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Emulsion polymerization is an effective means of synthesizing high-molecular-weight polymers in order to reduce the consumption of fossil energy. This paper adopts isoamyl octanoate to replace white oil as the continuous phase of emulsion polymerization to synthesize the high molecular weight polyacrylamide emulsion RSE. S-80 and T-80 were chosen as the emulsifiers, and the test results showed that the HLB value is 5.72 when S-80/T-80 is 6.5. When S-80/T-80 is 6.5, the HLB value is 5.72. Water and isoamyl octanoate can produce ultra-low interfacial tension and stable arrangement in the interfacial membrane to form a stable emulsion, and the particle size is uniformly distributed in a normal manner. The test of the exothermic process of polymerization shows that the emulsion can be stabilized into nuclei, and polyacrylamide polymers with a molar mass of more than 18 million are obtained. The microstructure test observes that RSE molecules are in an aggregated state in the aqueous solution. A three-dimensional spatial network structure is formed, and the polymerization can be stabilized in a three-dimensional network structure after 120 °C, 170 s−1 shear, which still has good stability. After the variable shear performance test proved that the aqueous solution of RSE belongs to the typical non-Newtonian fluids, to shock test the viscoelasticity of different concentrations of RSE polymer, G′ > G″, the RSE solution to elasticity is dominant. In this paper, the capacity to form stable emulsions of isoamyl octanoate in the emulsion polymerization process is investigated to provide a basis for seeking the selection of a continuous phase with little environmental pollution and high functionality. [ABSTRACT FROM AUTHOR]

Scheduling tasks in the cloud system is the main issue that needs to be addressed in order to improve customer satisfaction and system performance. This paper proposes DCOHHOTS, a novel multi-objective task scheduling algorithm based on a modified Harris hawks optimizer. In overall, this paper has two main stages. As the first step, DCOHHO is introduced as a new version of Harris Hawks Optimizer. Using the Differential Evolution algorithm, an optimal configuration is selected from the chaotic map, the opposition-based learning, and the ratio of the population. In order to improve the performance of the Harris Hawks Optimizer, this optimal configuration is applied to initialize the hawk's position. In the second stage, DCOHHOTS, a DCOHHO-based Task Scheduling algorithm, is proposed. Multi-objective behavior in the proposed task scheduling algorithm optimizes resource utilization to decrease the makespan, energy consumption, and execution cost. Moreover, prioritizing tasks before submitting them to the scheduler is done using the hierarchical process in the DCOHHOTS algorithm. For the purpose of investigating the performance of the proposed DCOHHO algorithm, a number of experiments are conducted using 20 standard functions and twelve algorithms. The experimental results demonstrate that the DCOHHO algorithm is superior at determining the optimal test function solutions. Additionally, makespan, execution cost, resource utilization, and energy efficiency of DCOHHOTS task scheduling algorithms are analyzed. Compared to existing algorithms, the proposed algorithm saves up to 16% energy in heavy loads. Additionally, resource utilization has increased by 17%. Compared to the conventional algorithm, the proposed algorithm reduced makepan and execution cost by 26% and 8%, respectively. [ABSTRACT FROM AUTHOR]

Given the increasing severity of energy shortages, the exploration of effective strategies to optimize energy structures has become imperative. This requires careful consideration of energy consumption patterns, especially since these data are fundamental inputs for policy formulation. Given the uncertainty in the rate of change in energy consumption, this paper proposes an optimized multivariable grey Bernoulli model that is rooted in the grey Bernoulli model and incorporates background values and genetic algorithms. The grey Bernoulli model effectively linearizes nonlinear problems, thus simplifying computational procedures. In addition, to account for random fluctuations of relevant factors that may affect the model's predictions, this model introduces nonlinear correction terms that allow simulation and prediction values to adhere to the grey index law. The incorporation of background values enhances the model's ability to process information, providing it with a superior grasp of real data. Genetic algorithms can be used to refine the model's parameters, increasing its adaptability and precision. Finally, this paper applies the refined model to examine China's energy consumption patterns, validating its efficacy and versatility. Furthermore, energy consumption patterns for the next four years are forecast, with the analysis revealing that the growth rate of energy consumption from 2021 to 2024 shows a downward trend, particularly notable in 2024, where the growth rate is 1.64%. [ABSTRACT FROM AUTHOR]

The objective of this paper is to investigate the nonlinear relationship between ICT and CO2 emissions by controlling for economic growth, foreign direct investment, energy consumption, and trade openness. Using data from 16 Middle East and North African (MENA) countries over the period 1990–2019, we apply the Panel Smooth Transition Regression (PSTR) model, as introduced by (González A, Teräsvirta T, vanDijk D (2005) Panel smooth transition regression models. SEE/EFI Working Paper Series in Economics and Finance, No. 604), to study the potential regime-switching behavior of the relationship between the variables. The results reveal the existence of a strong regime-switching effect between ICT and CO2 emissions. It was found that after reaching a certain threshold, ICT use and penetration starts to significantly mitigate environmental degradation. Our results show that high levels of ICT not only improve environmental quality but can also be part of the solution to combat the environmental challenges that the MENA region has faced over the past decades. In addition, to account for the potential endogeneity bias, we also develop and estimate a PSTR model with instrumental variables (IV-PSTR) using the approach of (Fouquau et al., Econ Model 25:284–299, 2008). The results obtained confirm those initially found by the PSTR model. The study concludes with policy implications. [ABSTRACT FROM AUTHOR]

