Cascade Reservoir System Research Articles

The Effect of Dam Break Speed on Flood Evolution in a Downstream Reservoir of a Cascade Reservoir System

The dam break flood is one of the potential causes of catastrophic events in cascade hydropower hub groups. Investigating the movement patterns of dam break flooding among reservoir groups under different dam break speeds is crucial for flood prevention and emergency response. In this study, the evolution characteristics of dam break floods were investigated in a cascading reservoir system, focusing on different break speeds of the upstream dam. The results indicate that the dam break speed determines the concavity or convexity of the water level curve changes in the upstream reservoir. Accordingly, dam breaks are classified into three modes: instant dam break, fast dam break, and slow dam break. An approximate critical speed has been identified to differentiate between the fast dam break and slow dam break. Further investigation into the evolution patterns of dam break floods in downstream reservoirs under different break modes was conducted. Correspondingly, the flood peak discharge and peak arrival time of the dam break floods vary differently with break speed under different break modes. Finally, a theoretical analysis for the flood peak discharge at the dam site during gradual dam break at a certain speed was established, which is able to predict the over-dam flood peak discharge in fast and slow dam break modes. This study is based on a combination of laboratory flume experiments and three-dimensional numerical simulations. This study has theoretical significance for the reinforcement of public infrastructure safety and the prevention of natural disasters.

Deciphering the impact of cascade reservoirs on nitrogen transport and nitrate transformation: Insights from multiple isotope analysis and machine learning

Construction of cascade reservoirs has altered nutrient dynamics and biogeochemical cycles, thereby influencing the composition and productivity of river ecosystems. The Lancang River (LCR), characterized by its cascade reservoir system, presents uncertainties in nitrogen transport and nitrate transformation mechanisms. Herein, we conducted monthly monitoring of hydrochemistry and multiple stable isotopes (δ15N-NO3-, δ18O-NO3-, δ18O-H2O, δD-H2O) throughout 2019 in both the natural river reach (NRR) and cascade reservoirs reach (CRR) of the LCR. Through the monthly detection of nitrogen forms and runoff in the import (M2) and export (M9) section, the average annual retention ratios for Total nitrogen (TN), Nitrate nitrogen (NO3--N), Particulate Nitrogen (PN) and Ammonium Nitrogen (NH4+-N) were about -35%, -53%, 48% and -65%, respectively. The retention rates were positively correlated with hydraulic retention time and negatively correlated with reservoir age, especially in the flood season. Compared to the NRR, the reservoir had significantly affected the nitrogen transport characteristics, especially for the large reservoirs (like Xiaowan and Nuozhadu), which enhanced phytoplankton uptake of NO3--N to form PN capabilities in the lentic environment and subsequently to precipitate or intercept it at the reservoir. This led to the overall decreasing trend of TN and PN concentrations along the CRR. The Bayesian stable isotope model quantified NO3--N sources from the NRR to the CRR. During this transition, soil nitrogen (SN) ratios decreased from 69.3% to 61.8%, while Manure & sewage (M&S) increased from 24.0% to 31.3%. Anthropogenic and natural factors, including urban sewage discharge, population density, and precipitation, were selected as key predictor variables. The eXtreme Gradient Boosting (XGBoost) model exhibited superior predictive performance for NO3--N concentrations, achieving an R2 of 0.70. These findings deepen our understanding of the impact of reservoirs on river ecology.

A combination approach for optimization operation of multi-objective cascade reservoir systems (Case study: Karun reservoirs)

