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

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

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

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

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

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

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

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

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

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

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

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

Faced with the difficult but urgent task of reducing carbon emission intensity, considering urban agglomeration as a fundamental spatial unit for this purpose has become increasingly important. However, as a long-standing and pivotal phenomenon in the development of urban agglomerations, the potential role of spatial functional division in urban agglomerations in reducing carbon emission intensity has been largely overlooked. To fill this gap, this paper explores the relationship between spatial functional division in urban agglomerations and carbon emission intensity, relying on a dataset of 19 urban agglomerations in China. The main results reveal an inverted U-shaped relationship between spatial functional division in urban agglomerations and carbon emission intensity. Spatial functional division in urban agglomerations increases carbon emission intensity in the initial stage; once it reaches a turning point, spatial functional division in urban agglomerations can reduce carbon emission intensity. This conclusion remains robust after conducting various robustness checks and addressing endogeneity concerns. This paper identifies productivity enhancement as an underlying mechanism that shapes this relationship. Moreover, the relationship between spatial functional division in urban agglomerations and carbon emission intensity exhibits heterogeneity based on function types and urban types. These findings emphasize that spatial functional division in urban agglomerations plays a unique role in reducing carbon emission intensity, which offers a new strategy for reducing carbon emission intensity from an individual city level to an entire urban agglomeration level. • An inverted U-shaped relationship exists between SFD and CEI. • Productivity enhancement derived from SFD is a crucial influencing mechanism. • Function types and urban types influence the relationship between SFD and CEI. • Improving SFD adds a novel urban agglomeration-level strategy to reduce CEI. [ABSTRACT FROM AUTHOR]

The partial shading condition (PSC) is the most common abnormality occurred in photovoltaic (PV) systems. Accurate quantitative evaluation of the shaded area and the severity of the shading is of potential importance in optimizing the maintenance strategy for PV systems. In this paper, we propose a PSC diagnosis and quantitative evaluation method by analyzing the measured string current–voltage (I–V) characteristic obtained from the PV inverter with the I–V scanning function, which includes pre-diagnosis of the system abnormality based on the operational power deviation, the diagnosis of PSCs based on the derivatives characteristics of the PV string, and the quantitative evaluation based on the I–V characteristic reconstruction. The quantitative evaluation of PSCs is the main focus in this paper, where the I–V characteristics of the unshaded PV modules in the partially shaded PV string are reconstructed according to different mismatch levels, respectively. The number of shaded PV modules and the corresponding severity of the partial shadings are estimated according to the reconstructed I–V characteristics. The simulation and experimental results verify that both the proposed diagnosis and quantitative evaluation method is effective with decent accuracy, especially for severe mismatch conditions. Experimental results show that the maximal mean absolute error of the quantified shaded area and quantified shaded rate are approximately 1.4446 units of 1/3 PV modules and 0.026, respectively. [Display omitted] • A new process for PSCs diagnosis and evaluation based on PV string I–V characteristics is proposed. • Normal portion of the measured string I–V characteristics is used to reconstruct healthy I–V curves. • The parameters used to quantify the shaded area and severity of shading are specified. [ABSTRACT FROM AUTHOR]

Demand response capacity is a key reference for demand-side participation in peak regulation and earning profit in the electricity market, and it is influenced by not only external public factors but demand-side internal individual factors. Demand change caused by internal individual reasons cannot be recognized by public features. Single participating in response industrial and commercial consumers cannot balance this change through large numbers of participants, unlike aggregators. The prediction model only considering the external public factors will cause significant errors. However, the actual industrial and commercial data is difficult to obtain because of the cost and privacy, and the simulation platform cannot accurately describe this situation, resulting in the problem being ignored. Therefore, this paper aims to prove the importance of internal factors and proposes an improved method considering the problem. First, based on an actual dataset, the optimal responsive behavior of consumers is modeled to quantify response capacity. Second, a support vector machine (SVM) method is applied to predict response capacity. Finally, combined with raw data, this paper proves that significant errors are caused by internal factors and provides different improvement methods for internal and external operators. The case study indicates that the improved model can provide better performance. • The impact of internal factors on response capacity prediction is proven, and prediction accuracy has been improved. • A method is proposed to improve the prediction accuracy for operators who cannot directly obtain internal information. • Based on the actual dataset, optimization and simulation of user behavior can better reflect the real response situation. • This study is developed under the price-based and incentive-based composite demand response program. • This study improves the modeling method of HVAC. [ABSTRACT FROM AUTHOR]