Nowadays, one of the main challenges facing green innovation management is how to enhance the performance of innovation processes by utilizing asymmetric input and output data. Therefore, this paper develops an improved SBM model analysis framework for evaluating the green innovation efficiency of asymmetric input and output data. The framework is applied to assess the technical (TE), managerial (PTE), and scale (SE) efficiencies of new energy companies under three input variables (R&D personnel input, R&D capital input, and comprehensive energy consumption input), two desirable output variables (green technology output and economic output), and one undesirable output variable (greenhouse gas emissions). Then, environmental factors and random factors are eliminated from the obtained input slack variables based on the SFA model, placing decision-making units in a homogeneous environment. The results demonstrate that TE, PTE, and SE are improved after eliminating environmental factors and random factors. Subsequently, based on the entropy method, this paper classifies companies' green innovation patterns into four categories and provides targeted solutions. The purpose of this paper is to provide an evaluation method for new energy companies to understand green innovation efficiency and assist decision makers in identifying the most optimal resource allocation approach. The proposed improved SBM model contributes to the literature and to industry practice by (1) providing a reliable evaluation of green innovation efficiency under asymmetric input and output data; (2) determining effective improvement actions based on a slack analysis of environmental variables and random variables that lead to improved process performance; and (3) making fuzzy innovation performance efficient to facilitate understanding and managing innovation resource allocation quality. [ABSTRACT FROM AUTHOR]

This paper presents the control strategies and performance analysis of doubly fed induction generator (DFIG) for grid-connected wind energy conversion system (WECS). The wind power produces environmentally sustainable electricity and helps to meet national energy demand as the amounts of non-renewable resources are declining. The development of the WECS can be seen with development in power electronics technology. Currently, two types of major generator used in WECS these are DFIG and the permanent magnet synchronous generator (PMSG). Both variable oprational speed are used in wind turbine. Because its high performance, reliability, controllability and high-power and voltage capabilities, multilevel converters have become a popular option for multi-MW WECSs. This paper focuses on the application of back-to-back modular multilevel converters in a DFIG and PMSG-based WECS. A comparative performance analysis of the two systems based on power quality, active, reactive power, and modular multilevel converter dynamics is presented. The comparison is based on simulation results created in MATLAB/Simulink software. [ABSTRACT FROM AUTHOR]

Electricity consumption in buildings is one of the major causes of energy usage and knowledge of this can help building owners and users increase energy efficiency and conservation efforts. For Pacific Island countries, building electricity demand data is not readily accessible or available for constructing models to predict electricity demand. This paper starts to fill this gap by studying the case of schools in Fiji. The aim of the paper is to assess the factors affecting electricity demand for grid-connected Fijian schools and use this assessment to build mathematical models (multiple linear regression (MLR) and artificial neural network (ANN)) to predict electricity consumption. The average grid-connected electricity demand in kWh/year was 1411 for early childhood education schools, 5403 for primary schools, and 23,895 for secondary schools. For predicting electricity demand (ED) for all grid-connected schools, the stepwise MLR model shows that taking logarithm transformations on both the dependent variable and independent variables (number of students, lights, and air conditioning systems) yields statistically significant independent variables with an R2 value of 73.3% and RMSE of 0.2248. To improve the predicting performance, ANN models were constructed on both the natural form of variables and transformed variables. The optimum ANN model had an R2 value of 95.3% and an RMSE of 59.4 kWh/year. The findings of this study can assist schools in putting measures in place to reduce their electricity demand, associated costs, and carbon footprint, as well as help government ministries make better-informed policies. [ABSTRACT FROM AUTHOR]

Global warming, pollution, and increasing energy demand have compelled electrification of the transport sector. Electric vehicles are not only an attractive and cleaner mode of transport, but they also possess the capacity to offer flexible storage alternative based on bidirectional vehicle-to-grid schemes. Vehicle-to-grid or V2G technology permits electric vehicles' batteries to store energy and discharge it back to the power grid during peak-load periods. However, the feasibility and economic viability of V2G is still a matter of concern and needs investigation. In this paper, the authors delved into the feasibility of V2G technology by analysing the real time-charging data of a V2G demonstration project named EV-elocity, located at the University of Nottingham campus in the UK. The authors analysed the charging data and trip-status data of two charging sites and put forward some insights regarding the feasibility of V2G and the behavioural traits of the vehicles. This paper will enlighten the research community regarding the feasibility and benefits of V2G in a real-world environment by analysing the charging/discharging and vehicle behaviour and reporting the opportunities and benefits of vehicle-to-grid technology. [ABSTRACT FROM AUTHOR]

Hydrogen is a kind of clean renewable energy with great development potential. However, traditional electrocatalytic water splitting methods often require high energy consumption and expensive catalysts, which limits their practical application. Transition metal selenides (TMSes), as a new type of hydrogen production materials from electrolytic water, have been the focus of research in recent years. The basic principle of electrochemical water cracking and the preparation of TMSes are reviewed in this paper. The electrocatalysts for hydrogen evolution, oxygen evolution and integral water cracking based on TMSes, including single metal selenides, binary metal selenides, and multinary mixed metal selenides, are discussed. Finally, the future research direction and the development prospect of TMSes electrocatalysts are prospected. • Eley-Rideal and Langmuir-Hinshelwood mechanisms were used to analyze the OER reaction mechanism. • The production and properties of transition metal selenides in recent years are introduced in detail. • In this paper, transition metal selenides are introduced from single TMSes, binary TMSes and multinary mixed TMSes. • The physical properties and electronic structures of transition metal selenides are described in detail. [ABSTRACT FROM AUTHOR]

