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Adsorption cooling and desalination systems have a distinct advantage over other systems that use low‐grade waste heat near ambient temperature. Since improving their performance, including reliability and failure prediction, is challenging, developing an efficient diagnostic system is of great practical significance. The paper introduces artificial intelligence (AI) and an automated machine learning approach (AutoML) in a real‐life application for a computational diagnostic system of existing adsorption cooling and desalination facilities. A total of 1769 simulated data points containing data indicating a failure status are applied to develop a comprehensive AI‐based Diagnostic (AID) system covering a wide range of 42 input parameters. The paper introduces a conditional monitoring system for adsorption cooling and desalination systems. The novelty of the presented study mainly consists of two aspects. First, the intelligent system predicts the health or failure states of various components in a complex three‐bed adsorption chiller installation using the extensive input data sets of 42 different operating parameters. The developed AID expert tool, based on selecting the best from 42 models generated by the DataRobot platform, was validated on the complex, existing three‐bed adsorption chiller. The AID system correctly identified healthy and failure states in various installation components. The developed expert system is very efficient (AUC = 0.988, RMSE = 0.20, LogLoss = 0.14) in predicting emergency states. The proposed method constitutes a quick and easy technique for failure prediction and represents a complementary tool compared to the other condition monitoring methods. [ABSTRACT FROM AUTHOR]

The paper proposes a new stochastic multiobjective technoeconomic model for integrating photovoltaic (PV) and wind energy resources in electricity price (EP)‐driven distribution systems. The primary goal of this paper is to determine the optimal location and capacity for renewable energy‐based distributed generation, specifically PV and wind resources, while considering weather and system uncertainties. These uncertainties include stochastic variations in PV illumination intensity, wind speed, EP, and load fluctuations. To address these uncertainties, the paper employs scenario modeling techniques named as Latin hypercube sampling with Cholesky decomposition. This technique generates multiple correlated scenarios that represent uncertain variables. Subsequently, a scenario reduction technique is applied to identify the scenario with the highest probability. Later, a mathematical model is developed to minimize an objective function that encompasses various factors like system losses, node voltage deviations, the cost of purchasing power from the grid; and simultaneously maximize the total annual energy savings. The objective is to find optimal solutions that strike a balance between different objectives. To obtain an efficient optimum solution, this paper employs an effective meta‐heuristic technique named as JAYA algorithm. The results obtained by the JAYA algorithm are juxtaposed with those obtained using particle swarm optimization and genetic algorithm techniques. The proposed method is evaluated using Institute of Electrical and Electronics Engineers (IEEE) 33‐node and IEEE 69‐node test feeders to validate its feasibility and effectiveness. However, the effectiveness of the proposed method is not limited to any size of test systems. [ABSTRACT FROM AUTHOR]

In this paper, a proper noun "Region" is used for nm‐scale n‐type dopant‐rich region in p‐type Si crystal. Using this Region, certain solar cells have been assumed. By resonance absorption between photon energy and potential barrier of the Region, the cell can absorb most photons for visible light frequency without passing loss or thermal loss. This light absorption mechanism is different from conventional band gap absorption. Despite this benefit, output voltage is anticipated to decline according to the principle of detail balance. To control the decline, two methods are proposed in this paper. Theoretical energy conversion efficiencies for several cases are calculated with an ideal condition. The calculation result is over 70% as a theoretical value. [ABSTRACT FROM AUTHOR]

In this paper, the wake characteristics of Zell 2000 wind turbine under different tip velocity ratios are studied by using the lattice Boltzmann method and large eddy simulation. The adaptive mesh refinement method is performed to capture the fine flow structure and wake characteristics development. In this paper, we mainly focus on the effect of the tip speed ratio on the flow structure and unsteady characteristics of wind turbine wake. The three‐dimensional flow vorticity structures, the section vorticity diagram, the pressure fluctuation of wake and the lift coefficient of wind turbine wake are utilized to explore the effect of the tip speed ratio on the unsteady physics mechanism of wind turbine wake. With the increase of the tip speed ratio, the distance between two adjacent vortex rings along the axial direction gradually decreases, as the position of the broken vortex circles gradually approaches the center of the blade, separated vortexes are rapidly generated, and the coherent structure appears closer to the wind turbine. A relationship is established between the tip speed ratios and the positions of the broken vortex circles. It is further found that the dominant frequency amplitude gradually increases with the increase of tip speed ratio and the pressure amplitude spectra of vortex increases with the decrease of the distance between the wake and the center of the blade axis. The above series of studies can provide significant physical insight into deep understanding the influence of the tip speed ratio on the wake characteristics of wind turbines. [ABSTRACT FROM AUTHOR]

Due to the difficulties of system modeling, nonlinearity effects, uncertainties, and the availability of Wind Turbines (WTs) SCADA system data, data‐driven Fault Detection and Isolation (FDI) methods for WTs have received increasing attention. In this paper, using the wind turbine SCADA data, an effective FDI scheme is proposed using the K‐Nearest Neighbors (KNN) classifier. The operational data set is labeled by the status and warning data sets, and the labeled operational data set, after eliminating invalid data, feature selection, and standardization, is used for training and validation of the FDI model. Data imbalance, which is common in real data sets, does not affect the performance of the proposed method, hence there is no need for data balancing methods in this algorithm and the performance is not deteriorated by occurring false alarms. Therefore, the proposed method has provided impressive performance in FDI compared with previous research on this data set. Also, many of the fault classes addressed in this paper were not considered in previous works on this data set. [ABSTRACT FROM AUTHOR]

The research addresses the pressing need to understand wind conditions for optimal wind energy utilization, a crucial aspect of achieving global energy sustainability. While previous studies have focused on assessing wind energy potential and terrain roughness, gaps persist in understanding how wind and spatial conditions affect blade geometry. Therefore, this study aims to investigate the impact of local wind conditions on wind turbine blade geometry, integrating nonuniform wind inflow due to wind shear into the theoretical model. Through the paper, it explores blade design for varying inflow conditions and analyses the relationship between terrain type and blade designs. A novel approach to wind turbine blade design, utilizing a theoretical model that combines the local momentum theorem with insights from propeller vortex theory was adopted. Through the analysis, the authors demonstrate that variations in terrain significantly influence blade geometry, underscoring the necessity for tailored turbine designs based on local conditions. The findings reveal discrepancies in blade geometry across different turbine capacities and terrain types, offering novel insights into wind turbine performance optimization. By tailoring blade geometry to specific locations enhances resource utilization, offering insights for energy policymakers. The research provides validated methods for optimizing turbine geometric parameters, opening avenues for increased local investment in wind energy and promoting distributed energy development. Illustrated with wind plants examples, the paper concludes by outlining future research directions in this field. [ABSTRACT FROM AUTHOR]

Real‐time monitoring of the coal caving process in fully mechanized mining is crucial for achieving intelligent and efficient top‐coal caving. While the coal gangue identification method, employing vision and deep learning, has advanced in the realm of intelligent monitoring, it exhibits a dependency on high‐performance hardware. This reliance poses challenges for deploying identification equipment on mobile terminals, hindering the widespread application of this method. To address the issues above, the paper presents a lightweight algorithm, utilizing You Only Look Once version 5s (YOLOv5s), utilizing YOLOv5s for the real‐time perception of the top‐coal caving state in fully mechanized caving mining. We replace the backbone network of YOLOv5s with the ShuffleNetv2 structure that is more suitable for lightweight deployment, and add the Simple Attention Mechanism attention mechanism to the network structure to enhance the model's receptive field and feature expression ability, and reduce the impact of falling debris on the detection results. A dynamic experimental platform for top‐coal caving in fully mechanized caving mining for thick coal seams is set up, and preprocessing operations such as brightness, sharpening, and denoising are performed on the image data sets collected by high‐speed industrial cameras. Research results show that compared with the traditional YOLOv5s, the improved model's P, mAP, F1 score, and other indicators have increased by 3.4%, 2.1%, and 1.1%, respectively, the model size is 70% of the original, and the detection frames per second value has increased by 48.1%. The lightweight algorithm stabilizes the accuracy of coal gangue identification dramatically in real time. It dramatically reduces the computing pressure on the mobile terminal, providing basic theory and practice for real‐time monitoring of fully mechanized coal caving mining. [ABSTRACT FROM AUTHOR]

