Hydroelectric Energy Production Research Articles

Mapping Longitudinal and Transverse Displacements of a Dam Crest Based on the Synergy of High-Precision Remote Sensing

Reservoirs are highly relevant infrastructure assets, and now, more than ever, they play an essential role in society’s welfare and national security. Their importance is related to regional socioeconomic development due to their capacity to store water for different uses, such as human consumption, agricultural irrigation, flood control, and hydroelectric energy production, among other important services. However, many reservoirs are reaching the end of their period of life, and others are showing undesired displacements and cracking. Four 3D surveys were conducted on a reservoir that serves the Metropolitan Area of Monterrey City in Mexico. These surveys were carried out over a period of 5 years using GNSS observation to assist in understanding the actual dam kinematics, i.e., the behavior of its longitudinal and transversal displacements and the possible correlation with the reservoir level. The high-precision leveling and close-range remote sensing data were assessed and then mapped. The high-precision geodetic and leveling techniques allowed us to locate and measure 84 established permanent control points with errors of about ± 0.003 m. The mapping of displacements was made possible by modeling the positive and negative translations. The highest uplifts (11 mm) occurred at the left riverbank, and the highest subsidences (−5 mm) occurred along the downstream piers from the middle of the dam crest to the right riverbank. A ground laser scanner (GLS) produced 3D digital models with geometrical and radiometric characteristics, detecting displacements among the dam crest elements. The synergy of GNSS and high-leveling techniques allows the possibility to measure displacements, while the use of geographical information system (GIS) and geomatic techniques allows a better visualization through 2D and 3D maps validated using traditional topographical methods.

Drought in the Po Valley: Identification, Impacts and Strategies to Manage the Events

The area surrounding the Po River, known as the Po Valley, provides a central contribution in the economy of Italy and is highly devoted to agriculture. Recently it has been hit by multiple droughts, among which the exceptional event of summer 2022 is considered the worst dry period of the past 200 years. In the near future, the frequency of such exceptional events is predicted to rise; thus, a deep knowledge of the past droughts that hit the area, the variables used to characterize the events, the impacts they caused and the mitigation strategies adopted to deal with dry periods is of the utmost importance for policy definitions and planning. This study maps the scientific literature published from 2000 to February 2024 on the topic of drought in the Po Valley using the Scopus and Web of Science databases. Overall, 44 articles have been identified and grouped in three main classes: event identification and characterization, impact analysis and management strategies. The main gaps found in the collected papers are the lack of evaluations of the impacts of drought events on human health, hydroelectric energy production and tourism. Furthermore, comprehensive drought management and planning in the area is never addressed in the considered articles. The mentioned aspects deserve more attention, especially the development of drought management plans and policies and the evaluation of their effectiveness.

Small Hydroelectric Power Plant Site Selection for Hydroelectric Energy Production with Geographic Information System: Sivas Kızılırmak Basin Example

Bu çalışmada, Coğrafi Bilgi Sistemi (CBS) tabanlı model yöntemi kullanılarak Kızılırmak Havzası'nda teorik olarak belirlenen mini hidroelektrik santrallerinin (MHES) lokasyonları tespit edilmiştir. Çalışma sonucunda, incelenen havzada kurulu güç potansiyeline göre 100-150 kWh arasında 151, 150-200 kWh arasında 19 ve 200-300 kWh arasında 13 yer tespit edildi. MHES potansiyelinin en yüksek olduğu noktaların Kızılırmak nehrinin ana kolu üzerinde yer aldığı tespit edilmiştir. Bu çalışma, bu havza içerisinde teorik olarak hesaplanabilecek hidroelektrik potansiyelini destekler nitelikte bir çalışmadır. Bu çalışma ile belirlenmiş enerji potansiyeli tahminleri, enerji kaynaklarının değerlendirilmesine yönelik daha fazla araştırma yapılmasını gerektirecek kadar önemlidir.

