Optimal Reservoir Operation Research Articles

Multi-purpose reservoir operation concomitant with estimating hydropower potential using multifarious hydrological models

The research aims at determining the optimal release rule to increase the capacity of Rib reservoir. The reservoir inflow using HBV-light hydrological model embracing optimal reservoir operation through HEC-ResSim model were used to prepare an optimum operational plan. The potential of the river for hydropower generation prioritise the demand at a specified level regarding storage capacity (m3), level of reservoir (m), and the relation between inflow and outflow of the reservoir. From the model performance features, the coefficient of correlation (R2) and Nash Sutcliffe Efficiency (NSE) were determined to be, respectively, 0.77 and 0.73 for calibration and 0.72 and 0.70 for validation. The Sobol approach was used for detailed sensitivity analysis of DROP model parameters based on the performance of C2M on outflows and volumes. The results suggest that the threshold coefficient characterizing the demand-controlled release level is the most significant parameter. According to the simulation's findings, the reservoir's average regulated release is calculated to be 22.86 m3/s, and its average monthly hydropower output is 6.73 MW. Average annual hydropower energy was estimated as 58.955 GW h/year and mean annual inflow of reservoir volume of water to be 223.54 Mm3. This volume of water is adequate to accommodate total annual irrigation demand, environmental obligation, and other respective requirements in the downstream. The demand for hydropower and irrigation and supply from reservoir capacity can be counterbalanced from the simulated result without any hindrance.

Analyzing environmental flow supply in the semi-arid area through integrating drought analysis and optimal operation of reservoir

This study proposes a novel form of environmental reservoir operation through integrating environmental flow supply, drought analysis, and evolutionary optimization. This study demonstrates that simultaneous supply of downstream environmental flow of reservoir as well as water demand is challenging in the semi-arid area especially in dry years. In this study, water supply and environmental flow supply were 40% and 30% in the droughts, respectively. Moreover, mean errors of supplying water demand as well as environmental flow in dry years were 6 and 9 m3/s, respectively. Hence, these results highlight that ecological stresses of the downstream aquatic habitats as well as water supply loss are considerably escalated in dry years, which implies even using environmental optimal operation is not able to protect downstream aquatic habitats properly in the severe droughts. Moreover, available storage in reservoir will be remarkably reduced (averagely more than 30×106 m3 compared with optimal storage equal to 70×106 m3), which implies strategic storage of reservoir might be threatened. Among used evolutionary algorithms, particle swarm optimization (PSO) was selected as the best algorithm for solving the novel proposed objective function. The significance of this study is to propose a novel objective function to optimize reservoir operation in which environmental flow supply is directly addressed and integrated with drought analysis. This novel form of optimization system can overcome uncertainties of the conventional objective function due to considering environmental flow in the objective function as well as drought analysis in the context of reservoir operation especially applicable in semi-arid areas. The results indicate that using either other water resources for water supply or reducing water demand is the only solution for managing downstream ecological impacts of the river ecosystem. In other words, the results highlighted that replanning of water resources in the study area is necessary. Replacing the conventional optimization system for reservoir operation in the semi-arid area with proposed optimization system is recommendable to minimize the negotiations between stakeholders and environmental managers.

A multi-objective cooperation search algorithm for cascade reservoirs operation optimization considering power generation and ecological flows

With growing attention focused on energy generation and environmental conservation, the operation of cascade reservoirs that considers power generation benefits and ecological flow requirements plays an increasingly important role in water resource and power systems. However, the traditional optimization methods may fail to solve complex cascade reservoirs operation models with strong spatial-temporal coupling physical constraints. To effectively address this problem, this study proposes an efficient multi-objective cooperation search algorithm (MOCSA) that utilizes the modified team communication, reflective learning operator and internal competition operators to enhance global exploration and local exploitation. MOCSA is tested on a group of multi-objective benchmark functions and real-world constrained engineering problems. The results demonstrate that MOCSA can provide better results for most benchmark test functions in comparison with the competitive algorithms. Besides, MOCSA exhibits excellent search ability to find feasible solutions of constrained engineering problems. The proposed method is then applied to resolve a real-world reservoir system under different operation scenarios. Simulations indicate that MOCSA provides diversified decision options for the operation of cascade reservoir system and find a more diverse set of non-dominated solutions within the feasible solution space. Overall, this research presents MOCSA as an effective multi-objective optimization tool for complex cascade reservoir operation problems.

