Hydropower Generation Research Articles

Multi-Objective Synergetic Operation for Cascade Reservoirs in the Upper Yellow River

The Yellow River, a critical water resource, faces challenges stemming from increasing water demand, which has led to detrimental effects on hydropower generation and ecological balance. This paper will address the complex task of balancing the interests of hydropower generation, water supply, and ecology within the context of cascade reservoirs, specifically Longyangxia and Liujiaxia reservoirs. Employing a systemic coupling coordination approach, we constructed a multi-objective synergetic model of the upper Yellow River in order to explore synergies and competitions among multiple objectives. The results reveal that there is a weak competitive relationship between hydropower generation and water supply, a strong synergy between hydropower generation and ecology, and a strong competitive relationship between water supply and ecology. The Pareto solution set analysis indicates a considerable percentage (59%, 20%, and 8% in wet, normal, and dry years, respectively) exhibiting excellent coordination. The probability of excellent coordination decreases with diminishing inflow. The optimization scheme with the highest coupling coordination demonstrates significant improvements in power generation, water supply, and ecological benefits in the upper Yellow River without compromising other objectives, fostering the sustainable operation of hydropower generation, water supply, and ecology in the upper Yellow River.

Effects of Cascading Dams on Streamflow within the Downstream Areas of the Rufiji River Basin in Tanzania

Despite their popularity, the construction and operation of hydropower reservoirs pose challenges to water resources. This study investigated the impacts of cascading dams on streamflow in Tanzania’s Rufiji River Basin. The SWAT model was developed to represent the entire Rufiji River Basin. The model simulated the streamflow for 41 years, from 1982 to 2022, and developed two main scenarios: with-dam and without-dam. To capture the influence of all dams, the results were emphasized from 2000 to 2022, when all three dams were operating. Calibration and validation were applied at the Rufiji-Stiegler and Kilombero-Swero stations with good performance. The results show that cascading dams annually decrease the streamflow by 1% at Rufiji-Stiegler station. In contrast, individually, the Mtera Dam displayed a 5% decrease while the Kidatu and Kihansi Dams exerted a 1% increase on the annual streamflow downstream at Rufiji-Stiegler. During 2000–2022, the Rufiji River Basin showed an annual reduction in streamflow contribution of 104.97 m3/s. Therefore, the reservoir’s operation significantly impacts the downstream streamflow. The findings are expected to guide policymakers, water resource managers, and environmentalists in mitigating potential adverse effects while optimizing the benefits of hydropower generation and water regulation within the region.

Evaluation of hydrological responses to climate change in the Gibe Gojeb catchment, southwestern Ethiopia

ABSTRACT The expected influences of climate alteration on hydrological responses in the Gibe Gojeb catchment, southwestern Ethiopia, were evaluated using the Soil and Water Assessment Tool (SWAT) model and regional climate models (RCMs). This study emphasizes the evaluation of the climate change impact on hydrological responses. The RCM data were downloaded from CORDEX-Africa. Power transformation and delta change methods were used for bias correction of precipitation and temperature, respectively. The calibration and validation of the SWAT model showed good agreement, and climate change impact was simulated. Accordingly, precipitation, soil water content, percolation, water yield, and groundwater are projected to decrease by 28.18, 7.5, 9.1, 5.4, and 9.2%, respectively, under RCP4.5 in the future (2021–2050). Besides, surface runoff and potential and actual evapotranspiration are projected to increase by 17.4, 11.32, and 5.06%, respectively. Soil water content, percolation, water yield, and precipitation are projected to decrease by 10.51, 5.63, 2.76, and 21.57%, respectively, under RCP8.5 in the future. Rain-fed crop production and hydropower generation in the catchment would be vulnerable to the effects of climate change. The results help to develop effective adaptation measures to reduce the impacts of climate change and draft long-term water resource management plans in the Gibe Gojeb catchment.

