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China is the top emitter of greenhouse gases in the world. Pulp and paper industry is one of eight the strictly regulated industries about carbon emission in China. However, estimates of carbon emissions from China's pulp and paper industry have been suffered from remarkable uncertainty. It is summarized that is aroused by the inaccuracy of factors evaluating energy consumption, and the huge gap between the calculation standards for assessing energy consumption in the production process and the actual level recorded in the factories. This paper re-evaluated the carbon emission of China's pulp and paper industry from the perspective of life cycle assessment, and applied more accurate emission factors, taking account of the actual energy consumption of the production process in factories, to estimate the actual carbon emissions in maximum. It is found that the national standards about the energy consumption quota of China's pulp and paper industry is no longer applicable, and the actual energy consumption regulated in the production process is far better than the advanced level given by the national standards. The greenhouse gas emission have been seriously overestimated approximately 12.00%–56.17% by international organizations. • Life Cycle Analysis of the China's pulp and paper Industry is conducted thoroughly. • The pulp flow of China's pulp and paper Industry is analyzed. • Energy regulated in factory is far better than the national standards' required. • Carbon emissions of China's pulp and paper Industry are overestimated. [ABSTRACT FROM AUTHOR]

Variable renewable energy-based power is expected to increase toward a sustainable society, although the power cannot be dispatched effectively due to its intermittent nature. To accelerate renewable energy implementation, we simulated energy flows of paper mills installing wind energy and evaluated their potentials to function as energy hubs for appropriate early-stage design. We targeted 39 Japanese paper mills assumed to have thermal energy storage, wind–thermal energy converters and regional woody biomass installed, and analyzed the amount of power and heat selling and life cycle greenhouse gas emissions. The results for the paper mills were compared with conventional mills to examine effective conditions for greenhouse gas reduction. The amount of power or heat selling depended on the capacity of the paper mill and the installed equipment. Most paper mills could reduce greenhouse gas using thermal energy storage, wind energy, and woody biomass. The paper mill with the highest life cycle greenhouse gas reduction, compared with conventional mills, could achieve a 190% reduction. To function as a cogeneration energy hub, the equipment installed in paper mills should be designed according to the conditions of the paper mills and their regions. • Paper mills have potential as energy hubs by integrating variable renewable energy. • Thermal energy storage and wind–thermal energy converters were studied. • Energy flows and greenhouse gas emissions of paper mills were simulated. • Most mills could provide stable power and/or heat supplies and reduce emissions. [ABSTRACT FROM AUTHOR]

In the present study, a thermodynamic analysis of a cogeneration system in a pulp and paper industry under different operating modes i.e., singular and hybrid with a variable ambient temperature is conducted according to actual operating data. For singular operating mode, the power boiler is only employed using natural gas while for hybrid operating mode, the power boiler with the recovery boiler is employed using natural gas and black liquor as main fuels, respectively. The results show that for hybrid operating mode in comparison with the singular one, the thermal efficiency of turbine and condenser is enhanced by 1.36 and 7.7%, respectively while it is reduced by 2.8% for the power boiler. In addition, the overall thermal efficiency under singular and hybrid operating modes is 87.4 and 53.7%, respectively. For hybrid operating mode, the exergy destruction of power boiler decreases by almost 10% compared with that for the singular operating mode. Also, at hybrid operating mode, the soda is recovered with a mass flow rate of 33 tons/hour that is required for the cooking process in the chemical pulp section and additionally the consumption of natural gas in the power boiler is reduced by 11.8%. [ABSTRACT FROM AUTHOR]

This study shows a comparative techno-economic analysis of the integration of biomass gasification for generation of producer gas to be used in electricity production and to replace natural gas in the lime kilns of the pulp and paper industry. Three study cases with alternative configurations of the energy system integrating biomass gasification were analysed, including or not the co-generation of electricity and thermal energy, namely: i) Case I, for generation of electricity from residual forest biomass (RFB) in an integrated gasification combined cycle system, ii) Case II, generation of producer gas by RFB gasification for replacement of natural gas in the burners of a lime kiln, and iii) Case III, as an integration of Cases I and II for combined production of electricity and heat. Residual forest biomass from eucalyptus logging activities for feedstock processing for the pulp and paper industry was considered as fuel for direct (air) gasification in bubbling fluidized bed. The analysis made is supported on review reports, namely, the evaluation of the existing literature on investment projects in biomass power plants, and on experimental investigation for determining the composition of the producer gas from gasification of the selected feedstock. An economic model was developed that combines the net present value, internal rate of return and payback period. The main results of the energy system analysis show that the net electricity generation is between 72 and 83 GWh/year, while the production of CaO in the lime kiln supported by combustion of producer gas, is 82.12 kt/year, and a global energy efficiency between 21.70 and 34.17 % is achieved for the analysed configurations of the energy system. The results of the economic analysis show a positive NPV, an IRR greater than the discount rate and a PBP less than the useful life of the plant. The sensitivity analysis shows quite favorable investment projections given the low probability that the NPV will reach negative values and compromise the economic viability of the project. • The technical-economic viability of the forest biomass residues gasification is addressed. • Integration of IGCC concepts in the pulp and paper industry is reviewed. • The opportunities and drawbacks of IGCC in the pulp and paper industry are discussed. • Replacement of natural gas for producer gas for electricity generation and lime production is analysed. • A Monte Carlo sensitivity analysis is performed. [ABSTRACT FROM AUTHOR]

Urgent need for waste disposal and renewable energy have opened up promising pathways for renewable and sustainable fuel production to substitute traditional fossil fuels. In this study, catalytic co-pyrolysis of waste paper (WP) and polyvinyl chloride (PVC) as typical municipal solid wastes was experimentally investigated towards hydrogen-rich syngas production. Results indicated the maximum H 2 yield (429 μmol·g cat −1·min−1) was obtained with 60% PVC weight ratio under 900 °C which was higher than single component pyrolysis. Higher hydrogen yield (681.76 μmol·g cat −1·min−1) appeared over Fe/CeO 2 –CaO under the optimal conditions compared to that of Ru/ZSM-5-CaO (445.54 μmol·g cat −1·min−1). From the calculated non-isothermal kinetic parameters, both catalysts could reduce activation energies. Proper porous structure and CaO content in the catalyst could enhance hydrogen production via carbon species fixation. • Catalytic co-pyrolysis of waste paper and PVC blends for hydrogen production is firstly investigated. • Enhanced interactions of PVC/waste paper during pyrolysis was found for H 2 production. • Non-isothermal kinetics were implemented to evaluate catalytic co-pyrolysis. • High H 2 yield was obtained over Fe/CeO 2 –CaO for its pore structure and sorption enhancing effect. [ABSTRACT FROM AUTHOR]

Among the various waste-to-energy conversion methods, gasification is a technically feasible option for municipal solid waste valorization, meeting current emission limits and reducing landfill disposal burden. This work investigates the potential of paper rich (⁓ 76 % wt.) commercial refuse-derived fuel (RDF) having high ash content (⁓ 17 % wt.) for gasification and co-gasification in downdraft gasifier. The ash formed from RDF, with higher proportion of alkaline earth metals (Ca and Mg) and lower proportion of alkali metals (Na and K), can effectively avoid agglomeration and clinker formation during gasification. The producer gas obtained from RDF gasification reports a maximum lower heating value of 4.34 MJ/Nm3at an equivalence ratio of 0.34 with corresponding cold gas efficiency of 59.24%.Co-gasification of RDF with biomass was examined as a strategy to reduce overall ash content of the mixture, using sawdust. Co-gasification has exhibited a synergy between RDF and sawdust when the RDF fraction in feedstock is at the higher side. A maximum lower heating value of 4.65 MJ/Nm3 was obtained during the co-gasification and the tar yield reduced from 13.6 g/Nm3to 8.8 g/Nm3 when RDF in the feed increased from 25% to 75% (w/w). • Investigates potential of paper-rich RDF for gasification and co-gasification. • Absence of agglomeration and clinker formation during gasification and co-gasification. • Producer gas with LHV and CGE of 4.34 MJ/Nm3 and 59.24%, respectively from RDF gasification. • RDF has exhibited synergy with biomass at higher RDF fraction during co-gasification. • Reduction in tar content with increase in RDF fraction during co-gasification. [ABSTRACT FROM AUTHOR]