Glacial Lakes of Mongolia

The over 2200 lakes of Mongolia are generally poorly studied, particularly the glacial lakes. This overview study presents a classification of the glacial lakes based on tectonic-geological and geomorphological dynamics. Selected representative lakes are described using results from fieldwork and satellite image analysis, including bathymetry, paleoshorelines, and recent lake-level fluctuations between 1987 and 2020. Generally, lake levels dropped from the early Holocene until recently, with the onset of the climate change-driven glacier recession that has resulted in lake-level rises and area expansion in almost all moraine-dammed, tongue-basin, and ice-contact lakes. In contrast, endorheic lakes have mainly been shrinking for the past forty years because of an increase in air temperature and evaporation rates and the effects of an intensifying water use within the catchment for irrigation, mining, and hydroelectric energy production in the form of dams. The creation of a lake monitoring system based on an in-depth inventory is recommended.

Application of soft computing and evolutionary algorithms to estimate hydropower potential in multi-purpose reservoirs

Hydropower is a clean and efficient technology for producing renewable energy. Assessment and forecasting of hydropower production are important for strategic decision-making. This study aimed to use machine learning models, including adaptive neuro-fuzzy inference system (ANFIS), gene expression programming, random forest (RF), and least square support vector regression (LSSVR), for predicting hydroelectric energy production. A total of eight input scenarios was defined with a combination of various observed variables, including evaporation, precipitation, inflow, and outflow to the reservoir, to predict the hydroelectric energy produced during the experimental period. The Mahabad reservoir near Lake Urmia in the northwest of Iran was selected as a study object. The results showed that a combination of hydroelectric energy produced in the previous month, evaporation, and outflow from the dam resulted in the highest prediction performance using the RF model. A scenario that included all input variables except the precipitation outperformed other scenarios using the LSSVR model. Among the models, LSSVR exerted the highest prediction performance for which RMSE, MAPE, and NSE were 442.7 (MWH), 328.3 (MWH), and 0.85, respectively. The results showed that Harris hawks optimization (HHO) (RMSE = 0.2 WMH, MAPE = 10 WMH, NSE = 0.90) was better than particle swarm optimization (PSO) (RMSE = 0.2 WMH, MAPE = 10 WMH, NSE = 0.90) in optimizing ANFIS during the prediction. The results of Taylor’s diagram indicated that the ANFIS-HHO model had the highest accuracy. The findings of this study showed that machine learning models can be used as an essential tool for decision-making in sustainable hydropower production.

CO2 Emissions from Renewable and Non-Renewable Electricity Generation Sources in the G7 Countries: Static and Dynamic Panel Assessment

The threat of global warming has increased due to industrialization, urbanization, population expansion, and changes in lifestyle among the Group of Seven(G7) Carbon dioxide emissions (CO2) directly affect how much electricity can be generated from various sources. This research aims to identify environmental hazards associated with various energy sources. Analyzing the impact of various energy sources on CO2 emissions from electricity and heat production using data from the G7. The data is analyzed using quantile regression (QR), generalized method of moments (GMM), random effects (RE), and fixed effects (FE). Our results indicate a substantial positive impact on CO2 emissions regardless of the technology used to generate coal and gas power. Coal-fired power plants have a larger impact on the environment than other sources of emissions. Also, all coal and gas coefficients are significant in FE, RE, GMM, and QR. Oil coefficients have a negative impact on environmental degradation and are significant for FE, RE, and D-GMM regressions. Hydroelectric and renewable energy production can reduce CO2 emissions in all regression models. Nuclear energy has a beneficial impact on the environment, but the coefficients are only significant for S-GMM and the last quantile. However, the most significant result of this study is the identification of a cause-and-effect relationship between CO2 emissions and energy production. Carbon dioxide (CO2) emissions can be lowered by shifting away from fossil fuels and toward renewable and hydroelectric sources. The research also suggests several renewable and alternative electricity production policies for sustainable energy.

Mapping and Quantifying Land Degradation in the Omo-Gibe River Basin, South-Western Ethiopia

ABSTRACT This study aimed to map and analyze the patterns of environmentally vulnerable regions to land degradation in the Omo-Gibe River Basin. The study used GIS techniques to map land sensitivity to degradation. The multifactorial approach-MEDALUS method, which used three quality indicators-soil, climate, and vegetation and nine parameters were used. About 48% of the basin was highly vulnerable to land degradation, and 36% showed clear signs of degradation. 15.8% of the area had a low potential for land degradation. Implementing proper land management strategies should focus on severely degraded areas to achieve sustainable agriculture and hydroelectric energy production.

