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Conventional energy resource problems made power system planners apply more renewable energy sources (RESs) in the power system. RESs have a significant impact on fulfilling the world's energy demand, protecting the environment at least in the exploration phase, and providing energy reliability as well as security for the new generation of power grids. In this regard, wind power generation is considered a promising solution. However, by increasing the share of wind power generation units in the power sector, some challenges, such as voltage problems, transient stability, increasing system losses, and line congestions can occur. Consequently, an alternative solution is required to accelerate wind power units' connections to the grid by preventing the problems caused by the penetration of wind power. In this regard, Flexible Alternating Current Transmission Systems (FACTS) devices can play an important role while they can improve power grid dynamic performance. This paper presents a review of the optimization techniques applied to accelerate the penetration of wind energy by exploiting FACTS devices. The topic identifies the objectives and optimization formulations, as well as schemes and models available in the published literature. This survey gives noteworthy insights to the researchers to cover available solutions in these regards. The literature in the field is summarized in different aspects, such as FACTS technology, objective functions, constraints, optimization methods, planning or operation purposes, and load flow types. At each aspect, a comparison is derived to recognize the most important issues. The result of the review is the finding of future work and contributions which can be done in the field of wind energy integration acceleration using FACTS devices and the output of the paper can be used as a guide for researchers to find a new path for research in this area. [ABSTRACT FROM AUTHOR]

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

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]

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]

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]

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]

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]

The industrial sector is China's major element of energy consumption and carbon emissions, and carbon emissions reduction in industrial parks has become an important foothold in carbon reduction work. Predicting the future evolution trend of carbon emissions in industrial parks under different policy orientations is of great significance for China to achieve the carbon emissions peaking and carbon neutrality goals. Based on the current status of electricity consumption and carbon emissions in industrial parks, this paper takes a light industrial park as the research object, analyzes the source of carbon emissions in this park, combines the characteristics of resource endowment and industrial structure, and considers the factors influencing society, load curve, energy supply and carbon sink, thus constructing a system dynamics model of carbon emission evolution for this park. Different influencing factors are selected to set multiple target scenarios, and the GDP, carbon emissions, carbon emissions reduction and near‐zero carbon evaluation coefficient of light industrial parks are used as characterization indicators for simulation analysis. The analysis results revealed that the comprehensive scenario set by increasing the maximum installed capacity of renewable energy units and raising the proportions of environmental and science and technology inputs while adjusting the industrial growth rate of the park performed best. In this scenario, the economic development of the park and the process of carbon emissions reduction are considered. [ABSTRACT FROM AUTHOR]

Tesla turbines, featuring bladeless turbomachinery and energy transformation using the viscosity of the working medium, have wide application prospects in renewable energy utilization as micropower equipment and portable power units and show better aerodynamic performance with a smaller turbine size. In this paper, a miniaturization method of Tesla turbines, including the stator and rotor, was proposed based on its sensitivity analysis results and flow similitude law. A typical Tesla turbine with a disc outer diameter of 100 mm was miniaturized to 50, 20, and 10 mm, respectively. In addition, the miniaturized Tesla turbines, including both the simplified single‐channel turbine model and the multichannel turbine model used in practical applications, were simulated numerically. The results show that the miniaturization method that maintains the two high‐impact dimensionless parameters at their optimal values is simple, reasonable, and effective. The isentropic efficiency of the single‐channel Tesla turbine decreases slightly with its scaling down. However, for the multichannel Tesla turbine in practical applications, the isentropic efficiency increases significantly with a decrease in turbine size, due to a decrease in the impact of the outermost disc channel on the flow fields of the inner disc channels (called the casing wall effect). This is embodied by the phenomenon that part of the working medium in the inner disc channels flows into the outermost disc channel through the nozzle‐rotor chamber, and the proportion of this part of the working medium decreases significantly with a decrease in turbine size. In conclusion, the Tesla turbine with a smaller turbine size exhibits better aerodynamic performance and has great potential in the field of microturbomachinery. [ABSTRACT FROM AUTHOR]

In recent years, with the development of battery technology, hybrid electric ship (HES), as a promising solution to reduce the fuel consumption and emissions, has become a research hotspot. However, frequent use of the battery will accelerate the aging of the battery, and the replacement of scrapped battery will increase the cost of the ship. Therefore, it is necessary to consider delaying battery aging into the energy control strategy of HES. The equivalent consumption minimization strategy (ECMS) is a feasible energy control strategy because it can be implemented in real time. However, under the condition of uncertain initial state of charge (SOC) of the battery, ECMS cannot effectively reduce the fuel consumption unless the equivalent factor (EF) is optimized in real time. In this paper, an adaptive equivalent consumption minimization strategy (A‐ECMS) is proposed, which extracts the global optimal EF trajectory from the dynamic programming (DP) solution and uses the back propagation (BP) neural network to adjust the EF in real time. A trade‐off between the fuel consumption and battery aging is made in the cost function by introducing a weight coefficient. Finally, the effectiveness and the adaptability of the proposed strategy are verified in MATLAB. [ABSTRACT FROM AUTHOR]

Energy consumption is closely related to industrial structure, economic prosperity, and population. Because of the Granger causal relationship between GDP and energy consumption, many researchers consider the regression method to predict energy consumption using economic variables. However, some other researchers regard the time series method (forecast) to estimate for energy consumption. To address the advantages and disadvantages of two methods in energy consumption prediction, we performed the deep learning (DL) based on the case of China. The experiment results show that the accuracy of time series forecast is much higher than regression methods. In addition, we also explored the relationship between industry, economy, population, and energy consumption. We found that GDP, population, secondary sector, and tertiary sector were significant to energy consumption. A tertiary sector can help to reduce energy consumption to some extent. This could give suggestions about changing industry structure. More and more attention has been paid to energy intensity. The strategic approach to sustainable development requires reducing energy intensity while developing the economy. Since China is committed to achieving carbon emission peak by 2030, this paper predicts the energy intensity of China in the next decade based on a DL algorithm. The prediction results show that energy intensity would slightly decrease in the next decade. Different types of energy represent the industrial structure behind economic growth. Therefore, this paper was conducted using ridge regression (RR) to explore the relationship between different types of energy and energy intensity. In addition, the effect (positive or negative) of different energy sources in reducing energy intensity has also been explored. We found that the best way of developing the economy and reducing energy consumption is to use natural gas and hydroelectric instead of coal. This can guide policy development about changing energy source structure. [ABSTRACT FROM AUTHOR]