This paper proposes a preventive maintenance decision model for electric vehicle charging stations based on mutation operators and lifecycle optimization to address the impact of potential faults on maintenance effectiveness. By introducing the particle swarm optimization algorithm with mutation operator, a comprehensive analysis of opportunity service age factor and safety failure probability factor was conducted to establish an indicator system for the operation status of charging piles, and a potential fault identification model was constructed. By optimizing the life cycle, the balance problem between optimal maintenance life and optimal opportunity maintenance life has been solved, thus completing preventive maintenance decisions. The experimental results show that the accuracy of this method in preventive maintenance decision‐making for electric vehicle charging piles can reach 98%, with an average preventive maintenance decision‐making time of 1.6 s for load piles. At the same time, the risk probability value and load loss value are effectively controlled. This study has good application prospects in improving the preventive maintenance effect of electric vehicle charging piles. [ABSTRACT FROM AUTHOR]

How to effectively utilize renewable energy and improve the economic efficiency of microgrid system and its ability to consume renewable energy has become one of the main problems facing China at present. In response to this challenge, this paper establishes a multiobjective capacity optimization model with the minimum levelized cost of energy, the maximum proportion of renewable energy consumption, and the minimum comprehensive system cost. Based on this model, a new improved beluga whale optimization algorithm is proposed to solve the multiobjective optimization problem in the capacity allocation process of wind–solar–storage microgrid system with the goal of ensuring that the microgrid can meet the maximum load demand at different moments throughout the year. In this paper, opposition‐based learning, artificial bee colony, dynamic opposite, and beluga whale optimization are combined to improve the population diversity and convergence accuracy, thereby enhancing the optimization performance of the algorithm. Finally, after finding the optimal Pareto front solution, the Technique for Order Preference by Similarity to an Ideal Solution is used to help decision‐makers select the optimal solution. Using real load data and meteorological data, the results of this paper show that the multiobjective capacity allocation optimization method of grid‐connected scenic storage microgrid system based on the improved beluga whale optimization algorithm can improve the economics of the wind–solar–storage microgrid system and promote the photovoltaic consumption simultaneously, providing a solution for the realization of low‐carbon power and regional economic development. The best‐found levelized cost of energy for the wind–solar–storage microgrid system is 0.192 yuan/kWh. [ABSTRACT FROM AUTHOR]

Microgrids with large‐scale photovoltaic systems constitute a large part of distributed renewable generation in many grids around the world. Managing the performance of such microgrids and especially their interaction with the main power grid is a challenging task, because it requires the control of renewable resources. This paper presents a comprehensive overview of DC‐DC converter structures used in microgrids and presents a new classification for converters. This paper also provides an overview of the control techniques of DC‐DC converters in DC microgrids and the advantages and disadvantages of the control methods are discussed. In connection with the increasing penetration of distributed generation sources (DGS) and renewable sources in power systems and their power management has been raised as a major concern and methods for power management have been investigated in this paper. Finally, a DC microgrid system, which includes a solar system, wind turbine, and battery, is simulated in MATLAB/Simulink software and its performance is analyzed. [ABSTRACT FROM AUTHOR]

The wind power generation system of a 5 MW horizontal axis wind turbine has a high wind energy conversion efficiency. The proportion of installed capacity in practical production applications is increasing year on year, so that the stability of its operation becomes a central factor in determining the productivity of the wind farm in question. This paper takes a 5 MW wind turbine as the research object and proposes a parameter‐adaptive robust model predictive control method to achieve self‐optimization of controller parameters through a Bayesian optimization approach. A robust model predictive control strategy, aiming to reduce the power fluctuation while maximizing the power output, is developed in this paper to enhance the dynamic economic performance under uncertain wind speed variation. A Bayesian algorithm is used in this paper to optimize the parameters of the controller. Moreover, wind speeds are simulated using TurbSim for different turbulence intensities of 5%, 10%, and 15% turbulence. Finally, the robust model predictive control toolbox in MATLAB is designed and simulated. The results show that the operational instability of the wind energy system is overcome. Meanwhile, the robustness of the wind energy system operation is improved compared to the traditional model predictive control approach. [ABSTRACT FROM AUTHOR]

In the context of a low‐carbon economy, considering the output uncertainties of the renewable energy sources and the fluctuation of electrical price, the cost–benefit analysis is proposed for multi‐investor virtual power plant (VPP) under different risk preferences, and multi‐investor VPP capacity sizing problem is researched. First, the structural organization and flexible regulation characteristics of multi‐investor VPPs are analyzed. Second, integrated model and cost–benefit analysis indexes of VPP are built. Third, an optimal capacity sizing model of multi‐investor VPP is established, aiming at income maximization. Game theory is used to configure the distributed generators in the VPP under various cooperative modes and noncooperative modes of multiple investors. Fourth, according to the installed proportion of each distributed generator and the risk preference coefficient of each investor, the income allocation correction model based on the improved Shapley value method is proposed to stabilize the optimal VPP alliance. Finally, the demonstration project in the animation industry park of Sino‐Singapore Tianjin Eco‐City is employed as representative scenarios, which is used to validate the effectiveness of the proposed model. The results prove that the method proposed in this paper can guide knowledge for the investors with different risk preferences when planning the optimal capacity of multiple distributed generators in the VPP. [ABSTRACT FROM AUTHOR]

Fuel cell technology is a promising alternative to traditional internal combustion engines in various applications, especially in transportation applications. However, the high cost and limited lifetime of fuel cells have hindered their widespread commercialization. Accurately predicting fuel cell lifetime is crucial for reducing the cost of ownership, ensuring safety, and promoting the adoption of this technology. The objective of the present work is to develop a tool that is able to estimate the lifespan of a proton exchange membrane fuel cell and to predict its behavior to anticipate failures. Therefore, this paper contributes to proposing a multi‐input time‐series prediction network based on an echo state network, which takes the future current into consideration. A degradation trend extraction method is proposed in this paper and the remaining useful life of the fuel cell is predicted. Results have shown that the proposed methods in both short‐term and long‐term prediction have achieved satisfying prediction accuracy. [ABSTRACT FROM AUTHOR]

In response to the issues of traditional backpropagation (BP) neural networks in state of charge (SOC) estimation, including easy convergence to local optima, slow convergence speed, and low accuracy, this paper proposes a novel adaptive crossover mutation strategy and dynamic sparrow search algorithm to optimize BP networks' initial values and thresholds (ACMSSA‐BP). The proposed method is based on the sparrow search algorithm, where the number of producers and scroungers is adjusted through an adaptive factor. This improvement effectively transitions the search process from extensive full exploration to localized fine‐tuning search. In the position update phase of the producers, crossover mutation and dynamic search strategies are introduced to increase the diversity of good populations, prevent the algorithm from converging to local optima, and maintain its local search capability in the later stage. Using real transportation data from coal mining flame‐proof tracked vehicles, we applied correlation theory to extract model feature parameters and constructed a training data set to estimate the SOC. The results of both static and dynamic validation experiments have indicated that the ACMSSA‐BP method has delivered impressive performance in predicting SOC, as reflected in the mean absolute error, root mean squared error, and mean absolute percentage error values of less than 1.5%, 1.5%, and 1.6%, respectively. Compared with BP, SSA‐BP, CMSSA‐BP, PSO‐BP, and NARX_NN methods, the ACMSSA‐BP approach demonstrates enhanced accuracy in estimation, significant robustness, and impressive generalization capabilities. [ABSTRACT FROM AUTHOR]

