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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]

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]

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]

With the development of smart grids and new power systems, the combination of non-intrusive load identification technology and smart home technology can provide users with the operating conditions of home appliances and equipment, thus reducing home energy loss and improving users' ability to demand a response. This paper proposes a non-intrusive load decomposition model with a parallel multiscale attention mechanism (PMAM). The model can extract both local and global feature information and fuse it through a parallel multiscale network. This improves the attention mechanism's ability to capture feature information over long time periods. To validate the model's decomposition ability, we combined the PMAM model with four benchmark models: the Long Short-Term Memory (LSTM) recurrent neural network model, the Time Pooling-based Load Disaggregation Model (TPNILM), the Extreme Learning Machine (ELM), and the Load Disaggregation Model without Parallel Multi-scalar Attention Mechanisms (UNPMAM). The model was trained on the publicly available UK-DALE dataset and tested. The models' test results were quantitatively evaluated using a confusion matrix. This involved calculating the F1 score of the load decomposition. A higher F1 score indicates better model decomposition performance. The results indicate that the PMAM model proposed in this paper maintains an F1 score above 0.9 for the decomposition of three types of electrical equipment under the same household user, which is 3% higher than that of the other benchmark models on average. In the cross-household test, the PMAM also demonstrated a better decomposition ability, with the F1 score maintained above 0.85, and the mean absolute error (MAE) decreased by 5.3% on average compared with that of the UNPMAM. [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 work in this paper is applied to the Zhangbei Power grid. In the flexible direct current (DC) power system, the fault current rises extremely fast when a DC fault occurs. The requirements for the peak of breaking current and fault energy absorption of DC circuit breakers (DCCBs) increase linearly, which significantly increases the cost of the equipment. Therefore, in order to reduce the design difficulty of DCCBs, this paper proposes a strategy to control energy after the fault occurs. Firstly, the energy dimension is added on the basis of the traditional vector control of MMC, which constitutes a three-dimensional energy direct control. Subsequently, the architectures of energy fluctuation control and feedforward control are proposed. The influencing mechanisms for the peak fault current, peak fault voltage and energy dissipation are analyzed. Finally, the simulation of energy fluctuation control and feedforward control is constructed on PSCAD/EMTDC. The simulation results show that the energy fluctuation control is obviously better than the conventional three-dimensional energy control, and the feedforward energy control is further improved on this basis. Compared with the conventional vector control, the peak energy is reduced by 45.43% and the peak current is reduced by 25.39%, which helps to simplify the equipment design and reduce the equipment cost. [ABSTRACT FROM AUTHOR]

This paper studies the THD and AC losses on the DC cables of offshore wind farm-based multi-terminal HVDC systems when they extract and deliver power from and to more than one connection point. In the paper, the study of a full system PLECS + Simulink model with two branches, including a wind resource, a wind turbine, a Permanent Magnet Synchronous Generator (PMSG), a Pulse Width Modulation (PWM) rectifier, a Single Active Bridge (SAB) DC–DC converter, an Input Parallel Output Series (IPOS) DC–DC converter, HVDC cables, and a simplified onshore system, is presented. It focuses on the investigation of the output ripple content of multiple DC–DC converters on DC cables under different wind conditions with different voltage and power ratings. The importance of the study is providing a general understanding of the operation of the innovative offshore wind farm-based DC system, as well as the interaction between different DC–DC converters and their influence on cable ripple content under different situations. [ABSTRACT FROM AUTHOR]

Urban areas generate more than 70% of the world's climate change emissions, mainly CO2, produced by the combustion of fossil fuels. Climate change is increasing cities' exposure to climate hazards, such as heat waves or floods. Therefore, there is a need to improve risk management with the inclusion of climate resilience in urban policy design. Despite improved urban climate monitoring, there are still relatively few scientific publications on climate change adaptation in urban areas. Adaptation to climate change is not achieved through specific action, but rather through the adoption and continuous implementation of adaptation actions such as housing rehabilitation, green space management and protection measures for vulnerable groups. This variety of actions makes it difficult not only to identify different indicators, but also to use common benchmarks. Considering the role of municipalities in adapting to climate change, it is crucial to identify adaptation indicators that serve as a basis for decision making, as well as evaluation methods that allow the effectiveness of planned and implemented measures in municipalities. It can be used to determine which measures increase the level of adaptation or lead to poor adaptation. Therefore, monitoring indicators makes it possible to evaluate the effectiveness of the measures, in addition to formulating new ones. This paper includes a literature review of existing index designed to address climate hazards and mitigate their impacts in urban areas. [ABSTRACT FROM AUTHOR]

