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In this paper, the aging characterization of a kind of insulating paper modified by magnetron sputtering MgO particles based on a microstrip resonant sensor was presented. Firstly, the modified insulating paper with 0, 15 and 30 min MgO particle sputtering times was prepared by a magnetron sputtering device. After that, the properties of the modified insulating paper with different sputtering times were analyzed through microscopic characterization, infrared spectrum, polymerization degree, dielectric constant, AC breakdown strength and thermal aging experiments. The results show that the dielectric constant of the modified insulating paper decreased obviously, the AC breakdown strength increased and the thermal aging resistance was better after 15 min of sputtering. The overall performance of the modified insulating paper after 30 min of sputtering is reduced due to excessive sputtering. In addition, microstrip resonant sensors are introduced to characterize the thermal aging degree of the modified insulating paper, and two microstrip resonant sensors are prepared: a complementary split ring resonator (CSRR) and an interdigital-capacitor-shaped defected ground structure resonator (IDCS-DGS). The resonance frequency deviation of the modified insulating paper samples after aging was measured by microstrip resonance sensors to show the influence of aging temperature on aging degree. The experimental results show that the test results of the microstrip resonance sensors are in good agreement with the traditional characterization methods and can characterize the various aging stages of the modified insulating paper to a certain extent, which proves the feasibility of the characterization method. [ABSTRACT FROM AUTHOR]

Oil–paper insulation is the critical insulation element in the modern power system. Under a harsh operating environment, oil–paper insulation will deteriorate gradually, resulting in electrical accidents. Thus, it is important to evaluate and monitor the insulation state of oil–paper insulation. Firstly, this paper introduces the geometric structure and physical components of oil–paper insulation and shows the main reasons and forms of oil–paper insulation's degradation. Then, this paper reviews the existing condition assessment techniques for oil–paper insulation, such as the dissolved gas ratio analysis, aging kinetic model, cellulose–water adsorption isotherm, oil–paper moisture balance curve, and dielectric response technique. Additionally, the advantages and limitations of the above condition assessment techniques are discussed. In particular, this paper highlights the dielectric response technique and introduces its evaluation principle in detail: (1) collecting the dielectric response data, (2) extracting the feature parameters from the collected dielectric response data, and (3) establishing the condition assessment models based on the extracted feature parameters and the machine learning techniques. Finally, two full potential studies are proposed, which research hotspots' oil–paper insulation and the electrical–chemical joint evaluation technique. In summary, this paper concludes the principles, advantages and limitation of the existing condition assessment techniques for oil–paper insulation, and we put forward two potential research avenues. [ABSTRACT FROM AUTHOR]

In order to reveal the migration and evolution of gas molecules in the actual oil–paper insulation composite system of transformer from the molecular level, the diffusion behavior of seven gas molecules (H2, CO, CO2, CH4, C2H2, C2H4, C2H6) generated during the operation and aging of oil-immersed transformers in the oil–paper composite insulation system is studied by molecular dynamics. Firstly, based on the molecular dynamics software, the model of the oil–paper composite insulation system and the gas molecule model is constructed. In order to compare and analyze the diffusion properties of gas molecules in a single medium, a single model of insulating oil and cellulose is also constructed. Then, the diffusion coefficients of gas molecules in the insulating oil, cellulose, and oil–paper insulating composite system are simulated and calculated. And the differences in the diffusion properties of gas molecules in the three insulating mediums are discussed. Finally, the microscopic mechanism of diffusion of different gas molecules in the three mediums is analyzed. The simulation results show that among the three mediums, the diffusion coefficient of H2 is the largest, while the diffusion coefficients of the other gas molecules are not very different. The diffusion coefficients of the seven gas molecules are the smallest in the oil-immersed paper composite insulation system, followed by cellulose, while the diffusion coefficients are the largest in mineral oil. It indicates that the diffusion of gas molecules is inhibited in oil–paper insulation systems where the insulating paper is completely immersed in oil. This is mainly due to the fact that the insulating oil completely penetrates into the paper, filling the pores and voids between the fibers, resulting in a reduction in the transition vacancies of the intermediate gas molecules, which hinders the diffusion of the gas molecules. [ABSTRACT FROM AUTHOR]

The pulp and paper industry is under increasing pressure to reduce its energy consumption and carbon footprint. This study examines the feasibility of integrating high-temperature heat pumps (HTHP) into tissue paper production to enhance energy efficiency and decarbonization. Focusing on the energy-intensive drying process, the study uses data from a typical tissue paper mill to simulate and optimize an HTHP system producing four tons per hour of nine-bar saturated steam. It also addresses necessary modifications for HTHP integration applicable across the sector. Various refrigerants were analyzed, achieving a maximum coefficient of performance (COP) of 2.01. Results showed that HTHP can reduce energy consumption and emissions by up to 17% and 40%, respectively, based on the European electricity mix. Although steam production costs increase by 55% compared to fossil fuel-based systems, HTHP is more cost-effective than direct electric resistance heating, which raises costs by 196%. With a CO2 price of EUR 100/t, HTHP offers a 12% cost reduction. However, without public funding, capital expenditures may be unsustainable in many regions, though viable in countries with favorable gas and electricity price differentials. The paper underscores the need for advancements in HTHP technology and cost reductions, emphasizing industry adaptation for seamless HTHP integration. [ABSTRACT FROM AUTHOR]

This paper focuses on proton exchange membrane fuel cell (PEMFC) operation at current densities in the order of 6 A/cm2. Such high current densities are conceivable when the traditional carbon fiber papers are replaced with perforated metal plates as the gas diffusion layer to enhance waste heat removal, and at the same time the relative humidity inside the fuel cell is kept below 100% by applying appropriate operating conditions as was found in previous one-dimensional modeling work. In the current paper, we applied a three-dimensional, multi-phase computational fluid dynamics model based on Ansys-CFX to obtain additional insight into the underlying physics. The calculated pressure drops are in very good agreement with previous one-dimensional modeling work, and the current densities in all case studies are in the order of 5–6 A/cm2, but different from the previous one-dimensional study, the results suggest that the relative humidity is very close to 100% throughout the entire channel length when the inlet relative humidity is 100%, ensuring best hydration cell conditions and hence best performance. Importantly, the model results suggest that fuel cell performance at a high current density in conjunction with relatively low stoichiometric flow ratios around 1.5–2 is possible. [ABSTRACT FROM AUTHOR]

