Abstract：
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.
摘要：
水坝（水库）引起的水流和水温变化可能威胁下游鱼类栖息地和本地水生生态系统。减轻大坝-水库对环境的负面影响，平衡水库运行的多种目的已引起广泛关注。尽管先前的研究已将生态流量要求纳入水库运营策略中，综合分析水电效益之间的取舍，生态流,缺乏生态水温需求。因此，本研究建立了多目标生态调度模型，考虑到总发电量，生态流量保障指数，与生态水温保障指标同步。该模型基于应用于三峡水库的集成多目标模拟优化（MOSO）框架。为此,首先,利用混合长短期记忆和一维卷积神经网络（LSTM_1DCNN）模型来模拟大坝泄流温度。然后，提出了一种改进的ε多目标蚁群优化连续域算法（ε-MOACOR）来研究竞争目标之间的权衡。结果表明，LSTM_1DCNN在预测大坝泄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。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以及水库优化问题方面具有很高的效率。更现实和务实的帕累托最优解的典型干燥，MOSO框架可以产生正常和潮湿的年份。生态水温保障指标目标,这应该在水库运行中考虑，可以随着入流流量的增加或大坝流量的时间分布变得更加不均匀而改善。
