Hasil untuk "Renewable energy sources"

Muhammad Yousaf Raza, Mohammad Maruf Hasan, Qasim Javed

Realizing affordable and clean energy requires a global transition away from reliance on fossil fuels toward sustainable energy sources; however, the significance of natural resource rents (NRR) in this transition remains surprisingly underexplored. The aim of this study is toward analyzing the impact of eight natural resource factors in the Bangladesh perspective, and this study covers the period 1980–2022. Additionally, the ARDL model is employed. We employed different types of regression model evaluation metrics (i.e., RMSE, MAE, and MAPE) to solve the desired problem at hand. The outcomes show that (i) all the variables at the first difference represent the cointegration test, while the bounds test outcomes confirm that there is long-run cointegration and relationships among energy transition, energy import, natural gas, total natural resources, oil, and forest rents. (ii) The natural resource rents and economic growth factors show a positive relationship with energy transition in Bangladesh between 0.55% and 3.90% in the short run and between 0.05% and 7.69% in the long run. This suggests that even a 1% change in resource rent and economic growth factors can still lead to optimistic growth, driven by rapid economic growth and the intense use of natural resources, which provides advanced development for its energy sector. (iii) The prediction results provide RMSE higher than MAE, while MAPE for energy transition is calculated at 1.45%, suggesting that energy transition processing in Bangladesh can be properly adopted. Finally, based on a comprehensive analysis of Bangladesh’s energy transition, resource rents, and economic factors, the study suggests its contributions to resource governance and plans evidence-based pathways for rent management, import structure, and renewable energy transition with long-run environmental stability.

Jiahang Chen, Zuhui Zhou, Jiaxing Guo et al.

Despite advances of single-atom catalysts (SACs) in sodium–sulfur (Na–S) batteries, their symmetric coordination geometry (e.g., M–N4) fundamentally restricts orbital-level modulation of sulfur redox kinetics. Herein, we demonstrate that hetero-diatomic Co–Y sites with Co–N4–Y–N4 coordination on N-doped carbon (Co–Y/NC) break the M–N4 symmetry constraint through d–d orbital hybridization, which is confirmed by an implementation of advanced characterizations, including the high-angle annular dark-field scanning transmission electron microscopy and x-ray absorption fine structure spectroscopy. In practical operation, the Co–Y/NC@S cathode with 61% sulfur mass fraction delivers a superior capacity (1,109 mAh/g) at 0.2 A/g, outperforming that of Co or Y SAC and further setting a new benchmark of diatomic catalysts for Na–S battery systems. Furthermore, the theoretical calculations show a hybridization-induced d-band splitting energy (ΔE = 0.5 eV), which induces electron-deficient Y sites for polysulfide adsorption (Na2S6) and electron-rich Co sites for S–S scission (barrier energy = 0.28 eV) via the d-p orbital hybridization of an asymmetric configuration. Our work establishes a strategy based on rare-earth-transition metal orbital hybridization to design asymmetric active sites for promoting multielectron sulfur redox reactions.

César Álvarez-Arroyo, Salvatore Vergine, Agustín Sánchez de la Nieta et al.

Fernando Martinez-Gil, Christopher Sansom, Aránzazu Fernández-García et al.

This review explores advanced maintenance techniques aimed at improving solar energy production efficiency. The study analyzes the rapid growth of solar energy and the challenges posed by environmental factors such as soiling, harsh climate conditions and hotspots, which reduce photovoltaic (PV) and concentrated solar power (CSP) system performance. Predictive models for solar energy generation and soiling detection, including artificial intelligence (AI) and machine learning (ML) algorithms and Internet of Things (IoT), are discussed as means for optimizing energy production and reducing maintenance costs. It is also emphasized the role of Unmanned Aerial Vehicles (UAVs) to capture images for fault detection and failure prediction, enhancing maintenance accuracy and minimizing downtime. The study concludes by analyzing the role of these techniques to reduce water consumption in cleaning tasks, as well as solutions to increase the operational lifespan and performance of solar plants such as anti-soiling coatings, robotic cleaning systems and accurate predictive models.