Optimal reservoir operation for transport timescales using an integrated methodology

Dam construction hinders the transport process of water constituents, resulting in various water quality issues in reservoir areas that impede the sustainable development of hydropower. Conventional reservoir operation optimizations to address these issues face challenges in mathematizing multiple water quality objectives and solving high-dimensional computational problems. Taking a comprehensive perspective, we propose a methodology that incorporates the concept of transport timescales into optimal reservoir operation. Firstly, a specific transport timescale is estimated through numerical tracer experiments using a 3D hydrodynamic model. Subsequently, a surrogate model is developed to approximate the hydrodynamic model for computationally efficient estimation. Finally, we employ a non-dominated ranking genetic algorithm, combined with the surrogate model, to search for a Pareto-optimal solution for multiple objectives. As a case study, we selected flushing time as the representation of transport timescales and applied it to Xiangxi Bay (XXB) in the Three Gorges Reservoir, which has experienced serious water quality problems since dam construction. Our results show that under the optimal operation scheme, the average flushing time for the entire XXB is 23.991 d, which represents a 10.9% reduction compared to the practical operation scheme. The reduction rate of flushing time along XXB shows a monotonically increasing trend towards the reservoir mainstream, with a maximum reduction of 90.9%. The proposed methodology provides a heuristic tool that links optimal reservoir operation and the transport process of holistic water constituents for comprehensive water quality management in reservoirs.

Multi-Dimensional Collaborative Operation Model and Evaluation of Cascade Reservoirs in the Middle Reaches of the Yellow River

Reservoir operation optimization is a technical measure for flood control and is beneficial owing to its reasonable and reliable control and application of existing water conservancy and hydropower hubs, while ensuring dam safety and flood control, as well as the normal operation of power supply and water supply. Considering the beneficial functions of reservoirs, namely flood control and ecological protection, this paper firstly established a two-objective optimal operation model for the reservoir group in the middle reaches of the Yellow River. We aim to maximize the average output of the cascade reservoir group and minimize the average change in ecological flow during the operation period under efficient sediment transport conditions, with the coordination degree of water and sediment as the constraints of reservoir discharge flows. The paper aims to construct an evaluation index system for reservoir operation schemes, apply a combined approach of objective and subjective evaluations, and introduce the gray target and cumulative prospect theories. By uniformly quantifying the established scheme evaluation index system, screening the reservoir operation schemes with the fuzzy evaluation method, and selecting the recommended scheme for each typical year, this paper provides a new scientific formulation of the operation schemes of reservoirs in the middle reaches of the Yellow River. The selected schemes are compared with actual data, demonstrating the effectiveness of joint reservoir operation and for multidimensional benefits in terms of power generation, ecology, and flood control.

Optimization of Cascade Reservoir Operation for Power Generation, Based on an Improved Lightning Search Algorithm

Cascade reservoir operation can ensure the optimal use of water and hydro-energy resources and improve the overall efficiency of hydropower stations. A large number of studies have used meta-heuristic algorithms to optimize reservoir operation, but there are still problems such as the inability to find a global optimal solution and slow convergence speed. Lightning search algorithm (LSA) is a new meta-heuristic algorithm, which has the advantages such as high convergence speed and few parameters to be adjusted. However, there is no study on the application of LSA in reservoir operation. In this paper, LSA is used to solve the problem of reservoir operation optimization to verify its feasibility. We also propose an improved LSA algorithm, the frog-leaping–particle swarm optimization–LSA (FPLSA), which was improved by using multiple strategies, and we address the shortcomings of LSA such as low solution accuracy and the tendency to fall into local optima. After preliminary verification of ten test functions, the effect is significantly enhanced. Using the lower Jinsha River–Three Gorges cascade reservoirs as an example, the calculation is carried out and compared with other algorithms. The results show that the FPLSA performed better than the other algorithms in all of the indices measured which means it has stronger optimization ability. Under the premise of satisfying the constraints of cascade reservoirs, an approximate optimal solution could be found to provide an effective output strategy for cascade reservoir scheduling.

The Optimization of Water Storage Timing in Upper Yangtze Reservoirs Affected by Water Transfer Projects

