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Because the carbon load inside a diesel particulate filters (DPF) affects the DPF regeneration, and the carbon load recognition is significant for the particulate matter (PM) emission control. It is necessary to investigate an on-board DPF carbon load recognition method because the carbon load cannot be directly measured by sensors. Aiming to build a DPF carbon load prediction model adopting the deep learning method, this paper proposes a DPF carbon load identification model based on different experimental parameters using a layered one dimension convolutional neural network (1D-CNN) method. To improve data validity, this paper adopts two data-processing methods. The data pre-processing adopts data splicing method to complete the construction of the original sample set, and the data after-processing uses wavelet packet transform method to establish the feature sample sets. The model adopts the optimal feature dataset constructed by three input parameters, i.e., temperature difference, pressure difference, and exhaust mass flow, and has both high training accuracy and test accuracy above 90 %. The pressure difference is the most important influencing input parameter, and the three-parameter sample set (Δ T + Δ P + Q) has great recognition accuracy and good model stability with the high training accuracy and test accuracy as well as less iteration. • A heavy-duty diesel engine was tested under various conditions of carbon load inside DPF. • The data processing was done by two methods: data splicing and wavelet packet transform. • We proposed a DPF carbon load identification model based on a one-dimension CNN method. • The model adopts 3 parameters, and the 3-parameter sample set has optimal performance. • The 1D-CNN model has both high training accuracy and test accuracy above 90 %. [ABSTRACT FROM AUTHOR]

Wind power is a critical source of renewable energy, but the variability of wind speed can impact the efficiency and reliability of wind power generation. To improve the accuracy of wind speed prediction, this paper proposes a novel system that combines deep learning and decomposition algorithms based on ensemble optimization methods. Firstly, the paper proposes an innovative hybrid modal decomposition (HMD) method that extracts accurate features from wind speed data using Singular Value Decomposition (SVD), modal number selection method, and Variational Mode Decomposition (VMD). Secondly, the variable screening method identifies relevant factors affecting wind speed as fixed inputs. Then, the stationary test method is used to identify the sequences of different features in the mode and establish different prediction models: a bidirectional long-term short-time neural network model (Bi-LSTM) model optimized by artificial hummingbird algorithm (AHA) is used for non-stationary sequences; an ARIMA model is used for stationary sequences. Data from Sotavento and Changma wind farms are used to test this forecasting system. Simulation experiments are carried out from multiple dimensions. The results show that all error indicators are significantly improved, and the proposed prediction system is better than other comparative forecasting schemes. • The research can be practically applied in the field of wind power generation. • The research can help improve the energy utilisation of wind power generation. • A new data decomposition algorithm called HMD is proposed. • A new prediction system based on ensemble optimization idea is proposed. • Combining AHA-optimized neural networks and ARIMA to predict wind speed. [ABSTRACT FROM AUTHOR]

With the appearance of digital computers and microprocessor technologies, the control systems used in nuclear reactors continue to transform from analog devices to digital ones in recent years. A heavy challenge is that applying the pre-existing intelligent load following control algorithms developed in the continuous-time domain to the current digital devices of the nuclear reactors may yield the so-called digital chattering effects, and even trigger system instability. To this end, this paper presents a discrete-time integral terminal sliding mode controller (DTITSMC) coupled with a discrete-time disturbance observer (DTDO) for the load following of a modular high-temperature gas-cooled reactor (MHTGR). The common continuous-time MHTGR model is first transformed into a discrete-time form with consideration of model uncertainties, exogenous disturbances, and discretization errors. Then, on the basis of this discrete-time MHTGR model, a DTDO that can estimate the lumped disturbances, composed of model uncertainties, exogenous disturbances, discretization errors, and unmeasured system states, is developed, subsequently being combined with the designed DTITSMC scheme in order to enhance the system robustness. The proposed DTITSMC scheme coupled with the DTDO is novel in that it can be directly implemented on the digital control system of the MHTGR, while at the same time providing an improved load following control performance, owing to the integration of the state-of-the-art sliding mode control techniques, backstepping control techniques, and the disturbance feedforward compensation supported by the disturbance observer techniques. Both the estimation error convergence of the DTDO and the overall system stability are theoretically proved by the Lyapunov stability theory in this paper. Finally, the feasibility and superiority of the proposed DTITSMC scheme coupled with the DTDO over the previous discrete-time linear sliding mode controller (DTLSMC) and discrete-time proportional–integral–derivative controller (DTPIDC) are further confirmed by simulation studies, where in the absence of lumped disturbances, the value of the accumulative absolute load error (AALE) produced by the proposed DTSMPPC scheme coupled with the DTDO is merely 0.0575% and 0.2675% to that of the previous DTLSMC and the optimized DTPIDC, respectively, while in the presence of lumped disturbances, the value of the AALE produced by the proposed DTSMPPC scheme coupled with the DTDO is merely 0.006% and 0.1952% to that of the previous DTLSMC and the optimized DTPIDC, respectively. • A discrete-time MHTGR model with lumped disturbances is constructed. • A DTDO that can estimate lumped disturbances is developed. • A DTITSMC scheme is proposed to accomplish load following operation of MHTGR. • Control scheme can be directly implemented on digital control system of MHTGR. • Simulations and comparisons reveal superiority of control scheme. [ABSTRACT FROM AUTHOR]

In order to optimize the urea injection volume of the SCR system after fuel change, this paper constructs the three-dimensional models of the marine engine and SCR system. When the engine is doped with hydrocarbon-based biodiesel, this paper determines the optimal doping ratio and injection advance angle of the engine to be 10 % and 27°CA BTDC based on the economy. Under these parameters, the simulation obtains the exhaust parameters of the engine, determines the urea injection area of the SCR system, and calculates the theoretical urea injection volume to meet the urea injection temperature operating point. After blending hydrocarbon-based biodiesel, when the SCR system continues to use the calibrated urea injection volume in pure diesel mode, the requirements cannot be met, so it is necessary to re-calibrate the SCR system urea injection volume. Subsequently, this paper proposes a NOx target conversion rate allocation method based on the average ammonia-nitrogen consumption ratio and working condition usage frequency and taking the engine unit power urea consumption as the optimization target. The results show that the optimized average ammonia specific consumption of the SCR system is 21.45g/(kW⋅h), which is reduced by 6.95 % compared with original calibrated average ammonia specific consumption (22.94 g/(kW⋅h)). • Marine Engine Blending with Hydrocarbon-Based Biodiesel to Reduce Emissions. • Demonstration of changes in engine exhaust parameters due to fuel change. • Performance study of SCR system for fuel change. • Proposing a new optimization method based on urea injection volume. [ABSTRACT FROM AUTHOR]

