The hybrid pumped storage-wind-photovoltaic multi-energy complementary system has broad application prospects. However, its capacity design needs to characterize the complex relationship between the water volume and electric power, and its economic evaluation should consider the rules of electricity markets. This paper describes a new two-stage optimization framework for optimizing operation and capacity decision. First, a consistent assumption for the target gross output is presented; and a double-objective operation optimization model is developed. Then, a discrete decision space is obtained through optimization based on a large number of medium and long-term operation cases. Finally, the scheme with the maximized net present value (NPV) is selected. Application in a case study of the clean energy base in the upper Yellow River gives the conclusion as follows. New energy capacities corresponding to high, medium and low acceptance degrees of load loss risks are 3.2-3.9 times, 2.4-3.0 times, and 1.6-2.1 times that of the mixed pumping and storage capacity, respectively. The peak to valley ratios of the system's monthly electricity delivery range from 1.36 to 1.45, indicating the power sources in the system are well complementary on the medium and long time scales.

Concrete is a key basic material for the construction of hydraulic and hydropower projects in China. However, as a typical porous medium material, it is prone to ionic erosion, product corrosion, and matrix cracking in the environment of hydraulic engineering, leading to corroded reinforcement and reduced structural bearing capacity. At present, the existing concrete materials are difficult to satisfy the requirements by the design of long-life, high-quality projects in hydraulic and hydropower engineering in the western plateau or the southern coastal regions. This paper first summarizes the damage mechanism and challenges faced by traditional hydraulic concrete structural materials under severe environments, and then discusses in detail the long-life design method and performance enhancement mechanism of these concrete materials. Finally, we give an outlook on the future application and development of artificial intelligence in the long-life design and use of such materials, so as to provide new methods and new ideas for safe operation and maintenance and long-lasting service of the national major hydraulic and hydropower projects.

The deformation and stability analysis of high steep slopes is a key technical problem in the construction and operation of hydropower projects. Over the past two decades, China has built a large number of large-scale water conservancy and hydropower projects. Many key technical problems of high dams and large reservoirs have been solved successfully, and remarkable progress has been achieved in the life cycle performance evolution and safety control of high, steep slopes of reservoirs. This paper takes the performance evaluation of high steep slopes in the southwest hydropower projects as the main research line, and focuses on the deformation and stability evolution of high steep slopes. We examine the research progress in determining the influencing factors of stability and failure modes of high steep slopes, stability evaluation and deformation analysis methods, seepage analysis, and safety control. The latest researches are discussed in detail on the strict three-dimensional limit equilibrium method, modified Hoek-Bray wedge method, rigid body spring method, parameter inversion method based on monitoring data, and slope seepage analysis. We also discuss the academic thinking and technical route and certain future challenges to the life-cycle deformation and stability evolution analysis of high steep slopes in hydropower engineering.

The achievement of carbon peaking and carbon neutrality goals mainly relies on innovation in clean energy projects such as hydropower, wind power, photovoltaic and pumped storage projects. Most of the previous studies focus on general scientific and technological management issues such as innovation strategies, management systems, and resource allocation, but lack a holistic understanding of the innovative characteristics of clean energy projects and an analysis of the existing empirical research on different kinds of clean energy project innovation. This paper presents a new system of innovation indexes for these clean energy projects, and reveals the status of innovation in their development through an industry survey. We also identify the main problems in the innovation and their causes, and suggest its future direction and scope. The results have both theoretical and practical implications for clean energy project innovation.

Cascade hydropower development disrupts the continuity of natural river water temperature, leading to a significant cumulative effect on the temperature and a series of ecological effects. This study examines the water temperature along the lower Jinsha River and in the Three Gorges reservoir area , and reveals its spatial and temporal variations on different scales before and after dam construction and the cumulative effects. The impact of water temperature changes on fish spawning is also discussed. The results show that after the Xiluodu and Xiangjiaba dams were constructed, the annual mean water temperature difference along the lower Jinsha becomes smaller; along the river, the annual highest temperature at the hydrological stations shows a decreasing trend, while the annual lowest is significantly elevated, especially in January and December. After the construction, the annual temperature variation is reduced, and the time period featuring water temperature distribution has a trend of ‘convergence’－the days of water temperature distribution changed from M-type to V-type. An examination on the cumulative effect indicator finds that after dam construction, a significant time crowding effect occurs－a lag time in the extreme water temperature, water temperature delayed up to 1 - 2 months, the fish of 48% and 44% affected to a high degree at the Panzhihua section and the section downstream of Xiangjiaba dam respectively－thereby affecting severely the fish spawning and reproduction in the downstream and the fish protection sections. The study demonstrates the cumulative effects of water temperature impacted by cascade dam construction and its impact on fish spawning, laying a basis to enhance the role of temperature changes caused by large-scale cascade dams and the downstream ecological restoration.