This paper presents the design of a modular and open‐source smart meter for educational and research purposes. The smart meter monitors the energy consumption, uploads the data to a cloud server, and can be used to control appliances remotely through a control relay. Using the Internet of Things (IoT) technology, the smart meter acts as a sensing node in a meshed network. This enables consumers to visualize their energy consumption profiles at the level of appliances and to take control actions to manage efficient energy consumption using the automation platforms or IoT with smart meters. In this paper, the energy consumed by different electrical appliances is computed and communicated to a server using the Arduino Uno board aided by the ESP8266 WiFi module. The energy consumption is also displayed on an LCD screen, and the consumption profiles are displayed through the home energy monitoring website "EmonCMS." The design can accommodate different transducer ratings with single‐phase and three‐phase measurements by configuring itself automatically using the plug‐and‐play approach. The realization of the proposed smart meter is a multicourse educational project that aims to polish students' skills in applying the knowledge gained in Electrical Engineering (ie, undergraduate courses) to produce a practical application. [ABSTRACT FROM AUTHOR]

With the increase in new energy power generation and the continuous augment in the penetration rate of electric vehicles, it is of crucial importance to use electric vehicles as energy storage devices to promote the consumption of new energy. Aiming at the uncertainty of electric vehicles, this paper proposes a method based on multiobjective optimization for electric vehicle‐supercapacitor hybrid energy storage system to track PV project output. The hybrid system consists of electric vehicles and supercapacitor. Electric vehicles and supercapacitors supplement the deviation of PV actual power and predicted power by charging and discharging. The electric vehicle is regarded as a nonspecific way to label a piece of equipment that can store energy. First and foremost, on the basis of traditional method of predicting PV output, a PSO‐BP prediction method based on PCA is proposed to improve the accuracy of PV output prediction. Secondarily, according to the different characteristics of electric vehicles and supercapacitors, the empirical mode decomposition (EMD) method is used to decompose the deviation that the hybrid energy storage system needs to bear with the purpose of initially allocating the energy. Furthermore, a multiobjective optimization model is established for the precise energy distribution of the hybrid energy storage system, and the NSGA‐III algorithm is used to solve it. Ultimately, the data of a domestic PV power station are used for simulation. After optimized control, the result shows that the standard deviation of the system output is reduced from 1967 to 75.77. The research in this article provides a theoretical basis for the application of electric vehicle virtual energy storage technology in the field of auxiliary new energy grid connection. [ABSTRACT FROM AUTHOR]

In this paper, an improved thermo‐economic diagnosis model based on the structural theory of thermo‐economics combined with advanced exergy analysis is proposed in a combined cycle fault diagnosis model. Diagnosis model simulation of a 590 MW combined cycle unit is implemented by EBSILON Professional in terms of advanced exergy analysis method. The characteristic fault value (CFV) required for diagnosis is quantitatively presented and precisely located in the diagnosis model. The results show that with 3% performance deterioration fault of the compressor, gas turbine and intermediate pressure cylinder of the turbine, gas turbine failure leads to three times fuel consumption to the unit compared to the compressor. In contrast, multiple fault diagnosis results show that the fault value generated by each component under multiple faults is not a simple superposition of a single fault. The impact of some component faults is partially offset or increased. However, the increased fuel consumption of the whole unit under multiple faults or malfunctions must be greater than the sum of the increased fuel consumption under a single fault. CFV can effectively perform precise positioning and quantitative analysis on single and multiple fault scenarios of the combined cycle unit. In addition, CFV has strong sensitivity and changes linearly with the performance deterioration of the components, which paves the way for a reliable approach for early‐stage warning and diagnosis. [ABSTRACT FROM AUTHOR]

In coastal cities, a large amount of cold energy is released into the nearby seawater during the process of regasification of liquefied natural gas (LNG) at the receiving terminal, and meanwhile, a large amount of exported and/or imported foods need to be frozen by bulk cold energy near the sea. In this paper, a new multistage cold energy recovery/utilization system is investigated to link the LNG cold energy directly to supply the coastal cold store. This design uses a realistic case in Ningbo LNG receiving station in China, and for the very first time considers how to utilize the LNG cold energy in an efficient manner regarding the technical, economic, and environmental benefits. A three‐stage LNG cold energy utilization system is designed and evaluated by considering different temperatures, pressures, and heat exchange parameters. Compared to conventional electric‐driven cold stores, the proposed multistage cold energy recovery/utilization system has a much cheaper capital cost during the initial investment stage, and also avoids the utility power consumption during the daily operation period. In the case study, from Ningbo LNG receiving station to a 10 MW class cold store, the proposed multienergy system can save the energy of 20.05 kilotons of standard coal, and the carbon emission reduction is up to 1.49 × 108 kg per year. [ABSTRACT FROM AUTHOR]

Clarifying the influencing factors of energy consumption in underground production processes of coal mine is the premise and foundation for the coal mining industry to control the energy consumption and intensity to achieve the goal of carbon peak and carbon neutrality. This paper aims to investigate and compare the differences in energy consumption and carbon emissions between a mining‐separating‐backfilling integrated coal mine (MSBICM) and a traditional coal mine and explore the effects of the main production links underground on carbon emission intensity in coal mines. Hence, taking an MSBICM in Henan province as an example, this study constructed an energy consumption and carbon emission model of the mine using system dynamics (SD) method. Based on this model, the energy consumption of the main production logistics system and auxiliary production system in MSBICM was simulated and analyzed. In addition, this study made a comparative analysis of the energy consumption of the different production links in the two types of coal mines. The results showed that: (1) the comprehensive mechanized coal mining with solid filling system and ventilation system were the subsystems with the highest and lowest energy consumption in MSBICM respectively. (2) The influence rates of raw coal mining, gangue filling, coal‐gangue separating, and material transportation on carbon emission intensity in coal mine were 1.09%, 1.34%, 1.09%, and 5.13%, respectively. (3) Under the same production and filling targets, energy consumption of underground transportation systems in MSBICM was found to be 336.66 tons of standard coal less than that in traditional coal mine. This study has helped clarify the complex relationships among the factors that influence energy consumption in coal mines and provided a reference for implementing dual control of energy consumption to promote low‐carbon mining of coal resources. [ABSTRACT FROM AUTHOR]

Mine integrated energy system (MIES) promotes the utilization of derived energy, multienergy complementation, and ecological protection, but the variable condition characteristics of key energy conversion equipment bring challenges to its operation optimization. Focused on this, the paper studies a MIES operation optimization model with integrated demand response under variable conditions of typical equipment. First, analyzing the coupling utilization mode of mine energy flow, a MIES operation structure including mine derived‐energy utilization and integrated demand response is constructed. Second, considering the supply characteristics of different derived energy sources, the variable condition dynamic energy efficiency model of typical energy conversion equipment is established. Furthermore, considering the flexible and controllable characteristics of multienergy loads, an integrated demand response mechanism including electric and thermal flexible loads is formed. Finally, the operation optimization model of MIES is constructed with the goal of the lowest operating costs of the system. The GUROBI solver is applied to solve the model, and experimental results under different operating scenarios show that the established MIES operation optimization model effectively improves the economy of system operation. [ABSTRACT FROM AUTHOR]

