Hasil untuk "Energy industries. Energy policy. Fuel trade"

Md Zawad Hossain, Davi M. Soares

Understanding the correlation between temperature and the electrochemical performance dynamics of materials in rechargeable batteries is crucial for developing next-generation rechargeable batteries. However, the complexity in understanding the electrochemical behavior of transition metal dichalcogenides (TMD) material makes it more difficult to explore at different temperatures. Presenting intriguing properties, such as superconductivity, a larger interlayer spacing, a conversion-type charge storage mechanism, and cycling stability; niobium diselenide (NbSe2) is a promising active material for the next generation of sodium-ion batteries (SIBs) and potassium-ion batteries (PIBs). To evaluate the electrochemical performance of NbSe2 as an electrode for SIB and PIB over different temperatures, a systematic electrochemical approach is adopted to analyze its electrochemical performance, ion transport kinetics, and ion storage mechanism at three different temperatures (15 °C, 25 °C, and 45 °C). This study examines the temperature effect on electrochemical activity, highlighting the increase in reaction resistance during phase transition, as well as the rise in equilibrium potential due to lower temperatures. Additionally, a long-cycle (1000 cycles) stability study at high current density (1 A/g) provides an extensive view of the NbSe2 material's performance at lower temperature (15 °C) and room temperature (25 °C) for both SIB and PIB. The denouement of this work provides comprehensive knowledge of the temperature-susceptible electrochemical properties of NbSe2, paving the way for the development of a negative electrode for next-generation sodium-ion batteries.

Percy Ngobeni, Phuti E. Ngoepe, Khomotso P. Maenetja

Due to the increased interest in vanadium cobaltite (VCo2O4) as a significant component of various catalysts, we have decided to investigate how its primary surfaces respond to oxidation and reduction processes. Our study employed computational modelling based on density functional theory to assess various surface types of geometries and surface free energies. This includes the stoichiometric plane and those containing either insufficient or excessive amounts of oxygen atoms. The most stable surface in the crystal is the (001) orientation. The crystal has an equilibrium morphology that resembles a cube with rounded corners. In our analysis, we identified the surface free energies of the most stable VCo2O4 (001) surface when oxygen atoms are adsorbed and reduced. The adsorption of oxygen atoms ensures the stability of the system, while their reduction causes it to become unstable. We analysed the oxygen adsorption (Γ= +1, +2) and vacancy formation energies (Γ= −1, −2); however, upon adsorption, we noticed the exothermic behaviour with decreasing adsorption energies. Conversely, the vacancy formation demonstrates an endothermic behaviour with increasing energies as oxygen atoms are reduced. The Bader charge provides insights into the interactions between atoms within a system. The reduction and adsorption of oxygen atoms result in minimal changes in the charge of the V and Co atoms, whether they are oxidized or reduced, compared to their original state. The interplanar distances indicate that the introduction of an oxygen atom leads to an expansion of the system, while its removal causes the system to contract. Understanding the work function aids in determining the system's level of reactivity. The presence of oxygen atoms reduces the system's reactivity, while their absence enhances it. We investigated and described the changes in the magnetic moment as the surface coverage increased. The findings will assist us in identifying a catalyst that can enhance the performance of the oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER), ultimately improving the efficiency of Zn-air batteries.

Shaoyang Yin, Xiaohua Yang, Qi Xu et al.