This work reports an innovative simulation method for effectively utilizing paper-mill sludge based on the co-gasification approach for energy-enriched renewable fuel production. An Aspen plus-based co-gasification model was developed to simulate and examine the synergetic effects of biomass and paper-mill sludge co-gasification. Initially, the model was validated using experimental data from the reported literature and found good agreement regarding gasification and co-gasification approaches. Sensitivity analysis of biomass gasification revealed that the maximum H 2 content of 29.6% and CO (36.0%), LHV (7.8 MJ/Nm3), 72.2% (CCE) was obtained at optimal (850 °C: temperature; 1 bar: pressure; 0.2: ER) conditions. Further, the H 2 proportion enhanced, and CO declined to 38.8 and 30.7% at the same operating conditions, respectively, with a co-gasification ratio of 50% compared to 0% (H 2 : 29.6% and CO: 36.0%). However, with increasing the co-gasification ratio beyond 20%, the CO content started to follow upward trends; at the same CGR, the H 2 , LHV, and CCE observed were 36.0%, 7.76 MJ/Nm3, and 65.2%, respectively. Hence, the developed co-gasification model can provide vital information for large-scale gasifier design, operating decisions, and optimization using different biomass blends. • Biomass and PMS utilization to produce renewable fuel. • Co-gasification parametric effects evaluation on product yield. • Increasing the proportion of PMS in feedstock mixture increases H 2 content. • 20% PMS co-gasification provided consistency with reported literature. [ABSTRACT FROM AUTHOR]

The generated gas bubbles in microfluidic fuel cells (MFCs) cover the active surface area of the electrode and greatly limit the cell performance. In this study, a feasible approach is proposed to effectively accelerate bubbles removal and enhance cell performance by embedding a paper separator in the middle of the microchannel. Visualization of bubble behaviors and the corresponding electrochemical measurements are performed to investigate the effect of bubbles on the cell performance. Periodical process of the bubble growth and removal leads to the fluctuation in the current density of the MFC. The rapid gas removal and the low ohmic resistance are observed after embedding a layer of filter paper in the microchannel, causing improved fuel transfer and smaller ohmic loss. Although the current density fluctuates more frequently, the fluctuation amplitude decreases and the cell performance is enhanced. Compared with the MFC without paper separator, the maximum power density and the limiting current density of the MFC with paper separator is increased by 25.2% and 130%, respectively. Moreover, the cell performance is improved with increasing the flow rate of reactant solution and the peak power density achieves 25.9 mW cm−2 under flow rate of 1500 μL min−1. • A microfluidic fuel cell with paper separator is proposed to enhance gas removal. • Bubble behaviors as well as its effect on the performance are studied. • The paper separator can restrict bubble size and improve cell performance. • Effect of flow rate on the bubble behavior and cell performance is studied. • Embedding a paper separator is a good and effective way for gas management. [ABSTRACT FROM AUTHOR]

Daytime subambient radiative cooling is a most promising alternative to electricity-free building cooling. However, optical performance degradation arising from surface contamination and inevitable parasitic heat gain still pose unprecedented challenges to cool building at subambient temperatures. This paper proposed a superhydrophobic cellulose aerogel cooler (SHB-CAC) as building envelope by integrating self-cleaning capacity, passive daytime radiative cooling and thermal insulation to reduce environmental heat gain. The SHB-CAC demonstrates high solar reflectance (93%) and long-wave infrared emittance (91%), accomplishing a temperature drop of 8.5 °C lower than the ambient under sunlight of 800 W/m2 in the outdoor experiment. Notably, the SHB-CAC possesses low thermal conductivity (28 mW/(m∙K)) that suppresses parasitic heat gain from warmer surrounding and reduces cooling energy consumption. The self-cleaning property resulting from superhydrophobicity protects SHB-CAC from water wetting and dust contamination but also maintains its good surface radiation capacities under different humidity environment. A building energy simulation was conducted and results showed that 43.4% of cooling energy on average could be saved compared to the building baseline consumption, if SHB-CAC was widely used in China, indicating that the strategy of optical and thermal conductivity management of cooler has the potential to reduce the energy consumption of buildings. [Display omitted] • Waste paper is converted into aerogel cooler for building cooling. • High R solar and ε IR , thermal insulation, and self-clean are achieved in one design. • The aerogel achieves a high sub-ambient temperature drop of ∼8.5 °C on daytime. • The aerogel shows that 43.4% of cooling energy could be saved. [ABSTRACT FROM AUTHOR]

With the continuous increase in underground engineering depth, the risk of coal–rock dynamic disasters, such as rockburst, is becoming increasingly serious and complex threatening the safety of coal resources. To study the effect of horizontal stress on fractal characteristics of rockburst fragments in coal mining, this paper uses a digital drilling instrument to perform rockburst proneness tests on andesite and granite specimens with different horizontal stresses. Fractal theory is used to calculate the fractal dimension of the rockburst fragments for several combinations of parameters. The residual elastic energy index is combined with the fractal dimension of rockburst fragments to assess the propensity of rockbursts from macroscopic and microscopic perspectives. The experimental results show that the positive correlation between the fractal dimension and the A EF obtained under the two methods. The fractal dimensions of andesite and granite are 1.507–1.80 and 1.734–1.979, respectively. According to the rockburst proneness criterion, it is predicted that andesite and granite are all strong rockbursts under confining pressures of 20 MPa, 30 MPa and 50 MPa. Therefore, it is of some theoretical relevance to investigate the rockburst mechanism in terms of both the A EF and fractal dimensions of fragments under different unloading conditions. • Rockburst proneness is studied for rocks using digital drilling instrument with different horizontal stresses. • The residual elastic energy index is obtained from the drilling parameters to estimate rockburst proneness. • The residual elastic energy index is combined with the fractal dimension to assess the rockburst proneness. • The fractal dimensions of andesite and granite are 1.507–1.80 and 1.734–1.979, respectively. • The andesite and granite are all strong rockbursts under confining pressures of 20 MPa, 30 MPa and 50 MPa. [ABSTRACT FROM AUTHOR]

The current study analyzes the potential of an environmentally benign integration of a parabolic trough solar collector (PTSC) with a heat-intensive multi-effect evaporator (MEE) used in pulp and paper industry. Actual plant data was used to simulate the MEE and estimate the steam economy (SE) and steam consumption (SC), which are used to judge the performance of PTSC-MEE system. Four distinct alternatives for the integration are investigated with Self-adaptive Differential Evolution (SaDE) utilized for optimization. Initially, a normal PTSC with four different model configurations of MEE, basic or with energy reduction schemes (ERSs) are tested and found that SE is slightly increased for each. Next, a geometrically optimized/combinatorial optimized PTSC with basic MEE is examined, a rise of ∼3.4% in SE is noted for both, with current integration outperforming one out of three ERSs. Subsequently, geometrical plus combinatorial optimized PTSC with basic MEE is inspected, increase in SE reached 7.59% and two of the three ERSs outperformed. Economic analysis is conducted to estimate the steam saving with different PTSC-MEE systems and corresponding payback periods. The possibility of decarbonization with the various integration possibilities is also explored by evaluating the fuel savings and related CO 2 savings. • Integration of PTSC with an MEE used in pulp and paper industry is investigated. • The fusion of PTSC with MEE is studied with various possibilities. • Self-adaptive Differential Evolution (SaDE) is used for various optimizations. • The steam savings and payback times are estimated for various PTSC-MEE systems. • Decarbonization is studied by analyzing fuel savings and CO 2 reductions. [ABSTRACT FROM AUTHOR]

A traditional system-level thermoelectric model requires enormous computing power and time for simulation analysis, especially when multiple optimization algorithms are combined. This paper initially proposed a comprehensive surrogate mode for the accurate modeling, field analysis, and fast optimization of thermoelectric generators. The surrogate model is made up of an artificial neural network (ANN) and a conditional generative adversarial network (cGAN), which can achieve a prediction accuracy of 97 % and a structural similarity (SSIM) of 0.954. Using a twisted tape annular thermoelectric generator (TT-ATEG) as an example, the generalization capability of the comprehensive surrogate model is demonstrated by studying the change of the twisted tape geometry parameters on the output performance and field distribution of the annular thermoelectric generator. The combination of the surrogate model and NSGA-II achieves fast optimization of key parameters under different working conditions, and the computational efficiency is improved by 99.97 %. Meanwhile, the cGAN part can predict the pressure and heat flow fields within 5s to provide intuitive visual feedback. The trained surrogate model requires less computer arithmetic power compared to the traditional multi-physics field simulation software. The successful application of the comprehensive surrogate model in this work provides a new solution idea and an optimization method for system-level TEG. • A comprehensive surrogate mode for performance prediction and optimization. • Surrogate Model Integrates ANN for Data-Based and cGAN for Image-Based Modeling. • Data-based performance computational efficiency improved by 99.97%. • Rapid physical field visualization provided within 5 seconds. [ABSTRACT FROM AUTHOR]

Solar photovoltaic thermal system (SPTS) can fully tap solar energy resources to realize thermal and electric supply for users simultaneously, but the volatility and uncertainty of renewable energy and load cause the imbalance of energy supply. This paper proposes a multi-time scale optimal scheduling method for SPTS based on event-triggered model predictive control (ET-MPC) considering battery performance deterioration. Firstly, SPTS is divided into day-ahead and day-in optimization layers from different time scales. Day-ahead scheduling takes the minimization economic cost as the optimization function to obtain pre-scheduling plan, and day-in scheduling adapts rolling optimization scheme based on model predictive control (MPC) to reduce power fluctuations and achieve optimal scheduling adjustment. Moreover, the battery performance decay parameter is introduced into the MPC model and embedded into the optimization problem to prolong the cyclic life deterioration, and an event-triggered mechanism is introduced into MPC to improve the computational efficiency and reduce the communication frequency. Finally, the results show that the proposed double-layer scheduling method improves 4.15 %, 66 % and 13.39 % respectively regarding cost, power fluctuation and battery maintenance, and improve the calculation efficiency of 48.89 % compared with standard MPC method, which indicates that the proposed method has good economy, stability and execution efficiency. • This paper proposed energy scheduling for solar photovoltaic thermal system (SPTS). • Double-layer optimal scheduling is proposed to realize the economy and stability. • Battery service time is prolonged through defining performance index. • An event-triggered mechanism is introduced to MPC to reduce the communication burden. • The proposed method improves performance from different perspectives. [ABSTRACT FROM AUTHOR]

