摘要
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.
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.
基金
supported by the National Natural Science Foundation of China (Grant No. 40971300)
the Fundamental Research Funds for the Central Universities (Grant No.10QX43)