To alleviate regional disparities in water resource distribution and consequent scarcity, China has initiated and planned a series of inter-basin water transfer projects using the Yangtze River Basin as the source. These projects are expected to divert approximately 33.4 billion cubic meters of water annually from the Yangtze River Basin. The implementation of these water transfer projects will inevitably alter the hydrological conditions in the upper reaches of the Yangtze River, impacting the reservoir storage strategies of cascading hydroelectric stations under current end-of-flood-season operational plans. This study quantitatively assesses the impact of water transfer projects on end-of-flood-season reservoir storage in cascading systems using the reservoir fullness ratio as an indicator. Employing reservoir storage analysis models, optimization techniques, and flood risk assessment methods, we simulated reservoir storage processes to evaluate associated flood risks and derive an optimized timing strategy for cascading reservoir storage. The results indicate that advancing the reservoir filling schedule by five days for both the Baihetan and Three Gorges dams can offset the adverse impacts of water transfer projects on reservoir storage efficiency. This adjustment restores the reservoir fullness ratio to levels observed in scenarios without water transfers while still meeting flood control requirements. After optimizing the timing of reservoir filling, the electricity generation capacity for the Baihetan and Three Gorges dams increased by 1.357 and 3.183 billion kWh, respectively, under non-transfer scenarios. In water transfer scenarios, the electricity generation for the Baihetan and Three Gorges dams increased by 1.48 and 2.759 billion kWh, respectively. By optimizing reservoir filling schedules, we not only improved the reservoir fullness ratio but also enhanced the electricity generation efficiency of the cascading systems, offering valuable insights for future reservoir operation optimization.

Rethinking environmental flow management strategies in reservoir operations from an integrated water quality perspective

River damming causes appreciable nutrient retention in reservoirs, increasing the risk of eutrophication in reservoir water. Environmental flow (e-flow) management strategies have been extensively studied to direct reservoir release to meet water quantity demands of downstream ecosystems. However, such strategies also impact reservoir nutrient transport and transformation as well as stored and discharged water quality, which has rarely been considered in existing e-flow management. This study combines a reservoir operation optimization model and a coupled hydrodynamic-eutrophication-sediment model to evaluate e-flow management strategies from an integrated water quality perspective. Multiple nutrients, water, and sediment processes are accounted for as well as the quality of both stored and discharged water. Five common e-flow strategies that focus on various ecosystem protection targets were used to generate operation scenarios. Subsequently, the coupled hydrodynamic-eutrophication-sediment model was used to simulate nutrient cycling processes and water quality under each scenario. Modeling results show that e-flow management strategies cause complex effects on reservoir nitrogen, phosphorus, and algal dynamics. In general, reservoir water quality was the worst in the scenario with largest e-flow release, implying that tradeoffs exist between water quantity and quality of reservoir release. Lower e-flow release resulted in reservoir storage level increases and promoted sediment nutrient release, but the effect was offset by the increase in the nutrient dilution capacity of reservoir water. Additionally, the comparison between inflow and discharged water quality showed that the reservoir contributed to reducing river nitrogen loads under low e-flow releases while increasing river phosphorus loads in all scenarios. The findings support e-flow management decision-making to account for both water quantity and quality targets as well as to coordinate reservoir services and ecological protection.

Optimization of Dam Reservoir Operation Using Metaheuristic Algorithms under Meteorological Drought Conditions (Case Study: Aydoghmoush Dam)

Optimization of Dam Reservoir Operation Using Metaheuristic Algorithms under Meteorological Drought Conditions (Case Study: Aydoghmoush Dam)

Study on reservoir optimal operation based on coupled adaptive ε constraint and multi strategy improved Pelican algorithm

The optimal operation of reservoir groups is a strongly constrained, multi-stage, and high-dimensional optimization problem. In response to this issue, this article couples the standard Pelican optimization algorithm with adaptive ε constraint methods, and further improves the optimization performance of the algorithm by initializing the population with a good point set, reverse differential evolution, and optimal individual t-distribution perturbation strategy. Based on this, an improved Pelican algorithm coupled with adaptive ε constraint method is proposed (ε-IPOA). The performance of the algorithm was tested through 24 constraint testing functions to find the optimal ability and solve constraint optimization problems. The results showed that the algorithm has strong optimization ability and stable performance. In this paper, we select Sanmenxia and Xiaolangdi reservoirs as the research objects, establish the maximum peak-cutting model of terrace reservoirs, apply the ε-IPOA algorithm to solve the model, and compare it with the ε-POA (Pelican algorithm coupled with adaptive ε constraint method) and ε-DE (Differential Evolution Algorithm) algorithms, the results indicate that ε. The peak flow rate of the Huayuankou control point solved by the IPOA algorithm is 12,319 m3/s, which is much lower than the safe overflow flow rate of 22,000 m3/s at the Huayuankou control point, with a peak shaving rate of 44%, and other algorithms do not find effective solutions meeting the constraint conditions. This paper provides a new idea for solving the problem of flood control optimal operation of cascade reservoirs.