The increasing global water pollution leads to the need for urgent development of rapid and accurate water quality monitoring methods. Microbial fuel cells (MFCs) have emerged as real-time biosensors for biochemical oxygen demand (BOD), but they grapple with several challenges, including issues related to reproducibility, operational stability, and cost-effectiveness. These challenges are substantially shaped by the selection of an appropriate air-breathing cathode. Previous studies indicated a critical influence of the cathode on both the enduring electrochemical performance of MFCs and the taxonomic diversity at the electroactive anode. However, the effect of different gas diffusion electrodes (GDE) on 3D-printed single-chamber MFCs for BOD biosensing application and its effect on the bioelectroactive anode was not investigated before. Our study focuses on comparing GDE cathode materials to enhance MFC performance for precise and rapid BOD analysis in wastewater. We examined for over 120 days two Pt-coated air-breathing cathodes with distinct carbonaceous gas diffusion layers (GDLs) and catalyst layers (CLs): cost-effective carbon paper (CP) with hand-coated CL and more expensive woven carbon cloth (CC) with CL pre-applied by the supplier. The results show significant differences in electrochemical characteristics and anodic biofilm composition between MFCs with CP and CC GDE cathodes. CP-MFCs exhibited lower sensitivity (16.6 C L mg−1 m−2) and a narrower dynamic range (25 to 600 mg L−1), attributed to biofouling-related degradation of the GDE. In contrast, CC-MFCs demonstrated superior performance with higher sensitivity (37.6 C L mg−1 m−2) and a broader dynamic range (25 to 800 mg L−1). In conclusion, our study underscores the pivotal role of cathode selection in 3D-printed MFC biosensors, influencing anodic biofilm enrichment time and overall BOD assessment performance. We recommend the use of cost-effective CP GDL with hand-coated CL for short-term MFC biosensor applications, while advocating for CC GDL supplied with CL as the preferred choice for long-term sensing implementations with enduring reliability. [ABSTRACT FROM AUTHOR]

Carbon emissions from the Yangtze River Economic Belt are an important element of China's carbon emission endeavor, and a study of its emission reduction pathway can provide a reference for the country's overall management of carbon emission reduction. From the perspective of energy consumption, this paper uses the carbon emission factor method to estimate the carbon emissions of the transportation industry in the Yangtze River Economic Belt during 2006–2020, based on the extended STIRPAT model, considering the influence of seven factors, i.e., population size, urbanization rate, GDP per capita, transportation added value, energy structure, energy intensity, and transportation intensity, on carbon emissions. Based on these factors, a scenario analysis, combined with a forecasting model, is used to predict the peak carbon performance of the transportation industry under different development scenarios. The results show that the overall carbon emissions of transportation in the YEB from 2006 to 2020 show a fluctuating upward trend, and the downstream carbon emissions are significantly higher than those in other regions. The main factors influencing carbon emissions from transportation in different upstream, midstream, and downstream regions vary, with both population and economic factors contributing to carbon emissions, while technical factors affect them differently. There are significant differences in the peak carbon performance of transportation under different development scenarios, and the government should take effective measures to work towards achieving the goals of the low-carbon or enhanced low-carbon scenarios. [ABSTRACT FROM AUTHOR]

The defossilisation of the agricultural sector is driven by intense worldwide academic research on non-fossil, renewable and energy-efficient agriculture, and the acknowledgment of the need for sustainable farming practices. For this purpose, not only technical transformations but also socio-technical system changes towards sustainability need to take place in a co-evolutionary manner. This paper investigates structural and qualitative characteristics of the knowledge produced by research on fossil-energy-free agriculture. We provide evidence on the worldwide research directions, as well as investigate whether academic research and publicly funded research projects foster knowledge creation for the desired transformation. Bibliographic maps are constructed using a query-based methodology as social networks to investigate the efficiency of the EU-funded research to achieve the goals set for the 2050 EU Green Deal. The H2020-funded papers are further analysed with dictionary-based text analysis to quantify the relative emphasis of different types of knowledge in the text. This approach is eventually used to relate transformational capacity to project profiles in the European Union, to evaluate past funding schemes and to improve the shape of future research programs on renewable and sustainable agriculture. [ABSTRACT FROM AUTHOR]

Microgrids (MGs) are the new paradigm of decentralized networks of renewable energy sources, loads, and storage devices that can operate independently or in coordination with the primary grid, incorporating significant flexibility and supply reliability. To increase reliability, traditional individual MGs can be replaced by networked microgrids (NMGs), which are more dependable. However, when it comes to operation and control, they also pose challenges for cyber security and communication reliability. Denial of service (DoS) is a common danger to DC microgrids with advanced controllers that rely on active information exchanges and has been recorded as the most frequent cause of cyber incidents. It can disrupt data transmission, leading to ineffective control and system instability. This paper proposes digital twin (DT) technology as an integrated solution, with new, advanced analytics technology using machine learning and artificial intelligence to provide simulation capabilities to predict and estimate future states. By twinning the cyber-physical dynamics of NMGs using data-driven models, DoS attacks targeting cyber-layer agents will be detected and mitigated. A long short-term memory (LSTM) model data-driven digital twin approach for DoS attack detection and mitigation is implemented, tested, and evaluated. [ABSTRACT FROM AUTHOR]

Mass-impregnated (MI) cables have been used for many years as cables in high-voltage direct current (HVDC) systems. In line commutated converter (LCC) HVDC systems, polarity reversal for power flow control can induce significant electrical stress on MI cables. Furthermore, the mass oil and kraft paper comprising the impregnated insulation have significantly different coefficients of thermal expansion. Load fluctuations in the cable lead to expansion and contraction of the mass, creating pressure within the insulation and causing redistribution of the impregnant. During this process, shrinkage cavities can form within the butt gaps. Since the dielectric strength of the cavities is lower than that of the surrounding impregnation, cavitation phenomena in impregnated paper insulation are considered a factor in degrading insulation performance. Consequently, this study analyzes the electrical conductivity of thermally aged materials and investigates the transient electric field characteristics within the cable. Additionally, it closely analyzes the formation and dissolution of cavities in MI cables during polarity reversal based on a numerical model of pressure behavior in porous media. The conductivity of the impregnated paper indicates that it has excellent resistance to thermal degradation. Simulation results for various load conditions highlight that the interval of load-off time and the magnitude of internal pressure significantly influence the cavitation phenomenon. Lastly, the study proposes stable system operation methods to prevent cavitation in MI cables. [ABSTRACT FROM AUTHOR]

Natural and synthetic esters are increasingly being chosen instead of mineral oil for environmental and fire safety reasons. However, their use in power transformers is limited due to insufficiently well-understood ageing processes affecting their properties and the properties of cellulosic materials impregnated with them. The research results presented in many scientific papers prove that the use of esters slows the ageing process of cellulosic materials. This article presents the results of research aimed at answering the question of whether the effect of slowing the ageing process will also occur in the case of insulation with very low initial moisture. The answer to this question will allow us to better understand the role of water in the ageing process of the transformer's insulation system. The thermal ageing process was carried out at a temperature of 150 °C in closed systems. The degree of cellulose polymerisation was taken as a measure of the degree of paper ageing. Great attention was paid to measuring the water content in both paper and electro-insulating liquids at various stages of their ageing. Furthermore, measurements of the properties of electro-insulating liquids were made, which are considered markers of ageing. The test results obtained indicate that in the case of a dry insulation system, corresponding to the initial moisture of the new units, the type of liquid used to impregnate the cellulosic material does not significantly affect its depolymerisation process. However, in the case of paper impregnated with natural esters, a lower dynamic of moisture increase in insulation was observed. [ABSTRACT FROM AUTHOR]