Harvesting Electricity from Water Pipeline in Hydro Power Generator

The purpose of this endeavour is to build a small-scale hydroelectric power plant that can produce energy for household use using water from a storage tank. One trustworthy and efficient kind of clean, green power is picohydroelectricity. It generates electricity with low maintenance and no fuel use. Water moving through residential pipes provides the kinetic energy to generate electricity for storage of energy. The design and construction of a pico-hydro generating system that extracts water from the water tanks of residential buildings are covered in this study. Three unique mechanical arrangements are added for improved operation and energy production: U-tube piping, a big nozzle pipe end, and an air bladder for sustaining water pressure. As a result, we might design a mechanical system which could produce electricity using the potential energy contained in a water storage tank from a water head. Key words: Renewable energy, Hydroelectricity, turbine , diaphragm pump, portable etc.

Design and experimental analysis of a new vertical ultra-low-head hydro turbine with the variation of outlet flow level on the head drop section of an open canal

Hydropower generation from ultra-low-head water resources is the salient category. The power can be tapped through different non-conventional turbines and is developing these days with different features. This research aims to develop a technology that applies to ultra-low-head resources from conceptualization to laboratory testing. Among several ultra-low-head water resources, it is concentrated on the power generation from the head drop section of the open canal flow through a new hydro turbine. A vertical shaft turbine with an axial flow type of runner having fixed, flat, and constant blade thickness including fixed guide vanes has been designed conventionally for 0.5 m of head and 0.088 m3/s of discharge. Several experiments have been conducted with fixing the level of flow outlet control from 0.0 m to 0.5 m creating the runner from free to submerged conditions is the novelty of this research. The results obtained for all the independent and dependent variables are presented and discussed for a constant flow of 0.088 m3/s and constant speed of 115 rpm conditions. The uncertainty on measurement was found ±1.85 % and the overall maximum efficiency obtained was 53.8 % for flow 10 % above the design flow. The research has concluded the use of outlet flow control is necessary to achieve the maximum efficiency of the turbine at a specific height and the overall efficiency can be advanced further through the optimization of blade profile.

Dynamic quantitative assessment of multiple uncertainty sources in future hydropower generation prediction of cascade reservoirs with hydrological variations

Hydropower is the dominant renewable energy source, which can significantly reduce carbon dioxide emissions while providing electric energy. It is a crucial way to achieve the dual-carbon goal of carbon peak and carbon neutrality. However, climate change inevitably threatens hydropower, either directly or indirectly. Global climate models (GCMs) and hydrological models (HMs) under different representative concentration pathways (RCPs) have been combined to investigate future hydropower generation responses to climate change. The contribution of multiple uncertainty sources to multi-scale hydropower generation uncertainties with hydrological variations remains unclear. Therefore, to address the above challenge, a three-step framework was proposed in this paper: (1) predicting future inflows to reservoirs using three HMs (HBV, SWAT and XAJ) with different structures driven by six GCMs (BNU-ESM, GFDL-ESM2G, GFDL-ESM2M, CanESM2, MIROC-ESM-CHEM and CSIRO-MK3-6-0) under three RCPs (i.e., RCP2.6, RCP4.5 and RCP8.5); (2) diagnosing future hydrological conditions using the Comprehensive Differential Split-sample Test; (3) predicting future hydropower generation of cascade reservoirs by developing a cascade hydropower generation model and dynamically quantifying the contribution of multiple uncertainty sources to multi-scale hydropower generation uncertainties with hydrological conditions based on the variance decomposition method through subsampling. The results indicate that GCMs, HMs, climate emission scenarios and their interactions significantly impacted the uncertainty of hydropower generation with hydrological variations. In the flood season, the relative contributions of GCMs and HMs were the largest, accounting for about 40 %. The interaction effects of multiple uncertainty sources gradually increased before, after and during the non-flood season, with its relative contribution accounting for about 36 %. These findings can facilitate the active response to the new challenges of strategic hydropower energy development under climate change, reducing the losses caused by climate change.