ABSTRACT Multi-reservoir systems that have diverse and conflicting objectives are challenging to design due to their uncertainties, non-linearities, dimensions and conflicts. The operation of multi-reservoir systems is crucial to increasing hydropower production. In this study, we have investigated the application and effectiveness of the new optimization algorithm MOAHA in multi-objective cascade reservoirs with conflicting objectives, and it has been investigated on a case-by-case basis on Karun cascade reservoirs (Karun 3, Karun 1, Masjed Soleyman and Gotvand). The suggested method (MOAHA) output with other optimization algorithms, MOALO, MOGWO and NSGA-II, were compared and evaluation criteria were used to select the best performance. Additionally, we employed the powerful TOPSIS method to determine the most suitable algorithm. The considered restrictions have also been observed. The results indicate that MOAHA's proposed method is better than the compared algorithms in solving optimal reservoir utilization problems in multi-reservoir water resource systems. The reduction of evaporation (losses) from the tank surface by 9% is accompanied by a 15% increase in hydropower energy production. MOAHA, scoring 0.90, is deemed the best algorithm in this study, whereas MOGWO, with a score of 0.10, is regarded as the least effective algorithm.

A deep learning modeling framework with uncertainty quantification for inflow-outflow predictions for cascade reservoirs

Accurate prediction of reservoir inflows and outflows and their uncertainties is essential for managing water resources and establishing early-warning systems. However, this can be a formidable challenge due to numerous uncertainties, particularly in cascade reservoir systems. To seamlessly quantify aleatoric (data-caused) and epistemic (model network–caused) uncertainties simultaneously in a single framework, we estimated the posterior distribution of parameters in a Bayesian neural network while measuring prediction variance to reflect the noise in data. By randomly discarding certain units within the network, the estimated posterior distribution can be combined with a new loss function. This sophisticated deep learning framework for a long short-term memory network has also included advanced supporting approaches, such as input variable selection, data transformation, and hyperparameter optimization. The model trained in this study was applied to two cascade reservoir systems with four reservoirs in Vietnam, providing uncertainty estimates from two distinct sources. Comparing three global reservoir operation schemes, we found that the proposed model achieved superior performance in all cases, and uncertainty in the forecasts was due primarily to data noise, rather than uncertainty in the model itself. Preprocessing data using a wavelet transform can reduce noise that the model cannot classify on its own, resulting in improved performance, particular with longer lead times. The satisfactory performance of the prediction results confirms that the framework can effectively assess the uncertainty of hydrologic predictions using deep learning.

Improving cascade reservoir inflow forecasting and extracting insights by decomposing the physical process using a hybrid model

Accurate and reliable inflow forecasting is essential for efficient reservoir operation, which serves various purposes such as flood control, hydropower generation, water supply and irrigation. Although data-driven models have been vastly used to improve forecasting, the lack of interpretability and physical insights, particularly for neural network models, limit their trustworthiness and usability. In this study, we introduce an interpretable hybrid model to decompose inflow into precipitation-runoff and routing processes. This approach not only improves model performance but also facilitates the extraction of valuable hydrological insights. When applied to the Missouri River Basin cascade reservoir systems, the hybrid model demonstrates strong performance when compared to the state-of-the-art hydrologic model HEC-HMS and the widely used Long Short-Term Memory (LSTM) model in predicting reservoir inflow, especially for lower and smaller reservoirs in the cascade reservoir systems. For the upper and larger reservoirs, it achieves substantial improvement in Nash-Sutcliffe efficiency, ranging from 0.05 to 0.08 and 0.28 to 0.32, except for Fort Peck reservoir, when compared with the LSTM and HEC-HMS models. The decomposition of physical processes enables the hybrid model to represent each component more effectively, thereby leading to improved results in simulation and forecasting. Furthermore, the hybrid model shows how meteorological information updates the memory state of the LSTM unit and routing parameters, which in turn influences inflow dynamics, particularly in situations involving snowmelt periods. The proposed approach also allows the extraction of valuable hydrological insights, including the assessment of the contributions of precipitation-runoff processes versus routing to inflow dynamics, dynamic water travel time, and routing coefficients. The case studies have demonstrated the promising capabilities of this interpretable hybrid model in improving reservoir inflow forecasting and providing meaningful hydrological insights into reservoir inflow dynamics.