Decision Support Framework for Optimal Reservoir Operation to Mitigate Cyanobacterial Blooms in Rivers

Flow control flushing water from reservoirs has been imposed in South Korea for mitigating harmful cyanobacterial blooms (CyanoHABs) in rivers. This measure, however, can cause water shortage in reservoirs, as the measure adopting this flow control may require an additional amount of water which exceeds the water demand allocated to the reservoirs. In terms of sustainability, a trade-off between improving water quality and alleviating water shortage needs to be considered. This study aimed at establishing a practical framework for a decision support system for optimal joint operation of the upstream reservoirs (Andong and Imha) to reduce the frequency of CyanoHABs in the Nakdong River, South Korea. Methodologically, three models were introduced: (1) a machine learning model (accuracy 88%) based on the k-NN (k-Nearest Neighbor) algorithm to predict the occurrence of CyanoHABs at a selected downstream location (the Chilgok Weir located approximately 140 km downstream from the Andong Dam), (2) a multiobjective optimization model employing NSGA-II (Nondominated Sorting Genetic Algorithm II) to determine both the quantity and quality of water released from the reservoirs, and (3) a river water quality model (R2 0.79) using HEC-RAS to simulate the water quality parameter at Chilgok Weir according to given upstream boundary conditions. The applicability of the framework was demonstrated by simulation results using observational data from 2015 to 2019. The simulation results based on the framework confirmed that the frequency of CyanoHABs would be decreased compared with the number of days when CyanoHABs were observed at Chilgok Weir. This framework, with a combination of several models, is a novelty in terms of efficiency, and it can be a part of a solution to the problem of CyanoHABs without using an additional amount of water from a reservoir.

A two-dimensional double layer-averaged model of hyperconcentrated turbidity currents with non-Newtonian rheology

Hyperconcentrated turbidity currents typically display non-Newtonian characteristics that influence sediment transport and morphological evolution in alluvial rivers. However, hydro-sediment-morphological processes involving hyperconcentrated turbidity currents are poorly understood, with little known about the effect of the non-Newtonian rheology. The current paper extends a recent two-dimensional double layer-averaged model to incorporate non-Newtonian constitutive relations. The extended model is benchmarked against experimental and numerical data for cases including subaerial mud flow, subaqueous debris flow, and reservoir turbidity currents. The computational results agree well with observations for the subaerial mud flow and independent numerical simulations of subaqueous debris flow. Differences between the non-Newtonian and Newtonian model results become more pronounced in terms of propagation distance and sediment transport rate as sediment concentration increases. The model is then applied to turbidity currents in the Guxian Reservoir planned for middle Yellow River, China, which connects to a tributary featuring hyperconcentrated sediment-laden flow. The non-Newtonian model predicts slower propagation of turbidity currents and more significant bed aggradation at the confluence between the tributary Wuding River and the Yellow River in the reservoir than its Newtonian counterpart. This difference in model performance could be of considerable importance when optimizing reservoir operation schemes.

A LSTM-based approximate dynamic programming method for hydropower reservoir operation optimization

Dynamic programming (DP) is a classical method developed to address the multi-stage hydropower reservoir operation problem, but still suffers from the serious dimensionality problem where the computational burden increases exponentially with the number of state variables. To improve the DP performance, this paper proposes a LSTM-based approximate dynamic programming (ADP) method for complex hydropower reservoir operation optimization. In ADP, the long short-term memory (LSTM) is treated as the response surface model to reduce redundant computations of power outputs in DP’s recursive equation, making obvious improvements in the execution efficiency. To fully assess its feasibility, the ADP method is used to find the scheduling schemes of a real-world reservoir system in China. Simulation results show that compared with the standard DP method, ADP effectively reduces the execution time while guarantee the solution quality in different cases. In the 1000-state and wet-year scenario, the ADP method achieves approximately 86.7% and 85.8% reductions in DP’s computation time for Longyangxia and Laxiwa reservoir with the goal of maximizing power generation. Thus, the LSTM-based response surface model is an effective tool to improve the DP performance in the hydropower reservoir operation problem.

Reservoir operation management using a new hybrid algorithm of Invasive Weed Optimization and Cuckoo Search Algorithm

Abstract Water scarcity throughout the world has led to major difficulties and complexities in managing water demands. These challenges gravitate towards the development of efficient methods for optimal reservoir operation. The present study aims to introduce a hybrid approach which integrates Invasive Weed Optimization (IWO) and Cuckoo Search Algorithm (CSA), with an objective to minimize the deficits for Indira Sagar Reservoir (ISR), India. To prevail over the limitations of the Weed Optimization Algorithm (WOA) and CSA, a critical comparison has been made in the study. The hybrid approach has improved the performance by 5 and 9% as compared to WOA and CSA, respectively. For the reservoir system, the Cv for 10 random runs was computed to be 0.0303 using the hybrid model, whereas for WOA and CSA, Cv was 0.22034 and 0.30698, respectively. Based on the performance measuring indices, results revealed that the hybrid model is more reliable and sustainable with the minimum error between release and demand. In addition, results reveal that the deficits have been reduced by 62% on average for the considered study period using the hybrid approach. Therefore, the results show that the proposed hybrid model has considerable potential to be used as an optimizer for complex reservoir operation problems.