Vulnerability and resilience of hydropower generation under climate change scenarios: Haditha dam reservoir case study

Hydropower is the primary renewable energy source that uses the energy of flowing water streams from rivers and reservoirs, and the hydrological patterns and conditions significantly influence its potential. The study aims to explore how climate change would impact the Euphrates River Basin's discharge patterns, the reservoir's inflow volume, and the potential of future hydropower plants at Haditha dam in the hyper-arid region by using downscaled five General Circulation Models (GCMs) driven by CMIP6 and three scenarios of the Shared Socioeconomic Pathway (SSPs). The forecasts indicated an overall basin temperature increase (up to 2.3 °C) and a significant decrease in precipitation (up to −6.2%). The flow declined by 18.28%, and the reduction in inflow volume of the reservoir ranged from 57 to 44 BCM, a reduction of up to 14.71%. The hydropower-generated output drawdown is up to 15%, ranging from 0.29 to 1.16 TWh per annum. The hydropower performance criteria indices under scenarios (SSP1–2.6, SSP2–4.5, and SSP5–8.5) revealed a downtrend in the resilience index, where the recovery rate (17.63, 34.25, and 41.75%), respectively, indicating that the reservoir requires a relatively long period to meet the target hydropower demand, leading to a vulnerability increase.

Predicting climate-driven changes in reservoir inflows and hydropower in Côte d'Ivoire using machine learning modeling

This study investigates the impact of climate change and variability on reservoir inflow and hydropower generation at three key hydropower plants in Côte d'Ivoire including Buyo, Kossou, and Taboo. To simulate inflow to reservoir and energy generation, the Random Forest (RF), a machine-learning algorithm allowing fewer input variables was applied. In three-step, RF k-fold cross validation (with k = 5) was used; (i) 12 and 6 multiple lags of precipitation and temperature at monthly increments were used as predictors, respectively; (ii) the five most important variables were used in addition to the current month's precipitation and temperature; and (iii) a residual RF was built. The bias-adjusted ensemble mean of eleven climate models output of the COordinated Regional Downscaling Experiment was used for the representative concentration pathways (RCP4.5 and RCP8.5). The model output was highly correlated with the observations, with Pearson correlations >0.90 for inflow and >0.85 for energy for the three hydropower plants. The temperature in the selected sub-catchments may increase significantly from 0.9 to 3 °C in the near (2040–2069) and from 1.7 to 4.2 °C in far (2070–2099) future periods relative to the reference period (1981–2010). A time series of precipitation showed a change in range −7 and 15 % in the near and −8 to 20 % in the far future and more years are with increasing change. Depending on the sub-catchment, the magnitude of temperature and precipitation changes will increase as greenhouse gas emissions (GHG)(greater in RCP8.5 than RCP4.5) rise. At all time scales (monthly, seasonal, and annual), the simulated inflow and energy changes were related to climate variables such as temperature and precipitation. At the annual time scale, the inflow is projected to change between −10 and 37 % and variability may depend on the reservoir. However, the energy change is promised to change between −10 and 25 %, −30 to 15 %, and 5–40 % relative to the historical (1981–2010) period for Taabo, Kossou, and Buyo dams, respectively at an annual scale. The changes may vary according to the year, the RCPs, and the dam. Consequently, decision-makers are recommended to take into consideration an energy mix plan to meet the energy demand in these seasons.

Evolution and future prospects of hydropower sector in Nepal: A review

Nepal is one of the pioneers of hydropower development among Asian countries. The plethora of fast-flowing rivers provides immense potential for hydropower generation. However, Nepal still lacks a clear blueprint for the overall development and management of this sector. This paper aims to review the evolution of hydropower development, future prospects and roadblocks to hydropower development. With the growing energy demands projected to reach as high as 41,264.82 Gigawatt hours (GWh) in 2030 and 115,294.4 GWh in 2040 under different scenarios, this paper highlights the huge prospects the sector holds. It also proposes a focus on storage-type hydropower plants and concepts of energy banking to address the incipient condition of seasonal energy mismatch in the country, which has developed a condition of energy shortage during the winter and energy surplus during the monsoon. Moreover, projected changes in hydro-climatic extremes under the climate change scenarios is likely to affect water availability and subsequently the energy production in the majority of hydropower projects. Thus, this review can serve as a guideline to help understand the current scenario and make rational decisions and policies for the future management of the hydropower sector of the country.

