Hydroelectric Power Production Research Articles

Stamina and the embodiment of expertise: resources of transnational activism to counter corporate slow violence

AbstractAnalyses from political ecology and other disciplines show on the one hand how projects of hydroelectric power production can lead to situations of corporate slow violence. On the other hand, contributions of the same disciplines analyse how transnational activism can change shape, course, and power structures in environmental conflicts. My contribution brings together these debates, which have so far been conducted separately, by analysing the conflict around a run-of-the-river hydroelectric power plant in rural Ecuador. The power plant has been backed by a legitimising discourse of contributing to sustainable non-carbon-based solutions to global climate change. Despite this good image, the company ignored the national legislation and the knowledge of the local population about their river, as well as their needs, and thus involved them in a situation of corporate slow violence. Parts of the community subsequently joined with activists from the U.S. and formed an NGO to convert the physical protest-dominated resistance strategy into one of scientific and legal activism. My analysis shows how this case of corporate slow violence was confronted by these activists mobilising a variety of resources, among them two that are not taken into account in the relevant literature so far, namely the necessary time, residency, and stamina to cope with the violence, and the use of the embodiment of expertise of the white colonialist other as a strategic tool to change power structures in the region.

Trade openness, hydroelectric power production, foreign direct investment and economic growth nexus in Nigeria

Trade openness, hydroelectric power production, foreign direct investment and economic growth nexus in Nigeria

Evaluating machine learning models in predicting dam inflow and hydroelectric power production in multi-purpose dams (case study: Mahabad Dam, Iran)

This study aimed to forecast dam inflows and subsequently predict its capability in producing HEPP using machine learning and evolutionary optimization techniques. Mahabad Dam, located in the northwest of Iran and recognized as one of the nation’s key dams, served as a case study. First, artificial neural networks (ANN) and support vector regression (SVR) were employed to predict dam inflows, with optimization of parameters achieved through Harris hawks optimization (HHO), a robust optimization technique. The data of temperature, precipitation, and dam inflow over a 24-year period on a monthly basis, incorporating various lag times, were used to train these machines. Then, HEPP from the dam was predicted using temperature, precipitation, dam inflow, and dam evaporation as input variables. The models were applied to data covering the years 2000 to 2020. The results of the first part indicated both hybrid models (HHO-ANFIS and HHO-SVR) improved the prediction performance compared to the single models. Based on the results of Taylor’s diagram and the error evaluation criteria, the HHO-ANFIS hybrid model (RMSE, MAE, and NSE of 3.90, 2.41, and 0.86, respectively) exerted better performance than HHO-SVR (RMSE, MAE, and NSE of 4.39, 2.70, and 0.86, respectively). The results of the second part showed that using the HHO algorithm to optimize single models (RMSE, MAE, and NSE of 0.2, 10, and 0.90, respectively) predicted HEPP better than single models (RMSE, MAE, and NSE of 0.2, 10, and 0.90, respectively). The results of Taylor’s diagram also showed that the HHO-ANFIS model exerted better performance. The findings of this study indicated the promising performance of machine learning models optimized by metaheuristic algorithms in the simultaneous prediction of dam inflows and HEPP in multi-purpose dams for better management and allocation of surface water resources.

Pathways to sustainable development: an overview of the progress and obstacles of hydroelectric power production in Angola

Abstract This article offers an in-depth examination of the historical trajectory of Angola’s hydroelectric energy sector, contextualizing its evolution within the country’s political and economic landscape. It begins by tracing the origins of hydroelectric development. Subsequently, it delves into the post-independence era marked by political turmoil and civil conflict, elucidating the challenges faced by the energy infrastructure amidst the ravages of war. Moreover, the article scrutinizes Angola’s post-conflict reconstruction efforts, particularly in revitalizing hydroelectric facilities and formulating strategic energy policies. It assesses the regulatory framework governing the energy sector, identifying deficiencies and proposing avenues for enhancement to ensure transparency and efficiency. This study explores the prospects and challenges associated with the Baynes Binational Hydroelectric Project on the Angola–Namibia border, highlighting its potential to foster regional cooperation and bolster energy security. The article underscores institutional reforms and strategic investments to propel Angola towards a more resilient and inclusive energy future.

