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Piezoelectric Energy Harvesting (PEH) has emerged as a promising alternative to traditional batteries for self-powered sensors. This paper proposed a multi-pillar piezoelectric stacks oscillating float wave energy harvesting device that utilizes a unique rack, pinion, and cam system to convert the heaving motion of the float into a unidirectional rotation. Subsequently, the cam actuates a linkage rod, driving multiple piezoelectric stack pillars to generate electrical energy. The innovative cam mechanism introduces frequency upconversion to improve the efficiency of power generation in conjunction with piezoelectricity. A theoretical methodology is also derived and validated with simulation and experimental model tests to characterize the output power. The results demonstrate that the system attains a peak output voltage of 32 V and a power output of 3.5 mW when utilizing 10 piezoelectric translation units connected in parallel. This achievement is facilitated by the oceanic force generated by wave motion, which exerts a force of 500 N over a wave period of 3 s. The sustainable energy solutions are developed for remote sensor applications in marine environments, offering potential advancements in the field of autonomous and self-powered sensing systems. • Multi-pillar piezoelectric stack ocean wave energy harvester with oscillating float buoy. • Cam mechanism up-converts wave frequency to improve the efficiency of power generation. • A theoretical methodology is developed and verified to characterize piezoelectric energy harvesting. [ABSTRACT FROM AUTHOR]

The widespread adoption of electric vehicles has become pivotal in attaining emissions reduction objectives, and the rationale behind designing and situating charging stations determines the unfurling of electric vehicles. This scholarly work fully mines the geographical situational particulars underlying the functioning statistics of charging stations in Beijing during the period from January to March 2016. By incorporating the urban road network, we construct a charging stations accessibility index to investigate the influence of stations accessibility on utilization rates. The findings reveal a substantial positive relationship between the overall accessibility of charging stations and their utilization rates. However, the limited number of charging stations hinders the agglomeration effect from significantly boosting utilization rates. Moreover, the impact of charging station accessibility on utilization rates varies significantly across regions with different levels of development, indicating pronounced heterogeneity. The research conclusions of this paper provide useful suggestions for promoting the rational layout of charging stations. [Display omitted] • Charging station accessibility depends on driving distance to important city areas. • The accessibility of charging stations significantly impacts their utilization rate. • Charging station accessibility's impact on utilization rate varies significantly across regions. • Insufficient charging stations limit agglomeration's impact on utilization rates. [ABSTRACT FROM AUTHOR]

In this paper, a cascade refrigeration system equipped with a single-screw compressor (SSC-CRS) is proposed and built in order to achieve the goal of high load cooling capacity under ultra-low temperature conditions. Firstly, the isentropic efficiency curve of the single-screw compressor (SSC) is fitted through experimental method in response to the characteristics of low suction temperature and large pressure ratio of the compressor. In addition, the analysis of SSC-CRS with 28 refrigerant pairs are made to compare the coefficient of refrigeration (COP) and power consumption. On the basis of the cascade system, the paper compares the improvement of performance between two circuits with SSC: vapor injection cascade refrigeration system (SSC-ICRS) and ejector vapor injection cascade refrigeration system (SSC-EICRS). The results indicate the COP improvement rate of SSC-ICRS system is 17.39 %–41.98 % and the COP improvement rate of SSC-EICRS system is 49.46 %–68.37 % compared with SSC-CRS. • The isentropic efficiency curve of the single-screw compressor is fitted through experimental method. • The analysis of SSC-CRS with 28 refrigerant pairs are made to compare the coefficient of refrigeration and power consumption. • The COP improvement rate of SSC-EICRS system is 49.46 %–68.37 % compared with SSC-CRS. [ABSTRACT FROM AUTHOR]

Solar chimneys (SCs) are effective in inducing natural ventilation. However, extending SCs from low-rise to high-rise buildings causes non-uniform flowrate distribution among storeys, where the flowrates on the lower storeys can be 300 % higher than those on the higher storeys. This paper aims to improve the uniformity of flowrate distribution among storeys in high-rise SCs up to 40 storeys. A concise one-equation theoretical model was derived to predict the flowrate distribution among storeys. This theoretical model was well verified by an experimentally validated numerical model. Furthermore, based on this flowrate distribution equation, the optimum shape (i.e., square-root-law (SRL) structure) was derived and proposed for high-rise solar chimneys to achieve a highly uniform flowrate distribution. Numerical simulations showed that the SRL structure reduced the relative difference in flowrate among storeys from 330.6 % to 15.0 % in a 20-storey SC. The findings of this study can provide guidance on how to design optimum high-rise SCs and maximise its ventilation performance. • A simple mathematical equation to predict flowrate distribution was derived. • Flowrate distribution among storeys is affected by the solar chimney shape. • Square-root-law structure can produce highly uniform flowrate distribution. [ABSTRACT FROM AUTHOR]

Effectively utilizing low-grade thermal energy is a promising approach to mitigating greenhouse gas emissions while reducing the burden on centralized power grids. Current thermoacoustic heat pumps and power generators face challenges such as high onset temperature differentials and low performance. This paper addresses these challenges by introducing a gas-liquid resonator into a thermoacoustic combined heat and power systems to recover low-grade thermal energy in residential buildings. Through Sage modeling and calculations, the internal characteristics of the proposed system and its output performance under different operating conditions are explored. At a heating temperature of 350 °C, the system can generate 6.4 kW of output thermal power, 0.9 kW of electricity, and overall exergy efficiency is 79.3 %. Combining neural network models with case studies conducted in Spain and Finland, the system can annually save 5.6 MWh and 20.7 MWh in fuel energy, reduce emissions of 1374 kg and 5180 kg of carbon dioxide, and save a total cost of €611 and €2324, respectively. Furthermore, comparisons with other emerging micro-CHP systems highlight the efficiency of the proposed system. These results indicate the high potential of thermoacoustic combined heat and power systems in recovering low-grade thermal energy and achieving energy savings and emission reductions. • Introducing an innovative thermoacoustic micro-CHP system. • Achieving an exergy efficiency of 79.3 % under standard operating conditions. • One system can annually reduce household CO 2 emissions by 5180 kg. • Ideal for households, balancing lower output levels for both heat and electricity. [ABSTRACT FROM AUTHOR]

This paper studies time-frequency connectedness using the Diebold Yilmaz method and the Baruník and Křehlík method to examine the relationship among geopolitical risk, traditional energy (coal, oil and gas) and carbon markets. First, geopolitical risk shows stable positive net spillovers in the medium and long term, but fluctuates in the short term. Second, the total connectedness is greater in the short term, and strengthened in the period of rising geopolitical risk with the changes of connectedness paths. Third, in general, the carbon market has greater impact on the geopolitical risk than it has been affected. Finally, as a sub-indicator of geopolitical risk index, geopolitical acts index generally contributes more, but natural gas is more affected by geopolitical threats index. Our results provide useful knowledge for governments to realize their goal of policymaking, and help industrial enterprises make timely adjustments to their energy strategies. In addition, we hope that investors can pay attention to the spillover effect and reduce investment risk. • The impact of geopolitical risk on energy markets tends to stabilize over time. • Geopolitical risk is more affected by carbon market, especially in short term. • High geopolitical uncertainty changes the paths of the connectedness network. • There are differences in the performance of geopolitical risk sub-indices. [ABSTRACT FROM AUTHOR]

High-pressure angle valves have suffered from eccentric jet flow characteristic in the diffuser pipe section due to the inherent structures and throttling effect. The eccentric jet flow will continuously scour the valve body and lead to washout on the side wall of the valve seat, largely impacting the valve service life and safety. With regard to this issue, this paper has conducted a numerical characterization study to investigate the influences of valve spool structures including the prototype, parallel and spherical spools on the eccentric jet flow in the first diffuser pipe section of a high-pressure angle valve. Results show that the parallel and spherical valve spool structures can achieve a lower pressure difference across the pipe section than the prototype spool structure, while the spherical spool has shown the minimum pressure difference in the first diffuser pipe section. Moreover, compared with the prototype and parallel spools, separated flows are distributed more uniformly on both side walls in the first diffuser pipe section for the valve with the spherical spool. The relative uniformly distributed separation vortexes makes the high-speed mainstream along the pipe center line and can successfully mitigate the eccentric jet flow. The motivation of this work is to reveal the influencing mechanisms of spool structures on the eccentric jet characteristic in high-pressure angle valves and provide useful guidelines for advanced angle valve design with low erosion and long service life. • The influences of valve spool shapes on the eccentric jet flow characteristic are studied in a high-pressure angle valve. • Separated flows are distributed more uniformly in the first diffuser section of the valve with spherical spool. • The spherical spool achieves a smaller pressure difference across the diffuser pipe than the prototype and parallel spools. • The spherical spool is beneficial for suppressing the eccentric jet flow characteristic. [ABSTRACT FROM AUTHOR]