Conventional energy sources, including diesel and petrol, are decreasing day by day, while their reserves are limited. However, it is difficult to find alternative fuels to replace them. Hence, to fulfill the growing energy demand and reduce environmental emissions, it is vital to find a wide variety of oxygenated compounds to conventional diesel fuels that show low emission potential from transport engines. In this research, we investigated diesel engine performance, combustion, and exhaust emissions with the addition of 10 %, 15 %, 25 %, and 30 % of three different oxygenated blends, including Diethylene Glycol Dimethyl ether (DGM), Di- n -Butyl Ether (DBE), and Tripropylene-Glycol Monomethyl ether (TPGM). This paper presents the brake-specific fuel consumption (BSFC), Brake Thermal Efficiency (BTE), Brake Mean Effective Pressure (BMEP), and Brake Specific Energy Consumption (BSEC) for these fuel blends. The generated emissions of carbon dioxide (CO 2), Carbon Monoxide (CO), and nitrogen oxide (NO) were quantified for four different engine speeds of 1500, 1800, 2000, and 2400 rpm for neat (100 %) oxygenated blends. This research also shows the in-cylinder pressure and heat release rate (HRR) of these three oxygenated blends. The outcome of this paper will help future researchers to develop automobiles and vehicles for oxygenated blends with conventional fuels. • Up to 30% DGM, DBE, and TPGM biodiesel provides similar engine performance to diesel. • Variation of BSFC is low for DGM, DBE, and TPGM biodiesel for a wide range of engine speeds. • DGM, DBE, and TPGM oxygenated fuels show lower CO and CO 2 but high NO emissions. • DGM, DBE, and TPGM provide similar in-cylinder pressure and heat release rate (HRR) to diesel. [ABSTRACT FROM AUTHOR]

To advance the field of lithium-ion battery (LIB) research, this paper unveils an accurate modelling of LIB that primarily relies on the equivalent circuit model, backed by the Osprey Optimization Algorithm (OOA). In the modelling stage, both single and double resistance-capacitance models are evaluated to depict the charge dynamics, incorporating the effects of fading, load, and temperature variations. The OOA approach is utilized to minimize integral squared errors between the actual measured and model-predicted battery voltages under constraints imposed by the model design variables. This approach is applied to a commercial 2.6 Ahr Panasonic LIB, with the performance of the OOA-based model being benchmarked against models developed by means of other optimization algorithms for further validation. Moreover, the robustness of the OOA method is assessed under battery uncertainty conditions or model parameter variation. A sensitivity analysis is performed on the battery model by employing a proposed approach that evaluates the impact of varying each parameter of the battery model by ±5 %, in a sequence that ascends and descends from 0 to 5 %. The single resistance-capacitance model is selected for in-depth validations. Notably, the OOA approach excels in estimating parameters for LIB modeling under both normal and abnormal operating conditions. • This paper presents novel Osprey optimization algorithm-based optimal lithium-ion battery model. • The model can be used with electric vehicles and power grid applications. • Practical measurements are implemented to verify the battery model and sensitivity analyses. • The OOA approach is proposed to solve the optimization problem. • The effectiveness of the proposed method is compared with other methods. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) panels' photoelectric conversion efficiency will decrease as dust deposition on their surface. An approach to dust removal on the PV panel's surface by longitudinal high-speed airflow was investigated to increase the output power. In this paper, commercial CFD software was used to numerically simulate the characteristics of dust removal by longitudinal high-speed airflow. The PV panels' output characteristic model under the action of dust removal by high-speed airflow is established by Simulink software, and the influence of high-speed airflow on the output characteristic is studied. Firstly, the dust removal mechanism of the PV panel under high-speed airflow is studied. Secondly, the impact of different tilt angles of the PV panel, dust particle size, airflow velocity, and blowing time on the dust removal effect of the PV panel surface were studied. The optimal airflow velocity and blowing time were obtained according to the airflow velocity and blowing time and their influence on the dust removal rate. Finally, the effect of high-speed airflow dust removal on PV power generation's efficiency and output characteristics is studied. The findings demonstrate that as the tilt angle increases, so does the dust removal rate increases continuously. In addition, the rate at which dust is removed increases with airflow velocity and blowing time. When the tilt angle is 30°, the best airflow velocity of dust removal is 5 m/s, and the best blowing time is 8 s. When the tilt angle is 45°, the best airflow velocity of dust removal is 10 m/s, and the best blowing time is 5 s. With the increase in airflow velocity and blowing time, the output power of PV panels continues to increase. When the airflow velocity is 10 m/s, and the blowing time is 5 s, 7.5 s, 12.5 s, and 15 s, the maximum output power after dust removal is increased by 15.44 %, 23.05 %, 23.38 %, and 23.39 %. This paper's research results can guide the design of the practical engineering application of longitudinal blowing high-speed airflow in the dust removal of PV panels. • A dust removal method based on longitudinal high-speed airflow on the surface of PV panels. • Dust removal under the action of longitudinal high-speed airflow is simulated by CFD. • The effect of high-speed airflow dust removal on the output power is studied. [ABSTRACT FROM AUTHOR]

District heating and cooling systems have been present on the European market for more than a century, constantly evolving into more efficient energy infrastructures important for decarbonization of the heating and cooling sectors. With roughly 17,000 operational district heating systems, serving over 70 million citizens, currently, the final energy consumption attributed to district heating in Europe is estimated at over 450 TWh. As the European district heating and cooling sector varies extensively in every analyzed aspect, there are vast differences between European countries regarding technical preferences, technological development, market structure and regulatory frameworks of these systems. The main purpose of this paper is to provide a comprehensive overview of the present status of the district heating and cooling systems in Europe with insight(s) and suggestions into future trends. In addition, the provided data serves as a valuable baseline for future research. Finally, this paper draws attention to the new socio-demographic approach, observed when analyzing the district heating and cooling systems, that has been recently introduced into the scientific community. This new approach also reveals the opportunity for creating new indicators that will influence the modeling of existing and future district heating and cooling systems. • Provides a review on ongoing trends in European district heating and cooling sector. • Analyses European DHC systems from technological, market, and regulatory aspect. • Provides an overview on commonly used performance indicators and energy models. • Discusses the possibility of creating new DHC indicators. • Discusses the trends in future DHC development. [ABSTRACT FROM AUTHOR]

Due to the rapid development of the automotive industry and the increasing shift towards cleaner energy, electric and hydrogen vehicles have great potential for growth. Therefore, charging stations need to transition from single charging forms to electric-hydrogen coupling charging. This paper focuses on the problem of mismatch between source and load in time and space when operating a Charging-Hydrogenation Composite Station (CHCS) independently. This leads to resource waste in the full-cycle and long-time sequence, making it difficult to achieve economic benefits. The characteristics that differentiate CHCS, such as geographic location, operating equipment, charging demand, and temporal attributes, are taken into account. CHCS in suburban, residential, tourist attractions, and industrial areas are modeled differently. Through improved Nash negotiation game modeling, a peer-to-peer (P2P) energy trading mechanism is established. The CHCS leverages its resource endowment advantages to ensure that each subject and the alliance as a whole can benefit through the coupled electricity-hydrogen energy trading. To ensure privacy in CHCS transactions, a distributed alternating direction method of multipliers (ADMM) algorithm is used to iteratively solve the problem and achieve a fair distribution based on the energy contribution size of each CHCS. Finally, simulation examples are used to verify the validity of the proposed model. The study shows that the electricity-hydrogen energy coupling trading strategy proposed in this paper can realize the synergistic mutual benefit of each CHCS, and in terms of economy, the overall benefit of the alliance has been improved by 7.58 %, and in terms of new energy consumption, the regional PV has gone from a consumption rate of 49.98 % to realize 100 % full consumption, and the wind power consumption rate has increased from 52.38 % to 96.34 %. • The coupled trading and revenue sharing of electric and hydrogen energy among multiple CHCSs. • The differentiation of temporal attributes, spatial geography, equipment types, and load characteristics are considered. • Implications of Nash negotiations and energy sharing on the CHCSs costs and consumption of new energy are investigated. • Distributed ADMM algorithms are used to solve the Nash bargaining problem. [ABSTRACT FROM AUTHOR]