Abstract This study conducts an in-depth analysis of the optimization model of the industrial value chain under the Energy Internet environment, and provides strong data support for the optimization of the industrial value chain through the presentation of specific data. Research data show that the application effect of Energy Internet in the industrial value chain is remarkable. In terms of energy consumption, the implementation of Energy Internet technology has resulted in a notable 25% reduction in the average energy consumption cost across the industrial value chain. Specifically, considering a prominent manufacturing enterprise as a case study, the introduction of the Energy Internet has led to a substantial 30% decline in its energy consumption cost, thereby enhancing the company's cost control capabilities significantly. In terms of production efficiency, the utilization of Energy Internet technology has brought forth remarkable improvements. According to statistics, the production efficiency of enterprises implementing Energy Internet technology has increased by an average of 18%. Especially in the manufacturing industry, some leading companies have realized the intelligence and automation of production processes through the Energy Internet, and the increase in production efficiency has reached more than 25%. Based on these specific data, this study builds an industrial value chain optimization model. The proposed model takes into account the multifaceted impact of the Energy Internet on each link of the industrial value chain, achieving comprehensive optimization by rationalizing resource allocation, enhancing energy utilization efficiency, and mitigating operational expenses. The simulation data reveal that, guided by this optimization model, enterprises can achieve a marked enhancement in their overall competitiveness. Furthermore, it is anticipated that this approach will potentially lead to a further 10% improvement in energy efficiency and a reduction of 15% in operational costs. In addition, this study also combines the actual cases of many industries to verify the optimization model. Following the implementation of the optimization model, the participating case companies have exhibited remarkable outcomes. Specifically, they have achieved an average reduction of 22% in energy costs and a corresponding increase of 17% in production efficiency. These findings further corroborate the efficacy of the optimization model.

Wenjing Zhang, Hongjing Yan, Lili Xiang et al.

Abstract As an important renewable energy source, wind power generation is highly stochastic and uncertain due to various environmental factors affecting its output. To raise the accuracy of wind power generation prediction, a bidirectional long short-term memory network combination model based on sparrow search algorithm and firefly algorithm optimization is designed. The model first employs a bidirectional long short-term memory network to capture the long-term dependency features of time series, and uses random forests for nonlinear modeling and feature selection. Then, the sparrow search algorithm and firefly algorithm are combined to optimize the hyperparameter configuration, improving the predictive performance and global search ability of the model. The findings denote that the accuracy of the designed model reaches 98.5%, with a mean square error as low as 0.005 and a prediction time as short as 0.18 s. The simulation analysis results show that the predicted values of the developed model almost coincide with the actual values, with small errors. The research outcomes denote that the optimized model greatly raises the accuracy and efficiency of wind power generation prediction, and has good application prospects.

Ruike Lyu, Anna Li, Jianxiao Wang et al.

In many countries, declining demand in energy-intensive industries such as cement, steel, and aluminum is leading to industrial overcapacity. Although industrial overcapacity is traditionally envisioned as problematic and resource-wasteful, it could unlock energy-intensive industries' flexibility in electricity use. Here, using China's aluminum smelting industry as a case study, we evaluate the system-level cost-benefit of retaining energy-intensive industries overcapacity for flexible electricity use in decarbonized energy systems. We find that overcapacity can enable aluminum smelters to adopt a seasonal operation paradigm, ceasing production during winter load peaks that are exacerbated by heating electrification and renewable seasonality. This seasonal operation paradigm could reduce the investment and operational costs of China's decarbonized electricity system by 23-32 billion CNY/year (11-15% of the aluminum smelting industry's product value), sufficient to offset the increased smelter maintenance and product storage costs associated with overcapacity. It may also provide an opportunity for seasonally complementary labor deployment across the aluminum smelting and thermal power generation sectors, offering a potential pathway for mitigating socio-economic disruptions caused by industrial restructuring and energy decarbonization.

Boris Ragula, Eduardo Martín-Martínez

Quantum energy teleportation (QET) exploits the existence of correlations to enable remote energy transfer without the need for physical energy carriers between emitter and receiver. This paper presents a review of the thermodynamic foundations of QET and reviews its first experimental demonstration (performed using Nuclear Magnetic Resonance), along with its implementation on publicly available superconducting quantum hardware. Additionally, we review an application of QET in the field of quantum thermodynamics as an efficient algorithmic cooling technique to cool down individual parts of interacting systems. Finally, we will review how QET can be employed to optimally generate exotic quantum states characterized by negative average stress-energy densities, offering a new operational approach to engineering such states which are promising in the context of semiclassical gravity.

Sher Muhammad Ghoto, Ramez Raja, Sajjad Bhnagwar et al.