New energy vehicles (NEV) have become an important driving force for carbon reduction in the transportation industry. This paper adopts the extended environmental pressure assessment (STIRPAT) model and the time-varying difference in differences (DID) model to evaluate the effectiveness of the NEV subsidy policy to achieve sustainable governance. The results show that the NEV subsidy policy can significantly decrease the carbon emission of the transport industry. The mechanism analysis results indicate that the NEV subsidy policy can decrease carbon emissions by promoting a green transformation of the consumer car purchasing structure. Further analysis found that a "fraudulent subsidy" can significantly weaken the policy effect of subsidies. The results of heterogeneity analysis indicate that the subsidy policies introduced by different levels of government have different effects. The subsidy policy of the central government has a more significant effect, and repeated subsidies from central and local governments will weaken the policy's effectiveness. These findings provide valuable insights for policymakers in developing countries to optimize subsidies for new energy vehicles in reducing carbon emissions in the transportation industry. • We explore the impact of the NEV subsidy policy on carbon emissions of the urban transport industry in Chinese cities. • The NEV subsidy policy significantly reduces the carbon emissions of the urban transport industry. • The NEV subsidy policy can promote a green transformation of the consumer car purchasing structure. • The fraud of government subsidies will weaken the carbon reduction effect of the NEV subsidy policy. • The impact of NEV subsidy policy from central and local governments on urban carbon reduction may vary. [ABSTRACT FROM AUTHOR]

The Joule cycle Linear Engine Generator (LEG) is a promising power generation technology with the potential to achieve zero carbon emissions. However, the LEG expander valve actuation system presents unique challenges due to its lack of a traditional crankshaft, the need for swift valve lift and reversal, and variable lift. This paper presents a Linear Variable Valve Actuation (LVVA) system for a LEG prototype. The LVVA system is powered by voice coil motors. Rigorous experimental investigations were conducted to analyze crucial performance factors, including energy consumption, force balance, energy flow distribution, and the relationship between valve lift duration and energy consumption. The results show that the LVVA system can achieve the desired valve lift and timing, as well as very small variations in LEG performance compared to the model using an ideal lift curve. The LVVA accounts for approximately 3.59 % of the LEG power output. The energy consumption of 1.607 J per valve stroke provides a slight advantage over traditional actuation systems. The obtained optimal lift curves were used to refine the LEG model. The influence of valve lift curves on LEG performance was evaluated which reveals rapid valve openings and relatively short duration contributing to improved LEG performance. [Display omitted] • Valve actuation validation contributes to a 1D Linear Engine model. • The valve actuation system consumes about 1.607 J per valve lift cycle. • Valve openings durations improves energy consumption of valves and engine performance. • The valve actuation energy demands 3.59 % of Linear Engine's power output. [ABSTRACT FROM AUTHOR]

The injection rate and injection volume are essential parameters for the combustion process, which greatly affect the engine efficiency and emission performance. However, at the current time the injection information cannot be obtained in real time during the actual operation of engine. This paper presents a novel real-time estimation method for the injection rate and injection volume based on the measurable rail pressure. A comprehensive dynamic model is derived by considering both the effect of fuel injection and supply process on the pressure fluctuation. Then a linear time-varying state space model is constructed by choosing three appropriate state variables. On this basis, considering the measurement noise and model uncertainty, a Kalman filter-based estimation method of the injection information is investigated. And the noise covariance matrix Q is optimally designed to adapt to a wide range of operating conditions. The proposed estimation method is validated on the different conditions, and the results show the injection rate estimation errors are within 4.5 %, the injection volume estimation errors are within 4 %. And on the situation with overlap of injection and supply process, the injection rate and injection volume estimation errors are all within 4.5 %. • A novel real-time estimation method for the fuel injection rate and injection volume is proposed. • To decouple the effects of fuel injection and supply on the pressure fluctuation, a comprehensive dynamic model is derived. • A linear time-varying state space model with three state variables pdown, p ˙ down , pup is proposed and built. • A Kalman filter-based estimation for the injection information in real-time is investigated. • The accuracy estimation results are achieved at the overlap of fuel injection and supply condition. [ABSTRACT FROM AUTHOR]

This paper explores methods to improve the fuel economy of a two-stroke free piston engine generator (FPEG) with no additional cost. In order to solve the problem of low fuel capture and poor fuel economy of two-stroke FPEG using port fuel injection (PFI), the fuel injection method is optimised in this paper. This fuel injection system is semi-direct injection (SDI). In contrast to PFI, SDI also uses a low-pressure injector, which introduces fuel directly into the ports of the engine. This study explores the feasibility of SDI on a free-piston engine generator through numerical simulation. The results show that SDI can effectively avoid short-circuit losses and improve the fuel capture rate by more than 60 % relative to PFI. Meanwhile, the ISFC is reduced by 42.3 %, and the lowest fuel consumption rate is 259.7 g/(kW·h). The combustion efficiency of SDI is maintained at over 95 % under warm-up conditions. Compared with PFI, the stagnation period is shortened by up to 10.9 % and the centre of gravity of combustion is advanced by up to 16.7 %. SDI effectively reduces HC emissions and has a lower NOx emission level than PFI. At the same time, SDI produces more CO due to stratified combustion. • A free piston engine generator simulation model with port fuel and semi-direct injection was established and validated. • Semi direct injection can effectively reduce short-circuit losses and indicated specific fuel consumption. • Using semi direct injection can effectively improve early flame propagation. • Semi direct injection can reduce HC and NOx emissions. [ABSTRACT FROM AUTHOR]

During marine hydrate production, some potentially undesirable consequences of casing strength failure are prone to occur. However, conventional methods used in casing mechanical analysis is unable to handle casing failure accurately, as the macro constitutive model employed in current simulation could not reflect the real stress-strain relationship of hydrate reservoir. This paper aimed at proposing an experimental method to investigate dynamic mechanical responses of casing during hydrate decomposition based on the self-developed experiment system, in which overlying stress was simulated to construct a similar mechanical environment of casing. By conducting laboratory-scale tests, the effect of overlying stress on casing mechanical behaviors could be revealed, and comparative analysis of casing axial stress obtained from experiment and simulation was carried out. Experimental results indicated that axial stress and extrusion pressure of casing increased 20.91 % and 7.18 % with the increase of overlying stress, respectively. Casing axial stress increased first and then gradually reached stable in different depressurization stage, which agreed well with the results calculated by numerical simulation. The maximum error between experimental data and simulation results was 11.7 %, 11.1 % and 10.2 % in different depressurization stage, respectively. The proposed approach could provide reference for casing safety design of natural gas hydrate production. • An experimental method to research dynamic mechanical responses of casing during hydrate decomposition was proposed. • A novel experiment of hydrate formation and decomposition considering overlying stress was conducted. • The influence of overlying stress on casing mechanical behaviors could be revealed. • Comparative analysis of casing axial stress obtained from experiment and simulation was carried out. [ABSTRACT FROM AUTHOR]

The surge motion of the floating platform can affect the hydrodynamic performance and wake characteristics of tidal turbines, and variable speed control may be able to improve this issue. In this paper, CFD methods are utilized to investigate the variation of power coefficient, thrust coefficient, and wake flow for fixed speed and variable speed-controlled (fixed tip speed ratio) tidal turbines at different surge amplitudes and periods. The results show that under the same conditions, the variable speed control can significantly increase the mean output power of the tidal turbine compared with the fixed speed, and increase the fluctuation amplitude of the power coefficient and thrust coefficient. There is an obvious wake interaction phenomenon in the surge motion, and the variable speed control can avoid blade stall in the surge motion and improve the hydrodynamic performance of the tidal turbine. The near-field wake velocity deficit for the variable speed control is severe, but the wake recovery is much faster, and the recovery of the far-field wake is essentially the same as for the fixed speed. In addition, the variable speed control is conducive to cutting the intensity of the wake vortex, accelerating the dissipation of the wake vortex, and promoting the recovery of the wake flow field. Taken together, under the same surge conditions, variable speed control improves the generation efficiency and promotes wake instability and vortex interactions, which have a positive effect on wake recovery. • The hydrodynamics of the tidal turbine with variable speed control exhibit more pronounced fluctuations under surge motion. • Variable speed control increases the mean power of the tidal turbine under surge motion compared to a fixed speed. • Heavy near-field wake velocity deficit but faster wake velocity recovery for the tidal turbine with variable speed control. • Variable speed control facilitates reduce vortex ring strength and accelerates wake vortex dissipation. [ABSTRACT FROM AUTHOR]

