Hydropower Generation Research Articles

Research on Medium- and Long-Term Hydropower Generation Forecasting Method Based on LSTM and Transformer

Hydropower generation is influenced by various factors such as precipitation, temperature, and installed capacity, with hydrometeorological factors exhibiting significant temporal variability. This study proposes a hydropower generation forecasting method based on Transformer and SE-Attention for different provinces. In the model, the outputs of the Transformer and SE-Attention modules are fed into an LSTM layer to capture long-term data dependencies. The SE-Attention module is reintroduced to enhance the model’s focus on important temporal features, and a linear layer maps the hidden state of the last time step to the final output. The proposed Transformer-LSTM-SE model was tested using provincial hydropower generation data from Yunnan, Sichuan, and Chongqing. The experimental results demonstrate that this model achieves high accuracy and stability in medium- and long-term hydropower forecasting at the provincial level, with an average accuracy improvement of 33.79% over the LSTM model and 24.30% over the Transformer-LSTM model.

The Political Economy of Variations in Energy Debt Financing by Two Chinese Policy Banks in Africa

ABSTRACTThis article examines the puzzle of why China's two policy banks, China Development Bank (CDB) and the Export‐Import Bank of China (Eximbank), have lending portfolios for power‐generation projects in Africa that have drastically different levels of carbon dioxide emissions. From the supplier side, Eximbank balances two imperatives: Beijing's ideational ambition as a new development provider to African recipients with sustainability commitments, and China's industrial goal to offshore non‐renewable capacity. In contrast, the CDB prioritizes its commercial interests, which results in the bank lending solely for coal projects. On the demand side, Eximbank's concessional capital has emerged as a second‐best option among international financial sources for renewable and hydropower generation projects. Conversely, CDB's market‐rate lending makes it the fiscal last resort for host countries seeking financial support for thermal‐power projects which are shunned by other financiers. This divergence can be understood through the polycentric development finance model, which captures the parallel decision‐making institutions governing Chinese energy financing in Africa. Specifically, the lending decisions of Eximbank are linked with institutionalized policy processes, translating priorities of Chinese and African state actors. Meanwhile, the loan origination processes of CDB are more independent of state actors, allowing greater autonomy for the financier to pursue commercial interests.

Patch-scale habitat dynamics: three metrics to assess ecological impacts of frequent hydropeaking

Human activities significantly alter natural river flows, impacting ecosystem functioning and biodiversity worldwide. Hydropeaking, resulting from intermittent on-demand hydropower generation, introduces sub-daily flow fluctuations exceeding natural variability. While the effects of single hydropeaking events are well-studied, the cumulative impacts of frequent hydropeaking requires further exploration. This study aims to develop metrics that captures changes in habitat dynamics at the patch scale (i.e. individual micro-habitats within the habitat mosaic) due to reoccurring hydropeaking. Using hydrodynamic simulations, we introduce three patch-scale metrics to quantify habitat dynamics with high spatial (0.5 m) and temporal (10 min) resolution: (M1) Habitat probability within patches, assessing spatio-temporal diversity of habitats; (M2) Habitat shifts within patches, evaluating habitat persistence for sessile organisms (e.g. vegetation, invertebrates); and (M3) Spatial shifts of habitats, indicating habitat relocation affecting mobile species (e.g. adult fish). Using eight hydro-morphological scenarios representing different levels of anthropogenic modification of flow and morphology, we demonstrate that these metrics effectively quantify changes in habitat dynamics at patch-scale. The results highlight the ecological relevance of these metrics and their potentially utility for river management. By identifying areas susceptible to ecological impacts, these metrics may serve as tools for hydropeaking mitigation, enabling more targeted and spatially explicit habitat management and restoration.

Resolving the conflict between the need for electricity generation upstream and the water supply for economic development downstream in the Ca River Basin, Vietnam

ABSTRACT Vietnam has vigorously developed hydroelectric reservoirs in main river basins with hydroelectricity reserves to produce electricity and exploit and use water resources efficiently and minimally – risks to meet socio-economic development requirements – in the downstream area. The study has shown evidence that the water level's daily mean value of the Ca River is more and more reduced. However, the number of occurrences of the daily minimum flow was more than that of the period when it was not hydropower reservoirs and was affected by climate change. The study calculated and built five scenarios for water use in the downstream areas. These scenarios are calculated based on the water demand for domestic, industry, irrigation, and water sea level. A simulation model for optimal hydropower generation of reservoir system upstream and forced to discharge downstream with five scenarios. This study's results demonstrated that four upstream reservoirs could operate with scenario 3 corresponding HNam Dan = 0.7 m. The results suggested options for the operation of hydroelectric reservoirs and simultaneously developing scenarios for taking water for agricultural irrigation downstream of the Ca River Basin with water level for irrigation drains and domestic water factories. The benefit of electricity in four reservoirs in scenario 3 reached 1,009 billion VND.