Rapid urbanization worldwide is driving increased demand for petroleum products. Yet, crude oil reserves—finite, geographically concentrated resources—are insufficient to meet this rising need, especially in countries lacking substantial fossil fuel reserves. This situation underscores the urgency of shifting toward alternative energy sources before reserves are exhausted. This study conducted particulate matter emissions and endurance testing using diesel fuel mixed with aluminum oxide nanoparticles. The endurance test involved a single-cylinder, horizontal diesel engine, running for 60 hours without modifications. Two fuel samples were examined: D100 (pure diesel) as the baseline and D97Al?O? (97% diesel with 3% aluminum oxide nanoparticles). Engine performance metrics and sound pressure levels were recorded at a constant 1400 RPM, with variable loads from 0.0 to 1.6 Kg-m, incremented by 0.1 Kg-m. The load was set at 1.0 Kg-m for endurance testing with a constant 1400 RPM. Visual inspection of fuel injector tips helped analyze the deposition of aromatic compounds on injector surfaces for each fuel sample. Electron microscopy provided detailed insights into deposit formation, showing that carbon deposition was reduced by 22.22% when aluminum oxide was used as an additive further analysis of the particulate matter emissions the results shows that PM reduced by 12.08% in aluminum oxide compared to the diesel fuel. Because they aid in the creation of cleaner fuel technologies that can lessen reliance on traditional petroleum products and minimize pollution, the study's findings have wider energy and environmental ramifications.

Lukáš Tichý, Zbyněk Dubský

The article explores the EU's energy security discourse regarding Russia during the period of 2010–2021. Three sub-discourses of this discourse are identified: the geopolitical, securitisation and diversification sub-discourses, as the same topics of the relationship are communicated differently in each of them. We use the method of discourse analysis (the main data analysis method is content analysis) and interpret the influence of the individual sub-discourses on the formation of the EU identity and interests. Through this, the role of discourse in relation to the formation of the EU as an energy actor is demonstrated. The discourse has an impact on the views of the reality of the EU energy cooperation with Russia, and it subordinated the energy cooperation to the political situation before the war in Ukraine.

Paul Maximilian Röhrig, Nancy Radermacher, Luis Böttcher et al.

In order to achieve greenhouse gas neutrality by 2045, the Climate Protection Act sets emission reduction targets for the years 2030 and 2040, as well as decreasing annual emission volumes for some sectors, including the building sector. Measures to decarbonize the building sector include energy retrofits and the expansion of renewable, decentralized power generators and low-CO2 heat generators. These measures thus change both the load and the generation of the future energy supply concept. Considering the interactions of the changed installed technologies on the building level and their influence on the electrical grid infrastructure is necessary. The grid operator will remedy the future congested grid states by grid expansion measures and pass on the costs to the connected grid users, which in turn could influence their behaviour and decisions. The aim of this work is a holistic analysis of the staggered interactions of generation expansion and grid expansion for a future decentralized energy supply concept conditioned by the expansion in the field of self-generation. To enable the analysis of the interactions, a multi-criteria optimization procedure for expansion and operation decisions at the building level is combined with an approach to determine grid expansion. As part of this work, the effect of an expansion of hosting capacity on the grid charges and thus the decision-making behaviour was investigated.

Jialin Tian, Zhe Zhang, Chenghang Liu et al.

Driven by the gradual development of oil and natural gas resources, the global use of rigs and the number of wells rise steadily. Modular rigs play an important role in oil exploration because of their efficient transport, economy, echnological advancement, and rig reliability. The derrick is a key part of the modular rig, and since many essential components in the derrick are welded, the fatigue life of the derrick weld is particularly relevant. In this paper, the modular rig is the object of the research, proposoing the calculation formula of fatigue life of the key weld of the derrick. Using modular rig ZJ90D as an example, and combining the rig working conditions with the structural characteristics of the derrick, the key calculation parameters are obtained by numerical analysis. Thus, the life expectancy of the key weld is calculated according to the calculation formula of fatigue life presented herein. The research results have important reference significance and guiding value for the design and optimization of modular rig derricks.