Gas-liquid two-phase flow exists in nuclear reactor evaporation, vehicle cooling, chemical production falling film evaporation and other processes, and the dynamic measurement of the interfacial wave is of great significance for industrial process monitoring and production optimization. Accurate identification and characterization of interfacial wave is an important prerequisite for scientific research and engineering practice. In this paper, interfacial wave measurement experiments have been carried out using an ultrasonic phased array system and measurement method to obtain gas-liquid phase interface measurement information in gas-liquid flow system. The liquid film thickness is extracted by fitting the gas-liquid interface through two-dimensional data matrix to ladder, detecting the liquid level data, and constructing a three-dimensional interfacial wave distribution model based on dynamic real-time data. The interfacial wave amplitude prediction correlation is developed using various machine learning regression models, which are optimized by parameters tuning algorithms. Laboratory results indicate that the wave amplitude prediction correlation is highly correlated with five inlet parameters, including superficial gas velocity, superficial liquid velocity, gas Reynolds number, liquid Reynolds number, and Lockhart-Martinelli (L-M) parameter. In the seven typical wave amplitude prediction correlations for stratified and plug flow, Least Squares Boosting (LSBoost) provided the best fit with MAPEs of 11.26 % and 18.42 %. The Gray Wolf Optimizer (GWO) is proposed and used to optimized the wave amplitude prediction correlation of LSBoost, and the MAPEs of optimized model are 4.38 % and 17.26 %. This paper provides a reliable method for accurate measurement and methodological analysis of interfacial wave spatial-temporal characteristic parameters of gas-liquid two-phase flow. • A three-dimensional measurement method of interfacial wave based on ultrasonic phased array is designed. • A three-dimensional interfacial wave distribution model based on dynamic real-time data is constructed. • The interfacial wave amplitude prediction correlation is established using multiple machine learning regression models. • Optimization of the wave amplitude prediction correlation by multiple parameter tuning algorithms. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of dealing with epistemic, i.e. non-probabilistic, uncertainties in strategic energy planning modelling. Current models have limited consideration of this type of uncertainty compared to probabilistic uncertainty, and also typically lead to overly conservative results. To address this issue, the contribution of the paper is to propose a novel decision support method which combines two decision-making methodologies into a single, internally consistent algorithm, and to show its applicability to real-size energy planning studies. Robust optimization is applied to address constraint uncertainties, while the minimax regret criterion is utilized for uncertainties in the objective function. This approach facilitates energy modelling exercises that can be more closely aligned with decision-makers' preferences for both feasibility and optimality. To demonstrate its effectiveness, the method is applied to a real-size strategic energy planning model, and the algorithm is shown to be able to provide detailed solutions in reasonable times. Ex-post evaluations confirm that this approach maintains robust optimization performance by effectively reducing the occurrence and magnitude of infeasibilities, while satisfying the minimax regret criterion across the entire range of uncertainties. Therefore, this integration preserves the distinct advantages of each methodology without any adverse effects when used together. • Robustness framework for decision-making in the face of epistemic uncertainty. • Decision support method combines robust optimization with min-max regret criterion. • Methodology accounts for decision-makers' feasibility and optimality preferences. • Real-size energy planning model proves applicability in other models or disciplines. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative decision support system for improving ship energy efficiency. It combines an engine optimization model and an artificial neural network (ANN). Real-time data regarding the ship and engine performance is obtained along a specific ship navigation, and an engine optimization model is built using Ricardo Wave software. This model is validated with high accuracy. Ship-specific parameters are derived, and fuel consumption is estimated using ANN models with different structures. The study identifies the most suitable ANN model with 1 hidden layer and 5 neurons based on error metric evaluation where the 0.99697 R2, 0.00035 RMSE, and 0.06470 MAPE scores are calculated. This approach offers a cost-effective solution for shipping companies to monitor critical engine parameters in real-time without investing in sensors or data collection systems. Thus, it contributes to dealing with the problem of the scarcity of analyzable data in the maritime literature, which is one of the significant issues in the papers related to energy efficiency, machine learning, and deep learning. It is an innovative approach since the parameters that are related to real-time operations rather than considering only instruction book information are aggregated, and the produced data is analyzed by an intelligent tool. • A system is proposed to control ship energy efficiency level by combining an optimization model and a neural network • A simulation model is created in the Ricardo Wave tool and the engine model is validated by actual data. • The ship fuel consumption is estimated by an artificial neural network based on the derived input parameters. • The prediction process is evaluated by error metrics to determine its success. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between financial development and access to electricity for rural and urban India by incorporating economic growth, trade, and foreign direct investment in energy production function. In doing so, we have applied autoregressive distributed lag bounds testing to examine long run cointegration between the variables by accommodating structural break(s) stemming in the series. Our empirical results indicate the presence of cointegration association between access to electricity and its determinants. We note that financial development enhances access to electricity in rural India but declines it in urban India. Economic growth has positive effect on access to electricity. Trade openness and foreign direct investment lower access to electricity in rural India. In urban India, trade openness and foreign direct investment increase access to electricity. The association between financial development and access to electricity is inverted-U shaped for rural and urban India. This paper would present new policy direction for Indian government to enhance access to electricity by using financial development as an economic tool. • Financial development and access to electricity have a strong linkage. • In India financial development enhances access to electricity in rural area but has adverse effect in urban area. • Rise in economic growth of India has increased the access to electricity. • Growth of Trade Openness and Foreign Direct investment increase access to electricity. [ABSTRACT FROM AUTHOR]

As installed wind power capacity continues to rise, the cost of onshore wind power generation in China has fallen, far exceeding the world average. The purpose of this study is to explore the main factors affecting onshore wind power in China and to identify ways to reduce costs. So as to reduce the cost of wind power and promote the large-scale grid-connected use of wind power. Therefore, this paper summarizes various factors that affect the development of onshore wind power. We also provide a comprehensive and repeatable MFLC (multi-factor learning curve) method to evaluate the factors affecting the cost of onshore wind power. Subsequently, the learning rates of various factors are calculated using this method. The results show that empirical learning by increasing installed capacity/generation, improving technology and selecting sites which are rich in natural resources, and appropriately reducing capital investment and material prices can significantly reduce the LCOE (levelized cost of electricity) of onshore wind power in China. Compared with wind power giants of the United States and Germany, the reduction in the cost of onshore wind power generation in China is more dependent on inputs such as capital investment and raw materials, while experience plays a relatively minor role. The ability to make full use of natural resources and to convert wind energy into electricity more efficiently is crucial to reducing the cost of wind power. • This paper summarizes various factors that affect the development of onshore wind power. • A comprehensive and repeatable MFLC method is built in this paper. • The learning rates of various factors are calculated. [ABSTRACT FROM AUTHOR]

Piezoelectric energy harvesters (PEH) are widely used to collect piezoelectric energy. However, adapting to different engineering application environments is difficult for traditional PEHs. To improve the engineering application value of PEH, a hanger piezoelectric energy harvester (HPEH) based on low frequency vibration of hanger is proposed in this paper. A dynamic electromechanical coupling model was established for hanger structure, and the effects of the external load resistance, the polyvinylidene fluoride (PVDF) piezoelectric film installation location, the stability of the PVDF piezoelectric film with different winding methods, and the external excitation amplitude on the energy output performance of the HPEH were studied. Results indicate that the optimal load resistance of the HPEH studied in this paper is 1.0 MΩ; the PVDF piezoelectric film location has a significant impact on the performance of the HPEH, and the output power is highest at hanger L c/4; the PVDF piezoelectric films during ring winding is better than those in spiral winding for the same external cycle excitation; in the external excitation amplitude range of 2 d –6 d (d : hanger diameter), appropriately increasing the external excitation amplitude is conducive to improving the output voltage of the HPEH, with the root mean square voltage increasing by 95.11 % at external excitation amplitude A = 6 d compared with that at the initial excitation amplitude. It provides an innovative solution for the field of cable structures and new energy sources, and the further research and application of this technology is expected to have a profound impact in the field of energy sustainability and engineering technology. • This paper proposes a novel hanger piezoelectric energy harvester. • The key parameters affecting the energy harvesting performance are analyzed. • The engineering application value is increased compare with conventional harvesters. • It is suitable for low-frequency vibrations of cable structures. • It provides a new idea for energy harvesting of cable structures. [ABSTRACT FROM AUTHOR]

