Hasil untuk "Renewable energy sources"

Siang Fui Tie, Chee Wei Tan

I. B. Kantola, E. Blanc‐Betes, A. vonHaden et al.

ABSTRACT Six years after replacing a maize/soybean cropping system, perennial grasses miscanthus (Miscanthus × giganteus) and switchgrass (Panicum virgatum), and a 28‐species restored prairie increased particulate organic carbon in surface soils without increasing soil organic carbon (SOC). To resolve potential changes in the quantity and distribution of SOC, soils were resampled after seven to thirteen years to measure bulk density, carbon (C) content, and stable C isotopes to a depth of 1 m. SOC stocks increased between 1.75 and 2.5 Mg ha−1 year−1 in all perennial crops between 2008 and 2016 (nine growing seasons). Despite relatively low litter inputs and belowground biomass, the highest rate of SOC accrual was in restored prairie (2.5 Mg ha−1 year−1), followed by miscanthus (2.0 Mg ha−1 year−1) and switchgrass (1.75 Mg ha−1 year−1). The change in SOC in maize/soybean was not significant. After 2016, total SOC decreased in maize/soybean and miscanthus, resulting in slower overall rates of SOC accumulation over the full sampling period for miscanthus (0.8 Mg ha−1 year−1). The rate of SOC accumulation was greatest below 50 cm depth for restored prairie and switchgrass but in the top 10 cm for miscanthus. Stable isotope analysis showed 13C enrichment in all depths of switchgrass soils, an indication of new organic C accumulation, but mixed results in all other crops. Planting perennial crops on land formerly in an annual maize/soybean cropping system can slow or reverse soil carbon losses, with the greatest increases in SOC from species‐rich prairie.

Walid Abdelfattah, Munthar Kadhim Abosaoda, Krunal Vaghela et al.

Accurate prediction of biochar yield from biomass pyrolysis is essential for optimizing production in sustainable agriculture, yet remains technically challenging due to multiple interacting factors. This study developed a predictive framework using a curated dataset of 211 samples, each including 14 normalized input features (chemical, physical, operational) and one output variable (biochar yield, wt%). Machine learning modeling utilized Gradient Boosted Decision Trees (GBDT), with hyperparameters exhaustively tuned via Gaussian Processes Optimization (GPO), Evolutionary Strategies (ES), Bayesian Probability Improvement (BPI), and Batch Bayesian Optimization (BBO). Models were evaluated on a train-test split (90% training, 10% testing) and the best performance was achieved by the GBDT–BPI model: total R² = 0.982, mean squared error (MSE) = 1.65, average absolute relative error percentage (AARE%) = 1.35; on the test set, R² = 0.693, MSE = 15.2, AARE% = 9.54. Comparative analysis showed GBDT–BPI outperformed GBDT–GPO (total R² = 0.978; MSE = 2.01; AARE% = 1.72), GBDT–ES (total R² = 0.976; MSE =  2.13; AARE% = 3.81), and GBDT–BBO (total R² = 0.980; MSE = 1.81; AARE% = 2.58). Sensitivity study presented reside duration, temperature, and fixed carbon as the top parameters of yield. Time efficiency was comparable for all optimizers, with BBO taking the longest (313 s/500 iterations). Diagnostic leverage analysis demonstrated high data quality, with less than 1% flagged as influential outliers. This integrated approach delivered high-accuracy, interpretable prediction, and revealed critical parameters for process optimization in biomass pyrolysis workflows.

Can Lv, Jun He, Jingjing Ma et al.

The integration of renewable energy sources, such as wind and solar power, into the grid is essential for achieving carbon peaking and neutrality goals. However, the inherent variability and unpredictability of these energy sources pose significant challenges to power system stability. Advanced energy storage systems (ESS) are critical for mitigating these challenges, with gravity energy storage systems (GESS) emerging as a promising solution due to their scalability, economic viability, and environmental benefits. This paper proposes a multi-objective economic capacity optimization model for GESS within a novel power system framework, considering the impacts on power network stability, environmental factors, and economic performance. The model is solved using an enhanced Grasshopper Optimization Algorithm (W-GOA) incorporating a whale spiral motion strategy to improve convergence and solution accuracy. Simulations on the IEEE 30-node system demonstrate that GESS reduces peak-to-valley load differences by 36.1% and curtailment rates by 42.3% (wind) and 18.7% (PV), with a 15% lower levelized cost than CAES. The results indicate that GESS effectively mitigates peak load pressures, stabilizes the grid, and provides a cost-effective solution for integrating high shares of renewable energy. This study highlights the potential of GESS as a key component in future low-carbon power systems, offering both technical and economic advantages over traditional energy storage technologies.