E. Tianlong, Kai Qin, Zhao Liu et al.

Abstract Ultra-high-voltage (UHV) autotransformers are widely employed in long-distance power transmission systems. Their operation involves complex energy conversion and coupling mechanisms, including high-intensity magnetic induction energy and strong induced currents. From the perspective of power systems and automation control, it is essential to construct a comprehensive equivalent control circuit for UHV autotransformers, integrating the analysis of induced current and magnetic flux density into the domain of analog electronics. Numerical analysis has become a core approach for investigating the external thermal physical characteristics of transformer power and various thermal management strategies. In this paper, the Message Passing Interface (MPI) and Portable, Extensible Toolkit for Scientific Computation (PETSc) parallel computing framework is adopted to compute and analyze the electro-thermal coupling in a UHV autotransformer. The dielectric loss of transformer components is thoroughly examined. A linear numerical simulation method for evaluating dielectric loss is assessed through parallel computation and validated via the design of a three-dimensional coupling model for leakage flux and core temperature rise. The dielectric loss calculation is applied to the transformer. Magnetostriction measurements under rated output power and various current and voltage conditions reveal the correlation between the coupled data and the thermal topology. The MPI-PETSc framework significantly enhances the computational efficiency of three-dimensional electro-thermal coupling problems in UHV autotransformers through distributed computing and efficient numerical solving, making it suitable for large-scale, high-precision engineering simulations.

Yanbo Tao, Yixun Xue, Yuan Du et al.

IntroductionThe accelerated development of renewable energy sources has confronted substantial challenges, primarily attributable to their intermittency and uncertainty. Consequently, the integration of green electricity has become a pressing concern. Hydrogen production from water electrolyzer has emerged as a key method for promoting local wind and solar energy consumption. However, extant studies tend to neglect the value of hydrogen as a chemical feedstock and rely on simplified linear models to describe the characteristics of electro-hydrogen coupling devices. This has resulted in discrepancies between optimization decisions and actual operational performance.MethodsTo address this gap, the present paper employs a nonlinear semi-empirical model with a focus on electrolyzer and fuel cell. It describes the energy conversion between electricity and hydrogen more accurately based on electrochemical mechanisms. On this basis, considering the dual value of hydrogen energy as both “energy carrier” and “chemical raw material”, the operation optimization model of electric-hydrogen coupling system for chemical parks is established. Furthermore, a convexification method for coupling device constraints is proposed to enhance solution efficiency.Results and DiscussionThe findings of the study demonstrate that the semi-empirical model provides a more accurate representation of actual equipment performance, thereby preventing deviations between real-world operation and outcomes derived from optimization. Furthermore, the collaborative optimization strategy that accounts for hydrogen’s dual value has been shown to significantly improve the system’s economic benefits.

Marco Stein Muzio

In this review we motivate ultrahigh energy neutrino searches and their connection to ultrahigh energy cosmic rays. We give an overview of neutrino production mechanisms and their potential sources. Several model-independent benchmarks of the ultrahigh energy neutrino flux are discussed. Finally, a brief discussion of approaches for model-dependent neutrino flux predictions are given, highlighting a few examples from the literature.

Kuan Lu, Menghao Huo, Yuxiao Li et al.

Accurate forecasting of renewable energy generation is fundamental to enhancing the dynamic performance of modern power grids, especially under high renewable penetration. This paper presents Channel-Time Patch Time-Series Transformer (CT-PatchTST), a novel deep learning model designed to provide long-term, high-fidelity forecasts of wind and solar power. Unlike conventional time-series models, CT-PatchTST captures both temporal dependencies and inter-channel correlations-features that are critical for effective energy storage planning, control, and dispatch. Reliable forecasting enables proactive deployment of energy storage systems (ESSs), helping to mitigate uncertainties in renewable output, reduce system response time, and optimize storage operation based on location-specific flow and voltage conditions. Evaluated on real-world datasets from Denmark's offshore wind, onshore wind, and solar generation, CT-PatchTST outperforms existing methods in both accuracy and robustness. By enabling predictive, data-driven coordination of ESSs across integrated source-grid-load-storage systems, this work contributes to the design of more stable, responsive, and cost-efficient power networks.