Theoretical analysis of green hydrogen from hydropower: A case study of the Northwest Columbia River system

Within the Pacific Northwest region of the United States, there is the unique opportunity to explore alternative energy management solutions of the Columbia River's multi-use hydropower system. As with various European hydropower systems that experience large variability in water runoff, but lack adequate reservoir storage capacity, the Columbia River System is a viable source for renewable hydrogen production. This paper studies the theoretical potential of green hydrogen production from excess hydropower energy from the Columbia River System. The potential surplus hydroelectric energy and hydrogen production potential from surplus energy (during March through July months) are estimated from 11 hydroelectric projects along with the Columbia River System. Results show that the system's total monthly average hydrogen production potential ranges from 2.22 × 106 to 8.96 × 106 kg H2 with the utilization of surplus energy over a historical 80 water year period (1928–2008). This study concludes that hydrogen production from spilled hydropower energy and its use in the transportation sector is a viable opportunity to lead the country towards a hydrogen economy.

Landscape changes over 30 years of intense economic activity in the upper Paraná River basin

In this study, we show the complexity associated with the recent land cover changes by elucidating the paths of 30 years of changes in the Upper Paraná River Basin (UPRB), a region severely impacted by agricultural activity, one of the areas with the highest density in the production of hydroelectricity, biofuels and food in the world. In this sense, a post-classification comparison approach based on Landsat images was used to identify detailed ‘from-to’ paths behind those land cover changes. The most expressive changes were the expansion of Cropland and Forest areas and the reduction in savannas, with a net change of 17.9%, 4.1%, and −16.9% of the UPRB area, respectively. Cropland areas showed an expressive increase between 1985 and 2015, rising from 249,439 km2 (27.7%) to 412,909 km2 (45.9%). Forest areas increased from 149,389 km2 to 185,839 km2 in the period. Notably, for this class, an intense spatial dynamic of losses (7.5%) and gains (11.6%) took place between 1985 and 2015. This behavior is related to the disappearance of native vegetation fragments in some sub-basins, as well as to afforestation, reforestation, and/or forest restoration in others. The Cerrado (a typical tropical savanna in South America), the most impacted natural biome of the Basin, decreased from 21.9% of the UPRB in 1985 (196,746 km2) to only about 5% of the whole UPRB area in 2015. Grassland areas, mostly used for livestock, decreased from 271,827 km2 (30.2%) to 229,007 km2 (25.5%). This net decrease was associated with a reduction of 160,830 km2 (17.8%) and the appearance of 118,010 km2 (13.2%) in new areas, previously occupied by tropical savannas in 1985. In conclusion, economic factors were the main drivers for land cover changes, especially agriculture and livestock activities, besides forestry and hydroelectric energy production. In addition, Grassland areas that predominated on the left banks of the UPRB in 1985 retreated with the advance of Cropland areas, mainly due to the expansion of sugarcane for ethanol production, a biofuel widely used in Brazil. In turn, pasture areas migrated to the right bank and occupied a significant part of the Cerrado. Finally, our results demonstrate that the transition dynamics among land cover classes can involve complex political-economical mechanisms that are not always captured by remote sensing.

Hydrological Drought Analysis for Bolu City with Streamflow Drought Index

Hydrological drought refers to the decrease in the amount of water found in surface or groundwater resources and monitoring of this natural disaster is very important due to causing serious problems in drinking and irrigation water supply and hydroelectric energy production. In this study, hydrological drought analysis was made for two streams in Bolu city located in the Western Black Sea Region in the north of Turkey and 3, 6 and 12 month streamflow drought indices were calculated by using the Streamflow Drought Index method. For this aim, the streamflow data of Bolu stream - Beşdeğirmen and Ulusu - Afatlar streamflow monitoring stations were used. According to the results, extreme and severedroughts were determined for two stream flow stations and it is seen that drought periods varied between 1 and 9 years. The most severe and longest droughts were detected for Bolu stream - Beşdeğirmen station. Mild droughts are much higher than moderate, severe and extreme droughts. It has been observed that the severity and number of drought for Bolu stream - Beşdeğirmen station have increased in recent years. In order to reduce the effects of hydrological drought, hydrological drought should be monitored continuously and necessary precautions should be taken regarding the management of water resources. People should be informed about water consumption and so waste should be prevented. Interms of irrigation water, drip irrigation method should be used instead of flood irrigation or sprinkler irrigation methods. For this aim, farmers should be made aware of this issue. Alternative renewable energy sources or other energy sources should be used for reductions in hydroelectric energy production.