Aircraft employing the boost-glide-cruise combination flight mode can achieve longer range, but traditional single configurations struggle to achieve good aerodynamic performance. This paper introduces a novel design method for a combined two-stage waverider, aiming to enhance aerodynamic performance while considering the integration of different engines. After completing the boosting and gliding phases, the lower surface and engines of the first-stage waverider are discarded to promptly reduce aircraft weight, allowing for improved aerodynamic performance and more efficient propulsion during the cruising phase. The airbreathing cruising waverider is designed based on the principle of osculating cone at a fixed Mach number, combining the waverider forebody, afterbody, and stretched body to obtain a complete waverider configuration, equipped with a scramjet engine. The flow field corresponding to the wide-speed-range gliding waverider is reverse-designed using the upper surface of the airbreathing waverider, allocating different Mach numbers and shock angles for the conical flow field, obtaining the wide-speed-range waverider configuration through streamline tracing, and equipping it with a rocket engine. The two waveriders are combined by sharing the upper surface, while constraints on the inlet cross-section are provided to ensure no intersection or overlap of the lower surfaces. Subsequently, numerical simulations were conducted to analyze the aerodynamic performance of the two-stage waverider, verifying the validity of the design method. Finally, the configuration was applied to a boost-glide-cruise flight trajectory, revealing that the two-stage waverider designed in this paper is highly suitable for long-distance flight. Compared with a single-stage wide-speed-range gliding waverider with the same configuration, the range is increased by 147.8 %, further validating the rationality of the design method presented in this paper. • A separable two-stage waverider design based on boost-glide-cruise combined trajectories is proposed. • A wide-speed-range waverider and a fixed Mach number airbreathing waverider are employed to optimize performance. • It achieves a significant range increase in the cruising phase than the single-stage waverider. [ABSTRACT FROM AUTHOR]

Frequency response analysis (FRA) methods are commonly used in the field of State of Health (SOH) estimation for Lithium-ion batteries (Libs). However, identifying their appropriate application scenarios can be challenging. This paper presents four FRA techniques, including electrochemical impedance spectra (EIS), mid-frequency and low-frequency domain equivalent circuit model (MLECM), distribution of relaxation time (DRT) and non-linear FRA (NFRA) technique. This paper proposes two estimation frameworks, machine learning and curve fitting, to be applied to each of the four techniques. Eight SOH estimation models are developed by linking the extracted feature parameters to the battery capacity variations. The paper compares the accuracy of estimation, estimation range, and other properties of the eight models. Application scenarios are identified for the techniques by using three classification methods: different estimation frameworks, frequency response linearity, and impedance technique. The results demonstrate that MLF is recommended for scenarios with a large amount of battery data, while CFF is recommended for scenarios with a small amount of data. NFRA could be applied to electric vehicle power batteries, while LFRA is recommended to be used for retired batteries. EIS method is recommended for complex and dynamic scenarios, while non-EIS method is recommended for scenarios that require high accuracy. • Four FRA techniques, Impedance, MLECM, DRT and NFRA-based method are developed. • Two frameworks, MLF and CFF, are applied to each of these four techniques. • The estimation accuracy and estimation range of the eight models are compared. • Application scenarios are identified by using three classification methods. [ABSTRACT FROM AUTHOR]

The park-level integrated energy system characterized by electricity heat cooling storage is regarded as a viable solution to energy and environmental crises due to its cleanliness and high efficiency. However, decision-makers encounter challenges in picking the optimum construction scheme. The paper introduces the matter element extension into the investment decision-making of the park-level integrated energy system. To begin, the paper analyzes the composition of the evaluation index system from the basic composition of the park-level integrated energy system, and establishes a standard system for comprehensive performance analysis, including economic, environmental, energy, security, and intelligence. Second, the subjective weight is calculated using the fuzzy analytic hierarchy process, the objective weight is calculated using the entropy weight. The matter element extension is used to evaluate the alternatives' performance. Finally, an industrial park in Jiangsu Province of China is selected as a case to verify. The results show that good comprehensive performance can be obtained by using photovoltaic, wind turbines, electric energy storage, ice storage, and heat pumps. The decision-making results will not be affected by the change in subjective and objective weight and the change in index weight within 20 %, but changes in matter-element classification would. • Intelligent performance criterion is introduced. • The evaluation system includes economy, environment, energy, security, intelligence. • A decision-making framework for the park-level integrated energy system is established. • A variety of sensitivity analyses were performed. • Using renewable energy and energy storage can obtain good comprehensive performance. [ABSTRACT FROM AUTHOR]

Exergoeconomics has played an important role in the study of new energy systems in the last decades. The use of exergy as a "carrier of value" has made it possible to define an unambiguous criterion for allocating costs among energy systems products. The paper shows how exergoecomic procedures of analytical optimization and design improvement on heuristic basis have been progressively replaced by more efficient procedures aimed at minimizing an objective cost function, leaving exergoecomic cost evaluation as the final step. It also highlights how growing concerns about climate change and the continued growth of inequalities in energy availability have broadened the set of objectives in the search for the optimal integration of the design and operation of energy conversion units and network with intelligent methodologies to reduce energy demands. The objective of the article is twofold: i) present the evolution of the main Exergoeconomic methods and show how they have paved the way for Thermo-X Optimization methods; and ii) outline the path for developing a general model of society's entire energy system that includes a broader set of objectives and constraints in addition to economic ones to help build the energy system of the future with a more sustainable perspective. • It is shown how Exergoeconomic procedures paved the way to Thermo-X Optimizations. • The paper outlines the path towards a general model of society's entire energy system. • The need to avoid wastes to reduce energy demand in wealthier nations is stressed. [ABSTRACT FROM AUTHOR]

The gas adsorption/desorption-induced coal deformation effect is a significant factor governing the evolution of coalbed permeability. Current theoretical investigations typically coal bulk and fracture deformation induced by gas are equivalent, neglecting the matrix-fracture interactions. Based on internal adsorption stress, this paper proposes Internal Differential Strain Coefficient (IDSC) to quantitatively characterize the relationship between coal bulk and fracture strain under equilibrium conditions. Coupling this coefficient constructs a binary gas permeability evolution model considering matrix-fracture interactions. Through numerical simulations of CO 2 -ECBM processes under various internal differential strain circumstances using this model, dynamic evolution patterns of diverse parameters are obtained. The research findings indicate that along the direction of CO 2 injection, matrix-fracture interactions exhibit a complex trend of initially increasing, then decreasing and then increasing, and the increase in internal differential strain levels results in a downward trend in permeability peak. Additionally, the evolutionary characteristics of CH 4 recovery and cumulative CO 2 storage rising with increasing internal differential strain levels were obtained on time scales using a fixed-point monitoring methodology. Inspired by the aforementioned laws, this paper discusses the macroscopic influence of burial depth on the effects of internal differential strain, providing new theoretical support for CO 2 sequestration injection methods in deep coal seams. • A coefficient capable of quantitatively analyzing the internal differential strain in the CO 2 -ECBM process is proposed. • Based on IDSC, a model describing the evolution of coal seam permeability in CO 2 -ECBM process is established. • Analyzed the influence of internal differential strain on the permeability of coal beds in the CO 2 -ECBM process. • Analyzed the influence of internal differential strain characteristics on CO 2 injection capacity. [ABSTRACT FROM AUTHOR]

The hybridization of energy storage systems consisting of fuel cells, batteries, and ultracapacitors has tremendous potential in fuel cell hybrid electric vehicles. However, the utilization of ultracapacitors in existing energy management strategies is insufficient, preventing complete exploitation of the energy storage system's benefits. To strengthen the utilization of ultracapacitors, this paper proposes an energy management strategy for fuel cell vehicles with three energy sources, considering the ideal state of charge (SOC) of ultracapacitors. The proposed method disassembles the motor power demand, with the ultracapacitor primarily supplying the acceleration portion. It calculates the ideal SOC of the ultracapacitor, and controlling the actual SOC of the ultracapacitor to match the ideal SOC. Furthermore, a fuzzy logic controller is designed to modify the fuel cell's output power, reducing power fluctuation. Additionally, according to the variation of the SOC of the battery, the fuel cell output power is modified to enhance the overall performance of the composite energy storage system. Simulation and hardware-in-loop results demonstrate that, compared to the adaptive filtering strategy with fuel cell power constraints and dynamic programming strategy, the strategy proposed in this paper reduces operational costs by 14.56 % and 4.99 %, respectively. • An energy management strategy considering ideal SOC in ultracapacitor is proposed. • The ideal SOC of UC is defined, with adjusting based on the average vehicle speed. • A fuzzy logic controller is designed to adjust the limits of the FC output power. • The output power of FC is adjusted according to the changes in the SOC of BAT. • This strategy offers better overall costs compared with the AFS strategy. [ABSTRACT FROM AUTHOR]