Corrosion-fatigue properties of 13%Cr4%Ni steel ForHydroturbine components

This paper thoroughly investigates the corrosion-fatigue properties of 13 % Cr-4 % Ni stainless steel, specifically focusing on its application in hydro turbine components. The study aims to comprehensively analyze the material’s resistance to corrosion and fatigue, elucidating the key factors influencing these properties and exploring the methodologies employed for testing and evaluation. By examining the practical implications of these properties for the design, operation, and maintenance of hydroelectric power plants, the research underscores the critical role of 13 % Cr-4 % Ni steel in facilitating sustainable and reliable hydropower generation. Through rigorous investigation and analysis, this paper contributes to advancing our understanding of material performance in hydropower applications, ultimately informing decision-making processes for the selection and utilization of materials in this vital energy sector.

Harnessing CFD simulations and CAD design to optimize hydropower efficiency of a propeller turbine in the Maliringan River: acase study

This research focuses on improving hydropower production by designing a turbine for the MaliringanRiver in Kalimantan Selatan. The rotational speed and torque will be the focus of maximizing power generation. This research used Computational Fluid Dynamics (CFD) to understand how the fluid flows and how efficiently the turbine works. The simulations helped us see how the fluid flowed, the pressure differences, and the speed of the water inside the turbine. The software COMSOL Multiphysics imitates how the fluid behaves and interacts in real-life situations. The utilization of SOLIDWORKS played a crucial role in the turbine's design process, facilitating an accurate representation of the turbine's geometry and the subsequent fabrication of a prototype propeller turbine, featuring an outer diameter measuring 0.27 meters and an inner diameter measuring 0.113 meters. The methodology resulted in a power efficiency of 76.45%, showcasing the possibility of significant enhancements in the efficiency of hydropower generation. The broader ramifications of this study emphasize the feasibility of tailor-made turbines for local hydropower initiatives, thereby supporting Indonesia's renewable energy plan by providing sustainable and efficient energy alternatives. This study emphasizes the collaborative utilization of Computer-Aided Design (CAD) and CFD technologies in the progression of turbine technology, thereby establishing a basis for future investigations in hydropower optimization.

Modeling and future projection of streamflow and sediment yield in a sub-basin of Euphrates River under the effect of climate change

ABSTRACT Recognizing the differential impacts of climate change across geographical scales, this study emphasizes the importance of statistical downscaling. Using Gene Expression Programming (GEP) and Linear Genetic Programming (LGP), statistical downscaling transforms broad climate trends into region-specific insights. This allowed for detailed analyses of anticipated changes in sediment yield and discharge within a Euphrates River sub-basin in Türkiye using large-scale variables from the CanESM2 model. The dataset is divided into calibration (1970–1995) and validation (1996–2005) periods. To assess the models’ accuracy, statistical measures such as RMSE, MAE, NSE, and R were used. The analysis revealed that LGP outperformed GEP in both discharge and sediment yield during validation, with RMSE = 51.79 m3/s and 4,325.66 tons/day, MAE = 27.14 m3/s and 1,593.34 tons/day, NSE = 0.684 and 0.627, and R = 0.841 and 0.788, respectively. However, when simulating future periods based on the observed period (2006–2020), the GEP model was superior to LGP under RCP2.6, RCP4.5, and RCP 8.5 scenarios from CanESM2. In 2021–2100, models suggest a moderate decrease in discharge and sediment yield, indicating potential shifts in the basin's hydrodynamics. These changes could disrupt hydropower generation, challenge water management practices, and alter riverine ecosystems. The results necessitate a thorough assessment of potential ecological consequences.

Water Level Fluctuation Effect on Fish Reproduction Success

Alteration of water levels of the lake/reservoir due to changes in river discharges, upstream abstraction, and drawdown regulation due to hydropower generation has been reported as among the major challenges to fish physiology and ultimately reproduction. Variations in lake/reservoir water levels influence biodiversity and the abundance of lake/reservoir biota. Variations have the greatest impacts in the littoral zones hence, interfering with fish spawning, incubation, and hatching of eggs, as well as the development of larvae, postlarvae, and juveniles. This particular paper reviews the literature available on the effect of the reservoirs/lakes’ water level fluctuations on fish reproduction success using the documentary view method. The literature argues that water level fluctuations have both positive and negative impacts depending on frequency, magnitude, and duration and the species exposed. Extreme fluctuations tend to bring more adverse impacts. It further indicates that extreme and untimely water level fluctuation has direct impacts on the aquatic habitats and ultimately impacts fish assemblage and their populations. Few articles indicate the range of decrease or increase of water level, duration of such events, and effects they have on reservoir/lake ecosystem functions and fish physiology. Nevertheless, quantification of moderate and extreme water level fluctuation and associated effects is lacking. Although water level fluctuation is an important environmental cue for aquatic organisms, literature shows that the reduction of extreme water level variations especially multiannual variations is vital for fish reproduction. Therefore, upstream water use and reservoir operations should take into consideration the effects of water level fluctuations on reservoir structure, biological functions, and ultimate effects on fish reproduction.