Due to the inherent intermittency in wind power production, reliable short‐term wind power production forecasting has become essential for the efficient grid and market integration of wind energy. The current wind power production forecasting schemes are predominantly developed for wind farms. With the rapid growth in the microgrid sector and the increasing number of wind turbines integrated with these local grids, power production forecasting schemes are becoming essential for distributed wind energy systems as well. This paper proposes a power production forecasting scheme developed explicitly for distributed wind energy projects. The proposed system integrates two submodels based on support vector regression: one for downscaling the wind speed predictions to the hub coordinates of the turbine and the other for predicting the site‐specific performance of the turbine under this wind condition. The forecasting horizons considered are the hour ahead (t + 1) and the day ahead (t + 36), which align with the Nord pool's energy market requirements. For the day‐ahead scheme, a multistep recursive approach is adopted. The accuracy and adaptability of the proposed forecasting scheme are demonstrated in the case of a distributed wind turbine. [ABSTRACT FROM AUTHOR]

Concerning the overwhelming advantages of solar energy, controlling and exploiting solar energy by using solar panels is one of the main fields of research in the domain of renewable energy. The choice of solar panel technology is highly significant to exploit as much energy as possible. In this paper, the main goal is to select the best technology for solar panels by investigating nine technologies from the first, second, and third generations of solar panels. Moreover, five sustainable criteria of electrical, mechanical, economic, technical, and climate, and 20 subcriteria are given for making decision analysis. Then, the best–worst method is employed according to the experts' opinions for weighting the criteria and for comparing the ranking of the solar energy technologies a framework based on the MULTIMOOSRAL multiple criteria decision‐making method is proposed. Finally, sensitivity analysis will be conducted on the ranking of the technologies. By exploiting the proposed methodology, CIS/CIGS and Perovskite Solar cell are ranked 1 and 2 as the best solar panel technologies for the selected locations. [ABSTRACT FROM AUTHOR]

Integrated energy systems and multi‐energy micro‐grids are complex multi‐agent collaborative optimization projects. According to the operating structure and characteristics of the multi‐energy system, considering the externality, economy and other factors, a scientific and effective comprehensive evaluation are important decision supports to ensure its implementation and promote its development. Therefore, this paper proposes a comprehensive benefit evaluation model of a multi‐energy micro‐grid based on the matter‐element extension model. With the research trends and practical requirements of multi‐energy micro‐grid benefit evaluation, an evaluation index system covering energy efficiency indicators, economic indicators, reliability indicators, and renewable energy consumption indicators has been constructed, and uncertainties have been resolved in cloud model and fuzzy set theory. Based on the advantages of the performance evaluation information, the system operation scenarios in the process of planning scheme formation are fully considered, and then a matter‐element‐extensible multi‐attribute decision‐making model is proposed to evaluate the comprehensive benefits of multi‐energy micro‐grids. Finally, the feasibility of the above method is verified by comparing the comprehensive benefit evaluation results of the multi‐energy micro‐grid under different planning scenarios. In addition, a certain decision‐making reference is provided for the formation of a multi‐energy micro‐grid configuration scheme. [ABSTRACT FROM AUTHOR]

Building energy assessment is essential to accomplish the sustainable energy targets of new and present buildings. Retrofitting of the existing buildings by assessing them through energy models is the most prominent method. Studies revealed that there is still blank information about the building stocks, and these affect the valuation of building energy efficiency policies. Literature also recommends that the existing energy models are too complex and unreliable to predict the energy use. Reliability of such energy models would improve through a better alignment of the input parameters and the model assumptions. The authors hypothesized that the reliability of models would be improved through identification of the most relevant energy use parameters for the building stocks in different regions and models. One of the most commonly accepted methods for detecting the most dominant input parameters is sensitivity analysis, though its shortcomings include the need for a large number of data samples and long computing time. In this research, the Energy, Carbon, and Cost Assessment for Buildings Stocks (ECCABS) model is adopted to identify the most important parameters of the presented model. The research team uses the model that has been validated by studies conducted for the UK building stock. Moreover, by assessing the feasibility study with the stepwise regression to identify the significant input parameters have been discussed. Results show that stepwise regression method could produce the same results compared to sensitivity analysis. This paper also indicates that stepwise regression is considerably faster and less computationally intensive compared to common sensitivity analysis methods. [ABSTRACT FROM AUTHOR]

In this paper, a three‐dimensional simulation model of the engine cylinder is established using commercial simulation software AVL‐Fire, and the accuracy of the model is verified with experimental results. The effects of diesel/biodiesel/n‐butanol blends on diesel engine performance, and combustion and emissions characteristics were investigated by performing simulation operations on the model. The combustion process in the cylinder was simulated for 0%, 5%, 10%, 15%, and 20% of n‐butanol in the fuel mixture. The results showed that at 75% load, the brake thermal efficiency of D95N5, D90N10, D85N15, and D80N20 engines increased by 0.84%, 1.68%, 2.48%, and 3.19%, respectively, and soot emissions decreased by 2.76%, 5.52%, 8.25%, and 10.96%, respectively, compared with D100. However, the brake power of the engine was reduced, the brake ratio fuel consumption increased, and NOx emission increased. In addition, at all loads, soot and CO emissions decrease as the percentage of n‐butanol in the fuel mixture increases. [ABSTRACT FROM AUTHOR]

The emission of greenhouse gases from fossil fuels is the main environmental impact of energy resources. Moving toward the use of renewable energy resources is the only solution to tackle these problems. In this paper, a configuration of the hybrid biomass and geothermal energy resource is examined for cooling and electricity generation. In this study, water as a geothermal working fluid is heated up due to combustion of syngas produced in a digester and produce power in a steam turbine. So, the remaining heat of this geofluid runs an absorption chiller for cooling production. The results reveal that the energy and exergy efficiencies of this proposed cycle are 30.8% and 15.5%, respectively. Moreover, the economic analysis of this cycle concludes that the payback period is 2.8 years and the simple payback period for this configuration is 2.6 years. Moreover, the highest exergy destruction rate of 52.4% belongs to the absorption refrigeration cycle. Whereas the lowest exergy destruction of the steam line is the lowest value of 17.4%. [ABSTRACT FROM AUTHOR]

