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Electrical vehicles are finding wide acceptance in the Jordanian transportation market; this has caused an accelerating shift in the emissions of greenhouse gases from the direct burning of fossil fuels consumed by the transportation sector towards the power generation sector. On the other hand, as electric vehicles gain more popularity, an extra load is added to the electrical power generation systems, raising essential concerns such as the capability of the power network to support this massive extra load and the increased emission of greenhouse gases caused by power plants. Studies show that Jordan's weather is known for being bright, sunny, and very suitable for the generation of electric power from solar energy sources. Therefore, with an infrastructure that can support convenient off-grid charging, a huge burden will be taken off the national grid and the environment. Therefore, this work proposes a basic design for an off-grid PV-covered carport and presents a study of the economics and effectiveness of using such a system to charge electric vehicles owned by the students and employees of the Applied Science Private University. The study is based on actual solar irradiance data collected on-site during university working hours (8 a.m.–5 p.m.) to allow students and employees to charge their electric vehicles from an off-grid carport system while on campus. Space limitations for carport design, initial design cost, return on investment, and annual electricity consumption are discussed to demonstrate the benefits of such a system for both the consumer (convenience and low charging cost) and the power company provider (less load to maintain). [ABSTRACT FROM AUTHOR]

This paper presents reduced-order modeling of thermal power dispatch (TPD) from a pressurized water reactor (PWR) for providing heat to nearby heat consuming industrial processes that seek to take advantage of nuclear heat to reduce carbon emissions. The reactor model includes the neutronics of the reactor core, thermal–hydraulics of the primary coolant cycle, and a three-lump model of the steam generator (SG). The secondary coolant cycle is represented with quasi-steady state mass and energy balance equations. The secondary cycle consists of a steam extraction system, high-pressure and low-pressure turbines, moisture separator and reheater, high-pressure and low-pressure feedwater heaters, deaerator, feedwater and condensate pumps, and a condenser. The steam produced by the SG is distributed between the turbines and the extraction steam line (XSL) that delivers steam to nearby industrial processes, such as production of clean hydrogen. The reduced-order simulator is verified by comparing predictions with results from separate validated steady-state and transient full-scope PWR simulators for TPD levels between 0% and 70% of the rated reactor power. All simulators indicate that the flow rate of steam in the main steam line and turbine systems decrease with increasing TPD, which causes a reduction in PWR electric power generation. The results are analyzed to assess the impact of TPD on system efficiency and feedwater flow control. Due to the simplicity of the proposed reduced-order model, it can be scaled to represent a PWR of any size with a few parametric changes. In the future, the proposed reduced-order model will be integrated into a power system model in a digital real-time simulator (DRTS) and physical hardware-in-the-loop simulations. [ABSTRACT FROM AUTHOR]

The electricity system and the industrial sector interrelate on the path to decarbonization. The study addresses the drivers and environmental pressure within the industrial sector on the National Electricity System (NES). This article aims to characterize the metabolic pattern of the NES and the industrial sector using the Multiscale Integrated Analysis of Social and Ecological Metabolism (MuSIASEM) from a bioeconomic perspective to identify fields of opportunity in the regulatory policy instruments. A set of extensive and intensive variables (2019) on energy, production factors, and emissions was used at different hierarchical levels based on both subsectors. Our results show that the NES used primary energy sources (PES) and secondary energy carriers to fulfill its functions, of which 72% were domestic sources and 28% were imported. México imported 79.5% of the natural gas (NG) for electricity generation. However, there are favorable conditions for renewable PES to increase the installed capacity of solar plants between 3 and 4 orders of magnitude and 2–3 in wind power from the current capacity. NES's energy consumption per hour of human time is 17,388 MJ/h, with 65% being the heat equivalent to a total energy input consumed of 2139 PJ/y. Public plants contributed 43.7% to the generation, and the independent and self-supply producers the rest. End uses needed the supply of 69.3% of electricity from baseload plants. Sixty-three percent of the metabolic pattern of the industrial sector is based on heat and fuel processes, which depend on NG imports. The NES is reorganizing and recovering its energy autonomy. [ABSTRACT FROM AUTHOR]

There has been a global expansion of solar electricity during the past decades. Solar photovoltaic (PV) electricity generation is not only simple but also environmentally benign and cost-effective. This is the most affordable and suitable power source for rural areas. The PV power output is unavoidably suppressed when there is partial shade. There were numerous maximum power point tracking (MPPT), arrays, and reconfiguration approaches that were available earlier. The authors have previously described an L-shaped propagating array arrangement. The L-shaped configuration creates the PV rows based on the chess game coin. The knight coin of this game will move in the shape of "L," which inspired new array configurations. This work discusses the hybridization of the L-shaped configuration with the conventional array configurations. The proposed hybrid L-shaped configuration has been developed in MATLAB/Simulink, where the conventional configuration and the hybrid configurations are validated under different test conditions. The test conditions are created based on the all possible real-word shading patterns. The power generation, mismatch losses, and characteristic curves were attained for each method, and this work presents all these validations with the inferences. In addition, the justification for the suitable array configuration with. [ABSTRACT FROM AUTHOR]

Using renewable energies is one of the alternatives to mitigate climate change. Among them, photovoltaic energy has shown a relevant growth of participation in the electric sector. In the backdrop of such growth, in countries such as Brazil, photovoltaic energy has surpassed the generation of electricity by petroleum derivatives since 2019. The significant growth in photovoltaic generation around the world can be attributed to several key factors, including abundant sunlight, supportive government policies, falling solar panel costs, environmental concerns, energy diversification goals, investor interest, job creation, and local manufacturing. However, photovoltaic system performance is heavily tied to weather variability. Different models are used to account for this meteorological dependence; however, there is a gap regarding the differences in the outputs of these models. The study presented here investigates the variability and sensitivity of the models used to estimate photovoltaic production ( P p v ). Six models were compared by percentage difference analysis. Statistical analyses from the perspective of variability revealed that the difference between the P p v estimated by these models reaches a 12.89% absolute power difference. Considering that temperature and solar irradiance are the meteorological variables that most influence P p v , the sensitivity analysis focused on these. Regarding sensitivity, in the context of temperature changes, the average relative difference in P p v between models can reach up to 5.32% for a 10 °C change, while in the context of changes in solar irradiance, the average relative difference can reach up to 19.05% for a change of 41.67 W/m2. The consideration of the variability and sensitivity of the main sets of equations used to estimate the potential of photovoltaic energy production can help refine methodologies and assumptions in future research in this area. There are variations and sensitivities, as observed, of such magnitude that, depending on the set of equations adopted in the study, they can alter the conclusion about photovoltaic energy production in a given region. Accurate estimations are pivotal not only for feasibility analyses but also for gauging economic and socio-environmental impacts. These divergences can, in turn, reformulate feasibility analyses and compromise the reliability of photovoltaic energy systems, thus leading to different economic and socio-environmental consequences. [ABSTRACT FROM AUTHOR]

