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Energy security is a main challenge in today's economies [[1]]. Bielowicz in the paper entitled "Petrographic Characteristics of Coal Gasification and Combustion By-Products from High Volatile Bituminous Coal" showed the benefits of coal gasification in a fluidized bed reactor with CO SB 2 sb as a gasifying agent at the temperature 889-980 °C [[18]]. Among the clean coal technologies, the new, innovative thermochemical conversion of coal technologies such a (combustion, gasification, pyrolysis, and hydrogenation) and co-gasification with biomass/biowaste/sewage sludge will be discussed. In this special issue "The effectiveness of clean coal technologies in global carbon dioxide mitigation", seven research papers were published as examples of recent developments in widely understand area of clean thermochemical conversion of fossil fuels with maximization of CO SB 2 sb emission reduction. [Extracted from the article]

For islanded microgrids, droop-based control concepts have been developed both in single and three-phase variants. The three-phase controllers often assume a balanced network; hence, unbalance sharing and/or mitigation remains a challenging issue. Therefore, in this paper, unbalance is considered in a three-phase islanded microgrid in which the distributed generation (DG) units are operated by the voltage-based droop (VBD) control. For this purpose, the VBD control, which has been developed for single-phase systems, is extended for a three-phase application and an additional control loop is added for unbalance mitigation and sharing. The method is based on an unbalance mitigation scheme by DG units in grid-connected systems, which is altered for usage in grid-forming DG units with droop control. The reaction of the DG units to unbalance is determined by the main parameter of the additional control loop, viz., the distortion damping resistance, Rd. The effect of Rd on the unbalance mitigation is studied in this paper, i.e., dependent on Rd, the DG units can be resistive for unbalance (RU) or they can contribute in the weakest phase (CW). The paper shows that the RU method decreases the line losses in the system and achieves better power equalization between the DG unit's phases. However, it leads to a larger voltage unbalance near the loads. The CW method leads to a more uneven power between the DG unit's phases and larger line losses, but a better voltage quality near the load. However, it can negatively affect the stability of the system. In microgrids with multiple DG units, the distortion damping resistance is set such that the unbalanced load can be shared between multiple DG units in an actively controlled manner rather than being determined by the microgrid configuration solely. The unit with the lowest distortion damping resistance provides relatively more of the unbalanced currents. [ABSTRACT FROM AUTHOR]

Power transformers play a crucial role in maintaining the stability and reliability of energy systems. Accurate moisture assessment of transformer oil-paper insulation is critical for ensuring safe operating conditions and power transformers' longevity in large interconnected electrical grids. The moisture can be predicted and quantified by extracting moisture-sensitive dielectric feature parameters. This article suggests a deep learning technique for transformer moisture diagnostics based on long short-term memory (LSTM) networks. The proposed method was tested using a dataset of transformer oil moisture readings, and the analysis revealed that the LSTM network performed well in diagnosing oil insulation moisture. The method's performance was assessed using various metrics, such as R-squared, mean absolute error, mean squared error, root mean squared error, and mean signed difference. The performance of the proposed model was also compared with linear regression and random forest (RF) models to evaluate its effectiveness. It was determined that the proposed method outperformed traditional methods in terms of accuracy and efficiency. This investigation demonstrates the potential of a deep learning approach for identifying transformer oil insulation moisture with a R 2 value of 0.899, thus providing a valuable tool for power system operators to monitor and manage the integrity of their transformer fleet. [ABSTRACT FROM AUTHOR]

Water can be an irritant to a power transformer, as it is recognized as a major hazard to the operation of transformers. The water content of a transformer insulation system comprises the water in the transformer insulation oil and in the cellulose paper. The increase in the water content in the insulation system leads to reduced breakdown voltage, accelerated aging of the oil–paper insulation system, and the possibility of producing bubbles at high temperatures. Therefore, various techniques have been applied to measure the water content in both oil and paper insulation. This article comprehensively reviews and analyzes the methods (technically or nontechnically) that have been used to monitor the water content in transformer insulation systems. Apart from discussing the advantages and major drawbacks of these methods, the accuracy, measurement time, and cost of each technique are also elucidated in this review. This review can be extremely useful to the utility in monitoring and maintaining the good condition of transformers. Based on the reviewed methods and their challenges, a few future research directions and prospects for determining the water content in transformer insulation systems are outlined, such as utilizing artificial intelligence and enhancing current techniques. [ABSTRACT FROM AUTHOR]

Charging electric vehicles (EVs) is of great concern both for future vehicle owners and grid operators, with charging at home being the preferred solution by 90% of owners. In addition, the supplied electricity needs to be clean in order to reduce emissions. This paper presents solutions for charging EVs at home using renewable electricity that fulfils such needs. It discusses: (1) the current landscape; (2) the latest hardware developments in the fields of renewable sources and storage; (3) software optimization for home energy management; (4) residential charging station standards and incentives offered by governments; (5) the evolution towards designing smart homes with low energy consumption from the grid; (6) case studies of particular interest. [ABSTRACT FROM AUTHOR]

This paper presents analysis, diagnosis and disposal with a typical internal breakdown failure of the resin impregnated paper (RIP) valve side bushing in high voltage direct current (HVDC) converter transformer. Based on the analysis of fault current characteristics at the time of the RIP valve side bushing failure, and field test results of insulation parameters, a method of diagnosing typical breakdown failures of valve side bushings is proposed. Through disassembly inspection of the internal overheating and arcing traces on the failure bushing, the root cause of this typical breakdown failure is found, which is upper axial flashover along the RIP condenser/SF6 interface caused by the abnormal contact of two current-carrying conductive tubes. Temperature distribution inside the bushing with an abnormal contact resistance between the copper conductive tube and aluminum conductive tube under different load current is simulated by using the finite element method. An special device is also developed for repairing defective bushing on-site, and 75 bushings with conductive contact defects have been repaired on the premise of not pushing converter transformers away from the valve hall and even without pulling out defective bushings. [ABSTRACT FROM AUTHOR]

The article announces the Best Review Papers Award given to authors including Manfred Lenzen, Vladimir Alvarado and Melanie Kuhn from "Energies" journal.

