Retraction notice to “Prediction of the effects of climate change on hydroelectric generation, electricity demand, and emissions of greenhouse gases under climatic scenarios and optimized ANN model” [Energy Rep. 7 (2021) 5431–5445

ABSTRACT The increasing integration of renewable energy sources into smart grids presents substantial challenges in solving the nonlinear and nonconvex optimal power flow (OPF) problem. This paper proposes a comprehensive OPF model that incorporates conventional thermal generators, solar photovoltaic generators, and hydroelectric power generators, while effectively addressing the uncertainties associated with renewable power generation. A lognormal probability distribution models solar irradiance variability in solar generators, while a Gumbel distribution captures water availability fluctuations in hydro generators. The paper proposes a novel hybrid optimization approach, hybrid osprey‐salp swarm optimization (HOSSO), to solve this complex OPF problem. The HOSSO leverages the exploration–exploitation balance of Osprey Optimization alongside the adaptive leadership and follower dynamics of Salp Swarm Optimization. The proposed methodology is validated on Institute of Electrical and Electronics Engineers 30‐, 57‐, and 118‐bus test systems across five distinct optimization scenarios: economic cost minimization, emission cost minimization, combined economic–environmental cost minimization, voltage deviation penalty cost minimization, and renewable generation uncertainty penalty cost minimization. The model incorporates reserve and penalty costs for renewable generation uncertainty and integrates carbon emission taxation to enhance grid reliability and sustainability. Comparative analysis against classical and hybrid optimization techniques demonstrates the superior performance of HOSSO across most test scenarios, consistently achieving competitive solutions while satisfying system constraints and stability requirements. The algorithm delivers improvements ranging from 0.4% to 17% in cost minimization, 3%–23% in voltage deviation minimization, and 2%–8% in uncertainty management over competing methods, with performance advantages becoming increasingly pronounced as system scale grows. The algorithm exhibits rapid convergence within 20–50 iterations, effectively avoids local optima, and proves well‐suited for both single‐ and multiobjective OPF problems in renewable energy‐integrated power systems. The results highlight HOSSO's potential for real‐time power system applications and its adaptability to smart grids with high renewable energy penetration.

Hydrovoltaic technology harnesses the ubiquitous and perpetual hydrologic cycle for solid–liquid interfacial energy conversion. However, contemporary hydrovoltaic systems exhibit multifaceted performance degradation, which includes dopant dissolution, destabilization of the electric double layer, and interfacial recombination. These factors collectively impair operational consistency and commercial viability. To address these limitations, we have developed sunlight-regenerable generators based on p-/n-type carbon nanotube (CNT) organic fabrics fabricated via scalable drop-casting techniques. The doped p-/n-type CNT organic fabrics facilitate photo-triggered recovery effects under AM 1.5 G irradiation (100 mW/cm2). Spectral and thermal analyses confirm that the recovery mechanism is primarily photo-triggered and is cooperatively assisted by photothermal effects, thereby restoring interfacial functionality. The system achieves markedly improved sustained voltage/current output, a higher peak power density of 16.46 μW at a 10 kΩ load resistance, and scalable integration with an output of 8.7 V from 20 serially connected units and 15.5 mA from 40 parallelly connected units. This work establishes a solar-hydro synergistic strategy for resolving the stability-compatibility dilemma in hydrovoltaic energy harvesting.

The role of critical minerals' price changes on the nexus between power sector CO2 emissions and disaggregated level renewable energy generation in China.

Ecohydraulics is a scholarly discipline and community of practice that represents the intersection of ecology and hydraulics/fluid dynamics. Although understanding the intersection of ecology and hydraulics is of fundamental interest, it is also highly relevant to the management and conservation of freshwater life and ecosystems, and consistent with calls for more integrative thinking. Here we provide an overview of the ways in which ecohydraulics has the potential to contribute to supporting the protection and recovery of freshwater biodiversity. For example, ecohydraulics can be used to identify environmental flows that benefit aquatic life while enabling hydroelectric generation. In the context of invasive species, ecohydraulics can be used to identify trapping designs that select invasive species whereas for reducing exploitation, it can be used to inform selective fishing gear designs. In terms of water quality management, ecohydraulics can inform the design of stormwater infrastructure that supports freshwater life. Habitat restoration can be guided by integrating morphodynamics and the habitat needs of species of interest or to ensure that aggregate water extraction is done in a manner and at sites that do not degrade freshwater ecosystems. Ecohydraulics also informs the maintenance or re-establishment of river connectivity through design of fish passage facilities. In summary, ecohydraulics has much to offer in the support of efforts to maintain and restore freshwater biodiversity. Doing so will require continued investment in fundamental and mission-oriented science, but also an emphasis on equipping practitioners with knowledge to implement actions that benefit freshwater biodiversity and people.