This paper aims to study the relationship between multi-frequency oscillation characteristics and parameters of hydro-turbine governing systems (HTGS), as well as improve the system oscillation quantification accuracy and primary frequency regulation performance. Firstly, an HTGS model with multi-frequency oscillation characteristics is established using an actual hydropower system as the object. Then, system oscillation components and the sources of each oscillating link are identified using eigenvalue and correlation analysis. To address the issue that sub-wave interference with dominant oscillation quantization, we innovatively incorporate correlation and frequency indexes into quantification process, and the dominant oscillations of complex HTGS are accurately quantified. On this basis, the detailed partitioning of control parameters is established to obtain the guide graph quickly reflecting the relationship between control parameters and system oscillations. Finally, an integrated optimization strategy with ultra-low frequency oscillation (ULFO) safety and regulation performance constraints is constructed, and control parameters are further optimized under two typical parameter setting methods. The results indicate over 90 % of the stable parameters enable the system to enter ULFO mode, and optimized control parameters utilizing proposed strategy increase regulation speed by 46 % while effectively suppressing ULFO. This paper provides a powerful tool and novel ideas for complex HTGS oscillation analysis and parameter optimization. [Display omitted] • Clarifying the multi-frequency oscillation sources of the complex HTRS and the influence law of key parameters on oscillations. • Proposing a novel oscillation quantization method to accurately identify dominant oscillations and quantify its oscillation characteristics. • Fully correlating control parameters with oscillation mode and stability margin as well as obtaining the ULFO security boundary of parameter adjustment. • Providing a high-quality optimization scheme to improve regulation rapidity while avoiding ULFO risk. [ABSTRACT FROM AUTHOR]

To achieve a win-win situation among multiple stakeholders within the integrated energy system, this paper proposes a low-carbon scheduling research of integrated energy system based on Stackelberg game under sharing mode. Firstly, an operation framework of integrated energy system based on leader-follower game was established, and a model of integrated energy system operator and user coalition was established considering carbon trading mechanism. Secondly, bi-directional long short-term memory is used to predict the travel data of electric vehicles, and a schedulable capacity model for electric vehicle cluster as shared energy storage was established. Then, an energy trading optimization model based on leader-follower game was constructed for integrated energy system operator and user coalition, and an improved Shapley value is adopted to realize cost allocation within user coalition. Finally, based on the demonstration project of Sino-Singapore Tianjin Eco-City is selected as the research object to make example analysis. The calculation results show this paper can achieve a reasonable benefit allocation among multiple participants, reduce carbon emissions of integrated energy system, and increase renewable energy accommodation capacity. The relevant achievements are a preliminary exploration for an effective mode of sharing economy in integrated energy system. • A low-carbon scheduling strategy of integrated energy system considers Stackelberg game under sharing mode. • A stepped carbon trading mechanism is introduced in the energy price decision of integrated energy system operator. • Bi-LSTM algorithm was used to predict EV cluster data, and Minkowski sum theory was used to get its schedulable capacity. • Everyone in user coalition participants in cooperative game, Individual cost can be allocated by improved Shapley value. • Dichotomy method is used to solve leader-follower game, example uses pilot project of Sino-Singapore Tianjin Eco-city. [ABSTRACT FROM AUTHOR]

In March 2023, the European Parliament and Council reached a consensus to increase the binding renewable energy target (RES) to a minimum of 42.5 % by 2030, effectively doubling the proportion of RES from the 2020 baseline. This significant development aligns the EU more closely with the objectives of the European Green Deal and the REPowerEU initiative. High-temperature heat pumps (HTHP), due to their appropriateness for industrial-scale applications, integrate perfectly within this progressive trajectory. They enable waste heat generated by various production processes to be recovered (temperatures typically ranges from around 50 °C–100 °C) and subsequent use at temperatures above 100 °C, thus reducing the consumption of fossil fuels and greenhouse gas emissions. The high operating temperatures and pressures of HTHPs are challenging. They require an in-depth analysis of the system processes involved, taking into account refrigerants, efficient heat pump cycles and key components. One possibility for a preliminary analysis of vapour compression HTHP system performance without incurring the costs associated with manufacturing and testing the device is modelling. However, there is no comprehensive review of research work on possible software. Commonly, the researchers report on one chosen methodology and the tools used. This paper provides a comprehensive review of modelling approaches. It also discusses aspects related to the principles of operation, refrigerants and system components. Additionally, the paper presents an overview of vapour compression heat pump applications in various sectors. The literature review conducted indicates the need for further research and development of HTHP covering not only technological aspects but also software development. [ABSTRACT FROM AUTHOR]

Battery health prognostics management is an important prerequisite for ensuring its safe use. As the battery is charging and discharging, its capacity deteriorates. Incremental capacity (IC) analysis is a common tool to analyse the degradation process based on the change rate of capacity relative to voltage. However, measuring battery voltage and current is difficult and there are inherent errors. This paper improves the IC curve from the perspective of time series, extracts a health indicator (HI), and uses the hybrid prediction model to predict the remaining useful life (RUL). Firstly, we convert the traditional IC-voltage (IC-V) curve to the IC-time (IC-T) curve. The time series corresponding to the peak of the improved curve is extracted. Secondly, the extracted degradation HI is predicted using grey forecasting model, and a probabilistic and iterative hybrid battery prediction method is established combined with Gaussian process regression. Finally, public datasets are used to validate the proposed method at different starting points and comparisons with other prediction methods are carried out. Results show that the HI based on the improved IC curve is highly correlated with the degradation process and the proposed method can provide an accurate result, which verifies its effectiveness and rationality. • This paper extracts the time corresponding to the improved IC-T curve peak as a new HI. • An iterative structure and probabilistic prediction method for RUL is established. • Grey forecasting model and Gaussian process regression are combined for RUL prediction. • Validation of the proposed method at different starting points for RUL is performed. [ABSTRACT FROM AUTHOR]

Purging offers a straightforward and cost-effective means of eliminating inert gases that accumulate in high-temperature proton exchange membrane fuel cells (HT-PEMFC) operating in dead-end anode (DEA) mode. Nevertheless, earlier studies primarily concentrated on inert gases, overlooking the significant challenge posed by the strong adsorption of CO within HT-PEMFC, complicating the use of inert gas methods for CO purification. This paper presents an analytical model that was validated experimentally. Leveraging this model, the paper analyzes the crucial parameter of CO concentration within the anode and deduces theoretical extreme values. Subsequently, it proposes purging strategies tailored to different operational conditions. The findings indicate that the minimum CO concentration within HT-PEMFC is influenced by current, feed gas flow rate, and feed gas CO concentration. Additionally, the maximum CO concentration is affected by the off-duration of the purge valve in addition to these factors. Specifically, under the operating condition of 473.15 K, adopting a CO concentration threshold purge strategy is recommended, whereas under the operating condition of 448.15 K, voltage drop and CO concentration threshold purge strategies should be respectively implemented above and below the cutoff line based on the current. This research contributes to mitigating the issue of CO accumulation in DEA mode. • A CO purging model was built in published literature. • The maximum and minimum values of CO molar concentration in CO purge were derived. • Purging strategies for different temperatures and current densities are proposed. • The maximum CO mole fraction that can be purified is increased to 3 %. [ABSTRACT FROM AUTHOR]

Conventional online evaluation methods for the mechanical/thermal behaviour of lithium-ion batteries fall short in terms of efficiency and accuracy, especially under extreme conditions coupling high-temperature and fast charging/discharging. This paper proposes an electrochemical/thermal/mechanical analytical model for prismatic lithium-ion batteries to assess their temperature, stress, deformation, and gas evolution. An electrochemical submodel is formulated, covering the lithium intercalation/deintercalation, solid-electrolyte interphase (SEI) decomposition/regeneration, and electrolyte decomposition. A thermal submodel is created to simulate heat transfer between the cell and its environment, considering reaction and Joule heating as heat sources. A mechanical submodel is developed to reveal the effects of nonlinear elastic constitutive properties and the mechanical/thermal/electrical states on cell deformation and stress, incorporating the reaction gas evolution. A coupled multidisciplinary analytical model is formed, using the state variables of temperature, stress, and deformation to link the submodels. The model's performance was validated against numerical and experimental results under high-temperature charge/discharge cycle conditions, demonstrating efficiency at the level of seconds, temperature errors below 0.6 %, and pressure errors below 4.6 %. The advantages in efficiency, accuracy, and applicability highlight its excellent prospects in energy storage and electric vehicle applications. This paper proposes an electrochemical/thermal/mechanical analytical model for prismatic lithium-ion batteries to assess their temperature, stress, deformation, and gas evolution. The temperature/pressure/SOC-dependent cell deformation is analyzed based on the nonlinear elastic property. [Display omitted] • A mechanical/thermal analytical model for prismatic lithium-ion cells is proposed. • The model integrates submodels of electrochemistry, heat transfer, and mechanics. • The mechanical submodel incorporates a nonlinear elastic constitutive equation. • The approach evaluates the state evolution in temperature, stress, and deformation. • The accuracy is validated by high-temperature charge/discharge cycle experiments. [ABSTRACT FROM AUTHOR]