The effects of damming and dam regulation on a river–lake-aquifer system: 3D groundwater flow modeling of Poyang Lake (China)

Large numbers of dams are planned or constructed for hydroelectric power production, water supply, and flood control in many rivers around the world. While the social and environmental impacts of dams are now well-understood, there is little knowledge about the effects of dams on groundwater-surface water interactions in a complex lake–river-aquifer system. This study adopts a three-dimensional groundwater numerical model to quantify the potential impacts of dam construction, on regional groundwater flow system, exemplified by the proposed Poyang Lake Hydraulic Dam (PLHD) in China. Once implemented, the PLHD will regulate lake levels, artificially controlling the lake inundation area during the recession season (September-October) and the dry season (November-December). Modeling results indicated that the increase in lake infiltration induced by PLHD regulation would alter the regional water exchange pattern. The PLHD regulation will likely enhance the groundwater storage by 20.44 % in the general dry year (i.e., 2018), alleviating 78.1 % of the groundwater deficit in the extreme dry year (i.e., 2019). Additionally, the response of groundwater dynamics to the PLHD regulation exhibits significant spatial differences between the upstream and downstream areas of the dam, primarily depending on topographical changes. Overall, the PLHD regulation is most likely to impact groundwater levels across an area of approximately 5,700 km2, and the associated groundwater storage is anticipated to reach a stable state, under future long-term regulation. Our findings may guide policy-makers wishing to develop more suitable PLHD scheduling plans and floodplain protection strategies by providing reliable information regarding groundwater storage change and ecosystem succession.

Smart hydropower management: utilizing machine learning and deep learning method to enhance dam’s energy generation efficiency

AbstractRenewable energy sources and hydroelectric power generation in large parts of the electricity market are crucial as environmental pollution worsens . Utilizing meteorological data from the region, where the Hirfanlı Dam is located, this study employs machine learning (ML) and introduces a novel hybrid Genetic Grey Wolf Optimizer (GGW0)-based Convolutional Neural Network/Recurrent Neural Network (CNN/RNN) regression technique to predict hydroelectric power production (PP). In the first section of the study, various ML techniques SVR (Support Vector Regression), ELM (Extreme Learning Machine), RFR (Random Forest Regression), ANN (Artificial Neural Networks) and WKNNR (Weighted K-Nearest Neighbor) are presented with the Principal Component Analysis (PCA) method and the minimum–maximum method in the normalization of the features. A novel GGWO and CNN/RNN model)-Long Short-Term Memory (LSTM) regression technique is introduced in the second section. GGWO is used to select features, while the proposed CNN/RNN-LSTM model is employed for feature extraction and prediction of PP. The study demonstrates that the ELM algorithm in Method I outperforms other ML models, achieving a correlation coefficient (r) of 0.977 and the mean absolute error (MAE) of 0.4 with the best feature subset. Additionally, the proposed CNN/RNN hybrid model in Method II yields even better results, with r and MAE values of 0.9802 and 0.314, respectively. The research contributes to the field of renewable energy prediction, and the results can aid in efficient decision making for electricity generation and resource management.

Examining the outstanding Euro-Mediterranean drought of 2021–2022 and its historical context

The Euro-Mediterranean region experienced a remarkable drought during the hydrological year 2021/22. Substantial and widespread impacts on water supply systems, agricultural crops, and the production of hydroelectric power were observed. This assessment characterises the drought from a long-term perspective using a multi-index approach and analyses the associated atmospheric circulation at the annual and monthly time scales. The main dynamical forcing of the drought was the unusual recurrence of high-pressure systems over western Europe, at least partly due to an anomalous southward shift in blocking activity and a remarkable occurrence of low-latitude blocks. This led to record-breaking positive geopotential height anomalies over western Europe and a poleward displacement of the North Atlantic eddy-driven jet. Although most of the region was affected by mild drought conditions, the 2021/22 event was not unprecedented in terms of precipitation deficits since other periods of the 20th century (e.g., in the 1920s, 1940s and 1970s) displayed moderate and severe drought conditions over larger areas. However, the 2021/22 drought has been the most intense since at least 1891 because of high atmospheric evaporative demand (AED) values associated with extreme temperatures, especially during the summer of 2022. This enhanced AED also contributed to depleting soil moisture and reducing runoff generation, leading to unprecedented deficits since at least 1965. Finally, we find important differences in the 2021/22 event as compared to other major historical droughts over the Euro-Mediterranean region. In particular, the contrasting effect of AED evidences its increasing role over the last decades and warns about the current risk of experiencing unprecedented droughts.

