Operation Of Cascade Reservoirs Research Articles

Spatiotemporal patterns and co-occurrence patterns of dissimilatory nitrate reduction to ammonium community in sediments of the Lancang River cascade reservoirs.

Dissimilatory nitrate reduction to ammonium (DNRA) is an important nitrate reduction pathway in freshwater sediments. Many studies have focused on the DNRA process in various natural habitats. However, the joint operation of cascade reservoirs will affect the physical and chemical properties of sediments, which may change the DNRA process and bacterial community pattern in the surface sediments of cascade reservoirs. Our study was the first to investigate the spatiotemporal distribution patterns of potential DNRA rate, nrfA gene abundances, and DNRA bacterial community diversity in surface sediments of the Lancang River cascade reservoirs. The results of slurry incubation experiments combined with the 15N isotope tracer experiment ascertained that the potential rates of DNRA were 0.01-0.15 nmol-N cm-3 h-1, and qPCR results indicated that the abundance range of nrfA was 1.08 × 105-2.51 × 106 copies g-1 dry weight. High throughput sequencing of the nrfA gene revealed that the relative abundance of Anaeromyxobacter (4.52% on average), Polyangium (4.09%), Archangium (1.86%), Geobacter (1.34%), and Lacunisphaera (1.32%) were high. Pearson and RDA correlation analysis exhibited that nrfA gene abundance was positively correlated with altitude, pH, OC, and sand concentration. Anaeromyxobacter was positively correlated with reservoir age and DNRA potential rate. The deterministic environmental selection process plays a crucial role in the formation of the DNRA bacterial community. Network analysis displayed that the dominant DNRA genus was the key population of the DNRA microbial community in the sediments of Lancang River cascade reservoirs. This study reveals that the variation of DNRA bacterial activity and community structure is largely driven by the construction of cascade reservoirs, and provides a new idea for further understanding the characteristics of the DNRA community in the cascade reservoir ecosystem.

Modelling the effect of cascade reservoir regulation on ice-jam flooding

In regulated rivers, dams and their reservoirs are the main means to change the flow conditions, and strategic reservoir operations can be an important measure for mitigating ice-jam flooding (IJF). Previous studies have shown the effect of single reservoir operation on IJF probabilities, but little is known about the effect of cascade reservoir operation on IJF occurrences and severity. This study presents a probabilistic modelling framework that couples machine learning (LSTM, Long Short-Term Memory) and river ice (RIVICE) models to assess the effects of regulated and naturalized flows on IJF backwater levels. The trained LTSM shows good performance in both validation (RMSE = 398.88, R = 0.86 and NSE = 0.67) and testing (RMSE = 366.57, R = 0.84 and NSE = 0.71) periods and the calibrated RIVICE can well simulate backwater levels caused by three IJFs from 2008 to 2010 with total volumes of incoming ice for 0.4 million m3, 0.55 million m3 and 0.32 million m3. The completed framework was used to assess IJF hazard along the Sanhuhekou bend, a reach of the upper Yellow River, whose hydraulic regime is impacted by eight upstream reservoirs. The effect of naturalized and regulated flows on IJF are compared from two aspects. Firstly, comparing the median of naturalized and regulated IJF backwater level ensembles, the results show that regulation increases the IJF backwater levels. Secondly, comparing the extreme values of naturalized and regulated IJF backwater level ensembles, results show that regulation can increase the hazard of IJF inundation when annual exceedance probability (AEP) is less severe than 1:100 and regulation can decrease the hazard of IJF when AEP is more severe than 1:125.