Predicting carbon prices is crucial for the growth of China's carbon trading industry. This paper proposes a residual correction model that considers multiple influencing factors. First, the best historical data and main external factors input by the model are determined by using the partial autocorrelation function and Spearman correlation analysis, and the carbon price forecasting index system is constructed. Second, the whale optimization algorithm (WOA) is utilized to determine the optimal parameters of the extreme gradient boosting (XGBoost), and the WOA‐XGBoost model is built to perform preliminary carbon price forecasts and obtain the residual series. Finally, the carbon price residual series undergoes decomposition into multiple components utilizing the complete ensemble empirical mode decomposition for subsequent forecasting and the aggregation of outcomes. Experiments are conducted to predict two carbon trading markets in Hubei and Guangzhou, and a feature importance analysis is performed. The results indicate that the proposed hybrid model consistently outperforms the comparative models in terms of prediction accuracy. Furthermore, it is revealed that historical carbon prices and European Union carbon prices are the key factors influencing the prediction of carbon market prices. [ABSTRACT FROM AUTHOR]

Environmental concerns have pushed toward electrified technologies for off‐road vehicle actuations that can lower greenhouse gas emissions and reduce energy consumption. Replacing a central diesel engine with a dedicated electric machine (EM) as a prime mover for the hydraulic supply offers several opportunities for so‐called ePumps (aka electric‐driven pumps) to maximize energy efficiency and limit the usage of electric materials. This paper discusses the impact of different choices for the ePumps architecture (i.e., fixed vs. variable displacement pump; variable speed vs. fixed speed electrical machine), and on their main design parameters in terms of size and efficiency. Although the procedure followed in the study could be extended to different types of electric and hydraulic units, the paper particularly considers ePumps based on permanent magnet synchronous machines combined with axial piston machines. The importance of properly considering the ePump drive cycle and its cooling requirements is taken into account while addressing energy efficiency, mass, and overall compactness of the solution. The results show that an ePump based on a variable displacement pump, when compared to fixed displacement ePumps, reduces the electrical machine size both in volume and mass up to 40%, when the high‐pressure demand is not combined with high flow rate demand, thus decreasing the cost of the EM. In all drive cycles, the variable speed EM–fixed displacement pump architecture has a higher efficiency, ranging from 1% to 5%, compared to the case of fixed speed EM–variable displacement pump. Finally, the paper compares the advantages and shortcomings of each ePump architecture presented, based on representative drive cycles. [ABSTRACT FROM AUTHOR]

With the escalating urgency for sustainable energy alternatives, solar power in urban landscapes has gained prominence. Building‐integrated photovoltaic (BIPV) systems are pivotal in this shift, blending efficient energy generation with architectural aesthetics. This review casts a spotlight on BIPV technologies, with a special emphasis on the less‐explored semitransparent photovoltaics (PVs). These systems are not only energy generators but also natural light facilitators, setting them apart from their opaque PV counterparts. Advancements in both transparent and opaque PV, such as crystalline silicon, are discussed. However, the paper prioritizes semitransparent PV's unique benefits, including harmonious integration with building design and simultaneous energy and daylight management. An exhaustive examination of current literature and developments sketches BIPV's trajectory, highlighting applications, challenges, and technological strides. The narrative extols the aesthetic, financial, and efficiency merits of BIPV, particularly the semitransparent variants' blend of functionality and design. Performance optimization of BIPV systems is scrutinized across various environments and architectural styles. This meticulous discussion seeks to clarify the status quo, emerging research avenues, and future prospects of BIPV in fostering a greener energy transition. The review compares BIPV configurations with traditional solar PV systems, charting a path for enhanced energy production, cost efficiency, and aesthetic integration, with semitransparent PV as a key player. By exploring the interplay between architectural design, energy efficiency, and urban planning, this paper aims to solidify the groundwork for future research, reinforcing the sustainability narrative in urban energy infrastructure. [ABSTRACT FROM AUTHOR]

With the continuous transformation of the global power system, ensuring the flexibility and stability of the power system has become a direction that the global energy industry has been striving for. This puts forward higher requirements for load forecasting, especially high‐precision short‐term residential load forecasting. However, the high volatility and uncertainty of electric consumption make this problem challenging. Existing methods usually focus on capturing temporal correlations and ignore the underlying spatial correlations in historical loads, which is crucial for accurate forecasting. In this paper, we propose a novel spatial–temporal gated fusion network (STGNet) for short‐term residential load forecasting including not only individual residential load but also aggregated load. By seeking both adaptive neighbors and temporal similarity neighbors, we construct a dynamic graph based on a data‐driven method. On this basis, we provide an adaptive graph gated fusion mechanism to capture fine‐grained node‐specific spatial dependence more accurately and comprehensively. Finally, a bidirectional gated recurrent unit (BiGRU) is utilized to learn the temporal dependence of load data. Extensive experiments on real‐world datasets are conducted and demonstrate the superiority of STGNet over the existing approaches. [ABSTRACT FROM AUTHOR]

Literature shows that only 56% of Kenyan households had access to electricity, with rural areas having the lowest access rate at percent. The high cost of extending the power grid to remote areas and power losses on distribution are significant challenges facing rural electrification. In addressing power accessibility problems, especially in rural areas, there is a need for tapping hydropower generation through the invention and implementation of in‐duct turbines to maximize the utilization of already existing pressurized water ducts that supply water in various parts of Kenya for hydropower generation. Makueni County is endowed with gravity‐fed water ducts with high potential for hydropower which can innovatively be produced by application of in‐duct turbines. This paper focuses on the assessment of energy needs and applications in rural areas. The research design was exploratory and experimental in nature. It was exploratory because, through an assessment, it sought to explore and identify the potential areas within the water supply lines for the production of hydropower to supply hydropower in Makueni County. It was experimental because the researcher developed (designed and fabricated) a hydro turbine for use in the production of hydropower from gravity water ducts of a diameter raging 100 mm. The research revealed that 62% (98) used solar power for lighting their homes, while 17% (28), 12% (20), and 8% (12) used lanterns, electricity, and kerosene lamps, respectively. Among the fuels assessed was firewood which was identified as the most used fuel at 89% (140). This was followed at a distance far by paraffin at 6% (9) of respondents. The households at 100% (158) identified electricity as a potential source of lighting for their household. The study recommends harnessing hydropower to enhance reach to 100% of the rural communities. The energy availability will provide opportunities for communities and institutions in rural areas to open their minds to business development and engage in income‐generating activities like the rearing of poultry and the development of light industries like the gridding of maize and other cereals. [ABSTRACT FROM AUTHOR]