A high-temperature oil–paper insulation system offers an opportunity to improve the overloading capability of distribution transformers facing seasonal load variation. A high-temperature electrical insulation system (EIS) was chosen due to thermal calculation based on a typical loading curve on the China Southern Power Grid. In order to evaluate candidate high-temperature insulation systems, Nomex® T910 (aramid-enhanced cellulose) immersed in FR3 (natural ester) was investigated by a dual-temperature thermal aging test compared with a conventional insulation system, Kraft paper impregnated with mineral oil. Throughout the thermal aging test, mechanical, chemical, and dielectric parameters of both paper and insulating oil were investigated in each aging cycle. The thermal aging results determined that the thermal class of the FR3-T910 insulation system meets the request of overloading transformer needs. [ABSTRACT FROM AUTHOR]

Mathematical models of energy systems have been mostly represented by either linear or nonlinear ordinary differential equations. This is consistent with lumped-parameter dynamic system modeling, where dynamics of system state variables can be fully described only in the time domain. However, when dynamic processes of energy systems display both temporal and spatial evolutions (as is the case of distributed-parameter systems), the use of partial differential equations is necessary. Distributed-parameter systems, being described by partial differential equations, are mathematically (and computationally) much more difficult for modeling, analysis, simulation, and control. Despite these difficulties in recent years, quite a significant number of papers that use partial differential equations to model and control energy processes and systems have appeared in journal and conference publications and in some books. As a matter of fact, distributed-parameter systems are a modern trend in the areas of control systems engineering and some energy systems. In this overview, we will limit our attention mostly to renewable energy systems, particularly to partial differential equation modeling, simulation, analysis, and control papers published on fuel cells, wind turbines, solar energy, batteries, and wave energy. In addition, we will indicate the state of some papers published on tidal energy systems that can be modelled, analyzed, simulated, and controlled using either lumped or distributed-parameter models. This paper will first of all provide a review of several important research topics and results obtained for several classes of renewable energy systems using partial differential equations. Due to a substantial number of papers published on these topics in the past decade, the time has come for an overview paper that will help researchers in these areas to develop a systematic approach to modeling, analysis, simulation, and control of energy processes and systems whose time–space evolutions are described by partial differential equations. The presented overview was written after the authors surveyed more than five hundred publications available in well-known databases such as IEEE, ASME, Wiley, Google, Scopus, and Web of Science. To the authors' best knowledge, no such overview on PDEs for energy systems is available in the scientific and engineering literature. Throughout the paper, the authors emphasize novelties, originalities, and new ideas, and identify open problems for future research. To achieve this goal, the authors reviewed more than five hundred journal articles and conference papers. [ABSTRACT FROM AUTHOR]

Industrial parks, characterized by the clustering of multiple factories and interconnected energy sources, require optimized operational strategies for their Integrated Energy Systems (IES). These strategies not only aim to conserve energy for industrial users but also relieve the burden on the power supply, reducing carbon emissions. In this context, this paper introduces an optimization strategy tailored to clustered factories, considering the incorporation of carbon trading and supply chain integration throughout the entire production process of each factory. First, a workshop model is established for each factory, accompanied by an energy consumption model that accounts for the strict sequencing of the production process and supply chain integration. Furthermore, energy unit models are devised for the IES and then a low-carbon and economically optimized scheduling model is outlined for the IES within the industrial park, aiming to minimize the total operational cost, including the cost of carbon trading. Finally, case studies are conducted within a paper-making industrial park located in the Zhejiang Province. Various scenarios are compared and analyzed. The numerical results underscore the model's economic and low-carbon merits, and it offers technical support for energy conservation and emission reduction in paper-making fields. [ABSTRACT FROM AUTHOR]

The three-phase imbalance in low-voltage distribution networks (LVDNs) seriously threatens the security and stability of the power system. At present, a standard solution is automatic phase commutation, but this method has limitations because it does not address the branch imbalance and premature convergence or instability of the commutation algorithm. Therefore, this paper proposes a novel refined regulation commutation system, combined with a modified optimized commutation algorithm, and designs a model and simulation for feasibility verification. The refined regulatory model incorporates branch control units into the traditional commutation system. This effectively disperses the main controller's functions to each branch and collaborates with intelligent fusion terminals for precise adjustment. The commutation algorithm designed in this paper, combined with the above model, adopts strategies such as symbol encoding, cubic chaotic mapping, and adaptive adjustment based on traditional genetic algorithms. In addition, in order to verify the effectiveness of the proposed method, this paper establishes a mathematical model with the minimum three-phase imbalance and commutation frequency as objectives and establishes a simulation model. The results of the simulation demonstrate that this method can successfully lower the three-phase imbalance of the low-voltage distribution network. It leads to a decrease of the main circuit's three-phase load imbalance rate from 27% to 6% and reduces each branch line's three-phase imbalance ratio to below 10%. After applying the method proposed in this paper, the main and branches circuit three-phase imbalance are both lower than the limit ratio of the LVDNs, which can improve the quality and safety of electricity consumption. Additionally, the results also prove that the commutation algorithm under this method has faster convergence speed, better application effect, and better stability, which has promotion and application value. [ABSTRACT FROM AUTHOR]

Different preservation temperatures for sample storage in space stations and corresponding refrigerator devices have been designed. In the below-freezing environment, frost will occur on the inner wall surface and the sealing place of the refrigerator device, which may have an impact on the operating performance and sealing performance of the refrigerator device. This paper analyzes the impact of frost on refrigerator storage devices, and designs and verifies a scheme of air cooling with a hollow seal method to reduce the impact of frost. The designed refrigerator was tested for air cooling, sealing, and long-term performance. The tests show that the air-cooling structure designed in this paper can effectively reduce the frost on the sample surface. A cumulative helium mass spectrometry leak detection method is applied to verify that the hollow seal structure designed has a better sealing property, which can prevent the diffusion of water vapor and frosting at the sealing position. The design with air cooling and hollow sealing has less frost and more stable performance for long-term operation. The results demonstrate this refrigerator would have the capability of operating on-orbit. The frost-preventing method for space station refrigerator designed in this paper has significance for the design of similar cryogenic equipment in microgravity environments. [ABSTRACT FROM AUTHOR]

Battery energy storage (BES) can provide many grid services, such as power flow management to reduce distribution grid overloading. It is desirable to minimise BES storage capacities to reduce investment costs. However, it is not always clear how battery sizing is affected by battery siting and power flow simultaneity (PFS). This paper describes a method to compare the battery capacity required to provide grid services for different battery siting configurations and variable PFSs. The method was implemented by modelling a standard test grid with artificial power flow patterns and different battery siting configurations. The storage capacity of each configuration was minimised to determine how these variables affect the minimum storage capacity required to maintain power flows below a given threshold. In this case, a battery located at the transformer required 10–20% more capacity than a battery located centrally on the grid, or several batteries distributed throughout the grid, depending on PFS. The differences in capacity requirements were largely attributed to the ability of a BES configuration to mitigate network losses. The method presented in this paper can be used to compare BES capacity requirements for different battery siting configurations, power flow patterns, grid services, and grid characteristics. [ABSTRACT FROM AUTHOR]

The dryer is an important part of the paper drying process, and the uniformity of the dryer wall temperature distribution has an important influence on paper production quality and efficiency. In this paper, improving the temperature uniformity of the traditional gas dryer wall is taken as the research goal, and the distribution trend and uniformity of the traditional gas dryer wall temperature are studied and analyzed, and the structural improvement plan is put forward. On the basis of this, in order to further improve the uniformity of the wall temperature of the improved gas dryer, the optimization scheme of applying endothermic coating in the low-temperature area of the inner wall of the dryer is proposed. The numerical simulation and experimental research methods are used to compare and analyze the temperature uniformity of the wall of the improved gas dryer. The results show that the axial uniformity of the wall temperature of the modified gas dryer is significantly improved. Compared with the traditional gas dryer, the temperature difference of the cylinder wall is reduced from 40 °C to 13 °C, the maximum axial temperature difference of the cylinder wall is reduced by 57%, and the temperature uniformity is increased from 66.7% to 89.6%. Compared with the improved gas dryer, after the endothermic coating is applied to the low-temperature area of the inner wall of the dryer, the temperature difference of the cylinder wall is reduced from 13 °C to 7 °C, the maximum axial temperature difference of the cylinder wall is further reduced by 46%, and the temperature uniformity is increased from 89.6% to 94.4%. [ABSTRACT FROM AUTHOR]