Optimal allocation of flood prevention storage and dynamic operation of water levels to increase cascade reservoir hydropower generation

The existing trade-off between flood control and water conservation measured by flood limited water level (FLWL) during flood season hinders multi-reservoir system from realizing comprehensive benefits. To overcome this barrier, we reasonably allocated the complementary flood prevention storage based on the aggregation-decomposition technique, theoretically deduced the mathematical formula of power generation variation simplified from the cascade reservoir operation model, and dynamically operated reservoir water levels considering the inflow forecasting information. The Wudongde (WDD) ∼ Baihetan (BHT) ∼ Xiluodu (XLD) ∼ Xiangjiaba (XJB) cascade reservoirs in the downstream Jinsha River in China is selected as the case study. Results demonstrate that: (1) The derived power generation function monotonically increases only with the growth of the pre-discharge water volume. (2) The reduced proportion of the flood prevention storage of the WDD (or XJB) is favorable to boost the hydroelectric benefits of the WDD ∼ BHT (or XLD ∼ XJB) cascade reservoirs. Optimal allocation of flood prevention storage can increase 1.933 billion kW∙h power generation annually (+2.15 %) for the WDD ∼ BHT ∼ XLD ∼ XJB cascade reservoirs. (3) Further cooperated with dynamic operation of water levels, the WDD ∼ BHT ∼ XLD ∼ XJB cascade reservoirs can totally generate 5.925 billion kW∙h more hydropower annually (+6.60 %) using effective 5-day lead-time inflow forecasting information, while maintaining the design flood prevention standards.

Stochastic multi-attribute group decision-making for long-term comprehensive operation of cascade reservoirs under multiple uncertainties

Study regionThe Three Gorges cascade (TGC) reservoirs, China. Study focusSince uncertainties inherent in inflow forecasting, elevation-storage, discharge capacity, flood routing, and the preferences of decision makers (DMs) persist in the attribute measurements (AMs) and weights (AWs), problems where multiple DMs select the best compromise scheme for the long-term comprehensive operation of cascade reservoirs considering numerous attributes become a stochastic multi-attribute group decision-making (MAGDM) process. To represent uncertain AMs and AWs and consider the bounded rationality of DMs, we propose a stochastic MAGDM with integrated stochastic multicriteria acceptability analysis (SMAA) and gray cumulative prospect theory (GCPT), namely, SMAA-GCPT. After quantifying the uncertainties, SMAA-GCPT is used to model uncertain AMs and imperfect AWs with calculable probability distributions and map the sampled AMs and AWs to utility values to reflect the overall performance assuming bounded rationality. New hydrological insights for the regionExperiments on the TGC indicate that SMAA-GCPT can effectively differentiate alternative schemes and select the best compromise scheme to harmonize the conflicting benefits and risks of the TGC, with higher reliability than SMAA-2 and SMAA-GRA. The influence of uncertain AMs on the reliability of SMAA-GCPT is more obvious than that of uncertain AWs. The decision-making error risk is primarily sensitive to the inflow forecasting uncertainty, followed by the flood routing uncertainty, and is less affected by the uncertainties in elevation-storage and discharge capacity curves.

Synoptic Analysis of Flood-Causing Rainfall and Flood Characteristics in the Source Area of the Yellow River

The source area of the Yellow River (SAYR) in China is an important water yield and water-conservation area in the Yellow River. Understanding the variability in rainfall and flood over the SAYR region and the related mechanism of flood-causing rainfall is of great importance for the utilization of flood water resources through the optimal operation of cascade reservoirs over the upper Yellow River such as Longyangxia and Liujiaxia, and even for the prevention of flood and drought disasters for the entire Yellow River. Based on the flow data of Tangnaihai hydrological station, the rainfall data of the SAYR region and NCEP-NCAR reanalysis data from 1961 to 2020, three meteorological conceptual models of flood-causing rainfall—namely westerly trough type, low vortex shear type, and subtropical high southwest flow type—are established by using the weather-type method. The mechanism of flood-causing rainfall and the corresponding flood characteristics of each weather type were investigated. The results show that during the process of flood-causing rainfall, in the westerly trough type, the mid- and high-latitude circulation is flat and fluctuating. In the low vortex shear type, the high pressures over the Ural Mountains and the Okhotsk Sea are stronger compared to other types in the same period, and a low vortex shear line is formed in the west of the SAYR region at the low level. The rain is formed during the eastward movement of the shear line. In the subtropical high southwest flow type, the low trough of Lake Balkhash and the subtropical high are stronger compared to other types in the same period. Flood-causing rainfall generally occurs in areas with low-level convergence, high-level negative vorticity, low-level positive vorticity, convergence of water vapor flux, a certain amount of atmospheric precipitable water, and low-level cold advection. In terms of flood peak increment and the maximum accumulated flood volume, the westerly trough type has a long duration and small flood volume, and the low vortex shear type and the subtropical high southwest flow type have a short duration and large flood volume.