The paper provides a novel approach for controllably meshing traditional medium-voltage networks by means of a fast-charging parking station with multiple points of delivery connected to different radial feeders. Regulating power flows at each point of delivery while the charging service is being provided, which means actively controlling power exchanges between radial distribution feeders can significantly increase the hosting capacity of the power system. Remarkable benefits are expected when the distribution networks to which the charging infrastructure is connected differ in terms of main characteristics, e.g., rated voltage level, end-user type and operating profiles, and the number and type of renewable plants. The paper focuses on technical targets, such as loss reduction and power quality in terms of admitted voltage deviation from the rated value. The power exchanges between distribution feeders are made possible by a controlled DC link, where bi-directional DC/DC converters are connected so as to charge or discharge vehicles according to the Vehicle-To-Grid approach. A multiplexer topology in which several vehicles can be alternatively connected to the same DC/DC converter is modeled. The proposed concept can contribute to network flexibility by controllably meshing distribution feeders and, jointly, by modulating charging processes according to assigned charging constraints. [ABSTRACT FROM AUTHOR]

Multi-stage fractured horizontal well technology is an effective development method for unconventional reservoirs; however, shale oil reservoirs with ultra-low permeability and micro/nanopore sizes are still not ideal for production and development. Injecting CO2 into the reservoir, after hydraulic fracturing, gas injection flooding often produces a gas channeling phenomenon, which affects the production of shale oil. In comparison, CO2 huff-n-puff development has become a superior method in the development of multi-stage fractured horizontal wells in shale reservoirs. CO2 huff and injection can not only improve shale oil recovery but also store the CO2 generated in industrial production in shale reservoirs, which can reduce greenhouse gas emissions to a certain extent and achieve carbon capture, utilization, and storage (CCUS). In this paper, the critical temperature and critical parameters of fluid in shale reservoirs are corrected by the critical point correction method in this paper, and the influence of reservoir pore radius on fluid phase behavior and shale oil production is analyzed. According to the shale reservoir applied in isolation to the actual state of the reservoir and under the condition of a complex network structure, we described the seepage characteristics of shale oil and gas and CO2 in the reservoir by embedding a discrete fracture technology structure and fracture network, and we established the numerical model of the CO2 huff-n-huff development of multi-stage fractured horizontal wells for shale oil. We used the actual production data of the field for historical fitting to verify the validity of the model. On this basis, CO2 huff-n-puff development under different gas injection rates, huff-n-puff cycles, soaking times, and other factors was simulated; cumulative oil production and CO2 storage were compared; and the influence of each factor on development and storage was analyzed, which provided theoretical basis and specific ideas for the optimization of oilfield development modes and the study of CO2 storage. [ABSTRACT FROM AUTHOR]

Due to the favourable pro-environmental properties of timber, including the origin of the raw material from renewable sources, ease of reuse, negative carbon footprint, low specific weight, possibility of prefabrication, etc., there is increasing interest in the use of timber in construction. This paper takes a closer look at the new uses of timber as a load-bearing structure for high and high-rise buildings. Cases described in the literature concerning this type of building with residential and public functions erected worldwide were analysed. The first buildings of this type were put into use in 2009. The aim of this paper is to show new possibilities and to extend the use of timber as a load-bearing structure of high and high-rise buildings previously made of reinforced concrete or steel. The scope of the analysis includes two postulates of sustainable construction, directly related to the above-mentioned goals: limiting interference in the natural areas of cities through efficient use of building plots for high or high-rise buildings and the use of renewable materials—timber—for the load-bearing structure of buildings. A research method based on a case study was used. Conclusions were made on the pro-environmental spatial–functional and material–structural design of these high and high-rise buildings. [ABSTRACT FROM AUTHOR]

The increasing penetration rate of distributed resources in the distribution network has brought about significant volatility and uncertainty problems. Demand response (DR) can flexibly change the energy consumption method of the user to balance supply and demand. This paper first considers the spatial distribution characteristics of DR resources to schedule DR resources to construct a distributed resource cloud-edge collaborative scheduling framework. Based on this, the distribution network scheduling requirements are combined with the multi-time scale characteristics of DR. A three-stage cloud-edge collaborative optimization scheduling strategy for distributed resources in the distribution network is proposed, which allocates the multi-time scale scheduling tasks of DR resources to the cloud and edge. Secondly, taking the cloud and edge as the optimization platform, a three-stage optimization decision-making model of the distribution network is established. In the day-ahead stage, the global optimization decision is made by combining cloud-centralized optimization with edge-independent optimization. In the intraday stage, edge-rolling optimization is carried out. In the real-time stage, the edge-distributed calculation is based on the consensus algorithm. Finally, the effectiveness and economy of the proposed model and strategy are verified by an example analysis. [ABSTRACT FROM AUTHOR]