Low-Level Virtual Machine (LLVM) compiler infrastructure is a useful tool for building just-in-time (JIT) compilers, besides its reliable front end represented by a clang compiler and its elaborated middle end containing different optimizations that improve the runtime performance. This paper specifically addresses the part of building a JIT compiler using an LLVM with the scope of obtaining the hardware architecture details of the underlying machine such as the number of cores and the number of logical cores per processing unit and providing them to the NUMA-BTLP static thread classification algorithm and to the NUMA-BTDM static thread mapping algorithm. Afterwards, the hardware-aware algorithms are run using the JIT compiler within an optimization pass. The JIT compiler in this paper is designed to run on a parallel C/C++ application (which creates threads using Pthreads), before the first time the application is executed on a machine. To achieve this, the JIT compiler takes the native code of the application, obtains the corresponding LLVM IR (Intermediate Representation) for the native code and executes the hardware-aware thread classification and the thread mapping algorithms on the IR. The NUMA-Balanced Task and Loop Parallelism (NUMA-BTLP) and NUMA-Balanced Thread and Data Mapping (NUMA-BTDM) are expected to optimize the energy consumption by up to 15% on the NUMA systems. [ABSTRACT FROM AUTHOR]

Frequency domain spectroscopy is considered to be a promising and novel method for the assessment of the insulation condition of power equipment. This work has practical significance as it explains the microscopic mechanism of this method in a wide temperature range and further establishes its quantitative model. To achieve this, in the present paper, we select oil-impregnated paper—one of the most common insulation materials for power equipment with a complex microstructure—as a test sample, deduce a formula based on the relationship between the real and imaginary parts of the complex permittivity to extract the spectra of independent dielectric processes and measure the frequency domain spectra of oil-impregnated paper under different temperatures, as well as its thermally stimulated depolarization current with a series of bias voltages. The analysis results reveal that oil-impregnated paper's frequency domain spectra in a wide temperature range are mainly determined by dielectric processes whose generation mechanisms are low-frequency dispersion, DC conduction, electrode relaxation, interfacial relaxation and dipole relaxation, respectively. Moreover, due to the different thermal properties of charge motions, the macroscopic characteristics and microscopic generation mechanisms of both spectra vary significantly with the sample's temperature. After verifying the generation mechanisms of the spectra in high, middle and low-temperature ranges, function models for those spectra with clear physical meanings are established separately, providing sufficient physical parameters to carry out insulation assessment. [ABSTRACT FROM AUTHOR]

The research aims were to construct an air-breathing paper-based microfluidic fuel cell (paper-based μ FC) and to evaluated it with different soft drinks to provide energy for their prospective use in portable devices as an emergency power source. First, in a half-cell configuration, cyclic voltammetry showed that glucose, maltose, and fructose had specific oxidation zones in the presence of platinum-ruthenium on carbon (PtRu/C) when they were individual. Still, when they were mixed, glucose was observed to be oxidized to a greater extent than fructose and maltose. After, when a paper-based μ FC was constructed, PtRu/C and platinum on carbon (Pt/C) were used as anode and cathode, the performance of this μ FC was mostly influenced by the concentration of glucose present in each soft drink, obtaining maximum power densities at room temperature of 0.061, 0.063, 0.060, and 0.073 mW cm − 2 for Coca Cola ® , Pepsi ® , Dr. Pepper ® , and 7up ® , respectively. Interestingly, when the soft drinks were cooled, the performance was increased up to 85%. Furthermore, a four-cell stack μ FC was constructed to demonstrate its usefulness as a possible power supply, obtaining a power density of 0.4 mW cm − 2 , using Coca Cola ® as fuel and air as oxidant. Together, the results of the present study indicate an alternative application of an μ FC using soft drinks as a backup source of energy in emergencies. [ABSTRACT FROM AUTHOR]

The reference characteristics of complex permittivity of the transformers insulation solid component were determined for use in the precise diagnostics of the power transformers insulation state. The solid component is a composite of cellulose, insulating oil and water nanoparticles. Measurements were made in the frequency range from 10−4 Hz to 5000 Hz at temperatures from 293.15 to 333.15 K. Uncertainty of temperature measurements was less than ±0.01 K. Pressboard impregnated with insulating oil with a water content of (5.0 ± 0.2) by weight moistened in a manner maximally similar to the moistening process in power transformers was investigated. It was found that there are two stages of changes in permittivity and imaginary permittivity components, occurring for low and high frequency. As the temperature increases, the frequency dependencies of the permittivity and imaginary permittivity component shifts to the higher frequency region. This phenomenon is related to the change of relaxation time with the increase in temperature. The values of relaxation time activation energies of the permittivity ΔWτε′ ≈ (0.827 ± 0.0094) eV and the imaginary permittivity component ΔWτε″ = 0.883 eV were determined. It was found that Cole-Cole charts for the first stage are asymmetric and similar to those described by the Dawidson–Cole relaxation. For stage two, the charts are arc-shaped, corresponding to the Cole-Cole relaxation. It has been established that in the moistened pressboard impregnated with insulating oil, there is an additional polarization mechanism associated with the occurrence of water in the form of nanodrops and the tunneling of electrons between them. [ABSTRACT FROM AUTHOR]

This study explores the possibility of enhancing both mechanical and breakdown properties of insulating presspaper by the introduction of an organic nano additive. Four different concentrations of nanofibrillated cellulose (NFC) were taken into account: 0.5 wt %, 2.5 wt %, 5 wt %, and 10 wt %. Presspaper containing no NFC was also prepared as a reference. Obtained samples were characterized by scanning electron microscope (SEM), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD). Mechanical properties and breakdown behaviors were measured. Results show that the addition of 10 wt % NFC to softwood fibers can achieve the best performance. Tensile strength of reference presspaper is 109 MPa, whereas that of presspaper modified by 10 wt % NFC is 136 MPa, resulting in a 25% increase. The improved tensile strength can be attributed to the increased density and inter fiber bond strength. More importantly, presspaper reinforced by 10 wt % NFC can also achieve enhanced AC and DC breakdown strengths, which are 19% and 21% higher than those of the reference presspaper. It is concluded that NFC is likely to be a promising nano additive for cellulose insulation. [ABSTRACT FROM AUTHOR]