Monitoring the health of lithium batteries is a crucial undertaking in ensuring the safe and dependable functioning of electric vehicles. Data‐driven methods have been proved to be an effective method for identifying the complex degradation process of batteries. To augment the precision of predicting the remaining useful life (RUL), this paper introduces a pioneering architecture for a denoising autoencoder (DAE). This architecture integrates a stacked convolutional neural network with subsequent layers of bidirectional gated recurrent units within an encoder–decoder framework. The utilization of the DAE network is employed as a means to effectively capture and represent the intricate and nonlinear knowledge associated with degradation data acquired from measured sources. Simultaneously, the reconstruction loss is incorporated into the total loss to improve the accuracy and generalization of the prediction model. The efficacy of the proposed approach is substantiated through the utilization of data sets sourced from the NASA Ames Prognostics Data Repository. The comparative findings suggest that the proposed approach demonstrates an exceptional ability to achieve precise and robust estimation in predicting the RUL, surpassing other advanced methodologies. [ABSTRACT FROM AUTHOR]

The high‐voltage pulse discharge technology, which is based on the electrohydraulic effect, can generate powerful, controllable, and repetitive electrohydraulic shock waves (EHSWs) in underground confined space, so it has been applied in the petroleum industry to improve the permeable of the reservoir. In this paper, a numerical model based on LS‐DYNA is built to study the damage analysis of the cement sheath and rock subjected to EHSWs during perforation completion. The results show that the stress state of the cement sheath and rock surrounding the perforation hole is determined by the shock pressure from both the perforation hole and the wellbore; the damage of the cement sheath and rock is mainly caused by tensile stress; the damage zone of the cement sheath is mainly distributed on the inner surface and near the perforation hole, while the damage zone of rock is distributed near the perforation hole; after multiple discharges, the damage of cement sheath and rock gradually accumulates, and the damage zone eventually forms a vertical plane along perforation holes. Overall, these results provide guidance for the safe application of the high‐voltage pulse discharge technology in the oil field. [ABSTRACT FROM AUTHOR]

The prediction of the efficiency of oil well pumping systems plays an important role in optimizing the energy efficiency parameters of these systems. Currently, the prediction of oil well pumping system efficiency relies primarily on mechanistic models, but these models are often overly complex in predicting efficiency. Some researchers have attempted to use deep learning to predict system efficiency, but due to insufficient consideration of influencing factors and the causal relationships between these factors and system efficiency, they often include irrelevant variables as influencing factors, leading to less accurate prediction models. In this paper, a hybrid model (MDS–SSA–GNN) is proposed for the prediction of pumping well system efficiency. The model consists of six parts: Pearson's product moment correlation coefficient (PPMCC), multidimensional scaling (MDS) transform, maximum–minimum normalization, sparrow optimization algorithm (SSA), graph neural network (GNN), and maximum–minimum inverse normalization. First, the size of the correlation coefficient between each influencing factor and the system efficiency is quantitatively calculated by using PPMCC. Second, the main influencing factors are downscaled by using MDS and normalized based on the principle of maximum–minimum normalization. Third, the GNN algorithm is used for the prediction of the pumping unit system efficiency, and the SSA algorithm is used for the optimization of the initial values of the network parameters. Finally, the prediction results are obtained by the maximum–minimum antinormalization. To validate the model's accuracy, this study randomly selected 100 actual oil wells for comparative analysis and analyzed the impact of structural parameters of the hybrid algorithm on the prediction accuracy of system efficiency. The analysis results demonstrate that the proposed model can effectively predict system efficiency and has a certain role in improving the accuracy of oil well pumping system efficiency predictions. [ABSTRACT FROM AUTHOR]

Managed pressure cementing (MPC) is a new technology based on managed pressure drilling, which has a greater advantage in facing narrow density window formations. However, the existing pressure prediction models during MPC injection stage consider fewer factors and have lower accuracy. To this end, combined with the characteristics of the injection stage, a predictive model of the distribution of annular fluid type was first proposed. Then, based on the experimental results, fluid density and rheology as a function of temperature and pressure were fitted. The governing equation of temperature‐pressure field was established. Eventually the fluid performance parameters–temperature–pressure coupling prediction model was developed in this paper. By comparing the predicted pump pressure with the measured pump pressure, the maximum relative error is not more than 10%. Using this model, the fluid type distribution, temperature field distribution, and pressure field distribution were investigated. The results indicated that the distribution of fluid types in the wellbore presented a complex variation, with up to 10 fluids in the casing and up to five fluids in the annulus. The trend of temperature field is complex, with three turning points. The larger the formation temperature gradient, the higher the fluid temperature in the annulus. The influence law of fluid heat conduction coefficient is reversed at 6750 m. Decreasing drilling fluid density will trigger gas channeling, while increasing drilling fluid density will increase the risk of fracturing formation, and safe operation can be realized by MPC. The variation of the static pressure in the casing is more complicated than in the annulus and the annular circulation pressure in the eccentric casing is smaller than that of the concentric casing, which is due to the smaller annular friction pressure. This study can provide a theoretical basis for the prediction of hydrodynamic parameters during the MPC injection stage. [ABSTRACT FROM AUTHOR]

A large number of renewable distributed generation (DG) systems connect to the distribution network, affecting the structure of the traditional distribution network and forming an active distribution network (ADN). Although the accelerating grid penetration of DGs brings significant challenges to the distribution network operation, the islanded operation capability of DGs provides a flexible solution to the self‐healing of ADNs from faults. To ensure that ADN can quickly recover and reconfigure in the event of a fault and continue to maintain safe, economical, and reliable operation, this paper proposes a dynamic microgrid formation method for ADNs combined with the dynamic network reconfiguration and intentional islanding operation of DGs. An optimization model is designed to represent the proposed self‐healing method, maximizing the load restoration and minimizing the DG cost, line network loss, and voltage excursion simultaneously. A binary hybrid optimization solver is applied to pursue the optimal self‐healing schedules from the optimization model. The self‐healing method is evaluated on the Institute of Electrical and Electronics Engineers (IEEE) 33‐node system and the IEEE 123‐node system, which indicate its rationality and effectiveness fully verified. By optimizing the on and off states of the normally open switches and the on‐grid and off‐grid operation states of DGs, ADNs not only get healed with minimum load curtailment, but also achieve minimal DG generation cost, network loss, and node voltage deviation. In addition, compared with traditional solvers, the proposed solver has a slightly higher computational time than the corresponding solver. [ABSTRACT FROM AUTHOR]

Solid fossil fuels still support the development of the energy and chemical industry. The clean and efficient utilization of solid fossil fuels can effectively alleviate the problems of energy shortage and environmental pollution. An in‐depth understanding of the kinetic process, models, and mechanism of pyrolysis of solid fossil fuels is also of great significance for the exploration and utilization technologies of petroleum resources. Thermogravimetric analysis is an effective method to study the pyrolysis process and kinetics with the advantages of high sensitivity, repeatability, and reliability for data acquisition. Researchers have conducted a lot of research on the thermogravimetric kinetics of solid fossil fuels in recent years, but there is a lack of systematic summary and comparison of kinetic methods, research progress, and trends. Therefore, a comprehensive study on pyrolysis kinetic principles, methods, and models of solid fossil fuels by thermogravimetric analysis is carried out in this paper. The influencing factors of pyrolysis kinetics of solid fossil fuels using thermogravimetry are elaborated and discussed in detail. This study also provides progress in recent years and prospects of thermogravimetric kinetics of solid fossil fuels. It is expected that this paper can provide certain technical support and theoretical guidance for the research on pyrolysis kinetics based on thermogravimetric analysis, and the exploration and development, clean, and efficient utilization of solid fossil fuels. [ABSTRACT FROM AUTHOR]

