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The paper presents an original method for synthesizing high-density composite ceramics with a Mo 2 C/Mo 3 Co 3 C composition using the technology of high-speed Spark Plasma Sintering (SPS) of a mechanically activated powder mixture of Mo 2 C-10 wt%Co. The activated homogeneous starting mixture was obtained by wet milling/activation of the initial powders of Mo 2 C and Co in an anhydrous isopropanol medium. The ceramic samples were consolidated by SPS under vacuum (10−5 atm.) at 1000, 1100, 1150, and 1200 °C with a heating rate of 87.3 °C·min−1 under constant external uniaxial pressing pressure of 50 MPa. The sintering kinetics were studied, the twofold nature of sintering was established, and the temperature range of active densification was determined. The ceramic synthesis is accompanied by a reactive interaction of the components, resulting in the formation of the Mo 3 Co 3 C phase and a change in the crystal lattice of the non-stoichiometric Mo 2 C x compound. The ceramic obtained in the temperature interval of the isothermal sintering stage of 1150–1200 °C is homogeneous in the distribution of Mo and Co, consists of a Mo 2 C/Mo 3 Co 3 C phase mixture, has a density value of 98.13 ± 0.5 % theoretical density, and has an average Vickers hardness value of ∼1526 HV. Increasing the sintering temperature to 1200 °C results in the formation of almost monolithic structures in the samples, but also induces the formation of large defects due to the collective consolidation of pores within the bulk material and increases the susceptibility of the ceramic to cracking. The paper presents preliminary results from electrochemical testing. The proposed method for synthesizing Mo 2 C/Mo 3 Co 3 C ceramics has potential for use in optimizing the composition and production methods of electrode materials for the realization of active components in heterostructured electrodes used for hydrogen evolution. [ABSTRACT FROM AUTHOR]

Closed-form models are important because they offer more design insights, require less computing time and prevent convergence problems in modeling compliant mechanisms. However, most of the available closed-form models, e.g., beam constraint models, are only appropriate for beam flexures within the intermediate deflection range. In this paper, a method for developing closed-form models based on the chained beam constraint model through the combined use of strain energy and Castigliano's first theorem is proposed. A closed-form model for beam flexures with a large prediction range is derived using this method. By modeling simple beams and compliant mechanisms using the proposed closed-form model, it is shown that the model has a significantly broader prediction range than the beam constraint model and is more efficient than the chained beam constraint model. The large prediction range and high efficiency of the proposed closed-form model make it potentially useful for optimizing compliant mechanisms. • A method to develop closed-form models for beam flexures is proposed. • A closed-form model for CBCM with 3 discretized elements is obtained. • Closed-form load-displacement relations for beam flexures are obtained. • The proposed molded is demonstrated to reduce the computational time. [ABSTRACT FROM AUTHOR]

• Novel damped conformable fractional accumulation method. • Prediction of energy related series with the uncertainties. • Design of damped conformable fractional grey bernoulli model. • Improved prediction accuracy, robustness and applicability. • Prediction of world coal consumption and production in 2025–2026. Energy resources, such as oil, coal are important to people's life and the development of the economy. Predicting the energy consumption and production with uncertainties can help the government and policymakers make the reasonable energy strategy and give constructive guidance. In this paper, a novel damped conformable fractional accumulation operator with two parameters are proposed to have control of both accumulation generation and new information involvement. The new accumulation can be adjusted from linear approximation to high order accumulation. A structure adaptive grey Bernoulli model based on proposed accumulation method is established, with the parameters optimized by particle swarm optimization algorithm. For testifying the effectiveness and the robustness of the model, two validation cases and two application cases are used to build the prediction model of diverse series with unexpected trends or fluctuations in energy fields. Compared with other existing fractional grey models and non-grey models, the proposed model has best prediction accuracy, better model stability and greatly improved applicability. The global coal consumption by industry is predicted as 30.85×106 TJ in 2025 and 30.81×106 TJ in 2026. The world coal production could be 182.93–187.73 exajoules in 2025 and 180.85–182.16 exajoules in 2026. [ABSTRACT FROM AUTHOR]

This paper explores the feasibility of utilizing lead-free multilayer piezo stacks, particularly focusing on Zr-doped 0.95(Bi 0. 5 Na 0.5)TiO 3 -0.05(BaTiO 3) piezoceramics, for energy harvesting. Various lead-free BNT-BT disk samples with different Zr concentrations undergo structural, microstructural, and electrical characterizations. The results illustrate that Zr-doping improves piezoelectric constitutive parameters, notably at a 2.0 mol% concentration. Electromechanical tests were conducted on multilayer piezo stacks, involving variations in electric load, mechanical load, frequency, and number of stacked disks. These tests demonstrate the ability of the multilayer transducer to produce up to 1.5 V (peak) under particular conditions: a 250 Hz frequency, a 10 N mechanical load, and an open circuit electric load, utilizing a stack comprising 5 disks. Employing an analytical mathematical model, an effective d 33 e f f of the system is determined, yielding values ranging from 588 to 625 pC/N for a stack composed of 5 disks. These findings highlight the considerable potential of lead-free multilayer piezo stacks in energy harvesting applications, offering crucial insights for the development of sustainable and eco-friendly energy solutions. [ABSTRACT FROM AUTHOR]

In this paper, a novel Model Predictive Control (MPC) technique for multi-satellite formation flying geometry acquisition and maintenance in high-drag environment is presented. The proposed MPC relies on a linearized and convexified quasi-nonsingular Relative Orbital Elements (ROE) model based on state transition matrices propagation, allowing to include the effect of perturbations in the prediction to optimize fuel efficiency and tracking accuracy. The formation is controlled with respect to a non-decaying orbiting point to perform absolute and relative station keeping simultaneously. For this purpose, a new dedicated plant matrix to include drag effects on ROE in the propagation is derived and validated with respect to numerical results. In all simulations, the satellites are assumed to be equipped with a single low-thrust propulsion unit, therefore, specific constraints are included in the controller to obtain a feasible solution in a real operational scenario. Moreover, a collision avoidance constraint is added in case of close proximity operations exploiting a linear mapping between the set of ROE and cartesian coordinates expressed in the Local-Vertical-Local-Horizontal (LVLH) reference frame. The controller response is simulated in several realistic mission contexts with a high-fidelity orbital propagator and the results are validated for fuel efficiency by comparing them to similar approaches available in literature and to optimal solutions obtained respectively with a direct single shooting algorithm and with a closed-form impulsive formulation. [ABSTRACT FROM AUTHOR]

In an era of increasing energy demands and environmental concerns, optimizing energy consumption within buildings is crucial. Despite the vast improvements in HVAC and lighting systems, plug loads remain an under-studied area for enhancing building energy efficiency. This paper studies smart plug active operating mode detection, plug-level load forecasting, and plug scheduling methodologies. This research leverages a unique dataset from the University of California, San Diego, consisting of readings from over 150 smart plugs in several office buildings for more than a year, notably during the post-Covid era. This dataset is made publicly available. A comprehensive literature review on plug, i.e. , appliances operating mode detection is presented. Novel unsupervised learning approaches are applied to identify plug operating modes. A pipeline integrating the detected modes with forecasting and scheduling is developed, aiming at building energy consumption reduction. Our findings offer valuable insights and promising results into smart plug management for energy-efficient buildings. • A novel year-long dataset of power readings from 169 office smart plugs is introduced. • The first review of plug load mode detection methods and terminology is presented. • The paper proposes an unsupervised method to identify plugs load operating modes. • The proposed smart plug scheduling pipeline reduces energy consumption by 20%. [ABSTRACT FROM AUTHOR]

This paper investigates the position and attitude (pose) tracking control of a rigid body with uncertain mass and inertia. By virtue of the dual quaternion description, a six-degree-of-freedom (6-DOF) relative motion model is established in an integrated and compact way. Then an adaptive pose tracking controller is proposed based on the immersion and invariance (I&I) theory, where the key integrability obstacle is overcome by the dynamic scaling technique. Compared with the existing filter system-based method that constructs a filter system to circumvent the integrability obstacle, the dynamic scaling-based method is directly designed based on the original system, which is capable of retaining all benefits of the I&I design with much lower dimension dynamic extensions. Comparisons with the filter system-based I&I method and the classical certainty equivalent method are first carried out from the theoretical analyses, which elucidate benefits of the proposed method in lower control demands. Then the comparative simulations are conducted to verify the features and effectiveness of the proposed dynamic scaling-based I&I adaptive control scheme. • In the framework of dual quaternions, a six-degree-of-freedom adaptive pose tracking controller is proposed based on the immersion and invariance. • Substantial dimension reduction and structure simplification are achieved by using the dynamic scaling technique. • Ideal closed-loop performances are guaranteed to be recovered by the proposed adaptive controller. • Control demands and energy consumptions are barely affected by the adaptive gain and can always be kept close to those in the ideal case. • Thorough theoretical analyses are presented to elucidate the benefits of the proposed adaptive controller. [ABSTRACT FROM AUTHOR]

