Given the increasingly worsening ecology issues in the lower Yellow River, the Xiaolangdi reservoir is chosen as the regulation and control target, and the single and multi-objective operation by ecology and power generation in the lower Yellow River is studied in this paper. This paper first proposes the following three indicators: the ecological elasticity coefficient (f1), the power generation elasticity coefficient (f2), and the ecological power generation profit and loss ratio (k). This paper then conducts a multi-target single dispatching study on ecology and power generation in the lower Yellow River. A genetic algorithm (GA) and an improved non-dominated genetic algorithm (NSGA-II) combining constraint processing and feasible space search techniques were used to solve the single-objective model with the largest power generation and the multi-objective optimal scheduling model considering both ecology and power generation. The calculation results show that: (1) the effectiveness of the NSGA-Ⅱcombined with constraint processing and feasible spatial search technology in reservoir dispatching is verified by an example; (2) compared with the operation model of maximizing power generation, the power generation of the target model was reduced by 0.87%, the ecological guarantee rate was increased by 18.75%, and the degree of the impact of ecological targets on the operating results was quantified; (3) in each typical year, the solution spatial distribution and dimensions of the single-target and multi-target models of change are represented by the Pareto-front curve, and a multi-objective operation plan is generated for decision makers to choose; (4) the f1, f2, and k indicators are selected to analyze the sensitivity of the five multi-objective plans and to quantify the interaction between ecological targets and power generation targets. Ultimately, this paper discusses the conversion relationship and finally recommends the best equilibrium solution in the multi-objective global equilibrium solution set. The results provide a decision-making basis for the multi-objective dispatching of the Xiaolangdi reservoir and have important practical significance for further improving the ecological health of the lower Yellow River.

中文翻译：

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黄河中下游河流生态健康单多目标优化调度研究

鉴于黄河下游生态问题日益恶化，本文以小浪底水库为调控目标，研究了黄河下游生态与发电的单目标和多目标运行。本文首先提出以下三个指标：生态弹性系数（f1）、发电弹性系数（f2）、生态发电盈亏比（k）。然后本文对黄河下游生态和发电进行了多目标单调度研究。结合约束处理和可行空间搜索技术的遗传算法(GA)和改进的非支配遗传算法(NSGA-II)求解发电量最大的单目标模型和考虑多目标优化调度的模型。既生态又发电。计算结果表明：（1）通过实例验证了NSGA-Ⅱ结合约束处理和可行空间搜索技术在水库调度中的有效性；(2)与发电最大化运行模式相比，目标模式发电量降低0.87%，生态保障率提高18.75%，生态目标对运行结果的影响程度为量化；(3) 在每个典型年份，单目标和多目标变化模型的解空间分布和维度用Pareto-front曲线表示，生成多目标操作方案供决策者选择；(4)选取f1、f2、k指标对5个多目标计划的敏感性进行分析，量化生态目标与发电目标之间的交互作用。最后，本文讨论了转换关系，最终在多目标全局均衡解集中推荐了最佳均衡解。研究结果为小浪底水库多目标调度提供决策依据，对进一步改善黄河下游生态健康具有重要的现实意义。