This paper presents the results of the study of the direct current (DC) and alternating current (AC) electrical properties of an electrical pressboard–bio-insulating oil–water composite in a wide range of water content and temperatures used in electric power transformers. These parameters allow the level of insulation reliability to be determined after many years of operation of power transformers. To analyse the experimental results, a model of the DC and AC conductivities of nanocomposites based on the quantum-mechanical phenomenon of electron tunnelling was used. It was found that in a low-frequency region, the conductivities of AC and DC and their activation energy are equal. The relaxation times of AC conductivity and permittivity are also equal. It was found that the dependence of the DC conductivity on the distance between water molecules is an exponential function. On the basis of the model of conductivity by electron tunnelling between potential wells, the average number of water molecules in a nanodroplet, located in a composite of electrical pressboard–bio-insulating oil–moisture was determined to be (126 ± 20). It was found that the measured dependencies of DC and AC conductivity, permeability and dielectric relaxation times are consistent with the results of computer simulations performed on the basis of the model. This study showed that the composite of pressboard impregnated with bio-oil spontaneously transforms through water absorption into a pressboard–bio-oil–water nanocomposite. These will serve as the basis for the application of actual conductivity and dielectric relaxation mechanisms to improve the accuracy of moisture estimation in the solid component of power transformer insulation carried out on the basis of measurements of DC and AC properties. This will improve the operational safety of the transformers, minimise the occurrence of transformer failure and the associated environmental pollution. [ABSTRACT FROM AUTHOR]

Energy-intensive enterprises lack a scientific and effective energy efficiency assessment framework and methodology. This lack leads to an inaccurate understanding of energy usage and its benefits. As a result, there is energy wastage and loss. This wastage and loss negatively affect product costs. They also present a challenge to effective energy management. To address these issues, this paper introduces a novel, comprehensive energy efficiency evaluation system. This system integrates both qualitative and quantitative measures. It proposes an evaluation model based on the Particle Swarm Optimization (PSO) combined with the Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE), wherein PSO is employed to optimize the weights determined by AHP, ensuring that the significance attributed to various indicators is scientific, objective, and rational. The FCE method is utilized to convert diverse factors affecting corporate energy efficiency, across different types and scales, into standardized 0–1 values, enabling a comparative analysis of the impact of each process and indicator on energy efficiency. Furthermore, the paper introduces an energy efficiency prediction model employing a multivariate linear regression algorithm, which demonstrates a good fit, facilitating the transition from retrospective energy efficiency evaluation to proactive improvements. Utilizing data on actual consumption of water, electricity, and steam from an enterprise, along with expert assessments on the implementation levels of new processes, technologies, equipment, personnel scheduling proficiency, steam recovery rates, and adherence to policies and assessments, a simulation experiment of the proposed model was conducted using Python. The evaluation yielded an energy efficiency score of 0.68; this is consistent with the real-world scenario of the studied enterprise. The predicted mean square error of 9.035416039503998 ×   10 − 9 indicates a high model accuracy, validating the practical applicability and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper introduces a new concept in transmission expansion planning based on unconventional lines, termed "smart transmission expansion planning". Traditionally, the domains of transmission expansion planning (TEP) and transmission line design are separate entities. TEP planners typically rely on the electrical specifications of a limited set of standard conventional line designs to evaluate planning scenarios, ultimately leading to the construction of the selected candidate line. In this context, it is noted that cost-effective scenarios often diverge from meeting the technical criteria of load flow analysis. To address this discrepancy, this paper proposes an alternative approach wherein TEP is conducted based on the specific requirements of the system earmarked for expansion. The transmission expansion planner initiates the process by determining optimal line parameter values that not only meet the operational criteria but also ensure cost-effectiveness. Subsequently, a line is designed to embody these optimal parameters. A detailed comparative analysis is conducted in this study, comparing the outcomes of TEP analyses conducted with conventional lines, unconventional lines, and lines featuring optimal parameters. Through extensive load flow analysis performed under normal and all single-contingency scenarios across three distinct loading conditions (peak load, dominant load representing 60% of peak load, and light load representing 40% of peak load), the results reveal that transmission lines engineered with optimal parameters demonstrate effective operation, with fewer transmission lines required to meet identical demands compared to other approaches. [ABSTRACT FROM AUTHOR]