Hydrothermal carbonization (HTC) is a promising process for the upgrading of wet biomass residues. Models of HTC processes, in particular at continuous pilot-scale, are needed to move HTC from lab-scale to industrial scale. This study presents a process model for mild HTC, dewatering and conversion to intermediate energy carriers (bio-pellets and biogas for power and/or heat production) of three wet biomass residue streams: paper sludge, olive pomace and orange peels, based on lab- and pilot-scale experiments. In addition, the process energy efficiency and feedstock utilization of the HTC process is calculated and compared with conventional treatment options for the chosen residues, i.e., direct anaerobic digestion (olive pomace, orange peels) or combustion after conventional dewatering (paper sludge). The process model indicates that the HTC pilot-scale process is much more efficient in terms of feedstock utilization to produce heat and/or power than the reference scenarios. The process energy efficiency of the HTC process (pilot-scale) was calculated to be 26 %, 63 % and 40 % for paper sludge, olive pomace and orange peel feedstocks, respectively. For all feedstocks, both the solid and liquid-generated products are equally important for improving the overall process energy efficiency. This study demonstrates the potential benefits of HTC processes for upgrading wet biomass waste streams based on continuous pilot-scale data. • Hydrothermal carbonization successfully upgraded wet biomass residues for energy. • Hydrothermal carbonization was more efficient for heat/power than reference cases. • Solid and liquid products are both important to improve process energy efficiency. [ABSTRACT FROM AUTHOR]

The partial denitrification (PD) coupled with anaerobic ammonium oxidation (Anammox) (PD/A) process is a unique biological denitrification method for sewage that concurrently removes nitrate (NO 3 −-N) and ammonium (NH 4 +-N) in sewage. Comparing PD/A to conventional nitrification and denitrification technologies, noticeable improvements are shown in energy consumption, carbon source demand, sludge generation and emissions of greenhouse gasses. The PD is vital to obtaining nitrites (NO 2 −-N) in the Anammox process. This paper provided valuable insight by introduced the basic principles and characteristics of the process and then summarized the strengthening strategies. The functional microorganisms and microbial competition have been discussed in details, the S-dependent denitrification-anammox has been analyzed in this review paper. Important factors affecting the PD/A process were examined from different aspects, and finally, the paper pointed out the shortcomings of the coupling process in experimental research and engineering applications. Thus, this research provided insightful information for the PD/A process's optimization technique in later treating many types of real and nitrate-based wastewater. The review paper also provided the prospective economic and environmental position for the actual design implementation of the PD/A process in the years to come. [Display omitted] • PD/A was considered the most economically and environmentally beneficial new nitrogen removal process. • Principles and recent advances of the PD/A process are discussed. • The functional microorganisms and their competition in PD/A process are analyzed. • Influencing factors control and reinforcement strategies of PD/A was classified and conceptualized. • Knowledge gaps and prospects are presented to improve the future of PD/A process. [ABSTRACT FROM AUTHOR]

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/policies/article-withdrawal). Post-publication, the editor discovered suspicious changes in authorship between the original submission and the revised version of this paper. In summary, The author names Baohua Shen (new First Author) and Minghai Li (new Corresponding Author) were added to the revised paper without explanation and without exceptional approval by the journal editor, which is contrary to the journal policy on changes to authorship. The editor reached out to the authors for an explanation, but they failed to provide a satisfactory explanation to these changes. In addition, it appears that Navid Bohlooli was claiming an affiliation with Sun-Life Company, Baku, Azerbaijan. When questioned, the author was unable to provide convincing evidence of the existence and nature of this company. Overall, the editor feels that the findings of the manuscript cannot be relied upon and that the article needs to be retracted. [ABSTRACT FROM AUTHOR]

• This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency. • A demand response program integrated with home energy storage systems is presented to optimize energy consumption. • To modify the consumption pattern of household consumers, a real-time pricing algorithm is proposed. • A self-healing system reconfiguration program integrated with distributed energy resources is presented. • Real data of California households are considered to model the home appliances and home energy storage systems. This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency of the Distribution Network (DN). A Demand Response Program (DRP) integrated with Home Energy Storage Systems (HESSs) is presented to optimize the energy consumption of household consumers. Each consumer implements a Smart Home Energy Management System (SHEMS) to optimize their energy consumption according to their desired comfort and preferences. To modify the consumption pattern of household consumers, a Real-Time Pricing (RTP) algorithm is proposed to reflect the energy price of the wholesale market to the retail market and consumers. In addition, a Self-Healing System Reconfiguration (SHSR) program integrated with Distributed Energy Resources (DER), reactive power compensation equipment, and Energy Storage Systems (ESSs) is presented to manage the DN energy and restore the network loads in disruptive events. The reconfiguration operation is performed by converting the isolated part of the DN from the upstream network to several self-sufficient networked virtual microgrids without executing any switching process. Real data of California households are considered to model the home appliances and HESSs. The proposed comprehensive program is validated on the modified IEEE 123-bus feeder in normal and emergency operating conditions. [ABSTRACT FROM AUTHOR]

CaO-CaCO 3 thermochemical energy storage is a promising technology for solar energy utilization and storage. Alkaline papermaking waste (APW) from paper mills, which is mainly composed of CaCO 3. Herein, TiO 2 /MnFe 2 O 4 co-modified APW was synthesized. The energy storage capacity of TiO 2 /MnFe 2 O 4 co-modified APW was studied during CaO-CaCO 3 thermochemical energy storage cycles in a duel fixed bed reactor. TiO 2 /MnFe 2 O 4 co-modified APW mainly consists of CaO, MgO, CaTiO 3 , CaMnO 3 , and Ca 2 Fe 2 O 5. The introductions of TiO 2 and MnFe 2 O 4 significantly improve the cyclic stability and optical absorption property of APW in 30 cycles. The co-modified APW (the mass ratio for CaO:TiO 2 :MnFe 2 O 4 = 100:5:5) possesses high sintering resistance, energy storage, and optical absorption capacities in 30 cycles. The energy storage conversion and capacity of the co-modified APW are 0.72 and 2288.57 kJ/kg after 30 cycles, respectively. The optical absorptance of the co-modified APW reaches 49%, which is 9.25 times as high as that of APW. CaTiO 3 and CaMnO 3 effectively mitigate the sintering of CaO grains. CaMnO 3 and Ca 2 Fe 2 O 5 significantly increase the optical absorptance of APW. The co-modified APW possesses stronger basicity and more oxygen vacancies, which is favourable for the carbonation reaction. Thus, TiO 2 /MnFe 2 O 4 co-modified APW is a good energy storage material. • TiO 2 /MnFe 2 O 4 co-modified papermaking waste is used for CaO-CaCO 3 energy storage. • The composite mainly consists of CaO, MgO, CaTiO 3 , CaMnO 3 , and Ca 2 Fe 2 O 5. • The composite has high and stable energy storage and optical absorption capacities. • The energy storage density of the composite is 2288.57 kJ/kg after 30 cycles. • The composite has high sintering resistance, stronger basicity, and oxygen vacancy. [ABSTRACT FROM AUTHOR]

In recent decades, the use of fossil fuels has had a harsh impact on the climate, and the frequency and intensity of various extreme weather events have increased, which has triggered an increased the use of renewable energy sources and reflection on technological advances. Many countries are trying to promote renewable energy to reduce their impact on climate change, but the impact of national energy policies in the climate risk framework remains poorly understood. Based on the panel data of 84 developed/developing countries around the world from 2006 to 2019, this paper studies and analyzes the impact of renewable energy use on climate risk. The study found that the increase in the proportion of renewable energy use can reduce the climate risk, and this result is still robust after undergoing robustness tests such as changing variables, samples, and research methods. The findings offer an important solution for European countries, where climate risks are most acute. The heterogeneity analysis shows that the higher the level of economic development, population density and the lower the level of incorruptibility, the weaker the inhibitory effect of renewable energy use on climate risk. The mechanism analysis also shows that the increase in the proportion of renewable energy use can reduce climate risk by reducing carbon dioxide (CO 2), methane (CH 4), nitrogen monoxide (NO), PM2.5 emissions, and the proportion of fossil fuel use, but it does not reduce climate risk by increasing biodiversity. In particular, we find that technological progress has not played a role in the use of renewable energy to reduce climate risk, breaking the stereotype. The policy recommendation of this paper is that under the increasing pressure of climate change, the transition to clean energy is an indispensable option, and the need to accelerate the energy transition becomes more urgent. There is a greater need to further protect biodiversity and enhance technological progress. • The increase in the proportion of renewable energy use can reduce the climate risk. • The mechanism is to reduce CO 2 , CH 4 , NO, PM2.5 emissions and fossil fuels use. • The use of renewable energy cannot reduce climate risks by increasing biodiversity. • The impact of renewable energy use on climate risk is heterogeneous. • Technological progress has not played a role in the use of renewable energy to reduce climate risk. [ABSTRACT FROM AUTHOR]