Hydrogen is considered a key energy vector and carrier for the decarbonization of global energy systems. However, the economics of green hydrogen systems hinder their widespread application. This paper presents a techno-economic analysis of a green hydrogen production system using high-temperature water electrolysis integrated with a concentrated solar power system (CSP-SOEC) for Western Australia. Real-time solar resource data with 30-min resolution for a typical meteorological year were used to assess the performance of the entire system. The intermittent nature of solar resources is accounted for by integrating the system with a thermal energy storage medium and performing the analysis in off-design mode. The validity of the electrolysis stack model is crucial for overall system performance, which was confirmed through experimental testing conducted on a 15-cell stack. The system was designed to generate a 1 MWe output, and the results showed that a field area of 29,000 m2 and thermal energy storage capacity of 382,500 kWh can fulfil the design criteria. The system generates 0.86 tonne/day of hydrogen at a cost of 8.87 US$/kg-H 2 with a solar-to-hydrogen efficiency of 13.80 %. The cost breakdown revealed that the storage medium has the most significant contribution. Moreover, the sensitivity of the system to the production capacity was analyzed, which showed that larger-scale hydrogen production systems have the potential to further reduce the cost. An 8 MWe system has the capacity to produce 7.18 tonne/day of hydrogen at a cost of 6.1 US$/kg-H 2. The molten salt is currently utilized only 39.3 % for the hydrogen production process. To optimize resource utilization, a cogeneration system is devised and assessed for simultaneous steam and hydrogen production. The results reveal that the cogeneration system can achieve an LCOH reduction of 9 % by reaching 8.07 US$/kg-H 2. These findings are invaluable for academic and industry stakeholders in making informed decisions and fostering the green hydrogen sector in Australia. • A techno economic assessment of green H 2 production for Australia is done. • The electrolysis model is corroborated using experimental results. • The cost of green H 2 for such system in Australia is 8.87 US$/kg-H 2. • Higher production capacity favors the system economics. • The cogeneration system reduces the cost by 9 %. [ABSTRACT FROM AUTHOR]

With increasing volatility in natural gas markets and the need for residential heat, research for alternative thermal systems based on green hydrogen-based fuels is necessary. Massive implementation of hydrogen in the current fleet presents a challenge for the decarbonisation of conventional thermal processes. This paper presents the integration of green methanol from a seasonal thermochemical energy storage system (TCES) coupled with district heating networks (DHN). It allows for a solar-based storage and low-temperature heat generation from the exothermic discharge reaction. The system uses concentrated solar thermal energy to decompose methanol into syngas at moderate temperatures (<315 °C), reducing GHG emissions from combustion to meet residential heat. Its potential is assessed through an analysis of 458 rural municipalities in Spain, achieving a substitution of 3.1 % of the natural gas demand. The system allows the production of green fuels from syngas using an open-loop approach, offering new market pathways in primary sector. In a closed-loop configuration, roundtrip efficiencies exceeding 60 % and chemical conversion efficiencies exceeding 70 % are reached, resulting in a global efficiency of 12 % for the DHN and methanol-TCES. The results show a levelized cost of stored energy highly competitive with other storage systems (<200€/MWh), given its simplicity. • A novel district heating network integration with methanol TCES is proposed. • Round-trip above 65 % and an overall efficiency of 11.6 % were achieved. • Analysis of presented DH-TCES in 499 rural municipalities in Spain. • Competitive levelized storage price values of €150/MWh was obtained. [ABSTRACT FROM AUTHOR]

Transportation is a major cause of energy consumption and emissions which can be reduced by optimizing routings and using alternative modes of transport. This paper relates to the strategic design of multimodal transportation networks. It presents a general model of green vehicle routing problems that supports strategic decision-making by identifying optimal solutions and provides data on costs and emissions. Three general linear programming models were developed that optimize multimodal distribution networks that could be applied in many industries. Model I evaluates carbon emissions; model II assesses carbon emissions and capacity constraints; and model III establishes total costs including transportation, handling, storage, fuel and carbon costs. Thailand is the third largest world sugar exporter in the world and is piloting carbon pricing, which will affect energy intensive industries, including the sugar industry. The models are applied using data obtained from a collaborating company. The research contributed to practice by informing managerial decisions relating to the export of sugar from the factory. This included evaluating the possible use of a dry port with rail connections, which could reduce transportation and carbon costs by 54.3 % and facilitate the building of another factory to increase exports. • LP models optimized outbound logistics networks with multiple modes of transport. • Minimized CO 2 emissions and total costs including transportation and carbon costs. • Modelling multimodal distribution networks including road, water, rail and dry port. • Used real data and supported strategic decision making for a Thai sugar company. • Provides a comprehensive review of LP models for optimizing distribution network. [ABSTRACT FROM AUTHOR]

This paper establishes the equivalent density and equivalent P-wave velocity models of coal bodies with different water saturation, and investigates the P-wave propagation evolution law of coal bodies with different water saturation from the perspective of numerical simulation. When the experimental coal samples continue to use high-pressure water after reaching the natural saturation state, the water saturation can be further increased, and the increase in water saturation is larger, and the corresponding P-wave velocity also appears to be increased more substantially, and this phenomenon also occurs in numerical simulation results, which indicates that the P-wave velocity will have an obvious increasing trend when the coal body is from nearly saturated to fully saturated. The experimental and numerical simulation results show that the P-wave velocity of coal samples increases with the water saturation degree in a similar exponential function, which further verifies the reasonableness and feasibility of the modelling based on the assumption of gas-liquid homogeneous medium. In addition, the propagation of P-wave in coal samples is affected by the pore and fracture structure, and the pore and fracture with small pore size has less influence on the P-wave, and vice versa. • Reveals the P-wave velocity evolution law from near saturation to full saturation in a coal body. • Equivalent density and equivalent P-wave velocity modelling of different water saturation were developed. • Numerical method for P-wave propagation in coals with different water saturations is realised. [ABSTRACT FROM AUTHOR]

Promoting energy transformation in rural areas, building a Rural Integrated Energy System, and developing multi-energy complementary and comprehensive utilization of rural energy in accordance with local conditions are important paths to support global low-carbon transformation and rural revitalization. However, how to consider the coupling synergy of multiple devices and plan the capacity of multiple devices in rural energy systems in an economical, low-carbon, and reliable manner is an urgent issue facing the development of Rural Integrated Energy System. This paper proposes a bi-level programming optimization method of Rural Integrated Energy System based on coupling coordination degree of energy equipment. Firstly, a multi-dimensional rural user energy utilization framework is designed that comprehensively considers agriculture, animal husbandry, industry, and lifestyle. Secondly, the bi-level optimization model of the Rural Integrated Energy System based on the coupling coordination degree of energy equipment is constructed. The upper-level planning model puts forward the equipment coupling coordination degree, and determines the equipment selection scheme for different types of Rural Integrated Energy System planning. The lower-level planning model considers economy, low carbon, and reliability, and uses the improved multi-objective Harris Hawk optimization algorithm to solve the capacity of different energy equipment in the Rural Integrated Energy System. Finally, the effectiveness and scientific nature of the model and method proposed in this paper are verified by the Integrated Energy Systems of a rural area in the north of China. In addition, by comparing the equipment selection scheme with the traditional method, it can be found that the optimization scheme in this paper that considers the coupling coordination of energy equipment reduces the annualized total cost by 29.34 %, reduces the annual carbon emissions by 60.43 %, and increases the reliability by 15.85 %. The collaborative planning of energy economy, low carbon energy and reliable energy has been realized. • A framework for the utilization of energy by multiple users in rural areas was designed. • A bi-level programming optimization method of RIES based on coupling coordination degree of energy equipment was proposed. • An improved multi-objective Harris Hawk optimization was constructed. [ABSTRACT FROM AUTHOR]

Electricity market-oriented reform and carbon emissions trading, as the main policies implemented in China's power sector, have a profound impact on China's green and low-carbon transition by promoting resource allocation efficiency and internalizing external environmental costs, respectively. Therefore, this paper constructed a dynamic recursive CGE model to measure the energy, economic, and environmental impacts of carbon emissions trading and electricity market-oriented reform from electricity market structure and pricing mechanism. The results show that the two policies have a synergistic effect on industrial structure optimization, energy consumption structure optimization, and carbon emission reduction. Specifically, the share of value-added of the tertiary sector, the growth rate of coal consumption, the share of thermal power consumption, and carbon emissions in 2030 are reduced by 5.70 %, 18.29 %, 2.55 %, and 2.838 trillion tons, respectively, compared with the scenario without carbon emissions trading and electricity market-oriented reform. However, imperfectly competitive market structures outperform perfectly competitive market structures in energy consumption structure and carbon emission reduction. Electricity price regulation is also more beneficial to GDP growth and increase in residential disposable income compared to price liberalization. To this end, returning emission trading scheme revenues to residents can effectively mitigate the adverse impacts caused by market-oriented reforms. • EMR is involved in market structure, pricing mechanisms, and market regulation. • EMR and CET have a synergistic effect on carbon reduction. • Energy consumption structure is better in imperfectly competitive market. • Electricity price regulation is more beneficial to GDP growth. • Returning ETS revenue to residents increases per capita disposable income. [ABSTRACT FROM AUTHOR]

