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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]

Aiming at the demand for subjective evaluation of driveability for fuel cell vehicles, the modeling and evaluation method of driveability for fuel cell vehicles were studied in this paper. Firstly, a real-time model of the fuel cell vehicle powertrain system was established, which included the fuel cell model, power battery model, DC/DC converter model and drive motor model. Secondly, it was integrated with the vehicle dynamics model to form a virtual prototype of a fuel cell vehicle. And a virtual subjective evaluation platform for fuel cell vehicles was built by combining the virtual prototype and high-fidelity driving simulator. Thirdly, a subjective evaluation method of driveability for fuel cell vehicles was proposed, which included the starting performance, acceleration performance, uniform speed performance and tip-in/tip-out performance. Finally, the paper used the platform and method mentioned above to conduct subjective evaluations of the fuel cell vehicles. [ABSTRACT FROM AUTHOR]

This review paper synthesizes the recent advancements in voltage regulation techniques for active distribution networks (ADNs), particularly in contexts with high renewable energy source (RES) penetration, using photovoltaics (PVs) as a highlighted example. It covers a comprehensive analysis of various innovative strategies and optimization algorithms aimed at mitigating voltage fluctuations, optimizing network performance, and integrating smart technologies like smart inverters and energy storage systems (ESSs). The review highlights key developments in decentralized control algorithms, multi-objective optimization techniques, and the integration of advanced technologies such as soft open points (SOPs) to enhance grid stability and efficiency. The paper categorizes these strategies into two main types: analytical methods and computational methods. In conclusion, this review underscores the critical need for advanced analytical and computational methods in the voltage regulation of ADNs with high renewable energy penetration levels, highlighting the potential for significant improvements in grid stability and efficiency. [ABSTRACT FROM AUTHOR]

This paper attempts to elucidate the transformative integration of computational techniques within power systems, underscoring their critical role in enhancing system modeling, control, and the efficient integration of renewable energy. It breaks down the two-sided nature of technological progress, highlighting both gains in operational efficiency and new challenges such as real-time processing, data management, and cybersecurity. Through meticulous analysis of query-based research patterns and mathematical frameworks, this study delves into the balancing act between specificity and breadth in scholarly inquiries while evaluating the impact and evolution of research trends through citation analysis. The convergence of interests and transient research trends is evident, particularly in Artificial Intelligence and optimization. This comprehensive narrative anticipates a sophisticated trajectory for power systems, advocating for continuous innovation and strategic research to foster sustainable, resilient, and intelligent energy networks. [ABSTRACT FROM AUTHOR]

The efficient utilization of low-cost carbon feedstocks, such as municipal solid waste (MSW), in biorefineries has become increasingly important for reducing GHG emissions and meeting the growing demand for renewable energy sources. However, MSW as a feedstock presents several challenges, including high moisture content, compositional variability, particle size and shape, density, and ash content. To address these challenges, the potential of mechanical dewatering and high-moisture pelleting processes for densifying MSW fractions, such as paper, cardboard, thin plastic, and thick plastic, into low-cost carbon feedstocks with improved handling and conversion properties were investigated. The effect of these preprocessing technologies on the critical quality attributes (CQAs) of the resulting pellets, including bulk density, durability, and size uniformity, were evaluated. The results showed that with these preprocessing technologies, the paper and cardboard fractions could be pelleted at moisture contents over 40% (w.b.) while achieving >99% durability and >300 kg/m3, while the high moisture plastic fractions were not suitable for pelleting. The thick plastic fraction processed in a screw press was shown to remove up to 30% of the moisture content in a single pass. These findings suggest that these mechanical preprocessing technologies can improve the physical properties of low-cost municipal solid waste fractions for biofuels production. [ABSTRACT FROM AUTHOR]

To advance carbon neutrality policies, many countries are increasingly integrating Renewable Energy Sources (RESs) into their energy mix. However, for harnessing natural energy in power generation, RESs present challenges in output control, potentially leading to power imbalances. Such imbalances, when coupled with an excessive reliance on unpredictable RES, may necessitate the curtailment of grid-integrated renewable generation, halting production despite available capacity. This paper proposes strategies to mitigate the economic and operational efficiency impacts of increased RESs on the power grid. It focuses on the optimal operation of Battery Energy Storage Systems (BESSs) and Power to Gas (P2G) technology, which flexibly converts surplus electricity into hydrogen. By employing Mixed Integer Linear Programming (MILP) for optimization and Stochastic Programming (SP) for the management of the uncertainty of renewable energy output, this paper evaluates the anticipated benefits of BESS and P2G applications. [ABSTRACT FROM AUTHOR]

High voltage connections will discharge through inductive voltage and combined transformers when they are switched off. In the past, there was a wide margin between the required capabilities and the actual capabilities of these components. Nowadays, however, this aspect is becoming more prominent due to a higher penetration by cable systems into the grid. Currently, none of the relevant international standards contain any definitions or requirements for line discharge, nor test methods which would validate the ability of the transformer to discharge a line of certain capacitance at a certain voltage. This is why the aim of this paper is to provide a foundation for how to specify, assess and verify the capacitive discharge performance of instrument transformers. This paper will present the basic theory, influencing parameters, multiple test sequences and acceptance criteria for cable discharge. It is clear that capacitive discharges present both thermal and mechanical stress to the unit, which is why this paper will detail how to guarantee both aspects. The idea is that this paper serves as a literal blueprint which can then be transferred to the relevant clauses in upcoming international standards. All conclusions within this paper are achieved through calculations and testing on actual 123 kV inductive voltage transformers and combined units. [ABSTRACT FROM AUTHOR]

Hydrothermal carbonization (HTC) of low quality, wet biogenic residues into intermediate bioenergy carriers can potentially contribute to a more flexible and stable renewable energy system and reduce environmental impacts compared to current residue disposal practices. This study quantifies the environmental impacts via life cycle assessment (LCA) of a novel hydrothermal process for the treatment on an industrial scale of application of three wet biogenic residues (paper bio-sludge, olive pomace, and orange peel) into bioenergy carriers, i.e., solid pellets and biogas. A comprehensive attributional cradle-to-gate life cycle assessment (LCA) was conducted; the life cycle impact assessment (LCIA) utilised the ReCiPe impact assessment method. A selection of 10 significant impact categories was prioritised. Reliability of this categorization was also ensured through a sensitivity analysis carried out using Monte Carlo simulation. Climate change, particulate matter formation and terrestrial acidification impact categories showed the highest reliability, while for freshwater ecotoxicity and freshwater eutrophication impact categories in the study suggest the need for more robust data and further investigation. The climate change impact category presents the following values, as kg CO2eq/tresidue: pulp and paper bio-sludge (PPB), 17.9; olive pomace (OP), −1290; orange peel (ORP), −1301. The LCA study compared electricity yields of the hydrothermal treatment process with conventional treatment processes for each of the target residue streams. The environmental performance of the proposed hydrothermal treatment benefits significantly from the combination of intermediate bioenergy carriers (pellets) from the solid fraction with biogas production from the liquid fraction. Avoided emissions due to the heat recovery provide further environmental benefits. The LCIA results show that the carbon footprint of the F-CUBED production system, as kgCO2eq/kWhe, accounts for –4.56, −0.63, and −0.25 for paper bio-sludge, olive pomace and orange peel, respectively. [ABSTRACT FROM AUTHOR]

