Search Results (82)

Climate variability and growing competition for limited water resources have made the operation of cascade reservoirs increasingly complex. This study develops a comprehensive system-based multi-objective optimization and evaluation framework that simultaneously integrates five goals: power generation, water supply, ecological protection, navigation reliability, and flood control as a constraint. The framework employs the NSGA-III evolutionary algorithm to address the high-dimensional optimization problem and combines Analytic Hierarchy Process (AHP), Entropy Weight Method, and TOPSIS to integrate subjective expertise with objective data in the evaluation of alternatives. Applied to the lower Jinsha River cascade under wet, normal, and dry hydrological scenarios, the model reveals distinct conflicts between hydropower and ecological or navigational requirements, partial synergies between hydropower and water supply, and tension between ecological and supply demands. Hydrological variability alters these relationships, with wet years intensifying conflicts and dry years heightening supply and ecological pressures. Functional differentiation among reservoirs is also evident, with Baihetan and Xiluodu showing pronounced power–ecology tensions, while Xiangjiaba primarily supports supply and navigation. The study not only advances the theory of multi-objective decision-making in water resources systems but also offers actionable guidance for sustainable reservoir governance and regional development. Full article

Cascade reservoir optimization faces significant challenges due to multi-dimensional, non-convex, and nonlinear characteristics with coupled constraints. As reservoir numbers increase, computational complexity escalates dramatically, limiting conventional optimization methods’ effectiveness. This paper proposes HHONMPA, a hybrid algorithm combining Harris Hawks Optimization (HHO) with Marine Predators Algorithm (MPA). The method uses SPM chaotic mapping for population initialization to enhance diversity and integrates both algorithms’ predatory behaviors. During exploration, it employs Brownian motion and improved Lévy flight strategies for global search, while exploitation uses enhanced HHO for local optimization. A novel Dual-Period Oscillation Attenuation Strategy dynamically adjusts parameters to balance exploration-exploitation. Performance validation using CEC2017 benchmark functions shows HHONMPA significantly outperforms the original HHO and MPA in solution accuracy and convergence speed, confirmed through statistical testing. Engineering validation applies the algorithm to the Lower Jinsha River—Three Gorges four-reservoir system, conducting experiments across various hydrological scenarios. Results demonstrate substantial improvements in search accuracy and convergence efficiency compared to existing methods. HHONMPA effectively addresses large-scale cascade reservoir optimization challenges, offering promising prospects for water resource management and hydropower scheduling applications. Full article

This paper investigates the integration of hydropower and solar energy within the Lower Olt River cascade as a pathway toward sustainable energy generation in Romania. The study focuses on the conceptual design of future hybrid power plants consisting of existing hydropower facilities where floating photovoltaic panels are proposed to be installed on the reservoir’s surfaces. An estimation of electricity production from both sources was performed, followed by the formulation of a trading strategy for the July–September 2025 period. The paper also explores the interaction between tactical and strategic management in hydropower operation and planning, describing how forecasting and decision-making processes are structured within the institutional framework. Finally, results for the selected hydropower plants demonstrate the positive influence of floating photovoltaic deployment on company performance, the national energy mix, and the overall sustainability of energy generation in Romania. Full article

This study develops a coupled hydropower–ecological optimization model to balance energy production and ecosystem sustainability. The ecological objective is quantified by a composite Eco-Index, derived via Principal Component Analysis from seven key parameters of 32 Indicators of Hydrologic Alteration, enhancing representativeness while reducing computational complexity. Hydrological years are classified into wet, normal, and dry types using the Standardized Runoff Index and runoff quantiles, showing that wet years exhibit the strongest hydropower–ecology coupling, followed by normal and dry years. The optimized average annual hydropower revenues are 3.75 billion USD in wet years, 3.10 billion USD in normal years, and 2.70 billion USD in dry years, with average EI values being 0.35, 0.27 and 0.26, respectively. Spatial analysis identifies Xiaowan and Nuozhadu reservoirs as critical control points sensitive to hydrological variability. Moreover, optimization substantially enhances system resilience and reduces vulnerability. These results demonstrate that coordinated cascade reservoir operation can improve system robustness while signaling a caveat for careful trade-offs between economic and ecological objectives. Full article

