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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]

A convenient and effective method based on vector analysis was proposed in this paper to quantitatively describe the pattern of the wind speed distribution curve. This method can accurately describe the steepness of the single‐peak curve by using two parameters of concentration degree (CD) and concentration period (CP). Through the analysis of wind speed data in China over the past 40 years, this paper found that regional wind resources with larger CD were poorer, while regional wind resources with larger CP were richer. In addition, there were obvious cubic relationships between the CP and the average wind power density. These two parameters could reflect the richness of wind energy and realize the comparisons of wind resources across time and scales. In comparison with conventional approaches, this method is simpler and avoids fitting process, which has broad promotion prospects in the field of power grids. [ABSTRACT FROM AUTHOR]

The gob‐side entry‐retaining by roof cutting and pressure relief (GERRC) method is an advanced no‐pillar mining method that can significantly improve coal recovery and reduce roadway accidents. However, according to case studies, there is relatively little research on the application of the GERRC method in geological conditions of soft rock formations (especially when the roof and floor are both mudstone), and such research is needed. Therefore, based on the typical soft strata of the Xinyi Coal Mine, this paper analyzes the failure mechanism of the roadways and proposes the GERRC method with coal rib and corner strengthened support (CRCSS). The evaluation indexes of the roof convergence rate and cable safety margin are established, and the effects of roof cutting and CRCSS on roadway deformation control are explored by a numerical comparison test. The results show that roof cutting has a limited effect on roadway deformation. The deformation can be reduced by 75.77% by implementing CRCSS on the basis of GERRC. Then, the evolution characteristics of roadway stress and deformation in the process of mining are further discussed through field contrast tests. The field application results show that compared with that of GERRC with conventional support, the surrounding rock deformation of GERRC with CRCSS is reduced by more than 55%, the safety margin of the cable is improved. The overpressure of the hydraulic prop is diminished, and the stability distance of the roadway is reduced by approximately 30%. Finally, this paper discusses the mechanism to control roadway deformation by CRCSS. This research expounds the rationality of CRCSS, and the results can provide a reference for other situations with similar geological conditions. [ABSTRACT FROM AUTHOR]

This paper deals with the stability analysis and enhancement in the AC‐microgrid system comprising parallel‐operated voltage‐source inverters (VSIs) supplying an active‐load. The active‐load behaves such as a constant power load from the AC‐side. In the paper, two models for the active‐load modeling in the AC microgrid are considered: (i) simple model in which the active‐load is modeled as an ideal current source, (ii) detailed model in which all dynamics of the active‐load converter and related controllers are taken into account. The main findings and contribution of this paper are as follows. (a) All linearized state equations of the study system are extracted and transferred into a unified common dq‐reference frame, and then, small‐signal stability of the study system is carried out in the common reference frame by using the simple and detailed models of the active‐load. (b) It is shown that the simple model of the active‐load, as a current source, leads to more pessimistic results in comparison to the active‐load detailed model. (c) Impacts of the active‐load power and bandwidth of the active‐load voltage control loop on the system stability are examined. (d) Control of the generation‐side VSIs is modified and a stabilizing approach, called generation‐side virtual capacitor, is proposed for stabilizing the system under high active‐load penetration. Next, modal analysis and time‐domain simulations are used to investigate the study system responses with and without the proposed stabilizing approach. [ABSTRACT FROM AUTHOR]

Solar collectors utilize solar energy, a sustainable and renewable source, to cater to both household and industrial needs. This paper investigated the usage of mono and binary nanoparticles in parabolic trough collectors. This paper used multiwalled carbon nanotube/Fe3O4 nanoparticles as the additives to the collector heat transfer fluid (in Syltherm 800 oil) and also evaluated the performance implications of energy systems in six scenarios. A MATLAB‐based computational model is provided to investigate the influence of employing binary nanoparticles on a solar collector's performance. The influence of nanoparticle modifications and volume percentages on energy, exergy, economic, and environmental aspects is studied. Finally, the impact of nanoparticles on each of the four mentioned areas is examined. At the same total energy rate, in the best case, adding 4% by volume of Fe3O4 (Case 2) to the base fluid increases the valuable energy rate absorbed from 7.32 kW in the base state to 7.50 kW (2.5%). The carbon dioxide produced in the system life cycle by using useful energy is called EnergoEnvironmental, and the carbon dioxide produced in the system life cycle by using useful exergy is called ExergoEnvironmental. At last, the calculation of the parameters EnergoEnviroEconomic and ExergoEnviroEconomic are done, the penalty policy is considered, and the calculations are generalized to solar collector power plants due to the carbon dioxide cost policies. [ABSTRACT FROM AUTHOR]