This paper focuses on the key issue of the Three Gorges hydropower station’s in-plant economic operation, which is aimed at achieving a real-time load allocation of large-scale units for minimizing water consumption. Dynamic programming usually encounters the curse of dimensionality when dealing with a large-scale hydropower unit cluster, and therefore, it cannot meet the requirement of real-time dispatching decision for the station. For training a multi-period unit load distribution model and its decision-making, we develop a deep reinforcement learning-based framework to train the deep neural network and generates unit load distribution plans through a pre-trained network model. We apply a group theory idea to processing the state and action features of the learning, so as to compress the state and action space significantly and improve model training efficiency. The results indicate that compared to dynamic programming, our new method shortens the decision-making time by two orders of magnitude at a cost of less than 1% benefit loss. Thus, it offers a rapid and efficient solution for the unit load allocations in large-scale hydropower stations.

New hydropower energy has been faced with new opportunities and challenges against the backdrop of the strategic goal of peaking carbon emissions and achieving carbon neutrality proposed in the national 14th Five-Year Plan. As the scale of hydropower stations expands, traditional manual inspection combined with industrial monitoring often faces more problems such as inability to automatically identify and judge faults, and low sensitivity to information feedback. This paper describes a new method for applying variational mode decomposition and image grayscale processing techniques to an analysis of the operating data of hydropower plant units, through combining 5G technology and artificial intelligence. The results show that the fractal dimension of the images features two typical frequencies of 30 Hz and 85 Hz, with the corresponding amplitudes of 0.02 and 0.009 respectively, which are detected as the dominant and secondary frequencies, far stronger than other clutter frequencies. The VMD method successfully decomposes the signals of pressure pulsation at each monitoring point so as to obtain the characteristics of various modal functions in time and frequency domains. By examining the VMD decomposition results for two monitoring points at the tail water pipe, we have found that their frequency components are similar and consistent with those monitored inside the volute. This study would provide important support for construction of intelligent hydropower stations, along with an effective means for their operation and maintenance.

Budget estimate quotas and mechanical quotas are the basis of construction resource analysis and engineering budget estimate compilation. Traditional manual binding of the process and quota is inefficient, cumbersome and error-prone. Based on establishing a quota paradigm database, this paper presents a new intelligent binding method for budget estimate quotas of construction process by using information retrieval and natural language processing technologies. For the budget estimate quotas with positive and negative relevance, a fuzzy retrieval model is developed using the morpheme segmentation of text keywords and combining Word2vec semantic analysis with inverse document frequency weights. And we construct a priori cascading search method for budget estimate quotas in response to the characteristics of hierarchical search of the quotas. Experiments show that the retrieval model achieves a recall precision of 92.29% for budget estimate quotas and 97.28% for construction machinery hourly rate quotas, and the search method reduces design man-hours significantly. Application of this binding method will be a strong support to intelligent designs in hydropower engineering.

The ensemble Kalman filter approach has been used to correct the state variable in hydrological models. Difficulties of its application include how to select the state variable for correction, whether or not to synchronize parameter correction with the state variable, and how to set up the filter algorithm's hyperparameters. To address these issues, we take the calibrated GR5J model for the Qijiang River basin as a prototype tool to assimilate observed streamflows and correct model state variables using feedback correction. We use synthesis experiments and rolling forecast tests to examine the impacts of state variable selection, model parameter disruption, and hyperparameter optimization of the filter algorithm on forecast accuracy. The results suggest that while the biased initial state could be specified, the ensemble Kalman filter does raise forecast accuracy; otherwise, a better way is to fix the runoff generation variable and the flow confluence variable simultaneously to avoid overcorrection on model states. In the case of biased model parameters, it is best to identify the parameter first and then adjust the state variable. Increasing the ensemble members and warm-up periods generally improve correction accuracy, but the impacts of model noises and observation noises on the correction accuracy are non-monotonic. The filter algorithm is superior to the warm-up method, though its forecast accuracy decreases with an increasing forecast period. The findings would help apply the state correction method to operational forecasting.