The present research on photovoltaic (PV) forecasting is devoted to the use of spatial information about PV sites to improve the accuracy of the models, but most of the models have to increase their network complexity to learn the spatial dependence. In this paper, we propose to take advantage of the known geographic location information of PV sites and embed them directly into the input information of Decoder, which makes it easier for the model to focus its attention. We refer to this process as relevance amplification. Based on this, this paper proposes Relevance Amplification based DeepFEDformer (RAD-FEDformer), where DeepFEDformer adds multiple Multi-Layer Perceptron (MLP) layers to FEDformer to improve the perception of deep features. The Relevance Amplification Module (RAM) is designed to receive geographic correlation information as a way to enhance its influence on the Seasonality component of the Decoder input and improve the performance of the attention mechanism in the model. Using the power generation data from PV plants distributed in 11 regions of Belgium as our case study, we evaluated the performance of RAD-FEDformer in predicting PV data at 48/96/192 time steps into the future for each plant. Compared to other Transformer family models, RAD-FEDformer achieved SOTA results by demonstrating significant R 2 improvements of 16.70%, 51.45%, and 23.50% over FEDformer, along with an average MSE reduction of 16.9%. We designed Ablation Experiments to validate and compare the performance of MLPs of different sizes based on the ETTm1 dataset and discussed the performance of RAM and its effect on the attention mechanism based on the PV dataset. The results show that our model enhances the performance of the attention mechanism in Multi-Site PV prediction scenarios, and conclude that the optimization of RAM is more effective in longer sequence predictions. • Propose a module for embedding geographic information into input data with low overhead. • Improving the FEDformer model using Multi-layer Perception. • Discuss the performance of the model on multivariate prediction task based on ETT and PV datasets. • Compare several popular Transformer family forecasting models in the case study. [ABSTRACT FROM AUTHOR]

With tidal energy demonstration projects with one or a small number of turbines having provided very positive results, the technology is moving to the commissioning and operation of tidal energy farms. For this next step, the understanding of the wakes generated downstream of the turbines is crucial to optimize the array performance. To date, the analysis of wakes of MW-scale tidal turbines has been made by numerical methods or experimentally with downscaled rotors. However, no consensus on a methodology to characterize wakes generated by full-scale turbines based on data measured on-site has yet been reached. The present paper introduces a new method to compare current velocity data measured before and during turbine operation that minimizes the impact of the spatial and temporal variability of tidal currents, thus enabling the estimation of the velocity deficit caused downstream of the turbine. Through this method, a characterization of the near wake was possible, with velocity deficits of 0.537, 0.463, 0.469 and 0.431 at 2D, 3D, 4D and 5D from the turbine. Results from this paper present a very valuable tool for the validation of numerical models aiming to estimate the wake losses in tidal energy farms. • Wake from a 0.5 MW bottom-fixed tidal turbine in a tidal site is characterized. • A new method to calculate velocity deficit with vessel-mounted ADCP data is presented. • Velocity deficit at the rotor hub height decreases from the turbine to a 5D distance. • Farther from 5D, the turbine impact on current velocity dissipates. [ABSTRACT FROM AUTHOR]