Due to the rapid development of today's industry, it not only greatly accelerates the consumption of energy, but also causes serious pollution to the environment. Therefore, reducing energy waste and recovering energy has become an important research direction today. This paper introduces the future development trend of renewable energy, discusses the progress of recovery of nonpolluting energy such as marine, bioenergy and thermal energy, and analyzes the efficiency and social benefits of various types of energy recovery. Energy recovery methods for natural gas pipelines are briefly analyzed, and current research advances in energy pressure recovery devices are provided, leading to a summary of methods to improve the efficiency of fluid energy recovery. Finally, it is concluded that although the current energy sources are diversified, the most valued energy sources at present and in the future should be solar energy and ocean, which are infinitely recyclable. This paper will benefit researchers to further understand and study the various aspects of energy recovery. [ABSTRACT FROM AUTHOR]

Hydrogen energy is playing an increasingly important role in integrated energy systems (IES) due to its multiple uses. Green hydrogen can be produced from electricity generated from renewable energy, reducing carbon emissions and environmental pollution. Previous work usually only considers electric vehicles (EVs) as flexible loads, and there are few studies on the production and application process of green hydrogen. In this paper, hydrogen electrolysis centers (HECs) and EVs are simultaneously schedulable loads, integrated into IES, and optimized. The distribution network model is established, and the optimization goal is to minimize network loss while ensuring the safe operation of the power grid. Through the power flow optimization method of mixed‐integer second‐order cone programming, the space optimization scheduling of EVs in the IES is carried out, and the optimal location of the HEC is reasonably planned and set. A simulation instance of IEEE 33 nodes is used to verify the proposed method. The final results show that this method can minimize network loss in the range of safe operation of the system for the spatial scheduling of electrolytic hydrogen centers and EVs. At the same time, it can also provide a reference for HEC addressing. [ABSTRACT FROM AUTHOR]

The filler is the core component of the cooling tower, filler performance refers to both its thermal and flow resistance characteristics, which use empirical formulas of tower characteristic N, volumetric mass transfer coefficient βxv, and pressure drop ΔP obtained through experimentation under specific conditions. However, the performance equations for identical countercurrent fillers can vary at different heights or seawater concentrations. Linear interpolation is the conventional method for obtaining the filler performance under different conditions, but its uncertainty limits the application. This paper explores the use of the radial basis function artificial neural network (RBF ANN) to analyze filler performance based on existing performance equations. The data set is generated by the filler performance equations. The results demonstrate that RBF ANN has a preferable prediction effect with high correlation (the determination coefficient R2 > 0.99) and prediction accuracy (the proportion of relative error within 10% N10 > 90%). Furthermore, the predicted results are consistent with the experimental results of the filler performance. Therefore, RBF ANN can accurately predict filler performance at varying heights and seawater concentrations, making it universal and providing a basis for cooling tower design. [ABSTRACT FROM AUTHOR]

During the multistage fracturing of shale gas horizontal wells, casing damage frequently occurs, making it essential to evaluate casing stress and deformation. This paper establishes a mathematical model of thermo‐hydro‐mechanical coupling and a finite element model of casing‐cement sheath‐formation (CCF). This paper examines the effects of temperature, casing thickness, nonuniform in situ stress, mechanical properties of cement sheath, and treating pressure on the stress and deformation of CCF. Finally, the study investigates the variation of local in situ stress at the heel and casing stress from toe to heel. The results show that the low temperature of CCF, high elastic modulus of cement sheath, small casing thickness, high treating pressure, and large nonuniform in situ stress can exacerbate casing stress. After pumping off, the local in situ stress and its nonuniformity near heel gradually increased, and the effective stress of casing increases compared with the pumping on the process. As the fracturing stages increase, the effective stress of casing at heel increases, and the casing at heel preferentially damages. The results provide a theoretical basis and guidance for maintaining wellbore integrity and preventing casing damage in the multistage fracturing of shale gas horizontal wells. [ABSTRACT FROM AUTHOR]

Since the "double carbon" target was proposed in 2020, emissions reduction has become the focus of the Chinese government's work. Industrial development is a crucial factor contributing to the increase in carbon emissions. Therefore, implementing carbon reduction in the industrial sector is an important part of China's goal of "double carbon." In this context, this paper uses a three‐stage data envelopment analysis model to calculate the green total factor productivity (GTFP) of 18 industry sectors in Shanxi Province. The results show that: (1) the external environment has influence on the GTFP of energy revolution reform pilot area industrial sectors, and the efficiency value of two industries is overestimated before excluding environmental factors; (2) the industry difference of GTFP is significant, among which pharmaceutical manufacturing industry has the greatest promoting effect on GTFP, and ferrous metal mining and dressing industry has the greatest inhibiting effect on GTFP; (3) there is a serious redundancy in R&D investment of energy revolution reform pilot area in China, and only six industries belong to zero redundancy. (4) To reflect the GTFP of various industrial sectors more clearly, this paper divides four industries: "Double‐high" industry, "High‐low" industry, "Low‐high" industry, and "Double‐low" industry. [ABSTRACT FROM AUTHOR]