Off‐grid renewable energy systems are a solution for power generation in areas where access to the grid is not possible or cost‐effective; in particular, low population rural areas located far away from grid lines or in rugged terrains. Furthermore, over‐reliance on fossil fuels for supplying global energy demand has led to the depletion of nonrenewable resources and environmental issues. This paper presents a feasibility assessment of an off‐grid hybrid renewable energy system for a remote rural area in Kohgiluye and Boyer‐Ahmad Province in Iran. Regarding the available energy resources in the region, a photovoltaic (PV)‐biomass energy system is considered. HOMER Pro software is utilized to find the optimized sizing of the PV‐biomass system to fulfill this load demand. The objective function in this optimization is the system's total net present cost. Due to the rapid fluctuation of economic factors in the country, several rates of inflation and discount rate are considered to investigate their influence on the costs of the system. The optimization result for the current inflation rate of 40% and the discount rate of 18% in the country proposes a hybrid energy system consisting of a 3 kW biogas‐fueled generator, 4.74 kW PV, 10 kWh battery, and 2.07 kW converter to meet the 2.64 kW peak load and 14.53 kWh/day consumption of the community. The total net present cost and cost of energy are $93,057 and 0.0933 $/kWh, respectively. Finally, the environmental assessment of the proposed hybrid system shows an annual CO2 emission of 2.95 kg, which means 99.9% CO2 emissions mitigation compared to a conventional coal‐based electrical plant. [ABSTRACT FROM AUTHOR]

The integrated energy system is an important prerequisite for the sustainable transformation to the low‐carbon power system. Therefore, this paper aims to provide readers with insights into the existing research about the planning and operation models of integrated energy systems. First, the general structure of integrated energy systems is elaborated, and the new issues faced by the low‐carbon planning and operation for the integrated energy systems are represented. Next, the technical framework and the general model are proposed. Furthermore, the typical models and solution technologies of the low‐carbon planning and operation are summarized based on specific models operating in multiple scenarios. Finally, based on the characteristics and framework of the integrated energy system, the future research direction is given, which is expected to guide the sustainable development and low‐carbon transformation of energy systems. [ABSTRACT FROM AUTHOR]

Most of the crude oil produced in China is highly viscous and easy to condense, and the pipeline transportation method is mostly heated. At present, frequency conversion technology is gradually applied to oil pump units. In order to find the best pipeline operation scheme, this paper is based on MATLAB programming software and established a corresponding mathematical model of oil pipeline energy consumption. This model innovatively takes the rotational speed of the variable frequency pump as one of the model optimization variables, calculating the pressure change and temperature change between stations of the pipeline, predicting the energy consumption of the pipeline and can guide the optimized operation of the pipeline, which has a high popularization and application value. An example of Qingtie Fourth‐line crude oil pipeline shows that among the genetic algorithm, particle swarm optimization, and simulated annealing algorithms for solving the optimization model, the particle swarm optimization algorithm has fast convergence speed, short optimization time, obtains the most optimal scheme, and can reduce energy consumption by 7.84%, which proves the practicability and creativity of the optimization model. [ABSTRACT FROM AUTHOR]

Traffic and vehicle simulations are often developed individually. However, vehicle performance is heavily affected by traffic conditions. Cosimulations of traffic and vehicle under real‐road situations can reflect the semi‐real‐world performance of vehicles, with traffic conditions being taken into considerations. This paper proposed an approach to combine the traffic and vehicle simulations that are realized by simulation of urban mobility (SUMO) and GT‐Suite software, respectively. In this paper, the sensitivities of the road grade and vehicle speed to the fuel economy and exhaust emissions were investigated; vehicle fuel consumption and regular exhaust emissions on a real‐road were analyzed; the effect of the traffic accident and congestions on fuel consumption and exhaust emissions were quantified. The results indicated that nitrogen oxides (NOx) and soot emission were consistent with fuel consumption rate, which was dominated by vehicle acceleration whose effect was aggravated by road grade. The fuel penalties caused by accident were in the range of 0.015‐0.023 kg depending on the severity of the accidents. The fuel consumption increased from 1.199 to 1.312 kg and 1.559 kg for 900 and 1800 vehicles/h traffic flow cases compared with 180 vehicles/h traffic flow. [ABSTRACT FROM AUTHOR]

Exhaust emissions from diesel engine powered vehicles are considerably high during cold start and warm‐up, because of the poor catalyst performance due to the insufficient catalyst temperature. The controlled heat injection allowed by electrically heated catalysts can effectively reduce the catalyst light‐off time with relatively moderate fuel penalty. This paper compares the exhaust temperature and emissions of a case study diesel vehicle in cold and warm start conditions, and proposes two electrically heated catalyst control strategies, which are evaluated in terms of emission reduction and energy consumption with different target temperature settings. In addition, a new performance indicator, that is, the specific emission reduction, is used to evaluate the after‐treatment system and associated thermal management. For the worldwide harmonized light vehicle test cycle, the results without electrically heated catalyst show that from both cold and warm start conditions a large amount of operating points of the engine is located in the region of partial catalyst light off. Moreover, emissions, especially in terms of carbon monoxide and hydrocarbon, significantly decrease with the electrically heated catalyst implementation, for example, by at least 50% from cold start; however, they still tend to be rather substantial when the fuel is re‐injected after the engine cutoff phases. The exhaust temperature is lower than the target values in the sections of the driving cycle in which the electrically heated catalyst power is saturated according to the maximum level allowed by the device. The carbon dioxide penalty brought by the electrically heated catalyst ranges from 3.93% to 6.65% and from 6.49% to 9.35% for warm and cold start conditions, respectively. [ABSTRACT FROM AUTHOR]