Reliable long-term runoff prediction for Wudongde and Three Gorges reservoirs, two major reservoirs in the upper Yangtze River basin, is crucial for optimal operation of cascade reservoirs and hydropower generation planning. This study develops a data-driven model that integrates large-scale climate factors with a Gated Recurrent Unit (GRU) neural network to enhance runoff forecasting at lead times of 7–18 months. Key climate predictors were systematically selected using correlation analysis and stepwise regression before being fed into the GRU model. Evaluation results demonstrate that the proposed model can skillfully predict the variability and magnitude of reservoir inflow. For Wudongde Reservoir, the model achieved a mean correlation coefficient (CC) of 0.71 and Kling–Gupta Efficiency (KGE) of 0.57 during the training period, and values of 0.69 and 0.53 respectively during the testing period. For Three Gorges Reservoir, the CC was 0.67 (training) and 0.66 (testing), and the KGE was 0.52 and 0.49 respectively. The model exhibited robust forecasting capabilities across a range of lead times but showed distinct seasonal variations, with superior performance in summer and winter compared to transitional months (April and October). This framework provides a valuable tool for long-term runoff forecasting by effectively linking large-scale climate signals to local hydrological responses. Full article

Multi-source coordinated scheduling has become the predominant operational paradigm in power systems. However, substantial differences among hydropower, thermal power, wind power, and photovoltaic sources in terms of response speed, regulation capability, and operational constraints—particularly the complex generation characteristics and spatiotemporal hydraulic coupling of large-scale cascade hydropower stations—significantly increase the complexity of coordinated scheduling. Therefore, this study proposes an optimization method for determining the day-ahead generation intervals of cascade hydropower, applicable to multi-source coordinated scheduling scenarios. The method fully accounts for the operational characteristics of hydropower and the requirements of coordinated scheduling. By incorporating stochastic operational processes, such as reservoir levels and power outputs, feasible boundaries are constructed to represent the inherent uncertainties in hydropower operations. A stochastic optimization model is then formulated to determine the generation intervals. To enhance computational tractability and solution accuracy, a linearization technique for stochastic constraints based on duality theory is introduced, enabling efficient and reliable identification of hydropower generation capability intervals under varying system conditions. In practical applications, other energy sources can develop their generation schedules based on the feasible generation intervals provided by hydropower, thereby effectively reducing the complexity of multi-source coordination and fully leveraging the regulation potential of hydropower. Multi-scenario simulations conducted on six downstream cascade reservoirs in a river basin in Southwest China demonstrate that the proposed method significantly enhances system adaptability and scheduling efficiency. The method exhibits strong engineering applicability and provides robust support for multi-source coordinated operation. Full article

The efficient and rational development of hydropower in the Lancang–Mekong River Basin can promote green energy transition, reduce carbon emissions, prevent and mitigate flood and drought disasters, and ensure the sustainable development of the entire basin. In this study, based on publicly available hydrometeorological observation data and satellite remote sensing monitoring data from 2001 to 2020, a machine learning model of the Lancang–Mekong Basin was developed to reconstruct the basin’s hydrological processes, and identify the occurrence patterns and influencing mechanisms of water-related hazards. The results show that, against the background of climate change, the Lancang–Mekong Basin is affected by the increasing frequency and intensity of extreme precipitation events. In particular, Rx1day, Rx5day, R10mm, and R95p (extreme precipitation indicators determined by the World Meteorological Organization’s Expert Group on Climate Change Monitoring and Extreme Climate Events) in the northwestern part of the Mekong River Basin show upward trends, with the average maximum daily rainfall increasing by 1.8 mm/year and the total extreme precipitation increasing by 18 mm/year on average. The risks of flood and drought disasters will continue to rise. The flood peak period is mainly concentrated in August and September, with the annual maximum flood peak ranging from 5600 to 8500 m³/s. The Stung Treng Station exhibits longer drought duration, greater severity, and higher peak intensity than the Chiang Saen and Pakse Stations. At the Pakse Station, climate change and hydropower development have altered the non-drought proportion by −12.50% and +15.90%, respectively. For the Chiang Saen Station, the fragmentation degree of the drought index time series under the baseline, naturalized, and hydropower development scenarios is 0.901, 1.16, and 0.775, respectively. These results indicate that hydropower development has effectively reduced the frequency of rapid drought–flood transitions within the basin, thereby alleviating pressure on drought management efforts. The regulatory role of the cascade reservoirs in the Lancang River can mitigate risks posed by climate change, weaken adverse effects, reduce flood peak flows, alleviate hydrological droughts in the dry season, and decrease flash drought–flood transitions in the basin. The research findings can enable basin managers to proactively address climate change, develop science-based technical pathways for hydropower dispatch, and formulate adaptive disaster prevention and mitigation strategies. Full article