The energy sector prioritizes innovative clean energy production. This research explores a versatile hybrid solar-biomass trigeneration system, integrating organic Rankine cycle (ORC), a heat pump, and a proton exchange membrane electrolyser. Sustainable and adaptable, it produces electricity, heating, and hydrogen for Indian paper and pulp industries. The study assesses the system's thermodynamic, economic, and environmental feasibility. Initially, parametric analysis and first layer multi-objective optimization (MOO) are performed to select the efficient working fluid and operating temperatures. Benzene outperforms other fluids, achieving an energy utilization ratio (EUR) of 0.27 and exergy efficiency of 75 %. A typical heat requirement of 250 kW for processing 150 kg of paper necessitates collector areas of 3500 m2 and 4500 m2 in Aurangabad and Tiruchirappalli, respectively, and biomass waste of 0.05 kg/s. The combined system performance during day time and night time operation was studied by altering the critical solar radiation, ambient temperature and biomass waste flow rate. The second layer MOO revealed an optimum EUR of 0.25 and exergy efficiency of 9 % at the lowest cost of 33 $/h for biomass waste operation. In comparison to the conventional electricity-powered system, the proposed system would reduce CO 2 emissions by 90 kg/h to 270 kg/h. [Display omitted] • Solar and biomass waste powered systems proposed for paper and pulp applications. • ORC integrated heat pump system for heating, electricity, and green H 2 generation. • Collector area and biomass flow rate are designed for daily paper output of 300 kg. • Benzene is an efficient working fluid, produced 815 kW of heat and 3 kg/h of H 2. • The recommended system reduced CO 2 emissions by 90 kg/h to 270 kg/h. [ABSTRACT FROM AUTHOR]

To meet the requirements of the update for the supersonic landing and take-off (LTO) cycle standard, a methodology for studying the LTO supersonic standard based on pollutant emission characteristics is proposed in this paper to analyze the thrust and time of the LTO. In this study, three distinct supersonic engine models were developed based on subsonic engine cores. Subsequently, the pollutant emission calculation models were developed to calculate the pollutant emission index (I e) of supersonic engines and analyze the pollutant emission characteristics. Findings confirmed the presence of a correlation between standard formulations and pollutant emissions. Finally, considering that the current LTO standard leads to excessive noise during the climb phase and inefficient combustion during the taxi/ground idle phase, the LTO supersonic standard was analyzed for the trade-off, which resulted in the more applicable and fairer standard being formulated. The results indicate that the LTO supersonic standard formulated in this paper proves to be more reasonable than the current LTO standard and accurately represents the pollutant emission characteristics of distinct supersonic engines during both the climb and taxi/ground idle phases. Consequently, this study's results have guiding significance for formulating the future LTO supersonic standard and predicting pollutant emissions from supersonic engines. • Outdated supersonic landing and take-off cycle standards are updated. • A trade-off analysis of the supersonic standard is developed. • More comprehensive supersonic engine models and supersonic emission calculation methods are developed. • The updated supersonic climb standard more closely approximates the emissions from the noise-reducing climb trajectory. • The updated supersonic taxi/ground idle standard is better able to meet the combustion efficiency requirements. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient bi-level energy management strategy (EMS) to optimize the operation cost of a grid-connected microgrid, considering the system operational constraints and uncertainties for renewable energy sources and load demand. The first level is optimal day-ahead scheduling based on two stages: the first stage is finding the optimal operating points of sources during the next day while the second one is controllable loads management. The second level of the proposed EMS is rescheduling and updating the set-points of sources in real-time according to the actual solar irradiance, wind speed, load, and grid tariff. In this paper, a novel real-time strategy is proposed to keep the economic operation during real-time under uncertainties. Also, a recent meta-heuristic algorithm called Honey Badger Algorithm (HBA) is used to solve the problem of day-ahead scheduling of batteries, which is a complex constrained non-linear optimization problem. Results obtained demonstrate that the HBA based bi-level EMS provides the real-time optimal economic operation of a grid tied microgrid under uncertainties in weather, utility tariff and load forecasts. • Bi-level energy management strategy for optimal operation of grid tied microgrids. • Load shifting approach that reshapes the load according to the target load profile. • A novel real-time scheduling strategy of grid tied microgrids under uncertainties. [ABSTRACT FROM AUTHOR]