During the current transient loading process, the proton exchange membrane fuel cell (PEMFC) is prone to voltage undershoot phenomenon, which result in internal complex chemical reactions, water vapor changes, and even reaction gas starvation. In this paper, the dynamic response characteristics of hydrogen-oxygen PEMFC and the mechanism of water-gas-heat synergistic transport during current transient loading are investigated by combining experimental and simulation methods. The experimental results show that an increase in the current loading amplitude exacerbates gas starvation and membrane dehydration leading to the deterioration of the dynamic response characteristics of PEMFC during transient current loading. To compensate for the lack of experimental methods to study the PEMFC water-gas-heat synergistic transport mechanism, this paper establishes a three-dimensional PEMFC transient model. The minimum values of reactant gas concentration in the anode and cathode catalyst layers are 0.0205 kmol/m3 and 0.0220 kmol/m3 at 65 °C, 0 backpressure and variable load conditions between 800 mA/cm2 and 1400 mA/cm2, respectively, which leads to more gas starvation at the anode. While comparing the energy wastage during current loading, it can be found that smaller current loading amplitude, higher stoichiometric ratio, and higher temperature and backpressure all contribute to the reduction of energy wastage. • The dynamic response characteristics of PEMFC during current loading were experimentally studied. • Water-gas-heat synergistic transport mechanism during current loading was numerical investigated. • The energy wastage during the current loading was compared and evaluated. [ABSTRACT FROM AUTHOR]

This paper presents a method to find the optimal topology, pipe sizing, and operational parameters of a district heating system under consideration of one design point. The current high costs of district heating systems set limits regarding the minimum heat demand density required for economic network expansions. Optimized routing with ideal pipe sizing and optimal operating parameters offers a potential for cost reduction. Therefore, this paper introduces a new two-phase method for district heating network expansion planning. This method consists of consecutive optimizations, starting with a mixed-integer linear programming followed by a nonlinear optimization. During the mixed-integer linear programming, the district heating system is optimized with continuous diameters, and the nonlinear pressure and temperature dependencies must be linearized. The resulting topology and the continuous diameters are afterward handed over to a nonlinear sparse sequential quadratic programming. The continuous diameters are discretized using a numerical continuation strategy that gradually forces the continuous diameter variables into discrete diameter choices. As a proof of concept, the district heating system for a small town with 400 consumers is optimized and analyzed. The two-phase optimization is performed in 251.68 sec, and in most cases, discrete or near discrete diameters are achieved in a nonlinear continuous optimization. • Development of a two-phase nonlinear optimization for district heating systems. • Determination of discretized diameters during a continuous nonlinear optimization. • Determination of nonlinear temperature and pressure profiles. • Selection of diameters in the second phase is limited by those of the first phase. • As a proof of concept, a town with 400 consumers is optimized. [ABSTRACT FROM AUTHOR]

The increasing expansion of photovoltaic power generation leads to unpredictable fluctuations in electricity supply, which can potentially jeopardize the stability of the power grid and escalate the costs associated with grid imbalances. As a result, precise forecasts of photovoltaic power generation play a vital role in optimizing capacity deployment, enhancing consumption levels, improving planning strategies, and maintaining grid balance within systems characterized by significant penetration of solar energy. This paper proposes a structural adaptive grey seasonal model based on data reorganization. Solar photovoltaic power generation data typically exhibit seasonal fluctuations, which pose a challenge to existing prediction techniques. Therefore, this paper adopts the idea of data reorganization to eliminate the seasonal fluctuations of observations, and the adaptive accumulation operator can accurately simulate the change trend of the original data in different periods, overcoming the defect of insufficient adaptability of the traditional accumulation operator. Subsequently, the time trend items are incorporated into the model structure to identify the trend characteristics of system development, which can effectively explain the power generation trend of photovoltaic systems at different time periods and improve the prediction accuracy of the model. In addition, the compatibility and unbiased nature of the proposed model have been demonstrated to help us better perceive the model. The Grey Wolf Optimizer (GWO) is used to optimize the adaptive parameters of the model, endowing it with higher flexibility and stronger adaptability. In order to verify the effectiveness of the model, three practical cases (namely quarterly solar power generation in the United States, Japan, and Germany) were compared with existing econometric techniques, artificial neural networks, and grey prediction methods. The experimental results show that the new model outperforms other benchmark models in both simulation and prediction performance, and enjoys high robustness. • A novel grey seasonal model with data reorganization is established. • The proposed model can grasp nonlinear and fluctuant in various datasets. • The robustness analysis of the novel model is established. • Comparative study shows that the novel model is superior to other models. • Relevant suggestions are put forward according to the prediction results. [ABSTRACT FROM AUTHOR]

Humidity plays a significant role in both daily life and industrial manufacturing. Adsorption-based dehumidification attracts considerable attention due to advantages of temperature and humidity independent control and great energy-saving potential. For adsorption-based dehumidification systems, desiccants are the basis for optimizing heat transfer and moisture adsorption performance. Despite the rapid advancements in materials science, few systematic classifications concerning composite materials are available. The primary objective of this paper is to provide a holistic and explicit roadmap of recent developments in composite desiccants, directing at its application in adsorption-based dehumidification for energy efficient utilization. Improvements in heat transfer can be achieved by adding high thermal conductivity materials or by reasonably designing structures of dehumidification components. Moisture adsorption is enhanced by combining different desiccants to maximize their functions. Three main categories are classified, that is, salt-embedded composites, porous matrix-polymer composites and polymer-polymer composites. This paper can help to identify the research gaps and explore promising approaches for future study to further enhance the energy efficiency of adsorption-based dehumidification technologies. [Display omitted] • A review of composites for adsorption-based dehumidification was completed. • High thermal conductivity additives and heat exchanger types can boost heat transfer. • Three main categories are classified for moisture adsorption improvement. • A comparative study was conducted on composites. [ABSTRACT FROM AUTHOR]

The diffusion on large scale of the hydrogen-fueled technologies could be a strategy for boosting the expected decarbonisation of the civil sector. However, there are still a lot of unanswered questions about the real feasibility and the problems of the integration in a pre-existing building-plants system. Despite some conflicting opinions, the research about the application of the hydrogen system in the building sector must continue to make the technology more attractive and easily available at low cost. Therefore, first of all, the paper analyses advantages and issues through a broad overview. The main limits of the available studies regard both the approach and the technology integration. Indeed, there are few data about in-field performance analysis since the simulative/numerical investigations are usually based on in-lab prototypes. The most diffused systems referred to integrated energy solutions with proton exchange membrane fuel cell technology and in any case the integration in very low energy buildings such as the nearly zero energy (nZEB) ones is not discussed. Then basing on the discussed research gap, the paper focuses on the set-up of a new installation of micro-combined cooling, heating and power unit based on solid oxide fuel cell technology fueled by green hydrogen. The complexity in the design of the integration with other innovative plant configurations is discussed considering as real case study a nZEB in Mediterranean climate. Preliminarily experimental results show that the combination of fuel cell and photovoltaic system could bring to positive energy with surplus energy, during autumn season, of about 23.8 kWh. Moreover, it is also proposed the validation of a dynamic numerical model developed by means of TRNSYS (Transient System Simulation Tool) that will be used for further sensitivity analysis. [ABSTRACT FROM AUTHOR]

The unavoidable wind power prediction error poses a challenge for wind power to participate in day-ahead scheduling plan. This paper proposes a two-stage correction method considering NWP wind speed and power correction. Firstly, this paper fully considers the historical similarity of NWP wind speed, and adopts the improved clustering distance for segment matching and wind speed correction based on the extracted most relevant trend component, which enhances the utilization of NWP information and avoids the randomness and uncertainty of the intelligent correction method, and gram angular field (GAF) and stacked autoencoder (SAE) are used for feature extraction and conditional variational autoencoder (CVA) is used to generate specific samples to ensure the adequacy of the clustering process. Secondly, considering the historical similarity of error of predicted power, the power is further corrected based on the proposed weighted double-constraint to realize the secondary calibration. The Proposed method is applied to several wind farms in China to verify its effectiveness. The results show that the proposed method reduces the N RMSE by 3.82 % and N MAE by 3.40 % compared with direct prediction in the wind farms in western Inner Mongolia, which is important for promoting wind power consumption and maintaining the safe and stable operation of the power system. • Propose a method to extract the most relevant trend component form wind speed. • Propose a novel method for wind speed correction and power calibration. • Propose a sample condition generation method based on GAF and CVA. • Propose an improved clustering distance into the wind speed correction process. [ABSTRACT FROM AUTHOR]