Determination of effects of climate change on hydropower generation at Kamburu dam-Kenya.

This study aimed to determine the effects of climate change on hydropower generation at Kamburu Dam, Kenya. Kamburu power plant has been experiencing low volumes of water with the shortage of rainfall from its catchment area. This study primarily utilised rainfall and temperature data, as well as field survey data collected through questionnaires. Primary data was collected using questionnaires, photography, checklists, and observation guides. Secondary data sources included Kengen and the meteorological department's annual reports. The study used simple stratified sampling. The four villages of the Kivaa sub-location were sampled into 20 households, making a total of 80 households; Kengen and meteorological stations sampled every 10 respondents to make a total of 100 respondents. Collected data from the questionnaires were coded and analysed using SPSS 29. The results of this research showed that the area experiences increasing temperatures of 0.044? every year and decreasing rainfall of 0.5897mm every year. The research forecasted a decadal increase in temperature of 0.44? and a decadal decrease in rainfall of 5.897mm. The research also showed a decrease in hydropower of 18,790MWH every year, translating to a decadal decrease of 187,790MWH. The research concluded that climate change has greatly affected hydropower production at the Kamburu Dam power plant. The research recommends afforestation and re-afforestation activities, putting rules and regulations of watershed management, upgrading the Kamburu dam infrastructure to fit climate-resilient, expanding the capacity of the dam, and energy storage, among others.

The value of long-duration energy storage under various grid conditions in a zero-emissions future

Long-duration energy storage (LDES) is a key resource in enabling zero-emissions electricity grids but its role within different types of grids is not well understood. Using the Switch capacity expansion model, we model a zero-emissions Western Interconnect with high geographical resolution to understand the value of LDES under 39 scenarios with different generation mixes, transmission expansion, storage costs, and storage mandates. We find that a) LDES is particularly valuable in majority wind-powered regions and regions with diminishing hydropower generation, b) seasonal operation of storage becomes cost-effective if storage capital costs fall below US$5 kWh−1, and c) mandating the installation of enough LDES to enable year-long storage cycles would reduce electricity prices during times of high demand by over 70%. Given the asset and resource diversity of the Western Interconnect, our results can provide grid planners in many regions with guidance on how LDES impacts and is impacted by energy storage mandates, investments in LDES research and development, and generation mix and transmission expansion decisions.

Estimation of Monthly Irrigation Water Requirement of Crops Using CROPWAT

Water is increasingly becoming a limited resource due to rising demands for various uses, including hydropower generation, irrigation, and domestic supply. As the population grows, the need for water across different sectors constantly increases. However, the availability of water resources is restricted both spatially and temporally. Therefore, there is an urgent need for systematic and scientific planning to ensure its optimal use. Agriculture sector being the major consumer of water resources, the adoption of modern irrigation techniques can significantly reduce water consumption and promote water conservation. This advancement will expand the irrigated areas and ultimately lead to increased agricultural productivity. Irrigation water requirements of major crops, namely, Paddy, maize, sugarcane etc. are determined using the CROPWAT model. The present study aimed to find out the irrigation water requirement for the major crops grown in Krishna Central Delta, Andhra Pradesh. Results revealed that irrigation water requirement for kharif season ranges from 89-475 mm and in rabi season varies from 320-821 mm. For sugarcane irrigation water requirement was found to be 927 mm.

Stochastic Decision-Making Optimization Model for Large Electricity Self-Producers Using Natural Gas in Industrial Processes: An Approach Considering a Regret Cost Function

In the context of high energy costs and energy transition, the optimal use of energy resources for industrial consumption is of fundamental importance. This paper presents a decision-making structure for large consumers with flexibility to manage electricity or natural gas consumption to satisfy the demands of industrial processes. The proposed modelling energy system structure relates monthly medium and hourly short-term decisions to which these agents are subjected, represented by two connected optimization models. In the medium term, the decision occurs under uncertain conditions of energy and natural gas market prices, as well as hydropower generation (self-production). The monthly decision is represented by a risk-constrained optimization model. In the short term, hourly optimization considers the operational flexibility of energy and/or natural gas consumption, subject to the strategy defined in the medium term and mathematically connected by a regret cost function. The model application of a real case of a Brazilian aluminum producer indicates a measured energy cost reduction of USD 3.98 millions over a six-month analysis period.