Growing demand for electricity due to economic development contributes to increased greenhouse gas production, especially CO 2. However, emissions can be limited by enhancing the efficiency of primary energy conversion, such as integrating geothermal energy into coal-fired power plants. Therefore, this paper proposes replacing conventional feed-water heaters with geothermal preheaters to create a hybridized system. This study was based on a numerical model validated at a selected Polish power unit. The model was subsequently calibrated for off-design conditions to facilitate partial load analysis. The obtained characteristics outperformed those of the non-hybrid unit, generating over 18 MW of electric power output. Such an improvement could potentially boost the unit's net efficiency by more than 2.6 %. This enhancement is significant as power units typically operate under part load for approximately 90 % of the time, hence the need to evaluate the performance characteristics of hybridized units in those states. Furthermore, the research outlines the potential decrease in the plant's CO 2 emission factor, with reductions reaching up to 6.5 % under off-design conditions. Based on a gap analysis of the existing literature, this paper's comprehensive partial load evaluation serves as a new addition to research on hybridized systems. • An in-depth analysis of feed-water heaters operation is performed. • Turbine performance calculations for both design and off-design states is validated. • The comprehensive study of hybridized systems integrates three major factors. • Hybrid geothermal-fossil power cycle is vital for specific CO 2 emissions reduction. [ABSTRACT FROM AUTHOR]

Electricity load exhibits daily and weekly cyclical patterns as well as random characteristics. At present, prevailing deep learning models cannot learn electricity load cyclical and stochastic features adequately. This results in insufficient prediction accuracy and the scalability of current methods. To tackle these difficulties, this paper proposes a framework for electrical load prediction based on an Attention Mechanism Time Series Depthwise Separable Convolutional Neural Network (ELPF-ATDSCN). The framework starts by using the Maximum Information Coefficient for exogenous variable selection. It then incorporates a seasonal decomposition algorithm with manual feature engineering to extract the cyclical and stochastic features of the electrical load. Subsequently, the framework employs the ATDSCN to learn the cyclical and stochastic features of the electrical load. In addition, the Bayesian algorithm optimizes model hyperparameters for optimal model performance. Experimental results of point and interval load prediction on datasets from the US and Nordic power markets reveal that the ATDSCN model proposed in this paper enhances load prediction accuracy compared with other models. It can provide more reliable predictions for power system operation and dispatch. • A new attention mechanism is designed to improve the model memory capability. • The ATDSCN model is proposed for learning periodic and stochastic features of electricity loads. • A framework for electricity load prediction is proposed to improve the accuracy of electricity load forecasting. • The effectiveness of the proposed method is validated on the real world dataset. [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]

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]

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]

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]

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]

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]

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]

Preventing the impact of natural disasters on urban-rural income inequality helps to achieve shared prosperity. This paper uses the functional entropy weight method to calculate the level of natural disasters from 2004 to 2020. Then, this paper discusses the impact of natural disasters on the urban-rural income gap. The empirical results are as follows. (1) Natural disasters have expanded the urban-rural income gap. The regression coefficient is 0.2663, which is significant at the 1% level. (2) In areas with a high proportion of the rural population, the impact of natural disasters is more prominent. (3) Mechanism analysis indicates that natural disasters have widened the urban-rural income gap by restraining rural investment and agricultural production. Finally, this paper suggests strengthening investment in the agricultural sector to mitigate the negative impact of natural disasters. • This paper uses the functional entropy weight method to calculate the level of natural disasters. • This paper quantitatively measures the impact of natural disasters on the urban-rural income gap. • This paper examines the mechanism of the impact of natural disasters on the urban-rural income gap. • This paper provides policy suggestions for narrowing the urban-rural income gap. [ABSTRACT FROM AUTHOR]