Accurate prediction of energy prices is crucial to the development of energy security and environmental policies in various countries. This paper proposes a novel multi-step prediction hybrid model with genetic algorithm for variational mode decomposition, improved complete ensemble empirical modal decomposition with adaptive noise, bidirectional gated recurrent unit, temporal convolutional network, and multi-layer perceptron (GVMD-ICEEMDAN-BIGRU-TCN-MLP) for predicting carbon and natural gas futures prices. First genetic algorithm (GA) is used to fix the parameters of VMD model, the carbon and natural gas prices are decomposed into subsequences. Then the difference between the original series and the VMD after decomposition is further decomposed into subseries using ICEEMDAN. Next, the highest frequency series is predicted using the MLP model, and other subsequences are predicted using the BIGRU-TCN model. Finally, each predicted value is added linearly to determine the final result of steps 1, 3, and 5 of the entire forecasting process. According to the experimental results, it is shown that the model has lower prediction errors than the comparison model under mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and modified Diebold-Mariano test (MDM). The good prediction results of the novel hybrid model are demonstrated in multi-step ahead integrated prediction experiments, especially in the experiments with 1-step ahead prediction, as well as in the experiments with varying training ratios. • GVMD-ICEEMDAN-BIGRU-TCN-MLP model is used to forecast energy prices. • ICEEMDAN is used to further decompose the residuals. • Hierarchical prediction based on subsequence features is better. • Multi-step forecasting integrated study of energy prices. [ABSTRACT FROM AUTHOR]

In high-altitude areas, suppressing the flicker of methane laminar diffusion flame and reducing carbon emissions simultaneously is a challenge. This paper proposes a combustion strategy involving sub-atmospheric pressure and ammonia mixing. Through high-speed imaging and schlieren techniques, the study explores the flame flicker patterns under mixing ratios of 0–60% ammonia at 0.5–1.0 atm, as well as the structural and dynamic characteristics of shear layers and vortices. The stable combustion mechanism of sub-atmospheric pressure and ammonia mixing is also analyzed. It was found that reducing the pressure reduced the flame Richardson number, eased the formation of vortices and weakened the interaction with the flame. The heat release rate of the flame decreases after mixing ammonia, which leads to a decrease in the density gradient of the shear layer and also slows down the formation of vortices. The characteristic values of flicker were quantified. Decreasing the pressure, the flicker frequency decreases. Increasing the ammonia mixing ratio, the flicker frequency increases and the oscillation amplitude shows a highly nonlinear decreasing trend. It is verified that the flicker frequency still satisfies the function form of the Strouhal-Froude number under sub-atmospheric pressure and ammonia mixed combustion. • Established a combustion chamber capable of simulating plateau areas' pressure. • Provided vortex characteristic information through high-speed Schlieren imaging. • Complete suppression of flame flicker observed at 0.5 atm/50% ammonia mixture. • Ammonia mixture can both reduce carbon emissions and suppress flame flicker. • Pressure reduction and ammonia mixture alter density gradients in the shear layer. [ABSTRACT FROM AUTHOR]

Residential electric vehicle charging station integrated with photovoltaic and energy storage represents a burgeoning paradigm for the advancement of future charging infrastructures. This paper investigates its planning problem considering multiple load demand response and their uncertainties. First, a hybrid time series and Kalman Filter model is proposed for photovoltaic output prediction. Second, an orderly charging model and an incentive scheduling model are developed for electric vehicles to facilitate both price-based and incentive-based demand responses. Third, to address uncertainties in user response behavior, consumer psychology theory is applied to construct fuzzy response models for both charging and residential loads. Finally, a multi-objective capacity allocation model is constructed and optimized from the perspectives of economy, environment and safety. The simulation case studies the impact of different demand response strategies and their uncertainties on the planning results. The findings indicate that implementing multiple demand response strategies significantly increases annual revenue by 295.82 %, while reducing carbon emissions and power fluctuations by 16.48 % and 44.27 %, respectively. • A multi-objective optimal allocation model is proposed. • Multiple demand response strategies and their uncertainties are considered. • Hybrid time series and Kalman Filter is used for photovoltaic output prediction. • Orderly charging influences more than the incentive scheduling strategy. [ABSTRACT FROM AUTHOR]

The synergistic operation of integrated electricity and natural gas systems (IEGS) in the presence of wind power necessitates a distributed optimization framework that ensures information privacy. However, incorporating wind power penetration leads to distributed optimization problems including uncertainty and unstable distributed algorithm's convergence. With the increasing proportion of wind power in IEGS, fluctuating wind power penetration highly affects the convergence of distributed optimization solutions, resulting in uncontrollable optimization time of the distributed algorithm. Accordingly, this paper investigates the impact of wind fluctuation and uncertainty on distributed IEGS optimization and proposes a novel fast distributed co-optimization framework. Specifically, an adaptive alternating direction method of multipliers (ADMM) is developed to accommodate wind fluctuation. Based on designed rules, the penalty parameter is updated in every step to maximize the gradient of the optimization objective. The experiments are conducted using real wind power generation data sourced from a wind farm in Australia and a classical IEGS framework composed of the IEEE 24-bus electricity system and a 12-node natural gas system. Compared to original ADMM and residual balancing ADMM, the proposed framework achieves an average reduction of 0–91.4% in the number of iterative steps for multiple solution iterations across various scenarios, with a corresponding decrease in the standard deviation by 13.8%–93.0%. • Fast and stable convergence of IEGS distributed optimization problem. • ADMM with an adaptive penalty parameter guarantees the convergence. • Update rate further improves the sensibility of the penalty parameter. • Mean value and standard deviation of running iterations significantly decrease. [ABSTRACT FROM AUTHOR]

Currently, the worldwide implementation of the Montreal Protocol has accelerated the development of low GWP refrigerants. R290 as an alternative refrigerant with low GWP has great advantages over R134a for heat pump applications. However, the use of expansion valve throttling in the heat pump cycle has a large irreversible loss. Therefore, an ejector-enhanced sub-cooler vapor injection heat pump cycle (ESVIC) with R290 for water heating is proposed in this paper to improve the energy efficiency by applying ejector. The thermodynamic models are established based on the 4E (energy, exergy, economic and environmental) analysis to compare its performance with the basic ejector-enhanced heat pump cycle (BEEC) and the sub-cooler vapor injection heat pump cycle (SVIC). The energy analysis results show that compared with the BEEC and SVIC, the volumetric heating capacity of the ESVIC is improved by 42.0 % and 21.2 %, and the heating coefficient of performance (COP h) of the ESVIC is improved by 27.6 % and 5.3 %, respectively. The exergy analysis results indicate that in BEEC, SVIC and ESVIC the exergy destruction of the expansion valves is 0.3 %, 13.5 % and 0.8 %, respectively. It is indicated that the adoption of ejector can significantly decrease the exergy destruction of expansion valves. Moreover, the environmental analysis results reveal that the carbon emissions of ESVIC is 21.5 % and 5.0 % lower than those of BEEC and SVIC, respectively. Besides, for the ESVIC system, the carbon emissions of the life cycle climate performance when using R290 are 9.9 % lower than that of using R134a, indicating that the R290 is a potential substitute for R134a in heat pump applications. The economic analysis results demonstrate that the unit exergy production costs of ESVIC is 21.6 % and 5.1 % lower than BEEC and SVIC, respectively. • An ejector-enhanced R290 heat pump cycle with a sub-cooler is proposed. • Energy, exergy, economic and environmental analysis models are established. • The impact of key operation parameters on the cycle performance is discussed. • The 4E performance of the ESVIC is batter than the BEEC and the SVIC. [ABSTRACT FROM AUTHOR]

Accurate photovoltaic (PV) power forecast is crucial to power systems. Solar irradiance forecast is the fundamentals of PV power forecast. Current researches merely focus on improving the forecast accuracy. There hasn't been comprehensive study on the spatial-temporal characteristics of solar irradiance forecast errors. Thus, this paper analyzed the error characteristics of solar irradiance forecast provided by European Centre for Medium-Range Weather Forecasts (ECMWF), one of the most accurate numerical weather prediction (NWP) products. ECMWF's forecasts were compared with ERA5 solar irradiance reanalysis data to reveal the error distribution characteristics. Four-year (2017–2020) data from the geographical region bounded by 63°N, −126°W, 21°S, and 36°E (covering most parts of Europe and North America) were studied. Experiments show that solar irradiance forecast errors peak at noon of the local time. Furthermore, correlations between solar irradiance forecast errors and other weather variables were also revealed. Experiments show that solar irradiance forecast errors have negative correlation with low cloud cover forecast errors and positive correlation with air temperature forecast errors. The possible reasons for the correlation relationship were also analyzed in detail. This paper systematically reveals the spatial-temporal characteristics of solar irradiance forecast errors and provides a useful guideline for solar PV system operation. • Error distribution of ECMWF's solar irradiance forecast in Europe and North America is revealed. • Solar irradiance forecast errors in Europe are smaller than those in North America. • Solar irradiance forecast errors peak at noon of local time. • Solar irradiance forecast errors have slight seasonal and monthly differences. • Correlation between solar irradiance forecast errors and other weather variables is revealed. [ABSTRACT FROM AUTHOR]

