Journal of Hydroelectric Engineering

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2025 Vol. 44, No. 9 Published: 2025-09-25

	1	Day-ahead market clearing price interval prediction model considering electrical source-load uncertainty ^Hot!
		WANG Yuankun, GE Yadong, ZHANG Yanke, LU Yaojian, MENG Changqing
		DOI: 10.11660/slfdxb.20250901
		To improve the accuracy of clearing price estimation in electricity markets with multiple power sources under source-load uncertainty, this paper presents a day-ahead market clearing price interval prediction model. This model considers the uncertainty of generation-side outputs characterized by multidimensional stochastic distributions and the impact of load-side demand response, aimed at minimizing the total generation cost with the participation of hydropower, thermal, wind, and solar power, based on the physical clearing mechanism of electricity markets and the IEEE 30-bus power system. Its solutions give predictions of the interval of locational marginal price and generation commitments, taking source-load uncertainty into account. Case studies show its improvement on bidding efficiency and generation revenue; compared with traditional point forecasting methods, our new model achieves a 4.09% increase in overall economic efficiency and a 0.47% increase in expected revenue. The results verify its feasibility and effectiveness, and provide reliable decision-making support for generators in formulating stepwise bidding and output strategies in the day-ahead market.
		2025 Vol. 44 (9): 1-14 [Abstract] ( 58 ) PDF (3856 KB) ( 59 )

	15	Study on optimal scheduling of reservoir power generation considering dynamic capacity impact
		PENG Yang, CUI Yonghong, YU Xianliang, YAO Lishuang
		DOI: 10.11660/slfdxb.20250902
		For a river-type reservoir, a reasonable hydropower generation scheduling should consider the effects of its dynamic reservoir capacity and unsteady flow propagation in the mainstream and tributaries within a certain area. Aimed at such an issue, this paper constructs a reservoir optimal power generation scheduling model coupled with a one-dimensional river network unsteady flow model, which is solved using an OpenMP-based parallel dynamic programming algorithm and applied to a case study of the Three Gorges Reservoir (TGR). The results show this dynamic capacity model is effective in simulations of the spatial-temporal variations of discharges and water levels along the mainstream and tributaries within the TGR area. Compared with the static capacity model, it shows distinct differences in power generation process across operational periods—more stable variations in drawdown period and flood period, while experiencing significantly more fluctuations during impoundment period. Specifically, in these three periods, the total power generation decreases by 206 million kW·h, 129 million kW·h, and 47 million kW·h, respectively, with the most significant impact observed during drawdown period. And, during the initial stages of drawdown and impoundment, the model enhances power generation due to an increase in outflow discharges. This study would help refined simulations of the hydropower generation scheduling for river-type reservoirs.
		2025 Vol. 44 (9): 15-26 [Abstract] ( 35 ) PDF (4727 KB) ( 61 )

	27	Optimizing layout and capacity allocation for hydroelectric-wind-photovoltaic-pumped storage system in middle Yarlung Zangbo River basin
		FAN Zhiyong, YUAN Wenzhe, TANG Lihua, ZHANG Yi, WU Chuandong, YANG Dawen, WU Zhong, RUI Defan
		DOI: 10.11660/slfdxb.20250903
		In response to the challenge of capacity mismatch in the multi-energy complementary systems located in the complicated terrain areas of the plateau, this paper focuses on the optimization of capacity allocation for hydroelectric-wind-photovoltaic-pumped storage system in the study area of the middle Yarlung Zangbo River basin. We use the Ward's hierarchical clustering algorithm to the field of spatial layout optimization for wind and solar resources development, and construct a refined 8760-hour simulation framework for four-dimensional hydroelectric-wind-photovoltaic-pumped storage. This method breaks through the time resolution limitation of traditional monthly-scale planning. And to verify its applicability, we examine a study cases of the under-construction Jiexu hydropower station, and the planned Yongmu pumped storage power station within the basin. Based on high-precision meteorological data sets, fine evaluation of the wind and solar energy resources in the study area indicates the middle reaches have a development potential of 23.5 million kW for wind power and 238 million kW for solar energy. Our predictions show new energy consumption can be regulated significantly by a hydroelectric-wind-photovoltaic-pumped storage multi-energy complementary system that is based on the two power stations of Jiexu and Yongmu. Wind-solar complementarity enhances the consumption rate; the pumped-storage power stations can mitigate the volatility of wind-solar outputs and increase power grid stability. Furthermore, if considering the flexibility of the demand side, the installed capacity ratio of new energy to pumped-storage power stations would increase significantly.
		2025 Vol. 44 (9): 27-37 [Abstract] ( 50 ) PDF (2053 KB) ( 45 )