The smooth operation of the power system imposes high requirements on wind power forecasting. Given the forecasting challenges brought by the coexistence of strong and weak disturbances in wind power data, this paper proposes a hierarchical anti-disturbance mechanism to raise the accuracy of single-point wind power prediction. In the first-level disturbance processing, we prioritize the treatment of strong disturbances in wind power data through a dual-branch multi-source fusion prediction structure. The wind power subsequences with distinct time–frequency characteristics are divided into two sets, processed by the stationary set prediction branch and the drastic set prediction branch, respectively. For the drastic set, we incorporate microenvironmental multi-source data surrounding the wind turbine as input and propose a parallel-structured attention fusion module CA (Convolution attention fusion block) to provide effective data support for the prediction module, then extract rich fusion features through deep temporal LSTM algorithm and ultimately achieve fine-grained forecasting. For weak disturbances that have not been adequately addressed in the previous step, we conduct a secondary treatment through error correction strategy. By employing the hierarchical processing approach, this paper accomplishes a comprehensive handling of disturbances with different intensities. Experimental results on four data sets show that the proposed model DEWFM (Dual-branch and error correction wind power forecast) demonstrates remarkable performance advantages in comparing with the baseline model and other advanced models, the average values of MAE reaching 1.679, revealing the effectiveness of the proposed method in enhancing the precision of short-term wind power prediction. • Present a method to address the uncertainty in short-term wind power prediction. • A time–frequency hybrid prediction, differentially processing subsequences. • Enhanced LSTM with a multi-source data fusion mechanism for fine-grained prediction. • Error correction handles residual weak disturbances, enhancing prediction accuracy. • The performance evaluation shows the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

Real-time acquisition of dynamic wake field information has garnered substantial attention in the wind farm industry, as it provides a crucial data source for intelligent wind farm monitoring and control. However, the existing wind measurement technologies, such as light detection and ranging (LiDAR), are limited to sparse data point measurements. This paper explores the utilization of a Physics-Informed Neural Network (PINN) to reconstruct wind turbine wake dynamics, specifically focusing on the influence of active wind turbine yaw operation on wake evolution. The methodology involves creating a tailored loss function that combines sparse wake measurement data with the Navier-Stokes (NS) equations. More precisely, the neural network incorporates the NS equations as constraints to guide the prediction of physical quantities in the output, including downwind velocity, crosswind velocity, and pressure. Taking the dynamic wake during yawing as a case study, the proposed method showcases remarkable universality and robustness across diverse scenarios involving varying scanning angle intervals, measurement point spacings, frequencies, and noise levels. It successfully captures the dynamic trends in wake evolution during yawing and accurately forecasts the wake trajectory and deflection. Even when tested with actual wake measurement data, the method can still effectively reconstruct the flow field, indicating significant potential for the real wind farm yaw control. • Dynamic spatiotemporal wake reconstruction of a utility-scale yawed wind turbine is carried out in this paper. • A novel approach combining physics-informed neural network with sparse LiDAR measurement is proposed. • The neural network maps spatiotemporal coordinates [ t , x , y ] to flow physical quantities [ u , v , p ] integrated with NS equations. • Various cases with different LiDAR measurement parameters are investigated for sensitivity and robustness analysis. • The new approach shows great effectiveness in reconstructing dynamic wake behavior of trajectory and deflection changes. [ABSTRACT FROM AUTHOR]

China still faces relatively serious energy poverty, and technological innovation is considered effective at alleviating this issue. This paper empirically investigates the impact of regional technological innovation on energy poverty based on panel data from 30 provinces in China from 2001 to 2020. This paper explores the effect of industrial structure distortion in the process of technological innovation on energy poverty. This paper conducts asymmetry analysis and investigates the heterogeneous impact of technological innovation on energy poverty in different areas. The research conclusions are as follows: (1) Regional technological innovation can effectively alleviate energy poverty. (2) Industrial structure distortion moderates the impact of technological innovation on energy poverty, and it weakens the effect of technological innovation in alleviating energy poverty, although the moderating effect is not significant in coal-producing regions. (3) The effect of technological innovation on alleviating energy poverty is more obvious in regions where energy poverty is prominent. (4) The role of technological innovation in alleviating energy poverty shows spatial heterogeneity, and this alleviation effect is more obvious in the western and non-coal producing regions. We also propose several policy implications for alleviating energy poverty. • Regional technological innovation can effectively alleviate energy poverty. • Industrial structure distortion moderates the impact of technological innovation on energy poverty. • Industrial structure distortion hinders the ability of technological innovation to alleviate energy poverty. • Technological innovation is more effective in alleviating energy poverty in regions where energy poverty is severe. • Technological innovation is more effective at alleviating energy poverty in the west and non-coal-producing regions. [ABSTRACT FROM AUTHOR]

It is well known that energy forecasts play an important role in guiding energy policy, economic development and technological progress. Therefore, based on the purpose of energy consumption and production forecasting, this paper proposes a novel structure adaptive discrete grey Bernoulli model, which is innovative in terms of both accumulated generating operator and model structure. In terms of accumulated generating operator, a new fractional order accumulated generating operator is proposed in this paper. The new accumulated generating operator has a different information priority by adjusting the values of the parameters. In terms of model structure, a novel discrete grey Bernoulli model is proposed in this paper. The novel model is well adapted to time series data containing nonlinear information, and can well mine and utilize the information contained in the original data. In addition, the Particle Swarm Optimization (PSO) algorithm was chosen to optimize the model parameters based on algorithm comparison experiments. This enables the model to flexibly adapt to a variety of complex data and has the ability of structure adaptive. Moreover, this paper conducts comparative experiments between the novel model and eight other forecasting algorithms for time series data. The numerical results show that the novel model has better forecasting performance for the data of China's total energy consumption, China's total electricity generation and China's total domestic electricity consumption. In addition, for the model reliability problem caused by the optimization algorithm, the stability and accuracy of the model are verified by Monte Carlo simulation and probability density visualization analysis. Finally, the proposed model predicts the future development trend of energy consumption and production in China. • A novel structural adaptive discrete grey Bernoulli model 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]

The paper measures environmental efficiency and productivity change to then carry out a decomposition analysis of an environmental efficiency index and a productivity change index. The paper includes a novel comparative assessment to determine the drivers of environmental efficiency and the sources of productivity change of the manufacturing sector in Europe, Asia and the whole world. We explore the capability to convert in an environmentally efficient way, selected inputs into desirable and undesirable outputs. Our estimation results of environmental efficiency confirm that Europe is ahead the world's average. Our analysis shows, however, a slowdown of Europe compared with the productivity growth of Asia. The latter shows a better performance in management capabilities, optimal resource allocation, and economies of scale. The analysis carried out in a second stage using the dynamic effects of the generalized method of moments also shows that the annual growth rate of manufacturing value added and manufacturing exports positively influence the level of environmental efficiency in both Asia and Europe. Therefore, the internationalization and extroversion of manufacturing can be crucial for sustainable development in Europe and Asia. • Benchmarking the strategy for sustainable development in Asia and Europe. • Decomposition of environmental efficiency indices and productivity change. • Effect of manufacturing value added and exports on environmental efficiency. • Benchmarking the manufacturing sector in Asia and Europe. • Showing Asia's best practices and European pillar of industrial restructuring. [ABSTRACT FROM AUTHOR]