Farhad Amiri, Mohammad Hassan Moradi

In the power system, frequency stability is critical. The wind turbine oscillates (depending on the wind speed) and is of low inertia. Thus, wind turbines face the issue of power system frequency stability. Since the power system's resources are interconnected via communication networks, the presence of time delay also affects the frequency stability of the power system. When a disturbance occurs in the power system due to load or distributed generation sources (wind turbine), it leads to frequency deviations in the power system, exhibiting low damping speed. Although large conventional generators in the power system provide sufficient inertia and reduce frequency deviation, the damping speed of frequency fluctuations is slow, which may be due to time delays between power system resources. In this paper, virtual damping (a proposed method) is used to accelerate the damping of frequency deviations caused by load disturbances, distributed generation source disturbances, and the time delay between power system resources. The results of the proposed method are compared to those obtained using the conventional method in this field, demonstrating the superiority of the proposed method. The proposed method reduced frequency deviations in the power system caused by disturbances and time delays by 67 % (a 67 % improvement over existing methods in this field) and increased the damping speed of the frequency deviations by 62 % (a 62 % improvement over the methods used in this field).

Rohit Babu, Saurav Raj, Bishwajit Dey et al.

Abstract Power system instability primarily results from the deviation of the frequency from its predefined rated value. This deviation causes voltage collapse, which further leads to sudden blackouts of the power system network. It is often triggered by a lack of reactive capacity. The solution to the reactive capacity problem can be obtained in two stages. In the first stage, the vulnerable buses, also known as ‘weak buses’, where voltage failure might occur are identified, and the Var compensating devices are mounted at those locations. The proposed approach utilizes three simple vulnerable bus detection methods: the fast voltage stability index, line stability index, and voltage collapse proximity index (VCPI). In the second stage, various optimization algorithms are implemented to determine the optimal setting of Var sources, such as particle swarm optimization, differential evolution, the whale optimization algorithm, the grasshopper optimization algorithm, the salp swarm algorithm, grey wolf optimization, and oppositional grey wolf optimization (OGWO). The results indicate that the best approach to poor bus recognition is the VCPI, and the OGWO technique provides a much less expensive system than other optimization strategies used for problems of optimal reactive power planning.

Teng WANG

[Introduction] Magnetically confined fusion is an effective way to solve the energy problem. To direct to quasi-steady state discharges and their relevant physics, the superconducting magnet (especially the high field HTS magnet) had become the first choice for tokamak design in the future. [Method] This reserch was devoted to introduce the recent experimental progress and future research schemes of EAST, and summarized the latest progress of CFETR, especially the research on superconducting magnet. [Result] EAST device, the world's first full superconducting tokamak, has achieved a new pulse length world record (1056 seconds) for high temperature tokamak plasma by the end of 2021. [Conclusion] The designs of CFETR, which aim to bridge the gaps between the fusion experiment ITER and the demonstration reactor (DEMO), have been carried out within CFETR National design team.

Qianfeng GUAN, Yu WANG, Jianmin DONG

[Introduction] The construction of battery energy storage power stations is an inevitable trend in the future. The research aims to learn the economic and operational benefits of battery energy storage power stations under the present battery technologies and peak-valley price policy. [Method] For the grid-side energy storage power stations, the economic benefit index was used as the criterion to measure the economic benefit, and the delayed substation expansion was used to measure the operational benefit. Taking battery energy storage power stations in Dongguan as an example, the direct economic benefits, operational benefits, and other benefits were analyzed. [Result] The results showed that under the present battery technologies and peak-valley price policy, generally the economic benefits of battery energy storage power stations in Dongguan suffered slight loss. Nevertheless, considering other operational benefits of the construction of energy storage power stations, the development of battery energy storage power stations can produce a small profit. [Conclusion] The case study of economic and operational benefits of battery energy storage power stations in Dongguan can provide a reference for the benefit analysis of other battery energy storage power stations of the same type.

Jin-Fu Chen, Ruo-Xun Zhai, C. P. Sun et al.

To accomplish a task within limited operation time typically requires an excess expense of energy, whose minimum is of practical importance for the optimal design in various applications, especially in the industrial separation of mixtures for purification of components. Technological progress has been made to achieve better purification with lower energy expense, yet little is known about the fundamental limit on the least excess energy expense in finite operation time. We derive such a limit and show its proportionality to the square of a geometric distance between the initial and final states and inverse proportionality to the operation time $τ$. Our result demonstrates that optimizing the separation protocol is equivalent to finding the geodesic curve in a geometric space. Interestingly, we show the optimal control with the minimum energy expense is achieved by a symmetry-breaking protocol, where the two membranes are moved toward each other with different speeds.