This paper proposes a new high step‐up DC–DC converter with zero input current ripple for renewable energy sources applications, such as fuel cells. In this topology, a coupled inductor and a multiplier cell are used to achieve high voltage gains. The power switch in this proposed circuit turns on under zero voltage switching conditions. An active switch limits the maximum voltage stress across the main power switch. Moreover, all circuit diodes of the converter turn off without a reverse recovery problem. A detailed analysis of a high‐gain step‐up DC–DC converter is studied along with improved efficiency. The ripple cancellation of the input stage of the converter is done by adding an input inductor and capacitor. By including the parasitic elements, an approximate loss analysis was performed in PSIM and found the maximum efficiency of ∼96% in simulation and ∼94% in hardware for 200 W power level design for 30 V input voltage. Later on, the converter laboratory hardware prototype can be developed with selected parameters, and finally, experimental results have been used to validate the properties of the converter. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive exploration of the Advanced Impedance Mismatch Technique (AIMT), a novel approach designed for the accurate detection of simultaneous and varied faults within photovoltaic (PV) systems. This investigation integrates a spectrum of fault detection strategies, pinpointing reflectometry as a notably effective tool. Despite its utility, conventional reflectometry applications face critical constraints, notably the limitation to identify only the primary fault location within a PV array and the inability to distinguish between different fault types. This work introduces an innovative mathematical model that estimates the impedance of PV modules, enhancing the reflectometry method to enable the precise identification and localization of multiple defective modules within a string. The proposed technique exhibits a remarkable sensitivity to detect slight impedance differences between a functional PV string and one with defective modules. The validity of the AIMT's mathematical model is corroborated through simulation experiments on a string of seven PV modules afflicted with multiple simultaneous faults. These experiments rigorously evaluate the technique's accuracy in pinpointing the locations of defective modules within a PV string. The outcomes of our proposed N $N$‐times faster AIMT reveal a strong concordance between the simulated reflective signals and the impedance values forecasted by the model, highlighting the proposed method's proficiency in the detailed detection and diagnosis of progressive faults within PV systems. [ABSTRACT FROM AUTHOR]

Coal‐measure strata is a typical layered structure, a better understanding of the bearing characteristics of fully grouted bolts in layered rock mass is of great significance for roadway support. However, limited studies have been conducted to investigate the bearing performance of fully grouted bolts in layered rock mass. In this paper, a numerical model of fully grouted bolt in layered rock mass was established, and a tri‐linear bond–slip model of the anchorage interface was imported into the numerical model by the user‐defined Fish language. The effects of strata sequence, strata thickness, and strata number in layered rock mass on the bearing performance of fully grouted bolts were systematically analyzed. The results show that fully grouted bolts in soft–hard type (SH‐type) layered rock mass has higher bearing capacity. The greater the thickness of the soft rock in the layered rock mass, the more significant the influence of the strata sequence on the bearing performance of fully grouted bolts. The bearing capacity of fully grouted bolts decreases with the increase of the thickness ratio of soft rock to hard rock. The greater the thickness of the hard rock in the layered rock mass, the more beneficial to the bearing performance of fully grouted bolts. With the increase of the strata number, the bearing capacity of fully grouted bolts gradually decreases. For bolt support of layered rock mass, the deep anchoring foundation of fully grouted bolts should be arranged in hard rock or elastic zone rock as much as possible, and rock grouting can be adopted to reduce the difference in mechanical properties between layered rock mass and improve the durability of fully grouted bolts. The research results may provide useful guidance and reference for the support design of fully grouted bolts. [ABSTRACT FROM AUTHOR]

Hot dry rock (HDR) is regarded as a promising resource of geothermal energy and becomes an important field for future geothermal development due to its advantages of high temperature, wide distribution and huge reserves. At present, HDR research is mainly focused on the modeling and efficiency evaluation of power generation cycle, but its relationship with the source side of the system has not been considered in the field of integrated energy systems. Therefore, this paper proposes a day‐ahead scheduling method for regional integrated energy systems (RIES) with HDR based on information gap decision theory (IGDT). First, the heat transfer system model of HDR is established according to the energy flow model and basic structure of the HDR enhanced geothermal system (EGS). Second, a comprehensive geothermal energy system scheduling model is established from HDR based on the energy hub modeling structure. Then, the IGDT is introduced to analyze the renewable energy output uncertainty in the model. Finally, through a real RIES analysis, the simulation results verified the correctness and effectiveness of the proposed model. The scheduling cost was ¥47,073 when EGS participated in the scheduling. Access to EGS reduced the system's total 24‐h energy purchase by 8305 kW, natural gas consumption by 3051.9 m3, and total carbon emissions by 742.28 kg. The latter emphasized that the proposed model achieves the purpose of reducing the system cost, saving energy and reducing emissions. [ABSTRACT FROM AUTHOR]

The paper combines experimental and numerical simulation methods to investigate the tip leakage vortex (TLV) in an oil–gas multiphase pump. The study aims to understand the evolution and dynamic characteristics of the TLV. The vorticity decomposition theory is used to analyze the spatial–temporal evolution, transient behavior, and rigid vorticity of the TLV. The findings reveal that the TLV is formed near the pressure surface when the tip clearance jet passes through the shear layer. In one impeller rotation period, transient fluctuations of the TLV are attributed to pressure differences and velocity changes across the tip clearance, and the TLV undergoes two split–dissipation–recurrence processes within one impeller rotation cycle. The presence of the gas phase enhances the scale and strength of the TLV while prolonging its existence in the flow channel. Analysis based on the rigid vorticity transport equation shows that the growth of the TLV and collapse are primarily governed by the rigid vortex generation term. The Coriolis force term contributes to stabilizing the TLV, while the rigid vorticity stretching term affects its shape. Furthermore, the gas phase significantly increases the value of the Rigid vorticity Curl of pseudo‐Lamb vector Term (RCT), which plays a crucial role in the prolonged existence of the TLV under gas–liquid two‐phase conditions. From an inlet gas void fraction of 0%–20%, the RCT value increases by 4.8 × 106/s2, the entropy production value increases by 75.55%, and the hydraulic efficiency decreases by 16.29%. [ABSTRACT FROM AUTHOR]

In carbonate reservoirs characterized by the fracture‐cavity system as storage spaces, the drilling process is highly prone to the loss of drilling fluid. This not only affects drilling efficiency but can also lead to severe accidents, such as blowouts. Therefore, it is crucial to understand the distribution pattern of these fractures. However, the formation of carbonate rock fracture‐cavity system systems, being controlled by various factors, is difficult to precisely identify. This limitation hampers the efficient development of such types of oil and gas fields. This paper presents a case study of the M55 sub‐section carbonate gas reservoir in the Sulige gasfield, proposing an improved You Only Look Once v5s (YOLOv5s) deep learning algorithm. It utilizes enhanced training with conventional logging data to identify response characteristics of fractures in the carbonate reservoirs. And its identification results have been confirmed to be accurate by various fracture data obtained through different means, such as the core samples, cast thin section photographs, imaging logging data, and seismic attributes. This method incorporates the Ghost convolution module to replace the Conv module in the backbone network of the YOLOv5s model, and modifies the C3 module into a Ghost Bottleneck module, effectively making the model more lightweight. Additionally, a Convolutional Block Attention Module is integrated into the Neck network, enhancing the model's feature extraction capabilities. Finally, the method employs the Efficient Intersection over Union Loss function instead of the Complete Intersection over Union Loss, reducing the network's regression loss. The validation results using actual data demonstrate that this method achieves an average recognition accuracy of 87.3% for the fracture‐cavity system, which is a 3% improvement over the baseline model (YOLOv5s). This enhancement is beneficial for precisely locating the drilling fluid loss positions in carbonate reservoirs. [ABSTRACT FROM AUTHOR]