Study on the constraint handling method for high-dimensional optimization of cascade reservoirs

Multicomplex constraints often need to be considered in the optimal operation of cascade reservoirs with high-dimensional decision variables, making it difficult for traditional optimization methods and modern intelligent algorithms to solve such problems. Therefore, this study proposes a constraint handling method combining a penalty function nested DPSA-POA and an intelligent algorithm, to determine the optimal flood control operation of cascade reservoirs in the middle reaches of the Ganjiang River, a problem with decision variables of up to 2196 dimensions. The results indicate that the constraint handling method proposed in this paper can solve high-dimensional optimization problems in three modes: continuous nesting (Mode 1), optimization with intelligence after obtaining a feasible solution (Mode 2), and optimization with the DPSA-POA after obtaining a feasible solution (Mode 3). Of the three modes, Mode 2 has the highest accuracy, but its calculation time is approximately 10 h. Although the accuracy of Mode 3 is slightly worse, can only achieve 98%–99% of Mode 2, its calculation time is only approximately 1–3 h. The comprehensive performance of Mode 1 is poor, the convergence accuracy can only reach 97% of Mode 2, which corresponds to a calculation time of approximately 4–6 h. The existing superiority of feasibility (SF), stochastic ranking (SR), penalty function (PF), ε-constraint (EC) and adaptive ε-constraint (Adaptive EC) methods cannot converge to a feasible solution stably, and the accuracy of the results of these methods under the condition of obtaining a feasible solution is significantly lower than that of Mode 2 and Mode 3, reaching only 95%.

A novel method for measuring interaction among multiple objectives in reservoir operation using niche theory

Accurate capture and presentation of the interactive feedback relationships among various objectives in multi-objective reservoir operation is essential for maximizing operational benefits. In this study, the niche theory of ecology was innovatively applied to the field of reservoir operation, and a novel state–relationship (S–R) measurement analysis method was developed for multi-objective reservoir operation. This method enables the study of interaction among multiple objectives. This method was used to investigate the relationship among the objectives of power generation, water supply, and ecological protection for cascade reservoir operation in the Wujiang River Basin in China. The results indicated that the ecological protection objective was the most competitive in acquiring and capturing resources like flow and water level, while the water supply objective was the weakest. Power generation competed most strongly with ecological protection and relatively weakly with water supply. These findings facilitate decision-making throughout the reservoir operation process in the region. The S–R method based on the niche theory is convenient, efficient, and intuitive, allowing for the quantification of feedback relationships among objectives without requiring the solution of the Pareto frontier of a multi-objective problem in advance. This method provides a novel and feasible idea for studying multi-objective interactions.

Extracting operation rule of cascade reservoirs using a novel framework considering hydrometeorological spatiotemporal information based on artificial intelligence models