Along with the rapid economic development and social progress, the problem of energy and environment is becoming more and more outstanding. Therefore, it is very important to use new energy saving techniques to increase energy consumption and reduce energy consumption. In this paper, we have developed a hot water system, which is based on solar energy technology, gas-source heat pump, and phase change energy storage technology. The system was designed and constructed, and its performance was tested in various control conditions, with an emphasis on the analysis of the influence of phase change heat supply on heat storage properties. In this paper, the effect of the heat storage property is analyzed. Studies have shown that compared to a conventional tank that does not have a phase change heat storage device, the heat storage tanks equipped with phase change energy storage devices have longer discharge time, higher discharge temperature, better temperature, and better operation efficiency. In addition, the data show that flow rate control is also an important factor in optimizing the thermal storage characteristics of the system. [ABSTRACT FROM AUTHOR]

In the stochastic and dynamic edge-cloud collaborative environment, the computing resources of the host are limited, and the resource requirements of computing tasks are random and changeable. Therefore, how to efficiently schedule dynamic tasks and improve system performance becomes challenging. The scheduling algorithm based on deep reinforcement learning optimizes the delay and energy consumption of the system by dynamically interacting with the environment, which can solve the problem of dynamic and changeable environment to a certain extent, but there are still problems such as poor model adaptability, low training efficiency, and unbalanced system load. In this paper, aiming at optimizing the average response time of task scheduling and the average energy consumption of the system, a multi-objective task scheduling model is designed, and a task scheduling policy optimization algorithm based on improved asynchronous advantage actor-critic (A3C) is proposed. The residual convolutional neural network (RCNN) improves the network structure of A3C, using asynchronous multi-threaded training methods to interact with the edge-cloud collaborative environment and capturing the random dynamic characteristics of resources required for computing tasks and heterogeneous edge-cloud hosts resource change characteristics to better adapt to random dynamic environments, and it can quickly updating network parameters, improving training speed, and make full use of host resources to solve system load imbalance problems. Simulation results show that the scheduling algorithm proposed in this paper can effectively reduce task response time and system energy consumption in an edge-cloud collaborative environment. [ABSTRACT FROM AUTHOR]

Mining production, being one of the most energy-intensive industries globally, consumes substantial amounts of fossil fuels and contributes to extensive carbon emissions worldwide. The trend toward electrification and advanced developments in battery technology have shifted attention from diesel power to battery alternatives. These alternatives are appealing, as they contribute to decarbonisation efforts when compared to conventional diesel trucks. This paper presents a comprehensive review of recent technological advancements in powertrains for Mining Haulage Truck (MHT). It also compares these configurations based on mining system-level considerations to assess their future potential. The evaluated configurations include Diesel-Electric Truck (DET), Trolley Assist Truck (TAT), Battery-only Truck (BOT), Battery Trolley with Dynamic charging truck (BT-D), and Battery Trolley with Stationary charging truck (BT-S). According to the analysis, the energy demand for on-board diesel or battery power (excluding trolley power) in these alternative options is as follows: DET—681 kWh, BOT—645 kWh, TAT—511 kWh, BT-S—471 kWh, and BT-D—466 kWh. The paper also illustrates the theory of battery size design based on the current battery technology, battery material selection, battery package design, and battery size selection methods. In the case of tailored battery size selection, BOT, BT-D, and BT-S configurations require LiFePO4 (LFP) battery masses of 25 tonnes, 18 tonnes, and 18 tonnes, respectively. Based on a techno-economic assessment of battery MHT alternatives with a future perspective, it has been determined that BT-D requires the lowest amount of on-board battery energy. Furthermore, over a span of 20 years, BT-S has demonstrated the lowest on-board battery cost. [ABSTRACT FROM AUTHOR]

This paper introduces a groundbreaking approach to demand response management, aiming to empower consumers through innovative strategies. The key contribution is the concept of "acquiring flexibility rights", wherein consumers engage with power aggregators to curtail energy usage during peak-load periods, receiving incentives in return. A flexibility right coefficient is introduced, allowing consumers to tailor their participation in demand response programs, ensuring their well-being. Additionally, a lighting intensity control system is developed to enhance residential lighting network efficiency. The study demonstrates that high-energy consumers, adopting a satisfaction factor of 10, can achieve over 61% in electricity cost savings by combining the lighting control system and active participation in demand response programs. This not only reduces expenses but also generates income through the sale of flexibility rights. Conversely, low-energy consumers can fully offset their expenses and accumulate over USD 33 in earnings through the installation of solar panels. This paper formulates an optimization problem considering flexibility rights, lighting control, and time-of-use tariff rates. An algorithm is proposed for a distributed solution, and a sensitivity analysis is conducted for evaluation. The proposed method showcases significant benefits, including cost savings and income generation for consumers, while contributing to grid stability and reduced blackout occurrences. Real data from a residential district in Tehran validates the method's effectiveness. This study concludes that this approach holds promise for demand response management in smart grids, emphasizing the importance of consumer empowerment and sustainable energy practices. [ABSTRACT FROM AUTHOR]