Deep Learning (DL) models, such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), have been widely used to predict the intermittency of wind power; however, the non-linear activation functions and backpropagation mechanisms in DL models increase computational complexity and energy consumption. This paper proposes a prediction model based on Green Learning (GL) to reduce energy consumption. The proposed GL model replaces the feature extraction of activation functions with a hybrid feature extraction approach combining categorical and numerical features. We also employ cluster centroids and quantile regression forest for classification/regression to eliminate the need for backpropagation in optimizing hyperparameters. Using Taiwan as a case study, this paper evaluates the risk of fluctuations in regional wind power generation in 2030. In simulations, the proposed GL model achieved excellent accuracy with energy consumption significantly lower than that of DL models. Our analysis also revealed that by 2030, fluctuations in wind power generation during the winter will exceed 40% of the peak supply capacity in the central region, indicating the need to enhance the resilience of regional power systems. • This is the first study to apply green learning to wind power prediction. • This study discriminated seasonal climate patterns at the regional level. • This study assessed the fluctuation risk under large-scale wind power integration. • The maximum hourly variability in wind power is projected to exceed 6,010 MWh by 2030. • We recommend the continued deployment of regional energy storage systems. [ABSTRACT FROM AUTHOR]

The paper explores Coastal Energy-Based Industrial Clusters (EBICs) and their role in advancing energy efficiency and sustainability through collaborative innovation. Economic growth theory and energy sustainability have been introduced into industrial clusters to illustrate indicators that have a greater impact on the development of EBICs. This paper proposes an EBICs development model based on the Cobb-Douglas function, in which accounts for various factors that drive the progress of such clusters. The outcomes of the economic model also provide insights into how the interaction of various factors affects the economic growth of EBICs and their eventual dominance in the energy market of coastal regions, dependent on gradually investing in areas such as research and development (R&D). Different development strategies demonstrate that the final development of a cluster has low dependence on the cluster's initial advantages. The study also illustrates how clusters can gradually monopolize the energy market, even with initial disadvantages. Through quantitative analysis, it showcases the transformation process of developing advantageous clusters into sub-clusters. Next, an energy symbiosis framework for coastal is proposed, which places greater emphasis on the multi-energy complementary system and reduces production costs. Finally, this paper also sheds light on shaping energy strategies for public authorities who shape economic policies at various levels of aggregation and in diverse dimensions. • Multi-energy complementarity provides a new path for sustainable development of coastal energy. • Hardware investment is critical to the development of energy-based industry clusters. • Clusters with better natural advantages may become sub-clusters due to incorrect development strategies. • The essence of energy cluster development is the result of the interaction of multiple influencing factors. • The economic growth of energy clusters depends on the proportion of investment in software and hardware. [ABSTRACT FROM AUTHOR]

The core of the energy transformation under the carbon-neutral vision is the gradual replacement of high-carbon energy by zero-carbon and low-carbon energy. However, the large-scale deployment of clean energy technology in turn relies on key minerals such as copper, aluminum, zinc, and nickel. Based on this, this paper conducts research on the demand for key minerals in clean energy transition and the role of key minerals in clean energy transition. In particular, this paper calculated the demand for mineral supply for China's carbon neutrality target, and the impact of some mineral resource constraints on the carbon neutrality path. Relevant results can provide a decision-making basis for the future development and utilization of key mineral resources, as well as the implementation of future carbon-neutral goals. • We evaluate the impact of crucial mineral resource on the carbon neutrality path. • Copper's availability greatly affects the adoption of new energy technologies. • Limited resources hinder early carbon capture and storage development. • Negative emission technologies are necessary for China's carbon neutrality. [ABSTRACT FROM AUTHOR]