Correlation Between Precipitation Index and Energy Generation in Hydroelectric Plants

A recent water crisis that befell over several states in Brazil negatively affected hydroelectric plants energy production once rainfall regime exerts a major influence in predominantly hydraulic power systems. This paper analyses the correlation between power generation and data collected from tributary flow, reservoir level, standardized precipitation index (SPI) on hydroelectric plants located in the Sao Francisco river basin. The analysis is performed by Pearsons Coefficient of Correlation, for a period of 28 years that includes the dry season, and for the dry season separately. The results showed weak and moderate correlation coefficients between SPI and generation for both analyzed periods with slightly higher correlations in the last years when a decrease in power generation is observed. The correlations found between the affluent flow, reservoir level, and the generation were influenced by the reservoir characteristics, and were slightly higher than those of the dry periods.

Snow Cover Variability in the Greater Alpine Region in the MODIS Era (2000–2019)

Snow cover is particularly important in the Alps for tourism and the production of hydroelectric energy. In this study, we investigate the spatiotemporal variability in three snow cover metrics, i.e., the length of season (LOS), start of season (SOS) and end of season (EOS), obtained by gap-filling of MOD10A1 and MYD10A1, daily snow cover products of MODIS (Moderate-resolution Imaging Spectroradiometer). We analyze the period 2000–2019, evaluate snow cover patterns in the greater Alpine region (GAR) as a whole and further subdivide it into four subregions based on geographical and climate divides to investigate the drivers of local variability. We found differences both in space and time, with the northeastern region having generally the highest LOS (74 ± 4 days), compared to the southern regions, which exhibit a much shorter snow duration (48/49 ± 2 days). Spatially, the variability in LOS and the other metrics is clearly related to elevation (r2 = 0.85 for the LOS), while other topographic (slope, aspect and shading) and geographic variables (latitude and longitude) play a less important role at the MODIS scale. A high interannual variability was also observed from 2000 to 2019, as the average LOS in the GAR ranged between 41 and 85 days. As a result of high variability, no significant trends in snow cover metrics were seen over the GAR when considering all grid cells. Considering 500-m elevation bands and subregions, as well as individual grid points, we observed significant negative trends above 3000 m a.s.l., with an average of −17 days per decade. While some trends appeared to be caused by glacierized areas, removing grid cells covered by glaciers leads to an even higher frequency of grid cells with significant trends above 3000 m a.s.l., reaching 100% at 4000 m a.s.l. Trends are however to be considered with caution because of the limited length of the observation period.

Investigation of the optimal method for determining hydropower potential of small streams: a case study Batman Basin in Turkey

Global energy consumption has provoked a need to explore alternative energy sources including run-of-the-river hydropower projects. In Turkey, hydroelectric energy production by utilizing small rivers has not reached the desired level yet. The aim of this study is to provide that hydroelectric energy production can be increased at the desired level in the Batman sub-basin and will offer a simplified procedure to estimate the potential power generation that can be applied locally. In this study, geographic information system (GIS) was used for elevation and morphological features of the basins and, a hydrological model and instantaneous discharge used for flow discharges. In the hydrological model, the parameters of the proxy basins were calibrated and transferred to the sub-basins were used. As the river discharge data of ten sub-basins, results of a hydrological model and for streams without flow observation stations instantaneous discharge estimations based on velocity measurement were used made by floats and pitot tube methods. Then, gross hydroelectric potential (GHP), hypsographic curve (HC), and head-flow (HF) methods for ten sub-basins were calculated. Flow duration curve (FDC) was used for the hydroelectric feasibility analysis. The study revealed that the hydropower potential of a river basin can be correctly assessed by employing a digital elevation model, stream network data, and a hydrological model, such as the HEC model, within a GIS framework. Also, considering the hydroelectric potential results (51507 MWh), it means an important energy potential for the region as it will support rural development and contribute to the country’s economy.