In the European Union, increasing attention is already being paid not only to the development of renewable energy sources, but also to the establishment of solutions to achieve local energy self-sufficiency while increasing the role of citizens in managing the energy they generate. This approach is expected both to have a positive impact on the environment and the reduction of greenhouse gas emissions, and to enhance energy security—both in economic and civic terms by, i.a., combating energy poverty. The development of local energy communities promoted in the EU is supported i.a. by energy cooperatives. These contribute to the efficient harnessing of renewable energy potential in rural and urban-rural areas, and have been developing in Poland for several years now. In their previous studies, the authors of this research paper attempted to verify the generation (number, type and capacity of installed sources) and consumption (energy demand) configurations in which an energy cooperative would be a viable solution for prosumers who might establish it. However, over the past few years, the conditions for prosumers and the method of their accounting with the electricity seller have changed radically in Poland (shift from net metering to net billing). This situation has opened up space for further research and encouraged the authors to revisit the problem of analyzing the viability of establishing energy cooperatives in relation to the rules of operation of individual prosumers. This research was carried out for three scenarios, and the horizon of the analyses conducted and described extends to 2045. The comparative analysis included energy consumers without their own generation sources, prosumers with a photovoltaic generation installation covered by the net billing model, as well as a scenario involving prosumers' cooperation within an energy cooperative, which by law is settled in the net metering model. Conclusions from the research and simulations made it possible to confirm the claim that, despite changes in the rules of prosumer billing, developing energy independence in the energy community formula results in a significant reduction in the cost of purchasing electricity (even several times lower purchase costs in the timeframe analyzed) and can lead to a reduction in the payback time of investments in generation sources even by a factor of two. The results presented in this research paper open up space for further research. The outcomes allow us to assume that energy cooperatives—in the organizational and institutional model in Poland—are a good tool for reducing the phenomenon of energy poverty on a local scale. [ABSTRACT FROM AUTHOR]

Cities play a pivotal role in achieving worldwide carbon neutrality due to their significant contribution to global energy consumption and carbon emissions. Therefore, planning effective strategies and guiding evidence-based policymaking at the city scale becomes even more crucial. Composite indices serve as a valuable tool for monitoring urban energy transition trends. This paper aims to present a novel approach, robust and flexible even under conditions of data scarcity, for tracking the energy transition trend of a city by means of a composite index (UETI). The Turin case study is introduced to test the applicability of the proposed approach. Additionally, to demonstrate the robustness of the composite index framework, the paper includes the findings of correlation and sensitivity analyses. This study reveals a significant improvement in Turin's environmental and energy domains, while the socio-economic domain shows more modest improvement. Furthermore, the study highlights the need to address the shortage of urban data to enhance the accuracy and reliability of metric-based frameworks and to extend the assessment to a larger sample of cities. [ABSTRACT FROM AUTHOR]

AbstractDrainage is a common practice in geotechnical engineering concerning dredged marine soils. Current drainage techniques, including surcharge preloading, vacuum preloading, and combined vacuum-surcharge preloading, have been proven to be effective in soft soil treatment, but are also criticized for their high energy consumption. This paper made a brief review on existing drainage techniques and proposed some prospects for the next-generation techniques in response to the public concern of sustainability. It is found that all conventional preloading techniques have been well studied from tests to modeling, and improved vacuum preloading tends to be used in combination with other techniques. Drainage techniques with lower energy consumption can be realized either by using renewable energy or designing biomimetic devices. The paper is expected to provide a comprehensive while concise report on recent advances in drainage techniques for dredged marine soils and in the meanwhile give an insight into the further development towards a more sustainable future. [ABSTRACT FROM AUTHOR]

Integrating hybrid energy storage system (HESS) consisting of battery and supercapacitor provides balance over the power generation and load demand, ensuring the system's stability under transient conditions. Along with battery storage, to control its rate of charging/discharging a rate limiter (RTL) is usually employed. However, the impact of the RTL on battery storage efficiency and system performance has not been explored in the existing literature. This paper investigates the performance of a grid‐tied photovoltaic (PV) system with HESS under dynamic scenarios with different RTL values applied to the battery storage. The paper explores the simulation analysis considering transient, linear, and steady‐state conditions with varying PV generation and load demand. The simulation study shows that a lower RTL value may slow down the battery response, leading to inefficient utilization of battery storage. Alternatively, a very high RTL value will not appropriately regulate the battery current, which may lead to frequent charging and discharging and deterioration of battery life. For the proposed system, RTL with a value of 100 A/s seems appropriate, providing 1.29 Wh of energy through battery storage with an energy efficiency of 91.81% for the simulated time. The DC voltage is efficiently maintained at 400 V with a ripple factor of 0.45%. [ABSTRACT FROM AUTHOR]

The deregulation of the electricity market has been accompanied by the growing utilization of unpredictable renewable energy sources (RESs) such as solar, wind, and hydropower plants. Additionally, advancements in energy storage technologies and new energy demands have further contributed to this trend. As a result, the planning and operation of power systems are now surrounded by a higher level of uncertainty. In order to ensure the proper operation of power systems integrated with RESs, modern power systems are equipped with specific vital tools such as optimal power flow (OPF), which regulates generation and demand to achieve specific objectives. Hence, this paper conducts a comprehensive review of recently published research articles focusing on various solution strategies to address OPF problems in the presence of stochastic RESs and power demand. The review encompasses diverse solution methodologies, objective functions, constraints, and distinct techniques to simulate the stochastic behavior of RESs and dynamic loads. Additionally, the paper explores fundamental challenges, identifies critical research gaps, and highlights unexplored areas pertaining to optimal power system operation in the future. This review is essential for system operators who need to assess and pre-plan flexibility competency for their power systems to ensure practical and cost-effective operation under high RESs penetration. [ABSTRACT FROM AUTHOR]

