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Reliability is a crucial index of the system, and many safety-critical applications have reliability requirements and deadline constraints. In addition, in order to protect the environment and reduce system operating costs, it is necessary to minimize energy consumption as much as possible. This paper considers parallel applications on heterogeneous distributed systems and proposes two algorithms to minimize energy consumption for meeting the deadline and satisfying the reliability requirement of the applications. The first algorithm is called minimizing scheduling length while satisfying the reliability requirement (MSLSRR). It first transforms the reliability requirement of the application into the reliability requirement of the task and then assigns the task to the processor with the earliest finish time. Since the reliability generated by MSLSRR is often higher than the reliability requirement of the application, and the scheduling length is also less than the deadline, an algorithm called improving energy efficiency (IEE) is designed, which redefined the minimum reliability requirement for the task and applied dynamic voltage and frequency scaling (DVFS) technique for energy conservation. The proposed algorithms are compared with existing algorithms by using real parallel applications. Experimental results demonstrate that the proposed algorithms consume the least energy. [ABSTRACT FROM AUTHOR]

[Display omitted] • Microwave-assisted pyrolysis is a promising method for biomass/waste valorisation. • Life cycle analysis shows reduced energy consumption, low global warming potential. • Innovations in reactor design and operation parameters aids in cost reduction. Microwave-assisted pyrolysis (MAP) has been perceived as a promising technology for biomass and waste conversion due to its distinctive features, including fast, even, and precise heating. This results in higher energy efficiency when compared to conventional pyrolysis via thermal heating. However, the scaling up of MAP of biomass and waste poses challenges, with investigations ongoing to uncover not only the technological aspect, but also both the environmental impacts and economic feasibility associated with this process/technology. The possible environmental impacts associated with MAP processes can be analyzed through systematic life-cycle assessment (LCA), while the economic feasibility can be evaluated via techno-economic analysis (TEA). This paper presents an overview of the current research trend in MAP and the products produced, as well as the LCA and TEA of the pyrolysis technologies. The LCA study reported a 2.5 folds reduction in energy consumption and up to 62% reduction in global warming potential. TEA study revealed that conventional pyrolysis has a greater profit for long-term assessment due to a higher maturity and less complexity technology; however, MAP may be more economically feasible in the future owing to the increased maturity and more established technology. Finally, the challenges and future perspectives for LCA and TEA in MAP are elucidated. [ABSTRACT FROM AUTHOR]

Currently, building energy consumption prediction models are usually based on a large amount of historical operational data in high demands of building operating hours and monitoring systems. However, many buildings may lack operational data due to relatively limited monitoring systems, causing the failure to use data-driven methods to characterize the energy profile. In this context, transfer learning is a promising method to establish the knowledge transfer between many high-quality building operation datasets and a small amount of target building data, and to help predict energy consumption in the target building. This paper studies the possibility of employing transfer learning to achieve both short and long-term building energy consumption prediction. Firstly, a similarity analysis method, based on variable modal decomposition and dynamic time warping, is proposed for identifying the source buildings with the most similar energy features to the target building. Then, transfer learning for long-term prediction air-conditioning energy consumption is developed with weather parameters generated by the Morphing method as inputs. For the short-term single-step prediction, the proposed model CV-RMSE improves 81.3% (AEC) and 77.4% (EEC), respectively, compared to the prediction model that does not implement the transfer learning strategy and directly uses the target BEC data. As for the short-term multi-step prediction, the proposed model CV-RMSE improves 62.0% (AEC) and 65.5% (EEC), respectively. For the long-term prediction, the average CV-RMSE for the whole year is 6.62% and 11.15% for the proposed and directly target domain-based model, respectively. The proposed method explores the practicality of transfer learning in building energy forecasting, contributing to the use of existing building operation data for energy management at different timespan. • A similarity analysis method for building energy consumption data based on VMD-DTW is proposed. • The Seq2Seq-Transformer model is applied to short-term multi-step prediction of building energy consumption. • Transfer learning idea is first introduced to the long-term building energy consumption prediction. [ABSTRACT FROM AUTHOR]

The integration of distributed energy resources (DERs) such as wind, solar, batteries, etc. into the grid presents opportunities as well as challenges with regard to efficient coordination and trading. Virtual power plants (VPPs) have emerged as an important paradigm for aggregating and managing numerous DERs. This paper proposes a decentralized blockchain-based peer-to-peer (P2P) multilayer energy trading framework to facilitate optimized coordination of DERs within a VPP context. A control center, prosumers, and service operators constitute the model's multi-layered architecture. Profits from feed-in tariffs, ancillary services, and P2P sales are optimized for revenue while expenses are minimized using a mixed-integer linear programming formulation. Smart contracts enable prosumers to trade energy units in the Ethereum blockchain with ease via a participatory web platform. The effectiveness of the framework in synchronizing power supply and demand within the VPP by coordinating the charging and discharging of storage systems in accordance with consumption patterns is demonstrated through performance evaluation. In contrast to conventional centralized architectures, the proposed framework utilized decentralized architecture efficiently consolidates and optimizes distributed energy resources (DERs) to improve grid stability and renewable energy utilization. [Display omitted] • A novel multi-layer framework for peer-to-peer (P2P) energy trading, within VPP. • An interactive web-based trading platform using the Ethereum blockchain to facilitate exchange of energy units. • Implementation of MILP-based DER management model for optimal trading. [ABSTRACT FROM AUTHOR]

