长江科学院院报 ›› 2020, Vol. 37 ›› Issue (9): 31-38.DOI: 10.11988/ckyyb.20190622

• 水资源与环境 • 上一篇    下一篇

兼顾下游生态流量的溪洛渡-向家坝梯级水库蓄水期联合优化调度研究

蔡卓森1, 戴凌全1,2, 刘海波3, 戴会超2, 汤正阳3, 王煜1   

  1. 1.三峡大学 水利与环境学院,湖北 宜昌 443002;
    2.中国长江三峡集团有限公司,北京 100038;
    3.中国长江电力股份有限公司,湖北 宜昌 443002
  • 收稿日期:2019-05-27 修回日期:2019-08-20 出版日期:2020-09-01 发布日期:2020-09-25
  • 通讯作者: 戴凌全(1986-),男,湖北襄阳人,副教授,博士后,研究方向为水库优化调度。E-mail: dai_lingquan@163.com
  • 作者简介:蔡卓森(1996-),男,湖北咸宁人,硕士研究生,研究方向为水库优化调度。E-mail: cai_zhuosen@163.com
  • 基金资助:
    国家自然科学基金项目(51809150);长江科学院开放研究基金项目(CKWV2019725/KY);中国博士后科学基金特别资助项目(2019T120119)

Optimizing Joint Dispatching of Xiluodu-Xiangjiaba Cascade Reservoirs in Consideration of Appropriate Downstream Ecological Flow in Storage Period

CAI Zhuo-sen1, DAI Ling-quan1,2, LIU Hai-bo3, DAI Hui-chao2, TANG Zheng-yang3, WANG Yu1   

  1. 1. College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang 443002, China;
    2. China Three Gorges Corporation, Beijing 100038, China;
    3. China Yangtze Power Corporation, Yichang 443002, China
  • Received:2019-05-27 Revised:2019-08-20 Published:2020-09-01 Online:2020-09-25

摘要: 金沙江下游为珍稀特有鱼类国家级自然保护区,鱼类自然繁殖生长需要适宜的生态流量。为探究金沙江下游已建成的溪洛渡-向家坝梯级水库蓄水期的适宜生态流量改变度与梯级水库发电量间的关系,采用RVA法量化下游河道适宜生态流量,建立以调度期内发电量最大和下游河道适宜生态流量改变度最小为目标的梯级水库群多目标优化调度模型,并用NSGA-Ⅱ算法(非支配排序遗传算法)对模型进行求解。以典型丰水年、平水年、枯水年溪洛渡的入库流量进行优化调度计算,结果表明:在满足约束条件下,丰水年发电量最大增加0.7%,而对应的适宜生态流量改变度增大20.82%;而以生态为目标时,可通过发电量损耗0.48%来减少28.06%的适宜生态流量改变度。平水年发电量较常规调度相比最大可增加1.28%,此时适宜生态流量改变度增大13.87%;而以生态为目标时,适宜生态流量改变度减小22.53%,但发电量损耗0.62%。枯水年发电量较常规调度相比最大可增加1.89%,此时适宜生态流量改变度增大4.96%,而以生态为目标时,适宜生态流量改变度减少13.7%,但发电量损耗0.35%。研究成果可为金沙江下游溪洛渡-向家坝梯级水库生态调度方案的制订提供参考。

关键词: 生态调度, 多目标优化, NSGA-Ⅱ算法, 蓄水期, 金沙江下游

Abstract: The downstream of Jinsha River is a national nature reserve for rare fishes. The natural reproduction and growth of fishes need appropriate ecological flow (AEF). In the purpose of exploring the effect of Xiluodu-Xiangjiaba cascade reservoirs on the AEF in the downstream of Jinsha River, the Range of Variability Approach (RVA) was employed to quantify the downstream AEF. A multi-objective model for the scheduling optimization of cascade reservoir group was established with objectives of maximizing power generation and minimizing the change degree of downstream AEF. The model was solved by Non-dominated Sorting Genetic Algorithm II (NSGA-II). The inflow of typical wet, normal and dry years was selected for the optimization. Results demonstrate that under the constraints, power generation in wet year increases by 0.7% to the maximum and the corresponding change degree of AEF soars by 20.82%; with ecological flow as objective, the change degree of AEF can be reduced by 28.06% by losing 0.48% of the power generation. The power generation in normal year increases by 1.28% compared with conventional operation, meanwhile the change degree of AEF augments by 13.87%; such change degree could decline by 22.53% by cutting power generation by 0.62%. The power generation in dry years grows by 1.89% compared with conventional operation while the change degree of AEF raises 4.96%; under the ecological objective, the change degree of AEF could lessen by 13.7% by curtailing 0.35% power generation. The research findings offer reference for the planning of ecological scheduling of Xiluodu-Xiangjiaba cascade reservoirs in the lower reaches of Jinsha River.

Key words: ecological operation, multi-objective optimization, NSGA-Ⅱ, storage period, lower reaches of Jinsha River

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