Effective management of cascade reservoir systems is essential for balancing hydropower generation, flood control, and ecological sustainability, especially under increasingly uncertain runoff conditions driven by climate change. Traditional optimization methods, while widely used, often struggle with high dimensionality and fail to adequately address inflow variability. This study introduces a novel deep reinforcement learning (DRL) framework that tightly couples probabilistic runoff forecasting with adaptive reservoir scheduling. We integrate a Long Short-Term Memory (LSTM) neural network to model runoff uncertainty and generate probabilistic inflow forecasts, which are then embedded into a Proximal Policy Optimization (PPO) algorithm via Monte Carlo sampling. This unified forecast–optimize architecture allows for dynamic policy adjustment in response to stochastic hydrological conditions. A case study on China’s Xiluodu–Xiangjiaba cascade system demonstrates that the proposed LSTM-PPO framework achieves superior performance compared to traditional baselines, notably improving power output, storage utilization, and spillage reduction. The results highlight the method’s robustness and scalability, suggesting strong potential for supporting resilient water–energy nexus management under complex environmental uncertainty. Full article

In this paper, improved reinforcement learning (IRL) is designed for the multi-objective optimization operation of a cascade reservoir system. The primary improvement of IRL is searching within limited solution space, based on the derived monotonic property: the first-order derivative relationship between individual reservoir water release decisions for mainstream use (i.e., hydropower generation) as well as tributary use (i.e., regional water supply) and the cascade system’s or a particular reservoir’s water availability, along with the synchronicity and substitutability assumption of storage distribution in the cascade system. The improved algorithm is then applied to a real-world cascade reservoir system in the Yangtze River of China. The results demonstrate the high computational efficiency and reasonable interpretability of IRL. Full article

The daily operation of cascade hydropower stations induces periodic water level fluctuations (WLFs) that propagate as gravity waves, significantly affecting the hydrodynamics of reservoirs. Previous studies have mainly focused on the effects of individual stations, with little attention paid to the combined impacts of upstream and downstream operations. Taking the Wudongde Reservoir on the Jinsha River as a case study, we used a one-dimensional hydrodynamic model and cross-correlation analysis to simulate flow fluctuation patterns under joint daily operations. The results show that fluctuations from upstream stations attenuate rapidly in the reservoir, with greater attenuation during the dry season. Under joint operations, wave energy decayed exponentially near the reservoir tail and linearly in the main reservoir area, leading to a further reduction in the WLF amplitudes. The interactions between upstream- and downstream-propagating waves enhance energy dissipation. The wave type transitioned from kinematic to dynamic as the water depth increased. During the wet and dry seasons, the average wave velocities were approximately six and nine times higher, respectively, than those under natural conditions. Joint operations expand the range of potential slope instability but reduce the WLF rate compared to natural flows. These findings provide a scientific reference for optimising the daily operations of cascade hydropower stations and mitigating their ecological impacts. Full article

Hybrid pumped hydro storage plants, by integrating pump stations between cascade hydropower stations, have overcome the challenges associated with site selection and construction of pure pumped hydro storage systems, thereby becoming the optimal large-scale energy storage solution for enhancing the absorption of renewable energy. However, the multi-energy conversion between pump stations, hydropower, wind power, and photovoltaic plants poses challenges to both their planning schemes and operational performance. This study proposes a multistage stochastic coordinated planning model for cascade hydropower-wind-solar-thermal-pumped hydro storage (CHWS-PHS) systems. First, a Hybrid Pumped Hydro Storage Adaptive Initial Reservoir Capacity (HPHS-AIRC) strategy is developed to enhance the system’s regulation capability by optimizing initial reservoir levels that are synchronized with renewable generation patterns. Then, Non-anticipativity Constraints (NACs) are incorporated into this model to ensure the dynamic adaptation of investment decisions under multi-timescale uncertainties, including inter-annual natural water inflow (NWI) variations and hourly fluctuations in wind and solar power. Simulation results on the IEEE 118-bus system show that the proposed MSSP model reduces total costs by 6% compared with the traditional two-stage approach (TSSP). Moreover, the HPHS-AIRC strategy improves pumped hydro utilization by 33.8%, particularly benefiting scenarios with drought conditions or operational constraints. Full article