As the number of transistors on a chip surface increases, power consumption becomes more and more a serious concern. A promising solution to bridge the gap between resource-constrained gadgets and computation-intensive applications could be the approximate computing paradigm. This paper presents four efficient approximate full adder cells based on dynamic logic and carbon nanotube field-effect transistors (CNFETs). To the best of our knowledge, dynamic logic has never been deployed in the design of approximate full adders before. Comprehensive simulations and analyses are conducted to study the efficacy of the new circuits. Simulation results indicate remarkable improvements compared to state-of-the-art circuits. For instance, at 0.9 V power supply, our final proposed design improves the power-delay-area product (PDAP) metric by at least 63% compared to its peers. Moreover, the applicability of the proposed adders in the image sharpening application is examined by measuring peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) using the MATLAB tool. The proposed designs have also a reasonable performance in this regard. • Deploying dynamic logic (DL) in the design of approximate full adders for the first time. • Designing ultra-low area and energy consumption full adder structures. • Applying the proposed adders in the image processing application. [ABSTRACT FROM AUTHOR]

Computer-based machine learning algorithms that produce impressive performance results are computationally demanding and thus subject to high energy consumption during training and testing. Therefore, compact neuro-inspired devices are required to achieve efficiency in hardware resource consumption for the smooth implementation of neural network applications that require low energy and area. In this paper, learning characteristics and performances of the nanoscale titanium dioxide (TiO 2) based synaptic device have been analyzed by implementing it in the hardware-based neural network for digit classification. Our model is experimentally validated by using 32-nm CMOS technology and the results demonstrate that the model provides high computational ability with better accuracy and efficiency in resource consumption with low energy and less area. The proposed model exhibits 20% energy gain and 16.82% accuracy improvement and 18% less total latency compared to the state-of-the-art Ag : Si synaptic device-based neural network. Furthermore, when compared to the software-based (i.e., computer-based) implementation of neural networks, our TiO 2 -based model not only achieved an impressive accuracy rate of 90.01% on the MNIST dataset but also did so with reduced energy consumption. Consequently, our model, characterized by a low hardware implementation cost, emerges as a promising neuro-inspired hardware solution for various neural network applications. The proposed model has further demonstrated outstanding performance in experiments involving both the MNIST and Fisher's Iris datasets. On the latter dataset, the model exhibited notable precision (94.5%), recall (91.5%), and an impressive F1-score (92.9%), accompanied by a commendable accuracy rate of 93.04%. • TiO 2 -based synaptic devices offer high computational ability and efficiency with low energy. • Proposed hardware neural network with TiO 2 -based synaptic device and backpropagation. • The model provides 20% energy gain, 16.82% accuracy improvement, 18% less latency vs. Ag:Si. • TiO 2 -based model consumes less energy vs. software-based neural networks. [ABSTRACT FROM AUTHOR]

• A novel QZS dual harvester-absorber system (DHAS) is proposed. • The QZS DHAS mitigates vibrations and harvests energy at low frequency. • The QZS DHAS achieves a wide anti-resonance bandwidth. • The QZS DHAS is optimized with high output power and low resonance peaks. The single quasi-zero-stiffness harvester-absorber system has shown its effectiveness in harvesting vibration energy and suppressing low-frequency vibrations simultaneously. Nevertheless, drawbacks of narrow frequency range of vibration suppression and low output power are still challenges. In this paper, a novel quasi-zero-stiffness dual harvester-absorber system (QZS DHAS) is proposed for broadband synergistic vibration suppression and energy harvesting at low frequency. The QZS feature is realized by mounting an equal-strength piezoelectric beam and a pair of magnet rings in parallel. Firstly, the restoring force of both the piezoelectric beam and the magnet rings are theoretically derived, respectively, which are verified by finite element simulations. Then, the governing equations of a host oscillator with QZS DHAS are derived based on Newton's law and Kirchhoff's law, which are solved by the harmonic balanced method combined with the pseudo arc-length continuation method. Moreover, the effects of mass ratios, frequency ratios and damping ratios on the performance of the QZS DHAS are investigated in details. Finally, the parameters of the QZS DHAS are optimized to improve the performance of vibration suppression and energy harvesting by using the multi-objectives genetic algorithm in combination with the technique of establishing order preference by similarity to the ideal solution. The results show that the peak response of the host oscillator is reduced by 33.48 % and the peak power is increased by 531.38 % after optimization, which indicates that the dual objectives of broadband vibration absorption and high-efficiency energy harvesting are fulfilled. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for distributed management of energy communities. The proposed method utilizes a stochastic profile steering algorithm as a greedy heuristic. The optimization process considers random parameters such as local forecasted demand, photovoltaic (PV) production, and the initial energy of electric vehicles (EVs) as embedded scenarios. Profile steering coordinates the flexible electricity assets within an energy community by determining the contribution of each prosumer's profile to the average value of the objective function. It iteratively selects the prosumer that contributes the most until no further improvements can be made. This process scales linearly with the number of controllable prosumers and can achieve various community-level objectives, such as maximizing self-sufficiency or minimizing aggregated cost-of-energy, even when dealing with non-convex optimization problems for modeling each prosumer's local energy management system. The outcome of the proposed method optimizes the average value of the community's objective while ensuring that grid limitations are met within a specified probability. The proposed method is evaluated through simulations involving small-scale communities (5 households) and large-scale communities (100 households). The results demonstrate the efficiency, flexibility, and scalability of the proposed method, as well as its ability to reschedule the aggregated demand to ensure that grid limits are not violated with at least a 95% probability. • A new approach for distributed power aggregation of energy communities is proposed. • Stochastic profile steering is used as the main algorithm. • Average community-level goals are optimized while meeting probabilistic grid limits. • Local forecasted demand, PV production, and EVs of prosumers are considered as random variables. • Results demonstrate the efficiency, flexibility, and scalability of the proposed method. [ABSTRACT FROM AUTHOR]