Dhruv Suri, Jacques de Chalendar, Ines Azevedo

Wind and solar generation constitute an increasing share of electricity supply globally. We find that this leads to shifts in the operational dynamics of thermal power plants. Using fixed effects panel regression across seven major U.S. balancing authorities, we analyze the impact of renewable generation on coal, natural gas combined cycle plants, and natural gas combustion turbines. Wind generation consistently displaces thermal output, while effects from solar vary significantly by region, achieving substantial displacement in areas with high solar penetration such as the California Independent System Operator but limited impacts in coal reliant grids such as the Midcontinent Independent System Operator. Renewable energy sources effectively reduce carbon dioxide emissions in regions with flexible thermal plants, achieving displacement effectiveness as high as one hundred and two percent in the California Independent System Operator and the Electric Reliability Council of Texas. However, in coal heavy areas such as the Midcontinent Independent System Operator and the Pennsylvania New Jersey Maryland Interconnection, inefficiencies from ramping and cycling reduce carbon dioxide displacement to as low as seventeen percent and often lead to elevated nitrogen oxides and sulfur dioxide emissions. These findings underscore the critical role of grid design, fuel mix, and operational flexibility in shaping the emissions benefits of renewables. Targeted interventions, including retrofitting high emitting plants and deploying energy storage, are essential to maximize emissions reductions and support the decarbonization of electricity systems.

Jing Fan, Zhengxing Dai, Jian Cao et al.

Viscosity is one of the most important fundamental properties of fluids. However, accurate acquisition of viscosity for ionic liquids (ILs) remains a critical challenge. In this study, an approach integrating prior physical knowledge into the machine learning (ML) model was proposed to predict the viscosity reliably. The method was based on 16 quantum chemical descriptors determined from the first principles calculations and used as the input of the ML models to represent the size, structure, and interactions of the ILs. Three strategies based on the residuals of the COSMO-RS model were created as the output of ML, where the strategy directly using experimental data was also studied for comparison. The performance of six ML algorithms was compared in all strategies, and the CatBoost model was identified as the optimal one. The strategies employing the relative deviations were superior to that using the absolute deviation, and the relative ratio revealed the systematic prediction error of the COSMO-RS model. The CatBoost model based on the relative ratio achieved the highest prediction accuracy on the test set (R2 = 0.9999, MAE = 0.0325), reducing the average absolute relative deviation (AARD) in modeling from 52.45% to 1.54%. Features importance analysis indicated the average energy correction, solvation-free energy, and polarity moment were the key influencing the systematic deviation.

Giulio Raimondi, Gianluca Greco, Michele Ongis et al.

Nowadays, great emphasis is rightly given in the scientific community to hydrogen production from electrolysis. However, to achieve the politically stated target ambitions, all low-carbon sources for hydrogen production must be considered. The present work proposes a local production system of negative carbon hydrogen from lignocellulosic residual biomass using gasification and gas separation through H₂-selective membranes as enabling technologies. The feedstock is pruning. In addition, the system produces heat and power for a Renewable Energy Community (REC) to increase the economic feasibility of hydrogen production via their sale. A modular basic plant is sized, based on a simplified system envisaged for RECs under the current regulatory framework in Spain (electrical renewable output of 100 kW). A network of these modular basic plants in the province of Huesca (Aragón) is simulated to create a system of hydrogen refueling stations for mobility in that area. A Levelized Cost of Hydrogen (LCOH) is proposed, comprehending the whole production chain from “field to tank”, which is significant in areas where there is no infrastructure for the production and distribution of hydrogen for automotive purposes. The resulting LCOH for the whole system is 8.90 EUR/kg. Sensitivity analysis potentially values a lower LCOH, which unveils that hydrogen mobility can be largely competitive with diesel one.