Performance of rotational speed controlled small-scale head-dependent hydroelectric power plant

Small scale or micro scale hydroelectric power production is characterized by high investment costs compared to operation costs. In addition, the repayment time is often long, more than ten years (if not subsidized heavily) which requires that a small-scale hydropower plants can be profitable only if both the investment costs and the running costs are low. The performance and efficiency of a plant is very important because having long repayment time means that the efficiency improvement of one percentage unit often means the ten percentage unit improvement in profit. The most economical way to control the flow is sometimes a two point control where generators are switched on and off depending on high water level used as control variable. The water level cannot be kept constant using this method, but the traditionally used mechanical blade angle adjustment system is too expensive in very small (approximately <100 kW) units. The new method of controlling the upper water level (head) is the use of rotational speed control of turbine, implemented by a four quadrant frequency converter. The efficiency of a 4-quadrant frequency converter is approximately 97 %, which reduces the income compared to the situation where the generators are directly connected to electricity network. However, when the head is kept constant using a frequency converter, it is not required to switch off the generator circuit breakers which have a finite life time and the head height can be kept optimal in terms of performance. In this paper, a system structure, a pilot plant installation and example calculations on the feasibility of a frequency converter driven fixed blade hydro power turbine is given.

DEPOSITION OF Al2O3/Cr2O3 CERAMICS HVOF SPRAYED COATINGS FOR PROTECTION AGAINST SILT EROSION

Hydro turbines are one of the most vital and essential components for the production of hydroelectric power. Since the hydro turbine is always operating under the influence of water and the silt transported by the water from the hilly region, it poses a lot of threat and damage to the turbine blades working constantly under the action of silt laden water. Erosion wear is the most common type of damage, and drastically affects the life of turbine runners and blades. The introduction of thermal spray coatings, namely high-velocity oxy-fuel (HVOF) technique, has resulted in a major improvement in stainless steel erosion resistance. Ceramic coating powders primarily Al2O3, Cr2O3, and a composite mixture of composition 55% Cr2O[Formula: see text] Al2O3 were deposited on stainless steel substrates of grade 304. The thorough experiments utilizing silt slurry are conducted at different particle size of silt in the range 212–250, 150–212 and 53–106[Formula: see text][Formula: see text]m. The silt concentration has been varied in the range 12–42% by weight, and the substrate was rotated at a rotational speed in the range 600–1500[Formula: see text]rev/min for the test time of 4[Formula: see text]h. SEM images were used to assess the surface morphology of the required testing samples. The erosion observance and the material removal mechanism were investigated and discussed on the basis of microstructure studies. Cr2O3 coating is found to be more resistant to erosion than other coatings under all test conditions.

Reservoir Advanced Process Control for Hydroelectric Power Production

The present work is in the framework of water resource control and optimization. Specifically, an advanced process control system was designed and implemented in a hydroelectric power plant for water management. Two reservoirs (connected through a regulation gate) and a set of turbines for energy production constitute the main elements of the process. In-depth data analysis was carried out to determine the control variables and the major issues related to the previous conduction of the plant. A tailored modelization process was conducted, and satisfactory fitting performances were obtained with linear models. In particular, first-principles equations were combined with data-based techniques. The achievement of a reliable model of the plant and the availability of reliable forecasts of the measured disturbance variables—e.g., the hydroelectric power production plan—motivated the choice of a control approach based on model predictive control techniques. A tailored methodology was proposed to account for model uncertainties, and an ad hoc model mismatch compensation strategy was designed. Virtual environment simulations based on meaningful scenarios confirmed the validity of the proposed approach for reducing water waste while meeting the water demand for electric energy production. The control system was commissioned for the real plant, obtaining significant performance and a remarkable service factor.