The battery energy storage system (BESS) is considered as an effective way to solve the lack of power and frequency fluctuation caused by the uncertainty and the imbalance of renewable energy. Based on these, this paper proposes a mixed control strategy for the BESS. First, this paper divides the demand for frequency modulation, peak regulation, and state of charge (SOC) of the battery into different zones. Then the Kuramoto model modulates the frequency, and the self‐recovery strategy is used to optimize the SOC. Meanwhile, the proposed mixed control strategy is used to integrate frequency control, demand control, and SOC optimization. Finally, the simulation results verify the rationality of the strategy. [ABSTRACT FROM AUTHOR]

This paper proposes an improved deep learning‐based maximum power point tracking (MPPT) in solar photovoltaic cells considering various time series‐based environmental inputs. Generally, artificial neural network‐based MPPT algorithms use basic neural network architectures and inputs which do not represent the ambient conditions in a comprehensive manner. In this article, the ambient conditions of a location are represented through a comprehensive set of environmental features. Furthermore, the inclusion of time‐based features in the input data is considered to model cyclic patterns temporally within the atmospheric conditions leading to robust modeling of the MPPT algorithm. A transformer‐based deep learning architecture is trained as a time series prediction model using multidimensional time series input features. The model is trained on a dataset containing typical meteorological‐year data points of ambient weather conditions from 50 locations. The attention mechanism in the transformer modules allows the model to learn temporal patterns in the data efficiently. The proposed model achieves a 0.47% mean average percentage error of prediction on non‐zero operating voltage points in a test dataset consisting of data collected over a period of 200 consecutive hours; resulting in the average power efficiency of 99.54% and peak power efficiency of 99.98%. The proposed model is validated through real‐time simulations. The proposed model performs power point tracking in a robust, dynamic, and nonlatent manner, over a wide range of atmospheric conditions. [ABSTRACT FROM AUTHOR]

Based on the energy flow analysis method, this paper establishes the relationship between the heat accumulator and the thermal system from the perspective of heat transfer. Different types of heat accumulators are converted into different equivalent models based on energy flow, the existing sneak circuit analysis methods based on energy flow are optimized, and the starting point and end point of path search are redefined. The energy flow‐based correlation method proposed in this paper is applied to a system with two different types of heat accumulators. The results show that the sneak circuit analysis method based on energy flow proposed in this paper can search for more paths and discover new sneak circuits on the basis of the original method. Therefore, the new method is feasible and effective. [ABSTRACT FROM AUTHOR]

Commercial buildings are consuming an increasing amount of energy, and accurate load demand forecasting is critical for the reliable operation of power systems and the efficient use of resources. Therefore, in this paper, a short‐term commercial load forecasting model based on tunicate swarm algorithm (TSA) combined with an extreme learning machine (ELM) under peak‐valley features is proposed as a research case for a shopping mall in Romania. This paper's overall structure is divided into two steps. In the first step, the 24‐h day is divided into six periods by analyzing the daily load characteristics of the training set, and the peak and valley loads are obtained. The ELM optimized by TSA (TSA‐ELM) algorithm is then used to forecast the peak and valley values of the test set one day ahead. In the second step, the actual load, peak, and valley for the previous week of historical load are chosen using the maximum information coefficient (MIC). Following that, the MIC ≥ 0.8 features are added to the TSA‐ELM to achieve short‐term commercial electricity load forecasting. The results show that the PV (Peak & Valley)‐TSA‐ELM model proposed in this paper has higher prediction accuracy compared with other models. Taking ELM as an example, compared with the traditional ELM, the mean absolute error, root mean square error, and mean absolute percentage error of PV‐TSA‐ELM are reduced by 20.59%, 20.13%, and 19.19% on average in the three commercial data sets. The proposed model is validated with an industrial data set, and ideal results are obtained, which verifies the effectiveness and superiority of the proposed method. [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]

