| 摘要: |
| 水库的排沙调度往往需要水库降低水位运行,与发电需要的高水位运行产生了冲突,所以需要制定合理的调度方案使水库能够长期运行且发挥最大效益。因此本文把小浪底水库作为研究对象,以水库减淤与发电为核心目标,结合一维水沙模型与快速非支配遗传算法(Non-dominated Sorting Genetic Algorithm III, NSGA-Ⅲ),建立了小浪底水沙电优化调度模型。该模型综合考虑了水库的泥沙调度和发电调度,且在计算水库冲淤情况时,采用了相较于经验排沙比公式更精细的一维水沙模型。结果表明:一维水沙模型能够准确地模拟各测站水位过程；通过NSGA-Ⅲ优化后得到的一系列帕累托最优解突显了调度过程中水库减淤与发电间的矛盾,且帕累托曲线上的方案明显优于原方案,在淤积量不变情况下,发电量能增加11.4%,发电量不变情况下,淤积量能减少48%,在发电与减淤目标函数值比值不变的情况下,两者分别增加13%和50%。 |
| 关键词: 水库发电 库区减淤 多目标优化调度 非支配遗传算法 |
| DOI： |
| 分类号: |
| 基金项目:国家自然科学基金资助项目(U2243238；52279076)；中央高校基本科研业务费专项资金资助(2452023325) |
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| Research on Joint Optimization Scheduling of Water, Sediment, and Power in Xiaolangdi Reservoir Based on Non-Dominated Genetic Algorithm |
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Tang Wenxin1,2, Wang Zenghui1,2, Li Dingqian1,2, Qi Zhangxin1,2, Chu Pengbo1,2
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1.Northwest A&2.F University
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| Abstract: |
| The sediment discharge scheduling of reservoirs often requires a lower water level, conflicting with the higher water levels needed for power generation. Thus, a reasonable scheduling scheme is necessary to ensure long-term operation and maximize benefits. This study focuses on the Xiaolangdi Reservoir, aiming at siltation reduction and power generation. By integrating a one-dimensional sediment transport model with the Non-Dominated Sorting Genetic Algorithm III (NSGA-Ⅲ), an optimization scheduling model for water, sediment, and power was established. The model comprehensively considers both sediment and power generation scheduling, employing a refined one-dimensional sediment transport model for more accurate sedimentation calculations compared to empirical sediment discharge formulas. Results indicate that the one-dimensional model accurately simulates water level processes at various stations. The series of Pareto optimal solutions derived from NSGA-Ⅲ highlight the conflict between siltation reduction and power generation, with solutions on the Pareto front significantly outperforming the original plan. With sediment volume constant, power generation can increase by 11.4%; conversely, with power generation constant, sediment volume can be reduced by 48%. Maintaining the ratio of objectives, power generation and siltation reduction can increase by 13% and 50%, respectively. |
| Key words: reservoir power generation reservoir siltation reduction Multi-objective optimal scheduling Non-dominated Sorting Genetic Algorithm |