Econometric analysis of pricing and energy policy regulations in Japan electric power exchange spot market

This study aims to provide a quantitative overview of the energy policy regulations concerning the Japan spot market liberalization and analyzes the market clearing price (MCP) and the regional spot prices based on the hourly contribution of the supply stack mix and the merit order effect. In addition, it estimates the regional spot prices interdependency on the MCP. The study acquires an hourly dataset in panel data format from 2016 to 2020 with total observations of 438,240 data records. The developed econometric models include panel data regression using fixed-effect estimator, ordinary least square, and two-stage least square equations. From the results, a one GWh addition from nuclear, hydroelectric, geothermal, biomass, solar, or wind power production technologies decreases spot prices. Contrarily, adding one GWh from thermal, hydroelectric storage, or regional interconnections raises the spot prices by 27.4, 606, 103 ¥/MWh, respectively. The spot prices of Hokuriku and Chugoku tend to offset the price spikes in Kansai, while Tokyo and Tohoku spot prices are significantly related. Adding one ¥/kWh to Tohoku spot price is estimated to raise the MCP by 0.35 ¥/kWh, while the addition of one ¥/kWh to Tokyo spot price is estimated to increase the MCP by 0.04 ¥/kWh. Furthermore, Kyushu and Tohoku, followed by Chubu, have appreciable control over the MCP. The study further highlighted policy implications to constrain the applications of hydroelectric storage as the costliest solution to balance the intermittency of renewable energy sources.

The process of electrification in Darjeeling hills with special reference to Sidrapong hydropower project

The paper gives an overview of the origins and development of hydroelectricity in Darjeeling hills during colonial period and the process of electrification of the region. As the history of the city gained lots of attention from scholars due to its colonial heritage, but its legacy in the production of hydroelectric power wasn’t gained so much attention till now. Darjeeling was one those earliest cities in India, which experienced the luxury of electricity in 19th century India. Here the special focus will be shifted on the Sidrapong power plant, which is known as the earliest hydroelectricity plant in India. Even the production of hydroelectricity was started here with the Sidrapng plant, later various other production units and extensions were installed in the areas like Lebong, Karseong and Kalimpong. In this article, an attempt was made to discuss factors behind the establishment of the project, important events during its developmental stages, production capacity, management, later extensions and other related things elaborately. The paper also contains several unknown data and details and statistics about those projects.

Estimating Snowpack Density from Near-Infrared Spectral Reflectance Using a Hybrid Model

Improving the estimation of snow density is a key task in current snow research. Characterization of the variability of density in time and space is essential for the estimation of water equivalent, hydroelectric power production, assessment of natural hazards (avalanches, floods, etc.). Hyperspectral imaging is proving to be a promising and reliable tool for monitoring and estimating this physical property. Indeed, the spectral reflectance of snow is partly controlled by changes in its physical properties, particularly in the near-infrared (NIR) part of the spectrum. For this purpose, several models have been designed to estimate snow density from spectral information. However, none has yet achieved significant performance. One of the major difficulties is that the relationship between snow density and spectral reflectance is non-bijective (surjective). Indeed, several reflectance amplitudes can be associated with the same density and vice versa, so the correlation between density and spectral reflectance can be very poor. To resolve this issue, a hybrid snow density estimation model based on spectral data is proposed in this work. The principle behind this model is to classify the snow density prior to its estimation by means of a specific estimator corresponding to a predetermined snow density class. These additional steps eliminate the surjective relation by converting it into three bijective relations between density and spectral reflectance. The calibration step showed that the densities included within the three classes are sensitive to different spectral regions, with R2 > 0.80. The results of the cross-validation for the specific estimators were also satisfactory with R2 > 0.78 and RMSE < 36.36 kg m−3. The overall performance of the hybrid model (HM), when tested with independent data, demonstrated the effectiveness of using proximal NIR hyperspectral imagery to estimate snow density (R2 = NASH = 0.93).