	38	Connotation, dynamic evolution model and key technologies of green hydropower development
		FAN Qixiang, AN Ruinan, WANG Xiaoming, GONG Dehong, LIN Peng, WENG Wenlin, LI Guo, YU Zhuojing
		DOI: 10.11660/slfdxb.20250904
		Green hydropower is a hotspot in the interdisciplinary research of hydropower construction, environmental protection, construction management, etc., an important concept for understanding and advancing the concepts of hydropower exploration, technology, and management. First, this paper reviews the development history of hydropower exploration in China. The practices are fully acknowledged on their comprehensiveness of all aspects and adjustability regarding differences between projects; Social and ecological benefits of the hydropower projects are well recognized, where water ecosystem health has been improved and regional economic development has been promoted. Second, we present a basic definition of green hydropower, its characteristics, and a development evolution model based on the mutual coupling relationship of society, environment, and engineering. Finally, we discuss the core technologies and successful practices in the development of green hydropower, including environmental protection of hydrology, water and sediment, mitigation of gas supersaturation, and watershed connection for habitat and life cycle carbon footprint management. And we suggest two primary research areas－intelligent construction and smart management, which are key to the future hydropower projects to enhance their high-quality infrastructure design, construction, operation and maintenance, and final decommission. This study would help promote the development of green hydropower and its science, technology and management－including upcoming hydropower exploitation in the high-altitude Southwest China, clean energy project development, and hydropower-wind-solar-complemented green energy supplying.
		2025 Vol. 44 (9): 38-52 [Abstract] ( 49 ) PDF (1858 KB) ( 79 )

	53	Establishing water quality characteristic factor library for drainage networks in a typical southern city
		WU Zihao, LI Manjie, YANG Xiaozhou, ZHANG Zhenzhou, ZHOU Mengfan, GUO Hongyu, LIAO Daocheng, MA Jie
		DOI: 10.11660/slfdxb.20250905
		Inflow of external water of a city into its drainage pipelines impacts the efficiency and energy cost in sewage treatment works, increasing the risk of overflow and environmental pollution. This study is aimed at constructing a water quality characteristic factor library for the drainage network of a typical city, Foshan, laying a technical basis for subsequent identification of external water infiltration. Through a comprehensive analysis of the functional areas of a typical region in the city, we identify and focus on five water categories－domestic sewage, industrial wastewater, agricultural water, river water, and rainwater. Their quality characteristics are determined through sampling and laboratory measurements we have conducted in this study. For the five categories, characteristic factors are total phosphorus and nitrogen, chemical oxygen demand and manganese, nitrate, dissolved organic carbon, and salinity, respectively.
		2025 Vol. 44 (9): 53-62 [Abstract] ( 43 ) PDF (3777 KB) ( 18 )