This paper introduces a novel design for a solar photovoltaic/thermal (PV/T) air collector that utilizes staggered fins. The collector features double-layer flow channels in the same direction, with the upper channel equipped with transparent wave plates, and the lower channel fitted with staggered fins. The paper includes a calculation scheme for wave plate heat balance. The numerical model of the collector is established and verified by experimental results. A numerical model is used to analyze the height combination of the upper and lower runner and the fin arrangement of the lower runner. The results indicate that, under the given environmental parameters, as the PV panel moves down, the overall air heat transfer capacity decreases. When the height combination of the upper/lower runner is 23 mm/105 mm, the maximum value is 53.741%. With the increase of fin spacing, the heat transfer capacity of the lower runner decreases. With the increase of the number of fin rows, the heat transfer capacity of the lower runner increases. When the equivalent diameter of the rectangular channel and the number of fin rows were 70 mm and 3 rows, respectively, the maximum solar energy comprehensive utilization efficiency was 56.261%. [ABSTRACT FROM AUTHOR]

The concerns over greenhouse gas emissions, environmental impacts, climate change, and sustainability continue to grow. As a result of countermeasures, many modern gas turbine power plants and combined cycle power plants are considering to use hydrogen as a clean fuel alternative to fossil fuels in the power plant industry. We assessed the implications of such transition from natural gas to hydrogen as fuel in a gas turbine power plant's balance of plant (BOP) equipment. Using the DWSIM process simulation software and the methodology of compression power changes against different gas compositions, the impact of blending hydrogen with natural gas on temperature differentials, energy consumption, adiabatic efficiency, compression power, and economic implications in gas turbine power plants were examined in this paper. We discovered, through analysis, that there was not a noticeable boost in compression power or energy consumption when 50% hydrogen and 50% natural gas were blended. Similarly, there was no discernible difference in temperature differentials or adiabatic efficiency when 30% hydrogen and 70% natural gas were blended. Moreover, mixing 50% hydrogen and 50% natural gas did not result in a noticeable cost climb. In addition, the techno-economic analysis presented in this paper offered valuable insights for power plant engineers, power generation companies, investors in energy sectors, and policymakers, highlighting the nature of the fuel shift and its implications on the economy and technology. [ABSTRACT FROM AUTHOR]

In order to cope with the problems of energy shortage and environmental pollution, carbon emissions need to be reduced and so the structure of the power grid is constantly being optimized. Traditional centralized power networks are not as capable of controlling and distributing non-renewable energy as distributed power grids. Therefore, the optimal dispatch of microgrids faces increasing challenges. This paper proposes a multi-strategy fusion slime mould algorithm (MFSMA) to tackle the microgrid optimal dispatching problem. Traditional swarm intelligence algorithms suffer from slow convergence, low efficiency, and the risk of falling into local optima. The MFSMA employs reverse learning to enlarge the search space and avoid local optima to overcome these challenges. Furthermore, adaptive parameters ensure a thorough search during the algorithm iterations. The focus is on exploring the solution space in the early stages of the algorithm, while convergence is accelerated during the later stages to ensure efficiency and accuracy. The salp swarm algorithm's search mode is also incorporated to expedite convergence. MFSMA and other algorithms are compared on the benchmark functions, and the test showed that the effect of MFSMA is better. Simulation results demonstrate the superior performance of the MFSMA for function optimization, particularly in solving the 24 h microgrid optimal scheduling problem. This problem considers multiple energy sources such as wind turbines, photovoltaics, and energy storage. A microgrid model based on the MFSMA is established in this paper. Simulation of the proposed algorithm reveals its ability to enhance energy utilization efficiency, reduce total network costs, and minimize environmental pollution. The contributions of this paper are as follows: (1) A comprehensive microgrid dispatch model is proposed. (2) Environmental costs, operation and maintenance costs are taken into consideration. (3) Two modes of grid-tied operation and island operation are considered. (4) This paper uses a multi-strategy optimized slime mould algorithm to optimize scheduling, and the algorithm has excellent results. [ABSTRACT FROM AUTHOR]

Ride comfort is an important indicator to evaluate the dynamic performance of automotive vehicles. One method for improving ride comfort is to incorporate an active actuator into the passive suspension. However, the improvement is closely linked with the required actuation power and force. Increased actuation requirements may lead to higher energy consumption and a larger actuator size. To enhance the trade-off between ride comfort and active actuation requirements, a new design approach for searching for the optimal passive-active-combined suspension across many different mechanical component layouts incorporating an inerter is proposed in this paper. Compared with traditional designs where the suspension is limited to a few special layouts, via this approach the optimal passive part among all network possibilities with pre-determined numbers of each element type (springs, dampers and inerters), and optimal controller parameters of the parallel active actuator can be identified. Considering a quarter-car model, with a benchmark combined active-passive suspension in which the passive part is a spring-damper, the optimal inerter-based suspension can reduce the active force by more than 48% and regenerate 6 W more average power in the active part while achieving the same ride comfort. Note that in this case, two constraints are always considered: the road-holding ability (as indicated by the dynamic tyre load) and suspension travel of the identified suspension will not be worse than those of the benchmark suspension. The optimal trade-off obtained with this approach serves as a powerful tool in the automotive suspension design as it provides guidance on the following three aspects: the optimal ride comfort, the minimum power consumption and the feasibility of specific actuation requirements, where the second point is obtained in conjunction with the power conservation theorem proposed in this paper which proves that the total power consumed by the passive and active parts will not vary with changes in the passive part and active controller parameters. [ABSTRACT FROM AUTHOR]