Offshore wind energy is booming and floating offshore wind has been proved to be a promising clean energy. Wake effect is one of the biggest challenges in large-scale floating wind farm which results in power loss and increased fatigue load. In this paper, actuator line model (ALM) coupled with unsteady Reynolds-averaged Navier-Stokes (URANS) is adopted to investigate the wake characteristics and evolution of floating wind turbine under motion. Besides, modal analysis is adopted to extract typical features of turbine wake. Velocity fluctuation is the prominent characteristic of floating turbine wake. The increase of surge frequency results in the decrease of velocity fluctuating region and large surge amplitude leads to bigger velocity deficit fluctuations. In addition, the surge motion brings a faster recovery rate than fixed wind turbine. The induced velocity field generated by vortex ring structure is the key to understanding complex evolution of turbine wake and the vortex ring structure is analyzed in this paper. With the deep understanding of FOWT wake, an innovative vortex ring structure model is proposed in this paper. This work is the basic work of developing the engineering wake model of floating wind turbine, to cope with the wake effect challenge in the large-scale floating wind farm. • Wake characteristics and vortex structure is analyzed using actuator line method. • Wake evolution mechanism is clearly revealed through modal analysis. • Instability theory of helical vortices is introduced into the study of turbine wake. • A semi-empirical vortex structure model is proposed based on theoretical analysis. [ABSTRACT FROM AUTHOR]

The imperative for small aviation engines lies in the pursuit of heightened power-to-weight ratios and thermal efficiencies. Relying solely on piston engines and gas turbines is insufficient to concurrently meet these evolving performance demands. This paper introduces the concept of a combined cycle aviation engine (CCAE), amalgamating the cycle modes of piston engines and gas turbines. The CCAE achieves flexible control over turbine operating states and engine performance through the adjustment of the energy distribution between these two cycles. The air diversion strategy (α) and the burner's fuel supply strategy (λ b) are identified as the key determinants of system performance. To comprehensively investigate the impact of α and λ b on CCAE's performance, this paper constructs a theoretical model, a simulation model, and a test bench. The simulation model's accuracy is validated through test data, and the air-fuel ratio range for burner stable combustion is explored. The simulation results indicate a reduction of 50 % in the fluctuation of turbine speed within a single cycle. Under high-altitude conditions, CCAE's intake mass flow rate, power, and efficiency are notably enhanced when compared to conventional turbocharged piston engines. These findings contribute valuable insights that can inform the application of CCAE within the aviation domain. • Propose combined cycle operation mode for aviation engines. • Explore the range of air-fuel ratio for burner stable combustion. • Bypass ratios and fuel supply strategies affect CCAE's performance. • Compare CCAE high-altitude performance with turbocharged diesel engine. [ABSTRACT FROM AUTHOR]

Integrated Energy Systems (IES) play a pivotal role in achieving carbon peak and carbon neutrality goals. However, previous research in IES operation has mainly focused on energy flow constraints, neglecting the impacts of renewable energy output, energy prices, carbon emission, and storage status. To bridge this gap, this paper proposes a Comprehensive Self-Adaptive (CSA) operation strategy as a key component of our two-tier synergistic optimization method, optimizing an IES integrated with Concentrated Photovoltaic/Thermal systems. In the upper-tier capacity configuration, the proposed method considers the full life-cycle carbon emissions of equipment. In the lower-tier operational strategy optimization, the CSA operation strategy automatically adjusts energy generation, utilization, and storage by comprehensively evaluating hourly operating costs, hourly carbon emissions, and hourly storage benefits. Compared to traditional operation strategies and other adaptive methods, the newly developed strategy consistently demonstrates superior performance. When contrasted with separate energy systems, IES operating under the CSA operation strategy markedly stands out, achieving a cost savings rate of 25.87 %, a CO2 reduction rate of 61.54 %, and an electricity matching ratio of 91.24 %. Additionally, the paper explores the performance of the CSA operation strategy across various regions, confirming its feasibility and generalizability under diverse load and irradiance conditions. • An integrated energy system combines CPV/T, ORC, and multi-dimensional energy storage solutions. • A CSA operation strategy holistically assesses costs, carbon emissions, and storage benefits during operation. • A two-tier synergistic optimization method simultaneously addresses capacity configuration and operational strategy. • Comparative studies in various regions to validate the feasibility and generalizability of the CSA strategy and CPV/T-IES. [ABSTRACT FROM AUTHOR]

Industry 4.0 realizes intelligent interconnection through the sensor group of the Internet of Things. A key challenge is to achieve the self-powering of sensors and stable operation of precision instruments. This paper introduces a dual-functional structure (VIPEH) integrating energy harvesting (EH) and vibration isolation (VI) based on the quasi-zero stiffness (QZS) piezoelectric mechanism. The paper analyzes the nonlinear statics of piezoelectric flexible beams under large deformations and conducts parametric analysis. A nonlinear dynamic model with electromechanical coupling was developed to investigate the impact of mechanical and electrical parameters on the system's dynamic bifurcation behavior, EH, and VI performance. Finally, an experimental setup is created to evaluate the VIPEH's characteristics. Sweep frequency excitation experiments demonstrate that the initial isolation frequency of VIPEH is lower than 1.4 Hz. The important thing is that VIPEH can still power the sensor based on the isolation frequency, and the output power of a single piezoelectric material can reach 3.19 mW. This not only enables the isolation of low-frequency vibrations but also presents a highly promising application for achieving self-powering in sensor clusters within the context of Industry 4.0 and the IoTs. • VIPEH based on quasi-zero stiffness piezoelectric mechanism is proposed. • VIPEH achieves energy harvesting while isolating low-frequency vibrations. • The output power can reach 3.19 mW. • The initial vibration isolation frequency of VIPEH is less than 1.4 Hz. [ABSTRACT FROM AUTHOR]

The permeability rebound and recovery of coal reservoirs is one of the key factors affecting the efficient recovery of coalbed methane (CBM). Most studies focus on the permeability rebound and recovery of intact coal reservoirs. Conversely, the permeability rebound characteristics of tectonic coal have only been rarely investigated. In this paper, an improved fully coupled mathematical model for methane transport of tectonic coal and intact coal seam was established. Then, the phenomenon of permeability rebound and recovery was analyzed theoretically, and the permeability rebound time, rebound value and recovery time were proposed to compare the difference in permeability evolution. In addition, the permeability rebound characteristics of tectonic and intact coal were evaluated for different geological parameters of coal reservoirs. The results indicate that the rebound time and recovery time of both intact and tectonic coal increase with the increase of initial gas pressure. As the initial permeability increases, the permeability rebound time of intact and tectonic coal show a decreasing trend. The permeability rebound time of intact coal decreases with increasing initial diffusion coefficient, while that of tectonic coal shows the opposite trend. Furthermore, the change in permeability recovery time for tectonic coal is 6.59 times larger than for intact coal, indicating that the effect of permeability rebound phenomenon is more significant during gas extraction in tectonic coal reservoirs. Finally, a conceptual model was proposed to explain the differential mechanism of permeability rebound between tectonic and intact coal, and its implications for CBM recovery in tectonic coal reservoirs was discussed. Therefore, the results presented in this paper can provide a theoretical basis for the efficient development of CBM in tectonic coal reservoirs. • Methane transport mathematical models of tectonic and intact coal are developed. • The permeability rebound in tectonic and intact coal is quantitatively analyzed. • The evolutionary mechanism of permeability rebound of tectonic coal is revealed. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) load integration and environmental concerns in recent years have motivated energy users to step forward in reducing carbon footprints by promoting energy trading. In the energy trading market, different energy stakeholders participate in trading energy with each other to maximize their utilities and improve trading efficiency. The social welfare (SW) of the energy market plays a significant role in keeping the market going and motivating new users to engage in energy trading. The SW of the energy market depends on its structure, where energy flows from producer to end user in a fair way. This paper introduces a hierarchical energy trading framework, formulated as a three-stage Stackelberg game, to enhance SW. In the first stage of this hierarchy, the retailer, acting as the sole leader, seeks to maximize profits through optimized energy sales to charging stations (CS). Subsequently, in the second stage, each CS operates as a leader for EVs, aiming to maximize profits by minimizing energy trading costs, while also acting as a follower in response to the retailer's strategies. The third and final stage involves EVs collaboratively acting as a unified strategic agent to minimize their trading costs through optimized charging and discharging schedules. This paper also introduces an average price penalty function to further enhance SW in the energy market. The proposed model, addressing the complexities of energy trading interactions, is formulated as a non-linear optimization problem with constraints. It has been implemented in a Python environment and solved using the Gurobi optimization solver, demonstrating its potential to improve the efficiency, fairness, and overall social welfare of the energy trading market. • Introduces a hierarchical energy trading framework using a three-stage Stackelberg game to enhance market social welfare. • Incorporates renewable energy at the retailer level, diversifying energy sources and boosting ecological relevance. • Establishes comprehensive pricing functions across all levels, including reciprocal EV discharge signals, for dynamic market interaction. • Implements an average price penalty at charging stations to preserve social welfare and promote efficient energy use. • Demonstrates model efficiency and practical applicability through tests on the IEEE 33 bus system, showing superior profitability and convergence. [ABSTRACT FROM AUTHOR]

