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In this paper, a new simultaneous framework of distribution network operation is proposed based on the scheduling of photovoltaic/wind/battery multi-microgrids with network reconfiguration considering self-healing. The objective function is considered to minimize the energy losses, and multi-microgrids cost and improve the reliability indices including minimization of energy not-supplied (ENS) and minimizing system average interruption duration (SAIDI) and system average interruption frequency (SAIFI) indices. The optimization variables are defined as the situation of the distribution network switches to find the network's optimal configuration with the installation location and size of renewable resources and battery energy storage during 24 hours. An improved beluga whale optimization (IBWO) based on a nonlinearly diminishing inertia weight (NDIW) approach is used to find the optimal variable set of the problem. The recommended methodology is implemented on 33-bus and real 59-bus distribution networks. The results demonstrated that by obtaining an optimal state of the network switches in the event of a fault, as well as the optimal scheduling of the two microgrids, the energy losses have decreased and the reliability indices have improved. The outcomes of the proposed methodology based on the hybrid multi-microgrid allocation and network reconfiguration are stated that the losses, ENS, SAIDI, and SAIFI are reduced by 66.39%, 54.00%, 50.24%, and 33.61%, respectively, for 33-bus network and are declined by 65.29%, 57.44%, 48.63%, and 62.33%, respectively, for the real 59-bus Ahvaz network in comparison with the base network. The obtained results illustrated that in the condition of the network line outage, by simultaneously implementing the reconfiguration with the change of network switches and the optimal allocation and scheduling of HMGs in the network, self-healing is provided to prevent a significant weakening of the network performance so that the resilience is improved in addition to minimizing the energy losses and the cost of HMG energy injection compared to the base network. The findings revealed that the objectives including losses, ENS, SAIFI, and SAIDI are increased by 78.29%, 33.00%, 83.33%, and 35.11%, respectively, due to outage of line 22 of the 33-bus network and these objectives are increased by 52.32%, 10.30%, 32.29%, and 79.66%, respectively, due to outage of line 12 of the 59-bus network compared with not considering the line outage. Moreover, the superior capability of the recommended NDIW-based IBWO has been confirmed in comparison with the well-known particle swarm optimizer (PSO) and ant lion optimizer (ALO) in solving the problem to achieve better objective value. [ABSTRACT FROM AUTHOR]

The increasing global dependence on fossil fuels for energy has prompted researchers to explore alternative power generation sources that offer higher efficiency, cost-effectiveness, and low environmental impact. Developed countries have been making continuous efforts to reduce their reliance on depleting fossil fuel sources to curb carbon emissions. As a result, hydrogen has gained significant attention among researchers as a potential future fuel that meets all the necessary criteria. Many countries aiming for a hydrogen economy have invested in research on fuel cells. Among various fuel cells, the solid oxide fuel cell (SOFC) has emerged as a commercially viable power source at a small scale. This paper provides an extensive review of the components, materials, design, operation, and integration strategies of SOFCs with existing thermal-based power plants. The performance and prospects of various SOFCs in standalone and hybrid modes are summarized, along with the limitations. Additionally, the research and commercialization efforts that can enhance the potential applications of SOFCs as a sustainable and reliable system in the long term are outlined. [ABSTRACT FROM AUTHOR]

In the past few years, there has been a notable interest in the application of machine learning methods to enhance energy efficiency in the smart building industry. The paper discusses the use of machine learning in smart buildings to improve energy efficiency by analyzing data on energy usage, occupancy patterns, and environmental conditions. The study focuses on implementing and evaluating energy consumption prediction models using algorithms like long short-term memory (LSTM), random forest, and gradient boosting regressor. Real-life case studies on educational buildings are conducted to assess the practical applicability of these models. The data is rigorously analyzed and preprocessed, and performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to compare the effectiveness of the algorithms. The results highlight the importance of tailoring predictive models to the specific characteristics of each building's energy consumption. [ABSTRACT FROM AUTHOR]

Energy storage systems (ESS) are seeing rapid market growth due to the changing worldwide landscape of electricity distribution and consumption. An ESS must possess the capability to oversee the functioning of the system's modules under abnormal circumstances, while also having the ability to supervise, manage, and optimize the performance of one or more battery modules. At present, the condition of batteries is assessed based on two factors: the level of charge (SOC) and the overall condition (SOH). By using these two characteristics, it becomes feasible to compute the anticipated battery lifespan and evaluate a battery's efficiency. The assessment of SOH is a crucial determinant in guaranteeing the effectiveness, dependability, and security of batteries in electric vehicles (EVs). Nevertheless, the safety issues resulting from the imprecise estimation and forecasting of battery health status have garnered significant attention in academic circles. This study presents a comprehensive evaluation of several SOH monitoring techniques. In order to achieve this objective, various scientific and technical literatures are examined and the corresponding methodologies are categorized into distinct groupings. The groupings are categorized based on the manner in which the procedure is executed: methods and techniques used in experiments and models. This paper provides a comprehensive overview of the benefits and drawbacks of several SOH assessment and prediction techniques, along with the associated obstacles in SOH estimation. [ABSTRACT FROM AUTHOR]