This paper investigates a generalized Camassa–Holm equation with cubic nonlinearities (alias the mCH-Novikov equation), which is a generalization of some special equations. The mCH-Novikov equation possesses well-known peaked solitary waves that are called peakons. The peakons were proved orbital stable by Chen et al. in [Stability of peaked solitary waves for a class of cubic quasilinear shallow-water equations. Int Math Res Not. 2022;1–33]. We mainly prove the orbital stability of the multi-peakons in the mCH-Novikov equation. In this paper, it is proved that the sum of N fully decoupled peaks is orbitally stable in the energy space by using energy argument, combining the orbital stability of single peakons and local monotonicity of the method. [ABSTRACT FROM AUTHOR]

This paper focuses on optimizing the Zuker RNA folding algorithm, a bioinformatics task with non-serial polyadic dynamic programming and non-uniform loop dependencies. The intricate dependence pattern is represented using affine formulas, enabling the automatic application of tiling strategies via the polyhedral method. Three source-to-source compilers—PLUTO, TRACO, and DAPT—are employed, utilizing techniques such as affine transformations, the transitive closure of dependence relation graphs, and space–time tiling to generate cache-efficient codes, respectively. A dedicated transpose code technique for non-serial polyadic dynamic programming codes is also examined. The study evaluates the performance of these optimized codes for speed-up and scalability on multi-core machines and explores energy efficiency using RAPL. The paper provides insights into related approaches and outlines future research directions within the context of bioinformatics algorithm optimization. [ABSTRACT FROM AUTHOR]

• Innovative passive cooling system to reduce refrigerator energy consumptions. • Use of external air for condenser cooling enables a lower temperature heat source. • The heat from the condenser drives natural airflow circulation of the external air. • CFD analysis optimize device geometry and heat transfer. • Experimental test demonstrates significant energy savings of 36.0 % ± 5.2 %. The paper presents a solution aimed at enhancing the cooling efficiency of domestic refrigerators. The proposed approach involves a passive cooling system designed to leverage the colder external air during winter for the cooling of the fridge condenser. Indeed, the use of a colder external source helps to increase the efficiency of the refrigerator. The system provides a connection between the rear section of the refrigerator and the external environment via two ducts. The heat dissipated by the condenser induces the natural circulation of outdoor cooling air. The study includes experimental and CFD analysis. The experimental results shows that the configuration with the cooling system achieves higher useful effects and lower power consumption compared to the traditional setup, yielding a reduction in energy consumption of 36.0 % ± 5.2 % when operating with an external air temperature of 0 °C. The CFD model is validated through experimental data, and simulations are conducted to optimize the sizing and design of the system components. Parametric analysis indicates that duct diameters of 12 cm and a box depth of 15 cm behind the fridge are required to optimize heat transfer and aid natural circulation. In summary, the paper offers a suitable solution to increase the fridge efficiency in cold climatic locations. [ABSTRACT FROM AUTHOR]

Thermal power plants are pivotal in meeting global energy demands, yet enhancing their efficiency and sustainability remains an enduring challenge. While previous studies have scrutinized energy and exergy analyses of distinct plant components, there's a scarcity of comprehensive reviews integrating findings across diverse plant types. This paper bridges this gap by presenting a comprehensive synthesis of recent advancements in energy and exergy studies across coal, gas, biomass, oil, and combined cycle plants. The review focuses on critical aspects: optimizing operations through modeling and advanced controls, economic evaluations encompassing costs and revenue, and assessing environmental impacts such as emissions and water use. Achieving a balance between performance, cost-effectiveness, and environmental responsibility is crucial for sustainable thermal power generation worldwide. It requires an integrated approach that considers technical, economic, and environmental factors to ensure efficiency, profitability, and minimal adverse effects on health and climate. Key findings emphasize that, in most cases, the boiler emerges as the primary source of exergy destruction, accounting for over 50% of losses across varied plant configurations. Turbines and condensers also significantly contribute to energy losses. Supercritical and ultra-supercritical power plants exhibit higher efficiencies compared to subcritical counterparts. Integrating waste-to-energy technologies with coal plants holds promise, offering efficiency improvements and reduced environmental impact. Optimizing parameters such as pressure and temperature, along with component advancements, shows potential in curbing losses. These optimization endeavors have showcased a notable up to 6% enhancement in exergy efficiency. This review underscores the critical role of ongoing thermodynamic modeling and assessments in steering towards more sustainable thermal power generation. In summary, this paper delivers valuable insights into performance benchmarks and delineates effective strategies for augmenting thermal power plant efficiency through exhaustive energy and exergy analyses. [Display omitted] • Exergy analysis provides a broader perspective than energy analysis alone. • Boiler subsystem is a major source of exergy destruction (52-85% of total losses). • Supercritical and ultra-supercritical plants display higher efficiencies than subcritical ones. • Optimization of parameters like temperature and pressure can boost exergy efficiency by up to 6%. • Advanced combined cycles achieved over 50% efficiency, indicating potential for lower CO2/kWh. [ABSTRACT FROM AUTHOR]

Traditional buildings have a central role to play in Europe's plans to be the first climate-neutral continent by 2050, with the building sector being the single largest energy consumer in the EU with 40 per cent of total energy use. Seventy-five per cent of the existing housing stock is energy inefficient with minute numbers renovated each year. Existing buildings offer huge potential for energy use reduction. Research has shown that a historic retrofit can emit less carbon compared to demolishment and rebuilding. Reductions in carbon emissions of up to 62 per cent were observed across a body of case studies by Historic England, including a 95 per cent reduction in the mews case study described in this paper. Moreover, carefully managed thermal retrofits, taking a 'whole house approach', have many benefits, including improved indoor comfort and air quality as well as reduced operational costs. These are factors which are fundamental in ensuring the continued use of historic constructions while improving their standards of preservation and durability. Thermally upgrading traditional building is still a much-debated topic. Inappropriate thermal interventions with unsuitable materials and finishes can have unforeseen consequences, resulting in building damage and a reduction in internal comfort, health and well-being. This paper aims to contribute to this discussion by providing an overview of key considerations when thermally upgrading the walls of traditional buildings while preserving unique characteristics and functionality. Advantages of adopting thermal mortars such as cork lime are put forward, and their potential efficacy is discussed, concluding with a case study focusing on the deep retrofit of a London mews within a conservation area. [ABSTRACT FROM AUTHOR]