Heat pumps have the potential for several applications across various industrial sectors, showcasing significant promise, especially in sectors such as pulp and paper, food and beverage, chemical, non-metallic minerals, and machinery. Envisioning the near future, there is confidence that heat pumps can achieve temperatures above 200 °C, offering substantial potential for utilization in these sectors. Nevertheless, a crucial aspect for the advancement of high-temperature heat pumps is the selection of the fluid. Fluid selection involves considerations of both thermodynamic efficiency and environmental impact, requiring fluids with zero ODP, negligible GWP, and no PFAS. Moreover, it is essential to consider the risks to human health associated with a specific fluid. Despite extensive research, particularly in the realm of vapour compression heat pumps, choosing the most suitable working fluid for these applications is a complex undertaking. Therefore, this paper conducts a theoretical analysis to evaluate potential fluids with suitable thermodynamic properties for high-temperature heat pumps (HTHPs). The comparative results gleaned from this study provide valuable insights for the comprehensive analysis of fluids, showing promise within temperature ranges dictated by specific applications. The metrics employed in the comparison emphasise the merits of fluids in terms of the overall performance, dimensions, and operating ranges of applicable compressor, heat exchange capacity, transport properties, and safety. One noteworthy finding from the analysis is that maintaining a constant HTHP lift (at 40 K) results in having the highest COP across all fluids when the condensing temperature ranges between 85% and 90% of their respective critical temperatures. According to the results of the analysis, natural fluids, including water and alcohols like ethanol or methanol, emerge as particularly compelling candidates. [ABSTRACT FROM AUTHOR]

This article is a summary of many years of work by the author, in which the thermal properties of various types of insulating liquids, used in power transformers, were evaluated. Recently, esters have been displacing mineral oil. There is a common view that mineral oil has better thermal properties than esters. This claim is supported by comparative results of tests of both materials as a liquid only filling the remaining volume of the transformer. The effect of the type of liquid on the thermal properties of the paper–oil insulation has not been analyzed so far. On this basis, the conclusions formulated may be incomplete. For this reason, the author has analyzed the influence of the type of liquid on both the thermal properties of the liquid filling the remaining volume of the transformer and the paper–oil insulation. It was proved that the more effective liquid filling the remaining volume of the transformer was indeed mineral oil. On the other hand, a more effective electrical insulating liquid, which is an element of paper–oil insulation, is a natural ester. A comprehensive assessment that takes into account both the paper–oil insulation and the remaining transformer volume showed that the natural ester proved to be a slightly more effective electrical insulating liquid than the other analyzed liquids. [ABSTRACT FROM AUTHOR]

In the field of intelligent transportation, the planning of traffic flows that meet energy-efficient driving requirements necessitates the acquisition of energy consumption data for each vehicle within the traffic flow. The current methods for calculating vehicle energy consumption generally rely on longitudinal dynamics models, which require comprehensive knowledge of all vehicle power system parameters. While this approach is feasible for individual vehicle models, it becomes impractical for a large number of vehicle types. This paper proposes a digital model for vehicle driving energy consumption using vehicle speed, acceleration, and battery state of charge (SOC) as inputs and energy consumption as output. The model is trained using an optimized CNN-BiLSTM-Attention (OCBA) network architecture. In comparison to other methods, the OCBA-trained model for predicting PHEV driving energy consumption is more accurate in simulating the time-dependency between SOC and instantaneous fuel and power consumption, as well as the power distribution relationship within PHEVs. This provides an excellent framework for the digital modeling of complex power systems with multiple power sources. The model requires only 54 vehicle tests for training, which is significantly fewer than over 2000 tests typically needed to obtain parameters for power system components. The model's prediction error for fuel consumption under unknown conditions is reduced to 5%, outperforming the standard error benchmark of 10%. Furthermore, the model demonstrates high generalization capability with an R2 value of 0.97 for unknown conditions. [ABSTRACT FROM AUTHOR]

Confronted with the significant challenges of global climate change and environmental deterioration, the pursuit of carbon emission peaks and the realization of carbon neutrality have become a collective goal for countries worldwide. As an exemplary combustion technology noted for its efficiency and environmental friendliness, the circulating fluidized bed (CFB) is instrumental in curbing the release of carbon dioxide alongside other deleterious gases. The technology is pivotal in promoting the clean and efficient use of coal, simultaneously expediting the global shift towards a sustainable, green, and low-carbon future. This study employs a bibliometric analysis, a social network analysis, and information visualization techniques to delve into the evolution of CFB technology, leveraging the Web of Science database (SCI-EXPANDED and CPCI-S) and the Derwent Innovations Index (DII). Through a meticulous examination of academic papers and patent literature related to CFB technology, this research unveils the developmental trajectory and trends of CFB technology, providing a scientific foundation and reference for strategic technology planning and focused research in key areas. The findings indicate that, while there is a downward trend in the global publication of academic papers on CFB technology, the number of patent applications continues to grow steadily. CFB technology has achieved significant advancements in enhancing combustion efficiency, environmental protection, energy utilization, and waste management, and is progressing towards a direction of diversification and greater efficiency. Moving forward, the development of CFB technology should concentrate on pivotal areas such as material science, fluid dynamics simulation, environmental impact assessment, system integration, and intelligentization, to foster ongoing innovation and a broad application of the technology. [ABSTRACT FROM AUTHOR]

The aim of this research is to predict the mechanical properties along with the behaviors of standard insulating paper used in power transformers under thermal aging. This is conducted by applying an artificial neural network (ANN) trained with a multiple regression model and a particle swarm optimization (MR-PSO) model. The aging of the paper insulation is monitored directly by the tensile strength and the degree of polymerization of the solid insulation and indirectly by chemical markers using 2-furfuraldehyde compound content in oil (2-FAL). A mathematical model is then developed to simulate the mechanical properties (degree of polymerization (DPV) and tensile index (Tidx)) of the aged insulation paper. First, the datasets obtained from experimental results are used to create the MR model, and then the optimizer method PSO is used to optimize its coefficients in order to improve the MR model. Then, an ANN method is trained using the MR-PSO to create a nonlinear correlation between the DPV and the time, temperature, and 2-FAL values. The acquired results are assessed and compared with the experimental data. The model presents almost the same behavior. In particular, it has the capability to accurately simulate the nonlinear property behavior of insulation under thermal aging with an acceptable margin of error. Since the life expectancy of power transformers is directly related to that of the insulating paper, the proposed model can be useful to maintenance planners. [ABSTRACT FROM AUTHOR]

A transpiration-type thermoelectric-power-generating paper based on previously developed carbon nanotube (CNT) composite paper, which is a composite material of CNTs and pulp that can generate thermoelectric power, was developed. The newly developed thermoelectric-power-generating material does not require an external high-temperature heat source due to the ability of paper to absorb liquid through capillary action and heat of vaporization generated when the liquid evaporates. The aim of this study is to investigate the feasibility of realizing the transpiration-type thermoelectric-power-generating paper. To begin with, the type of paper used as raw material for the composite paper was examined, and the fabrication process was modified in order to obtain more efficient liquid absorption based on capillary action. Then, the absorbing ability of the liquid was evaluated. Next, the feasibility of thermoelectric power generation using the heat of vaporization was confirmed. Moreover, for more efficient thermoelectric conversion, multisheet structures were also studied. Through several experiments, the material's feasibility was verified, and it was confirmed that more power can be easily obtained through the use of multiple sheets. Specifically, a single sample spontaneously generated a temperature difference of up to 1.7 °C due to the heat of vaporization, generating an electromotive force of 36 μ V. From the sample with a five-sheet structure, an electromotive force of 356 μ V was obtained at a temperature difference of 2 °C. This material can be used in watery environments, such as rivers, lakes, and hot springs, and is expected to become a new energy-harvesting device. [ABSTRACT FROM AUTHOR]