In this paper, a risk‐based probabilistic short‐term scheduling of a smart energy hub (SEH) is presented considering the uncertain variables and the correlation between them. Neglecting the uncertainty of renewable energy sources (RESs), demands and market prices can make the obtained results unusable. In addition, correlations among uncertain variables may have similar importance on final solutions. To have a more realistic view, the stochastic nature of solar irradiation, wind generation, energy demands, and electrical/thermal/gas market prices are taken into consideration through uncertainty modeling. For this purpose, a probabilistic scenario‐based approach is implemented. The Monte Carlo simulation technique is employed to generate an adequate number of scenarios and the Cholesky decomposition technique combined with Nataf transformation is used to make the samples correlated. In addition, the k‐means data clustering technique is used to reduce the initial number of scenarios to the most representative 10 scenarios. The addressed SEH comprises photovoltaic panels/a wind turbine/a combined heat and power generation unit/a fuel‐cells power plant (FCPP)/a thermal/hydrogen storage system and plug‐in electric vehicles (PEVs). This study aims to optimize the economic aspects while reducing the pollution emissions of the SEH and controlling the risk level of SEH operation. To enhance the flexibility of the SEH in the management of supplying demands with lower costs, the thermal demand response program (DRP) is considered beside the electrical DRP. Two kinds of time of use (TOU) and real‐time pricing (RTP) DRPs are used for electrical and thermal loads. The conditional value at risk technique is taken into account to control the deviations of the SEH operation and emission costs. Simulation results show a reasonable reduction in operation and emission costs along with the risk level of the energy hub with the proposed approach. The operation emission, and risk costs are reduced by 37.39%, 32.11%, and 33.16%, respectively, with integrating PEVs, FCPP, and RTP‐DRPs. Moreover, integration of PEVs, FCPP along with TOU‐based DRPs contribute to reduce the operation emission, and risk costs by 10.47%, 9.03%, and 11.64%, respectively. [ABSTRACT FROM AUTHOR]

Increasing consciousness to address future concerns is a challenging subject, noticed considerably by policymakers and investors. Investigation of energy use in residential buildings in terms of quantity and sources of energy was the main focus of this study. Indeed, the paper was aimed to study the role of contextual factors on two points: the energy demand and the spreading of renewable technologies in the residential sector. It was theory‐driven research to highlight some future concerns. The first theory was to reveal how the growth of single‐family houses versus multi‐family houses could affect the growth of energy consumption and the application of renewable technologies. The second theory was built to highlight that spreading energy measures to increase efficiency or introducing a new energy carrier should be embedded in the context of new construction activities or in a template of renovation projects. The third theory was to emphasize the priority of thinking about managing the changes in residential buildings because of the lasting impact of the decision about the type of building. Finally, the purpose of the ongoing transition was argued to highlight that the growth of renewable technologies has not been necessarily aligned with getting away from fossil fuels. [ABSTRACT FROM AUTHOR]

In the paper, the method based on optimization of flow passage geometry of diesel injector nozzle to improve the quality of fuel injection and atomization process has been studied. Designs of injector nozzles have been developed to enhance the quality of these processes. These nozzles' feature is the implementation of local hydraulic resistances—different grooves on the needle tip, which enhance the flow turbulence in the nozzle's flow passage. Computational fluid dynamics (CFD) simulations were performed to study the effect of the nozzle flow passage geometry on parameters of fuel flow, such as mass flow rate, velocity, turbulence, and cavitation. The influence of local hydraulic resistances on the needle tip over these parameters has been evaluated. The optimal geometry of the grooves has been chosen. Experimental studies of a four‐cylinder diesel engine D‐243 equipped with injectors with serial and modified nozzles have been carried out. The possibility of improving the fuel efficiency and emission performances by using the modified nozzles has been confirmed. At the maximum power condition, the use of the modified nozzles reduced the brake‐specific fuel consumption from 275.2 to 270.4 g/(kW∙h) (by 1.7%) and reduced exhaust smoke from 23.5% to 12.5% on the Hartridge scale (by 46.8%). However, at the same time, the emission of NOx increased from 1743 to 1923 ppm (by 10.3%). Moreover, there was a tendency to decrease the content of CO and unburned HC in the exhaust gases. [ABSTRACT FROM AUTHOR]

The low energy efficiency is the source of the large energy consumption issue and the rapid growth of energy demand in the manufacturing industry sector in Indonesia. The driving forces behind the low energy efficiency situation in this sector are both economic and efficiency factors. The objective of this study is to generate the map of the energy flow in the manufacturing industry sector to investigate the energy utilization in the industrial process. The data sample that represents 80% of energy consumption in the manufacturing sector is used to generate this map. The generated map shows the heating system is the largest energy consumer among all energy equipment and as the primary source of the energy losses. Additionally, we found the industry groups such as sugar, cement, pulp and paper, and textile use enormous amounts of energy as their source for the heating system; meanwhile, the industries such as basic chemical, metal, and textile are the largest electricity consumers for their motor-driven machinery. The energy flow analysis together with the comparison of the Specific Energy Consumption shows the areas that should be the focus for further energy conservation measures. Recommended measures are also discussed. [ABSTRACT FROM AUTHOR]

Green building design is a promising approach to reduce the energy intensity of the building sector. However, green buildings often show important discrepancies between their predicted and actual energy use levels, in part due to varying operation patterns that are difficult to predict during design. This paper presents a data‐driven modeling and analysis approach to test the resilience of green‐certified buildings to uncertainty in the operation of building systems. Using building energy modeling coupled with an extensive empirical Monte Carlo analysis scheme, the framework quantifies and compares the response of a building to uncertainty in key technical and operational features before and after the adoption of green building certification specifications. The framework is illustrated and validated through a case study of an archetype commercial building located in the extreme hot climate of Abu Dhabi, UAE. Results show that adopting the green building features of the local "Estidama" building code reduces energy demand by an average of 17%. More importantly, the variability in demand is reduced (P <.05), confirming the increase in building resilience to uncertainty in design and operation factors. Finally, the techno‐economic potential for solar photovoltaic (PV) adoption is also assessed, showing an estimated 16% reduction in capital costs. [ABSTRACT FROM AUTHOR]

Many efforts have been made to increase the utilization of renewable energy resources (RESs) in Iran. This paper aimed to evaluate the techno‐economic performance of an introduced hybrid microgrid (HMG) in eight climate zones of Iran. Therefore, ten cities are selected from the eight climate conditions of Iran. An electricity pricing strategy is also implemented according to the electricity tariffs defined by the Ministry of Energy (MOE) of Iran. The proposed electricity pricing strategy is applied to the HOMER software for investigating the optimal system configuration, RES electricity generation, and the economics of each understudy city. Optimization results indicate that Urmia (in moderate and rainy climate zone) has the least net present cost (NPC) (−5839$) and levelized cost of energy (COE) (−0.0122 $/kWh), whereas Golestan (in semimoderate and rainy climate zone) has the highest NPC (4520 $) and COE (0.012 $/kWh). It is shown that the combination of photovoltaic (PV)/wind turbine (WT)/converter in the grid‐connected operation mode is the most economical configuration. Moreover, the cities with higher potentials of wind speed and solar irradiance have lower NPC and COE. It is concluded that the utilization of the battery energy storage (BES) is technically and economically infeasible for all eight climate zones, even if the stored electricity is sold to the grid. Two sensitivity analyses are conducted to the electricity feed‐in‐tariff (FiT) and solar module price, respectively. The first sensitivity analysis indicates that by increasing FiT, more contribution of RESs is seen, which leads to lower COE and NPC. Furthermore, the two cities of Urmia and Yazd have the highest NPC and COE reductions. The second sensitivity analysis studies the module price impacts on the NPC and COE of each understudy city. It is revealed that the PV module price has a considerable effect on NPC and COE. However, this effect is more significant in some cities such as Bam, where a linear relationship is seen between the module price and economic results (NPC and COE). [ABSTRACT FROM AUTHOR]