With the increasing complexity of the power grid, the precision of the thermal power units' execution of automatic generation control (AGC) commands is gradually increasing the impact on the online regulation of the power grid. The deviation between the actual output of thermal power units and the AGC command of the grid will not only affect the consumption of new energy output, but also endanger the safe operation of the grid. This paper introduces "deep learning" technology to solve the problem. Firstly, an AGC command execution effect identification and confidence evaluation algorithm (ACEEI-CEA) is proposed. The algorithm builds a neural network model to accurately predict the unit output and the confidence evaluation of the prediction results. Next, a high-dimensional input preprocessing strategy based on variational autoencoder (VAE) is proposed to reduce the dimensionality of the model input attributes, improving the convergence and accuracy of the model. Finally, an AGC optimal command fast inversion solution method (AOCFISM) is designed. This method transforms the unit output deviation problem into an objective optimisation problem. And improve the efficiency of solving the optimal AGC command value by constraining the unit command value. The calculation results show that the error of the prediction results of the model proposed in this paper is 5% lower than that of the traditional neural network. The difference between the output value of the optimal AGC command and the expected output value obtained is less than 0.5 MV, which can support AGC online decision-making.© 2017 Elsevier Inc. All rights reserved. • An AGC command execution effect identification and confidence evaluation algorithm (ACEEI-CEA) is proposed. • Results show the ACEEI-CEA performs well in command prediction compared to the state-of-the-art methods. • ACEEI-CEA achieves confidence assessment of predicted values. • The pre-processing strategy based on variational autoencoder shortens the training time of the model. • The AGC optimal command fast inversion solution method (AOCFISM) can obtain the AGC optimal command value. [ABSTRACT FROM AUTHOR]

Extant research has highlighted the pros and cons of the use of (a) competition and (b) positive (peer) pressure as two mechanisms for invoking peer effects to bring about long-term behavior change. However, these mechanisms have been studied separately and in different contexts. In this paper, we compare the differential impacts of competition versus positive pressure-induced pro-environmental behavior change in the same target population. This study spans a seven-year horizon between 2013 and 2019 (pre-COVID) during which the workforce of about 2500 employees in the Stephan P. Clark Government Center in Miami, Florida, was split into four groups - one control and three treatment groups. Each treatment group was subjected to (a) a healthy inter-group competition through tracking, benchmarking, and comparing monthly group level electricity saving, and (b) positive pressure through the appointment of champions who encouraged pro-environmental behavior among their group members. Studying these mechanisms concurrently in the same population allows for a nuanced comparison of their differential impacts on pro-environmental behavior change. • In this paper, we compare the differential impacts of competition-induced spillover and supervision-induced peer pressure in affecting pro-environmental behavior change in the same target over a seven-year analysis horizon. • The study spans 2013-2019 during which the workforce of about 2500 employees in a building was split into four groups (one control and three treatment) and was subjected to both peer pressure and healthy competition. • The study spans 2013-2019 during which the workforce of about 2500 employees in a building was split into four groups (one control and three treatment) and was subjected to both peer pressure and healthy competition. • These results have implications for the design of pro-environment campaigns that could leverage peer effects through fostering an environment of intragroup pride and healthy inter-group rivalry sprinkled with intermittent supervisory interventions for making long-term impacts. [ABSTRACT FROM AUTHOR]

Blockchain technology, with its peer-to-peer trading feature, influences the management of energy consumption by offering the potential to transform transparency, efficiency, and sustainability within the energy sector. Nonetheless, there is a need to develop analytical decision-making models tailored for managing peer-to-peer energy transactions to improve energy resilience. Therefore, the purpose of this paper is to address the research question: How can energy distribution systems be protected via blockchain technology to enhance energy resilience and mitigate vulnerabilities to disruptions ? This paper employs a conceptual research model design and a mathematical decision-making model to address the research question by capturing the peer-to-peer trading capability of blockchain technology. The theory of planned behavior provides theoretical explanations for the proposed model. The sample includes longitudinal energy consumption data from 2015 to 2023 in Texas. The findings indicate a significant improvement in energy efficiency along with a considerable decrease in total electricity consumption. Post hoc analysis results reveal that the seasonal autoregressive integrated moving average algorithm is effective as a reliable input for the proposed mathematical model. The significant implications are to implement blockchain-based smart grids in which energy systems become more resilient to disruptions, as the peer-to-peer capability enables users to trade energy. The proposed model suggests that energy will be used more efficiently and effectively. This paper contributes to prior works by introducing a mathematical model that captures the trading behavior of energy consumers. Moreover, this paper proposes the SARIMA algorithm to predict energy demand. • A mathematical peer-to-peer trading model is presented to improve resilience. • A blockchain-based smart grid is presented for consumer energy consumption. • Five machine learning algorithms are presented in energy demand management. • Longitudinal energy consumption data of Texas is used in the model. • Theoretical support is provided using the theory of planned behavior. [ABSTRACT FROM AUTHOR]

This paper investigates the performance and fuel-saving effect of a velocity control algorithm on modern 50 cc scooters (Euro 5). The European Parliament has adopted major CO 2 emission reductions by 2030. But modern combustion-powered scooters are inefficiently restricted and emit unnecessary amounts of CO 2. Replacing the original restriction method with the system presented in this paper, the engine's operating point is being improved significantly. A Throttle-by-Wire-System senses the rider's throttle command and manipulates the throttle valve. A redundant wheel speed sensor measures the precise vehicle velocity using the Hall effect. The entire system is managed by a central ECU, executing the actual velocity control, fail-safe functions, power supply and handling inputs/outputs. For velocity control, an adaptive PI-controller has been simulated, virtually tuned and implemented, limiting the max. velocity regulated by legal constraints (45 km/h). In this way, the environmentally harmful restrictors used today can be bypassed. By implementing a human–machine interface, including a virtual dashboard, the system is capable of interfacing with the rider. For evaluation purposes a measurement box has been developed, logging vehicle orientation, system/control variables and engine parameters. A Peugeot Kisbee 50 4T (Euro 5) is serving as test vehicle. Finally, the system has been evaluated regarding performance and fuel efficiency both through simulation and road testing. Fuel savings of 13.6% in real-world test scenarios were achieved while maintaining vehicle performance. • Efficiency is increased and the throttle valve opening is reduced by 50%. • The controlled air supply minimizes the fuel injected by up to 25%. • Significant improvements of fuel consumption and CO 2 emissions by 14%. • Consistent performance is demonstrated by using energy more effectively. [ABSTRACT FROM AUTHOR]

Since General Secretary Xi Jinping pledged the "30·60" targets to the world, the importance of pollution reduction and carbon reduction has become increasingly prominent. The previous focus on the pollution reduction effects of emission trading systems, while overlooking their carbon reduction impacts, as well as the limitations of research methods and scope, is no longer suitable for the needs of the new era. Therefore, this paper constructs a CGE model to study the synergistic effects, simulating the synergistic emission reduction effects of sulfur dioxide emission trading policies, and comes to the following conclusions: After the implementation of the sulfur dioxide emission trading mechanism, under different sulfur price scenarios, the synergistic reduction amounts of SO 2 and CO 2 vary greatly. As the sulfur price increases, the reduction amount becomes larger. In the five sulfur price scenarios set in this paper, the maximum reduction of SO 2 can reach about 111,400 tons, and the maximum for CO 2 is about 399 million tons. The CO 2 reduction rate is approximately 1.0767 times that of SO 2. This indicates that the sulfur dioxide emission trading policy is a very effective path for synergistic pollution and carbon reduction, with good reduction effects. In addition, sulfur dioxide emission trading will reduce the consumption of fossil energy, decrease industry output, raise product prices, cause GDP loss, and lead to reduced social welfare to varying degrees. [ABSTRACT FROM AUTHOR]

Energy development in industrial parks promotes both industrial and inter-park economies. However, as the number of industrial parks increases and the economic level of the parks rises, energy consumption is also increasing rapidly. The rapid expansion of inter-park trading markets is accompanied by a surge in carbon emissions, making industrial parks increasingly significant contributors to carbon emissions within the industrial sector. In response to this environmental challenge, this paper proposes an integrated trading approach within industrial parks that considers carbon constraints. By implementing a spot trading method for surplus industrial energy, the goal is to mitigate the carbon emission pressure within these parks. Firstly, the paper conducts an analysis of the characteristics of combined heat and power units in industrial parks. It introduces an integrated analysis method within the target region, consolidating information from various park units to enhance flexibility in energy utilization. Secondly, an energy storage model is established. This model efficiently leverages energy storage capacity to balance fluctuations in energy supply and demand within industrial parks, thereby alleviating carbon emission pressure. Finally, the study and analysis of an industrial park in Liaoning Province were conducted using the Yalmip + Gurobi commercial software on the MATLAB platform. Compared with the traditional industrial park, the carbon dioxide emission of the industrial park is reduced by 91.49t, and the energy cost is saved by 11.2 million RMB. Numerical results from instances indicate that this method surpasses traditional trading approaches, aligning with the rigorous standards of scientific research. • An energy storage model is proposed to address the shortage of energy storage in waste heat trading in industrial parks. • A coordinated scheduling program is presented to reduce energy scheduling imbalances between multiple campuses. • The carbon-constrained energy storage device is integrated to improve the decarbonization capacity. [ABSTRACT FROM AUTHOR]