Metal(loid) Partitioning and Transport in the Jinsha River, China: From Upper Natural Reaches to Lower Cascade Reservoirs-Regulated Reaches

Riverine contamination of metals and metalloids is a growing global concern. However, our understanding of how cascade reservoirs influence the fate of these pollutants remains limited. In this study, we focused on the Jinsha River, a typical large river polluted with metal(loid)s in Southwest China, to investigate the partitioning and transport of these substances from the upper natural reaches to the lower cascade reservoirs-regulated reaches during both flood and dry seasons. Results revealed that the rapid urbanization and industrial development following the construction of reservoirs led to significant copper (Cu), nickel (Ni), lead (Pb), chromium (Cr), and cadmium (Cd) pollution. Through various analytical techniques such as correlation analysis, principal component analysis, redundancy analysis, and structural equation modeling, we determined that reservoirs had a profound impact on solid-associated parameters, including particulate organic matter (POM), suspended solid concentration (SSC), and median particle diameter (D50). These parameters, in turn, affected the fate of metal(loid)s through processes like sorption-desorption and dissolution-precipitation. High POM concentrations and low SSC and D50 values resulted in the precipitation of most particulate metal(loid)s, thereby reducing their transport across reservoirs. As river water moved downstream across the reservoirs, the percentage of particulate metal(loid)s decreased by 6.1% for arsenic (As), 38.3% for Cu, 74.9% for Ni, 36.8% for Cr, 17.8% for Cd, and 41.9% for Pb, respectively. Furthermore, the cascade reservoirs had cumulative effects on the deposition of particulate metal(loid)s, particularly concerning Cr, Cd, and Pb in the final reservoirs. Human health risk assessments indicated that dissolved As and Pb posed potential non-carcinogenic threats to residents. Despite the fact that reservoirs captured most particulate metal(loid)s, the transport of dissolved As and Pb across the reservoirs still impacted the safety of downstream drinking water. Therefore, in cascade reservoir systems, the concern regarding dissolved metal(loid) pollution remains a priority for future reservoir management.

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.

Unraveling the antibiotic resistome in backwater zones of large cascade reservoirs: Co-occurrence patterns, horizontal transfer directions and health risks

The widespread occurrence of antibiotic resistant genes (ARGs) throughout aquatic environments has raised global concerns for public health. However, the profiles and patterns of antibiotic resistome in backwater zone of cascade reservoirs, where water flow is slowed down, are still poorly understood. Here, we proposed a metagenomic analysis framework to comprehensively reveal the diversity, abundance, co-occurrence patterns and transfer direction of ARGs in cascade reservoirs system and evaluated their health risks through a procedure based on contigs. A total of 364 ARGs subtypes conferring resistance to different antibiotics classes were detected in our water samples, and the dominant ARGs (macB, bacA, vanRA, bcrA) were similar in different reservoirs. Meanwhile, the distribution of ARGs was influenced by the presence of biotic factors such as metal resistant genes (MRGs) and mobile genetic elements (MGEs), as well as abiotic factors such as dissolved oxygen (DO) and pH. Remarkably, ARGs (vanR, rosB, MexT) co-occurred with plasmids and virulence factor genes (VFGs), which can lead to the emergence and spread of highly virulent and antibiotic resistant bacteria in microbial communities. Overall, this study helps administrators to better understand the complex patterns of ARGs in backwater zones of large cascade reservoirs and provides a proper procedure for detecting the presence of high-risk of ARGs.

Evaluation of sediment phosphorus dynamics in cascade reservoir systems: A case study of Weiyuan River, China

Reservoirs tend to accumulate phosphorus (P) originating from agriculture, industry, and other upstream sources in sediment, with this stored P later released. However, the spatiotemporal dynamics of sediment P release in reservoirs remains unclear. This study investigated the spatiotemporal dynamics in P of the sediment and water of three cascade reservoirs in the Weiyuan River (Tuojiang tributary). The results showed elevated P in sediment [total P (TP): 1208.93 mg kg−1] and water (TP: 0.23 mg L−1) during the low-water season (LWS), which could be attributed to notably higher organic matter content (9.65%), finer particle size (20.95 μm), and extended hydraulic retention time (HRT: 13.13 days) downstream of the cascade reservoirs. Further study employing static in-situ diffusive gradient in thin films (DGT) and dynamic ex-situ adsorption kinetic experiments confirmed that the downstream release of P from sediments [diffusion flux (Fd): 1.67 mg m−2 d−1, equilibrium P concentrations (EPC0): 0.22 ± 0.10 mg L−1] greatly exceeded those upstream (−0.66 ± 0.17 mg m−2 d−1, 0.07 ± 0.001 mg L−1), Fe (II) was a critical factor in regulating sedimentary P release. The combined effects of high P in overlying water and sediment significantly stimulated downstream phytoplankton growth, particularly among cyanobacteria (26.48%) and green algae (8.33%). Further regulatory steps are needed to regulate LWS algal blooms downstream of cascade reservoirs.

