Simulation-optimization methods are often used to derive operation rules for large-scale hydropower reservoir systems. The solution of the simulation-optimization models is complex and time-consuming, for many interco...
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Simulation-optimization methods are often used to derive operation rules for large-scale hydropower reservoir systems. The solution of the simulation-optimization models is complex and time-consuming, for many interconnected variables need to be optimized, and the objective functions need to be computed through simulation in many periods. Since global solutions are seldom obtained, the initial solutions are important to the solution quality. In this paper, a two-stage method is proposed to derive operation rules for large-scale hydropower systems. In the first stage, the optimal operation model is simplified and solved using sampling stochastic dynamic programming (SSDP). In the second stage, the optimal operation model is solved by using a genetic algorithm, taking the SSDP solution as an individual in the initial population. The proposed method is applied to a hydropower system in Southwest China, composed of cascaded reservoir systems of Hongshui River, Lancang River, and Wu River. The numerical result shows that the two-stage method can significantly improve the solution in an acceptable solution time.
This paper examines the problem of finding an optimal operating policy for Hydro-Quebec's Manicouagan River and Outardes River hydroelectric installations. The solution method is based on the samplingdynamic prog...
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This paper examines the problem of finding an optimal operating policy for Hydro-Quebec's Manicouagan River and Outardes River hydroelectric installations. The solution method is based on the samplingdynamicprogramming (SSDP) algorithm. We use a new hydrologic state variable to capture the inflows regime, and this variable is given by a linear combination of the snow water equivalent and soil moisture. In real-time operation, this variable is calculated by a hydrologic model and incorporated in the operating policy to calculate the water released at each power plant. The algorithm is compared to the lag-1 stochasticdynamicprogramming (SDP) already implemented at Hydro-Quebec, through a statistical analysis with a set of 40 synthetic historical inflow scenarios obtained by hydrologic modeling, using synthetic temperature and precipitation produced by a stochastic weather generator. The results of the analysis show that the SSDP operating policy is statistically superior to the operating policy of the lag-1 SDP model. The SSDP operating policy does not underestimate the volume of runoff occurring during the spring season contrary to SDP. Consequently, there is a reduction in the hm(3) of water spilled, while the average annual generation is increased.
This study proposes a new monthly ensemble streamflow prediction (ESP) forecasting system that can update the ESP in the middle of a month to reflect the meteorological and hydrological variations during that month. T...
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This study proposes a new monthly ensemble streamflow prediction (ESP) forecasting system that can update the ESP in the middle of a month to reflect the meteorological and hydrological variations during that month. The reservoir operating policies derived from a sampling stochastic dynamic programming model using ESP scenarios updated three times a month were applied to the Geum River basin to measure the value of updated ESP for 21 years with 100 initial storage combinations. The results clearly demonstrate that updating the ESP scenario improves the accuracy of the forecasts and consequently their operational benefit. This study also proves that the accuracy of the ESP scenario, particularly when high flows occur, has a considerable effect on the reservoir operations. Copyright (c) 2010 John Wiley & Sons, Ltd.
This study applies a state-of-art optimization technique, SSDP/ESP (sampling stochastic dynamic programming with Ensemble Streamflow Prediction), to derive a monthly joint operating policy for the Nakdong multi-reserv...
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This study applies a state-of-art optimization technique, SSDP/ESP (sampling stochastic dynamic programming with Ensemble Streamflow Prediction), to derive a monthly joint operating policy for the Nakdong multi-reservoir system in Korea. A rainfall-runoff model, SSARR (Streamflow Synthesis And Reservoir Regulation), is linked to the SSDP/ESP model to provide ESP scenarios for runoff during the next month in the Nakdong River basin. The primary advantage of the SSDP/ESP is that it updates the derived operating policy as new ESP forecasts become available. Another SSDP model that employs historical runoff scenarios (SSDP/Hist) is also developed. The main difference between the two SSDP models is that SSDP/Hist is an off-line model whereas the SSDP/ESP is on-line. The developed operating policies are tested with a simulation model using an object-oriented simulation software, STELLA. The simulation results show that SSDP/ESP is superior to SSDP/Hist with respect to the water supply criterion, although both models perform similarly with respect to the hydroelectric energy production criterion.
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