Regional data center clusters have flourished in recent years to serve customers in a major city with low latency. The optimal coordination of data centers in a regional cluster has become a pressing issue because of their rising energy consumptions. In this paper, an online algorithm based on Lyapunov optimization framework is developed for the combined computing workload and energy coordination of data centers in a regional cluster. The proposed online algorithm is prediction-free and easy to implement. We prove that the workload queues and battery energy level will be within their physical limits, though their related time-coupling constraints are not considered explicitly in the proposed algorithm. The previous online algorithms do not have such a guarantee. A theoretical upper bound on the optimality gap between the online and offline results is derived to provide a performance guarantee for the proposed algorithm. To enable distributed implementation, an accelerated distributed coordination algorithm is developed based on the alternating direction method of multipliers (ADMM) with iteration truncation and follow-up well-designed adjustments, whereby a nearly optimal solution is attained with much enhanced computational efficiency. Case studies show that the proposed algorithm reduces the operational costs and saves computation time compared to online benchmarks. • The combined computing workload and energy coordination of data centers is studied. • A prediction-free online coordination algorithm is built with proven properties. • An accelerated ADMM algorithm is developed for distributed implementation. • The proposed coordination method greatly reduces the total cost of data centers. • The computation time is largely reduced compared to the traditional ADMM. [ABSTRACT FROM AUTHOR]

A high-pressure hydrogen micromix combustor has been investigated using direct numerical simulation with detailed chemistry to examine the flame structure and stabilisation mechanism. The configuration of the combustor was based on the design by Schefer et al. [1] , using numerical periodicity to mimic a large square array. A precursor simulation of an opposed jet-in-crossflow was first conducted to generate appropriate partially-premixed inflow boundary conditions for the subsequent reacting simulation. The resulting flame can be described as an predominantly-lean inhomogeneously-premixed lifted jet flame. Five main zones were identified: a jet mixing region, a core flame, a peripheral flame, a recirculation zone, and combustion products. The core flame, situated over the jet mixing region, was found to burn as a thin reaction front, responsible for over 85% of the total fuel consumption. The peripheral flame shrouded the core flame, had low mean flow with high turbulence, and burned at very lean conditions (in the distributed burning regime). It was shown that turbulent premixed flame propagation was an order-of-magnitude too slow to stabilise the flame at these conditions. Stabilisation was identified to be due to ignition events resulting from turbulent mixing of fuel from the jet into mean recirculation of very lean hot products. Ignition events were found to correlate with shear-driven Kelvin–Helmholtz vortices, and increased in likelihood with streamwise distance. At the flame base, isolated events were observed, which developed into rapidly burning flame kernels that were blown downstream. Further downstream, near-simultaneous spatially-distributed ignition events were observed, which appeared more like ignition sheets. The paper concludes with a broader discussion that considers generalising from the conditions considered here. [ABSTRACT FROM AUTHOR]

Microgrids are essential in ensuring responsible energy consumption and production patterns to support and advance the United Nations' sustainable development goals. However, due to the limited supply in the off-grid microgrids, an increase in the integration of electric vehicles (EVs) into electrical networks for their charging operation is expected to create a void between the energy supply and demand. Demand response (DR) programs provide a better solution to the energy management problem of microgrids. However, the difference in the objectives of the stakeholders of the off-grid microgrid can create an issue in providing optimal solutions to its energy scheduling problem. This paper focuses on the multi-objective energy scheduling strategy of the off-grid microgrid to manage its limited energy supply optimally using an incentive-based DR program. A fast heuristic approach is also presented in this work to reconfigure off-grid microgrid optimally during its hourly operations. Furthermore, Hong's (2m+1) point estimation method is used in this work to consider the uncertainties in the EVs and other DR participants. The proposed work is analyzed on the 33-bus and 69-bus off-grid microgrid consisting of droop-controlled distributed generations, EV charging stations, and consumers with curtailable loads. For the day-ahead operation of the off-grid microgrids, the analysis shows that the proposed work reduces the operational cost of the two off-grid microgrids by 2.3% and 4.56%, respectively. [Display omitted] • Priority-ordered incentive-based DR program for the EV and non-EV loads is proposed. • Fast dynamic reconfiguration of the off-grid microgrid is presented. • Multi-objective energy scheduling problem for the off-grid microgrid is modeled. • Utopia-tracking approach is used to solve the conflicting multi-objective problem. [ABSTRACT FROM AUTHOR]

• Proposing a novel reversible QCA-based half-adder structure based on the coplanar layout and simple quantum cells, focusing on low occupied area and energy consumption;. • Recommending a single-layer reversible QCA-based full adder design based on the coplanar layout and simple quantum cells with emphasis on the low-occupied area and low latency;. • Suggesting the reversible QCA-based multiplier design using the suggested structures for IoT devices such as printed devices and microcontroller units;. • Designing the proposed reversible QCA-based multiplier using a powerful quantum tool for evidence of its near zero-energy dissipation. An enormous variety of items and things are connected via wired or wireless connections and specific addressing schemes, which is known as the Internet of Things (IoT). However, IoT devices that adopt aggressive duty-cycling for high power efficiency and prolonged lifespan necessitate the incorporation of ultra-low power consumption always-on blocks. The multiplier plays a crucial role in enhancing the capabilities of low-power IoT devices, particularly those operating with energy-efficient batteries that offer extended battery life. The previous multipliers have a struggling speed, enormous occupied area, and high energy consumption; therefore, all prior flaws must be fixed by implementing it in a suitable technology, like the quantum computing. Therefore, this paper examines the ultra-low power circuit for nano-scale IoT platforms. It also suggests novel quantum-based adders for multiplier structure. The proposed designs are simulated using the QCADesignerE 2.2 tool by focusing on energy-efficient and occupied areas for miniaturizing IoT systems. [Display omitted] [ABSTRACT FROM AUTHOR]