Discussion on different impoundment and release orders of huge cascade reservoir system and its effects in the course of flood regulation

Discussion on different impoundment and release orders of huge cascade reservoir system and its effects in the course of flood regulation

Equivalent Relationship between Flood Prevention Storage of Cascade Reservoirs in the Downstream Jinsha River and Three Gorges Reservoir

The equivalent relationships between flood prevention storage of a cascade reservoir system provide useful information for real-time operation. However, those relationships are implicitly hinted at in the optimized operation rules of downstream reservoirs, which may add difficultly in providing feasible operation strategies for engineering practice. In this study, we investigate the equivalent relationship between cascade reservoirs using a flood regional composition-based framework coupled with reservoir inflow and river flow routing techniques. We select the cascade reservoirs in the downstream Jinsha River and Three Gorges Reservoir (TGR) as the case study. Results reveal that (1) the equivalent relationship between flood prevention storage of cascade reservoirs in the downstream Jinsha River and TGR is approximately linear-related; (2) the conversion coefficient of equivalent relationship depends on the flood type, and the intercept term of equivalent relationship is mainly influenced by the design flood frequency; and (3) the TGR flood prevention storage (or water level) can be dynamically adjusted according to the corresponding reserved flood prevention storage of cascade reservoirs in the downstream Jinsha River, which can generate 3.56 billion kW·h (+8.5%) extra hydropower during flood season without increasing flood control risks.

Comparison of multi-objective genetic algorithms for optimization of cascade reservoir systems

Abstract Multi-objective genetic algorithms (MOGAs) are widely used for multi-reservoir systems’ optimization due to their high efficiency and fast convergence. However, the computational cost grows exponentially with the expansion of multi-reservoir systems and the increased dimensions of optimization problems, posing a great challenge to multi-reservoir operations. Therefore, it is important to find a suitable and efficient multi-objective algorithm for multi-reservoir system optimization. In this study, three representative MOGAs were selected, namely the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Non-Dominated Sorting Genetic Algorithm III (NSGA-III), and Reference Vector Guided Evolutionary Algorithm (RVEA), and three multi-objective optimization models were then developed accordingly. Numerical experiments were conducted to evaluate the performance of these three algorithms for the optimization of a cascaded reservoir system. The results show that for the two-objective model, the non-dominant size generation rate (NDSGR) of NSGA-II is 1.66 and 4.01 times that of NSGA-III and RVEA, respectively. NSGA-II is more suitable for solving the optimization operation problem with two objectives. Based on the comprehensive evaluation, NSGA-III seems to be more appropriate for more than two objectives. These results emphasize the importance of using appropriate algorithms and provide further insights into the choice of algorithms for the optimization of hydropower systems.

Spatial distribution characteristics and pollution levels of heavy metals in surface water and sediments of the Heihe cascade reservoir system, China

Abstract The operation of cascade reservoir systems has altered river hydrology and sediment distribution patterns. In this study, 31 surface water and sediment samples were collected from the Heihe River from July to August in 2019 and 2020 to investigate the spatial distribution and sources of heavy metals and assess their ecological risks. The results revealed that the concentrations of heavy metals in surface water were much lower than the quality standards for surface water in China, and there were no significant differences in the natural reaches, center and tail of the reservoir. Cd in surface sediments was at a heavy contamination and high risk level, and the heavy metal pollution levels in the main streams and tributaries differed greatly, especially in the graded reservoirs with a gradual accumulation trend. This may be related to the fact that there were many fine-grained sediments in the reservoir center near the dam. Factor analysis-multiple linear regression (FA-MLR) revealed that heavy metals mainly come from natural factors and anthropogenic input, with anthropogenic inputs mainly coming from mining activities in the tributaries and industrial and agricultural activities in the main stream.