Researchers and engineers are actively working on enhancing the efficiency of heat exchangers in engineering applications by developing novel designs, exploring new materials, and utilizing nanofluids. Three kinds of nanofluids with varying concentrations are investigated in this paper. The objective is to assess the performance of N‐shaped double‐pipe heat exchanger used in thermoelectric power plants. The performance has been evaluated using COMSOL Multiphysics software. The findings show that higher nanofluid concentrations resulted in elevated heat transfer coefficients and improved efficiency of N‐shape double pipe heat exchanger. The analysis revealed that a mere 1% rise in the volume fraction of nanofluids enhanced the efficiency of the heat exchanger on average 23% when compared to the base fluid (water). In comparison to the N‐shape double pipe (Inconel 625) heat exchangers, the N‐shape double pipe (copper) heat exchangers appear to be more efficient. The introduction of nanoparticles has a notable impact on the heat transfer coefficients. Specifically, within an N‐shaped double pipe (copper) heat exchanger, the inclusion of a 1% volume fraction results in a 2.09% enhancement in the heat transfer coefficient for Al2O3/water, a 1.3% improvement for Fe3O4/water, and a 1.15% increase for TiO2/water. It also exposed that adding 1% Al2O3/water led to a significant 0.623% increase in effectiveness, while TiO2/water showed a 0.259% rise, and Fe3O4/water exhibited a 0.375% improvement. Moreover, increasing the Reynolds number enhances the Nusselt number for Al2O3/water and Fe3O4/water nanofluids by 55.22%, and for TiO2/water by 54.60% at a 6% volume concentration, leading to additional increases in exchanger efficiency. Therefore, the augmentation in nanofluid concentration leads to a reduction in the temperature pinch points both at the intake and outflow. This observation suggests that nanofluids exhibit a superior ability compared to conventional fluids when it comes to effectively lowering temperatures. [ABSTRACT FROM AUTHOR]

The increasing trend of energy generation and management systems towards decentralized structures such as using renewable energy resources makes it necessary to use digital and smart platforms for exchanging information and even conducting financial transactions in a decentralized manner, known as the peer‐to‐peer model. The decentralized transaction verification of cryptocurrencies makes it possible to use these encrypted currencies and decentralized blockchain networks in energy management systems and carry out financial transactions related to carbon trading. Carbon and other greenhouse gas (GHG) emission trading systems reduce the competitiveness of fossil fuel projects in the market and accelerate investment in low‐carbon energy sources such as wind and photovoltaic power generation units. This market mechanism allows large entities such as countries and companies that emit GHGs into the atmosphere to buy and sell these gases. This paper reviews the blockchain solutions developed for carbon markets. Studies related to the design of smart contracts in the platform of blockchain are investigated. Special cryptocurrencies that are used in the field of green energy transactions and carbon trading are introduced. In addition, the application of artificial intelligence and game theory in energy trading is stated. The study of different blockchain frameworks for carbon trading shows that the use of decentralized platforms in carbon trading can have a significant impact on the trend towards low‐carbon measures and achieving the goals of the Kyoto Treaty, increasing the value of green cryptocurrencies and the volume of transactions. These technologies offer a promising avenue for creating a more decentralized, efficient, and environmentally conscious energy ecosystem. [ABSTRACT FROM AUTHOR]

From core observations of shales of the Niutitang formation in the northern part of Guizhou Province, China, calcite was often found to act as a natural fracture filler and affect the extension of fractures in hydraulic fracturing. Therefore, it is crucial to understand the tensile mechanical behavior of shales by calcite veins. In this paper, by computed tomography scanning of shales containing calcite veins of different dip angles from the Niutitang formation, a three‐dimensional numerical model reflecting the internal fine structure of the shale was constructed. Direct tensile numerical simulations were carried out to investigate the effect of calcite veins at different angles on the fine‐scale damage process and mechanical properties of shale. The experimental results show that the tensile capacity of the shale increases with the increase in calcite veins. Depending on the dip angle of the calcite vein, the damage pattern of the shale is divided into three types of damage: pull‐off damage along the calcite vein, jagged damage, and horizontal damage perpendicular to the loading method. At high dip angles, the shale damage is more intense and the fracture network more complex. The temporal and spatial characteristics of the acoustic emissions provide a good indication of the microscopic behavior of the shale specimens during the damage process. The box dimension method was used to calculate the fractal dimension of the shale specimens at the final damage, and it was found that the damage was more intense and the fracture network was more complex at high dip angles, and there was an angular threshold. [ABSTRACT FROM AUTHOR]

To accurately calculate the carbon emission of integrated energy system (IES), and fully explore the potential for load side on carbon emission reduction, this paper proposes a method of guiding load to participate in demand response based on node carbon emission intensity, and constructs a bi‐layer low‐carbon scheduling model for source‐load collaborative optimization. First, the carbon flow calculation model of IES in the total process is established, such as source, network, load, and storage. It can depict the carbon emission characteristics of energy conversion equipment and energy storage devices. The principle of proportional sharing is used to track carbon emission flows, the changes in carbon emission intensity at each node is perceived from a spatiotemporal perspective. Second, carbon flow analysis is incorporated into the load demand response mechanism, and a carbon emission model for load aggregator (LA) after demand response is established based on the node carbon emission intensity. Third, a bi‐layer low‐carbon scheduling model is constructed, which considers the source‐load collaborative optimization. The upper layer is the optimal economic dispatch of IES operators, while the lower layer is the optimal economic dispatch of LAs. Finally, the effectiveness of the proposed method is verified using the system as an example, such as improved 14‐node power grid, 6‐node heating network, and 6‐node nature gas network. [ABSTRACT FROM AUTHOR]