In an aero-engine rotor system, a blade-casing rub-impact fault can lead to a frequency modulation phenomenon of the vibration signal. This paper discusses the extraction of the modulation information in the fault signal using the variational nonlinear chirp mode decomposition (VNCMD) method to detect early rub-impact faults. The regularities of frequency modulation are revealed in both the simulated and experimental blade-casing rubbing signals. According to the frequency modulation law, an empirical formula is summarized to judge the rub-impact fault type. Then, we propose an evaluation index to judge the fault severity based on the energy theory by using the advantage of the VNCMD method that can reconstruct the decomposed fault signal. Finally, the fault severity in the simulated and experimental signals with different parameters is compared. The results show the correctness and reliability of this evaluation index. The research in this paper provides a theoretical basis for the detection and judgment of the rubbing severity of blade-casing rub-impact faults. • The time-frequency modulation rules of different rub-impact faults are found and summarized. • An empirical formula used to judge the type of rubbing is summarized in the early stage of the rub-impact fault. • An evaluation index for judging the fault severity is proposed based on energy theory. [ABSTRACT FROM AUTHOR]

This study aims to investigate the factors of renewable energy consumption in the fifth Technology Revolution with specific evidence from ASEAN countries. The study applies an autoregressive distributed lag (ARDL) approach. The paper also uses the unrestricted fixed and random panel data methods. The manuscript identifies significant factors such as technological innovation, governmental policies, public awareness, electricity consumption, country population, foreign direct investment inflows, imports, exports, and economic growth that have influenced the renewable energy sector in these nations. This study also investigates the determinants that influence renewable energy in the context of international integration and the fifth technology revolution in ASEAN countries. The research analyzes data spanning two decades, from 2000 to 2022, through a combination of econometric modeling and machine learning techniques. Preliminary findings indicate that technological innovation, government policies, and public awareness significantly affect renewable energy consumption in these countries. The empirical results offer insights for policymakers and stakeholders in leveraging technological advancements for sustainable energy development. The findings from this study on the determinants of renewable consumption in the context of the Fifth Technology Revolution in ASEAN countries also have several important policy implications for the sustainable development goal. • This study aims to investigate the factors of renewable energy consumption in the fifth Technology Revolution. • The study uses panel data analysis, and the paper identifies significant factors that have influenced the renewable energy sector. [ABSTRACT FROM AUTHOR]

The energy transition towards sustainable resources is more urgent than ever given the environmental and geopolitical challenges. Being one of the major energy users, cities need to understand their energy sector to accomplish its transition, by means of data. However, data are not easily accessible and have their own challenges. This paper presents a joint effort between researchers, city representatives and industry to provide an urban system service that supports research, accelerates urban innovation, and involves the community. An energy data portal, "NRGYHUB", has been developed, where hourly data from thousands of energy meters are available. These meters were collected from neighborhoods in the city of Västerås, Sweden, and they measure electrical and heating energy. In addition, the data are complemented by geometrical and non-geometrical information of the buildings, as well as demographic statistics of the areas. The paper describes the process of data collection, preprocessing, and visualization, in addition to the main challenges and limitations of the project. This dataset can be used for energy use benchmarking, prediction, and analysis. • The creation and implementation of an energy data portal is detailed and made available on nrgyhub.mdu.se. • Legal and licensing issues in data sharing are explored, offering insights for those interested in Sweden and beyond • By showcasing real-world problems, the impact and role of this data sharing initiative are demonstrated. [ABSTRACT FROM AUTHOR]

• This paper proposes the research on dimension reduction for visualization of simplified security region of integrated energy system considering renewable energy access. • The imbalance between energy demand and supply caused by the renewable energy uncertainty is defined as a special N-1 fault. • An interval security region model of integrated energy system is constructed, according to the N-1 safety constraints of equipment and pipeline. • An engineering simplification approach for interval security region model is proposed, in order to quantify the security boundary, security distance, and interval total supply capability. • A dimension reduction observation method based on observation variable optimization is proposed, which can enlarge the volume unaffected by renewable energy uncertainty. This paper proposes the research on dimension reduction for visualization of simplified security region of integrated energy system considering renewable energy access. Reason: Integrated energy system is conducive to achieving low-carbon transition and efficient utilization of energy. As the penetration of renewable energy increases, the security of integrated energy system faces severe challenges. It holds significance for comprehensively studying N-1 security test method in the operation scenario of integrated energy system, the impact of renewable energy on the IES security region model, and the optimal selection for observation variables. Methods: Firstly, a model for interval security region of integrated energy system is constructed, according to the N-1 safety constraints of energy hub key equipment and pipeline. The imbalance between energy demand and supply caused by the renewable energy uncertainty is defined as a special N-1 fault. Then, an engineering simplification approach for interval security region model is proposed, in order to quantify the security boundary, security distance, and interval total supply capability. On the basis, a dimension reduction observation method based on observation variable optimization is proposed, the selection of observation variables help to expand the volume of area unaffected by renewable energy uncertainty. Results: To the existed problem in current research, an example is presented to verify the effectiveness of proposed method, which brings forward N-1 security test method in the operation scenario of integrated energy system, analyzes the impact of renewable energy on the security region model, and realizes the optimal selection for observation variables of simplified security region. Conclusions: Interval security region of integrated energy system can intuitively estimate the security status of operating points and observe the security boundaries. The simplified model can satisfy the needs of calculating precision. Optimal selection of observation variables is necessary, and the volume of area unaffected by renewable energy uncertainty can be effectively increased. [ABSTRACT FROM AUTHOR]

• This paper proposes a concept that utilizes historical time-series data from transmission system operators (TSOs) to estimate the optimal day-ahead energy-mix proportion for a power system dominated by converters. • This paper employs a long short-term memory (LSTM) network as a data-driven model to forecast the day-ahead values of key parameters. • To ensure secure system functioning, an energy-mix operation and reserve scheduling model is developed. • This paper emphasizes the importance of power system security in incorporating inverter based resources (IBRs). Advancements in various scientific fields have encouraged the development of novel tools, techniques, components, methodologies, and innovations aimed at addressing the challenges encountered in modern power systems dominated by inverter-based resources (IBRs). This paper focuses on a concept that leverages historical time-series data obtained from transmission system operators (TSOs) to enhance the secure management and operation of power systems. By employing a data-driven model, the day-ahead values of power generation and load consumption are estimated and integrated with a dynamic model of the power system for further analysis. To optimize energy generation and ensure grid stability, an energy-mix operation and reserve scheduling model is utilized. This model optimally combines different power-generating technologies, including synchronous generators (SGs), grid-following converters (GFLs), and grid-forming converters (GFMs), to meet the energy demands of the day while enhancing the overall system strength. The findings are supported by quantitative analysis utilizing variables such as frequency, power production, terminal voltages, and system non-synchronous penetration (SNSP). Simulation results demonstrate that implementing the proposed concept enables the power system under consideration to operate securely, even in the face of a 38% increase in immediate load, with a maximum SNSP ratio of 59%. These findings highlight the effectiveness of the proposed approach in addressing the reliability, system dynamics, stability, control efficiency, and security challenges posed by IBR-dominated power systems. Furthermore, it is believed that this research contributes to the ongoing efforts in decarbonization, renewable energy integration, and combating global warming by facilitating the secure and optimized operation of renewable energy-dominated power systems. [ABSTRACT FROM AUTHOR]

Our paper analyzes the optimal capacity investment in solar panels system for consumers. During the daytime, solar panel system absorbs energy from the sun and supplies the energy generated to the building structure. Our model takes into consideration the variations in the daily consumption of electricity and the variations in the daily available sun energy. We model these variations in our paper as uncertainty in energy demand and uncertainty in energy supply. Excessive usage of electricity that is beyond the energy generation of the solar panels are bought at a higher price from the utility company than the payment received from the utility company for excess energy sold to the grid. We analytically solve for the optimal quantity of solar panels to purchase and install. Our paper fills the research gap in quantifying the impact changes in solar energy yield rate, consumption, and sell-back rate has on the consumer's optimal investment decision in solar panels. We find that the optimal quantity of solar panels increases with the cost of energy during the daytime and payment for excess energy sold to the grid. Interestingly, when solar yield increases, the optimal solution only increases initially, and then it will eventually decrease. Furthermore, our analytical models provide consumers with a decision support system to determine the optimal number of solar panels to purchase and install while taking into consideration the random yield of solar panels, variations in energy consumption, and potential for sell back of excess unused energy generated under complex billing structures from the utility company. Our mathematical models are adaptable to policy changes such as rebates and purchase and sell back rates for electricity. Our numerical study validates the mathematical models and further analyzes the impact changes in solar energy yield rate, energy consumption, and sell-back coefficient have on the consumer's capacity investment decision in solar panels. Our numerical studies find that the lower the reliability factor of solar yield, the more impact the daytime energy cost has on the optimal quantity. When the sell-back coefficient exceeds a certain threshold, the optimal quantity of solar panels increases exponentially. [Display omitted] • We model the optimal quantity of solar panels to purchase and install for consumers. • Optimal quantity increases with consumption, but the rate of increase is decreasing. • Optimal quantity increases with energy daytime cost and sell-back rate. • When solar yield is high, the impact of payment coefficient is significant. • Increased variability in consumption, optimal quantity increases. [ABSTRACT FROM AUTHOR]