This document explores the challenges and solutions related to the sustainable development of new power systems under dual carbon goals. The integration of renewable energy sources has impacted the stability of power systems, requiring measures such as grid control, optimization, and accurate forecasting of renewable energy generation. The accurate prediction of photovoltaic and wind power is crucial, and deep learning-based models have shown superior forecasting accuracy. Control and optimization strategies, such as voltage feedforward control and adaptive controllers, enhance system stability. Effective control of the power grid also has economic benefits, and planning and scheduling play a significant role in power system reliability and stability. System state management and intelligent question-answering systems contribute to efficient system operation. The advancement of artificial intelligence technology and big data analytics is crucial for enhancing power system stability and efficiency. Overall, these measures are essential for the sustainable development of power systems. [Extracted from the article]

The MGM (1, n) model has the characteristics of less data required, simple modeling, and high prediction accuracy. It has been successfully applied to short-term forecasting across various economic, social, and technological domains, yielding promising outcomes. There is insufficient attention paid to the interpolation coefficient of the model. The interpolation coefficients determine the extent of model fitting, which, in turn, impacts its prediction accuracy. This study made some improvements to the interpolation coefficients and proposed an improved MGM (1, n) model. IMGM (1, n) model and MGM (1, n) model were employed to compare the performance of the improved MGM (1, n) model. Upon a series of comparisons and analyses, it was concluded that the improved MGM (1, n) model has higher fitting and prediction accuracy than the other two forecasting methods. The method was used to forecast the short-term electricity consumption of Linfen City. The findings revealed that by 2030, the electricity demand in Linfen City is projected to be 563.7 billion kWh. [ABSTRACT FROM AUTHOR]

Urban expansion and fossil fuel dependence have led to energy and environmental concerns, highlighting the need for sustainable solutions. Rooftop photovoltaic (RPV) systems offer a viable solution for urban energy transition by utilizing idle rooftop space and meeting decentralized energy needs. However, due to limited information on building function attributes, detailed assessments of RPV potential at the city scale are still complicated. This study introduces a cost-effective framework that combines big Earth data and deep learning to evaluate RPV potential for various investment entities. We introduced a sample construction strategy that considers built environment and location awareness to improve the effectiveness and generalizability of the framework. Applied to Shanghai, our building function recognition model achieved 88.67%, 88.51%, and 67.18% for accuracy, weighted-F1, and kappa, respectively. We identified a potential installed capacity of 42 GW with annual electricity generation of 17 TWh for industrial and commercial, 16 TWh for residential, and 10 TWh for public RPVs. The levelized cost of electricity ranges from 0.32 to 0.41 CNY/kWh, demonstrating that both user-side and plant-side grid parity was achieved. This study supports sustainable development by providing detailed urban energy assessments and guiding local energy planning. The methods and findings may offer insights for similar studies globally. [ABSTRACT FROM AUTHOR]

The droop control strategy is popularly employed in DC microgrids. However, its virtual resistance will cause voltage deviation and reduce transient response. The DOB‐based method is proven to improve transient response in literature. However, it is analyzed in this study that this method will negatively influence the current sharing when employed in droop control. A weakened disturbance observation (WDOB) is proposed in this work to improve the drawbacks. To employ the proposed method, the equivalent models of the droop controller and the physical system are separately established, and several transformations are conducted. An auxiliary compensation is added and the current loop is considered as a whole to be transmitted into the control plant, making the traditional DOB method successfully adopted. It is obvious that the dynamic performance is improved, but it disabled virtual resistance in the steady‐state. And the current sharing cannot be achieved in a multi‐converter parallel system. The reason for this problem is analyzed from the control process and transfer function, and the WDOB solution is finally proposed. Through the proposed method, both aims of improving dynamic response and current sharing can be achieved, and the steady voltage deviation is much less than that of the traditional droop controller. [ABSTRACT FROM AUTHOR]

Introduction: Sedimentary basins are naturally porous and permeable subsurface formations that underlie approximately half of the United States. In addition to being targets for geologic CO2 storage, these resources could supply geothermal power: sedimentary basin geothermal heat can be extracted with water or CO2 and used to generate electricity. The geothermal power potential of these basins and the accompanying implication for geologic CO2 storage are, however, understudied. Methods: Here, we use the Sequestration of CO2 Tool (SCO2TPRO) and the generalizable GEOthermal techno-economic simulator (genGEO) to address this gap by a) estimating the cost and capacity of sedimentary basin geothermal power plants across the United States and b) comparing those results to nationwide CO2 sequestration cost and storage potential estimates. Results and discussion: We find that across the United States, using CO2 as a geothermal heat extraction fluid reduces the cost of sedimentary basin power compared to using water, and some of the lowest cost capacity occurs in locations not typically considered for their geothermal resources (e.g., Louisiana, South Dakota). Additionally, using CO2 effectively doubles the sedimentary basin geothermal resource base, equating to hundreds of gigawatts of new capacity, by enabling electricity generation in geologies that are otherwise (with water) too impermeable, too thin, too cold, or not deep enough. We find there is competition for the best sedimentary basin resources between water- and CO2-based power, but no overlap between the lowest-cost resources for CO2 storage and CO2-based power. In this way, our results suggest that deploying CO2-based power may increase the cost of water based systems (by using the best resources) and the cost of CO2 storage (by storing CO2 in locations that otherwise may not be targeted). As such, our findings demonstrate that determining the best role for sedimentary basins within the energy transition may require balancing tradeoffs between competing priorities. [ABSTRACT FROM AUTHOR]

The environmental effect of a manufacturing or service method is determined by the resource and energy inputs and outputs at each point of the product's life cycle. In Fused Deposition Modeling (FDM), generally, the material used for fabrication is plastic, and the raising of interest from different backgrounds of users could increase the issue of plastic pollution. Therefore, many scholars have proposed an initiative to employ bio-composite in FDM. In this study, an environmental assessment of global warming potential and fine particulate matter emission from the fabrication of bio-composite filament FDM was performed through its life cycle analysis using GaBi Software. Initially, data on resources and energy inputs and outputs were gathered. The functional unit in this study was the 1.0 kg wood/PLA composite filament extruded using a twin-screw extruder. All wastes were collected and recycled. The fabricated composite filaments were transported by container ship with a capacity of 5000 - 200 000 dwt gross weight for 100 km within Malaysia. Based on the results from the GaBi dashboard, the FDM process of bio-composite filament has contributed as much as 138.7 kg CO2 eq on the global warming potential and 1.71e-4 kg N eq. on fine particulate matter by the electricity power generation in extrusion and printing processes. The main factor for this issue is the consumption of coal in electric power generation, which is considered a non-renewable resource. Therefore, it is recommended that natural fibers such as wood fiber be employed in the filament of FDM to reduce the environmental impact. As shown in the study, the materials contribute less to the impact. Further study is suggested to compare the FDM technology with conventional technology using similar materials. [ABSTRACT FROM AUTHOR]

