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The annual trading declaration of the electricity market for Kaidu River Cascade Hydropower Station depends on an accurate assessment of their power generation capacity. In order to fully tap the power generation dispatching potential of the hydropower station, rationally declare the annual power generation plan, and guarantee the economic benefits of the hydropower station, it is imperative to carry out research on the power generation dispatching potential of Kaidu River Cascade Hydropower Station. This study focused on Kaidu River Cascade Hydropower Station, aiming to minimize water shortage rates and maximize power generation. A multi-objective optimization dispatching model was created, and it was solved by using an improved NSGA-II algorithm. The model results were compared with simulated dispatching to tap the power generation of optimization dispatching. The results show that: ① Compared with simulation dispatching, optimization dispatching improves output reliability and increases power generation. Under simulation dispatching, the annual average power generation of Kaidu River Cascade Hydropower Station is 1.678 billion kW·h. Optimization dispatching obtains an additional 133 million kW·h (8% increase) in the annual average power generation; ② in different typical years, optimization dispatching has different effects on power generation dispatching potential exploitation of Kaidu River Cascade Hydropower Station, especially in extreme dry years, where the increase is 1.95 times that of normal years and 6.68 times that of extreme wet years. These results provide a decision-making basis for annual trading plan declarations of cascade hydropower stations and ensure the economic benefits of cascade hydropower stations.

The annual trading declaration of the electricity market for Kaidu River Cascade Hydropower Station depends on an accurate assessment of their power generation capacity. In order to fully tap the power generation dispatching potential of the hydropower station, rationally declare the annual power generation plan, and guarantee the economic benefits of the hydropower station, it is imperative to carry out research on the power generation dispatching potential of Kaidu River Cascade Hydropower Station. This study focused on Kaidu River Cascade Hydropower Station, aiming to minimize water shortage rates and maximize power generation. A multi-objective optimization dispatching model was created, and it was solved by using an improved NSGA-II algorithm. The model results were compared with simulated dispatching to tap the power generation of optimization dispatching. The results show that: ① Compared with simulation dispatching, optimization dispatching improves output reliability and increases power generation. Under simulation dispatching, the annual average power generation of Kaidu River Cascade Hydropower Station is 1.678 billion kW·h. Optimization dispatching obtains an additional 133 million kW·h (8% increase) in the annual average power generation; ② in different typical years, optimization dispatching has different effects on power generation dispatching potential exploitation of Kaidu River Cascade Hydropower Station, especially in extreme dry years, where the increase is 1.95 times that of normal years and 6.68 times that of extreme wet years. These results provide a decision-making basis for annual trading plan declarations of cascade hydropower stations and ensure the economic benefits of cascade hydropower stations.

The water consumption rate for hydropower station power generation is an important index of the economic operation of hydropower stations, which has been widely considered and applied in the production practice of hydropower stations and has great significance for the full utilization of hydroelectric resources. This paper takes A and B cascade hydropower stations as the research objects to study the variation patterns of the water consumption rate of hydropower stations under different conditions, which mainly includes the following: (1) in this study, the relationship curve of output–gross head–flow (NHgrossQ) of each unit was established based on the relationship curve of output–net head–flow (NHQ) and the flow–head loss curve of each unit; (2) the mirror vertical shift method was used to calculate and plot the relationship curve of gross head–flow for combinations of multiple units; (3) the outflow–water–output characteristic curve of the hydropower station under the conditions of different reservoir water levels, different discharge flow, or different power generation output under the optimal operation of the hydropower station were calculated and drawn; (4) the outflow–water level–output curve of the hydropower station under various conditions was converted into an outflow–water level–water consumption curve; (5) the variation characteristics of the water consumption rate of the hydropower station under different conditions were analyzed; (6) on the basis of the definition of the water consumption rate of cascade hydropower stations, the research on the variation in the water consumption rate of cascade hydropower stations was carried out under the conditions of different water levels of the A reservoir, different water levels of the B reservoir, different discharge, and different total output of the hydropower stations. The variation pattern of the water consumption rate of hydropower stations under different conditions obtained in this paper can provide technical support for the real-time economic operation of cascade hydropower stations.