In this paper, a multigeneration cycle of electricity, cooling, and Bitcoin whose energy source is geothermal, has been subjected to energy, exergy, and economic analyses. The cycle under consideration includes the steam cycle (upstream cycle), the carbon dioxide cycle (downstream cycle), and the liquid–gas line to absorb the heat dissipated by the carbon dioxide cycle. In this cycle, the steam cycle condenser acts as the carbon dioxide cycle evaporator. Part of the electricity generated by this cycle is used to generate Bitcoins. Energy and exergy efficiencies at baseline (excluding Bitcoin production) are 45.8% and 38.1%, respectively. In this cycle, if more power is spent on producing Bitcoin as a product, the energy and exergy efficiencies of the cycle are reduced. Because Bitcoin itself is not valuable in terms of energy and exergy. Considering the average price of Bitcoin during the years 2015–2022 and if 100% of the electricity generated by the system is spent on Bitcoin production, the payback period in 2018, 2021, and 2022 when the price of Bitcoin is equal to $13,412.4, $21,398.8, and $47,743.0, respectively, are less than the baseline. Therefore, the production of Bitcoin with a variety of renewable energies can be considered as a solution. Of course, it should be noted that large changes in the price of Bitcoin can affect the issue of economic benefit. [ABSTRACT FROM AUTHOR]

Considering the dynamic behavior of doubly‐fed induction generators (DFIGs) under the influence of random factors, this paper not only analyzes the phenomenon of stochastic instability and bifurcation of a DFIG dynamic variable in its random space when they are affected by environmental noise, but also proposes a method based on the Tkagi–Sugneo (T–S) fuzzy control strategy to control its stochastic bifurcation. First, a four‐dimensional stochastic dynamic DFIG model is established by using multiplicative white noise to simulate the influence of environmental noise on electrical variables, and stochastic central manifold theory is used to reduce the dimensionality of a planar model in the bifurcation neighborhood. Then, the stochastic stability of the model is investigated based on singular boundary theory, while the steady‐state probability density of the stochastic DFIG is determined using the Fokker–Plank–Kolmogorov equation to obtain the location and probability density of its stochastic P‐bifurcation. Finally, the influence of stochastic bifurcation behavior is eliminated by H∞‐fuzzy output feedback control. The numerical simulation results indicate that the location and probability of stochastic bifurcation in a DFIG will vary with the change in noise intensity, and the bifurcation parameter values and stochastic stability domain are obtained. The harm caused by random factors can be solved based on H∞‐fuzzy output feedback, which provides a theoretical basis for the stable operation of the DFIG system. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of feature extraction and fault identification in fault diagnosis of wind power converters, a novel method for open circuit fault diagnosis of wind power converters based on variational mode decomposition (VMD) energy entropy (EE) and time domain feature analysis (TDFA) is proposed. Primarily, the three‐phase output current at the grid side of the wind power converter is collected as the original signal, and the VMD is used to decompose the original signal into a series of intrinsic mode functions (IMF). To reduce noise interference as much as possible, the Pearson correlation coefficient between each mode component and the original signal under different fault states is analyzed, and the IMF component containing the major failure features is selected to calculate the energy entropy of each component; afterward, according to the Pearson correlation coefficient results, the modal components of the first layer are selected for time domain feature analysis; finally, the feature matrix that combines energy entropy and time domain feature analysis is inputted into the long short‐term memory neural network for training and fault identification. The simulation and experimental results show that the open circuit fault diagnosis method proposed in this paper has high accuracy and robustness. [ABSTRACT FROM AUTHOR]

This work addresses the ongoing challenge of optimizing sand control screens, attributed to a limited understanding of screen blocking. A novel one‐way computational fluid dynamics and discrete element method (CFD–DEM) coupling technique is presented to analyze the structural parameters of unconsolidated prepacked gravel screens (PPGS). CFD–DEM involves systematically investigating the width of the punched slot on the outer protective screen, the size of the gravel, and the efficiency of gravel packing to improve the antiblocking capability of unconsolidated PPGS. Results from numerical simulations reveal that under conditions of effective sand control, the amount of sand entering the gravel layer to the bridge (referred to as the sand pass rate) is positively correlated with the antilocking ability (referred to as oil productivity index) for unconsolidated PPGS. The efficiency of gravel packing has the most substantial effect on the antilocking performance of unconsolidated PPGS. The data suggest that appropriately reducing the efficiency of gravel packing can enhance the antilocking capability of unconsolidated PPGS. To design an unconsolidated PPGS with enhanced antilocking capability, the efficiency of gravel packing be determined first. Then, the gravel size is designed based on this efficiency, and the width relative to the gravel size is decided. This contemporary design principle diverges considerably from previous design principles for gravel‐packed sand control screens. Supporting validation experiments agree with simulation outcomes, suggesting the established one‐way CFD–DEM coupling method in this paper is suitable for analyzing the plugging mechanism of unconsolidated PPGS. Thus, the CFD–DEM coupling method improves the design of such screens for improved antiblocking performance. [ABSTRACT FROM AUTHOR]