Internal arc testing is still a controversial topic in the instrument transformer world. The main reason for that is the fact that even a fully successful test guarantees only a certain degree of transformer safety. Furthermore, the test does not cover a plethora of operational fault scenarios and has requirements which are not defined clearly enough. In addition, there are very few data available in the literature on the internal arc performance of alternative, biodegradable dielectric liquids. Some liquids (such as natural and synthetic esters) do inherently come with higher flash and fire points compared to conventional mineral oil, but there is insufficient test experience to corroborate the influence this high fire point has on the actual operation. Specifically, to the authors' knowledge, internal arc tests on instrument transformers were never performed with biodegradable dielectric liquids, making the contributions of this paper a true world premiere. In short, this paper is intended to augment the existing standards, thus providing additional insight into how to test internal arc performance, what to look out for and what level of performance to expect, which is of broad interest to researchers, utility engineers and public alike. [ABSTRACT FROM AUTHOR]

To enhance the accuracy of theft detection for electricity consumers, this paper introduces a novel strategy based on the fusion of the dual-time feature and deep learning methods. Initially, considering electricity-consumption features at dual temporal scales, the paper employs temporal convolutional networks (TCN) with a long short-term memory (LSTM) multi-level feature extraction module (LSTM-TCN) and deep convolutional neural network (DCNN) to parallelly extract features at these scales. Subsequently, the extracted features are coupled and input into a fully connected (FC) layer for classification, enabling the precise detection of theft users. To validate the method's effectiveness, real electricity-consumption data from the State Grid Corporation of China (SGCC) is used for testing. The experimental results demonstrate that the proposed method achieves a remarkable detection accuracy of up to 94.7% during testing, showcasing excellent performance across various evaluation metrics. Specifically, it attained values of 0.932, 0.964, 0.948, and 0.986 for precision, recall, F1 score, and AUC, respectively. Additionally, the paper conducts a comparative analysis with mainstream theft identification approaches. In the comparison of training processes, the proposed method exhibits significant advantages in terms of identification accuracy and fitting degree. Moreover, with adjustments to the training set proportions, the proposed method shows minimal impact, indicating robustness. [ABSTRACT FROM AUTHOR]

The objective of this study is to analyze the economic viability of municipal household solid waste (organic matter + paper) for the production of gas, coke and biofuel through the pyrolysis and distillation process. The waste was collected in the city of Belém do Pará-Brazil and pretreated at the Federal University of Pará. The analyzed fraction (organic matter + paper) was subjected to the pretreatment of drying, crushing, and sieving and was subsequently subjected to proximate characterization and, finally, pyrolysis of the organic fraction (organic matter + paper) in a fixed bed reactor. Initially, it was necessary to review the literature, and with the yields obtained by pyrolysis of the fraction, economic feasibility analyses were carried out. The economic indicators for evaluating the most viable pyrolysis process were basic payback, discounted payback, net present value, internal rate of return, and profitability index, which are all financial metrics commonly used in investment analysis and decision making. These metrics provide valuable insights into the financial viability and attractiveness of investment projects. They are essential tools for assessing the feasibility and profitability of various ventures, helping decision-makers make informed choices in allocating resources. The analysis of the indicators showed the economic viability considering an analysis horizon of 10 years of materials based on organic material and paper. The breakeven point obtained was USD 0.96/dm3 and the minimum biofuel sales price found in this project was USD 1.30/dm3. The sensitivity research found that material costs (organic matter + paper), bio-oil yield, total project investment and electricity, respectively, are the variables that most affect the minimum biofuel sales price. [ABSTRACT FROM AUTHOR]

In this paper, in order to improve the fuel economy of the actual application of the engine under multi-operating conditions, an experimental study is carried out on a turbo-charged direct-injection engine based on different pre-chamber structures. The engine used for the study is a four-cylinder turbo-charged direct-injection gasoline engine with different structures of pre-chamber spark plugs. The operating conditions in this study include load characteristics at 2000 r/min and characteristic loads at different speeds, including 3000 r/min, 3200 r/min, and 3600 r/min. With stable BMEP or fully open throttle and pedal, the experiment was conducted by the spark angle scanning method to collect data of engine power, economy, and emission under each condition. It was found that the pre-chamber structure has a direct effect on engine performance, with a clear load demarcation line for its effect. Under the WOT condition, the power of pre-chamber ignition is 1.6% higher than that of conventional spark plugs; at the low load of 2 bar, the economy of pre-chamber ignition is degraded by 6%; at the medium load of 8 bar, the economy of the two is comparable; at the large load of 16 bar, the fuel economy proves advantageous. Compared with conventional spark plugs, the pre-chamber spark angle can be advanced by 2~3 °CA, and the pre-chamber ignition with separate ground electrodes is highly reliable. The emission levels of the pre-chamber spark plugs and conventional spark plugs are comparable at all loads. [ABSTRACT FROM AUTHOR]

This paper presents a detailed review of the existing literature on peer-to-peer (P2P) energy trading considering market architectures, trading strategies, and enabling technologies. P2P energy trading enables individual users in the electricity network to act as sellers or buyers and trade energy among each other. To facilitate the discussion on different aspects of P2P energy trading, this paper focuses on P2P market mechanisms, relevant bidding strategies, and auction models. In addition, to solve the energy management problems associated with P2P energy trading, this paper investigates widely used solution methods such as game-theoretic models, mathematical optimisation, as well as more recent machine learning techniques and evaluates them in a critical manner. The outcomes of this investigation along with the identification of the challenges and limitations will allow researchers to find suitable P2P energy trading mechanisms based on different market contexts. Moreover, the discussions on potential future research directions are expected to improve the effectiveness of P2P energy trading technologies. [ABSTRACT FROM AUTHOR]

At present, most studies use the direct method to analyze the oscillation problem of modern power systems. However, these studies often only simplify the external characteristics of the wind turbine and lack an in-depth understanding of its internal refined energy structure. In this paper, based on the direct-drive permanent magnetic synchronous generator's detailed model (D-PMSG), combined with the dynamic energy of its port, layers of analysis are performed on the wind turbine's internal connections, and a detailed model of the energy structure is created. Then, the interaction mechanism of each control link in the wind turbine is analyzed by combining the energy function of the wind turbine with the improved perturbation method. Finally, this paper constructs a sub-synchronous oscillation (SSO) scenario of weak damping and a forcing type and proves the accuracy and effectiveness of the traceability method based on the refined energy of D-PMSG. This traceability method based on refined energy is expected to provide a new solution to the stability problem caused by the integration of new energy. [ABSTRACT FROM AUTHOR]