Wind curtailment, caused by wind power uncertainty, has become a prominent issue with the large-scale grid connection of wind power. To fully account for the uncertainty of wind power output, a short-term hydro-wind-thermal operation method based on a wind power confidence interval is proposed. By utilizing the flexible start-stop and efficient ramp-up of cascade hydropower plants to smooth fluctuations in wind power output, a multi-objective optimal scheduling model that minimizes the cost of power generation and maximizes the consumption of clean energy is constructed. To reduce the solution’s complexity, we chunk the model according to the energy type using a hierarchical solution. The overall solution framework, which integrates a nonparametric method, a heuristic algorithm, and an improved particle swarm algorithm, is constructed to solve the model rapidly. The simulation results of a regional power grid show that the proposed method can attain an efficient solution in 83.5 seconds. Furthermore, the proposed method achieves an additional 455,600 kWh of hydropower and a reduction of ¥233,300 in the cost of coal consumption. These findings suggest that the proposed method is a good reference for the short-term operation of a hydro-wind-thermal combination in large-scale wind power access areas.

The optimal operation of cascade hydropower station is a hydropower system based on the optimal operation method of a single reservoir and considering the water relations between reservoirs. Based on the allocation capacity of the reservoir, Water resources are redistributed in cascade basins, so that the power generation can reach the maximum under the condition of limited water resources. The optimal operation of cascade hydropower stations is a nonlinear optimization problem with many variables and constraints. When solving nonlinear problems, this paper considers using interior point method, which has good robustness characteristics. In this paper, interior point method is used to solve the optimal operation of cascade hydropower stations with intermountain runoff flows into the reservoir. The method used in this paper can shorten the optimal operation calculation time of 24 h 4 reservoir cascade hydropower station to 0.16 s. Compared with the traditional manual operation calculation, the calculation in this paper can be used for online operation to realize real-time operation. The manually scheduled power generation and the calculated power generation are 4328MW and 4617.77MW. In the calculation result, the power generation is 6.7% more than that of the manual calculation result.

With the rapid development of China's economy,electricity demand has increased.As the largest clean energy in China,hydropower has been put into large-scale and centralized production,and the most complex large-scale hydropower system in the world is formed.However,short-term hydropower optimal scheduling faces the serious challenge of the curse of dimensionality due to massive cascade hydropower stations,numerous hydropower constraints,and time-sensitive scheduling requirements.Therefore,this paper proposes a short-term optimal scheduling method for cascade hydropower stations based on a segmented load construction strategy.First,the selection rules of the hydropower stations to be regulated are formulated from the perspective of the regulation performance,location,and task of the cascade hydropower stations.Then,a method is given to divide the complex original load into two sub-load processes,namely the segmented load process with time reduction scale and the residual load process with frequent fluctuations.Finally,the adjustment method is given,and the particle swarm algorithm is used for traversal and optimization at different stages,so as to find the load division process when the target is optimal.By taking seven cascade hydropower stations in a certain river basin in southwest China as research objects,a mature particle swarm algorithm is used to simulate the typical day during the flood and dry periods.The results show that the segmented load construction strategy proposed in this paper can effectively reduce the solution complexity,and the appropriate number of segments can improve the solution efficiency while ensuring the solution quality.In addition,this method greatly reduces the output fluctuation of auxiliary hydropower stations and helps to reduce the difficulty of scheduling and unit loss.Therefore,the method proposed in this paper can provide a reference for solving the short-term scheduling of large-scale cascade hydropower stations and is of good practical value.

Power generation dispatching is a large complex system problem with multi-dimensional and nonlinear characteristics. A mathematical model was established based on the principle of reservoir operation. A large quantity of optimal scheduling processes were obtained by calculating the daily runoff process within three typical years, and a large number of simulated daily runoff processes were obtained using the progressive optimality algorithm (POA) in combination with the genetic algorithm (GA). After analyzing the optimal scheduling processes, the corresponding scheduling rules were determined, and the practical formulas were obtained. These rules can make full use of the rolling runoff forecast and carry out the rolling scheduling. Compared with the optimized results, the maximum relative difference of the annual power generation obtained by the scheduling rules is no more than 1%. The effectiveness and practical applicability of the scheduling rules are demonstrated by a case study. This study provides a new perspective for formulating the rules of power generation dispatching.

Toward solving the actual operation problems of cascade hydropower stations under hydrologic uncertainty, this paper presents the process of extraction of statistical characteristics from long-term optimal cascade operation, and proposes a monthly operation function algorithm for the actual operation of cascade hydropower stations through the identification, processing, and screening of available information during long-term optimal operation. Applying the operation function to the cascade hydropower stations on the Jinshajiang-Yangtze River system, the modeled long-term electric generation is shown to have high precision and provide benefits. Through comparison with optimal operation, the simulation results show that the operation function proposed retains the characteristics of optimal operation. Also, the inadequacies and attribution of the algorithm are discussed based on case study, providing decision support and reference information for research on large-scale cascade operation work.