Thermal energy is one of the most widely encountered energy forms in our daily life. To ensure efficient utilization and conversion of this energy, the balance between supply and demand needs to be maintained. For this purpose, thermal energy storage is required. There are various thermal energy storage systems available; one of the most basic is sensible thermal energy storage which includes rock thermal energy storage (RTES). This rock‐based energy storage has recently gained significant attention due to its capability to hold large amounts of thermal energy, relatively simple storage mechanism and low cost of storage medium. Accordingly, numerous studies have been conducted to elucidate the basic flow and heat transfer mechanism and to evaluate the performance of this energy storage. The major technical challenges hindering the wide adoption of this technology are the enormous pressure drop across the storage and nonoptimal heat transfer from the heat transfer fluid to the storage medium and vice versa. These issues will directly and indirectly affect the overall cost (capital, operational, and maintenance costs) of the system. To eliminate this issue and assist further development of this technology, it is crucial to compile and extract important findings from these previous studies, identify the challenge and research gap, and draw guidelines for the upcoming research and development. At the moment, this kind of compilation does not exist. Hence, this paper is prepared with the primary objective to comprehensively review the current technology and development of RTES and to propose a potential way forward based on the pain point identified. Discussion on the nontechnical aspect such as policy and regulations as well as community awareness will also be outlined and discussed. [ABSTRACT FROM AUTHOR]

The capacity of lithium batteries varies under different temperature conditions. However, many studies still neglect the influence of temperature on battery capacity. Besides, some studies only considered the combination of the heat and electricity of the battery monomer and failed to study the performance of the battery pack in groups. This paper investigates a new method for dynamic situations that takes the influence of temperature on battery capacity into consideration. After acquiring the parameter through experiment, we can substitute the performance parameters into the thermoelectric coupling model and use MATLAB to realize the iterative calculation of thermoelectric coupling. By combining the grouping mode with the model, we can simulate the thermodynamic and electrical properties of the battery cell in the pack. The novelty is evident because the paper focused on the battery pack instead of a single cell and the lumped parameter thermal model is adopted. Besides, the method proposed in this paper considered the influence of temperature on battery capacity. The program of calculation process can be solidified on the vehicle system. After the simulation, we can get the available capacity of the battery pack at different initial temperatures, with different grouping modes and different inconsistency of parameters. The results predicted by MATLAB model are compared with the results of MATLAB‐FLUENT thermoelectric coupling simulation. The result reflects that the prediction by the MATLAB model about the available capacity, temperature, and residual electricity is of good precision. [ABSTRACT FROM AUTHOR]

Nowadays, the load monitoring system is an important element in smart buildings to reduce energy consumption. Nonintrusive load monitoring (NILM) is utilized to determine the power consumption of each appliance in smart homes. The main problem of NILM is how to separate each appliance's power from the signal of aggregated consumption. In this regard, this paper presents a combination between particle swarm optimization (PSO) and artificial neural networks (ANNs) to identify electrical appliances for demand‐side management. ANN is applied in NILM as a load identification task, and PSO is used to train the ANN algorithm. This combination enhances the NILM technique's accuracy, which is further verified by experiments on different datasets like Reference Energy Disaggregation Dataset, UK Domestic Appliance‐Level ElectricityUK‐DALE, and Indian data for Ambient Water and electricity Sensing. The high accuracy of the proposed algorithm is verified by comparisons with state of the art methods. Compared with other approaches, the total mean absolute error has decreased from 39.3566 to 18.607. Also, the normalized root mean square error (NRMSE) method has been used to compare the measured and predicted results. The NRMSE is in the range of 1.719%–16.514%, which means perfect consistency. This demonstrates the effectiveness of the proposed approach for home energy management. Furthermore, customer behavior has been studied, considering energy costs during day hours. [ABSTRACT FROM AUTHOR]

Condensers are often required in power generation systems to condense the exhaust steam generated at the end of the turbine. This paper will investigate the enhanced condensation process of spent steam in a parallel flow type. The operating parameters of steam temperature (Tcond), steam flow rate (Gin), cooling water temperature (Tin), and cooling water flow rate (Lw) will affect the condensation effect by changing the gas–liquid arrangement in the tower. In this experiment, an orthogonal test was designed in the Raschig ring to investigate the significance of these four operating parameters on the experimental results. Different types of packings have different condensation effects due to different accumulation methods. The same type of packing has a different surface area, resulting in different contact areas between the gas and liquid phases during condensation. In this experiment, four different packings will be investigated to find their condensation performance. This paper uses the size of subcooling (ΔT) as an indicator to evaluate the effectiveness of condensation, and the values of condensation rate (R), number of liquid phase heat transfer units (NTUL), and total volume heat transfer coefficient (Kv) are used as a reference for the effectiveness of condensation. The results show that the overall condensation effect of regular packing is better than that of random packing, The average condensation rate of regular packing is 95%, the average condensation rate of random packing is 90%, the subcooling of regular packing is about three to five smaller than that of random packing, the heat transfer coefficient of regular packing is about 1.5 times of that of random packing. And by fitting the Kv data for different packings, an empirical formula was obtained that can be used to predict the Kv size of other packings. [ABSTRACT FROM AUTHOR]