The impact of hydroclimate-driven periodic runoff on hydropower production and management

This study evaluates the impact of hydroclimate-driven periodic runoff on hydropower operations and production, with a focus on how the forecasted biennial periodicity of runoff time series could affect the efficiency of hydropower generation. Hydrologic stochastic processes are utilized to forecast long-term runoff, and seven hydroclimate scenarios are developed to be input into a production management model, allowing for an analysis of how periodic hydroclimate variations influence hydropower management and output. The results reveal that the biennial alternation between wet and dry years is a key factor affecting hydropower operations in the Dalälven River Basin. Notable differences between wet- and dry-year scenarios were observed in terms of power efficiency, production output, and forecasting accuracy. Operating hydropower systems based on dry-year runoff forecasts in wet years results in a 1.63% decrease in production efficiency and a reduction of 9,104 MWh in power generation. Conversely, applying wet-year forecasts in dry years slightly boosts production efficiency by 0.31% and increases power generation by 7,832 MWh. Scenarios that adhere to biennial periodicity offer the highest forecasting accuracy, particularly when applying dry-year forecasts in dry years in winter and spring, which produce the most precise predictions. In contrast, using dry-year forecasts in wet years results in the lowest forecasting accuracy.

A new integrated modelling and control of ternary pumped storage hydro power generation for small signal stability studies

Pumped storage hydro power generation (PSHPG), a reliable renewable energy source with an energy storage feature, can impart efficient damping torque to power system oscillations to improve small signal stability with appropriate modelling and additional compensation through the governor. But ternary PSHPG (T-PSHPG) has the unique feature of hydraulic short circuit (HSC) mode, where both pumping and generating modes operate simultaneously. This study presents a novel fourth-order modified Heffron Phillips (MHP) model of T-PSHPG in coordination with a multi-band Power system stabilizer (MB-PSS) employing 2-way penstock, where primary penstock circulates water from the upper reservoir to the lower reservoir through the turbine and secondary penstock divides primary penstock, thereby creating short-circuit path through the pump. Also, this model includes additional reactive power droop control to maintain the voltage profile. A new state space matrix has been developed for coordinating T-PSHPG in HSC mode with MB-PSS. The control parameters of the proposed coordinated model are set by a new multi-objective differential evolution-improved particle swam optimization (DE-IPSO) algorithm. With variable load, random solar and wind source penetrations, it has been found that the proposed model can provide adequate damping actions. The sensitivity of the proposed model has been observed by varying critical model parameters by ±50 %. For all conditions, the settling time of speed variation is within 2.672 s to 5.951 s and the minimum damping ratio lies within 0.105 to 0.455. The minimum damping ratio is 0.32 and the settling time is found to be 3.1 s for system response subject to sudden solar power variation, which has been observed by shifting system eigenvalues toward the left half of the complex plane. Time and frequency domain parameters with sensitivity analysis predict system oscillations being damped effectively with consistency for the proposed modelling and control strategy. The results are verified in real-time with OPAL-RT real-time digital simulator. It has been justified by the present study that appropriate modelling of T-PSHPG along with coordinated control action provided by MB-PSS can handle critical power system oscillations efficiently and effective damping torque can be imparted in HSC mode.

A Machine Learning Approach to Forecasting Hydropower Generation

A Machine Learning Approach to Forecasting Hydropower Generation

Scenario Simulation and Efficiency Study of Hydropower Development to Promote Regional Sustainable Development: An Empirical Analysis of a Province in Southwestern China

Hydropower is a significant component of China’s contemporary energy framework, with its construction and operation critically contributing to the advancement of sustainable development in the region. However, the influence of hydropower development on regional sustainable development is not evident, and the course of its effect is not clear. In this work, on the basis of assessing the effect of hydropower development on regional sustainable development, a system dynamics (SD) simulation model was created, and 15 distinct development modes were set and tested. The relative driving efficiency of hydropower growth on sustainable development in a province in Southwest China under several scenarios was assessed using the super-efficiency-slacks-based measurement (Super-SBM), and an efficiency analysis was undertaken. The study’s findings demonstrate that: (1) Hydropower development had a complex driving influence on sustainable development in the province in 2015–2022. (2) The relative driving efficiency of the province over the study period exhibited a changing pattern and was at a high level greater than 0.7. (3) New hydropower investment and hydropower generation are the major factors impacting the relative driving efficiency of hydropower development on regional sustainable development in the province. The study’s findings provide a platform and reference for supporting regional sustainable development through hydropower development.