Motivated by large oil price swings, high economic and geopolitical uncertainties, and the financialization of oil, this paper examines the frequency spillovers and co-movements between oil shocks (risk and demand) and the stock markets of top oil-producer and consumer countries, namely, Canada, China, Russia, Saudi Arabia, and the US. The analysis uses the time-domain spillover index of [1], the frequency-domain spillover of [2]; and the wavelet coherence approach. The findings reveal that spillovers run from the U.S., Canada, and, to a lesser extent, Russia to oil shocks. On the other hand, oil shocks, Saudi Arabia, and China constitute net receivers of shocks. The intensity of spillovers is heavier in the short-term frequency than in the intermediate- and long-term. Furthermore, the direction of spillovers is more defined in the long-term. The U.S. stock market exerts a strong impact on oil risk in general, but the impact is stronger in the short-term. Conversely, an oil demand shock is susceptible to innovations from Canada and Russia that are stronger in the long-term. This means that oil risk shock stemming from innovations in financial markets is short-lived and dissipates quickly due to quick reactions from market participants. On the contrary, long-term links characterize the relationship between oil demand shock and financial markets, mirroring the macroeconomic nature of the linkages. Finally, while the 2008 crisis, EDC, oil price crash, and the COVID-19 pandemic coincided with strong spillovers in the short-term, the COVID-19 era was marked by higher spillovers in the long term. The findings provide important information for investors and policymakers in terms of diversification, risk management, and efforts to mitigate contagion. • This paper examines the frequency spillovers and co-movements between oil shocks and the stock markets. • We use the time-frequency spillover index method and the wavelet coherence approach. • Spillovers run from the U.S., Canada, and, to a lesser extent, Russia to oil shocks. • Oil shocks, Saudi Arabia, and China constitute net receivers of shocks. • The intensity of spillovers is heavier in the short-term frequency than in the intermediate- and long-term. [ABSTRACT FROM AUTHOR]

Single-stage axial flow turbines are typically used in underground mining machines and underwater vehicles due to their safety and small size. However, the high-pressure ratio and small mass flow rates force the flow inside the turbine to be supersonic. The blade design of such turbines with high-flow velocity leads to high losses and low turbine efficiencies at conventional design values. In this paper, the 1D design of the turbine is carried out using the meanline design method. The results of the design algorithm are compared with both experimental, CFD (Computational Fluid Dynamics), and a well-known commercial software AxSTREAM. After validating the design, Taguchi method is used to analyze 6 different parameters (Blade Shape, Nozzle Shape, Cord Length, Solidity Ratio, Nozzle Angle and Blade Thickness) affecting the turbine performance and the optimum blade profile is compared with the conventional blade design. The optimum design isentropic efficiency (68.267%) outperformed the conventional design turbine isentropic efficiency (61.759%) by about 11%. This paper provides a different insight into the conventional turbine design method. • Single-stage axial turbine design was discussed in detail. • The optimum admission ratio was determined within manufacturing limits. • 1D calculations and CFD results were compared with experimental data. • Nozzle and Rotor blades were optimized with the Taguchi Method. • The effects of Tip Cr on performance were determined. [ABSTRACT FROM AUTHOR]

Transition to carbon-neutral energy generation is a reality, and great efforts are being made in this direction in the last years. Ammonia is a promising carbon-free fuel, and it can be used as a gas turbine fuel in existing power generation cycles without significant modification. This paper considers various aspects of the use of ammonia as a gas turbine fuel: it provides a review of existing ammonia-fired gas turbines and conducts a study of the prospective technology of using ammonia via thermochemical transformation into hydrogen-rich gas. Moreover, this paper considers on-board hydrogen production technology via thermochemical ammonia transformation. The ammonia fired chemically recuperated gas turbine is analyzed for which the thermal efficiency can be increased up to 5%–7% comparing to traditional gas turbines. Moreover, hydrogen-rich fuel with a hydrogen mole fraction up to 75% is used as a fuel, leading to more stable combustion with lower NO x emission up to 6–10 ppm. Additionally, an approach to on-board hydrogen production from ammonia via the utilization of solar energy is investigated. It is shown that solar energy can replace up to 25% of heat obtained via ammonia combustion. The paper discusses future perspectives in investigations for ammonia-fired gas turbines with on-board ammonia transformation. • Ammonia as a gas turbine fuel with zero CO 2 emission. • NH 3 containing blends show the better combustion and emission performances when pure NH 3. • Thermochemical ammonia transformation as an on-board hydrogen production system is considered. • Use of exhaust gas for ammonia decomposition leads to an increase in efficiency up to 7%. • Use of solar energy for ammonia decomposition can decrease the ammonia consumption up to 25%. [ABSTRACT FROM AUTHOR]

Nowadays, climate change is a major global societal challenge that significantly increases environmental stress. Most international organizations and policies have promoted initiatives to minimize emissions, reduce fossil fuel dependence and increase renewable energy resource integration into different sectors. An energy transformation towards more renewable systems is thus a priority. Under this scenario, the present paper describes and evaluates an alternative energy conversion matrix-based model to combine sector electrification, power generation units from renewables, and new clean technologies. The proposed green matrix-based model allows analyzing future scenarios, including electricity participation in end-use consumption and electric power generated by renewables – potentially integrated into different sectors –. The proposed model is evaluated in the state of Maine (United States). This case study is focused on decarbonizing both residential heating and transport sector through the integration of large offshore wind power plant. Results and discussion is also included in the paper, providing expected energy demand reductions and decreasing emissions through the integration of renewables. This energy transition integration case study is proposed in three road-maps with different penetration rates and time scales. The proposed green matrix-based model can be also applied to other areas and energy resources, as an alternative way to analyze and estimate renewable integration into different sectors. • Green Electrical Matrix to link electricity supply and end-use for energy transition. • Assesses renewable potential and electrification tech for end uses, e.g., transport. • Applies model to Maine, USA to show reductions in fossil fuels and emissions. • GIS and MCDM for optimal offshore wind location, maximizing electricity generation. • Short, medium, and long-term projections of renewable integration into energy system. [ABSTRACT FROM AUTHOR]

Currently, wind power prediction has so many problems in the ultra-short-term time scale (0–4h), which is difficult to improve the deterministic prediction and probability prediction accuracy of the wind farm cluster because it can not fully explore the spatio-temporal correlation between the physical change process and the wind farm. In this paper, a method of ultra-short-term deterministic and probability prediction of wind farm cluster power based on Graph Convolutional Network (GCN) considering fluctuation correlation (FC) is proposed. Firstly, the adjacency matrix is constructed based on the power sequence fluctuation information of each wind farm, and the GCN is designed. Secondly, the spatio-temporal features of power and Numerical Weather Prediction (NWP) wind speed are extracted and fused based on the network model of dual-channel and dual-adjacency matrix. Thirdly, in order to effectively improve the prediction accuracy, a trend switching mechanism is designed based on the effective trend of NWP. When the fluctuation information of NWP is not accurate, the graph structure is constructed by the adjacency matrix based on geographical location to achieve effective prediction. Finally, the method proposed in this paper is applied to wind farm clusters in three provinces of China, compared with some commonly used methods, the average RMSE is reduced by 1.34 %, 1.62 %, 2.07 %, respectively, and the average CWC is reduced by 6.12 %, 4.49 %, 6.62 %, which verifies the effectiveness of this method. • A method for calculating FCC is proposed. • Combined with FCC, the graph structure of wind farm cluster is constructed. • A two-module GCN is designed. • A trend-switching mechanism is designed. [ABSTRACT FROM AUTHOR]