The article describes a case study when the voltage collapse during lightning impulse tests of new power transformers was noticed and when the repeated tests finished with a positive result. The step-by-step process of reaching the conclusion on the basis of dissolved gas analysis (DGA) as a key method of the investigations was presented. The considerations on the possible source of the analysis showed that the Duval triangle method, used in the analysis of the concentration of gases dissolved in oil samples taken from bushings, more reliably and unambiguously than the ratio method recommended in the IEC 60599 Standard, indicated a phenomenon which was identified in the insulation structure of bushings analyzed. Additionally, the results from DGA were found to be consistent with an internal inspection of bushings, which showed a visible trace of discharge on the inside part of the epoxy housing, as a result of the lightning induced breakdown. [ABSTRACT FROM AUTHOR]

This document is a special issue of the journal Energies titled "The Future of Nuclear Power for Clean Energy Systems." It contains four scientific papers on various topics related to nuclear energy. The first paper discusses the application of an independent temporary spent fuel storage pool cooling system for the decommissioning of pressurized water reactor Kori units 3 and 4. The second paper focuses on the modeling of a hybrid multi-mission radioisotope thermoelectric generator (MMRTG) for space missions. The third paper investigates event-triggered communication in cooperative, adaptive model predictive control of a nuclear power plant's turbo-generator set. The fourth paper predicts the impact of full-system decontamination of Kori unit 1 on the radioactive waste classification of steam generator tubes. These papers contribute to the understanding and development of nuclear energy for clean energy systems. [Extracted from the article]

The process of carbon neutrality does have economic costs; however, few studies have measured the cost and the economic neutral opportunities. This paper uses a dynamic computable general equilibrium (CGE) model to simulate China's carbon neutrality path from 2020 to 2060 and analyzes its economic impact. This paper innovatively adjusts the CGE modeling technology and simulates the boundary of the Porter hypothesis on the premise of economic neutrality. The results show that the carbon neutrality target may reduce the annual GDP growth rate by about 0.8% in 2020–2060. To make the carbon pricing method under the carbon neutrality framework meet the strong version of the Porter hypothesis (or economic neutrality), China must increase its annual total factor productivity by 0.56–0.57% in 2020–2060; this is hard to achieve. In addition, the study finds that China's 2030 carbon target has little impact on the economy, but the achievement of the 2060 carbon neutrality target will have a significant effect. Therefore, the paper believes that the key to carbon neutrality lies in the coexistence of technological innovation and carbon pricing to ensure that we can cope with global warming with the lowest cost and resistance. [ABSTRACT FROM AUTHOR]

This work aims to investigate the effect of process temperature and catalyst content by pyrolysis and thermal catalytic cracking of (organic matter + paper) fraction from municipal household solid waste (MHSW) on the yields of reaction products (bio-oil, bio-char, H2O, and gas), acid value, chemical composition of bio-oils, and characterization of bio-chars in laboratory scale. The collecting sectors of MHSW in the municipality of Belém-Pará-Brazil were chosen based on geographic and socio-economic database. The MHSW collected and transported to the segregation area. The gravimetric analysis of MHSW was carried out and the fractions (Paper, Cardboard, Tetra Pack, Hard Plastic, Soft Plastic, Metal, Glass, Organic Matter, and Inert) were separated. The selected organic matter and paper were submitted to pre-treatment of crushing, drying, and sieving. The experiments carried out at 400, 450, and 475 °C and 1.0 atmosphere, and at 475 °C and 1.0 atmosphere, using 5.0, 10.0, and 15.0% (wt.) Ca(OH)2, in batch mode. The bio-oil was characterized for acid value. The chemical functions present in bio-oil were identified by FT-IR and the composition was identified by GC-MS. The bio-char was characterized by SEM, FT-IR, and XRD. The variance in mass (wt.%) for organic fractions of municipal household solid waste (OFMHSW), between 56.21 and 67.45% (wt.), lies with the interval of 56% (wt.) and 64% (wt.) of OFMHSW for middle- and low-income countries. The pyrolysis of MHSW fraction (organic matter + paper) shows bio-oil yields between 2.63 and 9.41% (wt.), aqueous phase yields between 28.58 and 35.08% (wt.), solid phase yields between 35.29 and 45.75% (wt.), and gas yields between 16.54 and 26.72% (wt.). The bio-oil yield increases with pyrolysis temperature. For the catalytic cracking, the bio-oil and gas yields increase slightly with CaO content, while that of bio-char decreases, and the H2O phase remains constant. The GC-MS of liquid reaction products identified the presence of hydrocarbons (alkanes, alkenes, alkynes, cycloalkanes, and aromatics) and oxygenates (carboxylic acids, ketones, esters, alcohols, phenols, and aldehydes), as well as compounds containing nitrogen, including amides and amines. The acidity of bio-oil decreases with increasing process temperature and with aid Ca(OH)2 as a catalyst. The concentration of hydrocarbons in bio-oil increases with increasing Ca(OH)2-to-OFMHSW fraction ratio due to the catalytic deoxygenation of fatty acid molecules, by means of decarboxylation/decarbonylation, producing aliphatic and aromatic hydrocarbons. [ABSTRACT FROM AUTHOR]

E-grocery is fast growing worldwide and represents a relevant issue for city logistics. Although in almost all countries the percentage of food e-buyers was lower than those purchasing other commodity categories, due to the pandemic, they have increased significantly in the last two years, with consequences that are difficult to fathom and estimate. This phenomenon therefore deserves more attention, especially with respect to its environmental impact, mostly at the urban scale. This paper presents a systematic literature review (SLR) on how e-grocery impacts the environment through the CO2 emissions generated and the equivalent energy consumption. The methodology used for the review follows a standard approach, with different combinations of keywords used for the search performed in SCOPUS and the Web of Science databases. Emissions and energy consumption assessments were performed for all of the papers considered. The results point to two different findings: some studies consider online grocery as an environmentally friendly channel, while others note that the energy consumption of this emerging channel is higher than alternative ones. This paper contributes by suggesting future research directions to be explored on the relationship between e-grocery and energy use and provides some reflections that are useful not only to e-grocers and logistics operators, but also to policy makers with an interest in developing sustainable urban plans and promoting less environmentally impacting distributions/configurations of grocery delivery systems within city logistics. [ABSTRACT FROM AUTHOR]

A decade after the publication of seminal papers on personal carbon trading (PCT), few empirical studies on its implementation exist. Investigating how to design, set up and implement a PCT scheme for a community or country raises several difficulties. For instance, it is unclear how to introduce a reduction rate of CO2 allowances to ensure a steady decrease in CO2 emissions from households. Computational approaches have been introduced to address these challenges of PCT by providing an opportunity to test counterfactual scenarios. Among the benefits of an agent-based modeling approach (ABM) is the potential to directly address dynamic developments and introduce counterfactual situations. In this paper, we review existing modeling approaches and present an ABM for PCT. With simulations of an artificial population of 1000 and 30,000 agents, we address questions on the price and reduction rate of allowances. A key contribution of our model is the inclusion of an adaptive reduction rate, which reduces the yearly allocated amount of allowances depending on a set CO2 abatement target. The results confirm that increased emissions targets are related to higher allowance prices and a higher proportion of buying households. Our analysis also suggests a significant path dependence in the dynamics of allowance prices and availability, but that adaptive reduction rates have little impact on outcomes other than the price. We discuss data availability and computational challenges to modeling a PCT scheme with an ABM. Ideal data to populate an ABM on PCT are not available due to the lack of real-world implementations of a PCT. Nonetheless, meaningful insights about the dynamics and the focal variables in a PCT scheme can be generated by the exploratory use of an ABM. [ABSTRACT FROM AUTHOR]

The article announces awards given to several editors which includes the Energies Best Paper Award 2013 to Ophelia Han, Review Article Award to Dong Jin Ham and Jae Sung Lee, and Research Article Award to Ugo Bardi and Alessandro Lavacchi.