Solar energy production is an intermittent process that is affected by weather and climate conditions. This can lead to unstable and fluctuating electricity generation, which can cause financial losses and damage to the power grid. To better control power production, it is important to predict solar energy production. Big data and machine learning algorithms have yielded excellent results in this regard. This study compares the performance of two different machine learning approaches to solar energy production prediction: regression and classification. The regression approach predicts the actual power output, while the classification approach predicts whether the power output will be above or below a certain threshold. The study found that the random forest regressor algorithm performed the best in terms of accuracy, with mean absolute errors and root mean square errors of 0.046 and 0.11, respectively. However, it did not predict peak power values effectively, which can lead to higher errors. The long short-term memory algorithm performed better in classifying peak power values. The study concluded that classification models may be better at generalizing than regression models. This proposed approach is valuable for interpreting model performance and improving prediction accuracy. [ABSTRACT FROM AUTHOR]

Wave energy conversion holds promise for renewable energy, but challenges like high initial costs hinder commercialization. Integrating wave-energy converters (WECs) into shore-protection structures creates dual-function structures for both electricity generation and coastal protection. Oscillating water columns (OWCs) have been well studied in the past with their simple generation mechanism and their out-of-water power take-off (PTO) system, which can minimize bio-fouling effects and maintenance costs compared to other submerged WECs. In addition, a slotted barrier allows for better circulation behind the breakwater while dissipating incoming wave energy through viscous damping. This study examines the performance of a new design which combines an OWC with a slotted breakwater. Small-scale (1:49) laboratory tests were performed with a piston-type wave generator. The performance is evaluated in terms of wave transmission, wave energy extraction, and wave loading under various wave conditions while focusing on the effects of the porosity of the slotted barrier and tide level changes. Results show that under larger waves, a decreasing wave transmission, increasing power extraction from the OWC, and energy dissipation from the slotted barrier are observed. On the other hand, under increasing wavelengths, wave transmission is observed to be constant; this is important for harbor design, which means that the breakwater is effective under a wider range of wavelengths. Porosity allows for more transmission while inducing less horizontal force on the structure. [ABSTRACT FROM AUTHOR]

This study investigates how differences in energy-related properties of cities influence the composition of a cost-efficient urban energy system, assuming electrification of the transport and industry sectors and zero-emission of CO2. These differences are evaluated for two scenarios regarding the capacities of the modeled cities to import electricity. A linear optimization model that encompasses the electricity, heating, industry, and transport sectors, using measured data from six cities in Sweden, is applied. Results show that when strict constraints on electricity imports are enforced, cities with a lower ratio of annual electricity demand for heat encourage the implementation of power-to-heat solutions in the heating sector. This study also reveals that under such stringent electricity import conditions, cities with a high level of flexibility in electricity demand favor a combination of batteries and solar photovoltaics as opposed to biomass-based electricity production. Conversely, when electricity importation is less restricted and biomass prices surpass 20 EUR/MWh, local electricity generation is outcompeted by imports, and large-scale heat pumps working in tandem with thermal energy storage dominate the heating sector in all modeled cities. This assertion holds true when the maximum electricity import capacity is utilized up to 5000 h annually. [ABSTRACT FROM AUTHOR]

This publication explores current and prospective methods for hydrogen production and purification, with a strong emphasis on membrane-based technologies for purification and separation. This focus is justified by the ongoing shift towards renewable energy sources (RESs) in electricity generation, necessitating strategic changes to increase hydrogen utilization, particularly in the automotive, heavy road, and rail sectors, by 2025–2030. The adoption of hydrogen from RESs in the construction, energy, and industrial sectors (e.g., for process heat or fertilizer production) is also under consideration, driving the need for innovative production, separation, and purification methods. Historically, industrial-scale hydrogen has been predominantly derived from fossil fuels, but renewable sources such as electrolysis, biological, and thermal processes now offer alternatives with varying production efficiencies (0.06–80%) and gas compositions. Therefore, selecting appropriate separation and purification methods is critical based on specific usage requirements and the gas composition. Industrial-scale hydrogen purification commonly employs pressure swing adsorption (PSA) technologies, capable of achieving up to 99.99% purity. Cryogenic distillation is suitable for applications needing up to 95% purity. Membrane technologies, including polymer, metallic, and electrolytic membranes, have traditionally been limited to moderate volumes of pure gas production but are crucial for hydrogen purification and separation. This publication critically evaluates the potential of membrane technology for hydrogen separation, particularly in response to the anticipated rise in demand for RES-derived hydrogen, including from renewable feedstocks. [ABSTRACT FROM AUTHOR]

The relationship between a region's dependency on variable renewable energy (VRE) and the viability of long-duration energy storage (LDES) technologies is recognised through various electricity grid modelling efforts in the contemporary literature. Numerous studies state a specific VRE penetration level in total electricity generation as an indicator of the emergence of an LDES market. However, there is considerable variability across studies when comparing VRE penetration levels in conjunction with LDES technology utilisation, and significant diversity exists in electricity grid modelling approaches. This review aims to highlight these inconsistencies by offering an overview of disparate findings and dissecting the influencing variables. Sixteen parameters are identified from reviewed studies, complemented by an additional five recognised through in-depth analysis. This comprehensive examination not only sheds light on critical aspects overlooked in previous reviews, requiring further investigation, but also provides novel insights into the complexity of this correlation, elevating the understanding of LDES market creation by unravelling the factors that influence the technology adoption across various contexts. Furthermore, it provides clarity in LDES research terminology by rectifying ambiguous language in the existing literature. Altogether, seven databases were explored to produce a trustworthy foundation for the study. [ABSTRACT FROM AUTHOR]

This simulation study explores the potential of a novel façade design with integrated control system comprising a dynamic photovoltaic (PV) facade integrated with dimming lighting control to enhance the work environment in office buildings and achieve energy-efficient solutions. Parametric modeling using the Grasshopper plug-in for Rhino software 7, coupled with energy simulation through the Honeybee environmental plug-in for the EnergyPlus program, are used in the methodology. The integrated control strategy was simulated to study in a single office space, utilizing the Daysim engine to assess indoor daylight quality and focusing on Daylight Factor (DF) and Daylight Glare Probability (DGP). Additionally, two artificial lighting control systems were examined for potential integration with the dynamic PV facade to minimize lighting load. The study employs the Galapagos evolutionary solver function embedded within Grasshopper to identify optimum solutions. The dynamic PV façade achieves substantial reductions in overall energy consumption, cutting it by 73% in June, 54% in July, 54.5% in August, and 52.55% in September. The results demonstrate substantial reductions in total energy consumption, with notable savings in heating and cooling due to the dynamic facade's ability to balance and control solar radiation during working hours. Moreover, the dynamic PV facade contributes to electricity generation, demonstrating its potential to improve visual comfort, decrease energy consumption, and generate electric energy through rotational adjustments and varying transparency levels. [ABSTRACT FROM AUTHOR]