Although several studies have been performed on energy systems, there is a gap in examining the impact of energy storage systems (ESSs) technologies on the technoeconomic design of optimal multicarrier microgrids (MCMGs), considering environmental concerns. This paper aims to address this research gap by developing an optimal technoeconomic reliability‐oriented design of MCMGs, specifically focusing on battery storage systems (BSSs) and hydrogen storage systems (HSSs). The study was applied to MCMG at an actual MCMG, using HOMER Pro software. Additionally, a reliability‐oriented sensitivity analysis is conducted to understand the effects of reliability constraints, such as desired maximum capacity shortage, on the performance of various ESS technologies. One of the main contributions is analyzing the HSS and BSS from different viewpoints, such as cost, emission, and other technical–economic features for MCMGs. The comparative test results show that if a highly reliable MCMG is desired, the BSS‐based MCMG is the more practical option in terms of technoeconomic indices. However, regardless of reliability constraints, the HSS‐based MCMGs offer better environmental conditions. The total net present cost of the HSS‐based MCMG is 1.68% higher than the BSS‐based one, while it has 17.99% less CO2 emissions, while the HSS one has 17.99% less CO2 emissions. [ABSTRACT FROM AUTHOR]

Natural gas pipelines are exposed to complex production and transportation environments, leading to blockages of varying compositions and forms due to various factors. This paper proposes a pressure pulsing wave‐based pipe blockage detection method, and a series of pipeline blockage detection experiments of different materials are carried out, including hydrate blockage, ice blockage, paraffin blockage, and water blockage. Through the blockage detection calculation theory and the reflection and transmission characteristics during pressure wave emission, the blockage position, length, and section blockage rate under different blockage materials are obtained, and the influence of different blockage materials on the reflection and transmission signal characteristics of the pressure pulse wave is analyzed. The results show that the solid blocking material does not noticeably impact the reflectivity and reflective properties of pressure waves, and different cross‐sectional shapes have a noticeable impact on the reflectivity of the pressure wave. The reflected wave waveform of the liquid blockage material is visible but irregular. The average error of blockage location is 0.78%. [ABSTRACT FROM AUTHOR]

To scavenge energy from imperceptible vibrations, this paper investigates the broadband response and output performance of a microvibrational piezoelectric energy harvesting system with mechanical stopper. The energy harvesting system comprises a cantilever beam made of piezoelectric material, which is affixed with a coil at its unbound end and a mechanical stopper. The coil is placed in a magnetic field to provide an ultra‐low level excitation. The electromechanical model is derived according to force integration method (FIM) and Hertz's contact theory, and numerical simulations are undertaken to evaluate the influence of the excitation level, and the gap on the performance. For the linear counterpart without stopper, experimental results indicate the system generates a peak power of 24.12 μW with matched resistance under excitation with a level of 0.003 N and a frequency of 200.3 Hz. When a polydimethylsiloxane (PDMS) stopper is introduced, the vibration of the piezoelectric beam exhibits an obvious nonlinearity with an amplitude of micron scale. Increasing the excitation level and decreasing the gap could efficiently broaden the response bandwidth. Experimental results demonstrate that a copper stopper with larger elastic modulus results in a wider response frequency range, and the half‐power bandwidth could reach 37.1 Hz under excitation with a level of 0.003 N. [ABSTRACT FROM AUTHOR]

In the process of lower coal roadway support under double gobs in close coal seams, there are problems such as the whole destruction of the lower coal seam and the intermediate strata, the thin roof of the lower coal roadway with the smaller support space, the upper rock of the lower coal roadway without anchor bearing layer, the roof falling, the anchor cable offing, and so on. On the basis of the engineering background of No. 11103 haulage roadway in Fenxi coal mine, the research methods of theoretical modeling analysis, numerical simulation analysis and field engineering test are comprehensively adopted. In this paper, the layout of the roadway with floor insertion in the lower coal seam, the distribution characteristics of surrounding rock deviatoric stress, the control principle of broken surrounding rock and the coupling support method of shed–cable–prop are systematically studied. The study shows that many measures are needed to ensure the stability of the surrounding rock in the strong mining roadway of the lower coal seam. The lower coal seam roadway section was modified from the original rectangular section to adopt an arched section to counteract the broken roof. The staggered position of the lower coal seam roadway layout was modified from the original coal roadway position to a coal–rock roadway, so as to avoid the area of high deviatoric stress in the coal pillar and the area of crushed coal body, and to leave an effective support space for the roof. The support scheme of the lower coal seam roadway was improved to use anchor cable and single prop to support the weak section of U‐type steel shed with local coupling, and realize unequal force coupling by adjusting the size of the force at different coupling points. In the field monitoring the deformation of the roadway is small, and the support effect is good. [ABSTRACT FROM AUTHOR]

This paper studies the optimization of hydraulic parameters for pipeline system of hydropower station with super long headrace tunnel (HSSLHT) based on mayfly algorithm considering operational scenarios. Firstly, the state equation of HSSLHT under load disturbance is derived. The optimization design of hydraulic parameters for pipeline system based on mayfly algorithm is proposed. Then, the optimization of hydraulic parameters is conducted and analyzed. Finally, the effects of pipeline diameters and transfer coefficients of turbines on the optimization of hydraulic parameters for pipeline systems are revealed. The results show that the optimization of hydraulic parameters for pipeline system is a multiobjective problem, and the several objective functions exhibit significant conflicts. Compared to the firefly algorithm and genetic algorithm, the objective function under mayfly algorithm is improved by 14.7% and 5.1%, respectively. The mayfly algorithm can make the hydraulic parameters for the pipeline system reach the Pareto optimal solution under both load decrease condition and load increase condition. The diameter of penstock has an obvious influence on the robustness of dynamic performance of HSSLHT. When the diameter of penstock increases by 30%, the robustness of HSSLHT becomes worse and the robustness index deteriorates by 57%. The reason is that the flow inertia of penstock becomes smaller with the increase of diameter, and the flow inertia of penstock is favorable for resisting disturbance of HSSLHT. The coefficient of throttled orifice head loss and the lengths and head losses of headrace tunnel and penstock are the key hydraulic parameters for matching the operation of HSSLHT. [ABSTRACT FROM AUTHOR]

In the chemical flooding process, the premature breakthrough of chemical agents in production wells results in a large waste of chemical agents and increases the volume and processing difficulty of the produced fluids. The early warning method of chemical agent channeling can predict the strength of agent channeling in advance. The practice of chemical flooding shows that the production performance can be used for early warning of chemical agent channeling. In this paper, we analyze the relationship between cumulative oil production and cumulative polymer production of production wells in polymer–surfactant flooding. Three types of curves according to the enhanced oil production characteristics of chemical flooding, including convex type, S‐type, and concave type. We use the drop speed of the water–oil ratio and the rapid‐decline speed of water cut as early warning indicators to predict the channeling coefficient. A Latin hypercube experimental design method is used to design a polymer–surfactant flooding scheme with the main control factors of the channeling coefficient and early warning indicators. Numerical simulation is used to calculate samples of the channeling coefficient and early warning indicators under various conditions. The drop speed of the water–oil ratio reference value model and the rapid‐decline speed of the water cut reference value model are determined with a multiple regression method. A prediction model for the chemical agent channeling coefficient is established using the deviation coefficient of an early warning index. The method is applied in the Ng54‐61 polymer–surfactant flooding pilot area in the west of the Gudong Seventh District, Shengli Oilfield, China, and the error between the predicted result and the actual value is less than 10%. This research is helpful in taking effective antichanneling measures and improving the oil recovery degree of chemical flooding reservoirs. [ABSTRACT FROM AUTHOR]