With the greatly increased penetration rate of wind power, photovoltaic, and other new energy sources in the power system, the proportion of controllable units gradually decreased, resulting in increased system uncertainty. The biogas power generation system can effectively alleviate the pressure caused by source-load uncertainty in such high-permeability systems of new energy sources such as wind power and photovoltaic. Hence, from the perspective of the power system, this paper introduces a capacity demand analysis method for a rural biogas power generation system capable of independent operation amidst source-load uncertainty. To enhance the depiction of pure load demand uncertainty, a scene set generation method is proposed, leveraging quantile regression analysis and Gaussian mixture model clustering. Each scene's data and probability of occurrence elucidate the uncertainty of pure load demand. An integrated optimal operation model for new energy and biogas-generating units, free from energy storage capacity constraints, is established based on the generated scenario set. Addressing considerations such as biogas utilization rate and system operation cost, a biogas storage correction model, utilizing the gas storage deviation degree index and the cost growth rate index, is developed to determine biogas demand and capacity. The example results demonstrate the significant reduction in gas storage construction costs and charging and discharging imbalances achieved by the proposed model while ensuring systemic operational cost effectiveness. [ABSTRACT FROM AUTHOR]

The environmental/economic dispatch (EED) of power systems addresses the environmental pollution problems caused by power generation at the operational level, offering macroscopic control without requiring additional construction and remediation costs, garnering widespread attention in recent years. This paper undertakes a comprehensive review of existing EED models, categorizing them according to the control of atmospheric pollutants into total air pollutant control (TAPC) and control considering the spatial and temporal diffusion (STD) of atmospheric pollutants. In addition, various methods employed to address the EED problems, as well as the current state of research on multi-area EED models, are presented. Finally, this paper analyzes and summarizes the literature on existing EED models, highlighting the deficiencies of the current work and future research directions. Through these explorations, the authors find that controlling the EED model by considering TAPC is more suitable for general macro planning, whereas the EED model considering the STD of air pollutant emissions enables more precise and effective control. Summarizing such models and techniques is conducive to developing dispatch plans adapted to local conditions, which is significantly beneficial for public welfare and government management, promoting sustainable and environmentally friendly power system dispatch methods. [ABSTRACT FROM AUTHOR]

In the high-temperature mainstream of gas turbines, there is a rim clearance between the rotor and the stator. A rim seal is to prevent the intrusion of high-temperature gas by spraying cool fluid from the inside of the rim clearance to the outside. In the past research on rim seals, the focus was on the overall performance of the sealing structure, and the flow in the disc cavity was studied more, but the high-radius flow was simplified. In recent years, additional research in the field has focused on more complex sealing structures and high-radius flows, such as the interface between the disk cavity and the mainstream. There is more work to be conducted in this area of research. In this paper, the unsteady numerical simulation of the flow in four different rim sealing geometries is carried out by the URANS method. The flow phenomena and the influence of geometry on the flow are studied. The numerical simulation results are validated with the experimental results. It is found that the fluid in the rim sealing obviously presents two distinct forms and confrontations according to the tangential velocity. The flow in the sealing structure presents obvious circumferential non-uniformity. Compared with the single-axial structure, in the single-radial structure, the mixing area is induced by the radial geometry, and more vortex structures are generated, the mixing process is more intense, and the sealing effect is better. In the double-sealing structure, the inner structure plays the role of a barrier, and the cavity geometry between the two layers has a major influence on the sealing performance. [ABSTRACT FROM AUTHOR]

To improve performance in terms of overshoot and motor response speed when a permanent-magnet synchronous motor (PMSM) with a proportional–integral (PI) controller is subjected to external disturbances, this paper proposes a speed control strategy based on an enhanced Beetle Antennae Search algorithm, which allows for adjustable parameters of the PI controller within a certain range. Firstly, to enhance the global and local search capabilities of each individual beetle, the step size was improved by linearly decreasing it. Secondly, a simulation model of a PMSM closed-loop control system was built to verify the effectiveness of the improved Beetle Antennae Search (BAS) algorithm. Finally, a linear feedback shift register model that generates four random numbers was developed on a field-programmable gate array (FPGA). The improved BAS algorithm for the PMSM control system was implemented on an FPGA using the Verilog hardware description language, and the feasibility of the system was verified through hardware simulation. Additionally, the hardware resource consumption on different FPGA platforms was analyzed. The simulation results demonstrate that the proposed new speed control strategy can reduce the overshoot and improve the motor response speed. [ABSTRACT FROM AUTHOR]