The escalating concern over global warming has garnered significant international attention, with carbon emission intensity emerging as a crucial barrier to sustainable economic development across various regions. While previous studies have largely focused on annual scales, this study introduces a novel examination of Tianjin's quarterly carbon emission intensity and its influencing factors from 2012 to 2022 using quarterly data and the Logarithmic Mean Divisia Index (LMDI) model. The analysis considers the carbon emission effects of thermal power generation, the power supply structure, power intensity effects, and economic activity intensity. The results indicate a general decline in Tianjin's carbon emission intensity from 2012 to 2020, followed by an increase in 2021 and 2022. This trend, exhibiting significant seasonal fluctuations, revealed the highest carbon emission intensity in the first quarter (an average of 1.4093) and the lowest in the second quarter (an average of 1.0019). Economic activity intensity emerged as the predominant factor influencing carbon emission intensity changes, particularly notable in the second quarter (an average of −0.0374). Thermal power generation and electricity intensity effects were significant in specific seasons, while the power supply structure's impact remained relatively minor yet stable. These findings provide essential insights for formulating targeted carbon reduction strategies, underscoring the need to optimize energy structures, enhance energy efficiency, and account for the seasonal impacts of economic activity patterns on carbon emissions. [ABSTRACT FROM AUTHOR]

Today, the silicon photovoltaic cell (SPVC) solar panels are being deployed across the globe by all most all the governments to generate renewable carbon neutral electricity to use in place of the one generated at thermal power plants by burning fossil fuels. These SPVC solar panels generate electricity with less than 20% efficiency, and heat energy with more than 30% efficiency. This 10% excess heat energy released into the atmosphere can cause a very severe damage to the environment and ecology as it is million times higher than the one caused by the equivalent CO2 gas in the atmosphere with a concentration of about 421 ppm. To avoid the harmful and danger effects of heat energy released by these SPVC solar panels into the atmosphere, either they have to be stopped using or the heat generated by them has to be trapped to use for the beneficial purposes of the society or any other alternate safe solar panels have to be developed. In this study, for the first time, the SLAPE (Semiconductor and Liquid Assisted Photothermal Effect) solar panels are introduced to generate electricity from sunlight. In these new solar panels, a semiconducting material along with a stable organic solvent is employed to capture the sunlight reaching the earth surface and to convert it into the heat energy. This in situ generated heat energy is then converted into a rotational mechanical energy with the help of an heat engine, and then into electricity with the help of suitable electric generator. To establish the Proof of Concept (poc), in this study, SPV cells as semiconducting material and γ-butyrolactone were employed to fabricate a one square meter area SLAPE solar panel and generated about 17 V AC electricity with the help of a custom manufactured reciprocally moved steam engine (RMSE) and an electric generator. Thus obtained results are presented and discussed along this article. [ABSTRACT FROM AUTHOR]

The Port of Valencia, a prominent maritime center, is actively working towards minimizing its carbon emissions and aims to become a completely carbon-neutral port soon. This research uses data-driven sensitivity analysis to explore realistic power-generating options for a seaport to reduce its emissions. This approach comprises changing key parameters in power consumption and deploying renewable energies (rather than electricity and infrastructure prices, which are beyond the scope of this study) to assess their impact on the port's overall emissions profile. Through sensitivity analysis, policymakers and managers discover each scenario's efficacy and find the best decarbonization strategies. After thoroughly examining four realistic scenarios, our research findings show that each scenario's emission reduction share and sensitivity are practical and feasible. It becomes clear that gradually replacing traditional fossil fuels for electricity generation with renewables is a reasonable and realistic option for emissions reduction. The results demonstrate that focusing on reasonable targets, such as replacing 30% and 50% of electricity generation with renewables, is more achievable and beneficial in the medium term than ambitious goals, like replacing all electricity with renewable energy. This research contributes to reducing emissions of the Port of Valencia by using data-driven sensitivity analysis to find practical renewable energy strategies. It provides actionable insights for managers and policymakers to implement feasible decarbonization plans, emphasizing gradual adoption of renewables over ambitious goals, thus supporting sustainable maritime operations. [ABSTRACT FROM AUTHOR]

Currently, renewable energy sources (RESs) are gradually replacing traditional power sources that use fossil fuels. In some countries, such as Vietnam, RESs are developed on a massive scale and are concentrated in some key areas. This causes negative impacts on a power system when its transmission system is not deployed synchronously to release their capacity from these new renewable energy plants. An important challenge today is to ensure frequency stability in power systems with high uncertainty in RES output power. Additionally, the system requires solutions to prevent transmission line overloads during periods when RESs make a substantial contribution to the electricity generation capacity. Therefore, this paper builds an automatic generation control (AGC) system for a two-area power system with high penetration of RESs. This AGC system model aims to maintain system frequency stability amid unpredictable changes in RESs while also ensuring that tie-lines transmit the predetermined power levels to mitigate frequent congestion. By continuously monitoring and adjusting the system's frequency, the challenges posed by the inherent variability of RESs can be effectively mitigated. The AGC model is simulated on DIgSILENT PowerFactory software and tested with a 106-bus system. The simulation results of this study show that the AGC system operates effectively, ensuring that the frequency returns to the rated value and maintaining the exchange capacity on the tie-lines after occurrences of RES power decrease events. [ABSTRACT FROM AUTHOR]

The quest for sustainable energy solutions has intensified interest in marine renewables, particularly wave energy. This study addresses the crucial need for an objective assessment of Wave Energy Converter (WEC) technologies, which are instrumental in harnessing ocean waves for electricity generation. To benchmark WEC technologies, we employed an integrated approach combining the MEthod based on the Removal Effects of Criteria (MEREC) and the Spherical Fuzzy Combine Compromise Solution (SF-CoCoSo). MEREC provided a systematic way to determine the importance of various benchmarking criteria, while SF-CoCoSo facilitated the synthesis of complex decision-making data into a coherent evaluation score for each technology. The results of the study offer a definitive ranking of WEC technologies, with findings emphasizing the importance of grid connectivity and adaptability to various wave conditions as pivotal to the technologies' success. While the study makes significant strides in the evaluation of WECs, it also recognizes limitations, including the potential for evolving market dynamics to influence criteria weightings and the assumption that the MCDM methods capture all decision-making complexities. Future work should expand the evaluative criteria and explore additional MCDM methods to validate and refine the benchmarking process further. [ABSTRACT FROM AUTHOR]