Abdulwahab Rawesat, Pericles Pilidis

In the quest for achieving decarbonisation, it is essential for different sectors of the economy to collaborate and invest significantly. This study presents an innovative approach that merges technological insights with philosophical considerations at a national scale, with the intention of shaping the national policy and practice. The aim of this research is to assist in formulating decarbonisation strategies for intricate economies. Libya, a major oil exporter that can diversify its energy revenue sources, is used as the case study. However, the principles can be applied to develop decarbonisation strategies across the globe. The decarbonisation framework evaluated in this study encompasses wind-based renewable electricity, hydrogen, and gas turbine combined cycles. A comprehensive set of both official and unofficial national data was assembled, integrated, and analysed to conduct this study. The developed analytical model considers a variety of factors, including consumption in different sectors, geographical data, weather patterns, wind potential, and consumption trends, amongst others. When gaps and inconsistencies were encountered, reasonable assumptions and projections were used to bridge them. This model is seen as a valuable foundation for developing replacement scenarios that can realistically guide production and user engagement towards decarbonisation. The aim of this model is to maintain the advantages of the current energy consumption level, assuming a 2% growth rate, and to assess changes in energy consumption in a fully green economy. While some level of speculation is present in the results, important qualitative and quantitative insights emerge, with the key takeaway being the use of hydrogen and the anticipated considerable increase in electricity demand. Two scenarios were evaluated: achieving energy self-sufficiency and replacing current oil exports with hydrogen exports on an energy content basis. This study offers, for the first time, a quantitative perspective on the wind-based infrastructure needs resulting from the evaluation of the two scenarios. In the first scenario, energy requirements were based on replacing fossil fuels with renewable sources. In contrast, the second scenario included maintaining energy exports at levels like the past, substituting oil with hydrogen. The findings clearly demonstrate that this transition will demand great changes and substantial investments. The primary requirements identified are 20,529 or 34,199 km² of land for wind turbine installations (for self-sufficiency and exports), and 44 single-shaft 600 MW combined-cycle hydrogen-fired gas turbines. This foundational analysis represents the commencement of the research, investment, and political agenda regarding the journey to achieving decarbonisation for a country.

Faiz Ahmad, Peter B Monk, Akhlesh Lakhtakia

The building sector accounts for 36% of energy consumption and 39% of energy-related greenhouse-gas emissions. Integrating bifacial photovoltaic solar cells in buildings could significantly reduce energy consumption and related greenhouse gas emissions. Bifacial solar cells should be flexible, bifacially balanced for electricity production, and perform reasonably well under weak-light conditions. Using rigorous optoelectronic simulation software and the differential evolution algorithm, we optimized symmetric/asymmetric bifacial CIGS solar cells with either (i) homogeneous or (ii) graded-bandgap photon-absorbing layers and a flexible central contact layer of aluminum-doped zinc oxide to harvest light outdoors as well as indoors. Indoor light was modeled as a fraction of the standard sunlight. Also, we computed the weak-light responses of the CIGS solar cells using LED illumination of different light intensities. The optimal bifacial CIGS solar cell with graded-bandgap photon-absorbing layers is predicted to perform with 18%–29% efficiency under 0.01–1.0-Sun illumination; furthermore, efficiencies of 26.08% and 28.30% under weak LED light illumination of 0.0964 mW cm ^−2 and 0.22 mW cm ^−2 intensities, respectively, are predicted.

Laurens P. Stoop, Erik Duijm, Ad J. Feelders et al.

The introduction of more renewable energy sources into the energy system increases the variability and weather dependence of electricity generation. Power system simulations are used to assess the adequacy and reliability of the electricity grid over decades, but often become computational intractable for such long simulation periods with high technical detail. To alleviate this computational burden, we investigate the use of outlier detection algorithms to find periods of extreme renewable energy generation which enables detailed modelling of the performance of power systems under these circumstances. Specifically, we apply the Maximum Divergent Intervals (MDI) algorithm to power generation time series that have been derived from ERA5 historical climate reanalysis covering the period from 1950 through 2019. By applying the MDI algorithm on these time series, we identified intervals of extreme low and high energy production. To determine the outlierness of an interval different divergence measures can be used. Where the cross-entropy measure results in shorter and strongly peaking outliers, the unbiased Kullback-Leibler divergence tends to detect longer and more persistent intervals. These intervals are regarded as potential risks for the electricity grid by domain experts, showcasing the capability of the MDI algorithm to detect critical events in these time series. For the historical period analysed, we found no trend in outlier intensity, or shift and lengthening of the outliers that could be attributed to climate change. By applying MDI on climate model output, power system modellers can investigate the adequacy and possible changes of risk for the current and future electricity grid under a wider range of scenarios.