Hydraulic modeling of the Periyar River using HEC-RAS: unraveling flow dynamics.

The Periyar River, a vital component of Kerala's ecosystem in India, serves as a lifeline supporting agriculture, hydropower generation, and ecological equilibrium. This study adopts a multifaceted approach to address critical challenges in the Periyar basin, with a primary focus on flood mitigation due to the region's susceptibility to devastating floods. Covering a length of 67.85 km, the study intricately segments the Periyar River into distinct reaches for a comprehensive steady flow analysis, considering factors such as seasonal monsoon fluctuations, diverse catchment topography, and human-induced alterations. Utilizing advanced modeling techniques, particularly HEC-RAS software, the study effectively predicts and simulates shifts in hydraulic behavior. The results, including velocity plots and cross-sectional maps, offer accurate insights into critical parameters, enabling the identification of areas with high velocity occurrence. This information proves instrumental in making informed decisions for the construction of river restoration structures, crucial for mitigating the impact of floods. The study's findings contribute valuable tools for future forecasting and sustainable management of the Periyar River, addressing the complex interplay of natural and anthropogenic factors.

Investigations on the performance of bottle blade overshot water wheel in very low head resources for pico hydropower

Abstract An overshot water wheel is considered as a low-cost and simple alternative for rural electrification. Hence, the aim of this study is to develop a noncomplex geometrical design of an overshot water wheel, namely, bottle blade overshot water wheel (BOWW), yet effectively operated for pico hydropower generation. The BOWW and hydro system were assessed at very low water head conditions, namely, 0.3, 0.4, and 0.5 m water head using a water test rig. According to the experimental results, the BOWW equipped with eight blades when operated at 0.5 m head produced significant mechanical power and electrical power up to 48.58 and 15.55 W. Analytically, the turbine with eight blades has 26.8–49.1% efficiency. Meanwhile, the effectiveness of four blades is between 5.66 and 28.96%. The results showed that eight blades performed well. Finally, it proves that, with a very low water head and low flow rate, the proposed system can perform and produce a significant power output for power generation.

Water-Level Prediction Analysis for the Three Gorges Reservoir Area Based on a Hybrid Model of LSTM and Its Variants

The Three Gorges Hydropower Station, the largest in the world, plays a pivotal role in hydroelectric power generation, flood control, navigation, and ecological conservation. The water level of the Three Gorges Reservoir has a direct impact on these aspects. Accurate prediction of the reservoir’s water level, especially in the dam area, is of utmost importance for downstream regions’ safety and economic development. This study investigates the application and performance of four distinct deep-learning models in predicting water levels. The models evaluated include the Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM), and Convolutional Neural Network–Attention–Long Short-Term Memory (CNN–Attention–LSTM). The performance of these models was assessed using several metrics, namely the Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings indicate that the CNN–Attention–LSTM model outperforms the others in all metrics, achieving an R2 value of 0.9940, MAE of 0.5296, RMSE of 0.6748, and MAPE of 0.0032. Moreover, the CNN–LSTM model exhibited exceptional predictive accuracy for lower water levels. These results underscore the potential of deep-learning models in water-level forecasting, particularly highlighting the efficacy of attention mechanisms in enhancing predictive accuracy. Precise water-level predictions are instrumental in optimizing hydropower generation and providing a scientific basis for effective flood control and water resource management.