When studies investigate the impact of average schooling or learning scores on economic development, they rarely focus on the effect of inequality in learning quality on economic development. Human capital is defined as inclusive sources combining citizens' knowledge and treatment skills. This study uses global learning data to investigate a novel effect of the inequality of reading, math, and science learning scores on economic development and energy selection based on links between and within countries. We found that reducing learning outcome inequality among secondary students is crucial for improving economic development from both short-term and long-term perspectives. Moreover, the positive association between students' learning quality and economic development is confirmed. Compared to the within-country effect, the greater between-country effect of the relationship between learning quality and economic development is confirmed, showing that expectations of the between-country effect might overestimate the favorable effect of education on economic development. On the contrary, the findings show that population learning inequality is less likely to affect national attitudes toward nuclear electricity generation or renewable energy adoption. The insightful policy implications suggest that policy makers should consider reducing learning outcome inequality among students and improving the within-country relationship between education and economic development. [ABSTRACT FROM AUTHOR]

Introduction: Fermentation-produced protein (FPP) is gaining global interest as a means of protein production with potentially lower cost and environmental footprint than conventionally-produced animal-sourced proteins. However, estimates on the potential performance of FPP vary substantially, limiting assessment of its scalability and utility. Methods: We integrate life cycle analysis data with nutritional and economic data in an interactive online tool, simulating the requirements and consequences of fermentation at a globally-relevant scale. Results: The tool demonstrates that production of an additional 18 million tons of protein annually via fermentation (~10% of 2020 global consumption) would necessitate 10-25 million hectares of feedstock cropland expansion/reallocation, utilize up to 1% of global electricity generation, produce 159 million tons CO2 equivalents, and have a total process input cost of 53.77 billion USD, with a negligible impact on nutrient supply beyond protein. Discussion: This tool should be used to inform the debate on the future use of fermentation in the food system. [ABSTRACT FROM AUTHOR]

Ensuring a balance between supply and demand is critical within electricity grids, requiring a supply composition that guarantees consistent service provision in the short and medium term. Between 2008 and 2017, Ecuador's electricity generation capacity expanded significantly, with an investment of approximately USD 8150 million into harnessing the potential energy of water. This led to the construction of five high-capacity hydroelectric projects by 2017, contributing 33.4% of the sector's energy output by 2023. However, it is important to note that if installed hydroelectric projects operate as run-of-river plants, with limited reservoir capacity, they can only regulate water use for electricity generation on an hourly basis. As of 2023, these run-of-river plants represent 68.8% of Ecuador's total hydroelectric capacity within the National Interconnected System (SNI). Consequently, during periods of low inflows, supplementary energy from other power generation plants is necessary to prevent energy crises. This paper addresses the impact on energy storing for electricity generation resulting from the evolution of hydroelectric power plant entry from 2006 to 2023. This aspect has not been thoroughly examined in hydrothermal systems, which primarily focus on potential energy obtained from dams. Our approach involves a statistical analysis of hydroelectric dam reservoir operational levels. We further explore the influence on demand service within Ecuador's electricity system, particularly during observed energy crises towards the end of 2023. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the impact of solar radiation intensity on the performance of direct-expansion solar PVT heat pump systems. To this end, an experimental setup was constructed for direct-expansion photovoltaic (PVT) solar heat pump water heating systems and photovoltaic (PV) power generation systems. The system performance and main parameters were analyzed and discussed under different solar radiation intensities. The winter experiments in southern China revealed that the exhaust temperature of the heat pump unit varied considerably under clear conditions, while the back temperature remained stable, fluctuating between approximately −13.5 °C and 24 °C. In contrast, the power generation of PVT panels increased with the increase in radiation intensity, from 78.33 W to 122.68 W, for an increase of 56.6%. Furthermore, the total electricity generation of the PVT panels was higher than that of PV panels, with an increase of 8.7–8.3%. Nevertheless, discrepancies between experimental and theoretical data were observed, particularly under overcast conditions, where the back panel temperature error was pronounced. Additionally, the system exhibited enhanced stability at elevated temperatures in comparable environments, accompanied by an improvement in the system's coefficient of performance (COP) by 5.67%. [ABSTRACT FROM AUTHOR]

This study presents a systematic analysis of Brayton batteries using Ebsilon Professional® simulations. Over 200,000 concept configurations were evaluated, with less than 1% proving physically feasible. The research aimed to assess electricity generation; coupled generation of electricity and heat; coupled generation of electricity and cooling; and coupled generation of electricity, heat, and cooling, all with or without waste heat integration. Efficiency ranged from 20% to 50% for electricity generation alone, with higher efficiencies at a compressor discharge temperature of 625 °C compared to 450 °C. Co-generation improved the overall efficiency, although at the expense of power efficiency. Notably, simultaneous electricity, heat, and cooling generation solutions were absent within the study's parameters. Lead concepts, predominantly air-based systems with or without charging line recuperators and heat exchange at various stages, were identified. These will undergo detailed dynamic system simulations, focusing on thermal energy storage. Comparison with the existing literature was limited due to differing parameters and topologies, highlighting the value of this systematic analysis in identifying optimal solutions. [ABSTRACT FROM AUTHOR]

This study delves into the adoption of the organic Rankine cycle (ORC) for recovering waste heat from data centers (DCs). Through a literature review, it examines energy reuse with a focus on electric power generation, the selection of working fluids, and system design principles. The objective is to develop a thorough framework for system design and analysis, beginning with a quantity and quality investigation of waste heat available. Air cooling systems, chosen often for their simplicity, account for about 70% of used cooling methods. Water cooling demonstrates greater effectiveness, albeit less commonly adopted. This study pays close attention to the selection of potential working fluids, meticulously considering the limitations presented by the available sources of heat and cold for vaporization and condensation, respectively. It reviews an ORC-based system setup, incorporating fluid streams for internal processes. The research includes a conceptual case study where the system is designed and simulations are conducted in the DWSIM environment. The simulation model considers hot air or hot liquid water returning from the data center cooling system for ORC working fluid evaporation. Ambient water serves for condensing, with pentane and isopentane identified as suitable organic fluids. Pentane assures ORC net electric efficiencies ranging between 3.1 and 7.1% when operating pressure ratios increase from 2.8 to 6.4. Isopentane systems, meanwhile, achieve efficiencies of 3.6–7.0% across pressure ratios of 2.7–6.0. Furthermore, the investigation provides key performance indicators for a reference data center in terms of power usage effectiveness (PUE), energy reuse factor (ERF), energy reuse effectiveness (ERE), and greenhouse gas (GHG) savings. This study concludes with guidelines for system analysis, including exergy considerations, and details the sizing process for evaporators and condensers. [ABSTRACT FROM AUTHOR]

Landfills are major emitters of greenhouse gases, especially methane (CH[sub 4]). To address this, the U.S. EPA promotes utilizing landfill gas (LFG) for renewable energy. Our study in Southeast Texas compared four LFG conversion strategies: flaring, electricity generation, renewable natural gas and methanol production. Scenario analysis was conducted to reveal the influence of market conditions and incentives on profitability. While electricity generation consistently yielded the highest returns, methanol production emerged as a close contender due to the strategic advantage of its proximity to the landfill site in this industrial zone. This study highlights the promising synergy between landfills and the hydrocarbon industry for sustainable development. [ABSTRACT FROM AUTHOR]