The scientific operation of cascade reservoirs is of great significance for completely utilizing water resources and maximizing their comprehensive benefits. The extraction of reservoir operation rules from massive historical operational data still remains a challenge. Therefore, a novel framework for extracting the operation rules of cascade reservoirs was proposed in this study based on artificial intelligence models by considering hydrometeorological spatiotemporal information. First, the observed precipitation was obtained by merging multi-source precipitation products through a convolutional long-short-term memory (ConvLSTM) network. Second, a novel extraction model by considering hydrometeorological spatiotemporal information was established, comprising a process of capturing the spatiotemporal information of interval precipitation using ConvLSTM and a feature aggregation process of aggregating precipitation and reservoir historical operation information using an LSTM network. Finally, three different extraction strategies (recursive, direct, and multi-input multi-output (MIMO) strategies) for cascade reservoir operation rules were developed and compared, and the influence of future multi-step forecast information was analyzed. Considering the example of Yalong River, the results are as follows: (1) the proposed extraction model has excellent fitting performance with a Nash-Sutcliffe model efficiency coefficient (NSE) of more than 0.95 and mean relative error (MRE) of less than 10 %, especially in reservoirs with large regulation capacity; (2) according to the regulation capacity of the reservoirs and reliability of forecast precipitation, appropriately adding multi-step forecast information can improve the performance of the extraction models; (3) each reservoir's NSE when using the three strategies is above 0.9, indicating that they can all be used to extract the operation rules of the cascade reservoir. Among them, the direct strategy is optimal.

Sediment deposition within cascade reservoirs: a case study of Baihetan Reservoir in the lower Jinshajiang River, China

Sediment deposition in cascade reservoirs is not only related to the utilization efficiency of the reservoir itself but also to the boundary conditions for the operation of other reservoirs in the same group. The Baihetan Reservoir is the largest hydropower project with the highest unit capacity in the world, and it is necessary to consider sediment deposition within it, as this affects the comprehensive operation of cascade reservoirs in the lower Jinshajiang River. In this study, the input water, sediment, and deposition characteristics were analyzed based on both field hydrological and topographic data of the Baihetan Reservoir during its initial impoundment period. The results showed that water entering Baihetan Reservoir was mainly derived from the upper main stream, and approximately 41% was concentrated in the third quarter. Ten times the amount of sediment derived from the main stream was received from tributaries and uncontrolled areas of the reservoir, and these are the main sediment input sources. The fluctuating backwater area influenced by the upstream Wudongde Reservoir was slightly eroded, and siltation mainly occurred in the dead storage capacity (below 765 m) of the main stream and tributary estuaries in the perennial backwater area; approximately 15.8 times that in the regulating storage capacity (between 765 and 785 m). The differences between the results of this study and those from the reservoir demonstration stage indicate that was a lack of understanding about how climate change, human activities, and uncontrolled areas would affect siltation patterns. In future projects, research focusing on climate trend analyses and the comprehensive consideration of human activities should be combined with extensive sediment production monitoring and model parameter calibration.

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.

Sedimentation Characteristics of the Fluctuating Backwater Area at the Tail of Cascade Reservoirs: A Case Study of the Three Gorges Reservoir

The construction of cascade reservoirs is associated with considerable uncertainty in sedimentation in the fluctuating backwater area of the terminal reservoir and poses challenges to water safety. The sedimentation characteristics under the influence of multiple factors in the main urban area of the Chongqing river section were analyzed as a case study for the operation of cascade reservoirs in the Jinsha River via the utilization of a large dataset spanning back to the normal storage of the Three Gorges Reservoir. The results of this study indicate that, owing to factors such as upstream water, sediment inflow, reservoir operation, and river sand mining, this river section experienced deposition on the sand bars and erosion in the main channel. The rate of sedimentation increased with sediment inflow, peak flow rate, and duration, while the location of sedimentation shifted as the concentration ratio changed. These results may provide technical support not only for the operation of the Three Gorges Reservoir, but also for the governance of the fluctuating backwater areas of other cascade reservoirs.

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.

Impact assessment of cascade freshwater reservoir using the ecological security assessment (ESA) model across a four-year timescale