This paper reviews evidence on how the fast growth in renewable energy technologies can trigger social tipping dynamics that potentially accelerate a system-wide energy transition. It does so by reviewing a variety of literature across several disciplines addressing socio-technical dimensions of energy transitions. The tipping dynamics in wind and solar power create potential for cascading effects to energy demand sectors, including household energy demand. These most likely start with shift actions and adoption of household-scale batteries and heat pumps. Key enablers are strong regulations incentivising reductions in demand and setting minimum efficiency levels for buildings and appliances. While there is evidence of spillovers to more environmentally friendly behaviour, the extent of these and the key leverage points to bring them about present a knowledge gap. Moreover, these behavioural feedback loops require strong additional policy support to "make them stick". Understanding the economic and social tipping dynamics in a system can empower decision-makers, fostering realistic energy transition policies. This paper highlights energy communities as a promising niche for leveraging tipping dynamics. Ultimately, bridging the gap between these tipping dynamics and institutional reforms is crucial for unlocking the full potential of sustainable energy systems. [ABSTRACT FROM AUTHOR]

The paper offers innovative cooking utensil designs for remote, isolated, and even peri-urban communities at a low price, with high reliability and simple construction. It can alleviate energy poverty and improve food security. This utensil uses only local solar energy directly and allows comfortable indoor cooking. This paper provides the design principles of a solar cooker/frying pan or generic heater, based on a PV panel or a plurality of them, which are directly connected to a plurality of Positive Thermal Coefficient (PTC) resistors to match the power. PTCs are nowadays produced in massive quantities and are widely available at low cost. The proposed device does not require an electronic controller or a battery for its operation. The aim is for family use, although the design can be easily scaled to a larger size or power, maintaining its simplicity. Electric heating inside or attached to the cooking pot, plus the temperature self-limiting effect of PTCs, allows for thermally insulating the cooking pot from its outside using ordinary materials. Insulation enhances energy efficiency during cooking and keeps cooked food warm for a long time. Clean development would receive a significant impulse with its application. A simple mathematical model describes its functioning and states guidelines for adequate design. Its results indicate a successful proof of concept and high efficiency both for water and oil as representatives of cooking. [ABSTRACT FROM AUTHOR]

This paper presents the process of creating a model for electric vehicle (EV) energy consumption, enabling the rapid generation of results and the creation of energy maps. The most robust validation indicators were exhibited by an artificial intelligence method, specifically neural networks. Within this framework, two predictive models for EV energy consumption were developed for winter and summer conditions, based on actual driving cycles. These models hold particular significance for microscale road analyses. The resultant model, for test data in summer conditions, demonstrates validation indicators of an R2 of 86% and an MSE of 1.4, while, for winter conditions, its values are 89% and 2.8, respectively, confirming its high precision. The paper also presents exemplary applications of the developed models, utilizing both real and simulated microscale data. The results obtained and the presented methodology can be especially advantageous for decision makers in the management of city roads and infrastructure planners, aiding both cognitive understanding and the better planning of charging infrastructure networks. [ABSTRACT FROM AUTHOR]

The primary contributors to elevated line losses in low-voltage distribution networks are three-phase load imbalances and variations in load peak–valley differentials. The conventional manual phase sequence adjustment fails to capitalize on the temporal characteristics of the load, and the proliferation of smart homes has opened up new scheduling possibilities for managing the load. Consequently, this paper introduces a loss-reduction method for low-voltage distribution networks that leverages load-timing characteristics and adjustment capabilities. This method combines dynamic and static methods to reduce energy consumption from different time scales. To commence, this paper introduced a hierarchical fuzzy C-means algorithm (H-FCM), taking into account the distance and similarity of load curves. Subsequently, a phase sequence adjustment method, grounded in load-timing characteristics, was developed. The typical user load curve, derived from the classification of user loads, serves as the foundation for constructing a long-term commutation model, therefore mitigating the impact of load fluctuations on artificial commutation. Following this, this paper addressed the interruptible and transferable characteristics of various smart homes. This paper proposed a multi-objective transferable load (TL) optimal timing task adjustment model and a peak-shaving control strategy specifically designed for maximum sustainable power reduction of temperature-controlled loads (TCL). These strategies aim to achieve real-time load adjustment, correct static commutation errors, and reduce peak-to-valley differences. Finally, a simulation verification model was established in MATLAB (R2022a). The results show that the proposed method mainly solves the problems of three-phase imbalance and large load peak–valley difference in low-voltage distribution networks and reduces the line loss of low-voltage distribution networks through manual commutation and load adjustment. [ABSTRACT FROM AUTHOR]

This paper presents the design of and transient time simulations for a four-pole magnetic bearing with permanent magnets. The usage of permanent magnets reduces the consumption of electric energy in comparison to a traditional active magnetic bearing. Permanent magnets are installed in the yoke of the stator core to limit the cross-coupling of the magnetic flux generated by the windings. The first part of this paper presents the design of the magnetic bearing and its finite-element model, while the second part describes the field-circuit indirectly coupled finite-element model for the transient time simulation. The presented simulation model was used to calculate the transient response for the rotor lifting from the starting position, the step change in the rotor position and the change in the rotor position under an external impact force applied along the y-axis. [ABSTRACT FROM AUTHOR]