This paper compares the macroeconomic impacts of different decarbonization storylines until 2050 using two Computable General Equilibrium models. The modeling shocks are harmonized across models to simulate four decarbonization scenarios through three main drivers: technical development, societal attitude, and policies. The study explores the contribution of each of these drivers to the European decarbonization. The results show that the decarbonization scenarios have moderate effects on GDP; decarbonization scenarios rather result in sectoral shifts. The impact of temperature on labor productivity minimally alters expected growth levels, since larger differences in temperature between the scenarios are only expected to occur in 2100, not yet in 2050. Electricity demand is increasing in all scenarios, particularly with stronger political guidance leading to additional tax and subsidy measures to encourage the use of electricity and discourage fossil-based fuels. When society is a driving factor, it is found that circular business models result in an increase in the service industry but might have a slightly negative impact on overall growth due to stronger spillover effects linked to the manufacturing industry. Technology appears to be the only driver to facilitate decarbonization while maintaining steady economic growth. • We address the macroeconomic effects of five climate scenarios using two CGE models. • We focus on the effects of changes in society, technology, and policy. • We incorporate climate change effects through labor productivity. • Effect of decarbonization on GDP is moderate under given technological improvement. • Decarbonization scenarios result in relatively large sectoral shifts. [ABSTRACT FROM AUTHOR]

Large-scale new energy access to the power grid poses significant challenges to its stable operation. Differentiated user-side power consumption patterns further widen peak-valley differences in power demand. This paper focuses on operation scheduling problems of virtual power plants with coordinated optimization of diverse flexible loads and new energy, through efficient aggregation and optimization control of new energy and demand-side resources, improving power supply and demand mismatch. Firstly, Long Short-Term Memory and Latin Hypercube Sampling were employed for predicting the day-ahead output of wind and photovoltaic power. Secondly, wind and photovoltaic power, batteries and a pumped storage plant were aggregated into a virtual power plant, and the day-ahead optimization scheduling model was constructed considering system operation costs, energy curtailment costs and demand response costs. Finally, a simulation analysis was conducted. The results show that when large-scale new energy accesses to the power grid, traditional "Generation varies with Load" regulation modes will cause massive energy waste, while the "Generation-Load Interaction" regulation mode can achieve the linkage optimization between the generation side and the demand side, enhancing the system acceptance of new energy. Demand response can optimize users' power consumption behaviors, reducing the charging costs by 52.13 % and heating costs by 0.84 %. • "Wind-Photovoltaic-Battery-Pumped Storage" virtual power plant model. • Aggregation and control of new energy and demand-side resources. • Coordinated optimization of "Generation-Load-Storage". • Cost-benefits evaluation of participating entities from a whole-part perspective. [ABSTRACT FROM AUTHOR]

The safety of battery packs is greatly affected by individual abnormal cells. However, it is challenging to diagnose abnormal aging batteries in the early stages due to the low abnormality rate and imperceptible initial performance deviations. This paper proposes a feature engineering and deep learning (DL)-based method for abnormal aging prognosis and end-of-life (EOL) prediction. The mathematical model of dimensionless indicators (DIs) is applied to partial voltage-capacity (V-Q) of one cycle to construct the optimal DIs with high sensitivity to abnormal aging. Taking the optimal DIs and partial V-Q as inputs, an abnormal aging prognosis model is established, and an ablation study is designed to verify the necessity of the selected DIs. Finally, the gated recurrent unit (GRU) is utilized to establish EOL prediction models for normal and abnormal degradation batteries, respectively. The proposed method is verified by a Lithium Cobalt Oxide (LiCoO 2) battery dataset. The results indicate that the 100% prognosis of abnormal degradation batteries is achieved and the EOL prediction accuracy is less than 3.7%. This work highlights the rapid abnormal battery detection using data of one cycle without excessive battery testing, which contributes to the rational deployment of batteries and reduces the probability of failures during operation. • The optimal DI set with high sensitivity to battery abnormal aging is constructed. • The abnormal aging batteries are detected before deployment based on CNN. • The EOL prediction is realized based on CNN-GRU. [ABSTRACT FROM AUTHOR]

As main by-product in iron and steel industry, high value-added utilization of blast furnace slag had received extensive attention. In this paper, a novelty technology was proposed for using blast furnace slag as raw material to obtain excellent CO 2 adsorbent-layered double oxides. The characteristics of layered double hydroxides and layered double oxides were investigated in detailed. Most notably, the laminate structure of layered double hydroxides collapsed and the pore structure, particle size distribution and functional groups of the sample had been greatly changed. The layered double oxides were occupied by phases of CaO, Ca 12 Al 14 O 32 Cl 2 and MgO. The transformation mechanism of the layered double oxides preparation was obtained. In addition, CO 2 concentration and temperature influencing on CO 2 adsorption of layered double oxides were studied and established a suitable kinetic model. The optimal kinetic mechanism model of CO 2 adsorption by layered double oxides was PSO. The activation energy and pre-exponential factors were 56.88 kJ‧mol−1 and 12.26 min−1, respectively. Ultimately, CO 2 adsorption capacity comparison and preliminary economic evaluation were conducted to assess the industrial feasibility of this technology. This work achieved the goals between CO 2 reduction and high value-added utilization of solid waste in iron and steel industry. [Display omitted] • Blast furnace slag used for high value-added CO 2 capture technology. • Successful transformation of slag-derived hydroxides into effective CO 2 adsorbent. • Detailed investigation of changes in structure and characteristics of the adsorbent. • Achieved CO 2 reduction and waste utilization in the iron and steel industry. [ABSTRACT FROM AUTHOR]

The regenerator plays a crucial role in determining the energy efficiency of green liquid desiccant air-conditioning systems. This paper presents a novel air gap membrane distillation regenerator featuring heat recovery and portable water production. Based on a validated numerical model, performance evaluation of the regenerator combined with an external heat exchanger is conducted in terms of regeneration capacity (RC) and energy consumption (Q in). The comparison of flow patterns demonstrates that the counter-flow has a slightly higher RC , while the parallel-flow has a significantly lower Q in and is therefore preferable from the perspective of COP. The effects of key parameters including feed and coolant desiccant inlet temperatures (T f,in & T c,in), mass flow rate (m dil), membrane length (L), air gap thickness (δ ag), desiccant concentration (X dil) and solution heat exchanger effectiveness (ε she) on the performance are investigated. The results show that T f,in is the dominant factor on performance compared to T c,in and m dil , and increasing L leads to a marked growth in both RC and COP regardless of the raised Q in. Additionally, it is found that narrowing the air gap from 3 mm to 1 mm can improve RC and COP by 69.9 % and 50.1 % respectively. As for ε she , it has no influence on RC but remarkable impacts on Q in and COP. Moreover, the heat recovery by the coolant channel is proved to save nearly 8 % of energy input under the involved conditions. Finally, the NSGA-Ⅱ based multi-objective optimization is performed to obtain the Pareto front for maximizing RC and minimizing Q in. And the optimal solution through TOPSIS decision-making offers a RC = 975.8 g/h and a Q in = 1938 W. The findings provide a basis for design and application of the novel regenerator. • A novel air gap membrane distillation liquid desiccant regenerator is investigated. • Parallel-flow pattern is superior to counter-flow pattern in terms of energy efficiency. • Heat recovery by the regenerator can achieve about 8 % energy savings. • Effects of various design and operation parameters on the performance are explored. • Trade-off between regeneration capacity and energy consumption is realized by NSGA-Ⅱ. [ABSTRACT FROM AUTHOR]

By purchasing emission reduction equipment or reducing production scale, it is possible to effectively decrease CO 2. Therefore, understanding the marginal abatement cost (MAC) associated with these methods is crucial for making decisions. However, previous studies using the directional distance function (DDF) approach often mis-specified production functions and neglected data noise. They also assumed decision-making units (DMUs) to be on the production frontier and used proportional changes in inputs and outputs as abatement paths. This paper addresses these limitations by developing the convex quantile non-radial directional distance function (CQR-NDDF) method, which estimates the MAC of CO 2 and determines optimal abatement paths for DMUs without assuming a specific production function, employing linear programming techniques. Applying this method to 30 provinces in mainland China from 2011 to 2019, the study finds that China's CO 2 MAC increased from 182 to 247 yuan/ton. The lowest-cost abatement path varies by province and time. The club convergence and ordered probit model are employed to conclude that the second industry and urbanization increase the MAC of CO 2 , while factors such as foreign direct investment, openness level, and human capital decrease the MAC. Moreover, the CQR-NDDF method yields significantly lower MAC estimates than the NDDF method. In conclusion, this paper provides new insights into China's CO 2 MAC, emphasizing the importance of considering inefficiency and data noise in MAC estimation. We anticipate that utilizing CO 2 MAC as a benchmark for carbon trading market prices could lead to an increase in prices within China's carbon trading market. • A novel method for estimating CO 2 MAC has been developed. • Ignoring inefficiency will result in an overestimation of CO 2 MAC. • Three different emission reduction pathways are considered in the estimation. • The heterogeneity of the lowest cost abatement pathways has been identified. [ABSTRACT FROM AUTHOR]

Based on Green Slow model, this paper builds a framework to achieve carbon peak driven by manufacturing, energy-conservation and energy substitution technology concurrently.Through a panel dataset from 2000 to 2018 at the province level, we find that the increasing carbon emissions effects from manufacturing technology are primarily due to increased levels of disposable income resulting from technological advances applied in production, which increases the demand for energy consumption and results in the emission of more carbon dioxide. China's carbon emissions have been significantly reduced by energy-conservation and energy-substitution technology, which reduce the amount of fossil energy used in the generation of clean energy while increasing its share. These results suggest that to achieve carbon peak and further improve the carbon reduction effect of energy-conservation and fossil fuel alternatives technologies, local governments should strive to create an enabling environment for technology development. • Theoretical analysis for achieving carbon peak is conducted. • Manufacturing technology drives carbon emission. • Enlarging the production scale is the reason contributes to the increased carbon emissions. • Energy conservation and substitution technology mainly cause this emissions reduction. [ABSTRACT FROM AUTHOR]