During the development of oil reservoirs, a rapid increase in water cut following reservoir flooding leads to inefficient or ineffective circulation of injected water, rendering a significant portion of the remaining oil in the reservoir inaccessible. The displacement method using polymer particle dispersion systems effectively solves the issue of rapid water breakthrough in oil reservoirs. Owing to the particle phase separation phenomenon, polymer particles can selectively penetrate into the larger pores where water circulation is inefficient, enhance their flow resistance, and thereby achieve equilibrium displacement along with an increased swept volume. This paper investigates the heterogeneous distribution of polymer particles within a porous medium, incorporates the red blood cell dendrite concentration distribution theory from biological fluid mechanics, and develops a mathematical model to delineate the viscosity characteristics of polymer particle dispersion systems, taking into account the phase separation phenomenon. Building on this foundation, it formulates a capillary bundle model for the polymer particle dispersion system specifically designed for oil displacement and proceeds to determine its relative permeability curve. Simulation outcomes reveal that at a water saturation level of 0.063, the concentration of polymer particles in fractured large pore capillaries is markedly elevated, yet capillaries with a pore size under 26 µm remain devoid of polymer particles. With the increase of water saturation, the concentration of polymer particles in large pore capillaries reduces, whereas it progressively augments in medium pore capillaries. Upon reaching a peak water saturation of 0.751, capillaries smaller than 18 µm are entirely free of polymer particles. These findings suggest that the heterogeneous distribution of polymer particles markedly inhibits the percolation capabilities of the dispersed system following a water phase breakthrough, facilitating the entry of more dispersion into oil-laden capillaries and thus enhancing the flow capacity of the oil phase. [ABSTRACT FROM AUTHOR]

In pressurized water reactor accident scenarios, the injection of water from the emergency core cooling system (ECCS) (ECC injection) might induce a pressurized thermal shock (PTS), affecting the reactor pressure vessel (RPV) integrity. Therefore, PTS is a vital research issue in reactor safety, and its analysis is essential for evaluating the integrity of RPVs, which determines the reactor life. The PTS analysis comprises a coupled analysis between thermal–hydraulic and structural analyses. The thermal–hydraulic approach is particularly crucial, and reliable computational fluid dynamic (CFD) simulations should play a vital role in the future because predicting the temperature gradient of the RPV wall requires data on the transient temperature distribution of the downcomer (DC). Since one-dimensional codes cannot predict the complex three-dimensional flow features during ECC injection, PTS is one reactor safety issue where CFD simulation can benefit from complement evaluations with thermal–hydraulic system analysis codes. This study reviewed from the viewpoint of the turbulence models most affecting PTS analysis based on papers published since 2010 on single- and two-phase flow CFD simulation for the experiment on PTS performed in the Rossendorf coolant mixing model (ROCOM), transient two-phase flow (TOPFLOW), upper plenum test facility (UPTF), and large-scale test facility (LSTF). The results revealed that in single-phase flow CFD simulation, where knowledge and experience are sufficient, various turbulence models have been considered, and many analyses using large eddy simulation (LES) have been reported. For two-phase flow analysis of air–water conditions, interface capturing/tracking methods were used in addition to two-fluid models. The standard k – ε and shear stress transport (SST) k – ω models were still in the validated phase, and various turbulence models have yet to be fully validated. In the two-phase flow analysis of steam–water conditions, many studies have used two-fluid models and Reynolds-averaged Navier-Stoke (RANS), and NEPTUNE_CFD, in particular, has been reported to show excellent prediction performance based on years of accumulated validation. [ABSTRACT FROM AUTHOR]

Recent experience with the strongest earthquakes greater than magnitude 5.4 in Korea leads to public interest in the safety and resilience of critical infrastructures. There are many nuclear power plants near the epicenters of the earthquakes. Nuclear power plants are essential infrastructures that provide stable and enough energy for human life, and simultaneously, control systems for the safety and security of nuclear power plants are critical due to the risk of nuclear accidents on public health and the environment. The nuclear area uses probabilistic risk assessment to estimate the risk of structures, systems, and components in nuclear power plants, and the evaluation of the fragility curve is a key process for probabilistic risk assessment. The challenges of a seismic fragility analysis lie in estimating the influence of various uncertainties in material, geometry, and earthquake and improving the existing fragility analysis methods, which require time-consuming nonlinear time history analysis. Thus, this paper conducts a multivariate seismic fragility analysis using surrogate models for reinforced concrete squat shear walls and proposes a simplified closed form equation for a chosen surrogate seismic demand model. The surrogate models are trained and validated by several approaches: response surface method, support vector machine, Gaussian process regression, and neural network. In addition, a correlation analysis is used to evaluate the relative importance of the variables to the seismic demand to simplify the surrogate model further. Finally, simplified surrogate models based on the importance of the variables are proposed as closed form of polynomials, and the performance of these models on the fragility analysis is evaluated. [ABSTRACT FROM AUTHOR]

Shell and tube-type crossflow heat exchangers with circular fins are widely used in various industries, including nuclear power plants. This study focuses on the sodium-to-air heat exchanger, which is one of the key safety features in sodium-cooled fast reactors. Tube alignment inside the heat exchanger is important because it directly determines the performance. Although heat exchangers are generally designed to have staggered tube alignment, inevitable inline-aligned zones exist. Therefore, this paper proposes a hybrid tube alignment modeling approach that considers both staggered and inline tube heat transfer phenomena. The results of the hybrid model calculations are verified and validated with liquid sodium experiments. The hybrid approach to the tube arrangement reduced the temperature deviation between experimental and calculated values to a difference of 4.55% and 7.38% for both sodium and air sides, respectively, while for the heat transfer, the difference was 15.12% and 9.06% for sodium and air sides, respectively. The results of this study can also be used as a basis for the safety evaluation of nuclear reactors and licensing processes for sodium-cooled fast reactors. In addition, this study is limited not only to sodium heat exchangers but also to any serpentine tube arrangement or crossflow heat exchanger under high-temperature systems, such as concentrated solar power and thermal energy storage industries. [ABSTRACT FROM AUTHOR]