Graphene oxide has attracted many interests in the recent decade due to its unique mechanical and chemical properties. This study focuses on the modification of graphene oxide and preparation proton exchange membrane (PEM) by sulfonated poly ether ether ketone (SPEEK) as base polymer for using in MFC as a modified membrane to remove COD and electricity generation. The scanning electron microscope (SEM) images, Fourier transform infrared (FTIR) and contact angle measurements were used to verify hydrophilic properties of the synthesized membranes. First, preparation procedures and properties of sulfonated reduced graphene oxide are briefly described. Subsequently, modification of proton exchange membrane from SPEEK polymer with prepared nano particle of sulfonated reduced graphene oxide 0.5 wt. % and its operation in MFC was considered. COD removal, power density, current density and coulombic efficiency were monitored during the process operation to evaluate the MFC performance. During the process operation, COD removal, power density, current density, and coulombic efficiency were tracked to assess the MFC performance. The power density and current density, 39.43 mW/m² and 161 mA/m² and the columbic efficiency 48.9 % was obtained, respectively. The COD removal of 89.5 % was obtained. [ABSTRACT FROM AUTHOR]

Smart grids are conceived as a system to optimally manage the set of means that are part of the electricity grid to achieve adequate performance in the distributed generation system in the supply of quality electricity. The objective of this study was to analyze the importance of smart grids in distributed generation and how the elements that make it up allow the implementation of this technological innovation. The methodology was based on the mixed approach, of experimental design with descriptive scope and documentary support, the experimental method was applied for programming with the use of Homer Pro software. The results were obtained by simulation, carried out for a distributed generation system with a consumption of 197.78 kWh/day, obtaining important data such as the amount of kW of electricity generation supplied in a year, with a value of 98,114 kWh/year. [ABSTRACT FROM AUTHOR]

Hybrid power plants have recently emerged as reliable and flexible electricity generation stations by combining multiple renewable energy sources, energy storage systems (ESS), and fossil-based output. However, the effective operation of the hybrid power plants to ensure continuous energy dispatch under challenging conditions is a complex task. This paper proposes a dispatch engine (DE) based on mixed-integer linear programming (MILP) for the planning and management of hybrid power plants. To maintain the committed electricity output, the dispatch engine will provide schedules for operation over extended time periods as well as monitor and reschedule the operation in real time. Through precise prediction of the load and the photovoltaic (PV) and wind power outputs, the proposed approach guarantees optimum scheduling. The precise predictions of the load, PV, and wind power levels are achieved by employing a predictor of the Feed-Forward Neural Network (FFNN) type. With such a dispatch engine, the operational costs of the hybrid power plants and the use of diesel generators (DGs) are both minimized. A case study is carried out to assess the feasibility of the proposed dispatch engine. Real-time measurement data pertaining to load and the wind and PV power outputs are obtained from different locations in the Sultanate of Oman. The real-time data are utilized to predict the future levels of power output from PV and from the wind farm over the course of 24 h. The predicted power levels are then used in combination with a PV–Wind–DG–ESS–Grid hybrid plant to evaluate the performance of the proposed dispatch engine. The proposed approach is implemented and simulated using MATLAB. The results of the simulation reveal the proposed FFNN's powerful forecasting abilities. In addition, the results demonstrate that adopting the proposed DE can minimize the use of DG units and reduce a plant's running expenses. [ABSTRACT FROM AUTHOR]

Currently, most studies on the optimization of wind farm layouts on flat terrain employ a discrete grid-based arrangement method and result in irregular layouts that may damage the visual appeal of wind farms. To meet the practical requirements of wind farms, a two-step optimization method called "grid–coordinate" based on a genetic algorithm is proposed in this paper. The core idea is to initially determine the number of wind turbines and their initial positions using a grid-based approach, followed by a fine-tuning of the wind farm layout by moving the turbines in a row/column manner. This two-step process not only achieves an aesthetically pleasing arrangement but also maximizes power generation. This algorithm is conducted to optimize a 2 km × 2 km wind farm under three classic wind conditions, one improved wind condition, and a real wind condition employing both the Jensen and Gaussian wake models. To validate the effectiveness of the proposed method, the optimization of configurations based on different wake models was conducted, yielding results including the efficiency, total power output, number of wind turbines, and unit cost of electricity generation. These results were compared and analyzed against the classical literature. The findings indicate that the unit cost of electricity generation using the two-step optimization approach with the Gaussian wake model is higher than that of the initial grid optimization method. Additionally, varying the number of wind turbines can lead to instances of high power generation coupled with low efficiency. This phenomenon should be carefully considered in the wind farm layout optimization process. [ABSTRACT FROM AUTHOR]

Recently, there has been a push by countries to diversify their energy mix considering various factors. In this regard, there have been several studies conducted to assess the potential for using sources such as wind and solar to generate supplemental energy to the already present energy generation setup. In this regard, this study explores the potential of wind for the Commonwealth of Kentucky. To perform this study, wind data were sourced for eight locations across Kentucky from the publicly accessible wind speed information present at Weather Underground for the years 2020–2021 (two years). An analysis was performed concerning the seasonal, monthly, and hourly variation in the wind speed so as to identify the expected times of sufficient wind energy generation. Moreover, a comparison of the collected data was performed with data from a home-based weather station as well as a deployed wind turbine to validate the variation pattern of the publicly sourced data. Finally, in order to investigate the variation patterns of wind and solar energy sources, a comparative analysis was also performed using data from a solar power generation plant in Kentucky. A seasonal and monthly complementarity was observed between the wind and solar energy. However, when considering daily patterns, the wind was found to follow solar generation with an offset. While further research is required, this analysis indicates that it is possible to deploy wind energy power generation projects in the Commonwealth of Kentucky. The seasonal complementary behavior of wind and solar energy can be used along with battery storage in conjunction with natural gas to provide a diversified electricity generation portfolio. [ABSTRACT FROM AUTHOR]

In this work, two kinds of primary batteries, both of which included a Zn anode, C rod cathode, copper wire and electrolyte composed of Cd2+ -contaminated water or soil, were constructed in the first attempt to both remove Cd2+ and generate electricity. Unlike traditional technologies such as electrokinetic remediation with high energy consumption, this technology could realize Cd2+ migration to aggregation and solidification and generate energy at the same time through simultaneous galvanic reactions. The passive surface of Zn and C was proven via electrochemical measurements to be porous to maintain the relatively active galvanic reactions for continuous Cd2+ precipitation. Cd2+ RE (removal efficiency) and electricity generation were investigated under different conditions, based on which two empirical models were established to predict them successfully. In soil, KCl was added to desorb Cd2+ from soil colloids to promote Cd2+ removal. These systems were also proven to remove Cd2+ efficiently when their effects on plants, zebrafish, and the soil bacterial community were tested. LEDs could be lit for days by utilizing the electricity produced herein. This work provides a novel, green, and low-cost route to remediate Cd2+ contamination and generate electricity simultaneously, which is of extensive practical significance in the environmental and energy fields. [ABSTRACT FROM AUTHOR]