This document is a compilation of research papers that explore the topics of Smart Cities and Positive Energy Districts (PEDs). The papers cover a range of subjects including energy efficiency, renewable energy communities, smart urban management, and financial schemes for sustainable neighborhoods. The findings emphasize the importance of energy renovation, potential energy and financial savings, and the need for collaborative approaches and policy direction to achieve sustainable urban development. The papers also provide insights into innovative solutions and technological advancements that can contribute to the realization of carbon-neutral and sustainable cities. This document serves as a valuable resource for library patrons conducting research on energy efficiency and sustainable building practices. [Extracted from the article]

This study explored the association between renewable energy uptake and digitalization in the sector by reviewing relevant literature (published 2010–2022), with the aim of identifying the existing utilization of digital technologies within the sector, challenges to adoption, and future prospects. Different search engines (SCOPUS, Web of Science, and Google Scholar) were used to locate relevant papers and documents. The results revealed the high significance of digital technologies in supporting the renewable energy sector, with high costs and security risks representing the key challenges. Most papers reviewed had a positive outlook, but recommended further research and development for effective energy transition and resilient infrastructure. The current drivers of the integration of digital technologies to support the diffusion of renewable energy sources appear to extend beyond energy demand and involve many aspects of sustainability and sustainable development. Compared with previous reviews, this work has unique scope and novelty since it considers the bigger picture of the coupling between digitalization and the renewable energy sector, with a greater focus on critical areas in these two interconnected bodies that need to be addressed. The relatively small sample of relevant papers (69 from 836 hits) located in the literature review confirms the need for more research covering the subject in greater depth. [ABSTRACT FROM AUTHOR]

Chemical compounds dissolved in insulating oil, as indicators can excellently monitor the paper aging condition, which has attracted increasing interest in areas of transformer condition monitoring and fault diagnosis. Because of their outstanding features, such as good correlation with the degree of polymerization of cellulose paper and the aid of non-destructive online monitoring, chemical indicators have been effectively used for transformer condition assessment. In this study, a comprehensive, in-depth insight into the indicators of the aging of insulating paper from aging characteristics, physico-chemical characteristics, shortcomings of various compounds, generation pathways and mechanisms, and monitoring technologies are provided. It is expected that these chemical indicators can provide better guidance for the evaluation of paper insulation performance and transformer aging. In addition, the latest research progress, as well as current challenges and future prospects are also outlined. This study provides a theoretical basis and reference for chemical indicators in the fields of microscopic formation mechanism, diffusion equilibrium phenomenon, and insulation aging state assessment. [ABSTRACT FROM AUTHOR]

The sliding window method is commonly used for non-intrusive load disaggregation. However, it is difficult to choose the appropriate window size, and the disaggregation effect is poor in low-frequency industrial environments. To better handle low-frequency industrial load data, in this paper, we propose a vertical non-intrusive load disaggregation model that is different from the sliding window method. By training multiple electrical parameters at a single point on the bus end with the corresponding load data at the branch end, the proposed method, called multiple electrical parameters to point (Mep2point), takes the electrical parameter data sampled at a single point on the bus end as its input and outputs the load data of the target device sampled at the corresponding point. First, the electrical parameters of the bus end are processed, and each item is normalized to the range from 0–1. Then, the electrical parameters are vertically arranged by their time point, and a convolutional neural network (CNN) is used to train the model. The proposed method is analyzed on low-frequency industrial user data sampled at a frequency of 1/120 Hz in the real world. We compare our method with three advanced sliding window methods, achieving an average improvement ranging from 9.23% to 22.51% in evaluation metrics, while showing substantial superiority in the actual decomposed images. Compared with three classical machine learning algorithms, our model, using the same amount of data, significantly outperforms these methods. Finally, we also compared our method with the multi-channel low window sequence-to-point (MLSP) method, which also selects multiple electrical parameters. Our model's complexity is much less than that of the MLSP model, and its performance remains high. The superiority of our model, as presented in this paper, is fully verified by experimental analysis, which can produce better actual load decomposition results from each branch and contribute to the analysis and monitoring of loads in industrial environments. [ABSTRACT FROM AUTHOR]

The hot pressing parameters and fiber ratio have an important influence on the dielectric properties of aramid insulating paper. In order to deeply explore its influence and the mechanism behind it, aramid insulating papers were made with different hot pressing temperatures and pressures as well as fiber ratios. Its tightness, dielectric constant, and AC breakdown strength were tested, and its microstructure was analyzed by scanning electron microscopy. It was found that with an increase in hot pressing temperature, pressure, and fibrid content, the overall dielectric constant of the insulating paper showed a slight upward trend, while the tightness and AC breakdown strength continued to increase. Hot pressing temperature and pressure have a synergistic effect on the dielectric properties of insulating paper. The effects of these two parameters on the dielectric properties of insulating paper are similar, while the AC breakdown strength is greatly affected by the fiber ratio. In this paper, the influence mechanism by which the microstructure and fiber crystallinity of insulating paper is affected in the hot pressing process is discussed. [ABSTRACT FROM AUTHOR]

The article presents studies on the impact of the significant process parameters of a paper machine with a Yankee cylinder on its production capacity and heat energy consumption for drying the paper web. Parameters such as machine speed, web moisture content before and after pressing, parameters of steam supplied to the cylinder and parameters of hot air flowing from the nozzles of the hood were analyzed. The study's results were used to optimize production to improve the energy efficiency and performance of the machine. In order to use the possible methods of improving the production capacity and heat energy consumption, the parameters of the production process were measured and the basic indicators characterizing the operation of the machine were calculated in the Yankee cylinder–dryer hood system. The correct functioning of the machine components and the possibility of their modernization were also analyzed. Technological and construction changes introduced based on the research results made it possible to increase the production capacity by 10% and to reduce the consumption of heat energy per 1 ton of produced paper by 16.3%. The article presents a description of changes in the technology of paper production and modernization of the tissue machine made in the years 2013–2022. [ABSTRACT FROM AUTHOR]

The high proportion of distributed photovoltaic (DPV) access has changed the traditional distribution network structure and operation mode, posing a huge threat to the stable operation and economy of the distribution network. Aiming at a reasonable access capacity of DPV in the distribution network, this paper proposes an economic access capacity evaluation method for DPV in the distribution network considering proper PV power curtailment. Firstly, a method for generating typical joint light intensity and load power operation scenarios based on an improved K-means clustering algorithm is proposed, which provides comprehensive scenario support for the evaluation. Secondly, based on active and reactive power regulation, this paper proposes a DPV access capacity enhancement method to improve the DPV access capacity. Thirdly, considering proper PV power curtailment, an evaluation model of DPV economic access capacity in the distribution network is established to solve the maximum DPV economic access capacity in the distribution network. And aiming at the nonlinear problem in the model, the second-order cone relaxation method is employed to transform the model into the second-order cone programming model, so as to solve the objective function conveniently and efficiently. Finally, based on the improved IEEE 33-node distribution network analysis, the results show that the proposed method can be more comprehensive and effective in evaluating the DPV economic access capacity in the distribution network, and proper PV power curtailment can significantly increase the DPV economic access capacity in the distribution network. [ABSTRACT FROM AUTHOR]