Are cascade reservoir systems sustainable agroecosystems? A comparative assessment of efficiency, effectiveness and resource footprint in a Sri Lankan micro-cascade

CONTEXTSri Lankan village tank cascades have been classified as a globally important agricultural heritage system (GIAHS) in 2018.Yet, a systematic benchmark of the performance and sustainability of these systems has not taken place to date. OBJECTIVEThis study assessed the efficiency, effectiveness and resource footprint of village tank cascade systems with a view towards assessing the resilience of the system as a key contributor to the food production in Sri Lanka, by comparing the above parameters in a selected cascade system at two time points. METHODSThe land uses at two time points, i.e. 1920 and 2020, were studied using historical, primary and secondary data. The study site was Mahakanumulla-Ulagalla village tank cascade located in the dry zone of Sri Lanka. Primary data were collected from the site including social, economic, hydrological and ecological parameters. Landscape mapping was conducted and eight parameters under efficiency, effectiveness and resource footprint were calculated and analyzed using collected data for the two time points to compare the cascade status. RESULTS AND CONCLUSIONSThe study finds that the structural balance of the cascade system in terms of the ratio between catchment forest, water surface and command area of 5:1:1 has changed to 1:2:1 over a 100 years' period, displaying a clear deterioration. From 1920 to 2020, the agricultural water productivity has increased from 0.65 kg per m3 to 0.81 kg per m3 in the upstream and in the downstream it has increased from 0.65 kg per m3 to 1.00 kg per m3. However, relative to the increase in land productivity (i.e. 2050 kgha−1 to 2962 kgha−1 in the upstream and 2050 kgha−1 to 3009 kgha−1 in the downstream) during the same period, the increase in water productivity has been low. The irrigation water demand has risen from 0.21 million m3 to 0.22 million m3 in the upstream and from 0.28 million m3 to 0.32 million m3 in the downstream. However, the ratio of cascade components has changed causing ecological imbalance in the system with the expansion of home gardens and farm lands. SIGNIFICANCEExcept for water and land productivity, this study reveals a negative trend in parameters of efficiency, effectiveness and resource footprint between the two time points of comparison, which emphasizes the need for restoring the sustainability. We consider the structural balance and ecological sustainability as key factors in managing and restoring comparable village tank cascade systems. In particular, we highlight that this historically sustainable system may not be able to function sustainably today.

HYDROLOGICAL PREDICTION FOR MAPPING THE POTENCY OF BREAK IN THE SAGULING DAM, WEST JAVA PROVINCE, INDONESIA

Dams are structures built across bodies of water to hold the water back. They offer various economic, environmental, and social benefits, including large-scale water storage, hydroelectric power generation, tourism, and recreation. However, in addition to offering benefits, dams are also associated with certain risks. One of the biggest risks is the possibility of a dam breaking, which may occur if the discharge inflow is overly high at the point it enters the reservoir. A very high discharge inflow can result in a probable maximum flood (PMF) discharge. This study investigates the potential of hydrological analysis with regard to mapping the dam break potency of the Saguling Dam in Indonesia. The reservoir system on the Citarum River consists of three reservoirs, namely Juanda (Jatiluhur), Cirata, and Saguling. Together, they comprise a cascade reservoir system. The Saguling Dam is located in the upstream, the Cirata Dam is in the middle stream, and the Juanda Dam is under the Cirata Dam. This study focuses on the Saguling and Cirata Dams. It analyzes the PMF using the Nakayasu, ITB-1, Gama-2, and Snyder methods. Moreover, the rainfall analysis involves the Thiessen polygon and the frequency distribution of the Gumbel, Log Pearson-III, and Log Normal, while the hourly rainfall is analyzed using the PSA-7 currently being investigated in Indonesia. The intensity of the rainfall in the catchment area of a dam is directly related to the level of the discharge inflow that enters the reservoir. The Saguling Dam faces a similar risk, which explains the need to conduct a comprehensive hydrological analysis to assess and map one of the key risk factors for the dam breaking.