Optimization strategies for green power and certificate trading in China considering seasonality: An evolutionary game-based system dynamics

China has adopted renewable portfolio standards and tradable green certificates (including hydropower) to increase the proportion of green power used and reduce carbon emissions. However, hydropower generation is affected by seasonal factors, making it difficult to increase green power consumption in water-scarce areas. Trading electricity and green certificates in regions with different resource endowments can effectively solve this problem. Therefore, this study uses evolutionary game theory to explore the impact of the change in the policy of issuing green certificates for hydropower on the strategic choices of the trading parties. Based on the evolutionary game model, a system dynamics model is established to explore the optimization strategies of green power and certificate trading under the influence of seasonal factors. The results of the case study show that (1) the price of green certificates varies with seasonal changes. (2) Sellers with abundant hydropower resources should increase the amount of hydropower connected to the grid during the water-abundant period to help deficit areas achieve their consumption targets (3) Expanding the scope of green certificates can gradually eliminate government subsidies and incentives and reduce the government's financial pressure.

Atmospheric Fungal Spore Injection: A Promising Breakthrough for Challenging the Impacts of Climate Change Through Cloud Seeding and Weather Modification

Cloud seeding is a technique used to enhance precipitation in drought-prone areas, support agricultural productivity, ensure water supply for human consumption, improve hydropower generation from dams, lessen hurricanes, cool urban heat, and disperse fog in airports. Growing global population size and climate change are the biggest impetus for weather modification and cloud seeding operations. Currently, salt powders like silver iodide, potassium iodide, sodium chloride, calcium chloride, dry ice (solid carbon dioxide), and liquid propane are widely used as ice nucleating particles for cloud seeding purposes while in natural cloud formation, and precipitation particles from dust storms, mineral dust and biological aerosols (like spores, pollen, bacteria) are the dominant ice nucleators. Having this knowledge on hand and the ubiquitous nature of fungi on the other hand; it is feasible to exploit the ice nucleating ability of fungal spores and use it as potential candidates for cloud seeding and weather modification operations.

Turkey’s Hydropower Potential in the Near Future and the Possible Impacts of Climate Change—A Case Study of the Euphrates–Tigris Basin

Hydropower is becoming an important renewable energy source in Turkey, but the ever-changing atmospheric and climatic conditions of Turkey make it very difficult to be projected efficiently. Thus, an efficient estimation technique is crucial for it to be adopted as a reliable energy source in the future. This study evaluates Turkey’s hydropower potential in the Euphrates–Tigris Basin under changing climatic conditions. We adapted an empirical equation to model reservoir outflows, considering the site-specific characteristics of 14 major dams. Initial results from employing a model with a constant empirical coefficient, α, yielded moderate predictive accuracy, with R2 values ranging from 0.289 to 0.612. A polynomial regression identified optimal α values tailored to each dam’s surface area, significantly improving model performance. The adjusted α reduced predictive bias and increased R2 values, enhancing forecast reliability. Seasonal analysis revealed distinct hydropower trends: Ataturk Dam showed a notable decrease of 5.5% in hydropower generation up to 2050, while Birecik and Keban Dams exhibited increases of 2.5% and 2.2%, respectively. By putting these discoveries into practice, water resource management may become more robust and sustainable, which is essential for meeting Turkey’s rising energy needs and preparing for future climatic challenges. This study contributes valuable insights for optimizing reservoir operations, ensuring long-term hydropower sustainability, and enhancing the resilience of water resource management systems globally.

Exploring the substitution within clean energy: Evidence from China's top 14 hydropower provinces

This paper quantitatively examines the substitution effects within China's clean energy sector, focusing on the hydropower and new energy generation sectors across the top 14 hydropower-producing provinces, which collectively contribute to over 80 % of the country's total hydropower output. To provide a comprehensive analysis of regions that significantly influence national trends, the study utilizes the Cross-Price Elasticity (CPE) and Morishima Elasticity of Substitution (MES). CPE measures how the quantity demanded of one energy source responds to a change in the price of another, while MES assesses the sensitivity of the ratio between two energy inputs to price changes. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model is employed to forecast energy substitution dynamics, offering robust predictive accuracy. The average MES between clean energy and thermal power is 0.663, indicating a moderate substitution relationship, with the effect more pronounced in summer. Additionally, the mean MES between hydropower and new energy generation is 2.067, reflecting a strong substitution effect between these two clean energy forms. Furthermore, the SARIMA model shows a mean squared error (MSE) as low as 0.0006 in some cases, demonstrating its robust predictive accuracy in forecasting energy substitution dynamics. These results offer empirical support for policies aimed at reducing reliance on thermal power and promoting clean energy development in key provinces.