This paper performs a technoeconomic analysis of the wind power potential and evaluates the cost of wind power generation in Evodoula, comparing two methods, the conventional method and the uncertainty method based on a comparative spatiotemporal approach using the geographic information system (GIS) software tool. This study is based on satellite wind data measured at 10 m above ground level (AGL) over a 40-year period (1980-2019), by the meteorological service NASA (National Aeronautics and Space Administration)/Goddard Space Flight Center (GSFC). The main objectives are to obtain an appropriate design for a proven optimal location and to assess the viability of wind power at Evodoula. For this type of study, there is little literature available. The optimization of an onshore wind farm and deployment in the wind energy development interest area (ZIDEE) is carried out to obtain a minimum and parsimonious discounted cost production unitary (CPU) of the electricity produced. The results showed that in Okok, a location with a large energy deficit, an onshore wind farm with an electricity generation capacity of 12.5 MW with 5 NORDEX N100-2.5MW wind turbines would have a total energy production (TEP) of about 64.0254825 TWh and a selling price of electricity that would be 0.0034 CAD$/kWh, which is very low compared to the utility price (about 98% cheaper). The total cost of the wind farm would be about $11 million, with a net present cost of about $218 million. The annual profit generated by the wind farm would be over $6 billion. The return on investment (ROI) of the project is estimated at 2880.882%. The constructed onshore wind farm would avoid CO2 emission at over 11 Mt CO 2 , eq /year as the energy generated is from the atmosphere. The wind farm would realize an average annual cash flow estimated at nearly $30 million after 20 years of operation. These savings would allow the installation of CO2 capture systems in conventional power plants. In addition, the analysis of uncertainties and risks was identified and quantified to estimate the confidence levels of the project development results. The risks have been assessed, and we recommend that the total uncertainty of the project is around 15%. The energy values in P 75 and P 90 are 10.08% and 19.22%, respectively, lower than the energy value in P 50 of 11.60 GWh/An. Finally, the main policy recommendations for an inclusive design process are highlighted. The contribution of this study is to assist policy makers in making appropriate decisions in the development and implementation of energy and environmental policy in Cameroon and in many continental areas. [ABSTRACT FROM AUTHOR]

Summary: Free‐standing, flexible, and binderless binary ZnO‐reduced graphene oxide (rGO), SnO2‐rGO, and ternary ZnO‐SnO2‐rGO Li‐ion anodes were produced by vacuum filtrating the mixture of metal oxides synthesized by sol‐gel method and graphene oxide produced by Hummers method. These paper anodes were designed by being decorated with metal oxides between rGO layers. To compare nanocomposite anode structures, field emission gun‐scanning electron microscopy, energy dispersive X‐ray spectrometer, X‐ray diffraction, and electrochemical analyses were applied. The results suggested that ternary ZnO‐SnO2‐rGO paper anode exhibited larger discharge capacity and capacity retention than the binary anodes even after 100 cycles. This finding is attributed to buffering effect of rGO on volume expansion of the anode structure designed by sandwich‐type production and the synergic effect of ZnO‐SnO2 oxides. ZnO‐SnO2/rGO composite has been produced as binder‐free, flexible, and free‐standing anode for Li‐ion batteries. Binary ZnO/rGO and SnO2/rGO composites have been compared with ternary ZnO‐SnO2/rGO composite anode to determine the synergistic effect of the combination of ZnO and SnO2 with each other and with graphene. As compared with literature, satisfying results have been obtained from free‐standing anode structure of ZnO‐SnO2/rGO providing good cycling stability, long cycle life, and high discharge capacity of 731 mAh g−1 at 0.2 C. [ABSTRACT FROM AUTHOR]

The article presents information on the fifth International Ege Energy Symposium & Exhibition (IEESE-5) to be held at the Energy Research and Application Center, Pamukkale University in Denizli, Turkey from June 27-30, 2010.

Summary: With the rapid development of the global economy and technology, the demand for fossil fuels in transportation and power industries is ever‐growing, which inevitably leads to lower fossil fuel storage and higher prices. Electric vehicles (EVs) are widely used as an effective solution to energy shortage and environmental pollution. However, there exist numerous obstacles, such as unmatchable capacity and unsuitable location. Therefore, an appropriate size and location of the charging station (CS) are very crucial. Based on a thorough investigation of 138 literature, this paper comprehensively summarizes the past from three aspects including charging technology, CS model, and methodologies. Besides, a comprehensive summary of the charging way and its level is proposed for a more overall analysis with enriched technologies diversity. Furthermore, two different CS models are discussed detailly in the aspects of the objective function, factors, advantages and disadvantages, applicability, and complexity. Additionally, a detailed summary of three methodologies, including meta‐heuristic, multi‐criteria decision making (MCDM), and analytical methods, are proposed and a comprehensive evaluation and their advantages/disadvantages, applicability, and complexity are summarized and assessed thoroughly. Finally, 11 valuable and insightful findings are drawn and two promising challenges, two opportunities, and three future directions are proposed for further research. Highlights: Key technical difficulties and future challenges of sizing and placement of charging stations (CSs) are discussed.Most representative papers are selected and presented in table form for readers to compare more conveniently.Two different kinds of models and evaluation indexes are introduced in detail and each model is compared and evaluated fairly from different angles.Thirty‐three methods of sizing and location determination are summarized and compared strictly, and the advantages and disadvantages, applicability, and complexity of each method are dedicated to being described.Eleven main findings and seven constructive suggestions are put forward because of the development status of electric vehicles. [ABSTRACT FROM AUTHOR]

