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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]

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]

This paper presents a practical genetic algorithm (GA)-based solution for solving the economic load dispatch problem (ELDP) and further compares the performance of the improved GA (IGA) with that of dynamic programming (DP). Specifically, their performance is comprehensively evaluated in terms of addressing the ELDP through a case study of 26 turbines in the Three Gorges Hydropower Plant with a focus on calculation accuracy, calculation time, and algorithm stability. Evaluation results show that the improved GA method can significantly reduce the ineffectiveness of the GA in current use and could avoid the running of the turbines in the cavitation/vibration zone, thereby ensuring the safety of the turbines during generating operations. Further, the analysis comparing the performance of the IGA and DP show that the IGA is superior to DP when a small number of turbines are involved. However, as the number of turbines increases, the IGA requires more calculation time than DP; moreover, its calculation accuracy and convergence rate are significantly reduced. It is difficult to guarantee the stability of IGA in high-dimension space even though the population grows, on account of the exponential expansion of the calculation dimension, the algorithm’s premature convergence, and the lack of a local search capability. The improvement of the GA as well as the evaluation method proposed in this paper provide a new approach for choosing and improving optimization algorithms to solve the ELDP of large-scale hydropower plants. [ABSTRACT FROM AUTHOR]