To use photovoltaic energy, voltage source inverters are playing an important role. The most of local AC loads such as AC motors need higher voltage than solar panel output voltage. Differential boost inverter produces an AC voltage that is greater than the DC input voltage in a single power stage. This inverter consists of two DC‐DC bilateral boost converters in which work via time‐variant duty cycles to produce sinusoidal voltage, and this feature causes complexity in control design. To deal with this complexity, new modeling so‐called generalized averaged model is used to achieve a linear time‐invariant model. In this paper, a nested control loop is designed. This control strategy consists of two control loops in which the inner loop is designed by linear quadratic regulator theory to displace poles in proper location and the second loop uses a PI‐PR controller to track desired voltage and reject disturbances. To evaluate the proposed control method, the sliding mode controller is applied and compared as well. As shown by simulations, the proposed control strategy has more reliable performance and provides higher robustness in comparison with sliding mode control technique in rigid situations such as dealing with nonlinear load, parameters values changes, and external disturbances. [ABSTRACT FROM AUTHOR]

Co‐gasification of wood mixed with 20% (mass fraction) of polyethylene (PE) in a two‐stage NOTAR® fixed‐bed gasifier was studied in the present paper. The ratio of air flow injected in the pyrolyzer and air flow injected in the oxidation zone is called the air ratio (AR). The AR of 0.35, 0.43, and 0.49 were used for test runs. Mass and energy balances were established. The study showed the feasibility of co‐gasification of wood mixed with 20% of PE in the two‐stage gasifier. However, considerable quantity of residues rich in fixed carbon content (17% of the fuel mass and 69% of fixed carbon content) was produced due to a low reactivity of the charcoal obtained after the pyrolysis. AR increasing reduces the gas CO content due to overconsumption of char at the pyrolysis stage. H2 and CH4 highest values are obtained for the intermediate AR = 0.43 which maximized the LHV, energy efficiency, and carbon conversion with respectively 4674 kJ/Nm3, 64.35%, and 74.52%. Low reactivity and carbon content of the pyrolysis char likely lead to lower performance of co‐gasification of PE mixed with wood compared to wood gasification. [ABSTRACT FROM AUTHOR]

Regional hybrid energy system (RHES) is an effective way to accommodate clean energy and reduce its uncertainty. It is also an important development direction for energy market reform, playing an important role in the field of energy supply and demand. Based on electricity price, thermal price, and the relationship between load supply and demand, the paper proposes an optimal scheduling model for regional energy systems to promote clean energy consumption. Firstly, the typical structure and system modules of the integrated regional energy system are introduced in detail, and the two subsystem operating models of electricity and heating are given. Secondly, a multi‐objective function that combines the economics and stability of the system is constructed. The model maximizes the economic benefits and minimizes the net load fluctuations on the basis of maximizing the consumption of clean energy. The multi‐objective singularity is realized by fuzzy membership function. The decision‐making risk caused by the uncertainty of clean energy output is analyzed by introducing robust optimization theory. Finally, the effectiveness of the proposed model is verified based on a region in northwestern China. The results show that the proposed multi‐objective model can combine different optimization demands together, maximize the economic characteristics of clean energy while properly controlling risks, and provide reasonable support for decision makers to develop optimal operation plans. [ABSTRACT FROM AUTHOR]

Recently, energy‐related CO2 emissions are considered as one of the most crucial issues and are promptly augmented due to further urbanization. In this paper, in order to model and calculate yearly CO2 emission, an artificial neural network is used. For the first time, the IWO‐SVM method has been applied in modeling energy‐related CO2 emissions. In this regard, consumption of different energy sources such as renewable energy, natural gas, coal, and oil, and GDP of the G8 countries in various years (from 1990 to 2016) are regarded as input in the present study. For the aim of evaluating the exact ability of the SVM and SVM‐IWO models, the performance of these models in three different modes is carried out on the basis of the number of data in the test and train sections. For this purpose, implementations are split into three categories (a = 80% of the data for the train section and 20% for the test section; b = 70% of the data for the train section and 30% for the test section; and c = 60% of the data for the train section and 40% for the test section). Furthermore, five scenarios were selected on the basis of the number of input parameters and input parameters for achieving the best model. As indicated in the results in all scenarios, the correlation of the model with the hybrid invasive weed algorithm based on SVM is more favorable than that in the support vector machine model, due to better training of the SVM‐IWO model than the SVM model. Moreover, the technological orientations of the G8 countries to mitigate CO2 emissions are determined through patent analysis. While the patents have essential information, investigating the published patent by a country could be helpful for determination of technological orientations. Hence, all published patents by these countries are extracted and deeply analyzed. In the next step, to find out main technological approaches, all patents and their intents have been studied. Eventually, the technological life cycles and trends of each main technology are drawn. [ABSTRACT FROM AUTHOR]

A solar‐aided coal‐fired power generation (SAPG) system is a new type of power generation system in the field of solar thermal power generation. However, the instability of solar radiation will cause fluctuations in steam temperature, which is unfavorable for the operation of boilers and steam turbines. Based on a 600 MW subcritical coal‐fired power generation unit, TRNSYS software is applied to establish a transient simulation model of SAPG in this paper. A dynamic feedforward control system for steam temperature is set up. Some essential parameters, such as the fuel consumption rate, solar‐heated steam extraction rate, water spraying flow rate, and flue gas damper opening, are under the control of direct normal irradiance (DNI). Then, the dynamic characteristics of SAPG are analyzed using typical weather data. The simulation results show that under the fuel‐saving mode, when solar irradiation changes, the SAPG system achieves a coal reduction of 4.9% by using the steam temperature control system with DNI feedforward control, and the main steam temperature fluctuation is less than 5°C with the reheat steam temperature reduced within a reasonable range. The power generation fluctuation is less than 10 MW, and the solar‐to‐electricity efficiency reaches 22.5%. Some parameters on both the flue gas side and the water/steam side in the utility boiler are simulated to demonstrate the effectiveness of DNI control. [ABSTRACT FROM AUTHOR]