Internet Protocol version 6 (IPv6) address configuration is needed to meet the needs of current portable devices and wearable applications, where each device needs its own unique identification number. High complexity in Wireless Sensor Network (WSN) and Internet of Things (IoT) environments has increased challenges, such as low energy consumption and high overhead in IPv6 address configuration. The proposed IPv6 generation is based on a hybrid mathematical approach that retains the IP address's confidentiality and prevents Denial of Service (DoS) attacks from new and existing nodes in the network. In this paper, the proposed hybrid protocol for DoS attacks from new and existing nodes is analyzed. It is evident from the results that the rate of successful configuration, overhead, and energy consumption is found to be better than the existing approach, namely, Programming Protocol-independent Packet Processors Duplicate Address Detection (P4DAD), match prevention technique, etc. • Focuses on IPv6 using a hybrid Duplicate Address Detection (DAD). • Reduces the energy consumption of the IPv6 as compared to P4DAD, etc. • Reduces the long wait time compared to EUI-64. • ASR is investigated for the Hybrid DAD under three different scenarios [ABSTRACT FROM AUTHOR]

To achieve the carbon neutrality goal, the industrial parks with heavy multi-energy consumption are gradually promoting green and low-carbon transitions. This transition is a long-term dynamic evolution process, which is affected by the strategies of different stakeholders and unpredictable events. It will lead to the industrial parks blindly promoting carbon emission reduction tasks. Therefore, it is necessary to analyze the stakeholders' strategy evolution trajectories based on the evolutionary game theory under changing scenarios to provide correct policy guidance for their carbon emission reduction process. In this paper, firstly, an evolutionary game model of carbon emission reduction in industrial parks is constructed, including an energy supplier, an industrial park operator, and the government. Secondly, the dynamic behavior characteristics of different stakeholders and the factors affecting the stability of the strategy equilibrium are analyzed thoroughly. Finally, different scenarios are formulated to simulate and analyze industrial parks' long-term dynamic evolutionary game law of carbon emission reduction. Furthermore, discussions and suggestions are provided to guide industrial parks' future carbon emission reduction transitions. [ABSTRACT FROM AUTHOR]

This paper investigates the role of moisture content on the measurement of the thermal conductivity of wood-based low-density fibreboards (LDFs) used for building insulation. To that purpose, a computational code of coupled transfers was used to simulate the widely used Heat Flow Meter (HFM) method. To widen the range of study, the thermal conductivity is obtained with a mixing law model to account for the properties and fractions of the constitutive phases. This mix law model was fitted from thermal conductivity values reported for commercial LDFs. The prediction of the heat flow density during the HFM test was compared with the measurements for dry and wet LDF. Through a series of case studies, the simulations showed that, in the case of moist products, the thermal conductivity determined by the HFM method was overestimated by up to 20%. This overestimate depends on the density distribution, thickness, moisture content, and measurement parameters such as time and the equilibrium criterion. Recommendations are made to limit this error. [ABSTRACT FROM AUTHOR]

The energy demand in Sweden is increasing due to the growing electrification, and to meet this need, the generation must be expanded. Wind power generation is a very attractive renewable power production alternative for Sweden because of the favorable weather conditions. Research looks at how much wind energy may be deemed sustainable. Increased energy demand puts pressure on the government and industry to build more wind farms and, as a result, produce more wind turbines. Raw materials are necessary for wind turbines to provide a secure transition to green energy technologies. To meet these demands, the materials from various countries should efficiently contribute towards the Sustainable Development Goals (SDGs). This paper gathers information about raw resources from diverse nations worldwide. All materials are mapped to the country where they are produced using social science criteria. A few nations are chosen based on a sample approach for further analysis, and the implications of mining operations are investigated. Finally, the direct and indirect effects of the SDGs are considered. Based on the data gathered, recommendations and considerations are given to avoid or mitigate the repercussions of raw materials mining and make wind power generation more socially and environmentally sustainable. • Evaluation of side effects of wind turbine raw materials. • The extraction of raw materials often violates human rights. • Mining of raw materials for wind turbines negatively affects the environment. • Extraction of wind turbine materials affects several Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

To meet the growing need for customized products, flexible production systems are gaining widespread application in modern factories. The inherent complexity of these flexible systems poses new challenges for production management. In this paper, we intend to contribute an operation-level analytical model for such systems, thus providing an effective evaluation tool for productivity and energy consumption analysis. Specifically, a multi-type serial production line with machines obeying the geometric reliability model and intermediate buffers having limited capacity is considered, in which K batches of parts are processed sequentially in each shift. Analytical solutions for key operation metrics are first developed for small systems using a Markov model. For multi-machine lines, a semi-analytical method based on aggregation is proposed. The effectiveness of the proposed analysis method is verified through simulation experiments. Besides, system properties are analyzed to provide insights for continuous improvement and optimization. • Flexible production line with unreliable machine and limited buffer. • Generalized analytical model for both productivity and energy consumption. • New semi-analytical approach to speed up metrics calculation. [ABSTRACT FROM AUTHOR]