This research paper analyses the potential of multi-angled façade systems to achieve the sustainability goals of reduced energy consumption and improved indoor climate quality, hence aligning with UN SDGs 3, 11, 12 and 13. The objective of this research paper is to define the suitable glass properties for the two windows of the multi-angled façade. The concept of a multi-angled façade system is based on proposing using two different window orientations in each façade on a vertical axis (right and left), but not tilted up and down. The large part of the multi-angled façade is oriented more to the north to optimise the use of daylight and the small part of the multi-angled façade more to the south to optimise the use of solar radiation through the façades. One can optimise using daylight and solar heat inside the office room through the multi-angled façade design concept, but with less complexity in the design, manufacture, and transport phases. This is the gap between 3D façades and the new multi-angled façade system concept, which indicates its novelty. To evaluate the energy consumption, the researcher used the software program IDA ICE version 4.8. to analyse the energy behaviour through the façade and the building's indoor climate, to compare the glass property impacts. The researcher modelled five scenarios for a multi-angled façade with different glass properties in both parts of the multi-angled façade. Researchers have used this software in simulations to validate, test, and compare statistics for more than 25 years according to ASHRAE 140, 2004, and CEN Standard EN 15255 and 15265, 2007. To conduct the analysis for the material's embodied energy in the multi-angled façade, the researcher used the software LCAbyg version 5 and compared it with the situation for a flat façade. The results for the energy consumption results revealed that the energy behaviour in the first scenario's glass type is recommended because the illumination was better and offered lower total yearly energy consumption and the accepted number of overheating hours inside the office room. The result of the embodied energy for the materials of the multi-angled façade shows that the area-weighting of the sum of the total energy consumption through the building's lifetime and embodied energy is higher when one renovates with a flat façade by about 11%. • Discussing the potential of multi-angled façade systems as a new concept for office buildings. • Defining the suitable glass properties for the multi-angled façade's windows. • Explaining a good visual, optical, and thermal indoor climate; and. • Evaluating the embodied energy amount for the multi-angled façade materials. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the water-energy-food nexus (WEFN) system, focusing on its interlinked structure and dynamic characteristics. This paper proposes a dynamic series-loop data envelopment analysis (DEA) model with a directional distance function for measuring WEFN efficiency in China. A spatial Durbin model is constructed to investigate the impact of extreme temperatures on WEFN efficiency. The results are as follows. (1) Compared with the black-box DEA model and series network DEA model, a series-loop network DEA model can reflect the "networking effect" and "looping effect" in the WEFN system. (2) The WEFN system performs well with an average series-loop efficiency of 0.875, but the efficiency of the water subsystem scores low and needs improvement. (3) At the national level, extremely high and low temperatures have no significant effect on the WEFN efficiency in local or adjacent provinces, whereas average temperature promotes the WEFN efficiency in local provinces but hinders it in neighboring provinces. In the regions with a low temperature difference, average temperature has no impact on the WEFN efficiency of both the province itself and its neighboring provinces. Overall, this study provides valuable insights into the complex interlinkages within the WEFN system and highlights the importance of considering these relationships when improving the system efficiency. [ABSTRACT FROM AUTHOR]

The OECD community consumes excessive amounts of raw materials and materials, which leads to pollution and increases the material footprint. The material footprint is directly linked to the Sustainable Development Goals (SDGs)-8 and 12. The main reason for the material footprint is the usage of fossil fuels, and the dependence of OECD countries on fossil fuels continues to this day. By expanding renewable energy utilization due to rising energy security risk, a community can take steps to improve ecological quality and reduce its material footprint. In this context, the paper focuses to examine the impact of energy security, green innovation, economic policy stringency and income on the fossil fuel material footprint for 24 OECD countries over the period 1995–2018. To this end, the paper uses the Augmented Mean Group (AMG) method and novel Half Panel Jackknife (HPJ) causality and estimation approach. The study adds value to existing knowledge by empirically testing the determinants of fossil fuel material footprint for OECD countries for the first time using the novel HPJ approach. The results of the AMG show that increasing energy security risk index reduces the material footprint, while green innovation and economic policy stringency have no impact on environmental degradation. The environmental Kuznets curve (EKC) in the form of an inverted U and an inverted N are valid for OECD countries. The HPJ approach also illustrates that there is a causality from green innovation and energy security risk to material footprint, and innovation and risk are support footprint reduction. Based on these results, the study recommends that OECD policymakers pay attention to ensuring energy security to reduce the material footprint by expanding renewable resources and allocate more economic resources to green innovations with higher returns, considering the validity of the EKC. [Display omitted] • Applies second-generation panel data methods. • Investigates the fossil fuel material footprint for 24 OECD countries. • The EKC hypothesis holds for the fossil fuel material footprint of OECD countries. • Green innovation and energy security risk reduce fossil fuel material footprint. • Increasing risks and technological development contribute to SDGs 8 and 12. [ABSTRACT FROM AUTHOR]

With the fragmentation of global production, the share of intermediate trade in international trade has increased significantly. Nevertheless, more research must be done on how intermediate trade factors influence energy consumption. This paper utilized the multi-regional input-output (MRIO) model to categorize China's energy utilization into six parts based on its position within the global production chain. Additionally, it examined the impact of intermediate factors on China's energy consumption through index decomposition analysis (IDA). The results revealed that: (1) from 2000 to 2014, intermediate factors led to an increase of 1380.96Mtces in China's energy consumption, accounting for around 37% of the national total; (2) the reduction in efficiency is the primary cause of the surge in domestic intermediate energy consumption, while the increase in market proportion is the dominant driver promoting international intermediate energy consumption growth; (3) the international intermediate proportion contributed the most among all intermediate factors. Drawing upon the findings above, this paper proposed policy recommendations to regulate China's rising energy consumption, precisely the escalation of intermediate process energy usage, focusing on the global production chain. [Display omitted] • Energy consumption is divided into six parts based on the intermediate destination. • Fifteen factors is decomposed based on index decomposition analysis. • The contributions of the structure, proportion, and efficiency changes is considered. • This paper shows a way of identifying the composition of energy consumption. [ABSTRACT FROM AUTHOR]

An effective way to promote the consumption of renewable energy resource generation is by forming a joint power supply system between photovoltaic (PV) power plants widely distributed along a river basin and large-scale cascade hydropower plants, using shared transmission lines to transmit power to the power grid system. However, renewable energy resource generation has significant prediction uncertainty and can flood into the power grid, which is detrimental to its safe and stable operation. To solve the above issues, this paper proposes an improved interval optimization method to model the uncertainties of the inflow runoff of a cascade hydropower plant and the photovoltaic power output. On this basis, a medium- and long-term interval optimization scheduling model is established for a cascade hydro-PV complementary system based on extreme scenarios. This model does not need to obtain an accurate probability density distribution for renewable energy, and can realize the overall optimization of runoff-PV uncertainty scenarios within a certain range, taking into account the economy and robustness of system operation. In order to ensure the solution efficiency, the original model is transformed into a mixed integer linear programming (MILP) model by a variety of deterministic transformation methods and linearization techniques. The case study shows that: 1) Under the premise that the system power output is relatively stable, the power curtailment of the interval optimized scheduling scheme proposed in this paper is reduced by 47.5% compared with the robust scheduling scheme. The ability to cope with extreme scenarios is also significantly improved compared with the deterministic scheduling scheme. 2) There is a game relationship between the robustness and economy of the obtained scheduling scheme. When the fluctuation interval width of random variables increases from 20% to 30% and 40%, the corresponding total power generation decreases by 2.4% and 4.7%, respectively. 3) A large penalty factor is conducive to reducing the amount of abandoned hydropower, but in general, the penalty factor and ecological flow level have no obvious influence on the dispatching results, and the values of both can be reasonably determined according to the actual operation requirements. • An improved interval optimization method is proposed to deal with the uncertainty of inflow runoff and PV power output. • A MILP model for the medium- and long-term operation of a cascade hydro-PV complementary system based on extreme scenarios is established. • The resulting scheduling scheme achieves balance between economy and robustness. • The influence of penalty factor and ecological flow on the dispatching results is not obvious. [ABSTRACT FROM AUTHOR]

