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In China, there are numerous single-reservoir and multicascade hydropower plants (SMHPs), which provide high-quality peak-shaving power supply due to their characteristics of rapid load tracking and flexible regulation. However, the short-term peak-shaving operation (SPSO) of SMHPs serving multiple power grids faces difficulties from complicated hydraulic and electrical connections, dimensional disasters and complex solution difficulties. To effectively address the above problems, this paper presents a new method combining a typical peak-shaving output curve and a spillage adjustment strategy for the SPSO of SMHPs. First, the typical peak-shaving output curves of SMHPs are determined by the fuzzy membership function, flat-to-peak ratio and valley-to-peak ratio to reduce the dimension of SPSO. The fuzzy membership function is applied to identify the peak, flat and valley periods of multigrid loads. Then, taking the maximum total final energy storage of the scheduling period as the objective function, an SPSO model of SMHPs serving multiple power grids is established to balance the energy storage and peak-shaving requirements. Energy storage refers to the maximum hydropower generation that a hydropower plant can theoretically produce under a certain storage state. Finally, a spillage adjustment strategy is adopted to modify the power outputs of the SMHPs to minimize water spillage. The results from a real-world hydropower system in China demonstrate that the proposed method can allow SMHPs to play an important role in peak shaving while obtaining relatively high energy storage with negligible water spillage.

Short-term hydro-unit load dispatching (SHULD) refers to the determination of the power output of each unit within a hydropower station over a planning horizon to minimize the operational cost or maximize the power-generation profit while satisfying hydraulic and electrical constraints. In China, huge hydropower stations, such as the Three Gorges (TG) and Xiluodu (XLD) stations, are composed of a large number of hydro units, which feature a high installed capacity and a high water head. SHULD models of these stations are more complex and difficult to solve compared with those of traditional stations, especially when the stations serve multiple power grids. This study develops a practical method for optimizing SHULD models by considering the XLD hydropower station as a case study. First, a SHULD model for huge hydropower stations with multiple vibration zones and multiple receiving power grids is presented. Second, classical and sophisticated dynamic programming (DP) is applied to the SHULD model, and a practical strategy is proposed to balance the available water in a reservoir’s left and right banks to satisfy their load demands. Finally, the Fork/Join framework is used to parallelize DP to reduce the computation time and fully utilize the computer resources. The wet and dry season results demonstrate that the approach is efficient and suitable for huge hydropower stations with a high water head and multiple receiving power grids, thereby demonstrating its potential practicability and validity for solving the SHULD problem.

A multi-core parallel Particle Swarm Optimization (MPPSO) algorithm is developed to improve computational efficiency for long-term optimal hydropower system operation, in response to rapidly increasing size and complexity of hydropower systems, especially in China. The MPPSO can be implemented in three steps with easily accessible multi-core hardware platforms. First, a multi-group parallel computing strategy is introduced to maintain the diversity of population for finding the global optima. Second, the fork/join framework based on divide-and-conquer strategy is adopted to distribute multiple populations to different CPU cores for parallel calculations to take full advantage of CPU performance. Third, the results generated in different CPUs are merged to achieve an improved acceleration effect on computational time cost and more accurate optimal scheduling solution. Results for a system of twelve hydropower stations in the Guizhou Power Grid in China demonstrate that the proposed algorithm makes full use of multi-core resources, and significantly improves the computational efficiency and accuracy of the optimal solution, in addition to its low parallelization cost and low implementation cost. These suggest that the proposed algorithm has great potential for future optimal operation of hydropower systems.