Capacity expansion models provide the basis on which to decide where, when, how much and what technology type to deploy. In systems with large shares of variable renewable energy, the low temporal detail of these models has been shown to introduce biases, prompting much recent work to reduce them. This paper shows that this issue is fairly secondary compared to the impact of maximum investment rates. Through this parameter, typically not discussed in capacity expansion studies, many notions can collectively be expressed, such as the rate at which capacity is financed, institutions approve development, manufacturers roll-out equipment, civil engineers build infrastructure, network operators connect plants etc. This paper shows that considering even ambitious development rates significantly increases total system costs, and drastically changes the structure of an optimal generation mix. The presented sensitivity analysis is based on a multi-region representation of the European power system, modelled using the open-source tool OSeMOSYS, to which a novel power transmission module has been added. Results stress the extent to which hopes of meeting climate targets hinge on society's collective ability to deploy new low-carbon infrastructure fast enough. Energy policy can enhance this ability by providing long-term visibility and stability, reducing investment risk. • Sensitivity analysis compares effects of temporal detail and maximum investment rate. • Power system planning is strongly impacted by infrastructure maximum investment rate. • Meeting climate targets hinges on our ability to roll-out infrastructure fast enough. • Careful choice of representative periods is more important than number of time-steps. • Novel power transmission module added to open-source modelling tool OSeMOSYS. [ABSTRACT FROM AUTHOR]

The low-carbon transformation is the key to China's high-quality economic development. This paper uses the panel data of 278 Chinese cities to measure low-carbon total factor productivity (LCTFP) and examines the impact of environmental protection tax (EPT) reform on LCTFP. EPT significantly promotes LCTFP. The promotion effect is more significant in the lower administrative level and resource-based cities in central and western regions. EPT promotes LCTFP through three channels including green technology innovation (GTI) effect, industrial structure upgrading effect and forcing effect. EPT has a negative spatial spillover effect on LCTFP in neighboring areas. This paper provides important empirical evidence for further advancing the EPT in China. • EPT significantly promotes LCTFP. • The promotion effect is more significant in low administrative level and resource-based cities in central-western region. • EPT promotes LCTFP through green technology innovation, industrial structure upgrading and forcing effect. • EPT has a negative spatial spillover effect on LCTFP in neighboring areas. [ABSTRACT FROM AUTHOR]

To achieve carbon neutrality quickly and deal with urban greenhouse effect, it is necessary to study the energy consumption of urban buildings. Energy consumption prediction is an important step in planning and managing energy use in buildings. At present, few studies have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings in urban built-up areas. In this paper, a spatial data-driven framework is introduced for the analysis and assessment of building energy consumption. The framework is grounded in the building centroid and is combined with single photon radar and point of interest data through the utilization of the nearest neighbor classification algorithm. Two megacities, Beijing and Shanghai, are taken as examples to illustrate the method and to generate the regional building energy use database. The results show that: a) building physical feature (geometric form, functional type) have a significant impact on energy consumption, and energy consumption is correlated with the economic level of the region; b) Energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphon effect is obvious in region with high energy consumption. The contribution of the framework is to provide insights into the feasibility of employing multiple spatial data fusion for the analysis of energy consumption within urban built-up areas. This can be employed to address energy planning concerns and help promote sustainable environmental practices in urban areas worldwide. • This paper attempts to provide a feasible framework for analyzing urban building energy use by utilizing real-time urban semantic perception information and building features. • The proposed framework have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings. • The proposed methodology can obtain regional energy consumption stocks and produce spatial distribution results, thus improving the interpretability of the data. • The results show that energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphoning effect is obvious in regions with high energy consumption. [ABSTRACT FROM AUTHOR]

In the background of exhaustion of the traditional fossil energy sources and environmental deterioration, developing renewable energy has become a strategic choice for countries to achieve energy sustainable utilization and carbon neutrality target. Different renewable energy technical plans have different characteristics under multiple criteria. Therefore, before the further exploitation of renewable energy sources, it is of great significance to evaluate the comprehensive performance of different plans and then determine the best renewable energy sources. However, the commonly used weight calculation methods in the existing renewable energy evaluation have obvious shortcomings, such as the randomness of the subjective method is strong and the gap in the weight allocation of the objective method is too large, which affect the reliability and accuracy of the sorting result. In this paper, a novel weight calculation method based on non-dominated sorting genetic algorithm (NSGA-II) is proposed, and is applied to renewable energy evaluation. The study in this paper focuses on methodology. Firstly, for the linear normalized and Zeros normalized evaluation data, the differences between renewable energy plans in the two normalized evaluation data are measured, and the ranking deviations of the two normalized data are calculated. Then, the multi-objective weight solving model is constructed in terms of plans differences minimization and ranking vectors deviations minimization. The NSGA-II algorithm is applied to the optimization of multi-objective functions to find the Pareto non-dominated solution, so as to determine the weight values of each evaluation index. Finally, the TOPSIS method is used to determine the relative closeness value of each plan, and the rankings of renewable energy technical plans are achieved. Based on the actual renewable energy development data of a province in China, experiments were carried out to investigate the effectiveness of the proposed method. Experimental results show that the proposed method performs better than some popular renewable energy evaluation methods. [Display omitted] • Propose a novel evaluation method for renewable energy technical plans. • Apply non-dominated sorting genetic algorithm II to determine the index weights. • Compared with the existing evaluation method, the proposed method performs better. [ABSTRACT FROM AUTHOR]

Gas drainage in boreholes is characterized as an essential means to control abnormal gas emissions during mining. Unfortunately, its drainage efficiency is terribly restricted by high in-situ stress in deep underground coal seams. Gas drainage involves gas emission under stress constraint conditions (SCCs), as well as multi-scale gas migration (gas desorption, diffusion, and seepage). Nevertheless, how SCCs influence gas emission behaviors remains unclear. Considering this status quo, this paper focuses on researching He and CH 4 emissions under different SCCs, and analyzes the influence of SCCs on gas emission by integrating the initial gas diffusion coefficient and the diffusion attenuation coefficient. In addition, a novel method for calculating equivalent diffusion and seepage pore radii by the T 2 spectrum of nuclear magnetic resonance was proposed. Finally, according to the multi-mechanism gas emission model that tracks the dynamic evolutions of the equivalent diffusion and seepage pore radii, the influences of equivalent pore radius and pressure on the conductance of different diffusion mechanisms were further quantitatively analyzed. The research in this paper is expected to provide a theoretical reference for efficient gas drainage in deep coal seams. • The influence of stress on the apparent gas diffusion coefficients was analyzed. • The equivalent diffusion and seepage pore radii were proposed and calculated by NMR. • A multi-mechanism gas emission model considering dynamic pore radius was developed. • A testing method of adsorbed CH 4 desorption amount in fractured coal was proposed. [ABSTRACT FROM AUTHOR]

The randomness and uncertainty of charging behavior of EV users bring great challenges to the short-term prediction of charging load of EV charging stations. Aiming at the low precision of short-term load forecasting of EV charging station under the influence of many factors, this paper takes the optimization of EV charging loss as the objective of optimization, firstly, the time series attributes are combined with the nonlinear attributes, and PLSR and LightGBM are used to extract the attributes from EV charging load series, and the fusion prediction model is established. Secondly, the Bayesian optimization method is introduced to optimize hyper-parameters of the model, and the self-adaptive adjustment of the hyper-parameters are realized, which improves performance of the algorithm and the prediction precision. Finally, taking the actual load data of EV charging pile in a certain area as an example, the experimental analysis is carried out, the second-order cone optimal dispatching scheme is adopted to optimize the EV charging schedule. The results show that the EV prediction model constructed by this paper has higher prediction accuracy than other artificial intelligence prediction algorithms, and provides more accurate data for the second-order cone optimal scheduling scheme, the utility model effectively realizes the safe and stable operation of the power grid. • The accuracy of EV load forecasting is improved by using fusion algorithm. • The generalization ability of prediction model is improved by Bayesian optimization algorithm. • It provides an accurate data base for EV optimal scheduling. [ABSTRACT FROM AUTHOR]