Forecasting energy demand has become increasingly important due to technological advances, especially new power systems and population growth. Accurate predictions of energy demand and supply are crucial for academics and policymakers. Also, energy consumption is non-stationary and dynamic in time, requiring an adaptive forecasting algorithm. This paper presents a new adaptive hybrid approach for energy time series forecasting using statistical methods. Some of the most popular energy demand time series applications are used to demonstrate the superiority of the proposed algorithm, which covers a wide range of critical energy demand forecasting situations at various scales. The paper's objectives are presented in three phases. In Phase I, ARFIMA is used to forecast the entire original time series as a statistical inspector and later for comparison. Although ARFIMA's fractionality feature helps to capture long and short-term memory behavior in time series, to be more adaptive and become close to a nonlinear algorithm that can handle non-stationarity, and Gaussian and non-Gaussian time series closely, a novel dynamic statistical structural break detection framework is developed, and used in phase II to identify the time series' change points and associated time indexes. Since time series are partitioned based on structural change detections, they approach one in terms of the Hurst exponent and exhibit pure long memory, which is ideal for ARFIMA modeling. Phase III captures non-stationary statistical properties and memory characteristics by applying adaptive ARFIMA modeling to segmented time series. Finally, the predicted partitions are concatenated. Various performance assessment metrics, e.g., Mean Absolute Percentage Error (MAPE), tested on the final achieved results show that the proposed adaptive algorithm outperforms the forecasting capabilities of the existing algorithms on the same energy demand time series used in the current paper. MAPE of the proposed approach for the four utilized real-life cases are 1.72%, 1.35%, 0.22%, and 4.4%, respectively. Therefore, the proposed model is a satisfactory method for energy demand forecasting due to its high accuracy. • A novel adaptive hybrid algorithm is developed for energy demand forecasting. • The prediction system uses ARFIMA and a novel dynamic structural break detection framework. • The adaptive prediction method can forecast energy demand with high accuracy. • The structural break detection works dynamically, allowing ARFIMA to be adaptive. • The prediction method captures the energy time series' nonstationary statistical features. [ABSTRACT FROM AUTHOR]

In this paper, we address the following question: How can self-organizing system designers steer the system towards expected global behaviors that satisfy multiple conflicting performance indicators? Self-organizing systems (SOS) have advantages in performing tasks in exploratory and hazardous domains that are not suitable for humans. The design of the SOS is however difficult because negative emergence with unwanted behaviors is likely to happen and multiple conflicting performance indicators need to be considered. To address this challenge, in this paper, we propose an SOS design method using surrogate models and the compromise Decision Support Problem (cDSP) construct. Surrogate models are used to capture the relationship between low-level rules or parameters and high-level emerging system performance. And the cDSP construct is used to explore "good enough" solutions (characterized by rule adoption rates) while managing the trade-offs among conflicting performance indicators. The efficacy of the proposed method is illustrated using a multi-agent box-pushing problem in the Webots simulation environment. It is shown in the results that our method leads to a 6.9% improvement in time efficiency, an 8.4% improvement in energy efficiency, and 26.2% in system reliability. [ABSTRACT FROM AUTHOR]

Environmental issues are closely related to the economic development of human society, and reducing environmental pollution during production is of great significance for environmental protection. This paper utilizes data from Chinese industrial enterprises (2001–2010), and establishes a fixed effects model to examine whether minimum wage standards are important in reducing non-clean energy consumption by enterprises. The results indicate that an increase in the monthly minimum wage leads to a significant reduction in coal consumption by firms, while the change in the hourly minimum wage do not exert a statistically significant influence on coal consumption by firms. The above empirical results remain robust after conducting a series of endogeneity tests. The mechanism test shows that innovation and exports serve as mediating variables between the minimum wage and coal consumption. Specifically, raising the minimum wage can incentivize enterprises to innovate in production and expand their exports, resulting in a decrease in the consumption of non-clean energy. Finally, through the heterogeneity test, we observe that increases in the minimum wage have varying effects on coal consumption among firms with different numbers of employees and different ages. Based on the research findings of this paper, we propose that the government should comprehensively consider and improve relevant systems, formulate scientifically-based minimum wage standards, take into account both workers' rights and interests as well as enterprises' profits, actively promote energy transformation in enterprises, and ultimately achieve long-term development of the social environment. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach that integrates participatory methods and a rapid appraisal process to identify constraints and select potentially innovative solutions aimed at improving water and energy use efficiency at different levels in collective irrigation systems. First, a set of quantitative performance indicators is calculated, allowing the identification of the main problems. The study then adopts a bottom-up approach and emphasizes the need for active involvement of stakeholders from different backgrounds to identify potential opportunities to improve the sustainability of the collective irrigation system. From this collaborative and integrative approach, an innovation basket is developed, that includes a variety of techniques, technologies and management practices tailored to the specific challenges of the Lucefecit Collective Irrigation System. The results show that high-energy consumption is the main problem, with high values for energy consumption per hectare (2919.9 kWh) and per m3 of water delivered at the hydrants (0.43 kWh). Another problem is the lack of support for farmers in irrigation scheduling, which leads to large variations in irrigation water productivity across farms. The results of this study provide valuable insights into the effectiveness of the participatory approach and the feasibility of implementing innovative solutions in collective irrigation systems. • A novel approach was developed integrating participatory methods and a rapid appraisal process. • The study adopts a bottom-up approach and emphasizes the need for active involvement of stakeholders. • An innovation basket with tailored techniques, technologies and management practices was designed. • High energy consumption per ha and m3 of water delivered is the main issue at the collective system scale. • Lack of support to farmers hampers innovation adoption, causing high water productivity differences across farms. [ABSTRACT FROM AUTHOR]

Energy efficiency is a complicated process in designing Wireless Sensor Networks (WSNs). Effective routing is a precise solution for the energy efficiency constraint in WSN owing to the higher sharing of energy consumption during communication. Thus, routing is an alternative way out to this issue along with hierarchy-based clustering techniques. The clustering in WSN is carried out by picking the Cluster Head (CH), which is assumed as the essential or leader node. They perform more tasks, and thus, more energy is consumed. The clustering classifies the nodes into groups to avoid the huge communication range that is deputized to the CH. Hence, this paper establishes a routing algorithm for saving energy using a new optimization strategy via solving the multi-function formulation of the developed protocol in WSN. Here, the cluster head selection is performed with the Adaptive Remora Optimization Algorithm (AROA) to derive the multi-function derivation. The multi-function formulation undergoes with known factors such as energy consumption, distance, throughput, Packet Delivery Ratio (PDR), and path loss. The experimental result indicates an enhanced network lifetime and saving the energy of the developed heuristic-based routing protocol in WSN. [ABSTRACT FROM AUTHOR]

• Improvement potential of deep IGCtH are first assessed by advanced exergy analysis. • Efficiency of deep IGCtH are measured and compared by CExC per kg hydrogen as index. • CExC per kg hydrogen reduction by 16.4% for deep IGCtH compared with Lurgi SGCtH. • Lowest cost by deep IGCtH and better ability to resist market risk than Lurgi SGCtH. • Carbon tax of 132 RMB/t CO 2 for deep IGCtH with 80% CR equalize cost of without CCS. The deep in-situ gasification based coal-to-hydrogen (deep IGCtH) is attracting attention for its unique advantage in utilizing unrecoverable coal seam. In this paper, its improvement scope in energy conversion and utilization is analyzed using advanced exergy analysis, and the efficiency from cumulative exergy consumption (CExC) perspective and economic competitiveness with Lurgi surface gasification based coal-to-hydrogen (Lurgi SGCtH), coke-oven gas-to-hydrogen (COGtH) and natural gas-to-hydrogen (NGtH) are compared. Results indicate 39.9 % and 25.1 % of the exergy destruction of deep IGCtH and Lurgi SGCtH are unavoidable, while steam methane reforming and coal gasification possess the largest improvable potential, with avoidable endogenous/exogenous destruction of 96.63 MW/81.58 MW and 162.52 MW/151.19 MW, suggesting thermodynamic improvement can also be achieved by improving other components or optimizing process structure in addition to increasing its own efficiency. The CExC of deep IGCtH outputting 1 kg hydrogen is only 83.6 % that of Lurgi SGCtH, indicating it is more efficient route with significantly energy consumption reduction and can better demonstrate its efficiency advantage under large production capacity condition. The deep IGCtH shows only 68.7 % that of Lurgi SGCtH in investment and also presents significant advantage in cost. However, its profitability advantage over NGtH loses with hydrogen price increasing and there exists lower limit on capacity of 0.46 billion Nm3 to maintain cost and profitability competitiveness over COGtH. Furthermore, deep IGCtH shows emission reduction cost advantage over Lurgi SGCtH when capture rate exceeds 80 %, and profitability will not be limited by carbon tax and coal seam thickness within the considered range. [ABSTRACT FROM AUTHOR]