This paper concerns the utilisation of a gas bladder hydraulic suppressor to mitigate oscillations in the delivery flow rate of positive displacement machines. The research focuses on two primary objectives: first, the experimental validation of the potential of this solution and second, the formulation of a one-dimensional fluid dynamic model for the suppressor. The foundational framework of the fluid dynamic model is based on the equations governing fluid motion with a one-dimensional approach. To accurately depict the fluid dynamics within the suppressor, a unique approach for determining the speed of sound was incorporated, and it implemented the instantaneous cross-sectional area and the inertial effect of the bladder. This paper is a development of a previous work to also investigate the positioning along the delivery pipe of the suppressor with respect to the pump. The study presents the performance of the suppressor and points out the effects of its relative position with respect to the pump that becomes particularly relevant at high speeds. [ABSTRACT FROM AUTHOR]

The digitization of distribution power systems has revolutionized the way data are collected and analyzed. In this paper, the critical task of harnessing this information to identify irregularities and anomalies in electricity consumption is tackled. The focus is on detecting non-technical losses (NTLs) and energy theft within distribution networks. A comprehensive overview of the methodologies employed to uncover NTLs and energy theft is presented, leveraging measurements of electricity consumption. The most common scenarios and prevalent cases of anomalies and theft among consumers are identified. Additionally, statistical indicators tailored to specific anomalies are proposed. In this research paper, the practical implementation of numerous artificial intelligence (AI) algorithms, including the artificial neural network (ANN), ANFIS, autoencoder neural network, and K-mean clustering, is highlighted. These algorithms play a central role in our research, and our primary objective is to showcase their effectiveness in identifying NTLs. Real-world data sourced directly from distribution networks are utilized. Additionally, we carefully assess how well statistical methods work and compare them to AI techniques by testing them with real data. The artificial neural network (ANN) accurately identifies various consumer types, exhibiting a frequency error of 7.62%. In contrast, the K-means algorithm shows a slightly higher frequency error of 9.26%, while the adaptive neuro-fuzzy inference system (ANFIS) fails to detect the initial anomaly type, resulting in a frequency error of 11.11%. Our research suggests that AI can make finding irregularities in electricity consumption even more effective. This approach, especially when using data from smart meters, can help us discover problems and safeguard distribution networks. [ABSTRACT FROM AUTHOR]

This paper introduces a groundbreaking approach to demand response management, aiming to empower consumers through innovative strategies. The key contribution is the concept of "acquiring flexibility rights", wherein consumers engage with power aggregators to curtail energy usage during peak-load periods, receiving incentives in return. A flexibility right coefficient is introduced, allowing consumers to tailor their participation in demand response programs, ensuring their well-being. Additionally, a lighting intensity control system is developed to enhance residential lighting network efficiency. The study demonstrates that high-energy consumers, adopting a satisfaction factor of 10, can achieve over 61% in electricity cost savings by combining the lighting control system and active participation in demand response programs. This not only reduces expenses but also generates income through the sale of flexibility rights. Conversely, low-energy consumers can fully offset their expenses and accumulate over USD 33 in earnings through the installation of solar panels. This paper formulates an optimization problem considering flexibility rights, lighting control, and time-of-use tariff rates. An algorithm is proposed for a distributed solution, and a sensitivity analysis is conducted for evaluation. The proposed method showcases significant benefits, including cost savings and income generation for consumers, while contributing to grid stability and reduced blackout occurrences. Real data from a residential district in Tehran validates the method's effectiveness. This study concludes that this approach holds promise for demand response management in smart grids, emphasizing the importance of consumer empowerment and sustainable energy practices. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study on the impacts of peer-to-peer (P2P) energy markets on distribution systems, specifically focusing on voltage, power loss, and congestion. While P2P energy markets create opportunities for direct trading between prosumers and consumers, ensuring compliance with distribution system constraints remains a challenge. This paper proposes an iterative method and graphical interpretation in order to assess complex interactions, addressing the persistent issue of network constraints. Additionally, this paper proposes a method to determine distribution locational marginal prices (DLMPs) for real-time traditional energy markets. This ensures effective coordination among sellers, buyers, and the distribution system operator. The proposed method aims to prevent negative impacts on distribution system operation via the determination of the allowable maximum trading power (MTP), ensuring empirical validity and practical implementation via operating conditions and forecast errors, thus distinguishing it from prior studies. This paper also establishes a model for P2P energy market interactions, utilizing linear estimations for efficient DLMP updates. The contributions of this paper enhance the understanding and operation of P2P energy markets, and is supported by simulation results validating the proposed method. [ABSTRACT FROM AUTHOR]

Low power brushless direct current (BLDC) motors are used in many consumer appliances. These motors have a relatively high winding resistance and therefore current control loop can be avoided in some cases, but fast and accurate speed regulation can be still needed. To minimize harmonics and generated sound noise, improved sinusoidal pulse width modulation (PWM) has been tested in the paper. As the most suitable commutation type, the sine wave with the third harmonic component has been selected. This type of communication reduced the torque ripple of the motor. This paper analyses the possibility to improve traditional proportional-integral-derivative (PID) speed regulator with Fuzzy logic block. A simulation model of BLDC motor, inverter, speed detection circuit and controller have been created. Simulation results showed that by applying the Fuzzy-based PID controller, the transient time can be reduced from 0.2 s to 0.05 s and overshoot can be avoided in comparison with traditional PID controller. Experimental results show a significant improvement in the motor dynamics—the overshoot and transient time were reduced twice. The difference with simulation results and experimental ones can be explained by delays introduced by the microcontroller. [ABSTRACT FROM AUTHOR]

The optics is the component that most affects the concentrating photovoltaic (CPV) system performance, depending above all on the concentration factor and optical efficiency. Hence, a basic aspect is the concentrated solar flux measure on the receiving area, the evaluation of which is principally realized by indirect measurement methods. First, a literature review on indirect and direct methods used for the evaluation of concentrated solar flux and optical parameters is presented in this paper. The experimental measurement procedure, which is able to evaluate the optical parameters and concentrated solar flux in CPV systems, is also presented. The main steps of this procedure are represented by experimental system setup, sensor selection for concentrated solar flux estimation, identification of all the factors affecting optical performances, and development of an experimental campaign and output analysis. In particular, the optical characterization results of a CPV system are obtained by means of in-depth experimental analysis using Triple-Junction (TJ) solar cells with areas of 5.5 × 5.5 mm2 and 10 × 10 mm2. Three different setups have been analyzed related to primary and secondary optics composition. The main aim of this paper is the determination of a direct measuring technique, rarely adopted in literature in comparison to the established techniques, that is able to evaluate experimentally the optical parameter values and that can be standardized for other CPV systems. In particular, equations that link the optical concentration factor (C) and efficiency (ηopt) with focal distance (h) represent the fundamental results. They can be used for similar point-focus configurations presenting the same TJ cell size and ranges of C, ηopt and h. Finally, the experimental results of the direct method are compared with those of an indirect method adopting the same CPV system and operational conditions. [ABSTRACT FROM AUTHOR]