To accelerate the realization of the "carbon neutrality and carbon peaking" (dual carbon) goal, the "Opinions on Financial Support for Carbon Neutrality and Carbon Peaking" (the "Opinions") advocates the comprehensive adoption of tax incentives, government procurement and market-diversified investment measures to support the green low-carbon innovation transformation of key industries. Taking China's strategic new energy vehicle (NEV) industry from 2009 to 2021 as a sample, this paper empirically investigates the innovation effect of the measures in the "Opinions" on listed NEV enterprises. The results show that: (1) Both "supply-side" tax incentives and "demand-side" government procurement encourage NEV enterprises to innovate independently, jointly and efficiently, and the "supply-side" tax incentives have a stronger innovation effect. At the same time, compared with joint innovation, government policies are more conducive to independent innovation of NEV enterprise. (2) Market venture capital also encourages NEV enterprises to innovate. Government policy signals to the market can attract venture capital, and the "demand side" government procurement signal effect is stronger. (3) Tax incentives and venture capital, government procurement and venture capital have played an innovative synergistic effect on NEV enterprises. (4) There is an innovation failure in the combination of tax incentives and government procurement, but the innovation effect appears after the introduction of market venture capital. This paper not only supports the effectiveness of the innovative measures in the "Opinions", but also recognizes the continuation of the vehicle purchase tax exemption policy for NEVs. • "Opinions" measures encourage NEV to innovate independently, jointly and efficiently. • "Supply-side" tax incentives have a stronger innovation effect. • "Demand side" government procurement has a stronger signal effect. • Government policy and venture capital play an innovative synergistic effect. • There is an innovation failure in policy mix before venture capital intervention. [ABSTRACT FROM AUTHOR]

The scale of pig breeding in China is the largest in the world, and the pollution problem of breeding wastewater is serious. Supercritical water gasification (SCWG) process is a clean and efficient treatment technology for breeding wastewater. This paper involved constructing the molecular model of the primary organic components in pig breeding wastewater. The effects of time, temperature and mass concentration, and the interactions of different components on the SCWG characteristics of pig breeding wastewater were studied by molecular dynamics (MD) simulation, and the decomposition process of lignin and lipid molecules in the SCWG process were analyzed, and the possible intermediates were explored. The results showed that the effect of parameters on carbon gasification efficiency (CE) and H 2 production follows the order of temperature > time > mass concentration. During the gasification of lignin and lipid molecules, the C–O–C bond was broken first, and the gasification rate of lignin molecules was slow due to the benzene ring. Interactions among different organic decompositions hindered the gasification process. This paper will provide theoretical guidance for the selection of operation parameters and the control of products in the actual pig breeding wastewater treatment process. • The MD model of wastewater containing complex organics was established. • The effects of key parameters on SCWG of pig breeding wastewater were studied. • The detailed SCWG mechanisms of pig breeding wastewater were analyzed. • The intermediate products from SCWG of pig breeding wastewater were explored. • The interaction mechanisms of different organics during SCWG were analyzed. [ABSTRACT FROM AUTHOR]

Currently, lignite-fired boilers often feature low thermal efficiency, difficult coal pulverizing and poor combustion stability owing to lignite having high water content. A novel lignite-fired power generation process is proposed in this paper with using a superheated-steam-drying open-pulverizing-system boiler (SSD-OPSB) and a deep utilization of waste heat from the steam for coal drying. Through using an open pulverizing system, the problems of low furnace temperature and low boiler efficiency are resolved. Meanwhile, the operation safety of the coal pulverizing system is ensured by using superheated steam as the drying agent. This paper described the working principle and engineering feasibility of the SSD-OPSB process, and established analysis model for its thermo-economic performance. Energy savings and environmental benefits of applying the process were calculated and discussed based on an in-service 600 MW supercritical power unit firing lignite with water content of 39.5% at its turbine heat acceptance condition. The result shows that for the power unit, applying the SSD-OPSB process can decrease coal consumption by 6.86% under the same power supply, meanwhile recovering water by 132.2 t/h and reducing CO 2 emission by 27.78 t/h. It indicates that the SSD-OPSB process would significantly benefit lignite-fired power plants regarding energy-saving and environmental protection. • Lignite-fired boilers can benefit greatly from applying open coal pulverizing systems. • The benefits include marked coal-savings, enhanced coal combustion and water recovery. • A novel superheated-steam-drying open-pulverizing-system was proposed for applications. • Waste heat and water recovery system was designed to the new power generation process. • The model of analyzing thermo-economic performance of this process has been developed. [ABSTRACT FROM AUTHOR]