Hydroelectricity generators capable of harvesting electricity directly from the chemical energy of atmospheric moisture represent a sustainable technology for self-powered sensing and wearable electronics. However, existing designs suffer from output decay after water saturation and unstable performance under environmental fluctuations, hindering their applications in dynamic and variable wearable conditions. Inspired by the water-transport function of plant stems and leaves, we developed a fabric-based self-sustaining hydroelectric generator (SHEG) that integrates moisture adsorption and water evaporation into a continuous energy-harvesting cycle. The SHEG features an asymmetric heterostructure comprising a hygroscopic ionic-gel layer for rapid moisture capture and proton release, and an evaporative layer based on MoS2/MXene van der Waals heterojunctions that provide confined nanochannels for selective ion transport and directional evaporation. This hierarchical design maintains a stable water gradient, delivering a sustained electrical output for over 120 h and a maximum power density of 377 nW m–2 at 60% RH. The SHEG also responds to wind, heat, and sunlight, which is demonstrated in wearable real-time sensors for respiration and motion monitoring, showcasing its promise as a self-powered platform for next-generation electronics.

The increasing demand for electrical energy has driven the utilization of renewable energy sources, one of which is water energy. Centrifugal pumps operated in reverse as Pump as Turbine (PAT) are a simple and economical solution for small-scale power plants. This study analyzes the effect of impeller shape variations open, half closed, and fully closed on PAT performance through experimental testing with valve opening variations (50–100%) and electrical loads (30-300 watts). The parameters observed include flow rate, rotational speed, and hydraulic efficiency. The results show that the open impeller produces the largest flow rate (63.369 m³/h), the semi closed impeller has the highest rotational speed (1958 rpm), while the fully closed impeller provides the highest efficiency (58%). The impeller shape has been proven to have a significant effect on PAT performance, particularly in increasing energy conversion efficiency. This research contributes to the selection of optimal impeller designs for small-scale hydroelectric power generation systems and the development of more efficient renewable energy technologies

This study presents the use of a Battery Energy Storage System (BESS) and a thermal power plant to enhance Primary Frequency Regulation (PFR) in a power system. This integration seeks to mitigate operational challenges, such as the reduction in system inertia and frequency regulation, which are heightened when increasing renewable energy use in power grids with high hydroelectric generation. The proposed solution enables thermal generators to operate at optimal capacity, while the BESS provides a rapid frequency response, thereby enhancing operational efficiency and compliance with national standards. The process was structured in five stages: criteria definition, analysis, design, models, and evaluation. A comprehensive methodological approach was adopted, including dynamic system modeling and BESS sizing based on regulatory parameters. The method was tested with real data from a thermal plant under the conditions of the Colombian electricity market. The simulation results highlight the effectiveness of the proposed BESS, with a response time of approximately 0.6 s and regulation maintenance for over 30 s, reducing mechanical stress and preventing frequency overshoot. The control strategy was designed to maintain the energy neutrality of the BESS, thereby stabilizing its state of charge over the operational horizon. The results show that the BESS targets high-frequency transients and the generator focuses on low-frequency adjustments, managed by an Energy Management System (EMS) with a unified control approach.

Droplet electricity generation via biogenic iron nanoparticle-coated fungal bulking sludge.

Abstract Sedimentation and waste are common challenges in reservoirs worldwide, including the Sengguruh Reservoir in the upstream Brantas River Basin, Indonesia. Since its operation in 1988, the reservoir has experienced rapid sedimentation, primarily from the Brantas River (Mount Arjuna) and Lesti River (Mount Semeru), which has significantly reduced its storage capacity and affected hydroelectric power generation. This study aims to optimize the Sengguruh Hydroelectric Power Plant (HPP) by addressing sedimentation and waste management. The methodology involves problem identification through primary and secondary data collection, literature review, evaluation of electricity generation data, measurement of sediment and waste volumes, and analysis of their impacts on HPP performance. A management plan was developed, combining periodic dredging with waste flow control using strategically installed trash booms, integrated with continuous monitoring to ensure effectiveness. The results show that sediment and waste management measures have successfully increased reservoir capacity, minimized waste accumulation, and improved electricity production efficiency by 10–15%. The study emphasizes that stakeholder coordination and appropriate technology application are critical for sustainable outcomes. Overall, this research contributes to the development of effective reservoir management strategies and supports energy resilience through the optimization of hydropower resources in the Brantas River Basin.