With a large proportion of wind farms connected to the power grid, it brings more pressure on the stable operation of power systems in shorter time scales. Efficient and accurate scheduling, operation control and decision making require high time resolution power forecasting algorithms with higher accuracy and real-time performance. In this paper, we innovatively propose a high temporal resolution wind power forecasting method based on a light convolutional architecture—DC_LCNN. The method starts from the source data and novelly designs the dual-channel data input mode to provide different combinations of feature data for the model, thus improving the upper limit of the learning ability of the whole model. The dual-channel convolutional neural network (CNN) structure extracts different spatial and temporal constraints of the input features. The light global maximum pooling method replaces the flat operation combined with the fully connected (FC) forecasting method in the traditional CNN, extracts the most significant features of the global method, and directly performs data downscaling at the same time, which significantly improves the forecasting accuracy and efficiency of the model. In this paper, the experiments are carried out on the 1 s resolution data of the actual wind field, and the single-step forecasting task with 1 s ahead of time and the multi-step forecasting task with 1~10 s ahead of time are executed, respectively. Comparing the experimental results with the classical deep learning models in the current field, the proposed model shows absolute accuracy advantages on both forecasting tasks. This also shows that the light architecture design based on simple deep learning models is also a good solution in performing high time resolution wind power forecasting tasks. [ABSTRACT FROM AUTHOR]

On Distributed Electric Propulsion (DEP) aircraft, the deployment of numerous high-lift propellers with small diameters on the wing's leading edge significantly enhances lift during low-speed flight. The increase in the number of propellers leads to a decrease in diameter, which increases the disc loading. In this paper, a rapid design method applicable to heavy-loaded propellers is developed and does not require iterative calculations compared to traditional heavy-loaded propeller design methods, enabling rapid completion of the propeller design. The results of CFD computation show that the relative thrust error of the method proposed in this paper is within 5% for disc loading ranging from 600 Pa to 1400 Pa, features a high-accuracy design of propellers with required thrust, and high thrust coefficients are achieved within large advance ratio range. [ABSTRACT FROM AUTHOR]

Today's energy challenges are multifaceted. Over the past 30–40 years, energy issues have been discussed and published on an extensive scale. The green transition involves concrete actions related to increasing energy efficiency, replacing fossil fuels with alternative fuels, producing energy using renewable resources, creating various means of transport that use electric motors, identifying technical solutions that generate an increased energy yield in the case of buildings, and waste reduction, reuse and recycling. In order to attain a climate-neutral environment, it is mandatory to impose regulations, measures and actions to help decarbonize the energy sector. The analysis of published articles on these issues is the subject of this large and information-dense review. Concretely, the transition to climate neutrality will generate obvious advantages at an economic, social and technological level, for example, the opportunity for economic growth, new business models and new markets, and the generation of new jobs or technological development. At the same time, this paper underscores the need for a multifaceted approach, integrating technological innovation, policy intervention and global cooperation for an effective energy transformation. The review suggests future issues and research directions, focusing on viable strategies for energy transition and its socio-economic environmental impacts. [ABSTRACT FROM AUTHOR]

The DC circuit breaker is a crucial equipment for eliminating faults in DC transmission lines. The electromagnetic release functions as a critical component that restricts the circuit breaker's lifespan. It is essential to prioritize its safety and reliability during usage and predicting its remaining useful life (RUL) is paramount. This paper proposes a new prediction technique based on particle filtering (PF) and the whale optimization algorithm (WOA) for the remaining useful life of electromagnetic release. The particle filtering algorithm is a commonly used technique in practical engineering fields such as target tracking and RUL prediction. It is a mainstream method for solving the parameter estimation of non-linear non-Gaussian systems. The WOA is introduced to improve the PF algorithm in order to ensure the diversity of particles and lessen the effect of particle degradation. The WOA replaces the traditional resampling process, and after each computation is finished, the weights of the particles in the particle set are reassigned in order to improve the particle distribution and increase algorithm accuracy. The rate of loss of the spring reaction force and the striker counterforce are chosen as the degradation characteristics after the degradation factors of electromagnetic release are analyzed. The degradation curves at different temperatures of electromagnetic release are obtained using the accelerated life test, and the full-life data during normal operation are derived using the Arrhenius equation. Finally, the RUL is predicted by comparing this paper's method with the conventional PF method. The experimental results demonstrate that the method presented in this paper can more accurately predict the RUL of the electromagnetic release and has a higher prediction accuracy. [ABSTRACT FROM AUTHOR]

A Hybrid Photovoltaic Thermal (PVT) system is one of the most emerging and energy-efficient technologies in the area of solar energy engineering. This review paper provides a comprehensive review of hybrid PVT systems in the context of the history of PVT, general classification, and parameter analysis. Several cell technologies with spectrum analysis are discussed to understand the application's ability and energy efficiency. Hybrid PVT concept, characteristics, and structure analysis is also discussed in this study. An extensive analysis on the classifications of hybrid PVT systems from the recent literature is also presented here. These literatures are identified based on several criteria. In order to provide a complete and energy-efficient technology, an innovative classification of the hybrid PVT system is proposed in this paper. This proposed classification is a combination and upgrade of various existing classifications mentioned in recent research studies. Parameters have a significant and unavoidable impact on the performance and efficiency of the hybrid PVT system. A brief analysis of different parameters and the optimization of the system is conducted after reviewing recent research articles. This analysis provides insights into the impact of parameter variations on the system. A novel parameter model comprising parametric and optimistic analyses is also presented in this paper. It provides a detailed parametric description that significantly affects the performance and efficiency of the hybrid PVT system. Finally, the assessment focuses on a critical analysis of the main challenges in adopting PVT technology and suggests ways to overcome these barriers. [ABSTRACT FROM AUTHOR]

The transportation sector remains a major contributor to greenhouse gas emissions. The understanding of energy-efficient driving behaviors and utilization of energy-efficient driving strategies are essential to reduce vehicles' fuel consumption. However, there is no comprehensive investigation into energy-efficient driving behaviors and strategies. Furthermore, many state-of-the-art AI models have been applied for the analysis of eco-friendly driving styles, but no overview is available. To fill the gap, this paper conducts a thorough literature review on ecological driving behaviors and styles, and analyzes the driving factors influencing energy consumption and state-of-the-art methodologies. With a thorough scoping review process, thirty-seven articles with full text were assessed, and the methodological and related data are compared. The results show that the factors that impact driving behaviors can be summarized into eleven features including speed, acceleration, deceleration, pedal, steering, gear, engine, distance, weather, traffic signal, and road parameters. This paper finds that supervised/unsupervised learning algorithms and reinforcement learning frameworks have been popularly used to model the vehicle's energy consumption with multi-dimensional data. Furthermore, the literature shows that the driving data are collected from either simulators or real-world experiments, and the real-world data are mainly stored and transmitted by meters, controller area networks, onboard data services, smartphones, and additional sensors installed in the vehicle. Based on driving behavior factors, driver characteristics, and safety rules, this paper recommends nine energy-efficient driving styles including four guidelines for the drivers' selection and adjustment of the vehicle parameters, three recommendations for the energy-efficient driving styles in different driving scenarios, and two subjective suggestions for different types of drivers and employers. [ABSTRACT FROM AUTHOR]