While proposing dual carbon strategy goals, research on carbon reduction in integrated energy systems (IESs) has become a focus of attention. Traditional IES carbon emission flow is mostly based on energy flow, without considering the differences in energy quality. Exergy can be used as a reasonable measure of energy quality. Combining the theory of exergy flow and carbon emission flow, a modeling and analysis method for IES carbon emission flow considering energy quality is proposed. First, exergy flow models for electric and natural gas systems, equivalent exergy flow models for heat systems, and exergy hub models are introduced. Afterward, the exergy branch losses are allocated to the nodes at both ends. A networked modeling method for the carbon emission flow of energy stations is proposed. A unified matrix calculation model for IES carbon emission flow considering energy quality is established. On this basis, an IES carbon emission flow calculation method considering energy quality is proposed. Finally, this method is applied to the test IES, and the results show that the carbon emission flow calculation results reflect the distribution of carbon emissions borne by the effective energy supplied by the source and consumed by the load in the system, taking into account both the quantity and quality of energy. The reasonable allocation of new energy is conducive to synchronously improving the energy efficiency, exergy efficiency, and cleanliness of the system. [ABSTRACT FROM AUTHOR]

This study addresses the pressing issue of quality electricity access in remote regions, with a specific focus on Tumbatu Island in Tanzania. Most studies on quality improvement concentrate on low‐voltage distribution lines and leave high‐voltage (HV) transmission lines behind. It is essential to address quality issues in HV lines due to their critical role in electricity transmission and distribution infrastructure. The objective of the study is to improve the voltage profile on the island's HV transmission line by identifying the optimal hybrid energy system comprising solar PV, wind turbine, and battery technologies. The study begins by presenting the total power demand and consumption on Tumbatu Island, which are important factors in designing an efficient energy system. The findings reveal a power demand of 7173 kW and a consumption of 28540 kWh/day, with an average scaled value of 1507.9 and 6000 kWh/day. To achieve the desired voltage profile improvement, the research incorporates HOMER Pro software for simulating and analyzing various hybrid system configurations. Essential input parameters, such as costs, resources, technology components, and load, are considered in the simulation process. The software generates a ranked list of options based on the system's net present cost (NPC). The simulation results demonstrate that the integration of solar PV, wind turbine, and HV lines provides a substantial improvement in the voltage profile, increasing it from 29.6 to 31.23 kV during peak demand periods. This solution proves to be the most economically viable, with the lowest NPC of $4,003,851 and a relatively short payback period of 3.79 years. In addition, a sensitivity analysis is performed to identify the most influential parameter in the system's performance. Wind speed is found to have the greatest impact, emphasizing its significance in the design and operation of the hybrid energy system. The implementation of this optimal hybrid renewable energy system on Tumbatu Island will not only improve the voltage profile and meet the island's energy needs but also shall contribute to global efforts in reducing pollution and the cost of electricity to the Tumbatu Island population. Moreover, it addresses the current and future demands for clean energy, underlining its importance in achieving sustainable and accessible electricity in sub‐Saharan Africa and beyond. [ABSTRACT FROM AUTHOR]

In pursuing advancing solar energy systems, this research uniquely occupies a position at the intersection of photovoltaic (PV) efficiency, innovative design and aesthetic integration into urban landscapes. This study explores the potential of thin solar cells applied to spherical and hemispherical surfaces and the influence of temperature variations throughout the day. By conducting a comprehensive analysis of voltage–current diagrams, the study deciphers the intricate interconnections of spherical geometries and their response to external thermal impacts, elucidating their subsequent effects on energy conversion efficiency. Moreover, it delved into the dynamics of connecting multiple spherical modules, unveiling their potential to form artistic and utilitarian constructs such as solar trees and architectural embellishments. The findings indicate that while spherical configurations provide superior aesthetics and a power generation profile comparable to solar tracking systems, hemispherical configurations offer a 32% increase in efficiency compared with the spherical configuration and a notable reduction in land footprint. This research underscores the importance of striking a balance between aesthetic appeal, efficiency and land utilization, providing valuable insights for the future of PV technology integration into urban and agricultural landscapes. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new analytical method to normalize and forecast the energy usage/loss of residential and commercial buildings. Weather conditions have large effects on energy and economic activity. Weather Normalization is an important step in building energy rating and retrofit measurements. It has also become increasingly important because of changes in the worlds weather patterns due to global warming. Accounting for the impacts of weather on energy use in buildings is an extremely exhaustive challenge because of the complexity and diversity in the operation of the mechanical and electrical systems. In traditional weather normalization methods some building parameters, such as building size, window size, construction joints, and the effect of flues, are missing. We present a Structure Dependent Energy Usage/Loss (SDE U/L) linear and nonlinear models by using Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) to capture and forecast the behavior of energy consumption/loss. This model considers different building and weather parameters. Using the (SDE U/L) model, we present an innovative approach for linear and nonlinear weather normalization. Our simulation results demonstrate the flexibility and advantages of our structure dependent weather normalization method. Unlike most existing methods, the (SDE U/L) method does not impose any constraints on a property on its property type, use details, and energy data to be able to perform weather normalization for any building over time. [ABSTRACT FROM AUTHOR]

In the present day, when centralized energy storage technology is becoming increasingly mature, the cooperative energy sharing framework between the combined cooling, heating, and power (CCHP) systems and a shared energy storage provider (SESP) can effectively promote the local consumption of renewable energy and reduce the social energy cost. In this study, the optimal operation models of CCHP and SESP are first developed by considering the electric energy trading of the CCHP and the SESP. Then the CCHP–SESP cooperative operation model is established based on Nash bargaining theory, followed by equating it into two subproblems of coalition social energy cost minimization and electric energy trading payment negotiation. The alternating direction method of multipliers approach is introduced to solve the energy‐sharing problems in a distributed way. Numerical simulation results show the effectiveness of the proposed energy‐sharing framework. Simulation results show that the proposed bargaining‐based energy‐sharing framework can significantly reduce the social energy cost and improve the utilization rate of energy storage resources. [ABSTRACT FROM AUTHOR]

The organic Rankine cycle (ORC) has received a lot of attention in recent years due to its wide application in energy recovery and the use of low‐temperature energy sources. In this article, the energy, exergy, and economic analyses of a high‐temperature ORC (HTORC) in combined heat and power production mode have been performed. In this system, the heating water at 90°C for domestic or industrial purposes is provided in the HTORC condenser. Two working fluids, hexamethyldisiloxane (MM) and siloxane mixture (MDM), have been evaluated and compared in HTORC. The system has been modeled in engineering equation solver software and key parameters such as energy efficiency and exergy of the system, output power, heat‐to‐power ratio, and levelized cost of electricity (LCOE) have been calculated. The energy and exergy efficiency of the system for the two working fluids MM and MDM are equal to 40.8%, 45.6%, 22.45%, and 19.3%, respectively. From the point of view of energy and exergy, the working fluid MM performs better. The LCOE of the system with MM working fluid is equal to 0.5946 US$/kWh, which is slightly higher than MDM working fluid (0.5702 US$/kWh). [ABSTRACT FROM AUTHOR]