Formulating a reasonable construction ventilation scheme is the key to ensuring safety and efficiency in underground powerhouse construction. Most of the previous studies on ventilation scheme optimization started from a single optimization objective such as ventilation smoke dissipation time and average pollutant concentration; traditional numerical simulation methods have the shortcomings of high modeling cost and low computational efficiency. This paper presents a new multi-objective optimization method for the construction ventilation schemes of an underground powerhouse based on an improved Least Squares Support Vector Regression (LSSVR) surrogate model of Improved Dung Beetle Optimizer (IDBO). First, a mathematical model for multi-objective optimization of the schemes is constructed, taking ventilation effect and ventilation cost as optimization objectives, and selecting ventilation parameters as design variables, such as fan airflow and the distance from the duct opening to the palm surface. Then, an IDBO-LSSVR surrogate model is constructed for prediction of the ventilation effect by combining the advantage of LSSVR in predicting small-sample data; the IDBO-improved LSSVR regularization parameter is used to optimize the LSSVR regularization parameter γ and kernel parameter σ, thereby overcoming the difficulty in model hyperparameter specification and achieving a fast prediction of the ventilation effect target. And combined with the NSGA-II algorithm, the surrogate model gives a multi-objective optimization solution. Finally, this method is applied to an underground plant project at the Luoning pumped storage power station, achieving the optimized construction ventilation scheme and a fast and accurate prediction of ventilation effect. The results show that the optimized scheme increases the ventilation and dust removal rate by 20.01%, and reduces the ventilation cost by 9.52%.

As the city expands, the river shoreline is constantly being encroached upon, and the river's flow capacity and water environment are damaged severely. Therefore, efficient methods for monitoring complicated river courses are urgently needed. This paper precents a new method for collecting images and data of a river and its banks using drones and enhancing data with the Generative Adversarial Network. We identify and locate five kinds of typical foreign bodies on the river, based on the YOLOv5 algorithm and the coordinate transformation localization algorithm. The target recognition algorithm of this model introduces the attention mechanism into the backbone network and uses EIOU-Focal Loss as its loss function to improve YOLOv5 in detection accuracy and convergence speed. The results show that data enhancement improves the model’s target recognition and raises the mean Average Precision (mAP) by 9.9%. The ablation experiment results verify that this model has the highest detection accuracy, with its maximum mAP of 0.96 or an increase of 11.6% relative to the one before improvement. Its positioning results show the real average error of the algorithm target object is not greater than 3m, which means a high accuracy. Application of the improved model to the Minjiang River section in Fujian has verified its higher accuracy in detecting target objects and its significance for related research.

During the operation of a dam, its original monitoring data exhibit complex, diverse, and time-varying characteristics, leading to gradual reduction in the effectiveness and accuracy of long-term monitoring warnings and thereby increasing disaster risks. Therefore, developing efficient and accurate deformation monitoring models is crucial to dam safety assessment. Traditional deterministic point predictions of a dam system, due to its inherent uncertainty, are faced with unavoidable challenges in error, bringing in low accuracy and a difficulty in determining the main factors of dam deformation. This paper presents a novel method that combines eXtreme Gradient Boosting with Bootstrap to construct prediction intervals. We use Elastic Net to extract the features of displacement influencing factors, and Bayesian Optimization to search for its optimal parameters. It can effectively estimate its own bias by combining multiple XGBoost models through Bootstrap; through residual training of the ensemble model, it further estimates the variance of random noise, quantifying the uncertainty of dam deformation. We validate this method in engineering case studies against the monitoring data from the Baihetan extra high arch dam under operation. Comparison of its predictions with the measurements and those predicted using a single model verifies its high accuracy and robustness, showing its root mean square error of only 0.0112. The accuracy of the model reaches 96%, and the efficiency is raised by up to 71% compared with the single model.