Capillary reinforcement induced fabrication of cotton fiber array based hydro-electric generator for enhanced voltage output

Tasmania, Australia’s largest producer of hydroelectric power, receives most of its rainfall from extratropical cyclones (ETCs) and cold fronts. Western Tasmania experiences up to 3 m of annual rainfall, primarily driven by midlatitude weather systems and their interaction with local topography, which supports hydroelectric power generation in the state. However, the weather systems influencing rainfall variability in the east – where most Tasmanians live and where rainfall is vital for agriculture – remain less studied. Using combined datasets of ETCs, cold fronts and thunderstorms spanning 1979–2015 over 5–50°S and 110–160°E, we examined the key weather systems driving Tasmania’s spatial and temporal rainfall variability. These weather systems collectively contribute over 80% of the state’s annual rainfall. A large proportion of total rainfall in eastern Tasmania is due to ETCs, whereas cold fronts play a greater role in the west. ETCs south of 40°S, particularly those passing through the Tasmanian region, are associated with heavy rainfall across the state. A statistically significant decline in rainfall (1979–2023) has been observed over western Tasmania, particularly during the warm season (November–April), raising concerns for hydroelectric resources. Our findings highlight the central role of midlatitude weather systems in sustaining Tasmania’s hydroclimate and underscore the need to better understand their future changes in a warming world.

The integration of new energy into the grid has significantly intensified power grid operational pressure, posing higher demands on hydropower system regulation. As a key unit for power grid load tracking and stability maintenance, parameter mismatch of the PID governor is prone to inducing system bifurcation, thus leading to oscillatory instability, which has emerged as a critical challenge affecting the reliable consumption and sustainable supply of new energy. To address this challenge, a hydroelectric power generation system (HPGS) model in the infinite-bus power system is established. Bifurcation analysis is employed to quantitatively identify the critical thresholds of PID parameters that cause HPGS instability. Based on this, system dynamic response processes under critical thresholds are clarified using time-domain analysis. Furthermore, the potential oscillation instability mechanism is revealed using eigenvalue analysis, and suggestions for PID parameter selection are provided. Key quantitative results indicate that variations in proportional gain, kp, induce five limit point bifurcations. The system enters an unstable region when kp exceeds 2.467, whereas operation within the range below 0.891 is conducive to system stability. A supercritical Hopf bifurcation arises when integral gain ki reaches 0.925, so strict restrictions should be imposed on ki to avoid operating around this critical value. Two supercritical Hopf bifurcations that may trigger system oscillatory instability are identified during differential gain kd changing, and it should be regulated to a level below 5.188 to ensure system stability. By integrating bifurcation analysis, time-domain analysis, and eigenvalue analysis, this study effectively improves the accuracy of characterizing system dynamic behaviors, providing a clear quantitative basis for PID parameter optimization and bifurcation suppression, as well as laying a theoretical foundation for hydropower system stable operation and the efficient absorption of new energy.

Raknamo Dam is a dam that has a storage capacity of 14.091 million/m3 of water with the aim of providing raw water, providing irrigation, developing tourism, freshwater fisheries and freshwater conservation, flood control and hydroelectric power generation (PLTA). This study aims to analyze how much elevation of water level drop and the amount of discharge that comes out (outflow) when flooding occurs if the early release model is applied as a flood controller with a scheme of utilizing the use of water pumps. From the results of the analysis with the early release scheme as a flood controller, the scenario used is the utilization of the use of water pumps for 5 days, 3 hours with a total of 4 submersible pumps calculated, each with a capacity of 6 m3/sec, so that it can reduce flooding in the 1000-year return period with a scheme of 1 pump unit with a discharge of 378.60 m3/sec from an elevation of +107.2 m to 106.9 m; scheme of 2 pump units with a discharge of 288.60 m3/s from elevation +107.2 m to +106.4 m; scheme of 3 pump units with a discharge of 198.60 m3/s from elevation +107.2 m to +105.9 m and scheme of 4 pump units with a discharge of 108.60 m3/s from elevation +107.2 m to +105.3 m.

The formation and stability of river ice covers in regulated waterways are critical for uninterrupted hydro-electric operations. This study investigates the modelling of ice cover development in the Beauharnois Canal along the St. Lawrence River with the presence and absence of ice booms. Ice booms are deployed in this canal to promote the rapid formation of a stable ice cover during freezing events, minimizing disruptions to dam operations. Remote sensing data were used to assess the spatial extent and temporal evolution of an ice cover and to calibrate the river ice model RIVICE. The model was applied to simulate ice formation for the 2019–2020 ice season, first for the canal with a series of three ice booms and then rerun under a scenario without booms. Comparative analysis reveals that the presence of ice booms facilitates the development of a relatively thinner and more uniform ice cover. In contrast, the absence of booms leads to thicker ice accumulations and increased risk of ice jamming, which could impact water management and hydroelectric generation operations. Computational efficiencies of the RIVICE model were also sought. RIVICE was originally compiled with a Fortran 77 compiler, which restricted modern optimization techniques. Recompiling with NVFortran significantly improved performance through advanced instruction scheduling, cache management, and automatic loop analysis, even without explicit optimization flags. Enabling optimization further accelerated execution, albeit marginally, reducing redundant operations and memory traffic while preserving numerical integrity. Tests across varying ice cross-sectional spacings confirmed that NVFortran reduced runtimes by roughly an order of magnitude compared to the original model. A test GPU (Graphics Processing Unit) version was able to run the data interpolation routines on the GPU, but frequent data transfers between the CPU (Central Processing Unit) and GPU caused by shared memory blocks and fixed-size arrays made it slower than the original CPU version. Achieving efficient GPU execution would require substantial code restructuring to eliminate global states, adopt persistent data regions, and parallelize at higher level loops, or alternatively, rewriting in a GPU-friendly language to fully exploit modern architectures.

