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This paper introduces an optimization method (SCE-SR) that combines shuffled complex evolution (SCE) and stochastic ranking (SR) to solve constrained reservoir scheduling problems, ranking individuals with both objectives and constrains considered. A specialized strategy is used in the evolution process to ensure that the optimal results are feasible individuals. This method is suitable for handling multiple conflicting constraints, and is easy to implement, requiring little parameter tuning. The search properties of the method are ensured through the combination of deterministic and probabilistic approaches. The proposed SCE-SR was tested against hydropower scheduling problems of a single reservoir and a multi-reservoir system, and its performance is compared with that of two classical methods (the dynamic programming and genetic algorithm). The results show that the SCE-SR method is an effective and efficient method for optimizing hydropower generation and locating feasible regions quickly, with sufficient global convergence properties and robustness. The operation schedules obtained satisfy the basic scheduling requirements of reservoirs. Keywords: Reservoir scheduling, Optimization method, Constraint handling, Shuffled complex evolution, Stochastic ranking

Reservoir scheduling based on evolutionary algorithms needs to handle potentially stringent physical and operational constraints. Both generic and reservoir scheduling problem-specific constraint-handling techniques (CHTs) have their own merits and limitations. No CHT currently available can yield better solutions than the others consistently. To ensure good reservoir operation schedules, we develop an ensemble of CHTs (ECHT) that can utilize the advantages of different individual CHTs. In the ensemble, each CHT has its own population. In every generation, the different offspring populations are mixed together and evaluated. Each CHT then assigns fitness to all individuals and selects some of them to form its new parent population. The ECHT has been tested against long-term hydropower scheduling of two large-scale reservoir systems in China. Results show that the ECHT outperforms the state-of-the-art CHTs, and its probability of returning feasible solutions is much higher. The reservoir levels optimized with the ECHT are well suited for hydropower generation, which also reduce the chance of reservoir spilling.

Reservoir operations significantly alter the hydrological regime of the downstream river and river-connected lake, which has far-reaching impacts on the lake ecosystem. To facilitate the management of lakes connected to regulated rivers, the following information must be provided: (1) the response of lake water levels to reservoir operation schedules in the near future and (2) the importance of different rivers in terms of affecting the water levels in different lake regions of interest. We develop an integrated modeling and analytical methodology for the water level management of such lakes. The data-driven method is used to model the lake level as it has the potential of producing quick and accurate predictions. A new genetic algorithm-based synchronized search is proposed to optimize input variable time lags and data-driven model parameters simultaneously. The methodology also involves the orthogonal design and range analysis for extracting the influence of an individual river from that of all the rivers. The integrated methodology is applied to the second largest freshwater lake in China, the Dongting Lake. The results show that: (1) the antecedent lake levels are of crucial importance for the current lake level prediction; (2) the selected river discharge time lags reflect the spatial heterogeneity of the rivers’ impacts on lake level changes; (3) the predicted lake levels are in very good agreement with the observed data (RMSE ≤ 0.091 m; R2 ≥ 0.9986). This study demonstrates the practical potential of the integrated methodology, which can provide both the lake level responses to future dam releases and the relative contributions of different rivers to lake level changes.

In developing regions, accurate rain gauge measurements and satellite precipitation estimates that effectively capture rainfall spatial variability are promising sources of rainfall information. In this study, the latest Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) research product, 3B42V7, was validated against ground measurements in the region surrounding the Dongting Lake in China. In the subsequent model-based evaluation and comparison, the two precipitation datasets were separately included as the inputs for data-driven predictive models of the daily Dongting Lake level. The results show that (i) the daily 3B42V7 agrees well with the gauge measurements (correlation coefficient: 0.64–0.73); (ii) 3B42V7 underestimates the frequency of low-intensity (0–30 mm/day) rainfall and the contribution of low-intensity rainfall to the total rainfall volume, but slightly overestimates those of more intense rainfall; (iii) the lake level models driven by rainfall data from the two sources have similar performance, highlighting the potential of using 3B42V7 in data-driven modeling and prediction of hydrological variables in data-scarce regions; and (iv) the inclusion of rainfall as the model input helps achieve a balance between underestimation and overestimation of the lake levels in terms of both magnitude and quantity.

Dongting Lake, a large river-connected lake in the Yangtze River watershed, plays important roles in flood control, drought mitigation, and biodiversity conservation. Its ecosystem has recently been severely affected by upstream water resource development such as reservoir operations. In this study, an optimization model is developed for the operation of a multi-reservoir system, including the Three Gorges Reservoir (TGR) on the upper Yangtze River and 8 major reservoirs on the tributaries to Dongting Lake. The optimal target in pursuit of the ecological objective is to maximize the environmental water demand (EWD) satisfaction of the lake. A support vector regression-based model is used to predict the response of the lake level to reservoir operations. The optimization is carried out under different scenarios for both normal and dry conditions, and the results show that: (i) the existing operation policy could result in significant hydrologic alteration; (ii) in the normal condition, the proposed optimal joint operation policy could increase the general EWD satisfaction rate of Dongting Lake from 85.4% to 95.7%; and (iii) the improvement of EWD satisfaction in the normal condition is mainly affected by the TGR, while in the dry condition, the southern and western lake regions are more sensitive to the tributaries' reservoirs.

Evolutionary optimization of reservoir operation is subject to complex physical and operational constraints. Constraint-handling techniques (CHTs) in this field are predominantly problem-sp...