Prediksi Tinggi Permukaan Air Waduk Menggunakan Artificial Neural Network Berbasis Sliding Window

The water level in the reservoir is an important factor in the operation of a hydroelectric turbine to control water overflow so that there is no excessive degradation. This water control has an influence on the performance and production of hydroelectric energy. The daily reservoir water level (tpaw) recording of PLTA Riam Kanan is carried out through a daily direct measurement and observation process on the reservoir measuring board which is recapitulated every month in excel form. This time series historical data continues to grow every day to become a data warehouse that is still useless if only stored. Extracting knowledge from the data warehouse can be done using one of the artificial neural network data mining techniques, namely backpropagation to predict the next day's tpaw. Historical data for the tpaw time series is presented with a sliding window concept approach based on the window sizes used, namely 7, 14, 21 and 28. The window size represents the number of days as an input layer variable in the backpropagation network architecture to predict the next day's tpaw. Some backpropagation network testing is carried out using a combination of the number of window sizes against the comparison of the amount of training data and test data on the network. The prediction results obtained with the smallest mean squared error (mse) in network testing is 0.000577 as a high accuracy value of the prediction results. The network architecture with the smallest mse using 28 input layers, 10 hidden layers and 1 output layer can be a knowledge that can help the hydropower plant as an alternative in making turbine operation decisions based on the predicted results of reservoir water level.

Addressing the Effects of Climate Change on Modeling Future Hydroelectric Energy Production in Chile

Despite the growing scientific evidence, the electricity market models used in Chile do not consider the effects of climate change on hydroelectric energy production. Based on a statistical analysis of the historical hydro-energy inflow dataset and a revision of the scientific literature, we suggest a set of technical and statistical criteria to determine an alternative representation of the hydro-energy uncertainty in the Chilean electricity market. Based on these criteria, we then propose an alternative range of historical hydrological data, which is built by shedding the first 35 years of the historical dataset (out of 59 years) and using only a reduced subset of 24 years. Additionally, we propose to capture the potential impacts of even more prolonged droughts on the Chilean electricity system by repeating the last nine years of data at the end of the 24 year-long series. The resulting extended subset of 33 hydro-years is approximately 10% drier on average than the original dataset of 59 years. The proposed range of hydrological data captures some of the anticipated effects of climate change on Chilean hydro-uncertainty reported in the literature and also preserves most of the intra-annual and spatial diversity of the original data.

Da mineração à hidrelétrica: a face recente das dinâmicas territoriais na Amazônia Oriental Brasileira

Since the period of military dictatorship in Brazil, Amazon has become the focus through public and credit policies to privilege the interests of capital over traditional people (riverine people, fishermen, quilombolas, indigenous people), secular settled population, and other subjects (landless workers, those settled in agrarian reform projects). They are forcibly deterritorialized in this logic of integration of national and international capital in the exploration and production of commodities in this space. This work aims to portray the recent perspective of the last two decades of territorialization process of international interest, with the inflow of large capital resources, such as mining, and hydroelectric energy production. The methodology used was the case study, with interviews, documentary and bibliography research. These enterprises, of great magnitude of socio-territorial transformations, tend to provoke a new process of deterritorialization of hundreds of families of rural workers, as in the case of those settled in agrarian reform projects, riverine people, quilombolas, and indigenous people.

RESEARCH ON ELECTRICAL PRODUCTION OF MULTI-PURPOSE SUSTAINABLE DAMS IN THE WORLD

Dams a facility that stores water in the and uses water in a sustainable way. The high use of imported energy resources, which is composed entirely of fossil fuels and which has negative impacts in terms of economic and political aspects, clearly shows that the use and potential of domestic and renewable energy resources is inevitable. Increasing the domestic and renewable primary energy source of hydroelectric energy production to the highest values is only possible with the development of the right tank management policies. Dams also had been built to produce motive power and electricity since the industrial revolution. Development priorities changed, experience accumulated with the construction and operation of dams. Although the importance of water is well known in the human life and civilization around the world, still various groups argue that expected economic benefits are not being produced and that major environmental, economic and social costs are not being taken into account

Optimizing Current and Future Hydroelectric Energy Production and Water Uses of the Complex Multi-Reservoir System in the Aliakmon River, Greece

