针对现有的水工程调度研究缺乏考虑温室气体管控目标，构建面向温室气体管控的水工程多目标优化调度 模型。采用第二代非支配排序遗传算法（Nondominated?Sorting?Genetic?Algorithm-II，NSGA-Ⅱ）高效求解调度模型， 推求考虑温室气体管控、水华防控与发电调度目标的 Pareto 前沿解集，解析调度目标之间的协同与竞争关系，以 汉江中下游 2017—2021 年枯水期的水华事件为实例开展相关研究。结果表明：温室气体管控目标同水华防控、 发电调度目标均呈现竞争关系；相比常规调度方案，优化调度方案可增加发电量 0.3 亿 kW·h（增幅 4.5%）、可减少 温室气体净排放 CO2当量 1.56?Gg（减幅 1.2%），协调方案的三目标效益均更优；协调方案可有效提升水工程综合 效益。研究成果可为实现碳减排与河流水环境水生态保障调控提供技术支撑。

In the context of global climate change, greenhouse gas emissions from water projects are received increasing attention. The impoundment of water projects submerges soil and vegetation, causing the degradation of the inner organic matter to produce greenhouse gases (CO2,CH4,N2O) and discharge them into the atmosphere. The drawdown area which is repeatedly flooded and exposed affected by the regulation of water projects is also a "hot spot" for producing greenhouse gases, while the organic carbon buried in the sediment of water bodies can store atmospheric CO2 thus forming carbon sinks in the short or long term. The assessment of greenhouse gas flux of water projects is a research hotspot, and the existing monitored data can be used for research.The middle and lower reaches of the Hanjiang River are important ecological and economic zones, and the development of society has damaged the health of the rivers in this area, for example more than ten algal bloom outbreaks have occurred so far, which is not conducive to the high-quality development of the basin. A rational operation of water projects is beneficial for alleviating algal bloom outbreaks and reducing greenhouse gas emissions. In order to effectively improve the ecological environment and ensure high-quality development of the watershed, multi-objective optimization operation research is always carried out to explore the optimal operation mode of water projects. However, most of the existing researches focused on ecological indicators such as ecological water demand, ecological flow change degree, fish habitat, and algal bloom prevention and control, lacking the consideration of greenhouse gas emissions. In this case a multi-objective optimization model that comprehensively considered power generation, algal bloom prevention and control, and greenhouse gas emission control was established to explore the optimal operation mode of water projects.To solve the multi-objective problems, more and more researchers are using evolutionary optimization algorithms, among which the non-dominated sorting genetic algorithm (NSGA-II) with elite strategy is the most representative, which can reduce the computational complexity of non-dominated sorting methods, has advantages such as fast running speed and good convergence of solution sets, and has been widely used in the field of multi-objective optimization of reservoir operation. Therefore, the NSGA-II algorithm was used to solve the proposed model.The results show that the objective of greenhouse gas emission control was in a competitive relationship with both the objective of power generation and algal bloom prevention and control. Compared with the conventional operation scheme, the optimal operation schemes can achieve improvement in some indicators. Among the three optimal operation schemes, the compromised scheme can achieve improvement of all the indicators, indicating the effectiveness of the proposed model. The analysis of the operation process during typical events reveals a close relationship between greenhouse gas emissions and water level fluctuations.Using the established model, the net greenhouse gas emissions of water projects during the operation period were estimated, and an optimal operation scheme which can achieve better benefits compared to the conventional operation scheme was found. The results can provide technical support for achieving the optimal operation of water projects, reducing greenhouse gas emissions, and repairing the ecological environment of rivers.