Densification is the most effective forming technology, the production of which, pellets are the most intensive energy content of biomass solid fuels. Increasing the overall utilization efficiency of bioenergy, extending available types of wastes and residues for pelleting, and improving the pellet quality comprehensively are necessary. This paper reviewed feedstocks used for pelleting, binding mechanism of biomass pellets, and evaluation systems of pellet qualities. To extend the supply amounts and improve the qualities of raw biomass, various feedstocks and formulation recipes were classified based on their regions, sources, and species. And applicable objects and conditions of several different pretreatment technologies for feedstocks were compared. Physical properties and biochemical analyses are also concluded to evaluate the flowability, compactibility, and potential characteristics as solid fuel. For a better understanding of energy consumption and conversion during the densification process, pelleting devices and binding mechanism are elaborated. Associations and differences between single pelleting press in lab-scale and industrial pelleting in pilot-scale are also discussed. Suggestions are proposed for the optimization design of pelleting devices and processes for different feedstock densification. Lastly, relationships between operating parameter factors of pelleting process and pellet quality are presented. This review could provide theoretical analyses for powder compression, new insights for waste to energy, and guidance for achieving global peak carbon and environmental strategic objectives. [Display omitted] • Biomass feedstocks and recipes used for pelleting are reviewed. • Pelleting process is explained from theoretical and experimental aspects. • Durability and water sorption of biomass pellets are discussed. • Suggestions are given for optimizing pelleting process and mechanical device design. • New methods and concepts in industrial pelleting are presented. [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]

• 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]

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]

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]

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]

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]

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]

• 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]

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]

• A method for new power electronic equipment is proposed. • Novel software for the developed hardware is proposed. • The device decentralises the aircraft's power system. • This solution increases overall efficiency and safety. The More Electric Aircraft concept has arisen in an attempt to achieve the goals stated by different international organisations in terms of the concept of future aircraft. While some of these requirements are related to reducing the negative effects of airfreight such as CO2 emissions or fuel consumption, other requirements focus on aircraft safety enhancement. Nevertheless, if the way to achieve the vast majority of these goals is based on increasing electrification level of aircrafts, then their electric power system must be reviewed and reconsidered. Currently, literature proposes device redundancy as the main option for meeting aircraft safety requirements. This paper proposes alternative architecture, which consists of merging a new source code and recently developed topology, which makes it possible to decentralise the aircraft's DC bus, in order to increase its safety and meet the stated goals. While the new topology has been validated through small-scale prototyping, proposed software strategy has been validated in a virtual environment using simulations. The architecture developed enables a path to be selected in the electric power system: examining different options in the system, analysing which of those options are the most efficient and, finally, sending signals to configure the power system connections to power the load. Based on the results of those tests, the alternative proposed satisfactorily powers selected loads. [ABSTRACT FROM AUTHOR]

With the rise of the energy Internet and the reform of the power market, the multi-energy collaborative trading mode plays a fundamental role and acts as the framework in the construction of the integrated energy market. An integrated energy system model and its trading market mechanism are built in this paper. A new transaction strategy in the system is proposed to solve the problem of cascade utilization of thermal energy and flexibility of future market relationships. To facilitate the role transformation of stakeholders, the multiple agents system is used to decouple the complex relationship of the market transaction into two Stackelberg games of "two leaders and one follower". In addition, The Lagrange multiplier method is used to optimize the objective function and the optimal result is obtained by continuous iteration, and the existence and uniqueness of Nash equilibrium are verified. Eventually, The simulation results validate that the model has greater benefits for all stakeholders, new energy consumption and environmental protection. • The trading strategy proposed solves the flexible trading of multiple energies. • Decoupling the multi-energy market transaction into two Stackelberg games models. • The existence and uniqueness of Nash equilibrium in transaction model are verified. [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]

Understanding the connection between water supply and energy consumption in a circular economy is crucial for managing the demands of these resources. Sankey diagrams have the potential to enhance the visualisation of resource flows. However, there is a knowledge gap regarding the effective use of Sankey diagrams to capture the water-energy nexus and circularity of water. This study focused on effectively utilizing Sankey diagrams to model the nexus of water and energy flows and to assess the circularity of water. A comprehensive literature review was conducted on major studies on water material flows and modelling. This was supported by a review of approaches for modelling circularity, which have evolved from the material circularity index. The newly developed framework in this paper models water resource flows at all stages of the water supply chain, embedding the nexus as an interaction and considering resource intensity and material circularity index at each node. A city-level case study in South Africa was then used to apply the recommendations, model circularity, and the water-energy nexus using Sankey diagrams. In terms of circularity, the results indicate that water loss in the study area was very high, accounting for up to 40% of the total water supply and the water recycle rate was still very low, ranging from 3% to 7%. In terms of the nexus, the residential sector was found to have the highest energy intensity for water supply, primarily due to energy demand for water heating. Exploring the use of renewable or solar energy for water heating will be of interest and value. The new approach presented in this study allows for the simultaneous consideration of objectives, enabling a more robust analysis of energy-effective water use and water circularity along the water supply chain. [Display omitted] • A new framework for modelling the water energy nexus enriched by circularity options, energy intensity factors and confidence modelling. • Options for embedded circularity through the water supply chain. • The urgent challenge to address massive water loses in water supply systems for water scarce regions. • New Sankey diagrams showcasing how to model nexus and circularity. [ABSTRACT FROM AUTHOR]