Optimal potential of cascading the hydro-power energy generation site, with a hydrokinetic turbine generation system. A case study analysis in the Southern African region

The production of hydroelectricity is well-established in many nations worldwide and has a reputation for being resilient, emitting little carbon dioxide, and having a long lifespan. This article begins by examining the hydropower sites capacity in the Southern African region and the potential for generation-site enhancement. The idea is developed from the emergence of new technologies in generating electricity, and the importance of generating more energy in meeting the growing demand. Hence, it is crucial to examine ways to recover the energy that is released shortly after the hydro-turbine rotates when generating energy, hydro flow in the form of kinetic energy of flowing water is released. This article proposes the installation of a hydrokinetic turbine in proximity down flow to the hydro-turbine to recuperate and maximize the surplus energy, using the same water flow. To achieve this goal, a model has been developed with MATLAB-Simulink software, in response to the research gap. This model will calculate the maximum flow rate and, consequently, the optimal location for a turbine in this cascaded configuration. The simulation's results show how adaptable the model is in suggesting a location for the turbine to be placed for maximum power generation. Simulations run in the cascaded hydropower and hydrokinetic setup system, with different scenarios of water volume release from the Dam. As a result, this study provides a sustainable framework for enhancing electricity generation through effective energy recovery while leveraging existing hydropower infrastructure.

Snow runoff modelling in the upper Indus River Basin and its implication to energy water food nexus

Pakistan's hydropower sector depends heavily on glacier and snowmelt water that originates from the Upper Indus Basin (UIB). It is expected that climate change may adversely affect future hydropower generation capacity as a result of fluctuations in the magnitude, seasonality and hydrological extremes of the Indus River flow. This study employed the Degree-Day Snowmelt Runoff Model alongside the Moderate Resolution Imaging Spectroradiometer MODIS and daily ground-based hydro-meteorological data to model the snowmelt runoff response in the UIB. The results indicated a significant increase in the annual and seasonal runoff under both RCP4.5 and RCP8.5 scenarios, suggesting more water availability for hydropower and irrigation. By the end of the century, annual river flow is projected to increase by 28 % to 69 % under the RCP4.5 and RCP8.5 climate scenarios. Consequently, rise in annual river flow is expected to increase the electricity generation capacity of future hydropower projects by 93 % to 167 % under the RCP4.5 and RCP8.5 scenarios, respectively. The construction of robust multipurpose dams may potentially reduce flood risks in downstream areas during peak flows, while also supplying water for hydropower generation and irrigation during low flows. This, in turn, may enhance the resilience of both the hydropower and agriculture sectors in the face of climate change.

Simulation Study and Experimental Validation on The Effect of Generator Blade Type and Arrangement on Maximum Torque Generated For Micro Hydropower Generator

Simulation Study and Experimental Validation on The Effect of Generator Blade Type and Arrangement on Maximum Torque Generated For Micro Hydropower Generator

Using ARIMA and ETS models for forecasting water level changes for sustainable environmental management

It is vital to provide useful hydrological forecasts for urban and agricultural water management, hydropower generation, flood protection and management, drought mitigation and alleviation, and river basin planning and management, among other things. This paper introduces a simple and flexible hydrological time series forecasting framework. Predicting water levels is crucial, given the need for sustainable environmental management. The prognosis should be reasonable to persuade individuals to take proper precautions. While many methods have been developed to predict water levels, here the effectiveness of two approaches to predicting river water levels was assessed. For this purpose, nine years of data were used, which were divided into input data (2014–2021) and validation data (2022), on water levels in the Morava e Binçës river for the Vitia station, in the form of monthly time series records, to identify the best model among those used and to identify the information necessary for water resource management and hazard control. Models Autoregressive Integrated Moving Average(ARIMA) and Error Trend and Seasonality, or Exponential Smoothing (ETS), were examined using the R package to determine the most accurate. The results indicate the applicability of both models, as evidenced by the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). The predictive analysis based on historical water levels allowed the identification of distinct periods characterized by high and low water levels between 2022 and 2024, which is important for the area in question due to the numerous flood events occurring here.