The Yellow River (YR), China’s second-largest river, is rich in water resources, particularly in its upper reaches, which are characterized by mountainous canyons and considerable hydropower potential. Since the 1950s, 24 reservoirs have been constructed along a 918 km stretch of the upper Yellow River (UYR), creating the highest concentration of cascade reservoirs. This development has had significant ecological impacts on the surrounding environment. This study examines the relationship between reservoir attributes and climate factors to evaluate the environmental effects of reservoirs in the UYR. (1) Following reservoir construction, the average annual temperature and precipitation increased by 3–10%, though seasonal and spatial distributions varied. Temperature increases were most pronounced in winter, while precipitation decreased in some regions during spring and summer, although the overall trend remained positive. (2) The ecosystem experienced significant post-construction changes, including reductions in arable land, grassland, and unused land, while water bodies, construction land, and forests expanded. Consequently, the ecosystem within the reservoir area now accounts for 5.2–12.5% of the total area of the region. (3) Temperature and precipitation were closely linked to reservoir attributes, with storage volume (CAP) and long-term average flow (DIS) significantly affecting precipitation, while surface area (AREA) and normal storage level (FSL) had a greater influence on temperature. In conclusion, the dual impacts of reservoir construction on local climate and land use highlight the complex environmental mechanisms involved, providing valuable insights for future reservoir development and ecological protection in the Yellow River Basin and similar regions. Full article

Due to water scarcity in the Yellow River basin, the existing operations for the Longyangxia and Liujiashan cascade reservoirs are insufficient to meet the demands of multiple objectives. This study establishes a coupled coordination model considering hydropower generation, water supply, and storage capacity at different periods during the year. At the same time, the model quantifies the impact of scheduling strategies on multiple objectives and determines the optimal operation for reservoirs at different periods. The results indicate that the scheduling strategy of the Longyangxia reservoir dominates the changes in hydropower generation, water supply, and storage capacity. Specifically, during the ice flood control period, the scenario of continuous release from Longyangxia and continuous storage at Liujiaxia achieves 1.26 billion kWh of hydropower generation, with a water supply shortage rate of 8.67%; During the non-flood period, releasing water from Longyangxia in April and May and storing it in June while Liujiaxia continuously releases water results in 4.68 billion kWh of hydropower generation and a shortage rate of 1.61%. During the flood control period, continuous storage at Longyangxia and controlling the water level of Liujiashan within flood control limits, with storage in September and release in October, achieves 5.65 billion kWh of hydropower generation and a shortage rate of 0%. Full article

Hydrological changes caused by dam construction are among the primary drivers of global freshwater biodiversity decline. To assess the current status of fish community diversity and examine the impacts of cascade hydropower development on fish diversity, this study employed environmental DNA (eDNA) technology from 2023 to 2024 to conduct seasonal surveys at 18 sampling sites across six river segments separated by five dams in the downstream section of the Fujiang River. The study aimed to uncover the temporal and spatial dynamics of fish diversity and community structure, as well as to analyze the influence of environmental factors on these patterns. The results identified 84 fish species spanning 60 genera, 19 families, and 7 orders, including 2 nationally protected species, 11 endemic species of the upper Yangtze River, and 13 alien species. The cascade dams were found to have significantly reduced fish diversity compared to historical records, with a marked decline in native species and a rise in alien species, contributing to the miniaturization and homogenization of fish communities. Environmental factor analysis revealed that chemical oxygen demand (COD), dissolved oxygen (DO), total dissolved solids (TDS), electrical conductivity (EC), and reservoir formation time were significant drivers of fish community structure and diversity. This study provides essential baseline data on fish diversity under the influence of cascade hydropower development in the Fujiang River. It also offers valuable insights into the current status of fish resources and supports efforts in fish conservation and aquatic ecosystem management in the region. Full article

The uncertainty of natural inflows and market behavior challenges ensuring a reliable power balance in hydropower-dominated electricity markets. This study proposes a novel framework integrating hourly load balancing on typical days into a monthly scheduling model solved with Gurobi11.0.1 to evaluate demand-met reliability across storage and inflow states. By employing total storage as a system state to reduce dimensional complexity and simulating future runoff scenarios based on current inflows, the method performs multi-year statistical simulations to assess reliability over the following year. Applied to a system of 39 hydropower reservoirs in China, the case studies of present models and procedures suggest: (1) controlling reservoir storage levels during the dry season is crucial for ensuring the power demand-met rate in the following year, with May being the most critical month; (2) the power demand-met rate does not monotonically increase with higher storage levels—there is an optimal storage level that maximizes the demand-met rate; and (3) June and October offer the greatest flexibility in storage adjustment to achieve the highest demand-met reliability. Full article