China's climate and environmental problems remained prominent. Energy transition was the key to improve climate and pollution, and it was at a critical period. Previous studies mostly focused on the impact of climate policies on the reduction of air pollutants and the environmental health impacts of individual sector or pathway policies. Thus, focusing on the synergistic abatement of CO 2 and air pollutants and health effects of energy transition in carbon neutrality was significant for building beautiful and healthy China. LEAP-HEA model was constructed to project energy demand, CO 2 and air pollutants, and assess energy transition's environmental health effects from 2020 to 2060. LMDI model was adopted to decompose the abatement contribution. Synergistic assessment index was used to estimate the coordinated abatement effects. Cost-effectiveness of energy transition and regional heterogeneity of PM 2.5 toxicity were not considered. The results indicated: (1) energy demand would decrease and structure would be optimized significantly under deep energy transition scenario. (2) Deep energy transition could promote earlier realization of carbon neutrality. It would contribute significantly to CO 2 and air pollutants abatement with strong synergistic effects. (3) Energy transition would reduce health risks with large economic benefits. Recommendations were proposed based on the conclusions. The framework of LEAP-HEA model. [Display omitted] • The synergistic abatement and health effect of energy transition were explored. • The models of LEAP, LMDI, exposure-response and the data of 2020 were used. • Energy transition would have significant synergistic abatement and health effect. • The paper has significance for promoting carbon neutrality and healthy China. [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]

The thermal management system (TMS) in electric vehicles (EVs) is a comprehensive system that integrates an air conditioning system for the cabin, a temperature control system for the battery, and a cooling system for the motor. The currently used PI control strategy can only meet the basic TMS functions and cause high energy consumption. In this paper, we present a novel model predictive control (MPC) strategy for the TMS to optimize operational performance in real time. Different from the independent PI control for the individual components, MPC can predict future operation conditions and provide the optimal operating inputs in advance. A complete control-oriented model for MPC is developed, and the MPC strategy is designed to minimize the total power consumption of the TMS under the control-oriented model and constraints. The evaluation is carried out under several cases including the fixed ambient temperature, realistic ambient temperature, and different vehicle speeds. The results showed that the novel MPC strategy saved energy consumption by 5.9%–10.3% in these cases when compared to the PI strategy, demonstrating the effectiveness and feasibility of the proposed MPC control. • Proposed a model predictive control strategy for thermal management systems in EVs. • Developed a control-oriented model based on data-driven methods and physical principles. • Minimizes total power consumption while maintaining cabin and battery temperatures. • Results demonstrate the effectiveness of the MPC strategy under various conditions. [ABSTRACT FROM AUTHOR]

COVID–19 has disrupted human life considerably since the beginning of 2020. The transport sector is one of the most affected sector due to restrictions imposed to curb COVID–19 pandemic. This paper evaluates the environmental impacts of COVID–19 response scenarios using life cycle assessment approach. Six regions selected for the study are Australia, China, European Union (EU), India, United Kingdom (UK), and United States of America (USA). Data for vehicle population, vehicle kilometers travelled, fleet type, and fuel efficiency were used for 2015–2020. The environmental impacts were calculated for five scenarios; Baseline–2019, Business As Usual (BAU) and COVID–19 Response Scenario (CRS) for 2020 and 2030 namely, 2020–BAU, 2020–CRS, 2030–BAU, and 2030–CRS. The results indicate that COVID–19 occurrence and related response measures did not contribute to significant environment benefits in terms of Global Warming Potential (GWP) and other pollutants in short term and long term. This implies that although immediate reductions in emissions were observed in many studies, the long term effects were minimal due to irregular policies and unmanaged demand. Further, to achieve environment benefits sustained measures should be implemented in long term with proper planning and supporting infrastructure. Although electric vehicle strategies will provide benefits; emphasis on vehicle production and fuel production technologies is also needed to achieve the emission reduction targets. The findings are relevant for policy makers while devising policies to achieve the targets. • Vehicle stock will grow significantly in 2030 for developing countries • China's 2030 transport sector GWP will be highest, 1772 MT CO 2 eq, surpassing USA • Only USA showed reductions for all pollutants in 2030 COVID-19 response scenario • All countries had similar trends for most pollutants at different life cycle stages • COVID–19 occurrence didn't contribute to significant benefits in short & long term [ABSTRACT FROM AUTHOR]

• Coupling of the drag force model and the reaction dynamics for CVD simulation system is reviewed. • Numerical calculations drive the study of CVD processes. In the last 30 years, the growth mechanism of carbon nanotubes (CNTs) has received significant amounts of attention. However, the exploration of data for high-temperature confined fluidized bed reactors is severely limited by experimental methods, and numerical simulation has become a necessary means for current research. This paper reviews the processes of studying the particle–fluid system in fluidized beds by using numerical simulation, going layer by layer from the selection of physical model, the modification of drag force model, the development of reaction kinetics, and the fluidized bed scale-up. The ultimate goal is to achieve the mastery of the growth mechanism of CNTs by simulating the particle–fluid system. At the same time, the interaction forces between fluids and particles, the non-homogeneous flow field structure induced by bubble motion, and the complex coupled chemical reaction mechanisms make the simulation of fluidized reactors challenging. This study will be of great significance for the optimal design of the fluidized bed structure and the improvement of energy utilization and will provide an important reference for the energy-directed preparation of CNTs in the future. [ABSTRACT FROM AUTHOR]