Reducing costs and improving durability are essential factors for commercializing gas foil bearings, crucial components of fuel cell vehicle air compressors. In this paper, a two-pad gas foil bearing is proposed. By reducing the number of top foils, adopting a symmetrical design, and eliminating the welding step, the cost of processing and assembly is reduced, and the potential source of failure caused by welding is eliminated. The feasibility of the two-pad gas foil bearing was verified through testing on a commercial fuel cell vehicle air compressor. Then, durability testing was performed in accordance with fuel cell vehicle usage requirements, including an accelerated random vibration test equivalent to 6,000 h of on-board operation and a 200,000-cycle start-stop test. These tests simulated the damage caused to the bearings during fuel cell vehicle operation and start-stop periods, respectively. The durability test results indicated that the two-pad gas foil bearing provides good start-stop durability but insufficient durability against on-board random vibration. The failure causes were analyzed, and improvement measures were proposed. Our findings can be utilized to guide the manufacturing of low-cost and highly durable gas foil bearings. [ABSTRACT FROM AUTHOR]

Compressors are one of the three major components of gas turbines, and their characteristic curves are used to analyze off-design performance. How to infer the characteristic curve based on different data is an important research topic. In this paper, PG9351FA gas turbine is taken as the research object. Two methods, artificial neural network and parameter estimation, are used to predict its characteristic curve, and the prediction accuracy and application conditions of the two methods are discussed. This article compares the two methods from the perspectives of known speed characteristic curve regression and unknown speed characteristic curve inference, analyzes the impact of sample size and sample error on their inference results, and quantitatively analyzes the error through statistical methods such as calculating the mean square deviation of the data. The application scope and conditions of different methods are provided. The research results show that the method based on neural network to infer the characteristic curve has high accuracy and is suitable for the prediction of known and unknown speed characteristic curves under sufficient data, but not for the prediction of unknown side curves. The elliptic equation fitting method based on parameter estimation has a slightly lower accuracy in processing the nearly vertical compressor characteristic curve, but it can be used as an effective and reliable method to infer the compressor characteristic curve in the case of a small amount of data. The modulization method based on parameter estimation has high accuracy and is applicable to the estimation of complete characteristic curve from partial data of known characteristic curve. In this paper, the application scope and conditions of these two methods are determined, which can provide reference for engineering practice. [ABSTRACT FROM AUTHOR]

Matrix converters (MCs) are AC-AC power conversion topologies widely explored and applied in the industry for their attractive features of sinusoidal input and output currents, considerable size reduction, and reliable operation due to the omission of bulky passive components. Considerable research has made this technology a serious contender to the indirect AC-DC-AC topologies in industrial applications. This paper comprehensively reviews the state-of-the-art MC technology and its prospects. Furthermore, it presents the advancements in the topological structures of direct/indirect MCs and their modulation and control algorithms over the past decade. It also reports the recent progress in multiphase direct/indirect MC structural and control structures. This paper is aimed at providing comprehensive information to the researchers and engineers on MC technology's current industrial applications and the research scope as the world moves towards AI and digitalization. [ABSTRACT FROM AUTHOR]

Summary: At present, pumped hydro energy storage plays the dominant role in electrical energy storage. However, its development is clearly restricted by the topography and adverse impacts on local residents. Underground pumped hydro energy storage (UPHES) using abandoned mine pits not only can effectively remedy these drawbacks but is also constructive to the management of abandoned mine pits. In this paper, we firstly conduct a comprehensive analysis of conventional pumped hydro energy storage (CPHES) and UPHES, using life cycle sustainability assessment (LCSA). Sustainability indicators in this paper include economic indicators, environmental indicators, and social indicators. Among all the indicators, blue water footprint (BWF) and ecological footprint (EF) are included for the first time to assess the social performance of CPHES and UPHES. Then, this paper employs multi‐attribute value theory (MAVT) and scenario analysis to evaluate the overall performance of energy storages. The results show that CPHES has better performance in economy and environment than UPHES because of the economies of scale, while the UPHES has better performance in social sustainability impact because of the absence of stages of excavation and backfilling. When using MAVT methodology, only when the weight for social indicator is three times higher than that of economy and environment; ie, the weight for social dimension is 0.6, and the weights for environmental and social dimension are 0.2; the score of UPHES is higher than CPHES. [ABSTRACT FROM AUTHOR]

Summary: The popularity of the electric vehicles has been growing due to the concern of increase in environmental pollution. In order to overcome the environmental issues, electric vehicles have been introduced all over the globe. As the use and number of electric vehicles is growing, there is a great demand to identify the sustainable solutions for intelligent charging station dedicated to electric vehicles. The objective of this paper is to explore the possibilities for intelligent charging station using 5G services. This paper is a state‐of‐the‐art concept to identify the current challenges and issues of intelligent charging station. It should be noted that in the future, the use of the smart charging station will dramatically improve the efficiency of current charging stations. It is identified based on several existing techniques that the biggest challenges are the connectivity between the vehicles and among different charging stations. This manuscript could be useful for researchers working on intelligent charging station worldwide and on their integration with the recent communication technologies like 5G. The features of intelligent charging stations combined with the fifth‐generation network represent a new path for humanity and introduce a safer and more advanced environment. From data privacy to human safety, the network provides enough resources to rise driving quality, time and fuel economy and human lives saving. The results of this research can be well applied in the industry or in the field of academic research. [ABSTRACT FROM AUTHOR]