The paper deals with a new method of drying cellulose insulation, which uses methanol vapour present in nitrogen as a drying medium. Compared to the insulation drying methods currently used in the industry, the method presented in the article has the following advantages: there is no cellulose depolymerization because there is no need to heat the insulation, there is no need to use large-size and energy-intensive dryers because the whole process takes place in the tank of the transformer. Important parameters of the drying process, such as methanol concentration in the nitrogen, flow rate, and process duration, were determined in four separate experiments. For the purposes of these experiments, a special system was constructed to control and measure the flow of drying medium. Controlling the flow rate of the carrier gas (nitrogen) made it possible to obtain different concentrations of methanol in nitrogen. Such values of concentration and flow rate were determined that allow drying of pressboard and paper in less than 144 h to a level lower than 1%. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) offers unique capabilities in terms of design freedom and customization, contributing to sustainable manufacturing. However, energy efficiency remains a challenge in the widespread adoption of AM processes. In this paper, we present a comprehensive review of the current research on energy efficiency in AM, addressing challenges, opportunities, and future directions. Our analysis reveals a lack of standardization in the measurement and reporting of energy consumption, making it difficult to evaluate and compare the energy performance of various systems. We propose a holistic framework to address energy efficiency throughout the entire life cycle of the AM process, highlighting the importance of design optimization, material selection, advanced control systems, and energy management strategies. The paper also emphasizes the need for further research on the interactions between process parameters, along with the potential of integrating renewable energy sources into AM systems. This review offers valuable insights for both academics and industry professionals, calling for standardized methodologies and a focus on energy management to optimize energy efficiency in AM processes, ultimately enhancing competitiveness and sustainability in modern manufacturing. [ABSTRACT FROM AUTHOR]

As a representative new energy source, solar energy has the advantages of easy access to resources and low pollution. However, due to the uncertainty of the external environment, photovoltaic (PV) modules that collect solar energy are often covered by foreign objects in the environment such as leaves and bird droppings, resulting in a decrease in photoelectric conversion efficiency, power losses, and even the "hot spot" phenomenon, resulting in damage to the modules. Existing methods mostly inspect foreign objects manually, which not only incurs high labor costs but also hinders real-time monitoring. To address these problems, this paper proposes an IDETR deep learning target detection model based on Deformable DETR combined with transfer learning and a convolutional block attention module, which can identify foreign object shading on the surfaces of PV modules in actual operating environments. This study contributes to the optimal operation and maintenance of PV systems. In addition, this paper collects data in the field and constructs a dataset of foreign objects of PV modules. The results show that the advanced model can significantly improve the target detection AP values. [ABSTRACT FROM AUTHOR]

The global energy sector is in a period of transition, during which time it is expected that renewable and low-carbon energy sources, such as wind and solar, will replace traditional fossil fuels, including oil, gas, and coal. The energy transition is happening not only to limit the environmental impact of fossil fuel production and consumption but also to ensure energy security, reliability, access, affordability, and sustainability. The importance of the energy transition has been amplified by recent events, notably the Russian-Ukraine conflict. Economic, financial, and trade sanctions against Russia, and in particular its oil and gas industry, have forced countries to find new suppliers in the short term, but also to investigate new and more sustainable sources to guarantee long-term energy security. Given the importance of energy, it is perhaps not unexpected that there is a considerable body of recent academic literature, particularly over the last 4–5 years, studying what industries, consumers, governments, and markets can do to help bring about a faster energy transition. In this paper, we provide a review of the literature that pertains to the economic aspects of the energy transition. While our initial search of the literature is targeted at uncovering all relevant articles on the subject, we focus most of our discussion on the most influential articles in prominent journals and articles published in this journal—Energies. This review is intended to help identify active topics and potential research gaps and provide future direction, so we hope it will prove useful to the readers and authors interested in this topic. [ABSTRACT FROM AUTHOR]

As the EU strives to achieve its climate goals, it is becoming increasingly crucial to understand the complex relationships between economic activity, energy consumption, and carbon emissions. In this context, our paper aims to investigate the correlation between carbon emissions, energy consumption, and economic development. To fulfill our aim, we have used Eurostat and OECD data for the EU-27 member states for a period of 13 years (2010–2022), using a linear regression as the main analysis method. Our results have shown that there is a strong correlation between demand-based and production-based CO2 emissions as well as between production-based CO2 emissions and final energy consumption, while at the same time, our findings have shown that there is no direct correlation between energy consumption and economic development, aligning our study with the neutrality hypothesis of the energy growth nexus. This paper expands the ongoing discussion on sustainable development and climate change mitigation by conducting a thorough analysis of the EU-27 countries over a span of thirteen years. The results emphasize the need for integrated strategies that address both production and consumption emissions, emphasize the vital role of energy efficiency, and raise questions about the effectiveness of increasing energy consumption to enhance economic productivity or CO2 efficiency. [ABSTRACT FROM AUTHOR]

Germany needs to reduce CO2 emissions from space heating in its old buildings to net zero by 2045 to fulfil its climate goals. However, direct CO2 reduction measures in existing buildings receive relatively little subsidy support from the federal government's German Development Bank, compared to generous subsidies for energy efficiency measures. This interdisciplinary paper evaluates this phenomenon by comparing costs and CO2 abatement effects of ever higher energy efficiency measures, alongside the costs of direct CO2 reduction through heat pumps and onsite photovoltaics. It uses a set of carefully selected reports on the costs and benefits of renovation to a range of energy efficiency standards in three common types of multi-apartment buildings in Germany, updating these for 2024 construction, energy, and finance costs. The cost of the CO2 saved is extremely high with energy efficiency measures and absurdly high with the highest energy efficiency standards, up to 20 times the cost of CO2 abatement through other means, such as offsite renewables. This reduces markedly with onsite CO2 reduction measures. This paper sets this analysis in the context of asking what social, cultural, and discursive factors extol energy efficiency so highly that policy tends to thwart its own stated goal of deeply reducing CO2 emissions. [ABSTRACT FROM AUTHOR]