In this work we study long-term maximization of hydroelectric energy generation from complex multi-purpose reservoir systems, using the reservoir system of the Aliakmon River, Greece, as an application example. This system serves various purposes, like urban water supply, irrigation, hydroelectric energy production, cooling thermoelectric power plants and flood control, while preserving environmental flow. The system operator uses institutional rules for the annual scheduling of the outflows of the 2 largest reservoirs (Ilarion and Polyfyto) for additional safety and smooth distribution of energy production through the year. In this work, we focus on maximization of energy production. We have considered three different hydrological scenarios (dry, average and wet), both for the current and for anticipated future water demand. The multi-reservoir system’s operation was simulated and then optimized using a rather simple form of genetic algorithms, in order to maximize hydro energy production. All other water uses were taken into account as constraints. Our conceptual and computational approach succeeded to identify and quantify hydro energy production increase and to indicate necessary changes to the operating rule curves of the reservoirs. The methodology can be easily adapted to other large-scale multi reservoir systems.

High Levels of Anabaenopeptins Detected in a Cyanobacteria Bloom from N.E. Spanish Sau-Susqueda-El Pasteral Reservoirs System by LC-HRMS.

The appearance of a bloom of cyanobacteria in the Sau-Susqueda-El Pasteral system (River Ter, NE Spain) in the autumn of 2015 has been the most recent episode of extensive bloom detected in Catalonia. This system is devoted mainly to urban supply, regulation of the river, irrigation and production of hydroelectric energy. In fact, it is one of the main supply systems for the metropolitan area of cities such as Barcelona and Girona. An assessment and management plan was implemented in order to minimize the risk associated to cyanobacteria. The reservoir was confined and periodic sampling was carried out. Low and high toxicity was detected by cell bioassays with human cell lines. Additionally, analysis studies were performed by enzyme-linked immunosorbent assay (ELISA) and liquid chromatography–high resolution mass spectrometry (LC–HRMS). A microcystin target analysis and suspect screening of microcystins, nodularins, cylindrosperpmopsin and related cyanobacterial peptides by LC–HRMS were applied. The results for the analysis of microcystins were negative (<0.3 μg/L) in all the surface samples. Only traces of microcystin-LR, -RR and -dmRR were detected by LC–HRMS in a few ng/L from both fractions, aqueous and sestonic. In contrast, different anabaenopeptins and oscillamide Y at unusually high concentrations (µg-mg/L) were observed. To our knowledge, no previous studies have detected these bioactive peptides at such high levels. The reliable identification of these cyanobacterial peptides was achieved by HRMS. Although recently these peptides are detected frequently worldwide, these bioactive compounds have received little attention. Therefore, more studies on these substances are recommended, especially on their toxicity, health risk and presence in water resources.

Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting

Due to the stochastic nature and complexity of flow, as well as the existence of hydrological uncertainties, predicting streamflow in dam reservoirs, especially in semi-arid and arid areas, is essential for the optimal and timely use of surface water resources. In this research, daily streamflow to the Ermenek hydroelectric dam reservoir located in Turkey is simulated using deep recurrent neural network (RNN) architectures, including bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), long short-term memory (LSTM), and simple recurrent neural networks (simple RNN). For this purpose, daily observational flow data are used during the period 2012–2018, and all models are coded in Python software programming language. Only delays of streamflow time series are used as the input of models. Then, based on the correlation coefficient (CC), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency coefficient (NS), results of deep-learning architectures are compared with one another and with an artificial neural network (ANN) with two hidden layers. Results indicate that the accuracy of deep-learning RNN methods are better and more accurate than ANN. Among methods used in deep learning, the LSTM method has the best accuracy, namely, the simulated streamflow to the dam reservoir with 90% accuracy in the training stage and 87% accuracy in the testing stage. However, the accuracies of ANN in training and testing stages are 86% and 85%, respectively. Considering that the Ermenek Dam is used for hydroelectric purposes and energy production, modeling inflow in the most realistic way may lead to an increase in energy production and income by optimizing water management. Hence, multi-percentage improvements can be extremely useful. According to results, deep-learning methods of RNNs can be used for estimating streamflow to the Ermenek Dam reservoir due to their accuracy.