Before starting stability analysis of the multivirtual synchronous machine (n‐VISMA) power system, it is necessary to obtain the steady‐state operating points (SSOPs) of all dynamic nodes in the network. Modified traditional iterative schemes using the concept of droop bus technique in an islanded microgrid are not feasible for load flow analysis of VISMA microgrid incorporating non‐control dynamics. This paper proposes closed‐form steady‐state, fundamental‐frequency models for islanded VISMA microgrids using the concept of virtual swing buses. In this technique, the virtual internal buses of all VISMAs in the network are governed by the swing equation. The voltage at all buses is variable except the virtual buses in which the pole wheel voltages are prespecified. The algorithm was extended by a droop control localized to each VISMA. The suitability of the proposed algorithm to obtaining SSOPs of VISMA was tested on IEEE‐9 bus system with VISMA replacing electromechanical synchronous machines and also on a low‐voltage distribution system. To validate the applicability of the proposed algorithm and prove its accuracy, the case study systems were also modeled in the SIMULINK environment for detailed time domain analysis. The algorithm was found to be computationally effective for a load flow analysis of the VISMA microgrid. The results also reveal that the addition of external droop control improves the frequency stability of the system. [ABSTRACT FROM AUTHOR]

Environmental issues are the most discussed in today's industrialized world, and this has always been associated with the emission of greenhouse gases. CO2 emissions, especially from power plants, are considered the major greenhouse gas source. Capturing CO2 from large emission sources is necessary to address the worst impacts of climate change, including more frequent and severe droughts, heat waves, and intermittent rainfall. Conventional post‐combustion CO2 capture has always been questioned because of its high impact on energy costs. Rotating packed bed technology has shown the potential to reduce the cost of CO2 capture. This paper presents an experimental study on an in‐house designed rotating packed bed absorber for the capture of CO2 from a flue gas stream. Effects of process parameters such as rotating speed, liquid‐to‐gas ratio, and monoethanolamine (MEA) concentration on CO2 capture efficiency and overall volumetric mass transfer coefficient were studied. The study showed that as rotor speed increased, the CO2 capture efficiency increased, this can be attributed to higher rotor speed, resulting in more droplet flow, indicating an increase in the area of contact between the flue gas and the MEA solvent. Also, as the concentration of MEA increased CO2 capture efficiency increased, since an increase in concentration results in an increase in the rate of reaction. Finally, the study showed that a rotating packed bed absorber is an efficient unit for CO2 capture since it enhances mass transfer performance. [ABSTRACT FROM AUTHOR]

Saturation characteristics of water and capillary pressure are important for hydrofracturing or water flooding, which is widely adopted in dust suppression, methane displacement, and gas and coal outburst elimination. In this paper, we have studied the water saturation characteristics of coal samples with different coal ranks and their effect on capillary pressure. It was found that the isothermal adsorption of water vapor from coal samples can be divided into three processes: monolayer adsorption, multilayer adsorption, and capillary condensation. The dent isothermal adsorption model can well describe water vapor adsorption of coal samples, indicating water molecules were first adsorbed on the first‐order adsorption site of pore surfaces at low relative humidity and then on the second‐order adsorption site with increasing relative humidity. An obvious hysteresis occurred in the process of water imbibition and drainage. A capillary pressure of ~325 MPa or even larger was required to displace the water from the coal. Besides, micro‐ and mesopores play a more important role in capillary pressure. The capillary pressure of coal samples was positively correlated with wettability at lower water saturation, but it is not significant at higher capillary pressures. Therefore, the article's results provide a new direction for solving the water‐locking effect in coal seams. [ABSTRACT FROM AUTHOR]

This paper investigates four energy utilization scenarios with or without home‐distributed photovoltaic generation (HDPG) to reduce the generation cost and pollutant emission of the dynamic economic emission dispatch with the charging/discharging of plug‐in electric vehicles (DEED‐PEV). The first scenario considers valley filling for the charging of PEVs. The second scenario combines valley filling and peak shaving for the charging and discharging of PEVs. The third scenario adds peak shaving of HDPG to the first scenario, followed by the peak shaving with the discharging of PEVs. The fourth scenario rearranges the distribution of photovoltaic (PV) power for the third scenario, and the PV power in the afternoon is stored by a photovoltaic energy storage system (PESS) and consumed in the evening. A universal procedure is designed for the valley filling and peak shaving of the four scenarios, which is beneficial for determining the filled and shaved loads with respect to certain time intervals. An NSGA‐II method based on a modified crossover and an elimination of individuals (NSGA‐II‐MCEI) is proposed for the multiobjective optimization of DEED‐PEV. The modified crossover can improve the convergence of NSGA‐II‐MCEI, and the elimination operator can maintain the evenness of the nondominated solutions. According to experimental results, scenario 4 achieves cost savings of 95386.62 $, 85636.87 $, and 6776.85 $, respectively, for Scenarios 1, 2, and 3, and it reaches emission reductions of 27617.64 kg, 17252.71 kg, and 220.98 kg, respectively, for scenarios 1, 2, and 3. Also, scenario 4 outperforms the other three scenarios for three weather conditions such as sunny day, cloudy day, and rainy day. [ABSTRACT FROM AUTHOR]

In recent years, energy sharing has attracted a lot of attention. However, the intermediate platforms in centralized energy‐sharing methods cause the rapid growth of communication complexity and the risk of privacy leakage. Unlike complex energy‐sharing market mechanisms, in this paper, a simple and efficient distributed energy sharing for microgrids (MGs) is proposed, where game theory is employed to form flexible prices for prosumers and only local information is used to improve the privacy protection of prosumers. First, prosumers located in different areas are characterized more precisely and a two‐tier carbon emission cost model is built. Next, a game‐theory‐based distributed energy‐sharing model is proposed, where a flexible pricing mechanism is developed to enable prosumers to independently reach price agreements and achieve supply–demand balance within MGs. In the process, optimization models for obtaining equilibrium are formed and only local information is needed. However, solving these optimization models is generally time‐consuming. So, a distributed optimization method based on weighted subgradients is proposed to accelerate the equilibrium‐finding process. Finally, four cases are designed, and simulation results demonstrate that the prosumers' costs of our method are reduced by 7.5%–22.5% compared to the costs obtained by feed‐in tariff. Moreover, in the case of solving the distributed trading model for an MG at a 24‐h time scale, the iteration numbers of our method are only 38.9% and 49.3% of the two traditional solving methods. [ABSTRACT FROM AUTHOR]

As the penetration of distributed generation (DG) systems in the grid is increasing, the challenge of combining large numbers of DGs in the power systems has to be carefully clarified and managed. The control strategy and management concept of the interconnected systems should be flexible and reliable to handle the various types of DGs. This can be suitably met by microgrids. This paper introduces the microgrid structure and elements and states the main objectives that should be achieved by the microgrid controllers and each DG controller in both operation modes (grid‐connected and island mode). It also presents the challenges of having multiple DG units in a microgrid in terms of accurate power control/sharing, voltage and frequency regulation, power management between DGs, different renewable energy sources integration and deployment, seamless mode transfer, and the modeling issues. The centralized and decentralized control techniques as potential solutions have been discussed and compared by highlighting the advantages and disadvantages of each. Furthermore, the recent control techniques for drooped alternating current microgrids and the main proposed solutions and contributions in the literature have been exposed to finally overcome the droop control limitations and obtain a flexible and smart distributed power system. [ABSTRACT FROM AUTHOR]