There is a general consensus that bio-waste is a suitable material for valorization by means of the fermentation process with the production of biogas. The success of a bio-waste closed-loop economy will ultimately be determined by the demand for the products made from it. Poor-quality composts and fermentation products will not be allowed on the market in the long term. This means that not only final products but also bio-waste from separate collections must also meet the quality requirements. The aim of this 12-month study was a monthly analysis determining the level of contaminants in bio-waste collected from rural communities, single-family neighborhoods in urban areas, and multi-family neighborhoods in urban areas. The share of contaminants in bio-waste from rural areas and single-family urban housing averaged 8.2% and 7.2%, respectively, while multi-family urban housing had a significantly higher average of 16.6%. The primary contaminants identified were treated wood, plastics, mineral wastes, paper, and glass in rural areas and plastics, paper, treated wood, glass, and textiles in urban areas. The close positive correlation found between the total content of pollutants and, in particular, with plastics and kitchen waste in bio-waste collected in rural communities and from multi-family housing in cities indicates that they are likely the main source of the origin of these pollutants. [ABSTRACT FROM AUTHOR]

Accurate forecasting of electric vehicle (EV) power consumption per unit mileage serves as the cornerstone for determining diurnal variations in EV charging loads. To enhance the prediction accuracy of EV power consumption per unit mileage, this paper proposes a modelling method grounded in an improved Ant Colony Optimization-Support Vector Regression (ACO-SVR) framework. This method integrates the effects of both temperature and speed on the power consumption per unit mileage of EVs. Initially, we analyze the influence mechanism of driving speed and ambient temperature on EV power consumption, elucidating the relationship between power consumption per unit mileage and these factors. Subsequently, we construct an ACO-SVR model utilizing an improved ant colony optimization algorithm, fitting the relationship between power consumption, speed, and temperature to derive the EV power consumption per unit mileage model. Finally, leveraging operational data from EVs in Guangdong, Hong Kong, and Macao as a case study, we validate the energy consumption model of EVs by considering factors such as ambient temperature and driving speed. The results demonstrate that the model proposed in this paper is both accurate and effective. [ABSTRACT FROM AUTHOR]

In general, the fuel cell electric vehicle (FCEV) is regarded as more environmentally friendly than other vehicles. However, the commercialization of FCEV technology is hardly fulfilled due to high-cost fuel cells and an inadequate refueling infrastructure. Different technological trajectories of fuel cells are fiercely competitive, and related technologies are iterating quickly. It is an open issue in terms of what are the technological advances achieved or the opportunities for innovators. The paper proposes a novel approach to identify the key components of an FCEV by constructing the directed co-occurrence network of the International Patent Classification (IPC) and then adopts the Natural Language Processing (NLP) to construct the matrix of technology characteristics and functions. It is suitable to analyze the sentence structure of Subject–Action–Object (SAO) in patent documents by utilizing the NLP technology, which can help computers understand the text and communicate with us. The paper finds that the advances achieved in the fuel cell field are fuel cell composition, manufacturing fuel cells, and providing energy using fuel cells, and the advance in electric motors is supplying power for fuel cell vehicles, while the advances in hydrogen storage are to manage and store hydrogen. By contrast, the opportunities for innovators are to develop the control, diagnosis, and performance of the control system and hydrogen filling. This paper will be a contribution towards a better understanding of the advances and opportunities for developing FCEV technology. [ABSTRACT FROM AUTHOR]

As an indispensable part of the power system, transformers need to be continuously monitored to detect anomalies or faults in a timely manner to avoid serious damage to the power grid and society. This article proposes a combined model for transformer state prediction, which integrates gas concentration prediction and fault diagnosis models. First, based on the historical monitoring data, each characteristic gas sequence is subjected to one optimal variational mode decomposition (OVMD) and one complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The decomposed sub-sequences are input into a bi-directional long short-term memory network (Bi-LSTM) optimized by the sparrow search algorithm (SSA) for prediction, and the predicted value of each sub-sequence was then superimposed to be the predicted value of the characteristic gas. We input the predicted values of each gas into the improved sparrow search algorithm-optimized support vector machine (ISSA-SVM) model, which can output the final fault type. After the construction of the combined model of state prediction is completed, this paper uses three actual cases to test the model, and at the same time, it uses the traditional fault diagnosis methods to judge the cases and compare these methods with the model in this paper. The results show that the combined model of transformer state prediction constructed in this paper is able to predict the type of transformer faults in the future effectively, and it is of great significance for the practical application of transformer fault type diagnosis. [ABSTRACT FROM AUTHOR]

In a rail transit system, there is a constant leakage of current from the subway rails to the earth, and these stray currents have complex propagation paths and a wide range of influence. Since no stray current collection devices are installed at subway depots, some of the stray current leaking from the mainline will converge at the depot, seriously corroding the structural reinforcement and buried metal of the station, thereby jeopardizing the normal operation of subway trains and passenger safety. In this paper, a field-circuit coupling method is proposed to analyze the current leakage and distribution law of the subway mainline and depot. It is found that the failure of the gauge block at the mainline will trigger the maximum leakage of rail current. Additionally, it is observed that the stray current distribution at the depot is mainly influenced by the operating status of the one-way conduction device (OWCD) and the change of rail potential. These results validate the applicability and effectiveness of the field-circuit coupling method proposed in this paper and provide new technical support for the study of stray current leakage distribution in subways. [ABSTRACT FROM AUTHOR]

The pulp and paper industry (PPI) has several opportunities to contribute to meeting prevailing climate targets. It can cut its own CO2 emissions, which currently account for 2% of global industrial fossil CO2 emissions, and it has an opportunity to produce renewable energy, fuels, and materials for other sectors. The purpose of this study is to improve understanding of the decarbonization prospects of the PPI. The study provides insights on the magnitude of needed annual renewal rates for several possible net-zero target years of industrial fossil CO2 emissions in the PPI and discusses decarbonization opportunities, namely, energy and material efficiency improvement, fuel switching, electrification, renewable energy production, carbon capture, and new products. The effects of climate policies on the decarbonization opportunities are critically evaluated to provide an overview of the current and future business environment of the European PPI. The focus is on Europe, but other regions are analyzed briefly to widen the view. The analysis shows that there are no major technical barriers to the fossil-free operation of the PPI, but the sector renovates slowly, and many new opportunities are not implemented on a large scale due to immature technology, poor economic feasibility, or unclear political environment. [ABSTRACT FROM AUTHOR]