As global climate change intensifies and extreme events occur more frequently, the safe and stable operation of hydro-wind-solar multi-energy complementary systems faces great challenges, necessitating a rational evaluation of the resilience of these systems under extreme event disturbances. This study first elaborates on the complementary characteristics of hydropower, wind, and solar energy, and examines the types of disturbances confronted by these integrated systems. Then, a new concept of resilience for a complementary system is discussed, and a three-stage conceptual model for resilience is developed. Based on this framework, we select monthly, seasonal, and annual timescales and specify power output thresholds for the complementary system, and formulate an evaluation index and methodology for resilience assessment based on power output loss. Finally, to evaluate the resilience, a case study is conducted on a clean energy base in the upper Yellow River. The assessment results validate the effectiveness of the evaluation index and methodology. Notably, hydropower energy demonstrates superior resilience compared to wind and solar power, and the system’s overall resilience is enhanced significantly through utilizing the complementary nature of hydropower, wind, and solar energy. This study helps decision-making for resource allocation, scheduling strategies, and safe operation of integrated hydro-wind-solar energy systems.

This paper describes a new model based on the microeconomic principle of diminishing marginal productivity during the production process to economically and reasonably exploit the regulatory capacity of hydropower station reservoirs and to facilitate the efficient integration of wind and solar resources. This model considers the regulating capability of the reservoirs as a variable factor, treating the efficient consumption of wind and solar resources as a "fixed factor". It aims at maximizing the benefits of hydro-wind-solar co-generation and can be solved to obtain optimized operation plans for the reservoirs to facilitate the economic consumption of wind and solar resources. Case analysis demonstrates that under the given condition of inflow, load demand, reservoir operation status, and other factors, the model effectively reflects the reservoir’s contribution to the economic consumption and the co-generation benefits during its participation in wind and solar output regulation. Optimized operation scheme is given for the reservoirs involved in wind and solar output regulation under different scenarios. The model helps leverage fully the regulating capacity of the reservoirs and enhance the co-generation benefits while ensuring the safety of power systems.

The design rule curves are used to simulate reservoir operation to verify the design values and obtain a benchmark for comparative study. Joint and multi-objective optimal operation models are developed for the lower Jinsha-Three Gorges-Gezhouba cascade reservoirs. We solve the optimal model and its Pareto frontier using a parameterization-simulation-optimization framework based on the Gaussian radial basis functions and a Borg many-objective evolutionary algorithm. Comparative study reveals that the design values of annual hydropower generation are reasonable and reliable, and the joint operation of cascade reservoirs must consider the backwater effect of downstream reservoirs. Compared to the rule curve scheme, the joint (optimal) operation scheme reduces water spillage by 20.38% (30.44%) and increases annual hydropower generation by 4.01% (5.45%), with significant increases in the generation by the reservoirs of Xiluodu, Xiangjiaba, and Gezhouba. Generally, a conflict between hydropower generation and impoundment efficiency occurs, and the Three Gorges Reservoir will be faced with a challenge in dry years if it starts impoundment on the tenth of September. We suggest that the Three Gorges Reservoir should optimize further its flood-limited water level and consider an earlier start of impoundment.

During the construction of a hydropower project, a large number of non-editable documents for hydraulic concrete materials are generated. Using manual interpretation methods to obtain texts is time-consuming, laborious and accuracy-uncontrollable, making it difficult to meet the demand for information management of material data. This paper develops an intelligent interpretation method for non-editable texts of hydraulic concrete materials. First, we construct a text detection model, HC-PSENet, based on pixel level segmentation, which integrates the backbone network of PP-HGNet to achieve accurate detection of text lines. Then, a professional corpus is created based on the domain knowledge to realize accurate character mapping. We construct a text recognition model HC-CRNN for hydraulic concrete materials, using detection text boxes and the professional corpus as its inputs, and adopt the backbone network of ResNet and the improved loss function C-CTC Loss to improve the accuracy of character classification. Finally, a transfer learning strategy is adopted to train the model with the self-designed dataset as an example; the effectiveness and superiority of our new method is verified through ablation and comparative experiments. The results show that it has a harmonic mean of 0.985 for detecting text regions and its accuracy of text recognition reaches 90.62%. It has an overall performance superior to classical methods and would provide new technical means for the automated reuse of non-editable text resources in concrete materials.