SiC MOSFETs have been a game-changer in the domain of power electronics, thanks to their exceptional electrical traits. They are endowed with a high breakdown voltage, reduced on-resistance, and superior thermal conductivity, which make them supremely suitable for high-power and resilient applications across aviation, automotive, and renewable energy sectors. Despite their intrinsic advantages, SiC MOSFETs also necessitate advanced safeguarding mechanisms to counteract the vulnerability to short-circuit conditions due to their lower short-circuit robustness. This review paper offers an in-depth analysis of the array of short-circuit protection (SCP) methods applied to SiC MOSFETs. This paper scrutinizes techniques such as desaturation detection, di/dt detection, gate charge characteristics monitoring, two-dimensional monitoring, Rogowski coil-based detection, and two-stage turn-off strategies. The paper meticulously explores the operational principles, merits, and limitations of each method, with an emphasis on their adaptability to various fault types, including hard switching faults and load-induced faults. This review acts as a thorough compendium, guiding the choice of pertinent SCP strategies, ensuring the secure and efficient functioning of SiC MOSFETs in demanding applications. [ABSTRACT FROM AUTHOR]

To study the impact of faults on the enrichment and mobility of shale oil in the Gulong area, representative rock samples were selected in this paper. Based on geochemical data and chemical kinetics methods, coupled with shale oil enrichment and mobility analysis techniques, the shale oil generation quantity and in situ oil content were evaluated from the perspectives of shale oil generation and micro migration, and the mobility of shale oil was revealed. At the same time, the hydrocarbon expulsion efficiency (HEE) of shale was qualitatively and quantitatively characterized, combined with the development of faults. The research results indicate that the study area mainly develops organic-rich felsic (ORF)/organic-containing felsic (OCF) shale, their proportion in both wells exceeds 65%, and the resource amount is the largest in this type of lithofacies. The development of a fault controls the enrichment of shale oil, and the in situ oil content and oil saturation index (OSI) of the shale in well Y58, which is close to the fault, are significantly worse than those in well S2. Well Y58 has 9.52 mg/g and 424.83 mg/g TOC respectively, while well S2 has 11.34 mg/g and 488.73 mg/g TOC respectively. The fault enhanced the migration of shale oil, increasing the efficiency of oil expulsion. As a result, the components with weak polarity or small molecules, such as saturated hydrocarbons and low carbon number n-alkanes, are prone to migration, reducing the mobility of shale oil. [ABSTRACT FROM AUTHOR]

The exhaust gas recirculation (EGR) valve plays an important role in improving engine fuel economy and reducing emissions. In order to improve the positioning accuracy and robustness of the EGR valve under uncertain dynamics and external disturbances, this paper proposes a positioning servo system design for an electromotive (EM) EGR valve based on the Kalman filter. Taking a novel valve driven by a central winding limited angle torque motor (LATM) as the object, we have fully considered the influence of the motor rotor position and load current, as well as the magnetic field saturation and cogging effect, improved the existing LTAM model, and derived accurate torque expression. The parameter uncertainty of the above internal model and the external stochastic disturbance were unified as "total disturbance", and a Kalman filter-based observer was designed for disturbance estimations and real-time feed-forward compensation. Furthermore, using non-contact magnetic angle measurements to obtain accurate valve position information, a position control model with real-time response and high accuracy was established. Numerous simulated and experimental data show that in the presence of ± 25% plant model parameter fluctuations and random shock-type disturbances, the servo system scheme proposed in this paper achieves a maximum position deviation of 0.3 mm, a repeatability of positioning accuracy after disturbances of 0.01 mm, and a disturbance recovery time of not more than 250 ms. In addition, the above performance is insensitive to the duration of the disturbance, which demonstrates the strong robustness, high accuracy, and excellent dynamic response capability of the proposed design. [ABSTRACT FROM AUTHOR]

Relative permeability is a critical parameter in reservoir numerical simulation and production prediction, intimately associated with reservoir architecture and fluid property. During gas injection development, substantial alterations in reservoir properties and fluid phase behavior induce dynamic changes in relative permeability. Clearly characterizing the time-varying features of relative permeability is very useful for an understanding of how gas injection influences fluid mobility within the reservoir and enhances recovery rates. In this paper, core displacement experiments are firstly conducted to obtain the characteristics of the relative permeability of oil and gas under various development stages and displacement conditions, further delineating the comprehensive shifts in reservoir properties at different gas injection stages. Subsequently, a novel reservoir numerical simulation method is proposed that considers the spatial and temporal segmentation of relative permeability curves in the reservoir simulation. Finally, a practical application is presented to clarify the effects of injection and production parameters on the development performance of gas flooding oil reservoirs. The results show the following: (i) Significant time-varying characteristics of relative permeability occur throughout gas injection development, in the early stages of gas injection, where most of the reservoir is at the gas injection front, and a rightward shift in relative oil and gas permeability indicates that gas injection promotes oil mobility. Conversely, in the later stages of gas injection, as the reservoir reaches the trailing edge of gas injection, the change trend in relative oil and gas permeability reverses, shifting leftward, thereby exacerbating the gas breakout phenomena. (ii) Increasing the rate of gas injection causes relative oil and gas permeability to move leftward, effectively enhancing the gas volume sweep coefficient and microscopic oil displacement efficiency at lower injection speeds while reducing development performance at higher injection speeds. (iii) An increase in gas injection pressure causes relative oil and gas permeability to shift rightward, and although it reduces residual oil saturation and enhances microscopic oil displacement efficiency, it also intensifies gas breakout phenomena and lowers the gas volume sweep coefficient. This paper provides theoretical guidance and technical support for the design of gas injection strategies, optimization of injection and production parameters, and production forecasting. [ABSTRACT FROM AUTHOR]

This paper aims to show how the switching frequency influences the properties of the digitally controlled voltage source inverter (VSI). The measurements of the Bode plots of the inverter are shown and discussed to present the existing signal delays and power conversion efficiency, depending on the switching/sampling frequency. Two types of controllers are presented, Single-Input–Single-Output (SISO) and Multi-Input–Single-Output (MISO), and adequate prediction units (the Smith Predictor for SISO—Coefficient Diagram Method and the full-state Luenberger Observer for MISO—Passivity Based Control) were used to compensate for the delays. It will be shown by comparing the THD of the VSI output voltage that prediction is useful with low VSI switching frequency (about 10 kHz) but is not important for the middle switching frequencies (about 25 kHz) or the high switching frequency (>50 kHz). This paper shows that increasing the switching frequency simplifies digital control without reasonably decreasing efficiency. The theoretical considerations, the Matlab/Simulink 2021b simulations, the final experimental laboratory verification are presented. [ABSTRACT FROM AUTHOR]