Insulation cellulose paper is a basic measure for a power transformer's remaining useful life, and its advantageous low cost, electrical, and mechanical properties have made it an extensive insulation system when impregnated in a dielectric liquid. Cellulose paper deteriorates as a result of ageing due to some chemical reactions like pyrolysis (heat), hydrolysis (moisture), and oxidation (oxygen) that affects its degree of polymerization. The condition analysis of cellulose paper has been a major concern since the collection of paper samples from an operational power transformer is almost impossible. However, some chemicals generated during cellulose paper deterioration, which were dissolved in dielectric liquid, have been used alternatively for this purpose as they show a direct correlation with the paper's degree of polymerization. Furthermore, online and non-destructive measurement of the degree of polymerization by optical sensors has been proposed recently but is yet to be available in the market and is yet generally acceptable. In mitigating the magnitude of paper deterioration, some ageing assessments have been proposed. Furthermore, researchers have successfully enhanced the insulating performance of oil-impregnated insulation paper by the addition of various types of nanoparticles. This study reviews the ageing assessment of oil-paper composite insulation and the effect of nanoparticles on tensile strength and electrical properties of oil-impregnated paper insulation. It includes not only significant tutorial elements but also some analyses, which open the door for further research on the topic. [ABSTRACT FROM AUTHOR]

One of the possible causes of transformer failures is high moisture in the winding insulation system. In an extreme case, when the critical temperature is exceeded, a sudden release of water vapor from the transformer insulation, called the bubble effect, can occur. This article analyzes the initiation temperature of the bubble phenomenon in various solid insulation materials (Kraft cellulose paper and aramid-based high-temperature papers such as Nomex® 910 and Nomex® 926) immersed in two electro-insulating liquids (mineral oil and Midel 7131 synthetic ester). The initiation temperature of the bubble effect depends mainly on the moisture content of the solid insulation, but it was found to be slightly lower for high-temperature materials than for cellulose. However, after taking into account the differences related to uneven water absorption of the tested materials, the differences in the initiation temperature of individual solid materials are very small. Synthetic ester, compared to mineral oil, slightly increases the bubble initiation temperature, regardless of the solid material used. [ABSTRACT FROM AUTHOR]

Finding sustainable solutions to the increasing waste generation in Ghana has received a lot of attention in recent years. Through several waste-to-energy processes, the energy potential of municipal solid waste has recently witnessed significant technological advancements. The Renewable Energy Master Plan has projected the production of about 122 MWp from waste-to-energy installations by 2030 in Ghana. To help policymakers and engineers achieve national goals, this paper reviews the waste generation in Ghana estimated from 2010 to 2030 and the status of various bioenergy technologies in Ghana. This paper further estimates the energy recovery potential of municipal solid waste in Ghana under incineration, anaerobic digestion, and landfill gas recovery technologies. The review establishes that, by 2030, municipal solid waste generation will increase by 123% of the 2023 quantities and may produce 1484.25 MW of installed electricity capacity and 13,002.03 GWh per year, which would amount to nearly 59% of Ghana's 2030 renewable energy target. Additionally, it was determined that anaerobic digestion, incineration, and landfill gas recovery technologies, when properly developed, will add 105.33 MW, 301.4 MW, and 377.31 MW of installed electrical capacity, respectively, to Ghana's energy mix in 2028. [ABSTRACT FROM AUTHOR]

Grid-connected photovoltaic (PV) power systems are one of the most promising technologies to address growing energy demand and ecological challenges. This paper proposes smart switching to mitigate inrush currents during the connection of single-phase transformers used in PV systems. An effective inrush current mitigation contributes to the reliability of PV systems. The inrush current severity is influenced by the pseudorandom residual flux at the transformer core and the energization point-on-wave. The most common approach to avoid inrush currents is controlled connection, which requires prior knowledge of the residual flux. However, the residual flux can differ in each case, and its measurement or estimation can be impractical. The proposed smart switching is based on a comprehensive analysis of the residual flux and the de-energization trajectories, and only requires two pieces of data (ϕRM and ϕ0, flux values of the static and dynamic loops when the respective currents are null), calculated from two simple no-load tests. It has a clear advantage over common approaches: no need to estimate or measure the residual flux before each connection, avoiding the need for expensive equipment or complex setups. Smart switching can be easily implemented in practical settings, as it considers different circuit breakers with distinctive aperture features, making it cost-effective for PV systems. [ABSTRACT FROM AUTHOR]

In order to extend the service life of a highway power supply system and the level of new energy consumption, a power dispatching strategy considering the health status of multi-energy conversion equipment is proposed in this paper. Firstly, the energy and load forms of the highway power supply system are introduced, and the structure of the multi-energy conversion equipment, the topological structures of the DC–DC and DC–AC modules, and the operating characteristics are analyzed. Secondly, the module temperatures and output voltages are used as main parameters to establish the health indexes of DC–DC and DC–AC modules, and then the health index of the multi-energy conversion equipment is further calculated. Thirdly, the new energy consumption index is defined, and a multi-objective optimization model for power dispatching of highway power supply systems is established with the goal of improving the health index of multi-energy conversion equipment and the new energy consumption index. The case study shows that the power dispatching strategy in this paper can better control the temperature of each module, improve the health status of multi-energy conversion equipment, and have a high level of new energy consumption. [ABSTRACT FROM AUTHOR]

Nowadays, the microgrid system is characterized by a diversification of power factors and a complex network structure. Existing studies on microgrid fault diagnosis and troubleshooting mostly focus on the fault detection and operation optimization of a single power device. However, for increasingly complex microgrid systems, it becomes increasingly challenging to effectively contain faults within a specific spatiotemporal range. This can lead to the spread of power faults, posing great harm to the safety of the microgrid. The topology optimization of the microgrid based on deep reinforcement learning proposed in this paper starts from the overall power grid and aims to minimize the overall failure rate of the microgrid by optimizing the topology of the power grid. This approach can limit internal faults within a small range, greatly improving the safety and reliability of microgrid operation. The method proposed in this paper can optimize the network topology for the single node fault and multi-node fault, reducing the influence range of the node fault by 21% and 58%, respectively. [ABSTRACT FROM AUTHOR]

This paper focuses on the evaluation of the operational effect of a pumped storage plant in a new power system. An evaluation index system is established by selecting key indicators from the four benefit dimensions of system economy, low carbon, flexibility, and reliability. The evaluation criteria are based on the values of indexes for pumped storage plants that have already been put into operation. Using this method, the operational effect of pumped storage plants with different installed capacities, regulation durations, and conversion efficiencies are comprehensively evaluated and analyzed. The calculation results show that the operation effect of a pumped storage plant with high regulation performance and high comprehensive conversion efficiency is better, indicating that the established index system and evaluation method can comprehensively and truly reflect the positive benefits brought by a pumped storage plant to a new power system. This study can provide a practical reference for the early planning and decision making of pumped storage in a new power system. [ABSTRACT FROM AUTHOR]