Michalis Loizos, Konstantinos Rogdakis, Ashitha Paingott Parambil et al.

Zhi Liu, Jinde Hao, Ronghua Yao et al.

Abstract The proton exchange membrane electrolysis cells (PEMECs) are electrochemical devices that efficiently produce high-purity hydrogen via electrical energy conversion, making them widely applicable in renewable energy storage and hydrogen infrastructure development. However, the external sodium ion (Na+) contamination can severely damage the catalyst layer and membrane in PEMEC, causing significant performance degradation. Therefore, a segmented diagnostic platform for PEMEC is developed to analyze the poisoning effects of Na+ contamination on a large scale PEMEC under various operating conditions. The results demonstrate that during the cycle test, the Na⁺ poisoning process is defined as three distinct stages of initial, sustained and stable contamination stages. An increased Na+ concentration enhances the occupations of active sites on the catalyst layer, resulting in significant voltage spike, dynamic voltage fluctuations, non-uniformity distributions of current density and temperature. Both the low water flow rate and high operating temperature improve the chemical reaction and PEMEC performance at high current density. The deionized water flushing will dissolve Na+ on the catalyst layer surface and realize 2.17% decrease in voltage at 2.0 A cm⁻2 after three cycles. This study is beneficial to consolidate the understanding of poisoning effects of sodium ion contamination in PEMEC under various operating conditions, thereby overcoming the obstacles for commercial application of green hydrogen production technology.

Wenxin Liu, Jiakun Fang, Shichang Cui et al.

The growing coupling among electricity, gas, and hydrogen systems is driven by green hydrogen blending into existing natural gas pipelines, paving the way toward a renewable-dominated energy future. However, the integration poses significant challenges, particularly ensuring efficient and safe operation under varying hydrogen penetration and infrastructure adaptability. This paper reviews progress in optimization and control technologies for hydrogen-blended integrated gas-electricity system. First, key technologies and international demonstration projects are introduced to provide an overview of current developments. Besides, advances in gas-electricity system integration, including modeling, scheduling, planning and market design, are reviewed respectively. Then, the potential for cross-system fault propagation is highlighted, and practical methods for safety analysis and control are proposed. Finally, several possible research directions are introduced, aiming to ensure efficient renewable integration and reliable operation.

Giovanni Micheli, Laureano F. Escudero, Francesca Maggioni et al.

In this paper we address the challenge of designing optimal domestic renewable energy systems under multiple sources of uncertainty appearing at different time scales. Long-term uncertainties, such as investment and maintenance costs of different technologies, are combined with short-term uncertainties, including solar radiation, electricity prices, and uncontrolled load variations. We formulate the problem as a multistage multi-horizon stochastic Mixed Integer Linear Programming (MILP) model, minimizing the total cost of a domestic building complex's energy system. The model integrates long-term investment decisions, such as the capacity of photovoltaic panels and battery energy storage systems, with short-term operational decisions, including energy dispatch, grid exchanges, and load supply. To ensure robust operation under extreme scenarios, first- and second-order stochastic dominance risk-averse measures are considered preserving the time consistency of the solution. Given the computational complexity of solving the stochastic MILP for large instances, a rolling horizon-based matheuristic algorithm is developed. Additionally, various lower-bound strategies are explored, including wait-and-see schemes, expected value approximations, multistage grouping and clustering schemes. An extensive computational experiment validates the effectiveness of the proposed approach on a case study based on a building complex in South Germany. We tackle models with over 43 million constraints and 12 million binary, 700 hundred integer and 10 million continuous variables; they are solved with up to 0.32% optimality gap in reasonable computing time, where the value of the stochastic decisions as well as the benefit of the integrated risk-averse measures are quantified.

Ritesh Robin Stanley

Suresh A, Raja kumar S, Belqasem Aljafari et al.

Wind energy conversion systems (WECS) have gained increasing attention in recent years as promising renewable energy sources. Despite their potential, a clear research gap exists: the majority of WECS underperform in low wind speed conditions, limiting their applicability in many regions. To address this problem, this study proposes a novel approach by developing a 100 W micro wind turbine using Polylactic Acid (PLA) to generate efficient power in low wind speed conditions. The proposed wind turbine design employs Blade Element Momentum Theory (BEMT), which is commonly used for modeling wind turbine performance. Geometric design, mechanical analysis, and aerodynamic analysis are the fundamental considerations for designing any machine. In this work, the CREO 3.0 three-dimensional modeling software is used to create the geometric design of the proposed work. The airfoil SD7080 is selected due to its superior aerodynamic performance, and mechanical properties such as Young's modulus, density, and Poisson's ratio are attained to evaluate the wind blade's performance. Additionally, ANSYS 15.0 is used to conduct a detailed analysis of the proposed wind turbine, evaluating properties such as equivalent stress, deformation, and equivalent strain. Both simulation (ANSYS 15.0) and experimental setups are used to investigate the proposed wind turbine's performance, and the corresponding results are presented and discussed in this manuscript. The results indicate a significant performance improvement of the proposed wind blade when compared to conventional and ABS wind blades, demonstrating its potential as a more efficient solution for WECS. This proposed wind turbine design overcomes the problems like underprformance in low wind speed conditions and the wind turbine efficiency in all regions.