Highly dense and integrated data centers face key challenges of realizing efficient cooling and improved energy efficiency. To overcome these challenges, this study experimentally investigated the flow and heat transfer characteristics of single-phase immersion liquid cooling (SPILC) systems. The influence of two opposite coolant flow directions on the SPILC performance was examined. Furthermore, a correlation mechanism between coolant thermal properties and SPILC performance was established, along with a control chart for regulating the working conditions of SPILC systems. The results indicate that compared to a pro-gravity flow, an anti-gravity flow scheme reduces the chip case temperature and thermal resistance by 33.8% and 55.6%, respectively, while decreasing the power usage effectiveness (PUE) by 1.4%. Using coolant with the lowest viscosity reduces the chip case temperature and thermal resistance by 9.3% and 10.5%, respectively, while decreasing the PUE by 0.4%. Moreover, the cooling water temperature has a greater impact on the performance of SPILC systems than the volume flow rate of coolants. Additionally, this paper provides control charts for the cooling water temperature and coolant flow rate to improve the PUE while ensuring the safe operation of SPILC systems, with the highest chip temperature and total electricity consumption as indicators. • Immersion cooling performance of system-level data center servers is experimentally studied. • PUE of the anti-gravity flow decreases by 1.4% compared to the pro-gravity flow. • Correlation between coolant properties and immersion cooling performance is established. • Energy-efficient control charts for cooling water and immersion coolant are provided. [ABSTRACT FROM AUTHOR]

In the process of vaporizing water within the biogas-driven CCHP system based on chemical recuperation, a significant temperature disparity in heat transfer exists, leading to increased irreversible loss, and the methane conversion rate is low because the gas turbine exhaust gas is difficult to drive efficient methane conversion. This paper proposed a novel biogas-driven CCHP system based on chemical reinjection. Through the reinjection of a portion of the exhaust gas into the reactor, a self-thermal reforming reaction process is engineered, leading to a substantial enhancement in methane conversion rate. The heat matching of the heat exchange unit is optimized by the reheat air, and the power generation and cooling capacity of the system are improved. Notably, the electricity and cooling generation of the new system increased by 55.52 kW (6.20 %) and 542.49 kW (101.49 %), respectively, while the exergy efficiency increased by 8.31 %. The study delves into the influence of key parameters on the system thermal performance. During the reforming process, the exhaust gas split ratio has a significant impact on carbon deposition and methane conversion. When the methane is nearly completely converted, there is no significant change in electricity generation of the system. Excessive turbine inlet temperature is found to be unfavorable for improving the exergy efficiency of the new system from a holistic system perspective. Finally, a comprehensive comparison of the thermal performance between the new system and the reference system is conducted under various ambient temperatures. This study offer a new design scheme for the efficient utilization of biogas. • A novel biogas-driven CCHP system based on chemical reinjection is proposed. • Partial exhaust gas reinjection for self-heating of biogas reforming reaction. • The thermal efficiency and exergy efficiency increased by 28.39 % and 8.31 % in the new system. • The system is subjected to parameter analysis in combination with carbon deposition analysis. [ABSTRACT FROM AUTHOR]

The integration of smart grids with Advanced Metering Infrastructure (AMI) has bridged the realms of the Internet of Vehicles (IoV) and Electric Vehicles (EVs), yet challenges persist in managing EV Battery Range and charging infrastructure effectively. This paper presents an innovative IoV-based framework tailored for EVs, with a specific focus on forecasting electricity consumption in a Vehicle-to-Grid (V2G) scenario. By exploring the hurdles surrounding electric vehicle usage, the research lays the foundation for Electric Vehicles (EVs). The proposed IoV model optimizes IoT sensor utilization through a fog layer and employs a Back Propagation (BP) neural network for battery State of Charge (SoC) estimation, integrating Principal Component Analysis (PCA) for data dimensionality reduction. Leveraging substantial computing capabilities, the cloud layer predicts Electricity Consumption Data (ECD) associated with EVs in V2G scenarios. Performance evaluation metrics like Akaike Information Criteria (AIC), Mean Error (ME), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE) are assessed across state-of-the-art forecasting algorithms. Incorporating EV potential into the optimal model reveals a significant 10% reduction in electricity demand. This research advances IoV-based frameworks, offering insights to enhance EV efficiency within the broader energy infrastructure. • An innovative Internet of Vehicles (IoV) framework for depicting electricity demand on the demand side in V2G scenarios. • In-depth experimentation and evaluation of the state-of-the-art approaches of clustering and forecasting. • The devised model successfully projected a 10% decrease in demand side for consumers. [ABSTRACT FROM AUTHOR]

This paper introduces a novel coupled model for real-time control of the point absorber wave energy converter (WEC) using parallelized deep reinforcement learning (DRL), where the WEC is situated within a numerical wave tank (NWT) built with the method of computational fluid dynamics (CFD). An in-house solver is developed to couple with the DRL and CFD to solve the interaction between WEC and the fluid environment. Validations on wave generation, wave-floater interaction, and power take-off (PTO) unit are carried out. Then, neglecting the detailed model for the PTO technologies, the DRL-based strategy dynamically adjusts the PTO force as a function of the wave features and floater motion. Based on the interaction data, the model-free DRL is outstanding in adaptability and robustness. Simulation results reveal that DRL control improves the wave energy absorption in irregular wave environments, resulting in improvement of 107.5 % compared to the resistive control, with better device protection performance than the model predictive control (MPC). An additional analysis of model-free characteristics of DRL demonstrates the optimization ability independent of floater modeling. This work is the first in-depth study of DRL control of WECs in CFD simulation, providing a more accurate simulation and an optimization process closer to the reality. • Real-time conversion performance of the point-absorber WEC under DRL control in irregular wave environments is studied. • A CFD-DRL coupled model is developed for the interaction between the DRL-control WEC and the numerical wave tank. • Accelerate the training with paralleled OpenFOAM solvers and emphasizing recent and prioritized experience replay buffer. • Further validate the generalization in the irregular-wave environment generated by JONSWAP spectrum. • Explore the model-free nature of DRL in the control strategy generation of WEC system. [ABSTRACT FROM AUTHOR]

The utilization of supercritical carbon dioxide (sCO 2) in the Brayton cycle presents several advantages, such as compact equipment, high efficiency, and rapid response time. The sCO 2 power cycle can be applied in coal-fired power, solar thermal power, and nuclear power systems. Over the past 10 years, many scholars have researched sCO 2 power systems. The compression (pressurization) process is a core thermodynamic process in sCO 2 power cycles, achieved through the use of compressors. Therefore, compressors are important components in sCO 2 power systems. This paper provides a summary of recent studies on compression processes and compressors for sCO 2 power systems. The impact of near-critical-point characteristics of the compression process on equipment and sCO 2 power cycle systems is discussed. The investigations of the performance parameters, design considerations, design methods, and performance prediction methods of sCO 2 compressors are reviewed. The typical research results on CO 2 condensation at the compressor inlet, the effects of operating conditions on compressor performance, as well as optimization of design parameters, are also summarized. Additionally, it provides a summary of sCO 2 compressor prototypes developed by research institutes worldwide and experimental studies. Finally, the current issues with sCO 2 compressors are addressed, and the main future research directions are proposed. This paper will contribute to the development of compressors and promote the acceleration of the commercialization of sCO 2 power systems. • Summary of recent studies on compression processes for sCO 2 power systems. • Near-critical-point characteristics of compression process is discussed. • Summary of compressor prototypes by research institutes and experimental studies. • Current issues and future research directions on sCO 2 compressors are presented. [ABSTRACT FROM AUTHOR]

The interaction between volatile and char is widespread in combustion. The effect of this interaction on the conversion of fuel-N to NO x is significant, but the mechanism remains to be comprehensively unveiled. Thus, in this paper, the NO and N 2 O conversion of nitrogen-containing biomass models (glutamate, glycine, phenylalanine) during combustion at high temperatures (800–1500 °C) is investigated using two combustion modes, separated combustion (in which volatile and char are burned separately) and coupled combustion (in which volatile and char are burned simultaneously), in an O 2 /Ar atmosphere. A new pathway for N 2 O formation resulting from the interaction between volatile and char is identified. At low temperatures, this interaction facilitates the conversion of fuel-N to N 2 O. For instance, during the separated combustion of glutamate at 800 °C, the conversion rates of fuel-N to N 2 O and NO are 26.3 % and 20.4 %, respectively. However, in coupled combustion, these conversion rates shift to 48.1 % for N 2 O and 3.6 % for NO. At high temperatures, this interaction promotes the conversion of fuel-N to NO. For instance, during the separated combustion and coupled combustion of glutamate at 1500 °C, the conversion rates of fuel-N to NO are 6.2 % and 16.6 %, respectively. Similar patterns are observed for the other two amino acids. In both combustion modes, the co-firing of cellulose, lignin, and hemicellulose with glutamic acid significantly suppresses the production of N 2 O. The conversion rate of N 2 O decreases by about 7 %–10 %, while the impact on NO release shows either a suppressive or promotive effect in different temperature intervals. These results play a crucial role in the development of efficient and clean combustion technology for biomass. [Display omitted] • A novel N 2 O formation pathway from volatile-char interaction has been identified. • Volatile-char interaction enhances NO formation at 1300–1500 °C. • Accurate volatile-NO/N 2 O and char-NO/N 2 O contributions were determined. • Co-firing with cellulose, lignin, and hemicellulose inhibits the formation of N 2 O. [ABSTRACT FROM AUTHOR]