Rui Feng, Dariusz Czarkowski

Abstract A dynamic optimal transport problem of electric vehicles (EVs) over a network is investigated. The EVs are considered to be transported from their initial locations to the destination nodes for charging purposes. In our framework, the operators of charging stations are strategic, and each of them designs their charging pricing optimally to maximise the revenue. Since EVs play an essential role as power loads at the charging stations, the designed transport strategy by the EV operator has an impact on the market energy price which in turn influences the charging prices. Therefore, to design an efficient transport plan, the EV operator needs to take into account its influence on the charging pricing and the market energy price due to their complex interplay. To achieve this goal, a unified framework is proposed for optimal EV transportation by considering factors including the delay, charging cost, and real‐time social demand of EVs over a finite‐time horizon. The balanced dynamic optimal transport strategy is enabled through a combined quadratic and entropic regularisation. To compute the equilibrium pricing for all charging stations, an iterative particle‐swarm optimisation scheme is designed which addresses a high‐dimensional nonlinear optimisation problem. Finally, case studies are used to illustrate and corroborate the obtained results.

Tohidi Elham, Sadeghnejad Saeid

Waterflooding is widely implemented to enhance oil recovery. The displacement efficiency of this method depends on multiple factors, including makeup water properties, native oil, and formation rock. The incompatibility of the injected fluid with the pore fluid can result in a chemical non-equilibrium state within porous media that may result in different chemical reactions that depend on the type and number of the existing components within the system. The present research aims to develop a mathematical model capable of handling multiple geochemical reactions to predict pH and ions concentration during an acid injection while considering alterations of rock and fluid properties. The dependency of fluid properties (viscosity and density) is considered based on the system ionic concentration, which can be more crucial in high saline media. The validity of the developed model was evaluated using the experimental literature data. The results reveal that in addition to the effect of injected fluid pH, the process efficiency can be influenced by the composition of the injected fluid. In other words, fluid properties dependency on salinity and the injected fluid composition is significant during geochemical simulations. Comparing homogenous and heterogenous mineral distribution shows an insignificant sensitivity to the amount heterogeneity while the total mineral contents remain constant in both cases.

Yilun Du, Shuang Li, Yash Sharma et al.

Humans are able to rapidly understand scenes by utilizing concepts extracted from prior experience. Such concepts are diverse, and include global scene descriptors, such as the weather or lighting, as well as local scene descriptors, such as the color or size of a particular object. So far, unsupervised discovery of concepts has focused on either modeling the global scene-level or the local object-level factors of variation, but not both. In this work, we propose COMET, which discovers and represents concepts as separate energy functions, enabling us to represent both global concepts as well as objects under a unified framework. COMET discovers energy functions through recomposing the input image, which we find captures independent factors without additional supervision. Sample generation in COMET is formulated as an optimization process on underlying energy functions, enabling us to generate images with permuted and composed concepts. Finally, discovered visual concepts in COMET generalize well, enabling us to compose concepts between separate modalities of images as well as with other concepts discovered by a separate instance of COMET trained on a different dataset. Code and data available at https://energy-based-model.github.io/comet/.

Xinxin Zhou, Jingru Feng, Yang Li

With the rapid development of science and technology, the problem of energy load monitoring and decomposition of electrical equipment has been receiving widespread attention from academia and industry. For the purpose of improving the performance of non-intrusive load decomposition, a non-intrusive load decomposition method based on a hybrid deep learning model is proposed. In this method, first of all, the data set is normalized and preprocessed. Secondly, a hybrid deep learning model integrating convolutional neural network (CNN) with long short-term memory network (LSTM) is constructed to fully excavate the spatial and temporal characteristics of load data. Finally, different evaluation indicators are used to analyze the mixture. The model is fully evaluated, and contrasted with the traditional single deep learning model. Experimental results on the open dataset UK-DALE show that the proposed algorithm improves the performance of the whole network system. In this paper, the proposed decomposition method is compared with the existing traditional deep learning load decomposition method. At the same time, compared with the obtained methods: spectral decomposition, EMS, LSTM-RNN, and other algorithms, the accuracy of load decomposition is significantly improved, and the test accuracy reaches 98%.

L. Martinelli, S. Filoso