This paper establishes a multi-objective optimization model for railway heavy-haul trains, focusing on reducing carbon emissions and improving transport efficiency. The model integrates optimization of the route and the vehicle load rate, significantly reducing carbon emissions and enhancing transport efficiency. It addresses the challenges and characteristics of heavy-haul trains, introducing multi-objective optimization problems related to transport carbon emissions and efficiency. Using a pigeon-inspired optimization algorithm, the model considers joint constraints between carbon emissions and transport efficiency objectives. To overcome challenges in multi-objective transportation problems, the paper proposes a forward-learning pigeon-inspired optimization algorithm based on a surrogate-assisted model. This approach calculates the quality of the candidate solution using a surrogate model, reducing time costs. The algorithm employs a forward-learning strategy to enhance learning from non-dominant solutions. Experimental validation with benchmark functions confirms the effectiveness of the model and offers optimized solutions. The proposed method reduces carbon emissions while maintaining transport efficiency, contributing innovative ideas for the development of sustainable heavy-duty trains. [Display omitted] • A model is build for carbon emissions reduction and transport efficiency enhancement. • The model combines route and load rate optimization. • The model achieves carbon emission reductions while improving transport efficiency. • A novel meta-heuristic algorithm for solving the railway heavy-haul trains model. • The proposed algorithm introduces fresh insights for railway heavy-haul trains. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of solar gas heaters that utilize an air gap between the glass cover and the absorber, along with radiating gas. By using a participating fluid as the working gas, thermal radiation can be absorbed, emitted, and scattered, enhancing the heat transfer between the absorber and the flowing gas. Free convection airflow inside the air gap adds to the forced convection airflow through the solar collector's duct. The study utilizes the κ-ε model and the discrete ordinate method for radiative intensity computation to analyze the flow and energy equations. Numerical results indicate a significant increase in thermal efficiency, particularly at lower gas mass flow rates, when employing radiating gases with high radiative absorption coefficients. The research shows about 40% improvement in thermal efficiency is obtained in test cases at a gas mass flow rate of 0.01 kg/s. A gas flow optical thickness of τ = 2 is found to be optimal among the investigated parameters. In addition, this paper demonstrates high efficiency of up to 70% for plane solar collectors without the need for configuration changes by using working gases with high radiative properties, such as pressurized CO2. • Effect of radiating gas on the performance of solar gas heaters (SGHs) is examined. • An air gap is embedded between the glass cover and the absorber. • The efficiency of SGH has increased 40% for low mass flow of 0.01 kg/s. • The optical thickness of τ = 2 for the gas flow gives the best performance. • A simple plane collector could have efficiency of 70% just by utilizing a working gas with high radiative properties. [ABSTRACT FROM AUTHOR]

Accurate prediction of wind speed is significant importance in various applications such as renewable energy management, weather prediction, and aviation safety. However, more papers only focus on the time series of wind speed, ignoring the impact of other factors on wind speed, which will reduce the effectiveness of wind speed prediction. Therefore, an innovative methodology is suggested for both point and interval forecasts of multivariate wind speed time series using a Dual-layer Long Short-Term Memory (NVDL) in this paper. The proposed multivariate forecasting model takes into account the dependencies and correlations among different variables, which are essential for capturing the complex dynamics of wind speed variations. The first layer of the Dual-layer LSTM is responsible for capturing the temporal dependencies within each variable individually, while the second layer captures the interdependencies among the variables. By incorporating this dual-layer architecture, our model effectively captures the complex spatiotemporal patterns present for multivariate wind speed information. The results obtained from both interval and point prediction demonstrate that the proposed methodology outperforms all comparative models in the precision and stability of wind speed forecasting. Therefore, the proposed forecasting methodology, characterized by minimal prediction errors and exceptional generalization ability, can be a reliable tool for smart grid programming. • This article proposes an enhanced version of the dual-layer LSTM structure. • A self-adaption preprocessing to manage the impact of noise-induced interference. • Fuzzy interval forecasting can give the upper and lower of wind speed effectively. [ABSTRACT FROM AUTHOR]