Application of Multi-Criteria Decision-Making of CODAS and SWARA in Reservoir Optimal Operation Using Marine Predator Algorithm Based on Game Theory

Application of Multi-Criteria Decision-Making of CODAS and SWARA in Reservoir Optimal Operation Using Marine Predator Algorithm Based on Game Theory

Multi-objective optimal reservoir operation considering algal bloom control in reservoirs

Reservoir operation strategies (ROSs) are considered an efficient and low-cost method to control algal blooms. However, reservoir operations must consider regular objectives, including flood prevention and power generation. To address this multi-objective optimization problem, we coupled the non-dominated sorting genetic algorithm-II (NSGA-II) model and the General Lake Model-Aquatic EcoDynamics library (GLM-AED) model to optimize reservoir operations. Taking the Zipingpu Reservoir as a case study, we found the peak of outflow discharge (POD) could be reduced from 1059.5 to 861.4 m3 s−1 (19%), the total power generation (TPG) could be increased from 6.6 × 108 to 7.1 × 108 kW h (8%), and the peak of chlorophyll a concentration (PCC) could be decreased from 42.7 to 27.2 μg L−1 (36%) compared with the original reservoir operation in the early flood period. The obtained Pareto frontier revealed the tradeoffs between algal bloom control, flood prevention, and power generation. Reservoir operation schemes that achieved low PCC were typically associated with large POD and moderate TPG. In particular, under fixed start and end water levels, maintaining a higher average water level during May and June could result in larger outflows, effectively inhibiting algal accumulation and bloom development, thereby leading to a lower PCC. Slight variations in average water age were found among the minimum PCC scheme, maximum TPG scheme, and minimum POD scheme, indicating that water exchange varied little and has not been responsible for the differences in PCC. Collectively, enhancing outflow was determined to play a vital role in reducing PCC, particularly when operating under constrained rules. These findings contribute new insights into optimal reservoir operations considering algal bloom control and emphasize the importance of enhancing outflow as a governing mechanism. Furthermore, the coupled model offers a transferable technical framework for reservoir managers to mitigate eutrophication through ROSs.

Optimizing environmental flow based on a new optimization model in balancing objectives among river ecology, water supply and power generation in a high-latitude river

Environmental flow plays an important role in maintaining the health of river ecosystems and aquatic habitats. Although ecological regulation of environmental flow has attracted the attention of scientists, managing the world's reservoir-regulated rivers to better meet the needs of human being and ecosystems is a complex social challenge. To address the above issues, we constructed a model for optimizing reservoir operation based on a balance in achieving multi objectives among environmental flow, water supply and power generation (EWP). The model was solved using an intelligent multi-objective optimization algorithm (ARNSGA-III). The developed model was demonstrated in a large reservoir, Laolongkou Reservoir in the Tumen River. The results showed that the reservoir altered environmental flows mainly in terms of flow magnitude, peak, times, duration and frequency, which result in a sharp decrease in spawning fish, and degradation and replacement of vegetation along the channels. In addition, the mutual feedback relationship between the objectives of environmental flows, water supply and power generation is not static, but varies over time and space. The constructed model based on Indicators of Hydrologic Alteration (IHAs) can effectively guarantee the environmental flow at daily scale. In detail, the river ecological benefit increased by 64% in wet year, 68% in normal year, 68% in dry year after optimizing regulation of reservoir, respectively. This study will provide a scientific reference for the optimizing of the management in other rivers affected by dams.

Optimization of reservoir operation at Eyvashan dam using the water cycle algorithm with the approach of water resource management in climate changes conditions

Optimization of reservoir operation at Eyvashan dam using the water cycle algorithm with the approach of water resource management in climate changes conditions

Investigation of Climate change effect on Optimization of Dam reservoir operation using Dolphin echolocation and Gravitational search Algorithms

Investigation of Climate change effect on Optimization of Dam reservoir operation using Dolphin echolocation and Gravitational search Algorithms

Use of climate scenarios for optimizing reservoir operation

Use of climate scenarios for optimizing reservoir operation