Evaluating power grid model hydropower feasibility with a river operations model

Production cost models (PCMs) simulate dispatch of generators across a large power grid and are used widely by planners to study the reliability of electricity supply. As energy systems transition away from the thermoelectric technologies that have traditionally balanced electricity supply and demand, hydropower and its representation in PCMs is of increasing importance. A limitation of PCMs applied to continental power grids with diverse generation portfolios is that hydropower generation is simulated without full consideration of complex river dynamics, leading to possible misrepresentation of grid flexibility and performance. In addition, data used in PCMs may reflect outdated operating policies. In this paper we propose a hydropower generation feasibility test for PCMs. The approach uses a detailed hydropower model to determine whether the hourly hydropower schedule from a PCM with simplified monthly parameterization can be attained after accounting for realistic river dynamics and operating policies, such as spill requirements and general water movement and balance through a cascade reservoir system. We perform this hydropower generation test for the ‘Big 10’ hydropower system on the Columbia River (part of the Western Interconnect of the United States), revealing 9% overestimation of available hydropower generation in a PCM solution in an average hydrologic year. Our evaluation provides insight on the cost and opportunities for better representing hydropower in PCMs.

Longitudinal Variations in Physiochemical Conditions and Their Consequent Effect on Phytoplankton Functional Diversity Within a Subtropical System of Cascade Reservoirs

The social and environmental impacts of large dams are quantifiable and have been well documented, while small dams have often been presumed to be less environmentally damaging than large dams. The purpose of this study was to analyze longitudinal gradients in environmental, hydrodynamic variables and their impact on phytoplankton function, within a cascade of four reservoirs (XuanMiaoGuan, XMG; TianFuMiao, TFM; XiBeiKou, XBK; ShangJiaHe, SJH) and one reservoir bay (Huangbohe Bay, HBH), located from upstream to downstream in the Huangbo River, Hubei Province, China. Our results showed that water temperature, total nitrogen, and soluble silicate increased along the cascade reservoir system, while the concentration of dissolved oxygen and total phosphorus decreased. We identified 16 phytoplankton functional groups, and the predominant groups, including D (Synedra and Stephanodiscus hantzschii), E (Dinobryon divergens), Lo (Dinoflagellate: Peridinium bipes and Peridiniopsis), X2 (Chroomona), and Y (Cryptomonas), changed longitudinally from up to down in the cascade reservoirs. The number of dominant functional groups increased along the longitudinal gradient, indicating that the function of the phytoplankton community was more stable. Functional group D was the dominant phytoplankton functional group among the four reservoirs, and Lo group was dominant except SJH. The phytoplankton functional groups in the HBH have been completely changed due to the backwater jacking of the main stream of the Yangtze River. Euphotic depth, suspended solids, and nutrients were apparently the key factors driving variations in phytoplankton functional groups among the reservoirs. Notably, the patterns we observed were not all consistent with the cascading reservoir continuum concept (CRCC) that typically characterizes large rivers. Thus, our findings contribute to the further theoretical development of the CRCC, which may not apply widely to all cascade systems.

ASSESSMENT OF WATER AVAILABILITY IN THE CASCADE RESERVOIR OF DURIANGKANG-MUKA KUNING FOR SUPPORTING THE INTEGRATED OPTIMIZATION