	63	Sediment carrying capacity formula of lower Yellow River under new flow and sediment conditions
		LIANG Dong, BAI Yuchuan, HUANG Zhe, XU Haijue, LI Yan, LIU Junzheng
		DOI: 10.11660/slfdxb.20250906
		How to determine the sediment carrying capacity of water flow significantly influences the calculations of sediment transport in rivers. Since the operation of the Xiaolangdi reservoir in 2000, low sediment concentration floods have occurred frequently in its lower reach, and changes in flow-sediment relationship have emerged, yet the existing methods for sediment carrying capacity calculation remain unimproved under these new conditions. Starting from the energy conservation theory, this study derives a formula for calculating sediment carrying capacity. By the criteria for channel erosion and deposition, we calibrate its parameters using 360 sets of relatively balanced flow-sediment data selected from the 2000-2010 records of the lower Yellow. And, additional 208 sets of balanced water-sediment data of 2011-2022 are used to validate its reliability and applicability. Results demonstrate the calculations align closely with measurements, confirming the formula’s accuracy in estimating this reach’s sediment transport capacity. A comparative analysis with classical sediment carrying capacity formulas, using 171 published water-sediment datasets, highlights the superiority of our new formula in application to low sediment concentration conditions. It is simple in structural form and enjoys a relatively high calculation accuracy; It is applicable to estimating the capacity of sediment transport in a low sediment river such as the lower Yellow, useful in practical river channel management and riverbed evolution analysis.
		2025 Vol. 44 (9): 63-72 [Abstract] ( 41 ) PDF (1829 KB) ( 55 )

	73	Multi-parameter time series prediction model for digital twin water conservancy monitoring sensor networks
		WANG Chao, ZHANG Yaofei, ZHANG Sherong, WANG Xiaohua
		DOI: 10.11660/slfdxb.20250907
		For digital twin hydraulic monitoring perception networks, traditional single-point time series prediction models fail to capture spatial relationships among the devices, and cause missing correlation features; Uncertainty issues arising from strong subjectivity in model structure and parameter design. To address these issues, this paper presents a multi-parameter time series prediction model for monitoring perception networks based on the Bayesian optimization and Hyperband (BOHB), self-learning graph structures, and Bidirectional Long Short-Term Memory (BiLSTM) networks. First, a self-learning graph structure is generated to extract spatial features of the perception network using graph neural networks. Then, the bidirectional Long Short-Term Memory networks are used to extract temporal features, and the BOHB method is used to optimize hyperparameters and improve prediction accuracy. Finally, the model is applied to proactive predictions of future states of the monitoring perception network. We have verified that our new model has achieved optimization rates higher more than 4.35%, 33.14%, 20.47%, 9.09% and 15.03% in R2, RMSE, MAE, MAPE and RMSRE respectively, enjoys higher accuracy and stronger generalization ability compared with a variety of previous prediction models, and has significant performance advantages.
		2025 Vol. 44 (9): 73-88 [Abstract] ( 55 ) PDF (4858 KB) ( 29 )

	89	Experimental study on dynamic strength of rock-filled concrete at medium and low strain rates
		REN Yisha, ZHOU Yuande, JIN Feng, ZHANG Chuhan
		DOI: 10.11660/slfdxb.20250908
		The application of rock-filled concrete (RFC) technology in high dams and other large-scale projects is on the rise. Given the elevated seismic fortification intensity at certain project sites is relatively high, there emerges a pressing need for enhanced structural design consideration for RFC dams, necessitating comprehensive research on the dynamic performance of RFC. This study employs a scaled-down testing approach to conduct a series of uniaxial and triaxial compression tests on RFC specimens subjected to varying confining pressures and strain rates. The results indicate that the uniaxial strength of RFC is positively correlated with the strain rate, while it will decline to some extent as the size of coarse aggregates or the dimension of the specimen increases. Confining pressure significantly increases the dynamic strength of RFC. Under the same confining pressure, specimens containing larger aggregates demonstrate lower uniaxial strength but exhibit higher triaxial strength. This observation suggests the mesoscopic characteristics of the self-sustaining skeleton formed by coarse aggregates play a crucial role in the strength of RFC. Based on the test results, a failure criterion is established using the octahedral stress, thereby providing experimental evidence for the seismic design of RFC structures.
		2025 Vol. 44 (9): 89-97 [Abstract] ( 46 ) PDF (1478 KB) ( 16 )