The existence of cascade reservoirs in complex ecosystems not only assists humans to regulate and use water resources efficiently, but the presence of hydropower also contributes to reducing carbon emissions. In recent decades, the ecology of the water sources comprising cascade reservoirs has been subject to a wide range of threats. Assessing these threats is essential for sustainable operation and management strategies of cascade reservoirs. This paper aimed to propose an ecological security assessment framework using an improved Driver-Pressure-State-Impact-Response model to assess a typical riverine cascade reservoir located in the subtropical region of Zhejiang, China. Combining quantitative and qualitative methods to describe the indicators involved in social, economic, ecological, and management. Simultaneously, the degree of deviation from the standard state of ecological security in the study area is assessed with the help of the ecological security index. The analysis shows that the ecological security index of the study area for the four years increased from 81.67 in 2019 to 90.14, where the lowest value has been the socio-economic impact index, indicating that the study area is in a state of ecological security with a score higher than 90.00, but the impact of socio-economic activities on the study area is more pronounced in comparison with other aspects. The model shows that control management with a score of 92.70 in 2022 plays a vital connecting role. Good control management increases socio-economic growth by 16.82%, improves ecological health by 10.23%, and enhances ecosystem services by 5.25% compared to 2019.

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.

Cascade reservoirs operation rules derivation using integrated decision-making method and combinatorial evolution network

Implicit reservoir operation rules map known reservoir conditions to discharge flow or water level decisions to guide reservoir operation. Existing data-driven operation rule extraction models are influenced by both features and hyperparameters, and lack subjective-objective coordination, hindering the formulation of reasonable water energy utilization strategies. To improve the comprehensive benefits of cascade reservoirs, this study proposes a novel multi-objective cascade reservoir operation rule derivation framework. First, a multi-objective optimal operation model for cascade reservoirs (MOOMCR) considering power generation, environment, and navigation is constructed and solved using a Speed-constrained Multi-objective Particle Swarm Optimization algorithm (SMPSO). The relationships among the requirements were explained using a four-dimensional Pareto front visualization analysis. Then, an Integrated Decision-making Method based on Multilevel Subspace Domination (IDMSD) is proposed to circumvent the subjective defects of single decision-making models and reliably select the optimal scheme with the most significant comprehensive benefits from all typical years. Finally, a feature-hyperparameter combination optimization technique based on the differential evolutionary algorithm (FHCC-DE/best/1) is proposed, which exhibits its strength in the identification of key influencing factors on the optimal discharge flow from the IDMSD. The method was embedded in a deep learning model to construct a Combinatorial Evolution Network (CEN) to extract the implicit reservoir operation rules from the optimal IDMSD schemes for the MOOMCR. The results show that the RMSE of the CEN simulating the optimal discharge flow is improved by 5.98%, 28.03%, and 27.89% for the XLD reservoir and by 11.01%, 1.66%, and 60.98% for the XJB reservoir, respectively, over the best results of all benchmark models. Meanwhile, the operation simulation results confirmed that the CEN outperforms conventional reservoir scheduling, with advanced multi-objective comprehensive effects. This suggests that multimethod integrated decision-making and deep learning evolution optimization can help improve the accuracy, generalization, and reliability of implicit reservoir operation rules.

Reassessing the cost of hydropower for multi-function hydropower bases: A case study from Sichuan

Hydropower is an essential resource for the future clean energy transition, in which an accurate assessment of the hydropower cost is vital for promoting its share in the energy market. The purpose of this paper is to comprehensively reassess the cost of hydropower from a new perspective, in which the total static investment cost of a hydropower base is allocated among multiple functions. An allocation coefficient is proposed based on indicators of economic value and physical volume of multi-function hydropower bases. The economic value of each function is evaluated using approaches including the shadow project, market valuation, and emergy evaluation methods. Meanwhile, the physical volume is obtained through a multi-objective optimization of cascade reservoir operation considering an ecological constraint. Four main functions consisting of hydropower generation, flood control, agricultural irrigation, and residential water supply of hydropower bases in Sichuan are investigated. The results show that the allocation coefficients under both economic and physical indicators vary significantly among hydropower bases, and change slightly over the years. The average comprehensive allocation coefficient of hydropower is 57.26% (range from 29.83% to 77.41%), and the hydropower cost can be reduced by 59% from the current level. The reassessed hydropower cost can lay a solid foundation for market pricing. Attention should be paid to the multi-functionality characteristics of hydropower bases to achieve fair sharing of cost and benefit.

