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The proportion of hydropower within the energy-resource structure is gradually increasing. However, the construction of reservoirs inevitably leads to ecological and environmental issues, especially the release of low-temperature water in summer and its detrimental effects on fish spawning and reproduction. These issues can be alleviated by optimizing the reservoir operation scheme, but the large amount of time required for predicting the reservoir outflow temperature emerges as the primary constraint. In this study, we considered Pubugou (PBG) Reservoir and proposed an efficient and high-precision prediction method for reservoir outflow temperature based on theory-guided machine learning (TGML) algorithms. This method was applied to the multi-objective optimization of the reservoir operations, and an optimization operation plan was proposed to improve the outflow temperature. The research results indicate that the LightGBM machine learning model shows good predictive performance, with a maximum deviation of no more than 1 °C in predicting the outflow temperature. There is a significant competitive relationship between reservoir power generation and the outflow temperature. Compared with the current operational scheme, the maximum power operation plan results in a 4.4% increase in total power generation but causes the average outflow temperature during the fish spawning period to decrease by 0.1 °C. The total power generation of the improved outflow temperature operation scheme was 0.8% lower than that of the actual scheme, but the average outflow temperature during fish spawning increased by 0.2 °C. Properly increasing the discharge flow from the reservoir and lowering the water level can thus help alleviate the issue of outflow temperature. [Display omitted] • A research framework for reservoir optimization operation with complex nonlinear ecological objectives was established. • Ensemble machine learning models outperform neural networks in predictive performance for small sample datasets. • There is a competitive relationship between economic benefit and ecological benefit. • An operation plan to increase the outflow water temperature of the PBG reservoir was proposed. [ABSTRACT FROM AUTHOR]

• A multi-objective simulation-optimization (MOSO) framework optimizes reservoir operation. • Trade-offs among hydropower generation, ecological flow and ecological water temperature demands are achieved. • LSTM_1DCNN outperforms conventional LSTM in predicting dam discharge temperature. • ε-MOACOR provides a superior convergence behavior with better diverse solutions. • Large inflow discharge and uneven dam discharge volume improve ecological water temperature guarantee index. 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. [Display omitted] [ABSTRACT FROM AUTHOR]

• Hierarchical gaming narrows space of competition in multi-objective reservoir operation. • Non-unique optimal operations lead to cooperation potential between flood control and hydropower generation. • Higher cooperation degree reduces efficiency loss of reservoir operation decisions. Quantifying the relationship between flood control and hydropower generation in reservoir operation optimization is challenged by both institutional barriers and ill-posedness. Different stakeholders fit within hierarchical institutional structures and parameter equifinality may exist. Compared to the assumption of centralized decision-making and only one optimal solution used in previous studies, this paper investigated the effects of ill-posedness on the competitive-cooperative relationship between flood control and hydropower generation within a hierarchical gaming process. Based on the ill-posed bilevel programming model for the Three Gorges Reservoir pre-impoundment problem, this study revealed that the ranges of potential flood risk and hydropower generation (i.e., competition space between two objectives) could be narrowed, with the increased minimum of hydropower generation and increased minimal occupancy rate of flood control storage. Importantly, different from the "as one falls, another rises" relationship implied by Pareto front solutions, cooperation of the water conservation department with the flood control department, namely selecting a refill rule curve in favor of flood safety, could reduce the flood control risk without sacrificing power generation benefits. Furthermore, a higher cooperation degree would decrease the efficiency loss related to hydropower generation without changing potential flood risk. [ABSTRACT FROM AUTHOR]

[Display omitted] • Reservoir optimization operation considering temperature stratification is conducted. • NSGA-II and MOPSO are combined with hydro-temperature model CE-QUAL-W2. • Releasing water late and large inflow rate can weaken the thermal stratification. • Thermal stability can be reduced to 14627.01 J/m2 (−5.32%) in the Pareto frontiers. • Large flow event should be optimized to regulate thermal regime for large reservoirs. Temperature stratification increases the thermal stability (TS) of the reservoir area, decreasing the vertical water exchange and going against the water environment improvement. It is affected by the reservoir operation scheme and should be considered with multiple objectives. Hence, considering the early flood season of Xiluodu Reservoir as a case, this study built a multiobjective optimization model of temperature stratification by coupling the hydro-temperature model CE-QUAL-W2. The non-dominated sorting genetic algorithm II and multiobjective particle swarm algorithm were used to solve the multiobjective optimization model, and the Pareto frontiers were evolved by reducing the peak rate of outflow (PRO), increasing total hydropower generation (THG), and decreasing TS. This paper shows that (1) the maximum-THG individual tends to release water late to increase the water level and hydraulic head, which is opposite for the minimum-TS individual. A temporally uniform outflow favors a decrease in PRO. (2) In the Pareto frontiers, THG can be increased from 47.42 × 108 to 52.15 × 108 kw·h (+9.97%), and TS can be reduced from 15448.27 to 14627.01 J/m2 (−5.32%). (3) When the inflow rate increases, the temperature stratification is further weakened, and the minimum TS is reduced from 14627.01 to 13233.54 J/m2. For large cascade reservoirs with low flood risk in the early or late flood season, their THG and thermal regime can be improved under a moderate outflow and should be considered for the administrations. [ABSTRACT FROM AUTHOR]

Studies have shown that the performance of multi-objective evolutionary algorithms depends to a large extent on the shape of the Pareto fronts of the problem. Although, most existing algorithms have poor applicability in dealing with this problem, especially in the multi-objective optimization operation of reservoirs with unknown Pareto fronts. Therefore, this paper introduces an evolutionary algorithm with strong versatility and robustness named the Multi-Objective Evolutionary Algorithm with Reference Point Adaptation (AR-MOEA). In this paper, we take two water conservancy hubs (Huangjinxia and Sanhekou) of the Hanjiang to Wei River Water Diversion Project as example, and build a multi-objective operation model including water supply, ecology, and power generation. We use the AR-MOEA, the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) and the Indicator-Based Evolutionary Algorithm (IBEA) to search the optimal solutions, respectively. We analyze the performance of four algorithms and the operation rules in continuous dry years. The results indicate that (1) the AR-MOEA can overcome the difficulty of the shape and distribution of the unknown Pareto fronts in the multi-objective model. (2) AR-MOEA can improve the convergence and uniformity of the Pareto solution. (3) If we make full use of the regulation ability of the Sanhekou reservoir in the dry season, the water supply for coping with possible continuous dry years can be guaranteed. The study results contribute to the identification of the relationship among objectives, and is valued for water resources management of the Hanjiang to Wei River Water Diversion Project. [ABSTRACT FROM AUTHOR]