Summary: Lithium‐ion batteries (LIBs) are widely used in electric vehicles due to its high energy density and low pollution. As the key monitoring parameters of battery management system (BMS), accurate estimation of the state of charge (SOC) and state of health (SOH) can promote the utilization rate of battery, which is of great significance to ensure the safe use of LIBs. In this paper, a novel dual Kalman filter method is proposed to achieve simultaneous SOC and SOH estimation. This paper improves the estimation accuracy of SOC and SOH from the following four aspects. Firstly, the widely used equivalent circuit model is established as the battery model in this paper, and the forgetting factor recursive least squares (FFRLS) method is applied to identify the model parameters. Secondly, two kinds of single‐variable battery states are established to analyze the influence of OCV‐SOC curve and battery capacity on SOC estimation. Based on this, an error model is proposed combined with Kalman filter to achieve better estimation results of SOC and SOH. Besides, to promote the accuracy of SOC estimation, based on the error innovation sequence (EIS) and residual innovation sequence (RIS), the improved dual adaptive extended Kalman filter (IDAEKF) algorithm based on dynamic window is proposed. Finally, the superiority of the proposed model is verified under different cycles. Experimental results show that the estimation error of SOC and SOH is controlled within 1%. [ABSTRACT FROM AUTHOR]

Summary: The Extensive Damage Mitigation Guideline (EDMG), which was developed by the U.S. Nuclear Energy Institute (NEI) owing to the motivation of 911 terrorist attacks and not allowed to spread before the Fukushima nuclear accident, was publicly available after Fukushima nuclear accident and paid attention to worldwide. Many countries have adopted the U.S. EDMG methodology and developed their strategies (some are not called EDMG but have similar functions), planning to introduce it into the emergency plans of their domestic nuclear power plants (NPPs). Based on the defense‐in‐depth (DID) concept of nuclear safety, the experience of the Fukushima nuclear accident, and the state‐of‐the‐art of EDMGs research, this paper will give a comprehensive review of EDMG. To illustrate the reason for the introduction of EDMG into the traditional NPP procedures/guidelines, the review work started from the perspective of DID in nuclear safety and the reason for the Fukushima nuclear accident from the viewpoint of DID, which showed the weaknesses of DID implementation strategy ‐ underestimation of the damage during a beyond‐design‐basis external event (BDBEE). EDMG is crucial complementary for the old framework of procedures/guidelines to enhance the concept of DID. The research progress of EDMG in different countries has been reviewed and a rough comparison has been made among them according to the present state‐of‐the‐art from literature. The methodology of EDMG is summarized as follows: (a) Loss of Large Area (LOLA) ‐ the originally designed scenario for EDMG; (b) elements of EDMG ‐ the summarized contents for EDMG implementation; (c) classification of EDMG ‐ initial response EDMGs and Technical Support Center (TSC) response EDMGs; (d) implementation of EDMG‐introduction of three phases of EDMG implementation step by step. Furthermore, three typical methods to integrate EDMG into the traditional procedures/guidelines are reviewed. Finally, this paper gives some concluding remarks for EDMG‐related critical issues and recommended future work, which may be challenges for the development and application of EDMG and useful for the related researchers. Novelty Statement: The studies of the extensive damage mitigation guideline (EDMG), its methodology, and the progress in different countries have been reviewed.Three typical methods to integrate EDMG into the traditional procedures/guidelines are summarized and reviewed.Concluding remarks for EDMG‐related critical issues and recommended future work are summarized. [ABSTRACT FROM AUTHOR]

Summary: The internet of things (IoT) is a distributed heterogeneous network of lightweight nodes with very minimal power and storage. The IoT energy system for smart applications such as smart grid, smart building, and smart transportations depends on the IoT architecture, determining the high or low‐energy consumption levels. Most of the IoT objects are power‐driven by batteries with short life spans that require replacement. The replacement phase is tedious; hence this paper comprehensively discussed the IoT energy system, energy resources, and energy storage as these three elements are crucial to enable energy efficiency for the IoT applications. In comparison to the battery‐powered solutions, the scavenging of infinite quantities of energy makes the IoT systems robust. Therefore, this paper further elaborates on understanding the current situation in terms of renewable energy harvesting for these low‐energy systems. The IoT energy storage highlighted in this paper includes fuel cell, lithium battery, and supercapacitor technology. This paper also provides the findings for IoT energy system challenges and open issues in management and storage in terms of bidirectional, continuity, autonomy, fluctuation, conversion, consumption, integration, multifunction, and stability. This paper will assist researchers in more quality yet practical energy usage and savings, better IoT system architecture, and smart application sustainability. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the newly prepared gas diffusion layer (GDL) is investigated and characterized. The GDL mainly includes the carbon paper layer and microporous layer (MPL). By the analysis of physical and electrochemical test results, it is found that the performance of GDL prepared by composite carbon black is obviously improved compared with conventional GDL prepared by single carbon black. This new GDL combines the characteristics of two original carbon materials and has a more reasonable pore structure, which can meet the good transmission of reaction gas and liquid water. Furthermore, the research shows that the pore size distribution of GDL can be effectively adjusted by changing the proportion of composite carbon black. The GDL with composite carbon black consisting of 70 wt.% Acetylene Black carbon and 30 wt.% Vulcan XC‐72 is finally confirmed to have the best fuel cell performance. [ABSTRACT FROM AUTHOR]