Water evaporation–induced electricity generators (WEGs) are regarded as one of the most promising solutions for addressing the increasingly severe environmental pollution and energy crisis. Owing to the potential carbon emission in the preparation process of WEGs, whether WEG represents a clean electricity generation technology is open to question. Here, a brand‐new strategy is proposed for manufacturing negative carbon emission WEG (CWEG). In this strategy, the microalgae film is used as the electricity generation interface of WEG, which achieves a stable open‐circuit voltage (Voc) of 0.25 V and a short‐circuit current (Isc) of 3.3 µA. Since microalgae can capture carbon dioxide during its growing process, CWEG holds great promise to generate electricity without carbon emissions in the full life cycle compared with other WEGs. To the best of the author's knowledge, this is the first work using microalgae films to fabricate WEG. Therefore, it is believed that this work not only provides a new direction for designing high‐efficiency and eco‐friendly WEG but also offers an innovative approach to the resource utilization of microalgae. [ABSTRACT FROM AUTHOR]

The decarbonisation of electricity generation requires real-time monitoring and control of grid components to efficiently and timely dispatch demand. This highly automated system, known as the Smart Grid, relies on smart or sensor-equipped distribution network components to optimise energy flow and minimise losses. However, energy theft, a major obstacle to efficient resource utilisation, poses a significant challenge to achieving this goal. This study proposes and evaluates a real-time telemetry and control system designed to mitigate energy theft in agricultural irrigation applications. The system increases energy efficiency by tracking the energy use in agricultural irrigation. The key challenge is to identify the source of illegal electricity consumption, classify it, and localise it. To address these difficulties, two distinct classification problems are addressed through the utilisation of machine learning methodologies. The initial classification task concerns the categorisation of loads that consume illegal electricity in agricultural irrigation. The subsequent classification problem pertains to the categorisation of feeder branches where such loads are activated. Therefore, a pilot distribution grid feeder has been simulated, and irrigation motors have been used as illegal loads which are activated at different points along the distribution feeder. The data collected from these simulations are used to create a data set where three-phase current data are collected from the transformer substation. The generated data set is employed to train machine learning models for the classification of illegal loads and feeder branches. The performance results of machine learning methods is obtained using the following metrics: accuracy, precision, recall, and F1-score. The results of the classification of loads stealing electricity in agricultural irrigation demonstrate that the bagged trees (BAT) algorithm achieves 99.64% in each criterion. In branch classification, the algorithm achieves the best results, with 97.64%, 97.40%, 96.22%, and 96.81%, respectively. Both classification performance results indicate that the proposed algorithm is effective for solving both classification problems. This research demonstrates the efficacy of ML-powered real-time monitoring and control in combating energy theft and promoting efficient resource utilisation within agricultural irrigation networks. It is a pioneering study in the field of determining and classifying illegal loads in agricultural irrigation. Further research will investigate the potential for expanding the system’s capabilities to include different load types and exploring alternative ML techniques for broader applicability within the context of low voltage distribution network monitoring. [ABSTRACT FROM AUTHOR]

Microbial fuel cells (MFCs) represent a promising technology to generate bio-electricity and synchronously reduce wastewater pollutants. The presence of exoelectrogens in wastewater is critical for bio-electricity and pollutant reduction, but the performance of exoelectrogens at different pH levels remains unknown. This study aims to bridge this gap by offering an integrated approach to understanding the performance of exoelectrogens under varying substrate pH, particularly in bio-electricity generation and pollutant reduction in sugarbeet processing wastewater (SBWW). Three pH levels (ranging from acidic to alkaline) were studied and MFC’s electricity output was measured. Later, current density, power density, and coulombic efficiency (CE) were calculated. Both pre- and post-experiment substrate samples were analysed with inductively coupled plasma (ICP). Furthermore, 16S rRNA gene analysis, DNA amplification, sequencing library preparation, and bioinformatics workflows on post-experiment samples of the substrate and anode samples were conducted. A diverse community of microorganisms was identified, especially Alphaproteobacteria, Gammaproteobacteria, and Deltaproteobacteria (Geobacter). Bacteroidetes and Desulfovibrio were the major exoelectrogens responsible for electricity generation. Among the three pH levels tested, the most alkaline pH level (9.5±0.1) outperformed the others, achieving a 54% higher power density, 21% greater current density, and a 40% higher CE compared to the acidic pH level (6.5±0.1). Around 50–99% of pollutants were removed from the SBWW. The study revealed that Gammaproteobacteria thrive and perform better in alkaline environment. [ABSTRACT FROM AUTHOR]

Climate change presents a substantial impediment for the global community and prompts policymakers worldwide to prioritize environmental goals when defining national development strategies. Prioritizing these goals is particularly challenging for governments of developing economies still relying on fossil fuels, foreign capital, and industry contribution to GDP. This study examines the relationships between carbon dioxide emissions, industry value added, financial development, and electricity generation in 15 Central and Eastern European countries from 1995 to 2021. To achieve this objective, we examined stationarity and cointegration and employed a vector error correction model to investigate causalities between the variables, along with a variance decomposition analysis. Our findings suggest that the short-run unidirectional causalities exist from industry value-added to carbon dioxide emissions and from carbon dioxide emissions to financial development and electricity generation. Long-term causality exists between carbon dioxide emissions and industry value added. The findings shed light on the challenges and opportunities these countries face in transitioning to climate neutrality and meeting the decarbonization targets. Within this context, the findings underscore the significance of crafting customized strategies for these economies to navigate the complex landscape of climate change while promoting sustainable industrial, electricity and financial sector development. [ABSTRACT FROM AUTHOR]

Semitransparent organic solar cells show attractive potential in the application of building‐integrated photovoltaics, agrivoltaics, floating photovoltaics, and wearable electronics, as their multiple functionalities of electric power generation, photopermeability, and color tunability. Design and exploration of semitransparent organic solar cells with optimal and balanced efficiency and average visible light transmittance and simultaneously high stability are in great demand. In this work, based on a layer‐by‐layer‐processed active layer and an ultrathin metal electrode, inverted semitransparent organic solar cells (ITO/AZO/PM6/BTP‐eC9/MoO3/Au/Ag) were fabricated. Optimal and balanced efficiency and average visible light transmittance were demonstrated, and simultaneously promising thermal and light stability were achieved for the obtained devices. The power conversion efficiency of 13.78–12.29% and corresponding average visible light transmittance of 14.58–25.80% were recorded for the ST‐OSC devices with 25–15 nm thick Ag electrodes, respectively. Superior thermal and light stability with ~90% and ~85% of initial efficiency retained in 400 h under 85 °C thermal stress and AM1.5 solar illumination were demonstrated, respectively. [ABSTRACT FROM AUTHOR]

