Study on multi-objective optimization scheduling of cascade small hydropower stations with inclusion of monthly ecological flow variation constraints

Abstract

[Objective] Current research on multi-objective optimization scheduling primarily focuses on interannual and intra-annual ecological flow variations in medium-and large-scale reservoir groups and hydropower stations, while research on monthly ecological flow constraints for cascade small hydropower stations in mountainous rivers remains limited. [Methods] Three cascade small hydropower stations(Liangkou Dam, Qingnian, and Shengli) in the Liuxi River Basin were selected as the research objects. A multi-objective optimization model for cascade hydropower stations was established based on monthly ecological flow constraints. The model aimed to maximize power generation and minimize ecological water shortages and spillages, and was solved using the non-dominated sorting genetic algorithm(NSGA-Ⅱ). Optimization scheduling schemes were selected using the Knee Point method and compared with a fixed ecological flow scheme based on the Tennant method(10%~80% standard). [Results] The result showed that the annual power generation under the optimized monthly scheduling scheme increased by 13% compared to the Tennant method scheme, while ecological water shortages and spillages were reduced by 12%. The power generation of the three stations increased by 26%, 32%, and 26.7%, respectively. The increase in the upper limit of ecological flow constraints significantly reduced ecological water shortages during high-load operations, achieving simultaneous optimization of power generation and ecological benefits. [Conclusion] The findings provide a scientific basis and technical support for the multi-objective optimization scheduling and management of cascade small hydropower stations in mountainous rivers, enhancing both ecological and economic benefits.

Keywords

ecological flow / Tennant method / monthly frequency method / cascaded hydropower station / ecological scheduling / NSGA-Ⅱ / influencing factors