Ruijia Hu, Ming Li, Tao Shen et al.

Considerable progress has been made in recent years to the development of sustainable polymers from bio-based feedstocks. In this study, 100% bio-based nylons were prepared via an integrated chemical and biological process from lignocellulose. These novel nylons were obtained by the melt polymerization of 3-propyladipic acid derived from lignin and 1,5-pentenediamine/1,4-butanediamine derived from carbohydrate sugar. Central to the concept is a three-step noble metal free catalytic chemical funnelling sequence (Raney Ni mediated reductive catalytic fractionation - reductive funnelling - oxidative funnelling), which allowed for obtaining a single component 3-propyladipic acid from lignin with high efficiency. The structural and thermodynamic properties of the obtained nylons have been systematically investigated, and thus obtained transparent bio-based nylons exhibited higher Mw (>32,000) and excellent thermal stability (Td5% > 265 °C). Considering their moderate Tg and good melt strength, these transparent bio-based nylons could serve as promising functional additives or temperature-responsive materials.

S. Abe, J. Abhir, A. Abhishek et al.

Instruments for gamma-ray astronomy at Very High Energies ($E>100\,{\rm GeV}$) have traditionally derived their scientific results through proprietary data and software. Data standardisation has become a prominent issue in this field both as a requirement for the dissemination of data from the next generation of gamma-ray observatories and as an effective solution to realise public data legacies of current-generation instruments. Specifications for a standardised gamma-ray data format have been proposed as a community effort and have already been successfully adopted by several instruments. We present the first production of standardised data from the Major Atmospheric Gamma-ray Imaging Cherenkov (MAGIC) telescopes. We converted $166\,{\rm h}$ of observations from different sources and validated their analysis with the open-source software Gammapy. We consider six data sets representing different scientific and technical analysis cases and compare the results obtained analysing the standardised data with open-source software against those produced with the MAGIC proprietary data and software. Aiming at a systematic production of MAGIC data in this standardised format, we also present the implementation of a database-driven pipeline automatically performing the MAGIC data reduction from the calibrated down to the standardised data level. In all the cases selected for the validation, we obtain results compatible with the MAGIC proprietary software, both for the manual and for the automatic data productions. Part of the validation data set is also made publicly available, thus representing the first large public release of MAGIC data. This effort and this first data release represent a technical milestone toward the realisation of a public MAGIC data legacy.

Hadi Nemati, Pedro Sánchez-Martín, Álvaro Ortega et al.

In this paper, a novel approach to define the optimal bidding of renewable-only virtual power plants (RVPPs) in the day-ahead, secondary reserve, and intra-day markets is proposed. To this aim, a robust optimization algorithm is developed to account for the asymmetric nature of the uncertainties that characterize the market prices, as well as the energy production of the RVPP stochastic sources and flexible demand consumption. Simulation results show increased RVPP benefits compared to other existing solutions and demonstrate the potential of renewable sources to further increase their economic competitiveness. The simplicity of the implementation, the computational efficiency, and the flexible robustness are also verified.

Jie Zhu, Yiwei Qiu, Yangjun Zeng et al.

Utility-scale off-grid renewable power-to-hydrogen systems (OReP2HSs) typically include photovoltaic plants, wind turbines, electrolyzers (ELs), and energy storage systems. As an island system, OReP2HS requires at least one component, generally the battery energy storage system (BESS), that operates for grid-forming control to provide frequency and voltage references and regulate them through transient power support and short-term energy balance regulation. While larger BESS capacity increases this ability, it also raises investment costs. This paper proposes a framework of layered multi-timescale energy management system (EMS) and evaluates the most cost-effective size of the grid-forming BESS in the OReP2HS. The proposed EMS covers the timescales ranging from those for power system transient behaviors to intra-day scheduling, coordinating renewable power, BESS, and ELs. Then, an iterative search procedure based on high-fidelity simulation is employed to determine the size of the BESS with minimal levelized cost of hydrogen (LCOH). Simulations over a reference year, based on the data from a planned OReP2HS project in Inner Mongolia, China, show that with the proposed EMS, the base-case optimal LCOH is 33.212 CNY/kg (4.581 USD/kg). The capital expenditure of the BESS accounts for 17.83% of the total, and the optimal BESS size accounts for 13.6% of the rated hourly energy output of power sources. Sensitivity analysis reveals that by reducing the electrolytic load adjustment time step from 90 to 5 s and increasing its ramping limit from 1% to 10% rated power per second, the BESS size decreases by 53.57%, and the LCOH decreases to 25.458 CNY/kg (3.511 USD/kg). Considering the cost of designing and manufacturing utility-scale ELs with fast load regulation capability, a load adjustment time step of 5-10 s and a ramping limit of 4-6% rated power per second are recommended.