The reliability of a pressurized water reactor power plant's control rod drive mechanism (CRDM) is affected by many factors, such as operation states, unit performance, and dynamic environments. Multiple sources of uncertainties, including random, interval, and fuzzy, exist when analyzing the reliability of CRDMs. Modeling reliability without considering the fusion of multisource uncertainties may result in model distortion and may not provide realistic assessment results. This paper proposes a multisource uncertainty fusion method powered by the margin-based variable transformation and the Bayesian network propagation. The interval and fuzzy variables are transformed into random variables to obtain the corresponding generalized probability density function of the CRDM's feature parameters. After that, the CRDM's reliability can be evaluated via probability quantization of the Bayesian network inference result through sampling algorithms. A magnetic-jack type CRDM case is presented to verify the proposed method, and the results show that this method can fuse three types of uncertainty variable in a unified way and effectively obtain reliability evaluation results. [ABSTRACT FROM AUTHOR]

The need for sustainable integrated energy systems to mitigate environmental impact is hindered by challenges in fluctuating demand, trading reliability, and trustworthiness. This paper proposes an innovative approach to tackle these challenges by introducing a blockchain-based integrated energy system trading model with smart contracts. It is intricately linked with the operation of carbon capture and storage (CCS) technology and power-to-gas (P2G) equipment. The CCS-P2G-coupled operation principle is first outlined, followed by the presentation of a comprehensive system model. The peer-to-peer (P2P) energy trading algorithm is enhanced using the Bloom filtering technique. Leveraging smart contracts, a distributed energy trading mechanism is employed, resulting in a meticulously constructed integrated energy system trading model for CCS-P2G-coupled operation. The proposed model is rigorously evaluated for energy trading efficiency and system performance, revealing significant improvements compared to prior studies and showcasing substantial cumulative benefits. Three operation scenarios are examined, with experimental results highlighting the model's superior carbon emission reduction capacity. This study introduces an innovative paradigm for trading and managing integrated energy systems, holding potential implications for the sustainable development and decarbonization transition of future energy systems. [ABSTRACT FROM AUTHOR]

In the past few years, different kinds of structural nonlinearities, such as external magnetic interaction and multistable structures, have been introduced into galloping energy harvesters to enhance energy harvesting efficiency. However, since the galloping-based piezoelectric energy harvester (GPEH) is a self-excited system, the conventional impedance matching method that is widely used in vibration energy harvesters is no longer valid for it. Therefore, the power characteristics of the nonlinear GPEH are still an open question to be solved. To this end, this paper is motivated to derive the approximate analytical solution of a monostable galloping-based piezoelectric energy harvester through the harmonic balance method and impedance matching theory. The analytical maximum power, power limit, and critical electromechanical coupling are studied from the perspective of the influence of structural nonlinearity on the power performance. Firstly, the approximate analytical solutions of output power, power limit, optimal resistance, and critical electromechanical coupling coefficient are derived. The accuracy of the analytical solution is verified by numerical simulation. It is found that the influence of structural nonlinearity on the power characteristics of the nonlinear GPEH is quite different under different wind speeds. After that, the power characteristics of the system under different wind speeds and different coupling conditions are investigated. The results showed that the maximum power of the system can be increased by introducing stiffness nonlinearity under low wind speed and weakly coupled configuration. Even more, the system can be shifted into a strongly coupled system when the nonlinearity is enhanced to a certain level. Reasonable design of stiffness nonlinearity can effectively reduce the critical electromechanical coupling, which indicates that stiffness nonlinearity is a feasible and effective way to improve the power performance of low-speed wind energy harvesting. [ABSTRACT FROM AUTHOR]

This paper investigates comprehensively the operational dynamics of a thermochemical heat storage (TCHS) reactor for low-temperature applications using polyaluminum sulfate and takes into consideration a developed simulation model with an experimentation validation. Significant concordances were found between the developed simulation model and the experimental results. Results reveal optimized conditions for the charging process using an electrical heater, with a heating temperature of 120°C at 10 K/min over 500 minutes. This achieves full material charging, with stabilized pressure drops at an equilibrium of 80 mbar, corresponding to a temperature of approximately 108°C, and yielding a thermal power of 950 W. An inlet vapor pressure of 18 mbar at 10°C for 5 hours is sufficient to completely discharge the bed, with pressure drops reaching around 30 mbar and a thermal power of 300 after reaction completion. Insights into conversion extents during both processes are provided, along with a remarkable thermal efficiency of 90% and a coefficient of performance (COP) of 97%, surpassing recommended theoretical values (50%). The study suggests further enhancing system performance through the design and implementation of a dedicated heat exchanger. The achievement of 75% of the targeted thermal power specification represents a significant milestone, offering valuable contributions towards the realization of sustainable technological advancements of both the reactor technology of TCHS and state-of-the-art thermal energy storage solutions. From the research, it is possible to infer further suggestions for enhancing this performance by designing and implementing a dedicated heat exchanger for both heat supply and retrieval mechanisms. [ABSTRACT FROM AUTHOR]