To address the low density of high-pressure gaseous hydrogen and evaporation issues associated with liquid hydrogen storage, cryogenic supercritical hydrogen storage method is presented in this paper. Based on a dual parallel mixed refrigerant cycle (DPMR) and a dual cascade mixed refrigerant cycle (DCMR), the density of supercritical hydrogen product is 70.74 kg/m3, closely approaching liquid hydrogen. The processes are simulated in Aspen HYSYS, optimized by genetic algorithm for lower specific energy consumption (SEC). Thermodynamic analysis is conducted to compare with hydrogen liquefaction processes, demonstrating the feasibility and superiority of proposed process. The results show that the DPMR process has an SEC of 6.422 kWh/kg H2 and exergy efficiency of 52.66%, while DCMR is 6.872 kWh/kg H2 and 49.24%. Additionally, the inclusion of ortho-para convertors in DPMR process is investigated to further analyze the impact of heat release for ortho-para conversion. Furthermore, a techno-economic analysis is conducted on three processes, validating the economic feasibility. The study indicates that cryogenic supercritical hydrogen storage system is of great significance to the development of high-density hydrogen storage technology. The DPMR and DCMR presented in this paper can significantly improve the energy efficiency and provide valuable information for the design of high-density hydrogen storage system. • Two high-density cryogenic supercritical hydrogen storage systems are proposed. • Mix refrigerant cycles are applied to reduce energy consumption. • The processes have lower SEC and higher FOM than conventional LH 2 processes. • The volume storage density of hydrogen can reach 70.74 kg/m3 in the systems. • OPC is considered to analyze the thermodynamic performance. [ABSTRACT FROM AUTHOR]

The specific CO 2 production of fuels has commonly been expressed empirically in terms of kg CO2 (kWh)−1. No specifications regarding combustion conditions, transformation technology used, and efficiency are usually presented in detail. In this paper, however, we propose replacing this approach by the use of rigorous chemical engineering calculations based on the composition and the combustion conditions of the fuels. The MS EXCEL program, which is provided as supplementary material to this paper, calculates the specific CO 2 production considering energy losses, adiabatic flame temperature, calorific value and heat of fuel combustion, flue gas temperature, energy losses through flue gas, slag, and the overall energy efficiency of the whole energy conversion process. Results are presented for three fuel groups: coal, hydrocarbons, and renewable fuels. The calculated results are compared with the literature data. A benefit of our approach is that the procedure can be generalized for any fuel of known composition and for different types of combustion-based energy transformation. • Techno-economic energy strategies require accurate knowledge of CO 2 emissions. • The attached chemical engineering program in EXCEL provides the necessary data. • Fuel type and combustion conditions affect CO 2 emission per kWh. • The overall efficiency of various energy production processes was calculated. • The estimated and observed overall efficiencies are in good agreement. [ABSTRACT FROM AUTHOR]

Co-gasification is considered an effective treatment method for waste containing plastics and biomass for conversion into producer gas. This paper investigates treating personal hygiene care products like sanitary napkin waste by co-gasification with sawdust biomass pellets in a 50 KWh capacity downdraft gasifier to generate producer gas for thermal applications. The present experimental study is one of its kind for treating sanitary napkins waste via co-gasification technology that focuses on energy generation. Water loaded sanitary napkin content was increased from 0 wt% to 3.8 wt% in the total feedstock to evaluate the producer gas quality. Moisture plays dominant role compared to presence of plastic in a napkin with increasing napkin content. Increasing the napkin content in total feedstock from 0 wt% to 3.8 wt% leads to temperature decrease of all reaction zones in the gasifier. In contrast, LHV and clinker formation was reduced from 4.03 MJ/Nm3 to 3.3 MJ/Nm3 and 1 wt% to 0.76 wt%, respectively. Napkin loading of 1.4 wt% gives the best LHV and CGE of 4.05 MJ/Nm3 and 61.7%. Results reveal that the reactor can smoothly operate up to 3.8 wt% loading, generating producer gas flame temperature above 950 °C suitable for a wide range of thermal applications. • Effective utilization of sanitary napkins by co-gasification with sawdust pellets in downdraft gasifier. • Moisture and plastic together in napkins effect temperature and gas yield. • Plastic increase the temperature of all the zones while moisture reduces it. • Producer gas obtained by co-gasification of napkins was suitable for wide range of thermal applications. [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]

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]

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]

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]

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 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]

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 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]

There is a long way for solid-state batteries from the laboratory to large-scale application and commercialization. To overcome a series of challenges, researchers and innovators seek to further understand the processing-structure-properties relationships of solid-state batteries. However, less literature explores the advances and opportunities in solid-state battery technology based on patent analysis. The paper adopts the technology of Natural Language Processing (NLP) to analyze patent documents and reveal the advances and opportunities for developing solid-state battery technology by constructing the patent Information Relation Matrix (IRM). This paper finds innovation activities in developing solid-state batteries have been increasingly active in recent decades, but are uneven across organizations. The electrolyte is a priority area of technology development, and the advances in developing solid-state batteries are perfecting conductivity, reducing interfacial resistance, and improving density and stability. By contrast, the opportunities are to reduce cost, prevent short circuits, and prolong the life cycle. The paper perfects the extant method of constructing IRM and gives insight into the advances and opportunities for developing solid-state batteries. Our findings can help innovators better understand advances in solid-state batteries or opportunities for developing solid-state batteries, from a global perspective. • Adopting the technology of Natural Language Processing to analyze patent documents. • Using the Information Relation Matrix to reveal technology advances and opportunities. • There are advances and opportunities for developing solid-state battery technology. • The advances are perfecting conductivity, reducing interfacial resistance, etc. • The opportunities are to reduce cost, prolong the life cycle, etc. [ABSTRACT FROM AUTHOR]

Wind speed prediction is a crucial aspect in the utilization of wind energy. In this paper, a wind speed prediction model based on an outlier-robust ensemble deep random vector functional link network (ORedRVFL) and arithmetic optimization algorithm-optimized variational mode decomposition (AOA-VMD) is designed. First, the penalty factor and the number of mode decompositions of VMD are optimized using the AOA algorithm and the original data are decomposed using the optimized VMD. Then the decomposed data is predicted using the ensemble deep random vector functional link network (edRVFL) model. The edRVFL uses rich intermediate features for the final decision, which can make the final result closer to the real data. In order to strengthen the anti-interference ability to the outliers, this paper robustly improves the edRVFL model, and the improved model is called ORedRVFL. ORedRVFL reduces the impact of outliers by introducing regularization and norm to balance the relationship between training error and weights. The experiments have proved that the model proposed in this paper outperforms other models in terms of anti-interference ability and prediction accuracy. • An outlier-robust edRVRFL prediction model is developed. • Use VMD to decompose the data and enhance the model prediction capability. • Fuzzy entropy is used as a criterion for data aggregation. • AOA optimizes the VMD hyperparameters to enhance the decomposition effect of VMD. [ABSTRACT FROM AUTHOR]