Thin wafers and thin wires are beneficial to the photovoltaic industry for reducing costs, increasing efficiency, and reducing the cost of electricity generation. It is a development trend in solar silicon wafer cutting. Thin wire cutting reduces the kerf between silicon wafers to less than 50 μm. Therefore, it is extremely difficult to supply cutting fluid to the cutting area. And this affects cutting performance. This paper proposes the use of the capillary effect produced by ultrasonic waves in fixed diamond wire slicing to improve the cutting fluid supply and reduce wafer adsorption. To explore the rules of ultrasonic capillary action between two plates and guide the industrial applications, the effects of the distance between parallel plates, the distance from the bottom of the parallel plates to the ultrasonic radiation surface, the non-parallelism between the plates, the temperature of the working fluid, the ultrasonic action time, and the type of working fluid on the liquid level rise height were studied. The conclusions can be used to guide the improvement of the supply of working fluid in fixed diamond wire slicing. [ABSTRACT FROM AUTHOR]

Renewable energy has grown substantially in recent years due to its efficacy in mitigating climate change. The rapid proliferation of solar photovoltaic (PV) systems and subsequent alterations in land use have led to concerns about the impact on local ecosystems. Particularly in Japan, seminatural grasslands, which are valuable habitats, are being developed as solar PVs. Here, we focused on stilt-mounted agrivoltaic systems, capable of both photovoltaic power generation and plant growth beneath solar panels. By repurposing this technology, the coexistence of vegetation and solar PV systems can be possibly ensured. To assess the feasibility of this proposed approach, we initially examined the suitability of installing solar PV in seminatural grasslands. The suitability of seminatural grasslands, solar PVs, and random points for solar PV was evaluated in terms of electricity generation and construction costs. The environmental conditions of seminatural grasslands were found to be advantageous for electricity generation. On the other hand, in terms of construction costs, seminatural grasslands were comparable to randomly selected sites. These findings indicate that the suitability of existing seminatural grasslands for solar PV plant installations is not considerably lower than that of other land types. The idea of reconciling solar power generation with ecosystem conservation holds promise and warrants further investigation toward its realization. [ABSTRACT FROM AUTHOR]

Environmental pollution is currently one of the most worrying factors that endangers human health. Therefore, attempts are being made to reduce it by various means. One of the most important sources of pollution in terms of the current RoHS and REACH directives is the pollution caused by the use of chemical products for the production of sources for the storage and generation of electricity. The aim of this article is therefore to develop supercapacitors made of biodegradable materials and to investigate their electrical performance. Among the materials used to make these electrodes, activated carbon was identified as the main material and different combinations of gelatin, calligraphy ink and glycerol were used as the binders. The electrolyte consists of a hydrogel based on gelatin, NaCl 20 wt% solution and glycerol. In the context of this research, the electrolyte, which has the consistency of a gel, fulfills the dual function of the separator in the structure of the manufactured cells. Due to its structure, the electrolyte has good mechanical properties and can easily block the contact between the two electrodes. Most of the materials used for the production of supercapacitor cells are interchangeable materials, which are mainly used in other application fields such as the food or cosmetics industries, but were also successfully used for the investigations carried out in this research. Thus, remarkable results were recorded regarding a specific capacitance between 101.46 F/g and 233.26 F/g and an energy density between 3.52 Wh/kg and 8.09 Wh/kg, with a slightly lower power density between 66.66 W/kg and 85.76 W/kg for the manufactured supercapacitors. [ABSTRACT FROM AUTHOR]

Unreliable resources in conventional power systems have created new challenges for the users of these systems. These sources of production, including wind and solar sources, with a significant share in the power generation of many power systems, cause problems in the safe and stable operation of power systems due to uncertainty. Therefore, it is very important to provide a method or model for safe and stable operation of power systems. The main innovation of this paper is to present a new SCUC model to maximize the flexibility of the system along with reducing the operating cost in the form of a security constrained multi-level robust optimization problem in the presence of wind uncertainty sources. Also, a set of demand response programs and rapid response hybrid cycle units are used as sources of flexibility. Accordingly, the tolerable range of uncertainty and system slope reservation is developed with a low-cost combination and with the commitment to flexible covering resources. The desired optimization problem has been solved with the help of the NCCG algorithm and MILP method. The efficiency and reliability of this proposed model have been simulated and evaluated in IEEE 6-bus and 118-bus standard power systems, and the simulation results confirm the technical and economic efficiency of this model. [ABSTRACT FROM AUTHOR]

Heterostructure photocatalytic materials with a high surface area have garnered significant attention in recent years. In this investigation, we successfully synthesized g-C 3 N 4 /TiO 2 tubes using an enhanced impregnation-calcination method. The photocatalytic efficiency of the composite tubes was evaluated by examining the degradation of tetracycline hydrochloride (TCH) solution, revealing a photocatalytic degradation rate 3.7 times and 8.0 times higher than that of pure TiO 2 and g-C 3 N 4 when subjected to simulated sunlight. The enhanced photocatalytic performance can be ascribed to the expanded specific surface area and enhanced separation rate of photo-generated charge carriers. Density functional theory (DFT) calculations and trapping experiments revealed a possible photogenerated electrons transfer pathway in the degradation process of TCH. The photocatalytic fuel cell (PFC) system composed of the g-C 3 N 4 /TiO 2 tubes demonstrated remarkable performance in the concurrent degradation of TCH and electricity generation when being exposed to light. It achieved a short circuit current density of 26.9 mA/g/cm2 and a power density of 5.51 mW/g/cm2. The present study offers a cost-effective approach for both organic waste treatment and electrical power generation. [ABSTRACT FROM AUTHOR]

Fossil fuels, particularly oil, face sustainability challenges due to depletion and their role in increasing atmospheric CO2 levels, contributing to climate change and impacting global agriculture. Renewable energy sources like solar power offer a viable alternative. This study explores the feasibility of agro-photovoltaic (APV) systems, which integrate solar panels with agricultural land to generate electricity while cultivating crops. Specifically, the impact of APV systems on rice production and quality was investigated. Solar modules with a total capacity of 99.84 kW were installed on a 2580 m² site, with two module configurations tested. Two rice (Oryza sativa L.) cultivars, Woonkwang and Saenuri, were transplanted and monitored for growth characteristics, chlorophyll content, and fluorescence, as well as yield and quality. Growth analysis of rice under APV systems showed minimal impact on plant height and tiller numbers, though chlorophyll content analysis indicated delayed leaf aging and extended maturation time. Rice yield decreased due to altered panicle and spikelet numbers. Quality analysis revealed changes in head rice rate and broken rice, stressing optimal harvest timing in APV systems. Shading conditions also affected physicochemical properties and taste profiles. Yields dropped by about 20% under APV systems, but the financial returns from electricity generation significantly outweighed the crop revenue loss. Despite the high initial installation costs and regulatory challenges, APV systems present a promising dual-use approach for enhancing farm income and promoting renewable energy. Continued research and investment are essential for optimizing APV systems and expanding their adoption. [ABSTRACT FROM AUTHOR]