In Korea, the demand for complete underground installation of power distribution equipment installed on roads and green areas is increasing. In addition, KEPCO is making efforts to build a more reliable system for the underground distribution system. To meet these needs, this paper proposes the S-substation. In the S-substation, an RMU, a large power transformer, and an LV-Board (including ATCB and MCCB) are installed within the underground structure. This paper proposes three models to apply the S-substation to the underground distribution system. Power flow analysis is conducted for each model by simulating a variety of loads and DERs, and the frequency fluctuations are also examined under different distribution system events. An economic analysis is also conducted to select the optimal model. The economic analysis focuses on VOLL and construction costs. Based on power flow and economic analysis, one model is selected, and the underground distribution system that the model is applied is presented. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning-based intelligent detection device for insulation pull rod defects, addressing the issues of low detection accuracy, poor timeliness of intelligent analysis, and the difficulty in preserving detection results. Firstly, by constructing the pull rod defects dataset and training the YOLOv5s network, along with commonly used object detection algorithms in industrial defect detection, the feasibility of deep learning networks for insulation pull rod defects detection is explored. Secondly, the trained model is combined to build an intelligent detection device for pull rod defects, integrating insulation pull rod image acquisition and defect detection into a unified system. The research results demonstrate that the YOLOv5s network can quickly and accurately detect pull rod defects. On the test set constructed in this paper, the detection performance metric mAP@0.5:0.95 of the trained model reached 54.7%. Specifically, the mAP@0.5 score was 86.9% at a threshold of 0.5. The detection speed FPS reached 169.5, significantly improving the detection efficiency and accuracy compared to traditional object detection algorithms. By establishing an organic connection between the image hardware acquisition device and the deep learning network, the existing problems of inefficient detection and difficult storage of detection results in pull rod defects detection methods are effectively addressed. This research provides new insights for detecting insulation pull rod defects. [ABSTRACT FROM AUTHOR]

Non-invasive load monitoring (NILM) deduces changes in energy consumption patterns and operational statuses of electrical equipment from power signals in the feed line. With the emergence of fine-grained power load distribution, the importance of utilizing this technology for implementing demand-side energy management in smart grid development has become increasingly prominent. To address the issue of low load identification accuracy stemming from complex and diverse load types, this paper introduces a NILM method based on uniform manifold approximation and projection (UMAP) reduction and enhanced density-based spatial clustering of applications with noise (DBSCAN). Firstly, this paper combines the characteristics of user load under transient and steady-state conditions and selects data with significant differences to construct a load-characteristic database. Additionally, UMAP is employed to reduce the dimensionality of high-dimensional load features and rebuild a load feature database. Subsequently, DBSCAN is utilized to categorize typical user loads, followed by a correlation analysis with the load-characteristic database to determine the types or classes of loads that involve switching actions. Finally, this paper simulates and analyzes the proposed method using the electricity consumption data of industrial users from the CER–Electricity–Data dataset. It identifies the electricity load data commonly utilized by users in a specific area of Zhejiang Province in China. The experimental results indicate that the accuracy of the proposed non-invasive load identification method reaches 95%. Compared to the wavelet transform, decision tree, and backpropagation network methods, the improvement is approximately 5%. [ABSTRACT FROM AUTHOR]

In order to achieve low carbon emissions in the power grid, the impact of new energy grid connections on the power grid should be reduced, as well as the peak-to-valley load difference caused by large-scale electric vehicle grid connections. This paper proposes a two-tier, low-carbon optimal dispatch master–slave game model involving virtual power plant operators as well as electric vehicle operators. Firstly, the carbon flow is tracked based on the proportional sharing principle, and the carbon emission factor during the charging and discharging process of electric vehicles is calculated. Secondly, the node carbon potential and time-sharing tariff are used to guide and change the charging behaviour of electric vehicles and to construct a master–slave game model for low-carbon optimal scheduling with the participation of multiple subjects, with economic scheduling at the upper level of the model and demand response scheduling at the lower level. Finally, the IEEE30 node system is used as an example to verify that the method adopted in this paper can effectively reduce the peak-to-valley difference of loads, reduce the carbon emissions of the grid, and reduce the cost of each participating entity. [ABSTRACT FROM AUTHOR]

Despite the tightening of energy performance standards for buildings in various countries and the increased use of efficient and renewable energy technologies, it is clear that the sector needs to change more rapidly to meet the Net Zero Emissions (NZE) scenario by 2050. One of the problems that have been analyzed intensively in recent years is that buildings in operation use much more energy than they were designed to. This problem, known as the energy performance gap, is found in many countries and buildings and is often attributed to the poor management of building energy systems. The application of Artificial Intelligence (AI) to Building Energy Management Systems (BEMS) has untapped potential to address this problem and lead to more sustainable buildings. This paper reviews different AI-based models that have been proposed for different applications and different buildings with the intention to reduce energy consumption. It compares the performance of the different AI-based models evaluated in the reviewed papers by presenting the accuracy and error rates of model performance and identifies where the greatest potential for energy savings could be achieved, and to what extent. The review showed that offices have the greatest potential for energy savings (up to 37%) when they employ AI models for HVAC control and optimization. In residential and educational buildings, the lower intelligence of the existing BEMS results in smaller energy savings (up to 23% and 21%, respectively). [ABSTRACT FROM AUTHOR]

Accurate power load forecasting can provide crucial insights for power system scheduling and energy planning. In this paper, to address the problem of low accuracy of power load prediction, we propose a method that combines secondary data cleaning and adaptive variational mode decomposition (VMD), convolutional neural networks (CNN), bi-directional long short-term memory (BILSTM), and adding attention mechanism (AM). The Inner Mongolia electricity load data were first cleaned use the K-means algorithm, and then further refined with the density-based spatial clustering of applications with the noise (DBSCAN) algorithm. Subsequently, the parameters of the VMD algorithm were optimized using a multi-strategy Cubic-T dung beetle optimization algorithm (CTDBO), after which the VMD algorithm was employed to decompose the twice-cleaned load sequences into a number of intrinsic mode functions (IMFs) with different frequencies. These IMFs were then used as inputs to the CNN-BILSTM-Attention model. In this model, a CNN is used for feature extraction, BILSTM for extracting information from the load sequence, and AM for assigning different weights to different features to optimize the prediction results. It is proved experimentally that the model proposed in this paper achieves the highest prediction accuracy and robustness compared to other models and exhibits high stability across different time periods. [ABSTRACT FROM AUTHOR]

Grid-forming inverters (GFMIs) have been identified as critical assets in ensuring modern power system reliability. Their ability to synthesize an internal voltage reference while emulating synthetic inertia has sparked extensive research. These characteristics have recently piqued interest in their capacity to provide blackstart ancillary services. The blackstart of a bulk power system poses significant challenges, namely the large transients from the energization of unloaded transformers, rotational motor loads, and long transmission cables, which have been effectively studied using conventional synchronous generators (SGs). The concept of an inverter-based resource (IBR)-based blackstart continues to be an open research area necessitating further investigations due to the known limitations of IBRs such as low short-circuit current capabilities. This paper presents a blackstart case study of a bulk power system investigating the performances of a conventional SG to a GFMI when utilizing hard switching methods. The paper qualitatively investigates the transient inrush currents from the transformer and rotational load energization sequences. Additional examinations into the significance of the GFMI's current-limiting schemes and voltage control loop compensator gains are presented. Furthermore, the harmonic distortions from the transformer energization sequence are also evaluated. Finally, a full network energization case is presented to demonstrate how both sources can provide blackstart provisioning services. The models are developed in EMTDC/PSCAD using real-world transmission planning data. [ABSTRACT FROM AUTHOR]