Power transformers are one of the most expensive and important equipment in the power system. Significant differences exist in the insulation lifespan of transformers that have been in operation for more than 20 years, and using identical maintenance or scrapping methods may result in significant economic losses. However, most existing transformer life assessment methods only consider the impact of moisture content on the life decay rate without considering the impact of oil circulation cooling modes, which leads to some evaluation errors. In this study, we established a new transformer life assessment method that considers the influence of moisture content and oil circulation cooling modes, which is more accurate than most life assessment methods. Then, the proposed life evaluation method was validated on the on-site transformers, demonstrating its accuracy and effectiveness. The novelty of this study is that it establishes a new on-site transformer life assessment method that considers the comprehensive effect of moisture content and oil circulation cooling mode, which helps to evaluate the remaining lifespan of power transformers more accurately and thus extends the transformer lifespan systematically. [ABSTRACT FROM AUTHOR]

The modern world is moving towards a zero-emission economy; therefore, various actions are being taken to reduce the share of fossil fuels in energy production. The article examines the potential for the continued expansion of photovoltaic farms, with a special emphasis on farms utilising east–west panel orientation. The east–west orientation is an innovative solution with many advantages over the traditional north–south arrangement. The paper also makes a detailed assessment of the photovoltaic farm environment by applying two analyses based on the following factors: Political, Economic, Social, and Technological (PEST) and Demographic, Economic, Environmental, Political, Legal, Informational, Social, and Technological (DEEPLIST) factors. This is followed by an insightful, comprehensive review of the most important factors that contribute to the efficiency of photovoltaic installations, namely site conditions, existing infrastructure, and ability to connect to the electricity grid. The paper also devotes space to an analysis of daily energy price changes that affect the economic efficiency of the installation and discusses the potential for energy storage in the context of photovoltaic farms. The development of photovoltaics also requires investing in energy storage. All discussed issues fall within the scope of photovoltaic farm development and optimal use of energy resources. [ABSTRACT FROM AUTHOR]

As the main source of energy supply and carbon emissions, the oil and gas industry has entered the comprehensive low-carbon transition stage driven by various factors. Since different oil companies possess distinct understandings of transition paths, the effect of low carbon transition varies greatly. Obviously, it is necessary to evaluate the performance of low-carbon transitions within the oil and gas industry. Therefore, in this paper, 10 major international oil companies are taken as examples, and a super-efficiency slack-based measurement (super-SBM) model is applied in the present study to analyze the efficiency of low-carbon transitions. Furthermore, the logarithmic mean Divisia index (LMDI) is used to decompose the driving factors of carbon emissions and analyze their impact on the low-carbon transition of international oil companies. The obtained results reveal that although major oil companies have taken different measures in low-carbon transition and achieved a year-on-year reduction in carbon emissions, from the perspective of the efficiency of the entire production and operation process, these oil companies are inefficient in carbon emissions and need to adopt more effective low-carbon transition strategies; Moreover, after the further decomposition of the carbon emission driving factors of the 10 companies, it is found that improving the energy consumption intensity and development level of oil companies can effectively improve the effect of low-carbon transition of international oil companies. Drawing on the above findings, this paper puts forward suggestions for the low-carbon transition of energy companies, thus providing theoretical support and guidance for energy companies in different countries to implement low-carbon transition and green development strategies. [ABSTRACT FROM AUTHOR]

Solar forecasting is becoming increasingly important due to the exponential growth in total global solar capacity each year. More photovoltaic (PV) penetration in the grid poses problems for grid stability due to the inherent intermittent and variable nature of PV power production. Therefore, forecasting of solar quantities becomes increasingly important to grid operators and market participants. This review presents the most recent relevant studies focusing on short-term forecasting of solar irradiance and PV power production. Recent research has increasingly turned to machine learning to address this challenge. The paper provides a discussion about building a solar forecasting model, including evaluation measures and machine learning method selection through analysed literature. Given that machine learning is data-driven, the focus of this review has been placed on data sources referenced in the literature. Open-access data sources have been compiled and explored. The main contribution of this paper is the establishment of a benchmark for assessing the performance of solar forecasting models. This benchmark utilizes the mentioned open-source datasets, offering a standardized platform for future research. It serves the crucial purpose of streamlining investigations and facilitating direct comparisons among different forecasting methodologies in the field of solar forecasting. [ABSTRACT FROM AUTHOR]

This research paper presents a comprehensive review of the literature on microgrid development in the UAE, focusing on the socio-economic costs and benefits, policy frameworks, market dynamics, and environmental implications. The analysis encompasses publications from 2011 to 2021, with a particular emphasis on the United Arab Emirates (UAE) and the Gulf Cooperation Council (GCC) countries. A total of 33 papers were identified and classified, revealing gaps in comprehensive valuation models, consideration of environmental and social governance factors, and research informing policy and investment decisions. The findings highlight the significance of microgrid technology in the UAE, its limited adoption and commercialization, and its predominant usage in remote areas and academic testbeds. The paper underscores the UAE's vision for net-zero emissions by 2050 and the potential of microgrids in supporting its realization. Recommendations include the development of a comprehensive valuation framework to drive effective investments in microgrid technology, aligning with the UAE's sustainable energy goals. The study contributes to the understanding of microgrid development in the UAE, offering insights into its socio-economic, policy, market, and environmental dimensions. [ABSTRACT FROM AUTHOR]

The government has set an ambitious target of renewable energy development in Malaysia. As a promising renewable energy source, wind energy plays an important role in the Malaysia renewable energy roadmap. Compared to onshore wind energy, offshore wind resources with better quality can be provided in the areas away from the coast, which has greater potential to contribute to electricity generation. Wind and bathymetry conditions are two important factors that determine the feasibility and economics of offshore wind turbines. In this paper, an investigation is conducted on wind and bathymetry conditions around Malaysia. The data source mainly originates from the Global Wind Atlas. The conditions of the coastal areas in different states and federal territories of both Peninsular Malaysia and East Malaysia are analysed, with a specific focus on wind speed, wind energy density, and bathymetry conditions in high-wind-speed regions. The data and survey are verified and compared with the past published literature. This paper aims to investigate the wind and bathymetry conditions around Malaysia, assess the potential of offshore wind energy, discuss the feasibility of offshore wind turbines, and provide references for offshore wind development in Malaysia. [ABSTRACT FROM AUTHOR]