To achieve efficient and low energy consumption mining under different cutting depths of a shearer, a multiparameter coupling optimization method for the shearer based on three‐dimensional (3D) characterization of the coal wall was proposed. First, a seven‐axis absolute articulated arm measuring machine was used to obtain 3D point cloud data of the coal wall, and then the 3D of the coal wall surface was reconstructed by using segmentation, filtering, and stitching processing, thereby obtaining the average thickness of different coal wall areas. Second, through the quadratic rotation regression orthogonal combination experiment, the optimal combination of drum speed, traction speed, and cutting depth was obtained, further obtaining the order of primary and secondary influences, and the regression model. Moreover, a particle swarm optimization algorithm was used to obtain the optimal drum speed and finally, the laboratory and field test experiments were conducted to verify the effectiveness of the proposed optimization algorithm in reducing the cutting energy consumption of shearer. The experiment results show that the given optimization algorithm can adaptively optimize the traction speed and drum speed based on the corresponding cutting depth, which significantly reduces the cutting specific energy consumption of the shearer. Thus, it provided an important technical means for the shearer to achieve low energy consumption and efficient mining. [ABSTRACT FROM AUTHOR]

Energy supply policy quality is an important factor that would impact national energy security. While existing research conducted policy evaluation from the lens of performance assessment, less study has been devoted to evaluating energy supply policy from the perspective of policy making. This study takes the energy supply policy documents issued by China's central government during the '13th Five‐Year Plan' period (2016–2020) as the research sample, and pioneers the use of the extended policy modelling consistency (PMC) index model combined with the text mining methodology to construct a policy evaluation index system with the characteristics of the energy supply policy which conducts a more in‐depth quantitative evaluation of the energy supply policy documents. The results show that the average PMC index value of China's energy supply policies is 7.26, and the overall quality is high. The concerns of existing policies are mainly focused on reform and development planning, safety production management and project engineering construction, but there is still room for improvement in policy predictability, coordination of policy areas, clarity of policy basis and goals and comprehensiveness of policy tool combinations. Based on this, China should improve the comprehensiveness of its energy supply policy tool combinations in terms of policy design norm; strengthen policy predictability and coordination of policy areas in terms of policy implementation guarantee; and clarify the basis and goals of policies in terms of policy orientation enhancement, to provide a reference basis for the formulation and improvement of future energy supply policies and to realise the sustainable development of energy supply. [ABSTRACT FROM AUTHOR]

The cement production process is energy intensive both in terms of the thermal energy (firing the kiln, drying and De carbonation) and electrical energy for driving the numerous drives within the process line. The average specific power consumption of the case study plant was 111 kWh/ton of cement with an average peak demand of 9.7 MW. The high cost of electric power at 0.14 USD/ kWh results in very high cost of production that significantly lowers the company's profit margin and limits its competitive advantage. The generation of electrical power from waste heat recovery would reduce the electricity power bill through partially substituting the power procured from the national grid. This research evaluated the potential that the plant has for generating electrical power from the hot waste gases vented into the atmosphere and it was found that the plant has the potential to generate 3.4 MWh of electrical power. This results to a net potential to generate 2.89 MWh of electrical power after factoring in the auxiliary power consumption by Waste heat recovery plant system at 15%. This ultimately gave a reduction of 33% in the electricity power bill of the case study plant. The paper recommends the installation of a steam rankine cycle for the power generating plant. In this work the authors designed the steam boilers for the waste heat recovery plant for conversion of thermal energy to electrical energy, selected a commercial steam turbine and evaluated its economic feasibility and established that the designed plant would have a simple payback period of 2.7 years. [ABSTRACT FROM AUTHOR]

With climate change concerns on the rise, finding efficient and sustainable ways to separate carbon dioxide from industrial gas mixtures has become increasingly important. Membrane‐based gas separation technologies offer a promising solution due to their low energy consumption, low emissions, and ease of operation. In this study, we dug deep into theoretical energy consumption and practical optimization strategies for CO2 separation using such technologies. Our results showed that CO2 separation can be achieved with relatively low specific energy consumption, ranging from 0.09 to 0.27 MJe/kg CO2, depending on feed CO2 concentration. By conducting process simulations, we determined the optimal feed gas pressure and membrane surface area required to achieve a CO2 absorption ratio of 90% (mol). We also analyzed the effects of CO2 permeation and selectivity on energy consumption and membrane area, revealing that increasing both can significantly reduce energy consumption, albeit with more membrane surface area required. Using seepage flow circulation was found to be a particularly effective way to improve feed CO2 concentration and reduce energy consumption. To optimize and improve the capturing process, we conducted exergy analysis in each of the five stages of optimization, reporting Cooling Utilities (MW), Heating Utilities (MW), and Total Utilities (MW) for each step. Our results showed that in the optimal mode, Total Utilities (MW) were reported as 228.1, highlighting the potential of membrane‐based CO2 separation for carbon capture and storage applications. This study provides valuable insights and practical strategies for achieving efficient and sustainable CO2 separation. [ABSTRACT FROM AUTHOR]

Owing to the complexity of metros, the energy consumption characteristics of metro systems exhibit variability and the energy‐saving management of the systems encounters challenges. To encapsulate the essential characteristics of energy usage and to objectively assess the energy performance of metro systems, this study presents a generalized framework and applies it to a case study conducted in Tianjin. The study also employs correlation analysis to investigate the applicability of the indicators relevant to ridership. The results indicate that the monthly traction electricity consumption exhibits slight variation, while station electricity usage demonstrates substantial fluctuation with seasonal changes. For Tianjin Metro, the passenger factor hardly shows any effect on the electricity use of metro lines. The median value of traction electricity use is approximately 2.0 kWh/(car‐km) and that of the average annual station electricity use of underground lines ranges from 95 to 155 kWh/m2. The emission from the traction sector is 12.2 kgCO2/(vehicle‐km) and from the station sector is 118.6 kgCO2/m2. The study also identifies the energy‐intensive lines of the Tianjin Metro and compares the energy utilization among various global metro systems. The authors hope that this study can help shed light on the assessment of the energy status of metro systems and serve as a source of information for other City‐Metros to implement energy‐saving management. [ABSTRACT FROM AUTHOR]