This paper analyses the effect of energy poverty on respiratory health for a sample of 34 sub-Saharan African countries over the period 2000–2020. Using ordinary least squares (OLS), fixed effect, Driscoll-Kraay, Lewbel 2SLS, Kiviet and S-GMM, the results provide strong evidence of a negative and significant effect of access to electricity and access to clean energy for cooking on respiratory disease in sub-Saharan Africa. This result remained robust to changes in the variables of interest and to alternative estimation techniques. Furthermore, the results showed the existence of a non-linear relationship between energy indicators and respiratory diseases, including a U-shape and an N-shape. Based on these results, we encourage policy makers to better orient their energy policies towards the least polluting sectors in sub-Saharan Africa. In this way, the respiratory health of the inhabitants of sub-Saharan Africa can be improved. • This paper differs from existing work on energy poverty worldwide in the following respects: • It proposes a relevant determinant of respiratory health that has not been sufficiently studied in Sub-Saharan Africa. • It uses robust analytical methods to produce these estimates (Lewbel 2SLS and Kiviet). • It proposes useful policy recommendations for reducing energy poverty and reducing respiratory disease. [ABSTRACT FROM AUTHOR]

Current load forecasting methods struggle with the randomness and complexity of multiple loads in regional integrated energy systems, resulting in less accurate predictions. To address this problem, this paper proposes a novel two-phase decomposition hybrid forecasting model that combines complementary ensemble empirical mode decomposition, sample entropy, variational mode decomposition, and genetic algorithm-bidirectional long short term memory. The proposed approach begins by decomposing the load sequence into multiple intrinsic mode function components at different frequencies using complementary ensemble empirical mode decomposition. Afterward, the sample entropy values are calculated for each intrinsic mode function. The intrinsic mode function with the highest sample entropy value is subjected to a two-stage decomposition using the variational mode decomposition method. Through this technique, a series of stationary components is acquired. Subsequently, the bidirectional long short term memory model is optimized using the genetic algorithm, and the genetic algorithm-bidirectional long short term memory approach is employed to predict all the decomposed components. Finally, the prediction results are combined and reconstructed to obtain the final prediction value. Experimental results demonstrate the effective handling of non-stationary load sequences by the forecasting model, showcasing the highest level of accuracy in regional integrated energy system multiple loads forecasting. • CEEMD technology reduces the complexity of data samples. • The combine prediction model effectively improves the prediction accuracy. • Use grey relation analysis technique to determine the input of prediction model. • The improve sparrow optimization algorithm is utilized for parameter optimization. [ABSTRACT FROM AUTHOR]

Chiller systems hold a critical role in upholding comfort and energy efficiency within commercial buildings. Precise prediction of chiller energy consumption is imperative for operational optimization and the reduction of energy expenditures. This paper introduces an innovative methodology that integrates deep learning (DL), specifically Fixed Forward Neural Networks (FFNN), with Teaching-Learning-Based Optimization (TLBO) to enhance the accuracy of chiller energy consumption forecasts. Drawing on a diverse dataset from a commercial building, encompassing vital input parameters such as Chilled Water Rate, Building Load, Cooling Water Temperature, Humidity, and Dew Point, the study conducts a comprehensive comparison of metaheuristic algorithms (Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Barnacles Mating Optimizer (BMO), Harmony Search Algorithm (HSA), Differential Evolution (DE), Ant Colony Optimization (ACO), and the latest RIME algorithm). TLBO's adept navigation of the intricate parameter space of DL yields highly precise predictions for chiller energy consumption. The study's outcomes underscore TLBO's potential, along with other metaheuristics, in optimizing DL and refining energy management practices in commercial buildings. This research significantly contributes to the evolving discourse on the symbiosis between DL, particularly FFNNs, and metaheuristic optimization, offering a robust framework for chiller energy consumption prediction, thereby advancing sustainability and cost-effectiveness in building operations. • Hybrid TLBO-DL is used in predicting the chiller energy consumption. • Real data from a commercial building is used for training and testing of TLBO-DL. • TLBO-DL shows potential in predicting chiller energy consumption compared to others. [ABSTRACT FROM AUTHOR]

Sensor networks are playing an increasingly important role in modern buildings. With the growing size of building sensor networks and the increasing use of low-cost sensors, the accuracy and reliability of these sensor networks face challenges. Therefore, in-situ calibration of sensor networks is crucial to maintain data quality. Various state-of-the-art methods typically require meeting stringent conditions, such as reference sensors or co-located sensors, accurate physical models, and a large amount of operational data, limiting their applicability in some scenarios. This paper addresses a common issue in sensor calibration: the non-differential calibration issue in uncontrolled environments. We propose an in-situ calibration method based on virtual samples and Autoencoder. Virtual samples are generated through Monte Carlo sampling to ensure the completeness of sample information. Autoencoder autonomously establishes relationships within the sensor network, integrating sensor fault detection and calibration into one step. Offline experiments optimize methods, and online experiments are utilized for verification and analysis. The online experiments demonstrated that the proposed method achieved a calibration accuracy above 98.9 % for single and multiple sensor faults, with a calibration error below 3 %. Compared to the SoT methods, our approach consistently delivered superior performance, confirming its outstanding efficacy. • A method for in-situ sensor calibration based on virtual samples is proposed for the building thermal system. • Monte Carlo sampling is utilized for the generation of virtual fault samples. • Both single and multiple sensor faults were verified offline and online, respectively. [ABSTRACT FROM AUTHOR]

The development of copper-based catalysts with superior stability, activity, and reduced CO selectivity has been a prominent subject of research in the domain of hydrogen production via methanol steam reforming. In this paper, the bimetallic Ni–Cu catalysts supported by t-ZrO 2 were synthesized, with varying concentrations of copper phase being considered. The prepared catalysts were characterized by XRD, XPS, BET, ICP, FESEM, TEM, H 2 -TPR, EPR and N 2 O titration, and the results showed that appropriate copper phase concentration and nickel doping obviously decreased the crystal size and promoted the development of pore structure. With the condition of W/M = 3 and LHSV = 5.6 h−1, the catalytic activity revealed the methanol conversion and H 2 yield of CNZs were evaluated. The CNZ-4 exhibited optimal performance and stability in the MSR process, with a methanol conversion rate of 100 % and H 2 yield of 311 mmol g−1 h−1 at a temperature of 533 K. Given the results that in-situ DRIFTS tests showed, the generation of methyl formate as an intermediate was evaluated. Moreover, further DFT insights pointed out that the ternary surface model showed the best surface adsorption and reaction performance, which provided evidence for the high activity of CNZ catalyst at the molecular level. [Display omitted] • Bimetallic Ni–Cu catalysts supported by t-ZrO 2 were synthesized. • The H 2 yield of 311 mmol g−1 h−1 was obtained at 533 K. • The OH groups from water are crucial in MSR to form CO 2 promptly. • After 10 h MSR reaction at 593 K, about 0.7 % carbon deposition was detected. • The reasons for high catalytic activity were revealed via DFT calculations. [ABSTRACT FROM AUTHOR]

This paper presents the energy-saving potential of early-stage autonomous vehicles (AVs) by analyzing empirical AV driving records from a public road test. Vehicle-specific power-based microscopic energy consumption models are used to estimate the energy efficiency of the tested AVs, including light-duty vehicles and heavy-duty trucks. Overall, AVs do not necessarily save energy, largely depending on driving scenarios, such as traffic conditions and road types. On expressways, autonomous driving (AD) has an insignificant energy-saving effect (within 10 %) due to similar characteristics to human driving (HD). On urban roads with lower traffic speeds and more interventions from surrounding traffic, AD performs higher energy efficiency due to its capability to accelerate more smoothly by up to 60 %; however, frequent human disengagement generates additional energy consumption of 8%–40 %, especially on urban arterials. We further assessed the marginal effect of trip average speed and AD ratio on energy efficiency, illustrating varied relationships for different driving scenarios and power types. The study suggests a more moderate AD acceleration to elevate the energy-saving effect. Nevertheless, applying AD can hardly reduce the energy of early-stage AVs; instead, control methods to maintain an appropriate speed of the mixed traffic flow can help decrease the total energy consumption. • Empirical AV driving records from a large-scale real-world pilot were collected. • The energy-saving effect of early-stage autonomous vehicles (AVs) is demonstrated. • Expressway AV driving reduces energy use mainly from proper cruising setting. • Arterial AVs save energy due to less harsh acceleration while the effect varies. • Human disengagement reduces the energy-saving benefits due to unstable speed. [ABSTRACT FROM AUTHOR]