This work describes the one-dimensional, thermo-hydraulic model of the sCO 2 cycle Sofia developed to investigate optimal control methods and the behaviour of the cycle during operation. This dynamic model includes all devices such as turbomachinery, heat exchangers, valves, and piping including heat loss, in line with concept of the 1 MWe sCO 2 cycle, to be realised in the site of a fossil power plant in the Czech Republic. The model assembly and calculations were conducted using the commercial Modelica-based library ClaRa + using the simulation environment Dymola and in combination with another Modelica-based library, UserInteraction; the real-time simulations, with some parameter changes during the calculation, are made and described in this paper. Nominal parameters were achieved during the steady-state simulation, except for the lower mass flow of sCO 2. Transient simulation of power turbine start-up from standby state and results are also presented in this paper. The nominal state is achieved with the semi-automatic procedure in approx. 3 h. The simulation results allow more detailed analyses of control methods and a better understanding of real device control and behaviour during start-up, shutdown, or other transients. Careful manipulation of turbine valves in coordination with the pressuriser operation was identified as crucial for optimal control of the system. Also, the initial amount of CO 2 in the pressuriser affects its behaviour during transients. • Models of the PCHE and BPHE heat exchangers were prepared, and turbomachines models determined by CFD simulations were used. • Control system of the electric heater, which is the largest facility in the Sofia cycle, was designed. • Real-time simulations were performed using ClaRa+ and UserInteraction Dymola libraries. • Careful trubine valves manipulation and corresponding pressuriser control were identified as key control interventions. • Initial amount of CO 2 in the pressuriser affects its behaviour during transients. [ABSTRACT FROM AUTHOR]

To identify the influence of digital transformation on energy consumption, this paper introduces digitalization and energy elements into the multi-sector energy efficiency analysis model. Furthermore, this paper empirically tests the influence of digital transformation on energy efficiency from the micro level. The results show that the digital transformation indeed improves the enterprises energy efficiency, in which enterprises with higher energy efficiency are more affected. The improvement effect of digital transformation on enterprise energy efficiency continues to increase over time. In comparison, digital transformation shows more effective improvement effect on energy efficiency in enterprises that substantive transformation priority, with same group and maturity stage. Mechanism analysis shows that digital transformation can improve the energy efficiency of enterprises by reducing costs, increasing income, improving production efficiency and promoting technological innovation. In addition, the digital inclusive finance, government subsidies, intellectual property protection and service industry opening show positive regulatory effects. This paper proposes that, with the goal of peak carbon dioxide emissions and carbon neutrality, enterprises should actively increase digital investment and strengthen digital management of energy utilization. [Display omitted] • Digital transformation indeed improves the enterprises energy efficiency. • The promotion effect of digital transformation increases over time. • Digital transformation drives efficiency improvement and technological innovation. • Digital transformation contributes to cost reduction and benefit increase. [ABSTRACT FROM AUTHOR]

Climate change leads to the frequent occurrence of natural disasters. It has had a severe impact on agricultural economic growth in China. Based on data in China from 2004 to 2020, this paper uses a fixed-effect model to discuss the effects of natural disasters on agricultural economic growth. The empirical results are as follows. (1) Natural disasters negatively affect agricultural economic growth. The regression coefficient of natural disasters on agricultural economic growth is −0.1183. (2) The inhibition of agricultural economic growth by natural disasters is more prominent in areas with low government attention and average agricultural production conditions. (3) The mechanism analysis shows that natural disasters inhibit agricultural economic growth through channels that cause the spread of diseases and pests. (4) Expanding the scale of agricultural insurance can prevent the impact of natural disasters on agricultural economic growth. This research provides policy recommendations for preventing the negative impacts of natural disasters. Agricultural producers should raise awareness of disaster management and improve the accuracy of disaster warning mechanisms. • This paper measures the level of natural disasters in China. • This paper expands the application field of functional data analysis methods. • This paper examines the mechanism of natural disasters affecting agricultural economic growth. • This paper provides policy suggestions for ensuring agricultural economic growth. [ABSTRACT FROM AUTHOR]

Carbon emission flow is an effective tool to obtain carbon emission distribution in power systems, which can guide the active carbon reductions of consumers through proper incentive schemes. With the increasing penetration of renewable energy, the existing single-valued carbon emission flow model based on deterministic forecasted power outputs cannot describe the impacts of renewable energy uncertainties on carbon flows. Therefore, this paper mainly focuses on the assessment of the probabilistic carbon emission flow through the power systems considering the impacts of load and renewable energy uncertainties. In this paper, the probabilistic carbon emission flow (PCEF) is innovatively proposed. With the PCEF, consumers can make better energy use plans considering their own risk appetites for carbon emission payment. Firstly, the impact factors of the PCEF are modeled, including load and renewable energy uncertainties and integrated carbon intensity of thermal plants. The uncertainties of load, wind power and photovoltaics are modeled based on a p-box method while the uncertainty of hydropower is modeled based on the multi-state model. Besides, the integrated carbon intensity model of thermal plants are proposed considering the carbon intensity variations during the operating, start-up, and shut-down stages. Furthermore, the probabilistic carbon emission evaluation framework is proposed to assess the probabilistic carbon distribution. In this framework, novel probabilistic carbon indices are defined to precisely characterize the carbon emission situation with probabilistic representations. A solution method based on an adaptive interval point estimation (AIPEM) algorithm is proposed to efficiently solve the developed evaluation problem. Tests on an IEEE-30 node system, and a real provincial power system in China validate the effectiveness and application of the proposed model. The results showed that the PCEF can be a useful tool for obtaining the probabilistic representation of carbon emission flow and guiding the consumers' active carbon reduction in power systems. • Probabilistic carbon emission flow is proposed considering multiple uncertainties. • Integrated carbon intensity model for thermal plants is developed. • A p-box model is used to describe the uncertainties of load and renewable energy. • PCEF can help consumers balance the cost and carbon risk when making decisions. [ABSTRACT FROM AUTHOR]

The paper object is high-quality Shenmu bituminous coal modified by fatty alcohol polyoxyethylene ether sodium sulfate (AES) surfactant. The changes in functional groups, free radicals, Zeta potential, and contact angle of coal modified in AES anionic surfactant were analyzed. Based on the molecular structure of Shenmu coal, a coal-AES-water three-phase dynamic model was constructed. The wetting mechanism of coal was studied by molecular dynamics. The research has demonstrated that as the mass fraction of AES surfactant increases, the proportion of polar groups (-OH, C– O –C, –COOH, –C O), free radical content and Zeta potential increased significantly. The size of contact angle displayed a major negative linear correlation with the proportion of polar groups and free radicals, but positively linearly correlated with the Zeta potential. In the coal-AES-water three-phase dynamic model, the AES molecules adhere to coal molecules as hydrophobic group, while hydrophilic groups extended into the water. This increased H 2 O diffusion coefficient, enhanced action energy between coal and water interface, decreasing surface tension and achieving effective wetting of the coal. This paper supplies s a new idea for selecting efficient surfactants to change the coal microstructure. It is of major importance for improving coal wettability and offers a theoretical foundation for gas drainage and disaster prevention. • After AES modification, the hydrophilic groups of coal significantly increased. • After AES modification, the coal wettability is significantly improved. • After AES modification, coal molecules are more likely to adsorb water molecules. [ABSTRACT FROM AUTHOR]

In this paper, the research on mechanical motion rectification-based electromagnetic vibration energy harvesting (MMR-based EMVEH) is reviewed comprehensively. Firstly, the basic principle of MMR based on linear vibration is introduced, and the general design method of MMR is summarized, including the vibration input module and motion transfer module. Secondly, the structure, dynamics, advantages and disadvantages of different types of EMVEH based on magnetic resonance are analyzed, discussed and compared, mainly the motion conversion mechanism. With the same structural parameters, the equivalent electromagnetic damping of the AC motor is 1.5 times that of the DC motor. Thirdly, the common validation method of MMR-based EMVEH is summarized, including simulation, laboratory experiment and field test. According to the experimental results, the energy conversion efficiency of MMR with different configurations is 30–70 %. The power is due to the transmission ratio of the accelerator, ranging from 100 to 102 W. Then, the application scenarios of MMR-based EMVEH are analyzed and discussed, including vehicle suspension, road speed bumps, railway tracks, ocean waves, and human motion. In addition, the difficulties faced by MMR-based EMVEH are also proposed and analyzed under different application scenarios. Finally, the paper addresses the challenges and further investigations. • A review of MMR-based electromagnetic vibration energy harvesting is presented. • Structure, dynamics, features of MVR-based EMVEH are analyzed. • The validation method of MVR-based EMVEH is summarized. • The typical application scenarios of MVR-based EMVEH are summarized. • Challenges, difficulties and future research points in this field are proposed. [ABSTRACT FROM AUTHOR]