Parametric analysis of gravity Vortex turbines as a low cost renewable energy alternative from low head hydraulic resources

The long existent worldwide trend for large scale hydroelectric power plants, relying on dams are now under severe criticism for the large areas their reservoirs occupy, which are often fertile agricultural areas and sometimes flood cultural heritage sites. However, there are also environment-friendly alternatives for hydroelectric power production, which are capable to obtain energy from small scale streams with relatively low heads. Such smaller scale sources with low cost facilities can be used for electric production by alternative schemes that use small streams, irrigation canals and divertions from rivers, tidal pools, overtopping wave energy converters and urban wastewater. One of the recent types of such plants are the gravity vortex turbines that use the naturally occurring “sink vortex” draining such water. They are highly efficient and able to obtain energy from sources with flow rates as low as 1 m3/s and heads as low as 0.80 m. Such water sources are abundant in most of the rural areas and it is possible to obtain either an important part or the total need of the energy requirement of the nearby communities with such systems. Gravity vortex turbines have low costs due to their simple structure and are easy to maintain. They can also be implemented for overtopping wave energy and tidal energy systems, as well as recovery units of pumped energy storage schemes. The purpose of this paper is to propose relations for the design and parametric analysis to size the relevant parts of the plant- the pool and the turbine. Potential flow is assumed throughout the analysis. Attempts to obtain optimized relations between the relative sizes and rotational speeds for the pool, water source, turbine are made and inputs for preliminary design are obtained.

The water-energy-climate nexus concept of “Hydrobattery”: Storing excess Variable Renewable Energy (VRE) at the Canyon Ferry Dam

Hydropower is the renewable energy with the highest power production worldwide. At the same time the output flow of the water that is being used for hydroelectric power production can fluctuate significantly. The ability to “recharge” the dams with water from excess variable renewable energy technologies (VRE) can be used for storage and as a buffer for smoothing the production peaks of VRE. This study presented the idea of the “Hydrobattery” scheme which combines the concepts of “open-loop” and “closed-loop” pumped storage technologies along with the concept of energy storage and water recirculation. The Canyon Ferry Dam was used as a case study and water was modelled to be recirculating back to the Dam from the Helena Valley reservoir. The hydropower production from the Canyon Ferry Dam is below the nominal value of 50 MW and has fluctuated from 34.74 MW up to 41.46 MW for the period between 2014 and 2018. The application of “Hydrobattery” would be able to increase the power output by 2.39–3.39 MW of additional power, and an increase of power production between 5.7% and 9.7%. Also, the concept of “Hydrobattery” can be used as a potential water management system for connected water reservoirs.

Renewable Energies in the Electricity System of Valle del Cauca (Colombia)

<p>W artykule opisano obecną sytuację i udział odnawialnych źródeł energii w systemie elektroenergetycznym jednego z departamentów Kolumbii – Valle del Cauca. W ramach badania dokonano interpretacji ram instytucjonalnych dotyczących energii odnawialnej w Kolumbii oraz ogólnej analizy światowego i kolumbijskiego systemu elektroenergetycznego. Kolejna kwestia to udział odnawialnych źródeł energii w wytwarzaniu energii elektrycznej w Valle del Cauca, głównie w zakresie produkcji i infrastruktury energii hydroelektrycznej, bioenergii i energii fotowoltaicznej. W dalszej części autorzy skoncentrowali się na kwestii potencjału elektrycznego energii odnawialnej w omawianym regionie. Ponadto przedstawiono szereg dyskusji na temat tego, jak omawiany departament skorzystał z ram instytucjonalnych dotyczących wykorzystania energii odnawialnej.</p>

Understanding the impact of the earthquake on circular tunnels in different rock mass: a numerical approach

Tunnels are the most important infrastructure components as it plays a major role in public infrastructure, water transport and the production of hydroelectric power. The constant variation in the geological conditions affects the tunnel stability which can be achieved using flexible rock support methods. The analysis and understanding of the prevalent complex rock mass in a region which is highly seismically active is a challenge and necessary for the successful execution of any tunnelling project. Therefore, a pseudo-static approach has been studied to understand the impact of earthquakes on circular tunnels using the Phase 2 software. The rock mass surrounding the tunnel is modeled using the continuum approach and continuum with interface approach where joints were incorporated in the model as an interface. For this study, a tunnel model with specification (diameter-6 m, depth-100 m) with a Q range of 1–30 was used to examine the impact of rock mass quality. The study revealed that while the seismic load remains unchanged, the magnitude of the axial force on the liner and the net increase due to seismic loading referred to as seismic axial force, increase as the rock mass quality decreases. The diameter of the circular tunnel at a depth of 100 m is increased from 6 to 24 m at an interval of 6 m to check the influence of the tunnel diameter under the seismic loading. It was observed that the seismic axial force decreases with the tunnel diameter for the rock mass Q = 1 (“very low” rock class) and strong rock, it is comparatively small.