The forecasting of charging demand for electric vehicles (EVs) plays a vital role in maintaining grid stability and optimizing energy distribution. Therefore, an innovative method for the prediction of EV charging load demand is proposed in this study to address the downside of the existing techniques in capturing the spatial–temporal heterogeneity of electric vehicle (EV) charging loads and predicting the charging demand of electric vehicles. Additionally, an innovative method of electric vehicle charging load demand forecasting is proposed, which is based on the weighted measurement fusion unscented Kalman filter (UKF) algorithm, to improve the accuracy and efficiency of forecasting. First, the data collected from OpenStreetMap and Amap are used to analyze the distribution of urban point-of-interest (POI), to accurately classify the functional areas of the city, and to determine the distribution of the urban road network, laying a foundation for modeling. Second, the travel chain theory was applied to thoroughly analyze the travel characteristics of EV users. The Improved Floyd (IFloyd) algorithm is used to determine the optimal route. Also, a Monte Carlo simulation is performed to estimate the charging load for electric vehicle users in a specific region. Then, a weighted measurement fusion UKF (WMF–UKF) state estimator is introduced to enhance the accuracy of prediction, which effectively integrates multi-source data and enables a more detailed prediction of the spatial–temporal distribution of load demand. Finally, the proposed method is validated comparatively against traffic survey data and the existing methods by conducting a simulation experiment in an urban area. The results show that the method proposed in this paper is applicable to predict the peak hours more accurately compared to the reference method, with the accuracy of first peak prediction improved by 53.53% and that of second peak prediction improved by 23.23%. The results not only demonstrate the high performance of the WMF–UKF prediction model in forecasting peak periods but also underscore its potential in supporting grid operations and management, which provides a new solution to improving the accuracy of EV load demand forecasting. [ABSTRACT FROM AUTHOR]

This review paper thoroughly explores the impact of artificial intelligence on the planning and operation of distributed energy systems in smart grids. With the rapid advancement of artificial intelligence techniques such as machine learning, optimization, and cognitive computing, new opportunities are emerging to enhance the efficiency and reliability of electrical grids. From demand and generation prediction to energy flow optimization and load management, artificial intelligence is playing a pivotal role in the transformation of energy infrastructure. This paper delves deeply into the latest advancements in specific artificial intelligence applications within the context of distributed energy systems, including the coordination of distributed energy resources, the integration of intermittent renewable energies, and the enhancement of demand response. Furthermore, it discusses the technical, economic, and regulatory challenges associated with the implementation of artificial intelligence-based solutions, as well as the ethical considerations related to automation and autonomous decision-making in the energy sector. This comprehensive analysis provides a detailed insight into how artificial intelligence is reshaping the planning and operation of smart grids and highlights future research and development areas that are crucial for achieving a more efficient, sustainable, and resilient electrical system. [ABSTRACT FROM AUTHOR]

With the increase in the proportion of photovoltaic and wind power access, the scale and form of distribution networks are becoming more and more complex. The traditional single distribution network vulnerability assessment method is difficult to use to identify the vulnerable links in the distribution network. Therefore, this paper proposes a method for identifying and evaluating vulnerable links in distribution networks based on minimum discriminant information. First, considering the influence of distributed grid connection, an improved probabilistic power flow calculation method is proposed, which improves the calculation efficiency and accuracy. Second, considering the correlation degree, transmission capacity, and voltage stability of branches in the distribution network, the identification index of vulnerable lines is defined. Based on power quality and operating state, the identification index of vulnerable nodes in a distribution network is defined. Finally, based on the indicators of vulnerable nodes and vulnerable lines, the vulnerable links in the distribution network are comprehensively evaluated based on the principle of minimum discriminant information, and the vulnerable links of the entire distribution network are evaluated according to different degrees of vulnerability. The rationality and effectiveness of the proposed method are verified via an example analysis of actual power grid data. [ABSTRACT FROM AUTHOR]

A new energy industry represented by photovoltaic and wind power has been developing rapidly in recent years, and its randomness and volatility will impact the stable operation of the power system. At present, it is proposed to enrich the regulation of the power grid by tapping the regulation potential of load-side resources. This paper evaluates the overall voltage regulation capability of substations under the premise of considering the impact on network voltage security and providing a theoretical basis for the participation of load-side resources of distribution networks in the regulation of the power grid. This paper proposes a Zbus linear power flow model based on Fixed-Point Power Iteration (FFPI) to enhance power flow analysis efficiency and resolve voltage sensitivity expression. Establishing the linear relationship between the voltages of PQ nodes, the voltage of the reference node, and the load power, this paper clarifies the impact of reactive power compensation devices and OLTC (on-load tap changer) tap changes on the voltages of various nodes along the feeder. It provides theoretical support for evaluating the voltage regulation range for substations. The day-ahead focus is on minimizing network losses by pre-optimizing OLTC tap positions, calculating the substation voltage regulation boundaries within the day, and simultaneously optimizing the total reactive power compensation across the entire network. By analyzing the calculated examples, it was found that a pre-scheduled OLTC (on-load tap changer) can effectively reduce network losses in the distribution grid. Compared with traditional methods, the voltage regulation range assessment method proposed in this paper can optimize the adjustment of reactive power compensation devices while ensuring the voltage safety of all nodes in the network. [ABSTRACT FROM AUTHOR]

In this paper, a consensus-based model predictive control (Cb-MPC) scheme is proposed to control the active power and voltage at all nodes in grid-connected active distribution networks (ADNs) with multiple distributed energy resources (DERs). The proposed design methodology is based on a multiple-input multiple-output (MIMO) model of an ADN which accounts for both the internal and external interactions among the control loops of the DERs. To achieve the control objective, each DER unit is equipped with a controller–observer system. In particular, the observer implements the consensus algorithm to estimate the collective system state by exchanging data only with its neighbors. The scope of the controller is to solve the MPC optimal problem based on its collective state estimate, and, due to the presence of an integral term in the control action, it is robust against any unknown scenarios of the ADN, which are represented by uncertainty in the model parameters. The results of numerical simulations validate the effectiveness of the proposed method in the presence of unknown changes in the operating conditions of the ADN and of communication using a sample and hold function. [ABSTRACT FROM AUTHOR]

To address the frequent external damage incidents to transmission line corridors caused by construction machinery such as excavators and cranes, this paper constructs a dataset of external damage hazards in transmission line corridors and proposes a detection method based on YOLO-LSDW for these hazards. Firstly, by incorporating the concept of large separable kernel attention (LSKA), the spatial pyramid pooling layer is improved to enhance the information exchange between different feature levels, effectively reducing background interference on external damage hazard targets. Secondly, in the neck network, the traditional convolution is replaced with a ghost-shuffle convolution (GSConv) method, introducing a lightweight slim-neck feature fusion structure. This improves the extraction capability for small object features by fusing deep semantic information with shallow detail features, while also reducing the model's computational load and parameter count. Then, the original YOLOv8 head is replaced with a dynamic head, which combines scale, spatial, and task attention mechanisms to enhance the model's detection performance. Finally, the wise intersection over union (WIoU) loss function is adopted to optimize the model's convergence speed and detection performance. Evaluated on the self-constructed dataset of external damage hazards in transmission line corridors, the improved algorithm shows significant improvements in key metrics, with mAP@0.5 and mAP@0.5:0.95 increasing by 3.4% and 4.6%, respectively, compared to YOLOv8s. Additionally, the model's computational load and parameter count are reduced, and it maintains a high detection speed of 96.2 frames per second, meeting real-time detection requirements. [ABSTRACT FROM AUTHOR]