In the circumstances of "Carbon Peak," it is imminent to implement green construction technology in the construction industry as it is a major source of CO2 emissions. Nevertheless, little study has been done on the impact of self‐face awareness (SA) on green building project performance (GP). On the basis of a large‐scale questionnaire survey in China and an in‐depth analysis of the four categories (cognition, behavior, satisfaction, and performance), this paper has added an SA dimension, and proposed a new impacting path. Then, based on the improved conceptual model for measuring the impact of green construction behavior on GP, this paper has conducted empirical tests by using a large sample of questionnaire data and partial least square‐structural equation modeling method. The results show that: (1) green construction behavior has a positive impact on GP, and the impact of perceived behavioral control of green construction technology is most significant for promoting green construction in the industry, and SA has a significant positive effect on green construction in the industry; (2) environmental perception and perceived behavioral control for green construction technology play a mediating role; and (3) significant differences indeed exist in the multigroup analysis, and the biggest difference is working experiences in the engineering field. Finally, this paper has proposed corresponding management implications based on the above findings. The main contribution of this study is that this study has developed a new dimension (SA) to the model considering Chinese national and cultural differences, in an attempt to reveal the impact of green construction technology on GP in the Chinese context. In addition, the promotion of green construction (PGC) in the construction industry was also added based on the sustainability perspective, which is the first attempt to explore the role of the PGC. [ABSTRACT FROM AUTHOR]

Accurately predicting the capacity of lithium battery is conducive to improving its safety. Affected by complex internal electrochemical reaction and external use conditions, the prediction accuracy is difficult to guarantee; at the same time, the existing prediction methods are unexplainable, resulting in the inability to trace the prediction process. Therefore, a capacity prediction model based on belief rule base with interpretability and interval optimization strategy is proposed in this paper. First, the reasoning process is designed according to interpretability modeling criteria. Second, to achieve a balance of accuracy and interpretability, based on the whale optimization algorithm, a model parameter optimization method using interval optimization strategy is proposed. Finally, through a case study, the model's effectiveness is verified. Comparison with other models shows that the proposed model has certain advantages in accuracy and interpretability. [ABSTRACT FROM AUTHOR]

In this paper, a novel model with a parallel structure is proposed to predict NOx emissions from coal‐fired boilers by using historical operational data, coal properties, and convolutional neural networks. The model inputs are processed and passed into three parallel subnetworks with well‐designed building blocks. The features learned by the three subnetworks are fused and used to predict NOx emissions from a 330‐MW pulverized coal‐fired utility boiler. A comprehensive comparison of different prediction models based on deep learning algorithms shows that the prediction model proposed in this paper outperforms other prediction models in terms of root mean square error criteria. The results show that the parallel structure is key to obtaining accurate predictions while reducing model complexity. This suggests that the model's performance can be improved by designing the model architecture. [ABSTRACT FROM AUTHOR]

This paper reviews state‐of‐the‐art numerical tools for the operation and maintenance (O&M) of offshore wind farms, focusing on decision support models for maintenance scheduling and the consideration of human and environmental uncertainty. In this review, various factors that can influence the successful conduct of maintenance operations will be examined and special attention will be paid to the most significant ones. Data‐driven technologies for improved offshore asset management are also examined and the most used data‐driven methods for modeling and optimizing turbine operation and maintenance are presented. A focus will be placed on the choice of maintenance strategy, which is the basis for the planning of operations and thus the optimization problem discussed. As offshore maintenance is a complex operation whose efficiency and safety depend on human and environmental factors, special attention will be paid to the planning strategy that minimizes the risks involved while maximizing efficiency by considering these factors. The choice of planning technique for turbine maintenance and better consideration of uncertainties are crucial areas of improvement as they can lead to better overall efficiency, higher profit margins, better safety, and improved sustainability of offshore wind farms. The paper covers the application of digital technologies for offshore wind O&M planning and the associated challenges. The paper also highlights the various environmental and human factors to be considered for the operation and maintenance of wind turbines. [ABSTRACT FROM AUTHOR]