Potential of Colletotrichum typhae H.C Greene mycoherbicide for bio-control of Southern cattail (Typha domingensis Pers.) plants

The anthropic interference in aquatic ecosystems favors the disordered colonization of T. domingensis, damaging the production of hydroelectric power and river traffic. Because of this, studies report the efficacy of fungal mycoherbicides, with control rates reaching as high as 90%. Thus, the objective of this study was to evaluate the potential of C. typhae as a mycoherbicide in bio control of T. domingensis, at in vitro and greenhouse conditions. 107 samples of symptomatic T. domingensis leaves were collected in flooded areas of rivers in Brazil, with identification and isolation of the collected fungal species. The concentration of inoculum was determined to evaluate the incidence and severity of the disease, the influence of temperature on mycelial growth and conidia germination, the effect of temperature and leaf wetness period on T. domingensis infection by C. typhae and the host range test in vitro. The growth of the colonies of C. typhae was higher at 25 to 30 ºC. There was no interference of the photoperiod on germination of the spores, but the highest percentage of germination was occurred at 20 ºC. The influence of environmental conditions on infection of inoculated leaves of T. dominguensis revealed that at 15 ºC and the period of leaf wetness of 48 hours the highest incidence of the disease was observed, as well as the severity for the same period of leaf wetness. The specificity test showed that C. typhae is specifically pathogenic to T. domingensis. This the first report of the occurrence of this pathogen in aquatic macrophytes of this species and in T. domingensis, a potential mycoherbicide for the control of this aquatic weed.

Macroinvertebrate oviposition habitat selectivity and egg-mass desiccation tolerances: Implications for population dynamics in large regulated rivers

Aquatic insects exhibit complex life cycles that include egg, larval, adult, and, in some instances, pupal stages. Disturbances at any of these life stages can affect overall population dynamics. Yet, efforts to understand the effects of disturbances, such as hydrologic alterations, overwhelmingly focus on the larval life stage of aquatic insects. We evaluated the potential for load-following flows associated with hydroelectric power production to act as a population bottleneck for aquatic insects via reductions in the availability and temporal persistence of optimal oviposition habitats. Specifically, we quantified the oviposition habitat selectivity of Baetis spp. (Baetidae), Brachycentrus occidentalis (Brachycentridae), Chironomidae (Diptera), and Hydropsyche occidentalis (Hydropsychidae) downstream of Flaming Gorge Dam, Utah, USA. We found that all taxa except H. occidentalis preferentially laid eggs on large emergent substrates located along the river edge. Peak discharge associated with load-following flows substantially reduced the number of emergent substrates available for oviposition, and daily low flows exposed eggs in these habitats to desiccation and drying. When subjected to experimental drying, both Baetis and H. occidentalis eggs experienced nearly 100% mortality after 2 h, whereas most B. occidentalis remained viable after 8 h. Our paired field and experimental results are consistent with the hypothesis that load-following flows from hydroelectric dams produce a population bottleneck for aquatic insects by short circuiting recruitment processes. Environmental flows that seek to improve the health of tailwater aquatic insect populations would benefit from consideration of habitat requirements for all life stages of aquatic insects.

Time Series Analysis for Predicting Hydroelectric Power Production: The Ecuador Case

Electrical generation in Ecuador mainly comes from hydroelectric and thermo-fossil sources, with the former amounting to almost half of the national production. Even though hydroelectric power sources are highly stable, there is a threat of droughts and floods affecting Ecuadorian water reservoirs and producing electrical faults, as highlighted by the 2009 Ecuador electricity crisis. Therefore, predicting the behavior of the hydroelectric system is crucial to develop appropriate planning strategies and a good starting point for energy policy decisions. In this paper, we developed a time series predictive model of hydroelectric power production in Ecuador. To this aim, we used production and precipitation data from 2000 to 2015 and compared the Box-Jenkins (ARIMA) and the Box-Tiao (ARIMAX) regression methods. The results showed that the best model is the ARIMAX (1,1,1) (1,0,0)12, which considers an exogenous variable precipitation in the Napo River basin and can accurately predict monthly production values up to a year in advance. This model can provide valuable insights to Ecuadorian energy managers and policymakers.