirs are located. The results regarding peak flow reduction at the catchment outlet are in good agreement with the theoretical values obtained with the developed methodology.

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“…Most of the studies on daily operation or real-time operation use deterministic approaches (Yazicigil et al 1983;Mujumdar & Ramesh 1997;Eschenbach et al 2001;Turgeon 2005;Kim et al 2005). However, deterministic models are incapable of considering hedging decisions and the risk of water supply shortage or flooding against the inherent uncertainty of the inflow.…”

Section: Introductionmentioning

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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Goal Programming Decision Support System for Multiobjective Operation of Reservoir Systems

Cited by 71 publications

References 6 publications

Generalized Models of River System Development and Management

Generalized Models of River System Development and Management

Optimization of the flood protection effect of a hydropower multi-reservoir system

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

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