The ever-intensifying threat of climate change renders the electric power system undergoing a profound transition toward net-zero emissions. Energy efficiency measures, such as demand response, facilitate the transformation to jointly relieve consumers' financial burden and improve the operability of the electric power grid, in a carbon-free way. In this paper, we design a cluster-based appliance-level-of-use demand response program, based on the massive volume of appliance consumption data, to expand the role demand response can play in the power grid's low-carbon transition. We systematically model the appliance-level utility function to distinguish consumers' distinct consumption patterns. We then develop a bi-level optimization model to capture the interactions between individual consumers and a distribution system operator (DSO) and enable appliance-level-of-use demand response functions. To further improve the efficiency and scalability of the proposed mechanism, we propose a cluster-based approach to capture the heterogeneity of users based on their energy consumption behaviors. Simulation results show that by capturing the detailed appliance-level response patterns, the proposed approach can systematically improve overall social welfare compared with conventional demand response mechanisms. • Data Empowered Appliance-level Utility Modeling. • Demand Response Design to Offer Appliance-level Incentive. • Heterogeneity Characterization to Improve Scalability and Efficiency. [ABSTRACT FROM AUTHOR]

• Gas bubbles and solid particles are ready-made vaporization centers. • Increase in the number of vaporization centers intensified puffing. • Dissolved gas flotation led to a reduction in the size of secondary fragments. • Puffing became the main breakup regime for higher-viscosity compositions. • Expressions were obtained for predicting the key breakup characteristics. Heterogeneous droplets (multi-phase and multi-component slurries, emulsions and solutions) are a viable alternative to homogeneous fuels. A dispersed phase, e.g. water, brings about micro-explosive breakup, which significantly improves the efficiency of combustion of fuel compositions and reduces the concentrations of anthropogenic emissions (sulfur and nitrogen oxides). The micro-explosion of heterogeneous droplets holds great promise as a method of their secondary atomization during combustion at power plants, especially with relatively high-viscosity compositions (more than 100 mPa·s). In this paper, the micro-explosive breakup behavior of fuel droplets containing water of different quality was investigated. Ready-made vaporization centers in droplets in the form of solid particles or bubbles of air, gases, and vapors were found to be the most important factor. The droplet lifetime and breakup delays were determined, along with the impact of the composition of droplets on their heating dynamics. Approximations were obtained in the form of the critical bubble size as a function of the ambient gas temperature for two-liquid droplets containing the dispersed phase of different quality. The understanding of how the 0 % to 60 % variation in the mass concentration of solid particles affects the breakup delay times of slurry droplets is important for fuel-burning plants. [Display omitted] [ABSTRACT FROM AUTHOR]

Enhancing vehicle utilization through a collaborative logistics approach allows businesses to contribute to the three pillars of sustainability by reducing fuel consumption and emissions, enhancing profit margins, and increasing customer satisfaction. This paper aims to introduce an Integer Linear Programming model for solving a vehicle allocation problem within a proposed vertical logistics collaboration setting. In this collaboration setup, production capacity allocation decisions are jointly made by collaborating customers and the logistics service provider, in order to create a more balanced and profitable logistics plan for all involved parties. Through the application of the proposed mathematical model, our numerical analyses illustrate the effects of the vertical logistics collaboration on the performance of transportation operations, including improved vehicle utilization rate and increased satisfied demand amount. Besides, we analyze the outputs of this operation related to the economic (by focusing on profit growth), environmental (by measuring the reduction in emissions), and social (by assessing the discounted transportation costs for customers) aspects of sustainability. To the best of our knowledge, no current study in the literature has established the vehicle allocation problem in the context of vertical logistics collaboration across all three sustainability pillars at the same time. The model can also serve as a tool for evaluating the potential economic, environmental, and social benefits arising from vertical collaboration and joint projects with customers, along with the support offered to logistics decisions. [ABSTRACT FROM AUTHOR]

Recovery and re-utilization of high-quality exhaust vapor are one of the most important technical means to improve the performance of a potassium Rankine cycle system. The recyclable potential of the 300-kW potassium Rankine conceptual design is evaluated with the grand composite curve, indicating that the organic Rankine cycle (ORC) placed under the pinch point to form a potassium top-organic bottom energy cascade utilization scheme is meaningful. To make the system performance concrete, a comprehensive performance evaluation system is established, which consists of thermal, economic, and weight. Several important contents are performed, for working medium selection, thermal process analysis, and cycle parameter matching on the organic bottom cycle. In addition, this paper also preliminarily analyzes the effect of the electricity market environment on the power system. The results show that MD 4 M in the siloxane series has remarkable comprehensive performance advantages. The bottom cycle running an evaporation temperature of 626 K and condensing temperature of 390 K has great potential. The total output power of the binary coupled system exceeds 500 kW, the thermal efficiency is 25.32%, and the growth rate is 73.54%. The high thermal performance not only breaks through the upper limit of the single alkali metal system but also has a stronger ability to resist the fluctuations of the market environment. • The alkali metal thermal system with high enthalpy properties has great recovery potential. • K-MD 4 M binary coupling Rankine, an energy cascade utilization scheme, is investigated. • The influence of the operating temperature on the system performance is non-unidirectional. • The high power contradicts the economy and lightweight of the multi-cycle system. • The resilience to market economy fluctuations shock is enhanced by improving performance. [ABSTRACT FROM AUTHOR]

Energy wastage and service level access are challenges of cloud networks, while efficient migration of virtual machines is a solution for them. Of course, uncontrolled migration increases overhead in cloud comprising: violation in service and high energy consumption. Performance requirements is very important in cloud, generally, it in the form of service level agreements are formalized. The main goal of migration is energy and performance trade-off. Detecting overloaded and under-loaded physical machine as sub-problem of migration, which effects the performance and energy. In this paper, with definition a system modelling, in first for detect overloaded and under-loaded physical machines a predict detection method is proposed. The forecast of the future utilization of the processor by using predict physical machines utilization method is provided. Predict physical machine's utilization module in predict detection method has been called. Unlike the previous physical machine detection methods, the current load of physical machines along with the future load of them is considered, and will lead to more exact results. Then for selection of virtual machines on overloaded physical machines a fuzzy selection method is proposed. Unlike the previous virtual machine-selection methods, all affective variables are considered and will lead to better results. In overall, the predict detection-fuzzy selection algorithm as intelligent approach has proposed and simulated with PlanetLab workload, CloudSim toolkit and Matlab. Simulation results demonstrate that significantly outperforms benchmark algorithms in terms of the energy consumption, migration count, service level agreement violation, energy-service level agreement violation, and energy-service level agreement violation-migration. [ABSTRACT FROM AUTHOR]