In order to meet the hydrogen refueling demand of fuel cell vehicles and reduce the cost of HRS operation process, it is necessary to study the hydrogen refueling process of cascade hydrogen storage system of hydrogen refueling station. In this paper, a thermodynamic model of the hydrogen refueling process for fuel cell vehicles is established, and the effect of the variation of these thermodynamic parameters on the specific energy consumption and utilization rate of the hydrogen refueling process is investigated in terms of the pressure ratio and capacity of the hydrogen storage tank, which is the core of the hydrogen refueling station, consisting of multiple hydrogen storage tanks. The results show that the specific energy consumption and hydrogen utilization of the hydrogen refueling station decreases as the ratio of the nominal pressure of the medium-pressure stage to the nominal pressure of the high-pressure stage increases over the course of the pressure ratio change. In addition, the capacity variation is only slightly changed as a result of the pressure ratio variation. Therefore, the lowest ratio of specific energy consumption can be found by fixing the utilization rate and by changing the pressure ratio. For example, in this paper, if the total mass of hydrogen in the station is 600 kg and the hydrogen utilization rate is required to be not less than 12 %, the optimal pressures for each level are 50.2 MPa, 65 MPa and 86.7 MPa. • A thermodynamic model of the hydrogen filling process for fuel cell vehicles is established. • The pressure ratio and capacity of cascade hydrogen storage systems were analyzed. • Specific energy consumption and hydrogen utilization are used as evaluation indicators. • The optimal pressures for each level are 50.2 MPa, 65 MPa and 86.7 MPa. [ABSTRACT FROM AUTHOR]

Microwave‐driven catalytic processes have attracted the attention of many researchers and industries as these processes are powerful strategies for use in industrial electrification. However, undetectable hot spots inside the catalyst bed can be misleading in terms of the deceptive effects of microwaves on acceleration of catalytic reactions because of the lack of physicochemical knowledge about these processes. In this paper, the hot spots temperatures were estimated based on catalytic ammonia (NH3) synthesis/decomposition equilibrium. The reaction temperature estimated from the chemical equilibrium was more than 100 °C higher than the catalyst bed surface temperature measured with an infrared thermometer under microwave irradiation. Assuming a formation of very steep local high temperature fields, rigorous kinetic analysis led to an estimate that the active site fraction which reaches the high temperature was only about 10 % of total sites. Compared with a resistivity heating furnace, the energy input was reduced by more than 85 % under microwave irradiation. The temperature estimated by the chemical equilibrium was almost corresponding to the temperature estimated by an oscillation of extended X‐ray absorption fine structure recorded under microwave irradiation. This paper presents a proposal for a method to estimate hot spots based on thermodynamic equilibrium, yielding a quantitative evaluation of energy consumption reduction caused by microwaves. [ABSTRACT FROM AUTHOR]

In this paper, a user thermal comfort criterion based on predicted mean vote (PMV) values is introduced to realize the optimal operation of an improved energy hub (IEH) while considering thermal inertia and user thermal behavior. A three-layer optimization model based on user thermal comfort is constructed which fully considers user thermal comfort demand, IEH operating costs, and energy network constraints. Moreover, since IEH optimization considering user thermal comfort is a multi-objective bilevel optimization (MNBO) problem, this paper proposes an improved multilayer nested quantum genetic algorithm (IMNQGA) to solve it. Finally, the effectiveness of the proposed optimization model and algorithm is verified through the analysis of the four modes. The examples show that the proposed optimal control method can reduce the system's operating costs and improve energy efficiency while satisfying user thermal comfort demand. [ABSTRACT FROM AUTHOR]

The global energy crisis, which began in 2021 due to the extraordinary economic recovery after the pandemic and intensified after Russia's invasion of Ukraine in February 2022, has changed the conditions of energy management, paying more attention to energy efficiency. Natural gas prices have reached record levels and, consequently, so have electricity prices in some markets. Oil prices have reached their highest level since 2008. Higher energy prices have contributed to sharply increased inflation. Households are again becoming interested in buying coal as a source of heat. High energy and gas prices have pushed many families into poverty and forced some factories to cut production or even close. They have also slowed economic growth to the point where some countries are heading for a serious recession. Paradoxically, the negative effects of the energy crisis may accelerate the introduction of cleaner, sustainable, renewable energy such as wind and solar energy. The energy crisis is comparable to the oil crisis of the 1970s, when it contributed to significant advances in energy efficiency. The current crisis has highlighted the importance of investments in renewable energy resources and initiated the process of integrating regional markets, developing energy efficiency and promoting renewable energies. The aim of this article is to comprehensively explore the complex relationship between energy awareness, consumption patterns, and energy efficiency, with a focus on both individual consumers and industries, during the global energy crisis. This paper is based on a literature review, overarching policy documents, energy reports, and other secondary documents. The primary research method was the systematic literature review method, based on which the impact of the global energy crisis on energy efficiency was evaluated. This study emphasizes the diverse influences on energy awareness, ranging from economic factors to consumer preferences and environmental consciousness. The findings of the paper underscore the significant responsibility of industries in contributing to energy-saving efforts and the active role of consumers in the energy market. The responsibility of industries in contributing to energy efficiency is highlighted, with a call for a comprehensive approach that integrates energy-saving criteria into product development and corporate social responsibility. [ABSTRACT FROM AUTHOR]