This paper presents a fast focusing inversion algorithm of magnetic data based on the conjugate gradient method, which can be used to describe the underground target geologic body efficiently and clearly. The proposed method realizes an effect similar to matrix compression by changing the computation order, calculating the inner product of vectors and equivalent expansion of expressions. Model tests show that this strategy successfully reduces the computation time of a single iteration of the conjugate gradient method, so the three-dimensional magnetic data inversion is realized under a certain number of iterations. In this paper, the detailed calculation steps of the proposed inversion method are given, and the effectiveness and high efficiency of the proposed fast focusing inversion method are verified by three theoretical model tests and a set of measured data. Finally, the fast focus inversion algorithm is applied to the magnetic data of Gonghe Basin, Qinghai Province, to describe the spatial distribution range of deep hot dry rock, which provides a direction for the continuous exploration of geothermal resources in this area. [ABSTRACT FROM AUTHOR]

A high proportion of distributed generators (DGs) connected to the distribution network causes a significant change in the normal and fault currents of the system as well as in the linearization of the characteristics. It is difficult to adapt to conventional protection. This paper theoretically analyzes the possible impact of fault current characteristics on traditional protection based on single-ended informativeness after connecting to DGs. From the perspective of protection action, the evaluation index system of DG protection is established by considering the maximum short-circuit current output from DG. Combined with the relay protection requirements, the calculation method of evaluation indexes is given concerning the protection characteristics and expert experience. An analytic hierarchy process (AHP) and a CRITIC combination assignment method based on the principle of minimum information identification are proposed. The scores of different types of protection before and after DG access are calculated using the proposed methodology employing a typical distribution network example. The proposed method can quantitatively obtain the distribution network protection adaptability boundary. In the actual calculation example selected in this paper, a DG can reasonably improve the adaptability of the three-stage current protection when it increases the current amplitude at a penetration rate of 50%; the DG needs to adjust the three-stage current protection rectification value when it decreases the current amplitude at a penetration rate of 20%; and adaptive overcurrent protection and inverse time limit current protection need to be adjusted when the penetration rate of DG is 50%. Compared with the traditional protection evaluation method, the method adopted in this paper can intuitively derive the weak link between protection handling faults after DG access as well as the appropriate capacity of DG to improve protection performance. It can provide a powerful reference for the optimization of protection schemes after the high percentage of DG access. [ABSTRACT FROM AUTHOR]

The frequent occurrence of the pantograph–catenary (PC) arc seriously threatens the safe and stable operation of electric multiple units (EMUs). In order to investigate the primary factors affecting the electrical characteristics of the PC arc, this paper first reveals the mechanism of arc generation when EMUs leave the split-zone insulator according to the actual mechanical structure of the PC articulated split-zone insulator. Then, based on the Habedank black box arc model, considering the dynamic changes in arc length during the actual operation of EMUs, a numerical simulation model suitable for describing the PC articulated split-zone insulator arc is established. Furthermore, by comparing with existing research results, the effectiveness of the proposed model is verified. Next, a series of simulation experiments were conducted to analyze the influence of different operating parameters of the EMUs on the electrical characteristics of the PC arc. More importantly, the paper proposes using the entropy weight method to calculate the impact strength of different operating parameters of the EMUs on the arc duration time of the contact wire split-zone region. The research results of this paper can provide some technical references for reducing the erosion of the PC contact wire and slide caused by arcs when the EMUs pass through the split-zone insulator. [ABSTRACT FROM AUTHOR]

As a key project of China's western development, the West-East Natural Gas Transmission Project (WENGT) has effectively alleviated China's uneven energy distribution and supply-demand issues. It has promoted the optimization of China's energy structure and sustainable development in the new era, helping to achieve the goal of "double carbon". Based on pollution data from Chinese industrial enterprises from 2000 to 2012, this paper analyzes the impact of the West-East Natural Gas Pipeline Project on the air pollution emissions of industrial enterprises using a difference-in-difference model and explores the moderating role of local government environmental regulations. The findings indicate that the project significantly reduced air pollutant emissions from industrial enterprises in cities along the route, with a more pronounced reduction effect in areas with higher environmental regulation intensity. Additionally, the emission reduction effect varied among different types of enterprises and regions, with private enterprises showing the smallest reduction impact and the eastern coastal region exhibiting the most significant effect. This paper delves into the relationship between sustainable cross-regional energy development and air pollution emissions from industrial enterprises, providing a theoretical basis for energy conservation and emission reduction policies in China. [ABSTRACT FROM AUTHOR]

In supporting the stable operation of high-penetration renewable energy grids, flywheel energy storage systems undergo frequent charge–discharge cycles, resulting in significant stress fluctuations in the rotor core. This paper investigates the fatigue life of flywheel energy storage rotors fabricated from 30Cr2Ni4MoV alloy steel, attempting to elucidate the material's mechanical properties, crack propagation behavior, and impact of internal defects on fatigue life. Tensile tests reveal that the material exhibited high yield (992 MPa) and tensile strengths (1130 MPa). The Paris formula is used to model crack growth rates, ending in good agreement with the experimental data. Fatigue tests at various stress conditions highlight the material's significant variability in fatigue life and emphasize the need for reliable design approaches. This paper also demonstrates that internal defect size and location critically affect fatigue life, calling for improvements in forging inspection standards. Overall, the present study provides a comprehensive analysis of 30Cr2Ni4MoV steel's suitability for flywheel rotors, balancing safety, and operational efficiency. [ABSTRACT FROM AUTHOR]

The green port multi-energy microgrid, featuring renewable energy generation, hydrogen energy, and energy storage systems, is an important gateway to achieve the net-zero emission goal. But there are many forms of energy in green port multi-energy microgrid systems, the power fluctuates frequently, and the port loads with large fluctuations and fast changes. These factors can easily lead to the problem of the state of charge exceeding the limit of the energy storage system. To distribute the fluctuating power in the green port multi-energy microgrid system reasonably and maintain the state of charge (SOC) of the hybrid energy storage system in an moderate range, an energy management strategy (EMS) based on dual-stage fuzzy control with a low pass-filter algorithm is proposed in this paper. First, the mathematical model of a green port multi-energy microgrid system is established. Then, fuzzy rules are designed, and the dual-stage fuzzy controller is used to change the time constant of the low-pass filter (LPF) and modify the initial power distribution by an LPF algorithm. Finally, simulation models are built in Matlab 2016a/Simulink. The simulation results demonstrate that, compared with other algorithms under the control of the EMS proposed in this paper, the high-frequency component in the flywheel power is smaller, and the SOC of the supercapacitor is maintained in a reasonable range of 34–78%, which extends the lifespan of the flywheel and supercapacitor. Additionally, it has a faster automatic adjustment ability for the state of charge of the energy storage system, which is conducive to better maintaining the stable operation of green port multi-energy microgrid systems. [ABSTRACT FROM AUTHOR]