The Duriangkang reservoir was built in 1992–1995. It was the first estuary dam built in Indonesia and has the largest storage of any dam in Batam City. Its volume of effective storage is 107 million m3, and it is fulfilling the water need at a rate of about 2,500 l/s. The Muka Kuning reservoir was completed in 1990 and began operating in 1991. Initially, the IPA Muka Kuning reservoir had a water production rate of 310 l/s, which increased to 600 l/s in 2015 because of Batam City’s increasing water needs. This research is aimed at assessing the water availability in the Duriangkang–Muka Kuning cascade reservoir system. This evaluation was carried out to support the integrated management of optimization in cascade reservoirs. In this cascade system of the Muka Kuning and Duriangkang reservoirs, water is taken from downstream into the upstream reservoir, and a pump is used to create inflow into the downstream reservoir. This study’s methodology comprises rainfall data tests, trend absence tests, stationery tests, variance stability tests, and mean stability tests. Moreover, we conducted an analysis of dependable discharge for the wet discharge condition (35% probability), the normal discharge condition (50% probability), and the dry discharge condition (65% probability). To evaluate the water availability, we used the reservoir water balance. The results show that the water availability in Duriangkang is enough to fulfill the water need under all conditions (wet, dry, and normal); however, for Muka Kuning, the water availability can be fulfilled in the wet and normal conditions only, and under the dry condition, the water availability cannot fulfill the water need. Therefore, ensuring the integrated management of optimization is necessary.

Improved progressive optimality algorithm and its application to determination of optimal release trajectory of long-term power generation operation of cascade reservoirs

Dynamic programming (DP) is an effective method for solving multi-stage decision-making problems and has been extensively applied to the optimization of reservoir operations. However, the method is limited by the dimensionality problem; hence, it cannot be directly applied to optimizing the operations of a reservoir system with more than three reservoirs at current computing power. The progressive optimality algorithm (POA) is a classic variant of DP and has been widely used for optimizing multi-reservoir operations. Despite the use of a static variable decoupling strategy to ease DP's dimensionality problem, the POA's performance is reduced by an inherent drawback of the blind search of the static variable decoupling strategy and the dimensionality problem in two-stage solutions. To enhance the POA's performance, we propose an improved version of the POA known as the dipole optimization procedure (DOP) for optimizing cascade reservoir operations. In the improved algorithm, we use a dynamic variable decoupling strategy to obtain search directions that are more targeted than those of the POA so as to improve the quality of the solutions. The dynamic decoupling of variables was achieved by constructing and solving a dipole optimization problem using a DP model developed in this study. Moreover, a perturbation mechanism was introduced to address the POA's dimensionality problem so that the algorithm can be extended to larger reservoir systems. The results of the simulation of a hypothetical four-reservoir system and a real-world cascade reservoir system showed the superiority of the DOP over the POA and other five available alternatives. The comparison was based on the quality of solutions and solving efficiency. The results indicate that the DOP is rational and feasible and has the potential to be applied to optimizing the operation of large-scale multi-reservoir systems with numerous reservoirs.

Multi-objective operation of cascade reservoirs based on short-term ensemble streamflow prediction

Ensemble streamflow predictions (ESPs) play an essential role in reservoir operation in terms of flood control, water supply and hydropower generation, etc. However, the working mechanism of dam-regulated ESP is rarely explored even though a large fraction of major waterways is affected by artificial hydraulic infrastructure, let alone its application value in multi-objective multi-reservoir operating system. Here this study develops three-layered hydrologically-relevant long and short-term memory (LSTM) models to implement 1–3 days ahead ESP of cascade reservoirs with the help of numerical weather prediction (NWP), and designs operating policies based on Gaussian radial basis function which can explicitly incorporate ESP information into reservoir operating decision. The framework of our proposed ESP-based operating way is demonstrated on a cascade reservoir system in the upper Hanjiang River basin, China and assessed against two benchmark control ways (i.e., no-forecast and deterministic-forecast operations). Results show that (1) LSTM models have reasonable accuracy in short-term NWP-based ESP of cascade reservoirs, especially for high-flow regimes; (2) compared with the no-forecast operation, our ESP-based operation could harvest additional approximately 36 million kW·h of hydropower generation per year with a slight improvement in water supply over 11 years of operation during the flood season. These findings highlight the application potential of our numerically efficient and skillful NWP-based ESP scheme for multi-objective cascade reservoir operation to address water-energy shortage issues.