Anselme Ndikumana, Kim Khoa Nguyen, Mohamed Cheriet

Due to the high costs of optical fiber deployment in Low-Density and Rural Areas (LDRAs), 5G Fixed Wireless Access (5G FWA) recently emerged as an affordable solution. A widely adopted deployment scenario of 5G FWA includes edge cloud that supports computing services and Radio Access Network (RAN) functions. Such edge cloud requires network and energy resources for 5G FWA. This paper proposes renewable energy powered and Open RAN-based architecture for 5G FWA serving LDRAs using three-level closed-loops. Open RAN is a new 5G RAN architecture allowing Open Central Unit and Open Distributed Unit to be distributed in virtualized environment. The first closed-loop distributes radio resources to Open RAN instances and slices at the edge cloud. The second closed-loop allocates radio resources to houses. We design a new energy model that leverages renewable energy. We jointly optimize radio and energy resource allocation in closed-loop 3. We formulate ultra-small and small-time scale optimization problems that link closed-loops to maximize communication utility while minimizing energy costs. We propose reinforcement learning and successive convex approximation to solve the formulated problems. Then, we use solution data and continual learning to improve resource allocation on a large timescale. Our proposal satisfies 97.14% slice delay budget.

Dmitry V. Malyshev

Excess of gamma rays around the Galactic center (GC) observed in the Fermi Large Area Telescope (LAT) data is one of the most intriguing features in the gamma-ray sky. The spherical morphology and the spectral energy distribution with a peak around a few GeV are consistent with emission from annihilation of dark matter particles. Other possible explanations include a distribution of millisecond pulsars (MSPs). One of the caveats of the MSP hypothesis is the relatively small number of associated MSPs near the GC. In this paper, we perform a multiclass classification of Fermi-LAT sources using machine learning and determine the contribution from unassociated MSP-like sources near the GC. The spectral energy distribution, spatial morphology, and the source count distribution are consistent with expectations for a population of MSPs that can explain the gamma-ray excess. Possible caveats of the contribution from the unassociated MSP-like sources are discussed.

M. Basu

Paweł Ziemba, Abdullah Zair

Due to the environmental policy adopted by the European Union (EU), EU countries are obliged to reduce greenhouse gas emissions. They reduce emissions largely through the energy transformation and switching to renewable energy sources (RES). Therefore, it is important to assess the progress of the energy transformation of individual EU countries. This is related to the aim of the article, which is a temporal analysis of the energy transformation process towards the transition to RES and reducing the use of fossil fuels in energy production. To achieve this goal, a new Temporal/Dynamic Multi-Criteria Decision-Making (T/DMCDM) method called Temporal PROSA was developed, based on the PROMETHEE and PROSA family of methods. The Temporal PROSA method, unlike many other T/DMCDM methods, enables the aggregation of data from many periods into a single final assessment, as well as the direct transfer of information from the examined periods to the overall result. As a result of the research, EU countries that dominated in terms of progress in energy transformation towards RES in the years 2004–2021were identified. Based on the data and methodology used, it was indicated that these countries are primarily Sweden and Portugal, and recently also Denmark and Finland. On the other hand, countries such as Belgium, Bulgaria, Cyprus, Luxembourg, and Poland made the least progress between 2004 and 2021.

Pankaj Sarsia, Akhileshwer Munshi, Fiza Sheikh et al.

The global energy shift towards sustainability and renewable power sources is pressing. Large-scale electric vehicles (EVs) play a pivotal role in accelerating this transition. They significantly curb carbon emissions, especially when charged with renewable energy like solar or wind, resulting in near-zero carbon footprints. EVs also enhance grid flexibility, acting as mobile energy storage, stabilizing power supply. Integrating EVs into renewable systems offers demand response programs, optimizing energy use. However, extensive infrastructure development, particularly charging networks, is a significant challenge. Collaboration among governments, utility companies, and private sectors is crucial to ensure a smooth transition to electric mobility.