The thermal performance of a lithium-ion battery pack for an electric vehicle by adding straight rib turbulators in battery cooling plate channels has been numerically investigated in this paper and the numerical model of the battery pack has been validated by experimental data, which exhibits a satisfactory prediction accuracy. The effects of rib shapes, rib angles, rib spacings, and irregular gradient rib arrangement configurations on the flow and heat transfer behaviors of battery pack cooling plates have been thoroughly explored and analyzed in this paper. In addition, the thermal performance of the ribbed battery cooling plates was examined at actual high-speed climbing and low-temperature heating operating conditions. The results indicate that compared to the original smooth cooling plate, the square-ribbed battery cooling plate with a 60° angle and 5 mm spacing reduced the maximum battery temperature by 0.3 °C, but increased the cross-sectional temperature difference by 0.357 °C. To address this issue, a gradient rib arrangement was proposed, which slightly reduced the maximum battery temperature and lowered the cross-sectional temperature difference by 0.445 °C, significantly improving temperature uniformity. The thermal performance of the battery thermal management system with this gradient rib configuration meets the requirements for typical electric vehicle operating conditions, such as high-speed climbing and low-temperature heating conditions. [ABSTRACT FROM AUTHOR]

The doubly-fed induction generator (DFIG) is vulnerable to overcurrent at the stator winding and overvoltage at the DC link due to voltage drop after the grid fault. The large wind farm may have a capacity of several million MWs, whose tripping yields a notable power imbalance and frequency drop in the power systems, which may be avoided by the low-voltage ride-through (LVRT) strategies implemented with the hardware or software. The latter has the merits of low cost and easy to realize, thus studied in this paper. Considering the grid fault uncertainty and DFIG parameters' correlation, this paper newly introduces the sliding mode structure into the passive control to improve the performance of the inner current control loop of the rotor-side converter (RSC), thus proposing a passive sliding mode control (P-SMC) based RSC control strategy to improve the LVRT capability of the DFIG. The time domain analysis with different fault severities, i.e., voltage drops, at the point of public coupling (PCC) is performed. The simulation results with the P-SMC control or not are obtained and compared to verify the control effect and the robustness of the proposed LVRT strategy. This study is beneficial for maintaining power system security against fast-increasing wind power. [ABSTRACT FROM AUTHOR]

With the development of the power system, power users begin to use their own power supply in order to improve the power economy, but this also leads to the occurrence of the risk of self-provided power supply. The actual distribution network has few samples of power supply risk and it is difficult to identify the power supply risk by using conventional deep learning methods. In order to achieve high accuracy of self-provided power supply risk identification with small samples, this paper proposes a combination of transfer learning, convolutional block attention module (CBAM), and convolutional neural network (CNN) to identify the risk of self-provided power supply in an active distribution network. Firstly, in order to be able to further identify whether or not a risk will be caused based on completing the identification of the faulty line, we propose that it is necessary to identify whether or not the captive power supply on the faulty line is in operation. Second, in order to achieve high-precision identification and high-efficiency feature extraction, we propose to embed the CBAM into a CNN to form a CBAM-CNN model, so as to achieve high-efficiency feature extraction and high-precision risk identification. Finally, the use of transfer learning is proposed to solve the problem of low risk identification accuracy due to the small number of actual fault samples. Simulation experiments show that compared with other methods, the proposed method has the highest recognition accuracy and the best effect, and the risk recognition accuracy of active distribution network backup power is high in the case of fewer samples. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have become an integral part of modern life, serving both civilian and military applications across various sectors. However, existing power supply systems, such as batteries, often fail to provide stable, long-duration flights, limiting their applications. Previous studies have primarily focused on battery-based power, which offers limited flight endurance due to lower energy densities and higher system mass. Proton exchange membrane (PEM) fuel cells present a promising alternative, providing high power and efficiency without noise, vibrations, or greenhouse gas emissions. Due to hydrogen's high specific energy, which is substantially higher than that of combustion engines and battery-based alternatives, UAV operational time can be significantly extended. This paper investigates the potential of PEM fuel cells as an alternative power source for electric propulsion in UAVs. This study introduces an adaptive, fully functioning PEM fuel cell model, developed using a reduced-order modeling approach and optimized for UAV applications. This research demonstrates that PEM fuel cells can effectively double the flight endurance of UAVs compared to traditional battery systems, achieving energy densities of around 1700 Wh/kg versus 150–250 Wh/kg for batteries. Despite a slight increase in system mass, fuel cells enable significantly longer UAV operations. The scope of this study encompasses the comparison of battery-based and fuel cell-based propulsion systems in terms of power, mass, and flight endurance. This paper identifies the limitations and optimal applications for fuel cells, providing strong evidence for their use in UAVs where extended flight time and efficiency are critical. [ABSTRACT FROM AUTHOR]

This research paper examines how multi-angled façade systems improve and optimise energy performance compared to a flat façade and meet sustainability targets for lower energy use to align with UN SDGs 3, 11, 12, and 13. The multi-angled façade system does not tilt up and down. Instead, it employs two different window orientations on a vertical axis (left and right). The large portion orients more to the north to allow more daylight to penetrate inside the room, and the small part is oriented more to the south to provide passive solar heating. The investigations in this research paper were carried out using version 4.8 of the IDA ICE software, and the researchers evaluated the energy consumption, the energy action through the façade, and the building's inside operative temperature. The results of this paper present the simulation findings for primary energy consumption in different scenarios. For example, the researchers explain that one can save 6.3 kWh/(m2·year) when using a multi-angled façade system compared to a flat façade. This is in addition to improving the thermal indoor climate that results from using the façades. The conclusions of the research show that the façade with multiple angles maximises using daylight and optimises solar power, thus avoiding overheating issues. [ABSTRACT FROM AUTHOR]

This paper reviews renewable energy integration with the electrical power grid through the use of advanced solutions at the device and system level, using smart operation with better utilisation of design margins and power flow optimisation with machine learning. This paper first highlights the significance of credible temperature measurements for devices with advanced power flow management, particularly the use of advanced fibre optic sensing technology. The potential to expand renewable energy generation capacity, particularly of existing wind farms, by exploiting thermal design margins is then explored. Dynamic and adaptive optimal power flow models are subsequently reviewed for optimisation of resource utilisation and minimisation of operational risks. This paper suggests that system-level automation of these processes could improve power capacity exploitation and network stability economically and environmentally. Further research is needed to achieve these goals. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of multi-source survey data sharing in power system engineering by proposing two improved methods: a survey data sharing method combined with differential privacy and a permission change method based on attribute encryption. The survey data sharing method integrated with differential privacy achieves effective cross-professional and cross-departmental data sharing while ensuring data security by introducing multi-discriminator architecture and dynamic noise adjustment. To reduce the computational and communication overhead when user permissions change during survey data sharing, the attribute encryption-based permission change method supports dynamic changes in user permissions. The effectiveness of the proposed methods has been validated through targeted experiments in different scenarios. The work in this thesis provides a new solution for dynamic sharing of survey data in power network engineering. It contributes to the digital transformation of power network projects. [ABSTRACT FROM AUTHOR]