Due to its cost-effectiveness and high product selectivity, tin oxide has been regarded as a promising catalyst for the electrochemical conversion of CO2 to formate. However, formate production is hindered by the high overpotential; there is a need to reduce the overpotential to enhance energy efficiency and lower electricity cost for the implementation of carbon utilization technology. Here, we report a facile synthesis method for 1 nm-sized SnO2 cluster catalysts, which can be used for CO2-to-formate conversion. SnO2 clusters were prepared through impregnation of porous carbon with a tin precursor solution. The SnO2 clusters showed a low overpotential, generating a current density of 10 mA cm-2 at a potential of -0.34 V vs. RHE in 1 M KOH. They also achieved high Faradaic efficiencies of 90.5% and 81.5% at 200 and 300 mA cm−2, respectively. Their electrocatalytic performance was strongly dependent on the annealing conditions, which affected the particle size, electrochemical active surface area, and metal oxidation state. This paper presents a versatile method for synthesizing metal oxide cluster catalysts, apart from providing insights into the catalytic activity for the electrochemical conversion of CO2 to formate. [ABSTRACT FROM AUTHOR]

Photovoltaic power generation can directly convert solar energy into electricity, but most of the solar energy absorbed by the photovoltaic panel is converted into heat, which significantly increases the operating temperature leading to a reduction in the power generation efficiency of the panels. To reduce the working temperature of photovoltaic panels and improve the photoelectric conversion efficiency, this paper installs aluminum fins and air channels at the traditional photovoltaic cell back sheets and cools them with forced-circulation cooling through fans. The relationships between fin spacing, fin height, air channel inlet wind speed and panel temperature, power generation, and electrical efficiency were investigated. The results show that the temperature of the photovoltaic panel decreases and then increases with the decrease of the fin spacing during natural convection and gradually decreases with the increase of the fin height. The use of forced-circulation cooling technology can effectively reduce the PV panel temperature and increase the net power generation. When the solar radiation intensity is 1000 W/m2, the ambient temperature is 35°C, the fin spacing is 6 mm, the height is 80 mm, the inlet wind speed is 1 m/s, the net generating power reaches the maximum value, and the net generating power and the electrical efficiency are increased by 14.6% and 2.25%, respectively. [ABSTRACT FROM AUTHOR]

While demand response programs inherently depend on consumer acceptance in order to be successful, consumer behavior is often overlooked when designing such programs. This paper addresses the impact of consumer flexibility in terms of appliance use on the success of a demand response program, measured through the overall grid stability assessed by the demand's peak-to-average ratio. We employ a bootstrapping approach to simulate energy communities from real-life consumer data and implement a state-of-the-art demand response system. Results suggest that higher consumer flexibility under real-time energy tariffs implies higher degrees of grid stability, with real-time pricing decreasing the average peak-to-average ratio by 4.6% compared to time-of-use tariff and with highly flexible consumers showing a 23% lower peak-to-average ratio than regular consumers on average. Yet, it is possible for higher flexibility to be detrimental to grid stability by increasing the peak-to-average ratio under a time-of-use tariff. This result highlights the importance of understanding the interplay between different factors that influence energy consumer behavior, a research stream that has been under investigated thus far. [ABSTRACT FROM AUTHOR]

This paper proposed a single-piston free piston expander-linear generator (SFPE-LG) prototype applied to organic Rankine cycle systems. Two valve timing control strategies, namely, time control strategy (TCS) and position control strategy (PCS), were developed. Based on the experimental data, a back propagation neural network (BPNN) prediction model was established. The effects of structural parameters such as neural network layers, transfer function, training function, hidden layer nodes, and learning rate on the prediction accuracy of this BPNN model were discussed. The training and prediction accuracy of the BPNN model was verified using 5-fold cross-validation and Wilcoxon signed-rank test. Moreover, the BPNN model was integrated with a genetic algorithm to predict and optimize the maximum output power of the SFPE-LG. The results showed that the BPNN model used to predict the motion characteristics and output performance of the SFPE-LG exhibits strong learning ability and high prediction accuracy. Notably, the prediction accuracy of the BPNN model is significantly higher under the PCS compared to TCS. The effect of hidden layer nodes on mean square error (MSE) and correlation coefficient (R) is greater than that of the learning rate. When the number of hidden layer nodes exceeds 30, the BPNN model consistently achieves low MSE and high R. The optimization results showed that the SFPE-LG can obtain a maximum output power of 141.69 W under the TCS, when the working parameters are inlet pressure of 0.7 MPa, intake duration of 35 ms, load resistance of 67 Ω, and expansion duration of 104 ms, respectively. [ABSTRACT FROM AUTHOR]

Stochastic programming has become increasingly vital in energy applications, especially in the context of the growing need for renewable energy solutions. This paper presents a significant advancement in this field by introducing an efficient and robust algorithm for optimally sizing hybrid renewable energy systems. Utilizing a two-stage stochastic programming approach, the proposed algorithm addresses the challenges posed by the unpredictability of renewable energy sources. The proposed solution leverages the three-block alternating direction method of multipliers (ADMM), a cutting-edge technique that facilitates parallel computation and enhances computational efficiency. The distinctiveness of this method lies in its ability to solve complex stochastic optimization problems without compromising the mathematical integrity of the model. This is achieved by applying first-order optimality conditions, ensuring both robustness and efficacy. To demonstrate the practical applicability and superiority of the algorithm, a case study was conducted in a rural area of South Africa. The proposed algorithm was applied to design an optimal hybrid renewable energy system, and its performance was compared against traditional methods such as progressive hedging and Monte Carlo techniques. Results affirm the superiority of the approach, saving approximately 8.16% capital cost when compared to progressive hedging. In addition, the proposed algorithm outperforms the Monte Carlo method both in terms of CPU time and the number of cost function evaluations. [ABSTRACT FROM AUTHOR]

