Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, PR China.
Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, PR China.
Water Res. 2024 Apr 1;253:121314. doi: 10.1016/j.watres.2024.121314. Epub 2024 Feb 14.
Dam (reservoir)-induced alterations of flow and water temperature regimes can threaten downstream fish habitats and native aquatic ecosystems. Alleviating the negative environmental impacts of dam-reservoir and balancing the multiple purposes of reservoir operation have attracted wide attention. While previous studies have incorporated ecological flow requirements in reservoir operation strategies, a comprehensive analysis of trade-offs among hydropower benefits, ecological flow, and ecological water temperature demands is lacking. Hence, this study develops a multi-objective ecological scheduling model, considering total power generation, ecological flow guarantee index, and ecological water temperature guarantee index simultaneously. The model is based on an integrated multi-objective simulation-optimization (MOSO) framework which is applied to Three Gorges Reservoir. To that end, first, a hybrid long short-term memory and one-dimensional convolutional neural network (LSTM_1DCNN) model is utilized to simulate the dam discharge temperature. Then, an improved epsilon multi-objective ant colony optimization for continuous domain algorithm (ε-MOACOR) is proposed to investigate the trade-offs among the competing objectives. Results show that LSTM _1DCNN outperforms other competing models in predicting dam discharge temperature. The conflicts among economic and ecological objectives are often prominent. The proposed ε-MOACOR has potential in resolving such conflicts and has high efficiency in solving multi-objective benchmark tests as well as reservoir optimization problem. More realistic and pragmatic Pareto-optimal solutions for typical dry, normal and wet years can be generated by the MOSO framework. The ecological water temperature guarantee index objective, which should be considered in reservoir operation, can be improved as inflow discharge increases or the temporal distribution of dam discharge volume becomes more uneven.
水坝（水库）改变水流和水温条件会威胁下游鱼类栖息地和本地水生生态系统。减轻水坝-水库的负面环境影响并平衡水库运行的多种目的引起了广泛关注。虽然先前的研究已经将生态流量要求纳入水库运行策略中，但缺乏对水电效益、生态流量和生态水温需求之间权衡的综合分析。因此，本研究开发了一种多目标生态调度模型，同时考虑总发电量、生态流量保障指数和生态水温保障指数。该模型基于综合多目标模拟-优化 (MOSO) 框架，应用于三峡水库。为此，首先，采用混合长短时记忆和一维卷积神经网络 (LSTM_1DCNN) 模型模拟大坝放水温度。然后，提出了一种改进的连续域 ε 多目标蚁群优化算法 (ε-MOACOR) 来研究竞争目标之间的权衡。结果表明，LSTM_1DCNN 在预测大坝放水温度方面优于其他竞争模型。经济和生态目标之间的冲突往往很突出。所提出的 ε-MOACOR 具有解决此类冲突的潜力，并且在解决多目标基准测试和水库优化问题方面具有高效率。通过 MOSO 框架可以生成更现实和实用的典型干旱、正常和湿润年份的 Pareto 最优解。可以通过增加来水流量或大坝放水体积的时间分布变得更加不均匀来提高生态水温保障指数目标，该目标应在水库运行中考虑。