Outphasing power amplifiers (OPAs) can be used in wireless information and power transfer systems due to their excellent conversion efficiency. With the rapid advancement of technology, there is a strong demand for OPAs to achieve wideband and multi-band operations to enhance frequency coverage. This paper provides a comprehensive review of the research progress on the development of broadband and multi-band board-level OPAs. Firstly, the paper introduces the fundamental components of an OPA. Then, the paper delves into a detailed analysis of the load modulation of OPAs. Subsequently, previous works on the circuit topology and design methods for broadband and multi-band OPAs are discussed and compared. The paper highlights various design strategies and their respective advantages and disadvantages. Finally, the paper explores some potential design methods for broadband and multi-band OPAs, discussing emerging technologies and highlighting potential research directions. [ABSTRACT FROM AUTHOR]

In response to the volatility of photovoltaic power generation, this paper proposes a short-term photovoltaic power generation prediction model (HWOA-MVMD-TPA-TCN) based on a Hybrid Whale Optimization Algorithm (HWOA), multivariate variational mode decomposition (MVMD), temporal pattern attention mechanism (TPA), and temporal convolutional network (TCN). In order to improve the accuracy of photovoltaic power generation forecasting, HWOA-MVMD is used for data decomposition, the Minimum Mode Overlap Component (MMOC) is used as the objective function, the photovoltaic power generation sequence is decomposed into finite Intrinsic Mode Functions (IMFs) according to the optimal solution, and the training set is formed with key meteorological variable data such as total radiation (unit: W/m2), ambient temperature, and humidity. Then, the TPA-TCN model is used to train the sub-sequences, the final predicted values are obtained after superimposing the reconstruction of the prediction results, and finally the prediction error of the photovoltaic power generation data is studied. The proposed method is applied to real photovoltaic power generation data from a commercial center in Tianjin and is compared with HWOA-MVMD-BiLSTM, GWO-MVMD-TPA-TCN, and TPA-TCN prediction models. The simulation results demonstrate that the MAE value of the forecast method proposed in this paper is 1.95 MW and the RMSE value is 2.55 MW, which can be reduced by up to 33.74% and 38.85%, respectively. The HWOA-MVMD-TPA-TCN-based short-term photovoltaic power generation prediction model presented in this paper achieves higher prediction accuracy and superior performance, serving as a valuable reference for related research. [ABSTRACT FROM AUTHOR]

The electric scroll compressor is driven by a built-in electric motor that rotates the scroll disk. It is known for its simple structure, adjustability, and high efficiency, making it highly promising for various applications. This paper reviews the current application and research status of electric scroll compressors. It covers topics such as the optimal design of scroll compressor profiles, scroll disk leakage sealing, and computer simulation optimization design methods. Additionally, the progress and development trends of vapor-injection scroll compressors (SCVIs) are discussed. This paper also presents the latest research progress on the application of the new refrigerant CO2 in electric scroll compressors, along with its latest applications that align with sustainable development requirements. Finally, this paper concludes with recommendations for the application of electric scroll compressors and suggests future directions for research. [ABSTRACT FROM AUTHOR]

This paper investigates optimizing the power exchange between electric vehicles (EVs) and the grid, with a specific focus on the DC-DC converters utilized in vehicle-to-grid (V2G) systems. It specifically explores using model predictive control (MPC) in synchronous boost converters to enhance efficiency and performance. Through experiments and simulations, this paper shows that replacing diodes with SIC MOSFETs in boost converters significantly improves efficiency, particularly in synchronous mode, by minimizing the deadtime of SIC MOSFETs during switching. Additionally, this study evaluates MPC's effectiveness in controlling boost converters, highlighting its advantages over traditional control methods. Real-world validations further validate the robustness and applicability of MPC in V2G systems. This study utilizes TMS320F28379D, one of Texas Instruments' leading digital signal processors, enabling the implementation of MPC with a high PWM frequency of up to 200 MHz. This processor features dual 32-bit CPUs and a 16-bit ADC, allowing for high-resolution readings from sensors. Leveraging digital signal processing technologies and advanced electronic circuits, this study advances the development of high-performance boost converters, achieving power outputs of up to 48 watts and output voltages of 24 volts. Electronic circuits (PCB boards) have been devised, implemented, and evaluated to showcase their significance in advancing efficient V2G integration. [ABSTRACT FROM AUTHOR]

Operating nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) element data that contain information about the status of components, assets, and systems. Some of this information is in textual form where the occurrence of abnormal events or maintenance activities are described. Analyses of NPP textual data via natural language processing (NLP) methods have expanded in the last decade, and only recently the true potential of such analyses has emerged. So far, applications of NLP methods have been mostly limited to classification and prediction in order to identify the nature of the given textual element (e.g., safety or non-safety relevant). In this paper, we target a more complex problem: the automatic generation of knowledge based on a textual element in order to assist system engineers in assessing an asset's historical health performance. The goal is to assist system engineers in the identification of anomalous behaviors, cause–effect relations between events, and their potential consequences, and to support decision-making such as the planning and scheduling of maintenance activities. "Knowledge extraction" is a very broad concept whose definition may vary depending on the application context. In our particular context, it refers to the process of examining an ER textual element to identify the systems or assets it mentions and the type of event it describes (e.g., component failure or maintenance activity). In addition, we wish to identify details such as measured quantities and temporal or cause–effect relations between events. This paper describes how ER textual data elements are first preprocessed to handle typos, acronyms, and abbreviations, then machine learning (ML) and rule-based algorithms are employed to identify physical entities (e.g., systems, assets, and components) and specific phenomena (e.g., failure or degradation). A few applications relevant from an NPP ER point of view are presented as well. [ABSTRACT FROM AUTHOR]

Due to a rising share of renewable energy sources on the production side and electrification of transport and heating on the consumption side, the efficient management of flexibility in distribution networks is crucial for ensuring optimal operation and utilization of resources. Nowadays, the sensitivity-based approach is mainly used in medium-voltage (MV) networks for regulating voltage profiles with reactive power of distributed energy resources (DER). The main disadvantage of the simplified sensitivity-based method is its inaccuracy in case of a high deviation of the network voltage from the nominal values. Furthermore, it was also noted that despite the fact that the method is well described in the literature, there is a lack of systematic approach to its implementation in real-life applications. Thus, the main objective of this paper is to address this disadvantage and to propose an algorithm designed to calculate required consumer flexibility in near real-time to ensure distribution grid operation within operational criteria. In the first part of the paper, network state, including line loading and node voltages, is assessed to determine distribution network node capacity. By analyzing the sensitivity of network busbars to changes in consumption and production, our algorithm effectively identifies the most efficient nodes and facilitates strategic decision-making for resource allocation. We demonstrate the effectiveness of our approach through simulations of real-world distribution network data, highlighting its ability to enhance network flexibility and improve resource utilization. Leveraging sensitivity coefficients, the algorithm enables flexible consumption and production management across various scenarios, supporting the transition to a more dynamic and efficient power system. [ABSTRACT FROM AUTHOR]