Changing the shape of the blade trailing edge profile of a centrifugal pump is one of the effective means to improve the internal unsteady flow and pressure pulsation of a centrifugal pump. In this paper, the effect of four shapes of the blade trailing edge profile on the internal nonconstant pressure pulsation and internal flow of a multistage centrifugal pump is investigated by numerical simulation. The four types of blade trailing edges include prototype impeller (original trailing edge), pressure surface cut (ellipse on pressure side [EPS]), suction surface cut (ellipse on suction side [ESS]), and two‐side cut (ellipse on both sides). The results show that reasonable cutting of the trailing edge of the blade can reduce the pressure pulsation of the centrifugal pump. By comparing the pressure pulsation at fBPF, we found that the blade trailing edge profile can change the pressure pulsation. When the speed decreases, the periodicity of pressure pulsation becomes worse and the amplitude of pressure pulsation at fBPF decreases. The static pressure distribution at the trailing edge of the blade shows that EPS and ESS can effectively reduce the area of the high‐pressure gradient region at the trailing edge of the blade, and the dynamic–static interference effect is weakened, which leads to a lower pressure pulsation amplitude. The distribution of turbulent energy in different blade trailing edge centrifugal pumps corresponds to the distribution of static pressure in different blade trailing edge centrifugal pumps, further demonstrating the reliability of the static pressure distribution. The results of this study will further provide theoretical support for the optimization of the blade trailing edge profile. [ABSTRACT FROM AUTHOR]

Wind power generation does not emit greenhouse gases or pollutants, but there are some carbon emissions from the manufacturing, transportation, operation, and waste disposal of wind turbines. Directly driven permanent magnet and doubly fed asynchronous wind turbines currently have the largest market share in China, but few Chinese studies have compared their differences in carbon reduction potential. This paper uses life cycle assessment (LCA) to quantitatively analyze the full life cycle carbon emissions of the two wind turbines to determine which type of wind turbine has greater carbon reduction potential, obtaining the following results. (1) The full life cycle greenhouse gas emissions of 2.5 MW directly driven permanent magnet and doubly fed asynchronous wind turbines are 8.48 and 10.43 g CO2/kWh, respectively. The direct‐driven permanent magnet wind turbine is superior in terms of carbon reduction. (2) The stage with the greatest impact and the greatest difference between the two wind turbines in the full life cycle is the production stage, during which the carbon emissions of the directly driven permanent magnet and doubly fed asynchronous wind turbines are 1.045 × 106 and 1.210 × 106 kg, respectively. (3) According to sensitivity analysis, proper waste disposal and transportation can reduce carbon emissions from wind turbines. These research findings can be used to help achieve carbon peaking and neutrality goals, as well as the technological development of wind power enterprises. [ABSTRACT FROM AUTHOR]

The degradation of photovoltaic modules has an impact on various parameters of photovoltaic modules. Ignoring the degradation of photovoltaic modules or inaccurate estimation of the degradation will lead to wrong power dispatching strategies and lead to economic losses. For PV module life estimation or reliability estimation, it is necessary to first establish an accurate statistical degradation model of PV module. The main goal of this paper is to analyze a selection of explicit PV module degradation model based on distribution. Since the degradation is related to time, the study assumed that those parameters in Gamma or Gaussian distributions are related to time. Five models are calculated based on maximum likelihood estimation and particle swarm optimization. Through verification and comparison on the measured PV module degradation data, the performance of these models in four cases: long‐term data fitting, long‐term data prediction, single‐module short‐term data fitting, and multimodule short‐term data fitting are evaluated. The results show that the model proposed in this paper has a great improvement over the original model, and the constant‐σ Gaussian distribution degradation model achieves the best performance. [ABSTRACT FROM AUTHOR]

Renewable energy (RE) sources play an important role that not only reduces the pressure on fossil fuels but also produces safe and clean energy by developing the microgrid (MG) technology that fulfills the excess demand for power throughout the world. The technological development and the blessing of information and communication technology converts the MG technology to a smarter one, termed as smart grid (SG) and virtual power plant, by establishing a two‐way communication between the consumers and service provider with the aid of smart metering infrastructure, dynamic pricing scheme, energy management system, smart transmission and distribution system. The transformation of the traditional power system to SG enhances the efficiency, flexibility, resiliency, and robustness of the power system due to the increasing level of cyber security and automation. A directional pathway from conventional to smart power system has been carried out in this paper by addressing the present status of the power system, challenges during the operations, and possible solutions. Initially, the control operation of MG with RE and energy storage system has been investigated which is essential to understand the operation complexity and challenges due to the variable nature of the RE, load dynamics, and uncertainties. But the facilities of end‐user feedback, dynamic pricing scheme, cyber security, and smart metering system are not present in MG technology that hampers the proper regulation of controlling operation, generating and distributing energy as well as energy losses. The development of SG with smart technologies overcomes these problems by proper controlling of power usage. Finally, this paper provides a path to move the MG toward SG by analyzing the technical and critical challenges of smart devices, equipment integration, and their control issues and provides possible solutions to overcome the integration and control challenges for a reliable and sustainable operation. [ABSTRACT FROM AUTHOR]