Value of long-term inflow forecast for hydropower operation: A case study in a low forecast precision region

Many studies have evaluated the value of long-term inflow forecast for hydropower operation in high-precision watersheds and explored the impact of forecast uncertainty on hydropower benefits. However, few of them focuses on the low-accuracy regions and the specific impact of forecast errors on hydropower benefits. Taking Hunjiang cascaded system in China as an example, this study investigated the value of low accuracy long-term inflow forecast for hydropower operation, revealed the influence mechanism of forecast errors on hydropower benefits, and determined the critical threshold for accuracy of beneficial forecast. Results show that low accuracy forecast but higher than critical threshold can increase hydropower generation. Hunjiang has a low accuracy long-term forecast with qualified rate being 30–40 %, but annual hydropower generation can be increased by 14.86–29.58 million kWh. The influence mechanism of forecast errors on hydropower operation presents three typical scenarios, two of which are harmless. One is that the error tolerance of operation rules can avoid some decision-making errors; the other one is that when current misreporting is consistent with the inflow in a longer lead time, the misreporting may instead be beneficial. We conclude that low accuracy long-term forecast is valuable but higher accuracy brings higher benefits.

Three-Dimensional Modeling for Mechanical Analysis of Hydropower Generators with Floating Rotor Rim

Three-Dimensional Modeling for Mechanical Analysis of Hydropower Generators with Floating Rotor Rim

Impact of dam construction on sediment transport capacity: A case study of the Three Gorges Reservoir

AbstractReservoir sedimentation affects lifespan, hydropower generation, water resource use, and ecosystem sustainability. In this study, using measured data from the world‐renowned Three Gorges Reservoir (TGR), sediment transport capacity was analysed from 2002 to 2020 at four TGR gauging stations (Cuntan, Qingxichang, Wanxian, and Miaohe). The result indicated that: (1) a significant decrease in sediment transport capacity occurred in the TGR before and after dam construction, with an overall reduction of 82% in the perennial backwater zone. Notably, Qingxichang, Wanxian, and Miaohe experienced reductions of 40%, 91%, and 59%, respectively. Since the initial operation phase, their maximum monthly sediment transport capacity has consistently occurred in July. (2) Changes in the water level in front of the dam, the incoming flow, and sediment influxes were the primary factors driving the annual variations in sediment transport capacity. Among the factors affecting the variation of sediment transport capacity along the TGR, the influence of the cross‐sectional morphology was more pronounced than that of the distance from the dam. Sediment transport capacities in the wide and shallow sections were considerably lower than those in the narrow and deep sections. (3) A sediment transport amplitude index () was proposed to assess the sediment dynamics within the TGR, which effectively characterized the sedimentation conditions based on the relative importance of these influencing factors. These findings provide a theoretical basis for operational strategies in the TGR and offer insights into the patterns of sediment transport capacity changes in reservoir areas before and after dam construction in other river‐type reservoirs.

Regional Precipitation Regimes and Evaluation of National Precipitation Datasets against Satellite-Based Precipitation Estimates, Republic of Georgia

Abstract Analyzing water resources in areas with few hydrometeorological stations, such as those in post-Soviet countries, is difficult due to station closures after 1989. In Caucasus, evaluations often rely on outdated data from nearby rivers. We evaluated one national-level precipitation dataset, the Water Balance of Georgia (WBG) with two satellite-based precipitation products from 1981 to 2021, including the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data and CHIRPS blended with a dense rain gauge network (geoCHIRPS). We modeled mean annual precipitation from geoCHIRPS as a function of coastal distance and elevation. CHIRPS underestimated precipitation in the cold and wet seasons (R2 = 0.74, r = 0.86) and overestimated dry season precipitation, while geoCHIRPS performed well in all seasons (R2 = 0.86, r = 0.92). Distance from the coast was a more important predictor of precipitation than elevation in western Georgia, while precipitation correlated positively with elevation in the east. At four hydroelectric plants, the underperformance as a percentage of capacity (∼37%) corresponds with the percentage difference between differences in precipitation products (∼38%), suggesting that plants designed based on WBG may be systematically overdesigned, but further work is needed to determine the reasons for the underperformance of the plants and frequency. We conclude that 1) the existing WBG does not accurately reflect elevation–precipitation relationships near the coast, and 2) for accurate analysis of spatiotemporal precipitation variability and its impacts on hydropower generation and environmental and sustainable water resource management, it is essential to calibrate satellite-based precipitation estimates with additional rain gauge data.