Prediction of hydroelectric power generation with machine learning and innovative combined deep learning techniques

This article explores an operational management model for desilting the Poechos Dam in Chira-Piura, considering the sedimentation rate, which is decreasing by 1% annually, significantly impacting water availability for irrigation, drinking, and hydroelectric power generation in northern Peru. The research was basic, descriptive, cross-sectional, and non-experimental. Two questionnaires, validated by five experts, were used as instruments, each with 23 items for the two variables and a Likert scale. The main findings include frequency tables that reveal a lack of information and uncertainty regarding desilting procedures. This is corroborated in the results discussion by studies conducted in the Moquegua region, such as Romero's 2022 work, and at the Gallito Ciego Dam, by Matute-Velásquez, also in 2022. The discussion incorporates ideas from the Integrated Water Resources Management Model Theory and the adoption of Lean practices, suggesting an organized approach to achieving transparency and sustainability. The proposed model aligns with SDGs 6 and 9, contributing to the 2030 Agenda through reservoir restoration and climate resilience. The conclusions highlight perception neutrality as an opportunity for training, positioning the work as a benchmark for water management in Latin America.

Dams are crucial for water supply, flood prevention, and hydroelectric power generation. Often located in seismically active regions, they are vulnerable to main shock-aftershock (MS-AS) sequences, which can compromise structural integrity and hydraulic safety. Critical aspects of dam response to MS–AS events remain unclear, particularly the required rest time between successive events and threshold AS-to-MS intensity measure ratios that could serve as predictors of additional damage. This study addresses these gaps by analyzing concrete gravity dam–reservoir systems of three heights (50 m, 100 m, and 150 m) using the developed discrete element–based approach coupled with displacement/pressure-based mixed finite elements for the reservoir. Empirical rest time equations were derived from 124 as-recorded ground motions, while seismic performance under varying intensity levels was evaluated using 14 as-recorded MS–AS sequences. Damage was quantified using discrete indices of base crack length, maximum base crack width, and maximum total upstream crack width. Results indicate that AS primarily propagate existing cracks at lower intensities, whereas higher intensities generate new cracks along the upstream face, increasing crack widths by 25–30% on average. The 50 m high dam remained within the mild damage category, while taller dams occasionally reached moderate levels, posing potential seepage risks. Threshold AS-to-MS ratios for four different intensity measures were identified. These findings provide mechanistic insight into crack propagation under MS-AS events, providing practical guidance for post-earthquake dam safety assessment, inspection prioritization, and incorporating sequential seismic effects into design and emergency planning.

Ta Ngao is the local name in Loc Thanh Village, Bao Lam District, Lam Dong Province. This district is a place that has rich mineral resources in the province with 10% in the total mineral resource value of Southeast. With a waterfall of 7 stages, it seems to be one beautiful site, a big resource for hydroelectricity generation. Besides, there are some natural landscapes and human resource for many forms; this is a conversing place of many peoples; therefore, we have a strong potential to develop the tourism. It is a wild area, everyone plants a little; therefore, here, it has not vegetables. The soil and the efficiency of the trees do not care. Here, we examined the heavy metals on the tomatoes and we studied fertilizers, and we want to improve the soil, to serve the product for the people better.

Hydrogel-based solar-driven interfacial evaporators have recently emerged as high-efficiency and sustainable technology for desalination. By leveraging the unique three-dimensional network, remarkable hydrophilicity, and tunable physicochemical properties of hydrogels, these systems achieve efficient solar absorption and thermal conversion, significantly enhancing water evaporation rates. This review summarizes design strategies based on physical and chemical cross-linking, and explores key approaches for performance enhancement, including reduction of evaporation enthalpy and structural optimization. Through regulation of water states and construction of multi-scale porous and biomimetic architectures, synergistic improvements in photothermal conversion, water transport, and thermal management have been realized. Furthermore, hydrogel-based evaporators demonstrate great potential in integrated applications such as wastewater treatment, salt collection, and hydroelectric generation. Finally, challenges related to water purification applications are discussed. This review offers valuable insights for the future design of hydrogel-based solar evaporators to mitigate global water scarcity.