Large ships are more utilized than ever before to facilitate transporting heavy goods and oils overseas. However, they rely on fossil fuels and cause severe environmental impacts. Utilizing alternative fuels and implementing new engine designs are considered promising methods to reduce greenhouse gas emissions and improve engine performance. This paper compares three developed, hybridized marine engines operated using five fuel blends of hydrogen, methanol, dimethyl ether, ethanol, and methane. In addition, the paper presents a multi-objective particle swarm optimization to increase the engine power and its exergetic efficiency and reduce the cost and environmental impact. A marine engine (SRC-GT-SOFC) containing a steam Rankine cycle, a gas turbine, and a solid oxide fuel cell is considered to weigh less and perform better. It is found that the optimized SRC-GT-SOFC engine power is increased by about 10 %, corresponding to 16269 kW with an increased exergetic efficiency of 70.6 %, respectively. Its specific fuel and product exergetic costs are found to be 12 $/GJ and 15.67 $/GJ, and its specific fuel and product environmental impacts are 7.4 Pt/GJ and 9.3 Pt/GJ, respectively. This results in a significant reduction in the relative cost difference and the relative environmental impact difference of the optimized SRC-GT-SOFC of 30 % and 25.4 %, respectively. • Three hybridized marine engines are compared and optimized to raise performance. • Optimized SRC-GT-SOFC engine has 16.3 MW power and 70.6 % exergetic efficiency. • Specific exergetic cost reduced to 12 $/GJ for fuel and 16 $/GJ for product. • Specific exergetic environmental impact for fuel and product becomes 7.4 and 9.3 Pt/GJ. • New operation conditions of SRC-GT-SOFC have low price and environmental impact. [ABSTRACT FROM AUTHOR]

The optimal capacity configuration of combined wind-storage systems (CWSSs) serves as a foundation and premise for building new electricity system. This paper proposes a novel capacity configuration model of CWSS. The model simultaneously considers economy, stability and low carbon by respectively minimizing Levelized cost of electricity (LCOE) and maximizing effective load carrying capacity (ELCC). The multi-objective optimization model is solved by Non-dominated Sorting Genetic Algorithm II (NSGA-II). And Multi-Attributive Border Approximation Area Comparison (MABAC) method is used to score the alternative solutions on the Pareto front and make decisions under different scenarios. The results of the case study show that: (1) The evaluation of the ELCC for CWSS can quantify its ability to replace the installed capacity of thermal power, thus greatly reducing the uncertainty of wind power output. (2) Under low-carbon scenario, economic scenario and standard scenario, the results of optimal capacity configuration for the CWSS show that LCOE and ELCC are negatively correlated. When the weights of ELCC and LCOE are equal, the three goals of economy, low carbon and safety and stability are achieved. (3) The results of a single sensitivity analysis on LCOE show that the sensitivity coefficient of electricity generation is the highest, exerting the greatest impact on the capacity configuration decision of the CWSS. A 20 % increase in power generation can reduce LCOE by 0.1203RMB/kWh. This paper provides energy planning recommendations for decision-makers in CWSSs, contributing to the development of a reliable new electricity system. • A multi-objective optimization of CWSSs considering ELCC and LCOE. • Multi-objective optimization model is established based on NSGA-II and MABAC. • The model of LCOE based on ELCC is proposed. • The model is presented to achieve the optimal capacity configuration of the CWSS. [ABSTRACT FROM AUTHOR]

High absorption of PV benefits heat collection in the daytime, but the heat loss to the space/environment because of its high emissivity also should be noticed. Although there are many papers investigating PVT, few of them cover the night behavior and negative running during late afternoon. This paper supplements detailed studies on related content to draw people's attention to the night and late-afternoon operation that helps better strategy establishment and less heat loss. From experiments, the clouds help hinder the PV radiant heat exchange with space. Under clear night, the PV bottom temperature can be 6.88 °C lower than air temperature and the water tank's bottom temperature drops 2.8 °C. Because of the fluid viscosity, no apparent temperature stratification and flow occur in the PVT water pipe, causing even PV temperature. The higher the water temperature, the easier the negative running occurs during late afternoon, although the irradiation is still high at 521W/m2. Heat loss power and energy are −199W and 1791 KJ from 15:10 to 17:40. Based on different regressed linear correlations of different PVT systems, the effect of water temperature, irradiation, and ambient temperature on the negative performance is also comparatively studied. • Night behavior and negative running of PVT system are studied to avoid heat loss. • The clouds helps hinder the PV radiant heat exchange with the space. • PV bottom temperature can be 6.88 °C lower than ambient temperature. • Even the irradiation is still high at 521W/m2, negative running may still happen. • Negative operation is also comparatively studied based on different PVT and ambient. [ABSTRACT FROM AUTHOR]

There has been a growing demand for energy consumption statistics in the manufacturing industry to establish national energy and greenhouse gas policies. Despite its importance, the Korean government faces significant challenges in collecting energy data at a facility level in a precise and timely manner. To address the lack of timely data, this paper employs machine-learning models to predict the annual total energy consumptions of each manufacturing facility. We first designed four prediction models that take into account the characteristics and energy consumption behaviors of industry sub-sectors. As input variables, these prediction models mainly included electricity consumption, employee size, energy types, gas consumption and other accessible data. Finally, we conducted numerical experiments on approximately 100,000 facilities and evaluated the prediction performance of various machine-learning algorithms such as linear regression, decision tree regression, random forest regression, gradient boost regression, and extreme gradient boosting regression. The numerical experiments provided insights into which model and algorithm offer the best prediction performance for each industry sub-sector. In addition, we identified the important variables for predicting total energy consumption, revealing that not only electricity but also various other energy sources and variables representing industry-specific characteristics play a crucial role in improving prediction performance. • This paper predicts the annual total energy consumption of individual manufacturing facilities in Korea. • Several prediction models capable of considering industry-specific features are presented. • This paper employs machine-learning models. • The numerical experiments provided insights into which model and algorithm offer the best prediction performance. • Not only electricity but also variables representing industry-specific characteristics play a crucial role. [ABSTRACT FROM AUTHOR]

Dynamic simulation is a powerful tool for accurately evaluating the thermal demands of buildings and assessing the impact of energy refurbishment actions on their final consumption. Conversely, semi-stationary models are widely adopted in commercial applications for its simplified approach, which reduce calculation times, resulting in standardized results showing a certain deviation with respect to the real energy This paper presents the energy and economic comparison between the dynamic simulation and semi-stationary approaches for the calculation of primary energy demand of residential buildings. The semi-stationary method, used by the legislation to calculate the buildings energy label, is based on an energy performance parameter, not representative of the real energy demand. Conversely, an approach based on dynamic simulation provides a more reliable estimation of the primary energy demand. The main novelty of this paper is to numerically prove that the energy and economic savings calculated by means of software based on the current legislation may be overestimated. In this work, the dynamic simulation of the building-plant system is performed by TRaNsient SYstem Simulation (TRNSYS) program. Each building apartment is divided in thermal zones, where the internal heat gains are defined in detail. The semi-stationary simulation of the building-plant system is performed according to the Italian standard UNI TS 11300. The models allow one to evaluate the yearly primary energy demand, along with the energy bill and CO 2 emissions. A specific case study is developed for a residential building located in Naples (Italy). The models are used to calculate the building energy demand for several scenarios, considering different thermal transmittances of the building elements. The results show that the semi-stationary method overestimates of primary energy saving, equal to 64.7 %, with respect to the one calculated with the dynamic approach, equal to 43.2 %. • Semi-stationary and dynamic simulations were compared for residential buildings. • Critical evaluation of energy and economic savings due to the building refurbishment. • Primary energy savings of 65 % and 43 % by semi-stationary and dynamic simulation. • Dynamic simulation is needed within the legal frame of energy efficient buildings. [ABSTRACT FROM AUTHOR]

In this paper, a novel combined heating and power (CHP) system is proposed to realize full-scale utilization of geothermal energy and efficient multi-generation, which not only performs preferable overall performance than previous homogeneous system, but also offers an effective energy cascade utilization approach for self-condensing transcritical CO 2 (TCO 2) Rankine cycle. Based on the established mathematical models, the performance comparison is conducted for proving the superiority of the novel CHP system. Then, an overall performance analysis is implemented to reveal the combined effects for six key parameters on system thermodynamic, exergoeconomic and exergoenvironmental performances. Furthermore, multi-objective optimization considering system overall performance is conducted. The results show that for the novel CHP system, the largest relative improvement rate of system exergy efficiency (η exg) and declining rate of total unit product exergy cost (c P , total ) versus the previous CHP system are 15.03 % and 18.89 %, respectively. The final optimization results of η exg , c P , total and total unit product exergy environmental impact (b P , total ) are determined as 51.10 %, 14.12 $/GJ and 9.00 mPts/GJ, respectively. This paper fulfills an elaborate performance analysis and optimization for the novel CHP system, which fills the research gap of efficient and promising CHP system based on self-condensing TCO 2 Rankine cycle. • A novel self-condensing transcritical CO 2 cycle based cogeneration system is proposed. • Full-scale utilization of geothermal energy is realized by the novel system. • Overall performance superiority of the novel system is proved by comparison study. • Combined effects of six key parameters on system overall performance are revealed. • Multi-objective optimization of system overall performance is performed. [ABSTRACT FROM AUTHOR]