In this paper, a novel model of an energy management system (EMS) for a microgrid (MG) under uncertain conditions is proposed. The MG consists of renewable photovoltaic and wind sources along with electric vehicle parking lots. Hence, the model incorporates the uncertainties of renewable DGs, parking lots, and also load. In this study, the MG operation cost and voltage stability index are considered objective functions. A novel combined algorithm (hMOPSO-HS) is proposed for microgrid energy management. The hMOPSO-HS algorithm is a combination of the mutant multiobjective particle swarm optimization (MOPSO) algorithm and the harmony search (HS) algorithm. The simulations are performed in two parts, with and without considering the uncertainty. The comparative analysis involves evaluating the optimization outcomes achieved by the hMOPSO-HS algorithm in contrast to various other metaheuristic algorithms for multiobjective optimization. The simulation findings validate the superior efficacy of the hMOPSO-HS algorithm compared to other approaches. Also, the simulation results showed that in the conditions of uncertainty, the operating cost is 6.1% higher and the microgrid stability index is 6.8% lower. Also, considering the uncertainty has caused the penalty for energy not supplied (ENS) and demand response program (DRP) costs to increase by 3% and 4%, respectively. [ABSTRACT FROM AUTHOR]

In recent years, due to the increase in electricity consumption and environmental problems, power system expansion planning requires new technologies. In this regard, the incorporation of renewable energy sources (RESs) and utilization of demand response (DR) programs need disruptive variations in the present power system configurations. This paper proposes a mixed-integer linear robust multiobjective model for generation and transmission expansion planning (GEP-TEP) taking into account wind farms (WFs) and a DR program based on time-of-use pricing. The suggested model is presented via mixed-integer nonlinear programming (MINLP) at the first stage and then transformed into mixed-integer linear programming (MILP) using the Big M linearization technique. Moreover, long- and short-term uncertainties of load demand and WFs are incorporated into the recommended model to achieve more accurate results. The interval-based method is applied for taking into account long-term uncertainties while the scenario-based stochastic model is applied for modeling short-term uncertainties in the recommended GEP–TEP model. Lastly, the suggested model is investigated on various standard test systems to evaluate the effectiveness of the GEP-TEP model. [ABSTRACT FROM AUTHOR]

The surge in interest surrounding renewable energy stems from concerns regarding pollution and the finite supply of nonrenewable resources. Solar PV and wind hybrid renewable energy systems (HRES) are increasingly recognized as practical and cost-effective solutions, particularly in remote areas. However, the intermittent nature of solar and wind power presents a challenge. To address this, incorporating a hydrogen source into the system has been proposed. This study focuses on modelling and sizing a hybrid energy system tailored for remote areas, accommodating both home and electric vehicle loads. The simulation is conducted for Siliguri, West Bengal, India, with the goal of optimizing productivity, minimizing expenses, and considering economic factors using HOMER Pro software. The integration of green hydrogen-based power generation with photovoltaic and wind HRES emerges as an effective solution. Solar power, in particular, showcases promising opportunities for the electrolysis process and HRES systems. The presented work facilitates the modelling of a green hydrogen-based green energy system, taking into account capacity, cost, and emission constraints. Various case studies are conducted to enhance system efficiency and reduce the costs of energy (COE). In this paper, three cases of grid-connected and three cases of off-grid or grid-disconnected systems are considered for highlighting the benefits of hydrogen energy incorporation in both types of systems. This research contributes to sustainable energy solutions, advancing a greener and more efficient energy landscape, especially in addressing the recent development in load combinations of home and electric vehicle loads in both grid-connected as well as grid-disconnected system. [ABSTRACT FROM AUTHOR]

In this paper, a multiobjective framework for simultaneous reconfiguration and allocation of photovoltaic (PV) energy resources in radial distribution networks is performed for minimizing the power losses, lowering network loading factor, and reducing cost of energy resources as well as increasing the voltage stability index. A novel algorithm called the improved clouded leopard algorithm is used to find the set of optimum decision factors in the combined execution of restructuring and also PV resource distribution. The clouded leopard optimization (CLO) algorithm, which is widely used, takes its cues from the sleeping and foraging habits of the animal, and the improved CLO (ICLO) is formed using adaptive inertia weight to overcome premature convergence. In five cases of base distribution network and different contribution of reconfiguration and PV allocation, single- and multiobjective approach has been applied on 33- and 69-bus distribution networks. According to the findings, the best case includes simultaneous reconfiguration and PV allocation in the radial networks based on the multiobjective approach unlike the single-objective method which obtained the highest network performance with the best compromise between different goals. Also, in the best lowest losses, the decrease in the network loading and cost of energy resources and the better voltage profile and stability are obtained satisfying the constraints. The losses, network loading, voltage profile, and voltage stability are improved by 60.40%, 37.89%, 6.17%, and 27.5% for 33-bus network and also enhanced by 71.77%, 41.23%, 4.76%, and 20.48% for 69-bus network, respectively. Also, the results showed that network reconfiguration only has the weakest performance among other cases based on reconfiguration or photovoltaic sources. Moreover, the superior capability of the ICLO in addressing the aim of the study is proved in comparison with the traditional CLO and well-known particle swarm optimization (PSO) and manta ray foraging optimization (MRFO) to obtain the better objective value and statistical criteria to solve the best case. [ABSTRACT FROM AUTHOR]