The 17 Sustainable Development Goals (SDGs) defined by the United Nations represent a blueprint for the adoption of global practices and policies to foster sustainability in societies and increase efficiencies in resources uses. The Italian National Trust, FAI, has embraced these goals, developing a holistic and transdisciplinary methodology to promote a more sustainable approach to preserve its natural and built heritage. This methodology combines heritage protection with the SDGs in various macro-areas of interventions, including energy, water, biosphere, responsible consumption, and sustainable communities. The aim is to increase access to affordable and clean energy, reduce water, waste, and soil footprints, promote responsible consumption, and develop human capital. The paper proposes a set of strategies to achieve the SDGs in heritage, involving the collaboration of several stakeholders, ranging from institutions to citizens. The methodology is applied to a specific Italian case study, Casa Macchi in Morazzone, to highlight the challenges in achieving the dual objective of preserving the historic building and promoting sustainability in the built environment. [Display omitted] • We propose a trans-disciplinary methodology for sustainable heritage preservation. • The heritage preservation project is analyzed in five macro-areas of intervention. • Assessment of effects on energy, water, biosphere, resources, and sustainability. • Casa Macchi preservation project to apply methodology and prove effectiveness. [ABSTRACT FROM AUTHOR]

• Revealed the damage zone characteristics of different tools penetrating rocks. • Identified the key factors of energy transfer efficiency in impact rock breaking. • Investigated the key influencing factors of the dynamic force-penetration curve. • Proposed a prediction formula for penetration force considering the tool shape. • Evaluated the proportion of energy used for rock cracking. The energy transfer efficiency and damage characteristics are the theoretical basis for the selection and optimization of hydraulic impact hammer tools. In this paper, quarter three-dimensional models of the piston-tool-rock system are established based on the continuum and discontinuum element method. The incident and reflected stress waves and rock damage characteristics obtained from the simulation model are in good agreement with experimental and theoretical results. The effects of piston impact velocity, tool shape, rock strength, confining pressure and repeated impacts on rock damage zone and energy transfer efficiency between tool and rock are analyzed. The force-penetration curves and penetration coefficients under different impact velocities, rock strength, and confining pressure conditions are obtained. The results illustrate that the energy transfer efficiency and penetration coefficient increase with the increase of rock strength, confining pressure and tip area of tool, but decrease with the increase of impact velocity. Based on the simulation results, a prediction formula for penetration force with consideration of the shape of the tool tip is proposed and the calculation accuracy is verified. Finally, the proportion of energy actually used for rock fracturing is analyzed. [ABSTRACT FROM AUTHOR]

This paper reviews the recent evolution of unmanned aerial vehicles (UAVs) through the lens of thermodynamic design. Emphasis is placed on using constructal theory in order to understand the relationship between different degrees of freedom within these complex dynamic systems. Size scale, the way in which UAVs are categorized based on weight and dimensions and one of the most significant factors in the design of any UAV, is analyzed in terms of its relationship to energy consumption. Specifically, constructal theory is used to predict the minimum size at which any UAV will need to be tethered in order to support its energy requirements. [ABSTRACT FROM AUTHOR]

Given the stochastic nature of solar energy, integrating PV arrays with energy storage systems (ESSs) is crucial for a consistent power supply. Effective maximum power point tracking (MPPT) control is of great importance in this context. The significance lies not just in achieving high energy efficiency but also in ensuring overall system safety and reliability, as rapid and stable power tracking control alleviates power stress on ESSs. Challenging factors like parameter uncertainty from the PV array linearization process, dynamic DC bus variations induced by pulsed power loads, and sudden irradiance variation under cloudy weather, make the maximum output power of PV arrays largely dependent on the effectiveness of the control laws. Therefore, this paper presents a comparative study for MPPT control using PV-interfaced DC/DC converters. The aim is to demonstrate the advantages of employing average large-signal model-based sliding mode-type controllers in PV system integration with health-conscious objectives. A comprehensive system-level control-oriented modeling process is presented, followed by the theoretical background of three types of controllers (classical proportional–integral (PI)-type controller, μ -synthesis-based robust controller, and sliding mode controller) for practical applications. Extensive tests with real experimental data reveal sliding mode-type controllers' superiority in various integration scenarios. For example, with random solar irradiance variation over 0.5 s, they achieve a 54.75% reduction in energy loss, outperforming the commonly used PI-type counterparts. [ABSTRACT FROM AUTHOR]

• Proposing a multi-source energy optimization approach for supersonic aircraft, with fuel consumption rate and thrust as objectives and engine safety as constraints. • Through mechanistic analysis, it has been determined that the system's energy efficiency exhibits two characteristics within the solution space: strong nonlinearity and a multi-modal distribution. • Proposing an advanced multi-objective optimization algorithm MOACCGA that effectively enhances the convergence capability for solving highly nonlinear problems and overcomes the challenge of becoming trapped in local optima. In the trend of increasing aircraft electrical power levels, future supersonic aircraft will have an urgent need for multi-source, adaptive, and high-energy-efficient approach to electrical power generation. In this paper, a multi-source energy optimization method based on multi-objective optimization algorithm MOACCGA is proposed. The method considers fuel consumption rate and thrust as the objective functions, with engine safety as a constraint. Based on the establishment of a small bypass ratio turbofan engine with multi-source energy extraction system, the influence mechanism of energy extraction on engine performance is analyzed. It is concluded that the generation of multi-source energy is optimizable, and the objective function has strong nonlinear and multi-peak distribution in the solution space. Then, the multi-objective adaptive covariance matrix and chaotic search group algorithm (MOACCGA) is proposed to solve the multi-source energy optimization problem. The algorithm utilizes the evolutionary approach with an adaptive covariance matrix to adapt to the strong nonlinearity of the system. Additionally, it overcomes the problem of getting trapped in local optima within the solution space characterized by multiple peak distributions by employing chaotic search. Finally, the multi-source energy optimization method is validated through simulations. In each condition, multi-source energy optimization can save fuel consumption by 0.7% of the total fuel consumption and reduce thrust loss by 1.5% of the total thrust. [ABSTRACT FROM AUTHOR]

• A Reference-Standard-Volume optimization method is proposed to improve CESPB. • The best aspect ratio of CESPB is 2–4 considering efficiencies and pressure drop. • The volume multiple is given at 1.1 with the highest exergy efficiency of 63.72 %. • The thermal and exergy efficiencies are increased by 2.03 % and 3.65 %, respectively. Cryogenic liquids (e.g., liquid air, liquid hydrogen, liquid carbon dioxide) have gained popularity in electricity storage due to their high energy density, no geographical constraints, and environmental friendliness. The cryogenic energy storage packed bed (CESPB) is widely employed as a cold recovery device to enhance the round-trip efficiency of cryogenic energy storage systems. Nonetheless, the cycle efficiencies of CESPB remain relatively low, with limited research investigating efficient methods for determining the design parameters. To address this, a novel optimization method named as the Reference-Standard-Volume (RSV) approach is introduced to enhance the thermodynamic performance of CESPB. In this paper, the complete optimization process of CESPB is illustrated, presenting the optimization outcomes in the view of heat and mass transfer. The findings reveal that a larger aspect ratio leads to the higher efficiencies but increased pressure drops. An optimal aspect ratio of 3 and a volume multiple of 1.1 is identified, achieving the highest exergy efficiency of 63.72 %. Meanwhile, increasing charging/discharging time enhances heat transfer in CESPB, however, it leads to an increase in external cold dissipation, which gives an optimal charging/discharging time of 3/3 h. By implementing the RSV optimization method, the percentage increase of discharging efficiency, thermal efficiency, and exergy efficiency of CESPB is 2.08 %, 2.03 %, and 3.65 %, respectively. These research outcomes offer valuable reference and guidance for future CESPB design and research. [ABSTRACT FROM AUTHOR]

This paper aims to present an autonomous intelligent system that improves the performance of conventional manufacturing processes to make them friendly to the environment. This improvement is made considering objectives that involve a reduction of supplies, use of tools, and low energy consumption; but maintaining desirable characteristics of machined parts in quality, mechanical properties, and dimensions. We present an architecture that integrates modeling with neural networks, validation using metrical statistics, and multi-objective optimization based on MOEA/D which takes experimental or historical data of the process and generates a set of parameters to satisfy all three criteria of sustainability, efficiency, and quality. Two cases for welding and machining process are presented to illustrate the use of the proposed approach which results in evidence of the feasibility of having green and intelligent manufacturing systems. In the welding process, the user can select a low amperage but increase lightly the cycles of operation to reduce energy consumption but satisfy the quality objective. In machining, the user can select a solution to avoid the use of cooling maintaining quality and efficiency in acceptable levels of operation. • Green and Intelligent Manufacturing Systems (GIMS) offer eco-efficient processes. • We propose an approach to convert conventional manufacturing processes to be GIMS. • Our approach allows a trade-off on economic, quality, and sustainability factors. • Efficiency is related to economic criteria, at once with green factors. • GIMS promotes new research on modeling and multi-objective optimization. [ABSTRACT FROM AUTHOR]