Stanley Eshiemogie, Peace Aielumoh, Tobechukwu Okamkpa et al.

This research proposes a framework for modeling and comparing two electricity scenarios for Nigeria by 2050, focusing on the inclusion and exclusion of electricity storage technologies. A Central Composite Design (CCD) was used to generate a design matrix for data collection, with EnergyPLAN software used to create energy system simulations on the CCD data for four outputs: total annual cost, CO2 emissions, critical excess electricity production (CEEP), and electricity import. Three machine learning algorithms, support vector regression (SVR), extreme gradient boosting (XGBoost), and multi-layer perceptron (MLP), were tuned using Bayesian optimization to develop models mapping the inputs to outputs. A genetic algorithm was employed for multi-objective optimization to determine the optimal input capacities that minimize the outputs. Results indicated that incorporating electricity storage technologies (EST) leads to a 37% increase in renewable electricity sources (RES) share, resulting in a 19.14% reduction in CO2 emissions. EST such as battery energy storage systems (BESS), pumped hydro storage (PHS), and vehicle-to-grid (V2G) storage allow for the storage of the critical excess electricity that comes with increasing RES share. Integrating EST in Nigeria's 2050 energy landscape is crucial for incorporating more renewable electricity sources into the energy system, thereby reducing CO2 emissions and managing excess electricity production. This study outlines a plan for optimal electricity production to meet Nigeria's 2050 demand, highlighting the need for a balanced approach that combines fossil fuels, renewable energy, nuclear power, and advanced storage solutions to achieve a sustainable and efficient electricity system.

Ugur Korkut Pata, Mustafa Tevfik Kartal

This study investigates the impacts of nuclear energy consumption on environmental quality from a different perspective by focusing on carbon dioxide (CO2) emissions, ecological footprint, and load capacity factor. In this context, the South Korea case, which is a leading country producing and consuming nuclear energy, is investigated by considering also economic growth, and the 1997 Asian crisis from 1977 to 2018. To this end, the study employs the autoregressive distributed lag (ARDL) approach. Different from previous literature, this study proposes a load capacity curve (LCC) and tests the LCC and environmental Kuznets curve (EKC) hypotheses simultaneously. The analysis results reveal that (i) the LCC and EKC hypotheses are valid in South Korea; (ii) nuclear energy has an improving effect on the environmental quality; (iii) renewable energy does not have a significant long-term impact on the environment; (iv) the 1997 Asian crisis had an increasing effect on the load capacity factor; (v) South Korea has not yet reached the turning point, identified as $55,411, where per capita income improves environmental quality. Overall, the results show the validity of the LCC and EKC hypotheses and prove the positive contribution of nuclear energy to South Korea's green development strategies.

Thuan Ngoc Vo, Ji Eun Kang, Hyocheol Lee et al.

Abstract Calcium ion batteries, though more sustainable than lithium‐ion batteries, still face significant challenges, including the lack of highly rechargeable electrodes. Prepared Prussian blue nanodisk electrodes can significantly extend the longevity of Ca‐based cells. The method involves precipitating 20‐nm‐thick nanodisks of Prussian green from Fe(NO3)3 and K3Fe(CN)6, followed by sodiation with NaI. The Prussian‐blue‐based cathode with polyacrylic acid/polyaniline binder delivers an initial discharging capacity of 77.6 mAh g−1 at 0.1 A g−1 and retains 91.0% capacity after 700 cycles. Polyvinyl fluoride is detrimental to the Ca‐based cell, as its coulombic efficiency decreases from 94.8% (120th cycle) to 86.4% (400th cycle). With the same cathode, the Ca‐based cell is much less sensitive to high current densities than the Na‐based cell. This can be because only half the amount of cations is required to move in Ca‐based systems compared to Na‐based systems; thus, the charge‐transfer resistance is noticeably reduced in Ca‐based systems.