In this paper, we selected representative electric buses (EBs) and diesel buses as research cases, aiming to explore a localized method suitable for various vehicle types that can be implemented in any region to measure the carbon emissions of urban buses throughout the lifecycle. We take into account the influence of seasonal factors over the lifecycle. We fully consider the production, assembly, and recycling stages of vehicle systems. We construct a model for estimating CO2 emissions during the fuel cycle. We analyze and compare the impacts of the optimized power grid structure on the lifecycle CO2 emissions of the two types of buses. The results indicate that seasonal factors affect both bus types' energy consumption, with EBs being more sensitive. Carbon emissions primarily stem from manufacturing the body, chassis, and notably, the lithium‐ion batteries, with the latter having a pronounced impact on EBs. A decrease in coal power generation from 71% to 21% in the grid reduced EB fuel‐cycle emissions by 70.7%. Although EBs are sensitive to seasonal factors, they still have the greatest potential for carbon reduction, depending on the proportion of clean energy in the power grid and the production processes of vehicle components. [ABSTRACT FROM AUTHOR]

Due to its high energy storage density, high instantaneous power, quick charging and discharging speeds, and high energy conversion efficiency, flywheel energy storage technology has emerged as a new player in the field of novel energy storage. With the wide application of flywheel energy storage system (FESS) in power systems, especially under changing grid conditions, the low‐voltage ride‐through (LVRT) problem has become an important challenge limiting their performance. In this paper, we propose a machine‐grid side coordinated control strategy based on model predictive current control (MPCC) for the insufficient LVRT capability of traditional FESS during grid faults. Excellent dynamic properties are demonstrated by the technique, which allows the grid‐side converter output current to swiftly follow the reference current instruction. The FESS's LVRT capability is increased when the grid‐side converter uses the MPCC current inner loop rather than the proportional–integral current inner loop during grid voltage dips. This greatly increases the reactive power response speed and efficiently supports the quick recovery of grid voltage. According to simulation verification carried out by Matlab/Simulink, the suggested control approach can assure the long‐term dependable operation of the FESS during voltage dips. This study can also be used as a reference for improving the FESS's LVRT capabilities in the future. [ABSTRACT FROM AUTHOR]

Accurate modeling and parameter identification of photovoltaic (PV) cells is a difficult task due to the nonlinear characteristics of PV cells. The goal of this paper is to propose a multi strategy sine–cosine algorithm (SCA), named enhanced sine–cosine algorithm (ESCA), to evaluate nondirectly measurable parameters of PV cells. The ESCA introduces the concept of population average position to increase the population exploration ability, and at the same time introduces the personal destination agent mutation mechanism and competitive selection mechanism into SCA to provide more search directions for ESCA while ensuring the search accuracy and diversity maintenance. To prove that the proposed ESCA is the best choice for extracting nondirectly measurable parameters of PV cells, ESCA is evaluated by the single‐diode model, the double‐diode model, the three‐diode model, and the photovoltaic module model (PVM), and compared with eight existing popular methods. Experimental results show that ESCA outperforms similar methods in terms of diversity maintenance, high efficiency, and stability. In particular, the proposed ESCA method is less than the SCA by 0.081, 0.144, and 0.578 in the standard deviation statistics metrics of the three PVM models (PV‐PWP201, STM6‐40/36, and STP6‐120/36), respectively. Therefore, the proposed ESCA is an accurate and reliable method for parameter identification of PV cells. [ABSTRACT FROM AUTHOR]

Aiming at the matter of poor precision in predicting the charge of lithium battery by applying conventional integer‐order models and offline parameter identification, this paper proposes a joint fractional‐order multi‐innovations unscented Kalman filter (FOUKF‐FOMIUKF) algorithm for predicting the cells' state of charge (SOC) online and uses the theory of singular‐value decomposition to tackle the issue of failure of the traceless transformation. Initially, the circuitry model of fractional order is built. The parameters of the model are recognized online by fractional‐order unscented Kalman filtering (FOUKF), and the obtained parameters are then transmitted to the method known as the fractional order multi‐innovations unscented Kalman filter (FOMIUKF) to calculate the SOC of the cell. The algorithm was validated under four working conditions such as FUDS (US Federal Urban Driving Distance), BJDST (Beijing Dynamic Stress Test), DST (Dynamic Stress Test), and US06 (Highway Driving Distance Test), respectively, and compared with the FOMIUKF, MIUKF, and FOUKF algorithms for offline identification. The conclusions demonstrate that the SOC estimated by the FOUKF‐FOMIUKF method is controlled within 0.5% of the mean absolute error under the four conditions and the root‐mean‐square error is controlled within 0.8%. It is not difficult to find that the FOUKF‐FOMIUKF algorithm estimates SOC with higher accuracy and robustness. [ABSTRACT FROM AUTHOR]

Industry 4.0 is a term used to refer to the fourth industrial revolution, characterized by the introduction of new technologies, such as the Internet of Things, Big Data, and artificial intelligence (AI). As the number of connected devices in industrial settings grows, energy optimization of such sensors becomes increasingly essential. This paper proposes an energy optimization framework for sensor nodes in Industry 4.0. The framework is based on energy efficiency, energy conservation, and energy harvesting principles. It is designed to optimize the energy consumption of sensor nodes while maintaining their performance. The framework includes dynamic power management, scheduling, and harvesting techniques to reduce energy consumption while maintaining performance. In addition, the framework provides a comprehensive approach to energy optimization, including advanced analytics and AI to predict energy consumption and optimize energy use. The proposed model reached 96.93% sensitivity, 91.36% false discovery rate, 11.28% false omission rate, 90.12% prevalence threshold, and 91.24% threat score. The proposed framework is expected to improve the performance of sensor nodes in Industry 4.0, enabling increased efficiency and cost savings. [ABSTRACT FROM AUTHOR]

Reservoir stimulation using hydraulic fracturing technology is one of the most common stimulation measures to develop tight oil and gas reservoirs, and the conductivity of fractures after fracturing is the key to the stimulation effect of oil fields. In this paper, the laboratory simulation system of fracture conductivity is used to study the long‐term fracture conductivity under the displacement of formation water and fracturing fluid. The experimental results show that the fracture conductivity decreases with the increase of displacement time under the displacement of formation water and fracturing fluid, and the decrease of fracture conductivity under the displacement of fracturing fluid with time is greater than that of formation water displacement because of the plugging effect of fracturing fluid residue on pore channels. The use of formation water and fracturing fluid circulation displacement can delay the decline rate of conductivity with time. The conductivity increases with the increase of sand concentration, proppant particle size, and elastic modulus, and decreases with the increase of closure pressure. In addition, by studying the influence of elastic modulus on fracture width, it is found that the smaller the elastic modulus is, the more serious the embedding phenomenon of proppant is, and the smaller the fracture width is. [ABSTRACT FROM AUTHOR]

Today, multilevel inverters are widely utilized in various applications. Reducing the number of components in the design of these converters is one of the crucial goals so that they have less volume and cost. This paper presents a single‐source structure for a multilevel switched‐capacitor inverter with voltage boost capability. The proposed structure can generate 25 sinusoidal‐like step voltage levels with a six times amplitude increase compared to the input. This converter uses only one direct current (DC) power source, 14 switches, and four capacitors. The nearest level control switching method has been used to control the converter, create voltage levels, and select the switching modes. A comprehensive comparison has been made with other recently presented structures to demonstrate the efficiency of the proposed structure. The advantages of the proposed structure include using only one DC voltage source, the ability to increase the output voltage six times compared to the input, the use of fewer power electronics components compared to the number of voltage levels, proper efficiency, self‐balancing of capacitors, and as a result, the lower cost. The performance accuracy of the proposed converter has been simulated in the MATLAB Simulink environment and then evaluated by a laboratory prototype. [ABSTRACT FROM AUTHOR]