	98	Application of CRITIC-Stacking ensemble learning in missing value processing of dam safety monitoring data
		SONG Jintao, DONG Jialei, YANG Jie, CHENG Lin, GE Jiahao
		DOI: 10.11660/slfdxb.20250909
		Missing value processing is an important foundation for analysis of dam safety monitoring data. Traditional methods for handling the missing values of a dam often use a single type of machine learning models for prediction and interpolation, ineffective in integrating the advantages of multiple types of machine learning models. This article integrates multiple classic machine learning and deep learning algorithms into a strong learner within the framework of ensemble learning. To address the issue of weight allocation to each model, we develop a new critic stacking (CS) weight allocation method so that we can construct a dam monitoring data interpolation hybrid model based on CS ensemble learning. The results show that compared to single base learners and traditional Stacking ensemble models, this CRITIC-Stacking ensemble learning method reduces the RMSE index by an average of 72.7% and 58%. This indicates that the method can fully leverage the predictive advantages of various machine learning models, and the improvement of weight allocation can also improve the predictive accuracy of ensemble learning models, thus providing a new solution for handling missing values in dam monitoring data and constructing prediction models.
		2025 Vol. 44 (9): 98-113 [Abstract] ( 51 ) PDF (5555 KB) ( 30 )

	114	Semantic segmentation model for concrete cracks integrating multi-scale features and attention mechanisms
		FENG Jingyi, LIANG Hui, QI Zhiyong, TAN Dawen, REN Qiubing, LI Mingchao
		DOI: 10.11660/slfdxb.20250910
		Cracking, as one of the most common defects in concrete dams, weakens the integrity and durability of dam structures; crack detection has been a crucial task in the operation and maintenance management of concrete dams. Aimed at the drawbacks of traditional image-processing techniques in crack detection－such as substantial manual intervention and limited generalization ability, this paper presents a semantic segmentation model of dam cracks that incorporates multi-scale features and attention mechanisms. This model uses ResNet-50 as its backbone network for integrating the Path Aggregation Network to recycle shallow features, and makes use of the mechanisms of channel attention and spatial attention. These mechanisms enhance the model's ability to identify critical features, thus effectively improving its segmentation accuracy. Then, based on its semantic segmentation results, the digital image technology is adopted to quantify the geometric characteristics of cracks, including area, length, average width, and maximum width. Tests on a crack image dataset show this new model achieves a crack segmentation Intersection over Union of 82.02% and an F1 score of 90.12%; Quantification results of geometric characteristics exhibit an excellent agreement with the real values and a satisfactory accuracy. Thus, our method demonstrates significant potential for application in crack detection and geometric characteristics quantification for concrete dams.
		2025 Vol. 44 (9): 114-124 [Abstract] ( 56 ) PDF (2517 KB) ( 47 )

	125	Water absorption characteristics and compressive strength of rock-filled concrete with soft aggregates under water environment
		HE Shiqin, WEN Shihao, WANG Hui, ZHOU Hu
		DOI: 10.11660/slfdxb.20250911
		To study the influence of internal soft rock water absorption on the strength of rock-filled concrete during pouring and working in water environment, we design and prepare rock-filled concrete specimens with soft sandstone in red bed area as coarse aggregates, and conduct water content tests and compressive strength tests on the specimens with different soaking time. The results show that compared with the self-compacting concrete, the specimens exhibit a greater capacity for continuous water absorption and an increased saturated water content. Specifically, the water absorption rate of the internal soft rock is 2 - 3.5 times that of the self-compacting concrete in the same region. Under wet conditions, the specimens’ failure mostly occurs in the external concrete, and their surface fractures after drying mostly extend through the soft rock aggregates. In the soaking environment, its strength remains stable after 2 days and has not been continuously 'weakened ' due to soaking. This study helps understand the compressive strength characteristics of rock-filled concrete with soft aggregates in water environment, also useful for engineering applications.
		2025 Vol. 44 (9): 125-134 [Abstract] ( 39 ) PDF (2134 KB) ( 22 )