In order to fully utilize the advantages of liquid phase discharge and achieve efficient hydrogen production. This paper designed an new-type electrode structure and determined the optimal the electrode length in the reactor. On this basis, the contribution of solution pH and H radical to methane reforming in liquid-phase microwave plasma for hydrogen production was mainly discussed. The results indicated that acidic conditions are more conducive to increasing hydrogen production, while alkaline conditions can increase the percentage content of hydrogen. The difference in hydrogen production performance is speculated to be related to the influence of H+ or OH− concentration in the solution on the relative strength of discharge radicals. The important role of H radical in the reaction system was determined through the addition of radical quenchers and spectral diagnosis. H radicals are not only important substances in the reaction to generate H 2 but also have an impact on the conversion of methane and the strength of other radicals, thereby changing the amount of other gas-phase products. On the premise of achieving the goal of stable discharge, this experiment provides theoretical guidance for the subsequent liquid phase discharge reforming of methane to produce hydrogen. • This paper designed a novel electrode structure combining boron nitride and tungsten. • The optimal length for the electrode is 50 mm in the reactor. • The pH can affect discharge characteristics and plasma chemical reactions. • The generation of H radical is beneficial for H 2 production and CH 4 conversion. [ABSTRACT FROM AUTHOR]

The supercritical carbon dioxide (S–CO 2) Brayton cycle is a potential technology for recovering waste heat from compressed natural gas (CNG) engines. To choose suitable cycle configurations is important considering the complex operating characteristics of CNG engines. The traditional evaluation models of the S–CO 2 cycles configuration are relatively one-sided without the comprehensive considerations of evaluation metrics. On the basis of the analytic hierarchy process and information entropy theory, this paper proposes a multi-dimensional comprehensive evaluation method for S–CO 2 cycles from three perspectives: thermodynamic, economic, and environmental performance. A thermoeconomic performance optimization is conducted using a non-dominated genetic algorithm Ⅱ. Unlike most of the existing researches that use a single stable operating condition, this paper considers the full operating conditions of engines. Results show that the recompression cycle has the best overall performance among the investigated cycles with an exergy efficiency and electricity production cost of 67.20 % and 0.45 $/kWh, respectively. The efficiency improvement of the CNG engine coupled with the WHR system can reach up to 6.31 %. This study can provide a new reference for the comprehensive multidimensional evaluation and performance characteristics and optimal performance acquisition under full engine operating conditions of the S–CO 2 cycle. • A multi-dimensional comprehensive evaluation method for S–CO 2 Brayton cycle. • Thermo-economic optimization under full operating conditions is conducted. • The optimal operating regions of decision variables are obtained. • The CNG engine efficiency with waste heat recovery can be improved by 6.31 %. [ABSTRACT FROM AUTHOR]

The natural hydraulic connections between cascade hydropower may make hydraulic and electric connections mismatch after the market clears. Aiming at relieving the contradiction between water surplus and water shortage after market clearing, this paper proposes a new market clearing method considering the coupling relationship between water and electricity. And the method is verified by the simulation of a virtual spot market consisting of eight stations in Sichuan Province, China. The main contributions of this paper lay on three aspects: firstly, a new index called water-electricity coupling degree (WCD) and its calculation method are proposed to measure the overall situation of water shortage and water abandonment in the clearing results. Secondly, a multi-objective clearing model for cascade hydropower stations is established with the goal of minimizing power purchase cost and maximizing WCD. Thirdly, a solution method combined with the characteristics of the clearing model is developed. The case results demonstrate that the built model increases the WCD value by 1.57 % and reduces the insufficient output and surplus water by 45.18 % without increasing the power purchase cost of the power grid; The proposed solution strategy lowers the complexity of the model and improves the solution efficiency, where the search space reduction and time period merging strategies can reduce the calculation time by about 3 % and 8 % respectively. The new clearing method provides a new idea for solving the uncoupling problem of water-electricity-price in the cascade hydropower spot market. • Propose a new index called water-electricity coupling degree (WCD). • Establish a multi-objective clearing model for cascade hydropower market. • Develop a model dimensionality reduction strategy for model solution. [ABSTRACT FROM AUTHOR]

Traditional flat PV heat pumps has low irradiation density, large PV area, and the PV evaporators are always fixed, causing large area occupation and lots of irradiation missing, which limit the application and system efficiency. To solve the problems, a novel concentrated PV-air dual-source heat pump system (CPV-DSHP) was proposed in the previous paper, but the annual performance and economic comparison with flat PV-DSHP (FPV-DSHP) are still blank, which are extremely concerning to researchers. Besides, the paper also fills related content about the impact of irradiation composition on the system performance and economics, as well as the irradiation comparison on tracking CPV and fixed FPV surface. From the calculation of the validated model, it's found that the CPV-DSHP system could capture 6098 MJ and 2213 MJ more solar energy, and produce 282.21 kWh and 100.16 kWh more electricity in Garze and Hefei in a year. The annual concentration efficiency is 82.73 % in Garze where direct irradiation accounts for 68.89 %, which is only 73.15 % in Hefei with 39.84 %.The COP PVT is annually lifted by 0.40 in Garze. Although the two systems could both get payback in 5∼6 years, the CPV-DSHP only shows economic advantages in places with good direct irradiation conditions like Garze. • A novel concentrated PV-air dual-source heat pump system (CPV-DSHP) is presented. • The annual performance and economic comparison in different cities are supplemented. • The CPV-DSHP could produce 282.21 kWh more electricity in Garze in a year. • The annual concentration efficiency is 82.73 % in Garze with better direct irradiation. • Payback time is 5∼6 years but CPV-DSHP only shows economic advantage in Garze. [ABSTRACT FROM AUTHOR]

New materials and designs have been developed and proposed to enhance the thermal and electrical performance of photovoltaic-thermal (PV/T) systems in recent years. Despite the significant attention received by PV/T technology, the available PV/T modules are scarce due to complex system designs, environmentally unfriendly working fluids, and low thermal efficiency. In this paper, a highly efficient water-based PV/T system is proposed by combining an innovatively designed cooling system with a PV module. An experimental evaluation of the designed PV/T system was carried out in laboratory conditions. The test results of the PV/T system showed that when the inlet mass flow rate and temperature were 0.0458 kg/s and 11.90 °C, the PV/T system reached the maximum thermal efficiency of 96.47 ± 1.40 %. The increase in mass flow rate from 0.0042 kg/s to 0.0375 kg/s and the decrease in inlet temperature from 17.70 °C to 11.40 °C led to a 12 % improvement in thermal efficiency of the PV/T system showing that the inlet mass flow rate and temperature have a significant influence on thermal efficiency. The developed 3-D numerical model accurately estimated the outlet temperature of the PV/T system. A maximum of 3.43 % error occurred between the experimental results and model output. • The paper proposes a novel, highly efficient, state-of-the-art PV/T system. • The designed PV/T system achieves a maximum thermal efficiency of 96.47 ± 1.40 %. • The designed cooling system reduces the PV module temperature by 40.72 °C. • The electrical conversion efficiency of the PV module is enhanced by 18.32 %. [ABSTRACT FROM AUTHOR]