Regional Integrated Energy Systems (RIESs) and Shared Energy Storage Systems (SESSs) have significant advantages in improving energy utilization efficiency. However, establishing a coordinated optimization strategy between RIESs and SESSs is an urgent problem to be solved. This paper constructs an operational framework for RIESs considering the participation of SESSs. It analyzes the game relationships between various entities based on the dual role of energy storage stations as both energy consumers and suppliers, and it establishes optimization models for each stakeholder. Finally, the improved Differential Evolution Algorithm (JADE) combined with the Gurobi solver is employed on the MATLAB 2021a platform to solve the cases, verifying that the proposed strategy can enhance the investment willingness of energy storage developers, balance the interests among the Integrated Energy Operator (IEO), Energy Storage Operator (ESO) and the user, and improve the overall economic efficiency of RIESs. [ABSTRACT FROM AUTHOR]

In the current source photovoltaic grid-connected system, to prevent the DC-link inductor from incurring an opening circuit fault, it is necessary to include the overlap time in the switching signals. However, current error and serious harmonic distortion in the inverter-side and grid-side currents are generated, which will cause additional losses and reduce the power quality of the grid, so it is important to compensate for the current error caused by the overlap time. In this paper, the relationship between the nonlinear current errors caused by the overlap time and the AC-side voltage is analyzed. Then, the mathematical expression of the low-order harmonics with losses caused by the overlap time is derived. On this basis, a current error compensation method with a discrete filter of AC-side voltage is proposed. Finally, a simulation and experiment are carried out to verify the correctness and effectiveness of the theoretical analysis and compensation scheme presented in this paper. With an overlap time of 3 μs, the THD of the grid-side current decreases from 5.93% to 1.59% after compensation. [ABSTRACT FROM AUTHOR]

Shale gas is an important supplement to the supply of natural gas resources and plays an important role on the world's energy stage. The efficient implementation of hydraulic fracturing is the key issue in the exploration and exploitation of shale gas. The existence of bedding structure results in a distinct anisotropy of shale rock formation. The anisotropic behaviors of shale rock have important impacts on wellbore stability, hydraulic fracture propagation, and the formation of complex fracture networks. This paper briefly reviews previous work on the anisotropic mechanical properties of shale rock and their relation to hydraulic fracturing in shale reservoirs. In this paper, the research status of work addressing the lithological characteristics of shale rock is summarized first, particularly work considering the mineral constituent, which determines its physical and mechanical behavior in essence. Then the anisotropic physical and mechanical properties of shale specimens, including ultrasonic anisotropy, mechanical behavior under uniaxial and triaxial compression tests, and tensile property under the Brazilian test, are summarized, and the state of the literature on fracture toughness anisotropy is discussed. The concerns of anisotropic mechanical behavior under laboratory tests are emphasized in this paper, particularly the evaluation of shale brittleness based on mechanical characteristics, which is discussed in detail. Finally, further concerns such as the effects of bedding plane on hydraulic fracturing failure strength, crack propagation, and failure pattern are also drawn out. This review study will provide a better understanding of current research findings on the anisotropic mechanical properties of shale rock, which can provide insight into the shale anisotropy related to the fracture propagation of hydraulic fracturing in shale reservoirs. [ABSTRACT FROM AUTHOR]

This paper introduces a simultaneous optimization approach to synthesizing work and heat exchange networks (WHENs). The proposed work and heat integration (WHI) superstructure enables different thermodynamic paths of pressure and temperature-changing streams. The superstructure is connected to a heat exchanger network (HEN) superstructure, enabling the heat integration of hot and cold streams identified within the WHI superstructure. A two-step solution strategy is proposed, consisting of initialization and design steps. In the first step, a thermodynamic path model based on the WHI superstructure is combined with a model for simultaneous optimization and heat integration. This nonlinear programming (NLP) model aims to minimize operating expenditures and provide an initial solution for the second optimization step. In addition, hot and cold streams are identified, enabling additional model reduction. In the second step of the proposed solution approach, a thermodynamic path model is combined with the modified HEN model to minimize the network's total annualized cost (TAC). The proposed mixed integer nonlinear programming (MINLP) model is validated by several examples, exploring the impact of the equipment costing and annualization factor on the optimal network design. The results from these case studies clearly indicate that the new synthesis approach proposed in this paper produces solutions that are consistently similar to or better than the designs presented in the literature using other methodologies. [ABSTRACT FROM AUTHOR]

Electricity consumption in buildings is one of the major causes of energy usage and knowledge of this can help building owners and users increase energy efficiency and conservation efforts. For Pacific Island countries, building electricity demand data is not readily accessible or available for constructing models to predict electricity demand. This paper starts to fill this gap by studying the case of schools in Fiji. The aim of the paper is to assess the factors affecting electricity demand for grid-connected Fijian schools and use this assessment to build mathematical models (multiple linear regression (MLR) and artificial neural network (ANN)) to predict electricity consumption. The average grid-connected electricity demand in kWh/year was 1411 for early childhood education schools, 5403 for primary schools, and 23,895 for secondary schools. For predicting electricity demand (ED) for all grid-connected schools, the stepwise MLR model shows that taking logarithm transformations on both the dependent variable and independent variables (number of students, lights, and air conditioning systems) yields statistically significant independent variables with an R2 value of 73.3% and RMSE of 0.2248. To improve the predicting performance, ANN models were constructed on both the natural form of variables and transformed variables. The optimum ANN model had an R2 value of 95.3% and an RMSE of 59.4 kWh/year. The findings of this study can assist schools in putting measures in place to reduce their electricity demand, associated costs, and carbon footprint, as well as help government ministries make better-informed policies. [ABSTRACT FROM AUTHOR]

Dry-type air-core shunt reactors are integral components in power transmission and distribution networks, designed to control reactive power and enhance system stability. However, inter-turn short-circuit faults (ISCFs) are common occurrences in shunt reactors, which are caused by various factors, including manufacturing defects, insulation degradation, or operational stresses. At the early stage of the ISCFs, the current does not reach a sufficient level to activate the protective equipment. These faults may lead to serious consequences, such as overheating, insulation breakdown, and even catastrophic failures, posing risks to the entire power system. Therefore, developing an effective and reliable detection method for ISCFs at the early stage is paramount. In this paper, a new method named the fault detection factor (FDF) based on equivalent resistance is presented to detect the slight ISCFs in dry-type air-core shunt reactors considering insulation resistance. In addition, the effect of noise signal existence in the monitoring process is taken into account. A moving average filter is adopted to guarantee both the sensitivity and the reliability of the proposed method. Ultimately, the simulation results of the FDF under different conditions are presented, which show the effectiveness and potential of the proposed method in observing and monitoring slight ISCFs. [ABSTRACT FROM AUTHOR]

Predicting the remaining useful life (RUL) of lithium-ion batteries is crucial for enhancing their reliability and safety. Addressing the issue of inaccurate RUL predictions caused by the nonlinear decay resulting from capacity regeneration, this paper proposes an indirect lithium-ion battery RUL prediction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and convolutional neural network (CNN)–bidirectional gated recurrent unit (BiGRU). The method extracts Health Indicators (HI) from the battery-charging stage and employs CEEMDAN to decompose HI into several components. These components are then input into a component prediction model for forecasting. Finally, the predicted component results are fused and input into a capacity prediction model to achieve indirect RUL prediction. Validation is conducted using the lithium-ion battery dataset provided by NASA. The results indicate that, under prediction starting points (STs) of 80 and 100, the maximum average absolute errors do not exceed 0.0096 and 0.0081, and the maximum root mean square errors do not exceed 0.0196 and 0.0115, demonstrating high precision and reliability. [ABSTRACT FROM AUTHOR]