Multi-dimensional interest game between reservoir and city stakeholders in the Yellow River Basin: a case study of the lower reaches

Reservoirs in sediment-laden rivers can bring multiple benefits, and the calculation and redistribution of these considerable benefits are the premises to ensure the efficient operation of reservoirs. Firstly, the benefits of social economy, ecological environment, flood discharge, and sediment transport under the joint operation of reservoirs are uniformly measured based on the emergy theory. Secondly, the stakeholders are divided into reservoir and city groups. A two-tier gains allocation model is established based on the Nash bargaining model and multi-attribute decision making theory. Finally, taking the lower reaches of the Yellow River, Sanmenxia Reservoir, and Xiaolangdi Reservoir as cases, the multi-dimensional benefits of reservoirs under the two operation modes in the face of typical floods are calculated, and the gains are distributed among stakeholders. The results show that: (1) Although the overall benefit of the system is optimal under one scheduling mode, 7/17 of the stakeholders prefer another mode. (2) Comparing the two operation modes of the reservoir group, it is found that XLD and SMX can improve the overall benefit of 4.12E + 09yuan at the cost of their sediment discharge benefits of 3.08E + 09yuan and 2.82E + 06yuan. (3) After gains distribution, the profit of all stakeholders can be optimized to varying degrees. This study broadens the dimension of benefit accounting under the joint operation of cascade reservoirs and the category of stakeholders in the gain allocation, which is conducive to promoting the ecological protection and sustainable development of sediment-laden rivers.

Regulating outflow temperature for multi-objective operation of cascade reservoirs: A case study

Thermal stratification widely exists in large reservoirs, causing significant inflow-outflow temperature difference (IOTD), which goes against the downstream ecology. Reservoir optimal operation has recently been conducted to regulate the outflow temperature, while its effects for cascade reservoirs urgently needed to be illuminated. Taking the typical Xiluodu-Xiangjiaba cascade reservoirs (XXCR) as study case, this paper had built a multi-objective optimal operation model combing the non-dominated sorting genetic algorithm II (NSGA-II) and a hydrodynamic-temperature model, and the total power generation (TPG) and IOTD were simultaneously considered. The effects of different inflow discharge and global warming scenarios were analyzed. This paper showed the following: (1) The IOTD and TPG of XXCR showed a positively linear relationship with the water level of the superior Xiluodu Reservoir (XLD), instead of the inferior Xiangjiaba Reservoir (XJB). (2) The reservoir with larger water volume and depth (such as XLD) determined the thermal structure and hydropower generation in cascade reservoirs, compared with the inferior reservoir (such as XJB). (3) As the inflow discharge or air temperature increased, the slope between the IOTD and TPG in the Pareto front curve increased. The results of this paper could support the ecological operation for cascade reservoir to regulate the outflow temperature.

An improved butterfly optimization algorithm and its application in cascade hydropower generation operation

Abstract Cascade reservoir operation is an effective nonstructural countermeasure for water resources management. In recent years, many metaheuristic algorithms are introduced to handle reservoir optimal operation due to their strong search capability and high efficiency. The butterfly optimization algorithm (BOA) is a newly developed metaheuristic method which has been widely used in solving various optimization problems. But it has local convergence and premature problems. Therefore, this paper proposed an improved version of BOA where three strategies are introduced: (1) the self-adaptive strategy to improve the initial population, (2) the dynamic switch strategy to balance exploration and exploitation, (3) the Levy-flight and standardized fragrance operators for position updating. The feasibility of the BOA, and IBOA are verified and compared with several commonly used algorithms (PSO, SCA, WOA and TSA) based on 19 test functions. Then, these methods are applied to address the optimization of cascade reservoirs that aims to maximize total hydropower generation. The results show that the proposed IBOA produces higher hydropower output and more stable results, indicating better scheduling schemes than BOA and the other four algorithms. In conclusion, IBOA is an effective and robust alternative optimization tool for cascade reservoir operation problems.

An improved many-objective artificial bee colony algorithm for cascade reservoir operation

An improved many-objective artificial bee colony algorithm for cascade reservoir operation