Ziwei Wang, Wenliang Yin, Lin Liu et al.

A hybrid drive wind turbine equipped with a speed regulating differential mechanism can generate electricity at the grid frequency by an electrically excited synchronous generator without requiring fully or partially rated converters. This mechanism has extensively been studied in recent years. To enhance the transient operation performance and low-voltage ride-through capacity of the proposed hybrid drive wind turbine, we aim to synthesize an advanced control scheme for the flexible regulation of synchronous generator excitation based on fractional-order sliding mode theory. Moreover, an extended state observer is constructed to cooperate with the designed controller and jointly compensate for parametric uncertainties and external disturbances. A dedicated simulation model of a 1.5 MW hybrid drive wind turbine is established and verified through an experimental platform. The results show satisfactory model performance with the maximum and average speed errors of 1.67% and 1.05%, respectively. Moreover, comparative case studies are carried out considering parametric uncertainties and different wind conditions and grid faults, by which the superiority of the proposed controller for improving system on-grid operation performance is verified.

Masoud Negahban, Mohammadreza Vazifeh Ardalani, Morteza Mollajafari et al.

The intermittent and uncertain behavior of renewable energy sources; moreover, the increase in penetration of these resources causes some drawbacks in power grids, particularly in low-inertia microgrids. In addition to supply-side challenges, changing load rate has a considerable negative effect on small-size microgrids. Therefore, according to these challenges, the lack of balance between generation and demand is a vital issue in microgrids. One way to face these challenges would be using a suitable control strategy. In this research, an adaptive fuzzy model predictive control has been proposed as a novel control approach and has been compared with conventional controllers such as an optimal PI, and an adaptive optimal model predictive control. It is important to mention that, various types of load changing and time-varying parameters have been considered for a new model of small size microgrids in this study. The comparison and simulation results obviously indicate the effectiveness of the proposed control strategy.

Lilian Velásquez-Yévenes, Rahul Ram

The dissolution of copper during the leaching of chalcopyrite in ammonia solutions is an attractive alternative to acid sulfate leaching in the treatment of ores with high consumption of acid. Despite considerable research into this complex leaching system, a lack of understanding of the fundamental chemical drivers has delayed the implementation of the ammonia process. In the present study, various ammonium salts solutions (chloride, sulfate, carbonate) have been used to study the effect of ion association on the dissociation constant of the ammonium ion at temperatures of 25 and 35 °C. Experimental and calculated solubilities of Cu2+ have been obtained under different conditions and plotted in speciation distribution diagrams, in other to assess the accuracy of predictions using available thermodynamic properties.Ion association was found to significantly affect the dissociation constant of the ammonium ion in solutions containing sulfate, chloride and carbonate anions; thus, influencing the free ammonia concentration in solution. Increasing temperature from 25 to 35 °C was found to decrease the dissociation constant of the ammonium ion. These findings highlight the importance of using the correct anionic ligands for the ammonium ions and temperature in order to obtain high dissolution of copper.It has been established that solubility of copper in ammonia solution is affected by the anionic ligands, temperature and addition of chloride ions. The NH3 ligand forms strong coordination compounds with cupric or cuprous ions depending on the anionic ligand, generating an increase in solubility between pH 8.5 and 10.0. The present study, therefore, identifies important constraints on the role of varying anion associated with ammonia, temperature, pH, and addition of chloride ions and the inter-dependence of these factors in controlling Cu2+ solubility in ammoniacal systems. The results from the present study provides experimental pKa values and solubility constants of the various ammoniacal systems to provide commercial processing via ammoniacal routes the optimal conditions in which to maximise Cu recovery and maintain free ammonia at levels to minimise volatility and loss. The findings are directly beneficial to future commercial application employing effective ammonium-anion lixiviant strategies in the sustainable recovery of Cu.