This paper introduces a straightforward control strategy aimed at the reduction of current fluctuations within the low-frequency domain of open-loop V/f control in induction motor drives. Traditional control techniques necessitate the addition of a current compensator based on motor parameters and the use of digital filters such as band-pass or high-pass filters. These methods, however, rely on precise motor parameters and involve complex filter design and implementation. The proposed control is capable of suppressing current fluctuations without controlling the slip of the induction motor. The proposed control strategy generates the forced rotation angle and command input voltage using the V/f block and outputs the d-axis voltage using a proportional integral controller to keep the d-axis current constant at zero. The difference between the command input voltage and the d-axis voltage is applied as the q-axis voltage and then applied through SVPWM. In order to verify the effectiveness of the proposed control, the proposed control is implemented and analyzed using power simulation based on the results of the analysis of the causes of current fluctuations in the induction motor. Finally, the effect of suppressing current fluctuations of the induction motor is verified through experimental results. In the 10~19 Hz range, where the conventional V/f control method resulted in current fluctuation rates exceeding 10% and peaking at 113.3% at 13 Hz, the proposed method suppressed the fluctuation rate to below 8.6% across all frequencies. This paper validates the effectiveness of the proposed control strategy through these results. [ABSTRACT FROM AUTHOR]

As the global energy landscape transitions towards a more sustainable future, hydrogen has emerged as a promising energy carrier due to its potential to decarbonize various sectors. However, the economic competitiveness of hydrogen production by water electrolysis strongly depends on renewable energy source (RES) availability. Thus, it is necessary to overcome the challenges related to the intermittent nature of RESs. This paper presents a comprehensive techno-economic analysis of complementing green hydrogen production with grid electricity. An evaluation model for the levelized cost of hydrogen (LCOH) is proposed, considering both CO2 emissions and the influence of RES fluctuations on electrolyzers. A minimum load restriction is required to avoid crossover gas. Moreover, a new operation strategy is developed for hydrogen production plants to determine optimal bidding in the grid electricity market to minimize the LCOH. We evaluate the feasibility of the proposed approach with a case study based on data from the Kyushu area in Japan. The results show that the proposed method can reduce the LCOH by 11% to 33%, and increase hydrogen productivity by 86% to 140%, without significantly increasing CO2 emission levels. [ABSTRACT FROM AUTHOR]

The external insulation strength of the power equipment sleeve in the substation and converter station will be reduced with the increase in altitude and atmosphere pollution. The weakness of external insulation seriously threatens the safe operation of electrical equipment. At present, there are few studies and conclusions that focus on the flashover characteristics of power equipment bushing in high-altitude polluted areas. Therefore, this paper takes the 220 kV voltage level bushing as the research object and studies the Alternating Current (AC) pollution flashover characteristics and lightning and switching impulse flashover characteristics at low pressure. The insulation configuration at high-altitude polluted areas is analyzed. Finally, the insulation configuration scheme, which is suitable for high-altitude polluted areas, is proposed. The results show that the AC pollution flashover voltages of the bushing decrease with the decrease in the atmospheric pressure and the increase in the salt density as a power exponent function. The impulse flashover voltages also decrease with the decrease in the atmospheric pressure as a power exponent function. The atmospheric pressure/pollution impact characteristic index is related to the type of voltage, the value of the atmospheric pressure, and the value of the salt density. The test bushing does not meet the insulation configuration requirements in some high-altitude polluted areas. Through the analysis and calculation of the test results, the bushing insulation configuration scheme in high-altitude polluted areas is proposed. The research results can provide a reference for the external insulation design of bushings in substations and converter stations in high-altitude polluted areas. [ABSTRACT FROM AUTHOR]

Recently, there has been a significant increase in the integration of distributed energy resources (DERs) such as small-scale photovoltaic systems and wind turbines in power distribution systems. When the aggregated outputs of DERs are combined, excessive reverse current may occur in distribution lines, leading to overvoltage issues and exceeding thermal limits of the distribution lines. To address these issues, it is necessary to limit the output of DERs to a certain level, which results in constraining the hosting capacity of DERs in the distribution system. In this paper, coordination control methodologies of DERs are developed and executed to mitigate the overvoltage and overcurrent induced by DERs, thereby increasing the hosting capacity for DERs of the distribution system. This paper proposes three coordinated approaches of active and reactive power control of DERs, namely Var Precedence, Watt Precedence, and Integrated Watt and Var Control. The Var and Watt Precedence prioritizes reactive power for voltage (Q–V) and active power for current (P–I) to address network congestion, thereby enhancing hosting capacity. Conversely, the Integrated Var and Watt Precedence propose a novel algorithm that combines four control indices (Q–V, P–V, Q–I, and P–I) to solve network problems while maximizing hosting capacity. The three proposed methods are based on the sensitivity analysis of voltage and current to the active and reactive power outputs at the DER installation locations on the distribution lines, aiming to minimize DER active power curtailment. Each sensitivity is derived from linearized power equations at the operating points of the distribution system. To minimize the computation burden of iterative computation, each proposed method decouples active and reactive power and proceeds with sequential control in its own unique way, iteratively determining the precise output control of distributed power sources to reduce linearization errors. The three proposed algorithms are verified via case studies, evaluating their performance compared to conventional approaches. The case studies exhibit superior control effectiveness of the proposed DER power control methods compared to conventional methods when issues such as overvoltage and overcurrent occur simultaneously in the distribution line so that the DER hosting capacity of the system can be improved. [ABSTRACT FROM AUTHOR]

High-voltage switchgear is a key control and protection unit in power systems. The reliable operation of the operating mechanism is essential for the reliable opening and closing of the switchgear. This is a vital guarantee for the safety and stability of the power systems. The spring operating mechanism is a commonly used actuator for circuit breakers at 12–252 kV. However, a reliability estimation method that is convenient, practical and accurate is still lacking. The aim of this paper is to explore a reliability estimation method based on a stress–strength interference reliability theory and a fatigue life theory. The changing rules of the reliability of the main axis, and the closing pawl and opening pawl in the spring operating mechanism during operations, are achieved. The accuracy of the calculated reliability based on theories of stress–strength interference and fatigue life is verified by adopting a test data statistical reliability model based on Weibull distribution. The results show that the reliability estimation method based on stress–strength interference and fatigue life theories has a high degree of confidence. The maximum tolerance is 0.024. The study would help in providing a useful reference for the optimization and durability estimation of a spring operating mechanism and its key components in high-voltage circuit breakers. [ABSTRACT FROM AUTHOR]