Field-Programmable Gate Arrays (FPGAs), renowned for their high performance per watt, are extensively utilized to accelerate Convolutional Neural Networks (CNNs) in edge computing environments, primarily employing dataflow-based and instruction set-based approaches. Compared to the instruction set-based approach that features fast and versatile circuit design, the dataflow-based approach can significantly enhance performance at the expense of design versatility. Nevertheless, edge computing environments require both high energy efficiency and adaptability to various scenarios. This paper proposes a novel end-to-end heterogeneous acceleration framework for CNN inference on FPGAs, named Pflow. The basic idea is to decouple network deployment and hardware details with a hardware–software co-design approach. First, a dataflow accelerator with an adaptive scheduling strategy is proposed. The adaptive scheduling strategy, along with a scalable design, maximizes hardware utilization in terms of computing resources and bandwidth. Secondly, we design a novel operator-perception method to automate the processes of network reconstruction and operator fusion. Thirdly, we integrate Pflow into the industrial-grade deep learning framework Paddle-Lite. We evaluate Pflow by implementing several networks on two representative FPGA platforms. Experimental results demonstrate that Pflow achieves energy efficiencies of 46.5 GOPS/W on Xilinx Zynq Ultrascale+ MPSoC 3EG and 59.4 GOPS/W on Virtex UltraScale+ XCVU13P. It also reaches a throughput of up to 255.7 GOPS on the former and 3.686 TOPS on the latter. • We propose a customized graph reconstruction method and integrate it into Paddle-Lite to reduce the overhead across various platforms. • Overlay shields the specific details in hardware, implement the integration of framework and accelerator. • Dataflow accelerator adopts adaptive scheduling strategy, increasing data reuse of input features. It is deeply pipelined. • Pflow achieves an energy efficiency of 59.4 GOPS/W on Virtex UltraScale+ XCVU13P and provides a throughput of 3.686 TOPS in end-to-end experiments. [ABSTRACT FROM AUTHOR]

This paper presents a new prosumer-centric peer-to-peer (P2P) energy trading framework incorporating the network-secure export–import limits, known as dynamic operating envelope (DOE), for prosumers considering their preferences to trade in the local market. In the proposed framework, a DOE-integrated energy management algorithm is developed for prosumers considering electric vehicles (EVs) charge–discharge and photovoltaic (PV) systems. The framework allows prosumers to operate within defined DOE, aligning with the preferences of EV-owning prosumers by managing the desired EV battery charge levels at their departure. An auction-based P2P energy trading mechanism for prosumers is proposed incorporating the allocated DOE to ensure secure network operation and valuing prosumers' preferences to benefit them. The proposed framework is implemented on a 78-node low-voltage (LV) residential distribution network to evaluate its performance. The results and comparative analyses verify the effectiveness of the proposed framework in addressing network issues such as voltage and line congestion problems, meeting prosumers' preferences in asset management, and providing benefits to all participants in the energy trading process. • A prosumer-centric P2P energy trading using network-secure export–import limits. • Hybrid DER marketplace model-based framework consisting of the DSO and the DMO. • Energy management within the limits considering EVs charge–discharge and PV systems. • DOE-integrated Double auction-based market mechanism for P2P energy. • Fairness in allocating DOEs with high RPI, PIEI, QoS, QoE and MiM values. [ABSTRACT FROM AUTHOR]

In recent years, the potential of Solid oxide fuel cell (SOFC) technologies as energy conversion devices with high efficiency and zero‑carbon emissions has been underestimated, where high fuel utilization plays a crucial role. Although the SOFC system with anode off-gas single recirculation has received much attention as a means to increase fuel utilization, this problem is not thoroughly addressed. To overcome this limitation, this paper proposes a novel SOFC system concept featuring anode off-gas "dual" recirculation, designed to maximize fuel utilization up to the theoretical limit of 100%. This innovative approach inherently eliminates carbon dioxide emissions through an integrated carbon capture process, thereby maintaining systematic carbon balance. A comparative analysis among the novel dual recirculation system, traditional single-pass, and other anode off-gas recirculation systems, reveals that the proposed system outperforms existing configurations. It achieves electrical and overall combined heat and power efficiencies of 49.6% and 85.7%, respectively, even after accounting for the energy required for carbon capture. This represents a 21.5% increase in electric efficiency over the single recirculation system, with a six-fold increase in tail-gas CO 2 concentration, reaching 26.3%. The study categorizes the four analyzed systems into "open" or "closed" based on whether the anode off-gas is directly discharged into the environment. Systematic investigations into carbon deposition potential, fuel utilization, flow rate, operating voltage, carbon capture ratio, and condensation temperature disclose distinct patterns between open and closed systems. The closed configurations exhibit superior performance, suggesting they are more suitable for SOFC applications. [Display omitted] • Merit of fuel utilization to SOFC systems is reevaluated. • Novel SOFC system with anode off-gas dual recirculation is proposed. • The system achieves systematic fuel utilization of 100%. • The system achieves superior energy and carbon capture performance. • Introduced "open" and "closed" SOFC system categories. [ABSTRACT FROM AUTHOR]