Robot comprehensive performance optimization is a high dimensional non-convex problem, which is of great significance in the practice of robotic intelligent manufacturing. Existing methods still face many challenges in performance evaluation, local optima, and planning efficiency. In this paper, a novel method based on accelerated gradient descent and directed-mutagenesis genetic algorithm (AGD-DGA) is proposed for efficient robotic motion planning and comprehensive performance optimization. Firstly, the time, energy consumption, and impact (T-E-I) are taken as the optimization objectives. In particular, the power loss sub-model is constructed, which is rarely considered in the previous research on motion optimization, and it is combined with the driving power sub-model to improve the evaluation accuracy. Next, a novel directed-mutagenesis genetic algorithm (DGA) is proposed to address the local optima problem. Compared with existing methods based on random sampling, the DGA further improves the success rate and efficiency by updating the probability model based on the adaptive Gaussian process. Moreover, a hierarchical weight updating mechanism is established to overcome the impact of additional performance indicators on the success rate of collision-free path planning. To improve planning efficiency, an incremental optimization mechanism and an adaptive fine-tuning strategy for population size are proposed to reduce unnecessary sampling, evaluation, and sorting. The simulation and experimental results in different scenarios demonstrate that the proposed AGD-DGA achieves better results than most of the mainstream algorithms in terms of optimization efficiency, success rate, and motion performance. In addition, it is also verified that the modified energy consumption model can achieve an average evaluation accuracy of more than 90% while maintaining the calculation efficiency. [ABSTRACT FROM AUTHOR]

Local electricity markets (LEMs) such as peer-to-peer (P2P) and community-based markets allow prosumers and consumers to exchange electricity products and services locally. In order to coordinate electricity trading and flexibility services, this paper proposes a hierarchical prosumer-centric market framework with a hybrid LEM and a local flexibility market (LFM). Multi-cut Benders decomposition (MCBD) is employed to decompose the integrated hybrid LEM into a centralized P2P market and multiple community-based markets. The aggregators coordinate energy sources and demands of households in low voltage (LV) distribution networks (DN) as virtual power plants (VPPs) and engage in trading through a P2P market over the medium voltage (MV) DN. In addition, a modified MCBD (M-MCBD) approach is proposed to accelerate the convergence process. The LFM is operated by the distribution system operator (DSO) and is formulated as a mixed-integer nonlinear programming (MINLP) problem which is further relaxed to a mixed-integer second-order cone programming (MI-SOCP) problem. The case study demonstrates that aggregators were able to collaborate on trading within the hybrid LEM to minimize the costs incurred by prosumers within the network. Furthermore, the proposed M-MCBD method improves the scalability of the MCBD by enhancing its convergence speed and accuracy, as demonstrated by testing on problems of varying scales. • A hierarchical market framework is designed comprising a hybrid LEM and an LFM. • The hybrid LEM combines a P2P market and community-based markets with VPPs. • The P2P market and community-based markets are coordinated by a modified MCBD. • The LFM is formulated as an MI-SOCP problem, with VPPs providing flexibility service. • VPPs bid block orders so that the operational characteristics of DERs are captured. [ABSTRACT FROM AUTHOR]

Sensitivity analysis has been widely used in building energy analysis to identify key variables influencing building energy and carbon emissions. However, most previous studies only focus on the single time scale energy use of buildings when conducting sensitivity analysis. The multiple time-scale sensitivity analysis would lead to increased complexity and challenges in building energy analysis. This paper proposes a new systematical procedure for global variance-based sensitivity analysis of multiple time-scale energy use of buildings. A case study of an office building is used to demonstrate the application of this approach using the Bayesian adaptive spline surface (BASS) models at three different time scales (annual, monthly, and daily). The results indicate that the procedure proposed here can provide fast and reliable sensitivity results for the multiple time-scale building energy assessment. The BASS learning models have the advantages of high predictive performance and the fast computation of sensitivity indicators in a closed form without Monte Carlo integrals. The application of principal component analysis can deal with the highly correlated multi-output energy use at a smaller time scale, which can further reduce computational costs. Moreover, the sensitivity analysis at the different time scales can provide new insights into energy characteristics due to the intrinsic variations of weather conditions. This systematical procedure can provide useful guidance for the multiple time-scale model calibration and multi-step ahead predictions of buildings. Moreover, the method proposed here can be extended to the sensitivity analysis of energy use in different time scales (sub-hourly, hourly, or daily) for other energy systems (such as PV, solar thermal, and wind). • Bayesian adaptive spline surfaces (BASS) in sensitivity analysis of building energy. • Multiple time-scale sensitivity analysis of buildings using BASS models. • Building energy assessment using principal component analysis and BASS. • Reduce computational time for sensitivity analysis of building energy using BASS. [ABSTRACT FROM AUTHOR]

With the high penetration of renewable generation systems in the power grid, the accurate simulation of the uncertainty in renewable energy generation is vital to the safe operation of the power system This paper proposes a novel controllable method for renewable scenario generation based on the improved VAEGAN model. The standard VAEGAN model is first improved using spectral normalization technique and the generator of GAN is trained using VAE. Then, the external and internal interpretable features in the latent space are learned as the controllable vector utilizing the principle of mutual information maximization. Finally, the renewable energy scenarios with overall features are generated using the external universal meteorological features, and renewable energy scenarios with specific features are generated by tuning along the internal interpretable feature of the controllable vector in the latent space. The proposed approach is used to produce real-time series data for renewable energy including wind and solar power. Experiments demonstrate that our method has a better performance in terms of controllable generation and enables the generation of preference patterns covering various statistical features. • A novel VAEGAN model with controllable interpretable features is proposed. • External universal meteorological features are used to generate scenarios with unknown patterns. • Internal interpretable latent features are designed to generate scenarios with specific features. • The results show a better performance of the proposed method on controllable scenario generation. [ABSTRACT FROM AUTHOR]

Integrating tasks with different functions and criticality levels onto the same hardware platform forms mixed-criticality systems (MCSs). Semi-clairvoyant scheduling, in which the mode transition is revealed at the arrival of high-criticality tasks, has attracted researchers' attention. Most studies focus on the schedulability test of MCS and do not consider energy consumption. In this paper, we simultaneously consider fault tolerance, energy management, and semi-clairvoyant scheduling in MCS. Firstly, we propose a schedulability test based on compensating adaptive mixed-criticality called CAMC-sem-cp. Secondly, we propose an energy-efficient scheduling algorithm based on the CAMC-sem-cp, called EE-CAMC-sem-cp. Finally, we conduct experiments to evaluate the performance of the proposed algorithms. The experimental results show that EE-CAMC-sem-cp can save 47.84% of the energy consumption compared to CAMC-sem-cp. [ABSTRACT FROM AUTHOR]

In low-inertia grids, distributed energy storage systems can provide fast frequency support to improve the frequency dynamics. However, the pre-determination of locational demands for distributed energy storage systems is difficult because the classical frequency dynamic equivalent response cannot capture the dynamic characteristics of the entire system. Additionally, optimal allocation of the distributed energy storage systems required for the different buses is challenging because of nonlinear constraints that account for these locational effects. This paper develops a method to evaluate locational frequency security. By simulating the worst-case G-1 contingency, the distribution network buses that violate the limit of the maximum rate of change of frequency are determined as the installation locations of distributed energy storage systems. We then propose a feasible region-based linearization method to replace the complex nonlinear constraints. Finally, we present a two-stage robust allocation model for distributed energy storage systems that is intended to maintain locational frequency security. The validity of the proposed method is verified through case studies performed on a modified IEEE RTS-24 bus system and a modified IEEE 118 bus system. The results show that the optimally allocated distributed energy storage systems effectively reduce the frequency safety risk that originally existed due to oscillation at the distribution network buses. • A locational frequency security evaluation method after G-1 contingencies is proposed. • Considering frequency oscillations, a feasible region-based linearization method is designed. • Distributed storage systems are optimally allocated for locational frequency security. [ABSTRACT FROM AUTHOR]

• Targeting United States residential HVAC electricity load using wind energy. • Partial load targeting concept using renewable energy source. • Capacity factor and estimation analysis of wind energy potential production. • Offshore versus onshore capacity factor estimated production performance. • Heating and cooling load curtailment methodology and analysis. This paper explores the concept of partial-load targeting through presenting a technical analysis for modelling the electrical energy demand of residential HVAC systems spanning the U.S. using onshore and offshore wind turbine technology. The proposed modeling technique uses DOE backed survey data and both known and theoretical capacity factor (CF) levels to determine the amount of wind turbines required to sustain the electrical load necessary for the heating and cooling components of residential housing units within all major U.S. climate regions. To perform the analysis, annual load data sourced from the energy information administration (EIA) surveys utilizing National Renewable Energy Laboratory (NREL) benchmark simulation house parameters are calculated and compared to wind turbine CF data to determine an equivalent range of turbines required to satisfy the target load. Two production profiles based on the CF range of each region are created using onshore and offshore scenarios. These profiles are then fitted to the corresponding average annual household load for the particular climate region. An analysis on the results is then conducted to determine overall potential load reduction as well as observe characteristics between the offshore and onshore data. [ABSTRACT FROM AUTHOR]