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This paper presents a nested approach for generating long-term, medium-term, and short-term reservoir scheduling models, which is based on the actual needs of the scheduling operation of the Three Gorges–Gezhouba (TG-GZB) cascade reservoirs. The approach has established a five-tier optimal scheduling model in which the time interval of the scheduling plan prepared by the model can be as short as 15 min, meeting the real-time scheduling requirements of the cascade hydropower station system. This study also presents a comparatively comprehensive introduction to all solving algorithms that have ever been adopted in the multi-time scale coordinated and optimized scheduling model. Based on that, some practical and efficient solving algorithms are developed for the characteristics of the scheduling model, including the coupled iterative method of alternating reservoirs (CIMAR)—the improved dynamic programming (IDP) algorithm and the improved genetic algorithm (IGA). In addition, optimized-scheduling solutions were generated by each of the three algorithms and were compared in terms of their convergence rate, calculation time, electric energy generated, and standard deviation of the algorithm. The results based on the Cascade Scheduling and Communication System (CSCS) of Three Gorges–Gezhouba, China, which includes two interlinked mega-scale reservoir projects, show that scheduling models have better efficiency and good convergence, and more importantly, the maximization of the power generation benefits of the hydropower plants has been achieved without violating any of the reservoir scheduling regulations. [ABSTRACT FROM AUTHOR]

The tournament operator genetic algorithm (TGA) often shows poor convergence and easily gets trapped in a local optimum when solving optimized reservoir scheduling problems. The selection operation is the most important operation determining an algorithm's convergence; therefore, this study proposes a proportional reproduction selection-based operator genetic algorithm (RGA) and a steady-state reproduction selection-based operator genetic algorithm (SGA) as alternatives to TGA. This study used TGA, RGA, and SGA to solve the maximum power generation model for the Gezhouba hydropower station, the largest runoff hydropower station in the world. Then, by using the maximum hydropower station output under a given typical runoff scenario as the optimization criterion, this study evaluated the optimized solution performance of the GA using different selection operators. The results show that TGA, SGA, and RGA can be applied to solve the short-term reservoir scheduling model. As the number of iterations increases, the hydropower station output optimized by these three GAs increases. Based on the maximum power generated by the Gezhouba hydropower station, SGA and RGA provide better results than TGA, and between SGA and RGA, the former provides better results. [ABSTRACT FROM AUTHOR]

A method is presented for optimal load dispatch in large-scale hydropower plants using a genetic algorithm integrated with simulated annealing. The genetic algorithm overcomes dependence on an initial value and provides parallel processing and fast convergence, whereas simulated annealing prevents prematurity and retrieving of the local instead of the global optimum. Thus, the integrated genetic-simulated annealing algorithm improves efficiency and robustness to obtain solutions close to the global optimum. We evaluated the proposed algorithm to determine the optimal load dispatch of 32 units of the Three Gorges Hydropower Plant in China. Test results show that the minimum water consumption obtained using the proposed algorithm is similar to the optimum obtained from a previously proposed "improved" genetic algorithm when the total load of the plant is relatively high. However, for reduced load, the proposed algorithm clearly outperforms the "improved" genetic algorithm. [ABSTRACT FROM AUTHOR]

• A new method of online, real-time prediction of downstream water levels is posed. • The Gezhouba Hydropower Plant in China is used as a reregulation case study. • A back propagation neural network-based prediction model is utilized. • Model water level predictions could fully meet real-time station dispatching needs. The forecasting accuracy of downstream water levels greatly impacts the economic operation of reregulating hydropower stations. If present, large errors in water level forecasting may require the hydropower station to discard water, which decreases the revenue from hydropower generation. Especially when the reregulating hydropower station undertakes the task of peak load regulation of the power grid, its power output may change drastically over a short period of time. Such large changes in the discharged flow of the hydropower station may inevitably cause an unsteady flow, which can lead to large fluctuations in the water levels of downstream rivers. In this case, the current forecasting methods, such as standard rating curves and empirical formulae, may inevitably result in large deviations when forecasting the change in the downstream water level. In order to reduce the volume of water discarded as a result of forecasting errors, a back propagation neural network-based forecasting model for downstream water levels of a reregulating hydropower station was constructed. The model enabled the direct, accurate, real-time forecasting of changes in downstream water levels by using measurable operation data from a hydropower station and process data on downstream water level changes. This method was applied to the actual hydropower generation dispatch of the Gezhouba Hydropower Plant in China. The results showed that as the peak load regulation volume increased, the forecasting errors of two conventional methods (standard rating curves and empirical formulae) became continuously superimposed. However, the water level forecasting error of the neural network-based method was small and could fully meet the real-time dispatching requirements of the hydropower station. In particular, under large peak load regulations, the maximum absolute values of the forecasting errors of the two conventional methods were close to 1 m, while that of the neural network-based method could be controlled within 0.3 m. [ABSTRACT FROM AUTHOR]

The completion of Shuibuya, the last hydropower station to be constructed in the Qingjiang cascade, signifies China's successful development of the world's largest mixed cascade hydropower generation system. Joint operation of such cascaded hydropower stations is considered necessary to improve hydropower output in China. In this study, two modeling methods – routine and optimal operations – were adopted, based on existing rules of reservoir operation, to determine the effects of joint operation. To investigate the realistic and potential effects, the two models were computed using observed runoff data (1951–2009). The potential effects were identified by comparing the total hydropower generation two single cascades and their cogeneration. Under the routine operational mode, the incremental power generation of joint operation would be 5.73 × 10 8 kWh, an increase of 5‰ compared to isolated operation. However, even in isolation, if reservoir operation is optimized through the dynamic programming algorithm, the incremental power generation of the cascade would be 42.24 × 10 8 kWh, which is seven times that of joint operation with routine reservoir operation (5.73 × 10 8 kWh). The results showed that, under current reservoir operating rules, there is little room for improvement in hydropower generation, although joint operation could increase hydropower generation to a certain extent, especially in the reservoirs' refill and release stages. China must amend its existing operational mode for reservoirs to enhance the economic benefits of cascade hydropower stations. Furthermore, to meet increasing demands for both water and energy, carefully considered planning of constructing clusters of these stations is required. [ABSTRACT FROM AUTHOR]