Integrating renewable energies into district heating systems has a large potential to reduce CO 2 -emissions in the heating sector. As district heating systems offer the possibility of incorporating renewable energies into the heat supply, new systems have to be built and the existing networks must be maintained. This study investigates ways to optimize existing district heating systems in order to ensure economic sustainability in the long-term. Previous case studies have elaborated on a variety of optimization measures. However, to date, these measures have neither been collected nor consistently assessed for a wider application range. Therefore, in the study presented here a system for assessing the ecological and economic benefits of optimization measures was developed and applied. The assessment method utilized showed that optimization of district heating consumer substations and adding of a central buffer storage tank has a high optimization potential in comparison to intermittent operation strategy, which has a significantly lower optimization potential. From this information and the transferability data, a district heating operator can determine which optimization measure should be prioritized, which is shown at the end of the paper on an example case. • A method was developed to assess benefits of optimizing district heating systems. • A survey was conducted to assess the ecological and economic value. • Optimization measures for DH systems were compared using the new method. • A guideline is provided to offer operators a suggested approach to take. [ABSTRACT FROM AUTHOR]

Power stations operating on PhotoVoltaic (PV) power generation are challenged by demand forecasting as PV power generation is random and intermittent. This paper presents an Efficient Shrinkage Temporal Convolutional Network (ESTCN) model, which combines the Temporal Convolutional Network (TCN) and an improved Deep Residual Shrinkage Network (DRSN) to forecast PV power output. First, the attention sub-network of the DRSN is improved, the fully connected layer of the sub-network is canceled, and feature extraction is done directly on the model features following global average pooling using a one-dimensional convolution to obtain cross-channel interaction and increased model efficiency. Next, an improved attention mechanism and adaptive soft thresholding are introduced into TCN to automatically determine the noise threshold to address the issue of information weight dispersion caused by redundant information in the input samples. By incorporating dilated causal convolution, attention module, soft thresholding, and residual connection, the ESTCN model is formed and is shown to enhance PV power output prediction with only a minimal increase in the number of parameters. The power station in Ningxia, China is adopted as a validation example with data taken from the year 2020. The fitting and prediction results of the ESTCN model are compared against the Convolutional Neural Network, Long Short-Term Memory, and TCN models. The ESTCN model yielded the following values of the evaluation metrics: Root Mean Square Error (RMSE) of 1.47 kW, Mean Absolute Error (MAE) of 0.79 kW, and coefficient of determination of 0.999. Compared to the TCN model, the prediction errors based on RMSE and MAE improved by 0.39 kW and 0.18 kW, respectively. Applying the ESTCN model to predict PV power generation of power stations in two regions in China, Ningxia and Xinjiang, shows the ESTCN model to be superior, scalable, and universal over other deep learning models. • Study the problem of photovoltaic power using a deep learning method. • A new Efficient Shrinkage Temporal Convolutional Network model is presented. • Attention sub-network of DRSN is improved by cancelling the fully connected layer. • Feature extraction is done on the model features using a one-dimensional convolution. • Improved attention mechanism and adaptive soft thresholding are introduced into TCN. [ABSTRACT FROM AUTHOR]

Prefabricated buildings (PB) are assembled using prefabricated components, known for their cost-effectiveness and high efficiency. They are seen as an effective means to achieving sustainable development goals and mitigating climate change. This paper examines the impact of prefabricated buildings on urban green total factor energy efficiency (GTFEE) using panel data from 278 cities in China. The analysis is conducted through the application of a super-efficiency slack-based measure (SBM) model and a difference-in-differences model. The results of the study indicate that the promotion of PB can significantly improve urban GTFEE; there is heterogeneity in the improvement of urban GTFEE by PB; the innovation effect, industrial effect and marketization effect of PB are the main ways to improve urban GTFEE. Specifically, PB exerts an innovation effect by promoting technological innovation. It influences consumer and investor demand, leading to increased activity in the secondary industry, thereby creating an industrial effect. Moreover, PB facilitates the marketization effect by phasing out outdated industries, encouraging autonomous enterprise innovation, and fostering the growth of new advanced enterprises. This study reveals the critical role of PB in urban GTFEE improvement. It clarifies its mechanism of action, which provides a reference for urban sustainable development strategies and energy efficiency. • Prefabricated buildings can improve green total-factor energy efficiency. • Urban green total-factor energy efficiency has been measured. • PB improves GTFEE through innovation, industry and marketization effect. [ABSTRACT FROM AUTHOR]

Achieving precision in the prediction of buildings' dynamic heat load is crucial for the advancement of smart heating systems. This research highlights the urgent need to enhance the accuracy of models predicting dynamic heat load. Through literature review, distinguished machine learning and regression algorithms were chosen to formulate prediction models. These models employ a data time-step adaptive strategy, a physics-guided loss function, and fundamental principles of heat transfer. Optimization algorithms of a mathematical nature were utilized to fine-tune the parameters and the framework of long short-term memory (LSTM) and multi-layer perceptron (MLP) models. An analytical comparison was undertaken between physics-guided models and those not guided by physics. Principal conclusions are: 1) Pelican optimization algorithm (POA)-LSTM model emerges as superior in heat load prediction accuracy of an office building, with percentage errors for actual and simulated datasets ranging from −6.7 % to 5.8 % and −5.2 %–4.5 %, respectively, and the mean absolute percentage error (MAPE) standing at 2.3 % and 1.3 %. 2) The linear regression model exhibits the lowest precision, with a MAPE of 17.5 % and 4.0 % for the 7-day prediction results in the actual and simulated datasets, respectively. These findings provide support for improving heat load prediction in heating systems. • The authors have implemented a total of 20 physically-guided models for predicting building thermal load, among which the POA-LSTM model demonstrates the highest accuracy. • The disparities in thermal load prediction accuracy between the physically-guided machine learning models and the physically-guided mathematical regression models have been identified. • The accuracy of thermal load prediction was compared between the five physical guidance models proposed in this paper and the five non-physical guidance models mentioned in other papers. [ABSTRACT FROM AUTHOR]

The solar chimney power plant (SCPP) provides the approach to achieving carbon neutrality goals. The power generation of the system decreases drastically due to the ambient crosswinds (ACWs). In this paper, three wind resistant structures, including blockage wall, radial partition wall (RPW), and porous wall are adopted to weaken the effects of the ACWs. In addition, the flow characteristics and power generation performance of each system are investigated in the first consideration of the 3D turbine by developing a numerical model. The results reveal that the negative effects caused by the ACWs on the system are attenuated by integrating the three structures. The adverse effect of weak winds on the SCPP is greatly weakened by integrating a blockage wall, but is not significant for robust winds. For SCPP with RPWs, the velocity is slightly increased due to the improved collector flow path by the RPWs at ACW = 0 m/s. The maximum power generation is 93.68 kW, an increase of 1.22 times over the SCPP. The uneven forces on the turbine blades due to the ACWs can be mitigated by integrating porous wall, enhancing turbine stability. In addition, the fluctuation of the power generation is less than 30 % under ACWs. • The performance of the SCPP is studied in open space. • Three structures are adopted to weaken the negative effects of the ACWs. • The effect of ACWs on the SCPP is weakened by integrating a blockage wall. • The maximum power generation increased by 22 % by integrating RPWs. • The fluctuation of the power generation is less than 30 % due to a porous wall. [ABSTRACT FROM AUTHOR]

Energy storage systems (ESS) address the uncertainty of renewable energy sources (RES) in renewable energy based isolated microgrids. One type of ESS is unable to provide 100 % supply reliability. Moreover, there are either excess energy or reliability issues due to a lack of energy management techniques. Therefore, this paper proposes hybrid RES (HRES) with multiple-ESS (MESS), including battery and hydrogen storage system (HSS). The techno-economic analysis and optimal design of HRES-based microgrids, which include wind turbine (WT), solar photovoltaic (PV), fuel cell (FC), electrolyzer, HSS, and battery energy storage (BES) are performed using HOMER. The design objectives are to minimize the net present cost (NPC) and cost of energy (COE) in the proposed HRES with MESS. The analysis is performed for a multi-energy storage system. The results show that WT/PV/FC/Electrolyzer/HSS/BES/converter is the optimal microgrid with the lowest NPC of $838832, $679605, and COE of 0.232 $/kWh, 0.189 $/kWh at capacity shortages of 0.0 % and 1.0 %, respectively. The excess energy and unmet loads are also the lowest for the system at the same capacity shortage. The sensitivity analysis shows the impact of uncertain techno-economical parameters on NPC and COE. • Optimal design of Microgrid with smaller dump load size and capital investment. • Analyze the impact of Multi Energy Storage System to minimize the cost of energy. • Techno-economic and sensitivity analys is identifying relationship to parameters. [ABSTRACT FROM AUTHOR]