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This paper proposes a distribution network fault emergency power supply recovery strategy based on 5G base station energy storage. This strategy introduces Theil's entropy and modified Gini coefficient to quantify the impact of power supply reliability in different regions on base station backup time, thereby establishing a more accurate base station's backup energy storage capacity model that can fully utilize the base station's energy storage resources to participate in the emergency power supply of distribution network faults. First, the optimal Copula function in each cycle is determined through the Akaike information criterion and squared Euclidean distance to establish a wind power-photovoltaic output scenario. Secondly, the traditional Gini coefficient is modified using the weighted average node degree and influence rate indicators. A comprehensive vulnerability model is established through the modified Gini coefficient and Theil's entropy indicators. Based on the comprehensive vulnerability model, a backup energy storage time model and a modified backup energy storage capacity model of the base station affected by power supply reliability are established. Thus, the callable energy storage capacity of base stations in different areas is obtained. Finally, a two-stage robust optimization model is introduced to minimize system operating costs to solve the volatility of 5G base station communications and wind-solar output, thereby establishing an emergency power supply recovery model for base station energy storage and wind-solar output. Simulated with the improved IEEE-33 node model, the results show that the proposed base station's energy storage model improves the utilization of the base station energy storage resources and, at the same time, effectively reduces the loss of load during the fault phase of the distribution network and improves the absorption of the PV output. • Modeling of 5G base station backup energy storage. Aiming at the shortcomings of existing studies that ignore the time-varying characteristics of base station's energy storage backup, based on the traditional base station energy storage capacity model in the paper [ 18 ], this paper establishes a distribution network vulnerability index to quantify the power supply reliability of the distribution network nodes by adopting the voltage Theil's entropy and the modified Gini coefficient. Based on the power supply reliability of power grid nodes and combined with load level weights, a model for the backup energy storage time of base stations affected by power supply reliability is established. • The model of 5G base station energy storage participating in power supply recovery In view of the impact of changes in communication volume on the emergency power supply output of base station energy storage in distribution network fault areas, this paper introduces a two-stage robust optimization model to deal with the uncertainty of communication volume. Based on the base station energy storage capacity model established in contribution (1), an objective function is established to minimize the system operating cost in the fault area, and the base station energy storage owned by mobile operators is used as an emergency power source to participate in power supply restoration. Establish sub-objective functions of the loss cost of base station energy storage charging and discharging, the subsidy cost of base station energy storage charging and discharging, and the base station energy storage operating cost to supplement the shortcomings of existing research. [ABSTRACT FROM AUTHOR]

In an integrated electricity-gas system (IEGS), gas-fired units with flexibility response capabilities address the uncertainty associated with renewable energy by swiftly adapting their output, which may result in uncertain fluctuations in the gas withdrawal of gas-fired units, potentially posing risks to the security of IEGS. Hence, it is necessary to efficiently evaluate the dynamic security region (DSR) of natural gas systems in IEGS by considering the real-time dynamic characteristics of natural gas systems, which are not satisfactorily handled in state-of-the-art works. To bridge this gap, this paper first conceptionally verifies the necessity of the DSR and establishes its mathematical model. Then, a dimensionality reduction method is proposed for the efficient solution and visualization of the high-dimensional DSR evaluation model. A fast evaluation (FE) algorithm is developed to address the difficulties of the nonconvex dynamic constraints in the reduced DSR model. Finally, the necessity and notable advantages of the proposed DSR model and FE are verified based on small and relatively large test systems in comparison with common security region models and algorithms. To the best of our knowledge, this is the first paper that comprehensively presents models and efficient algorithms regarding the DSR of natural gas systems in IEGS. • This paper proposes the novel dynamic security region (DSR) of the natural gas system with natural gas system dynamics. • A dimensionality reduction method for the efficient solution and visualization of the high-dimensional DSR is proposed. • A fast evaluation (FE) algorithm is developed to address the nonconvex dynamic constraints in the DSR model. • Case studies confirm the necessity and effectiveness of the DSR, dimensionality reduction method, and FE. [ABSTRACT FROM AUTHOR]

Compared with the conventional turbofan engine, the adaptive cycle engine (ACE) has more extensive flight envelope and the more types and number of adjustable components. While the performance of each mission profile is improved adaptively, the controller design of profile switching process will face strong flight condition disturbance and internal uncertainty. The traditional control system's single loop controller and discrete switching method of control parameters are to some extent difficult to meet the adaptive and robustness requirements of ACE. Thus, this paper proposed a multi-variable decoupling anti-disturbance controller for ACE based on the results of flight envelope partitioning with similar distances, embedded a state-dependent switching law. By analyzing the impact of inlet disturbances, internal actuator actuation, and random degradation of component performance on different switching laws, the adjust time and overshoot of the controller with state-dependent switching law proposed in this paper are reduced by an average of about 11.91 % and about 9.98 %, and the average thrust linearity and robustness has increased by about 0.096 % and 20.02 %. Finally, the reliability and feasibility of the control system were verified based on a hydraulic driven semi-physical experimental platform, providing reference for more complex and dynamic engine operation control in the future. • Consider external and internal disturbances during the ACE mission profile switching. • A multivariable ADRC controller with state-dependent switching law for ACE is proposed. • The controller robustness was analyzed through stochastic perturbation simulation. • The system reliability was verified based on RVABI semi-physical experimental platform. [ABSTRACT FROM AUTHOR]

This paper analyzes the impact of water and emission limitations on expansion planning of thermal power plants (TPPs), in Iran. TPPs need considerable amount of water for their cooling system. In Iran, water crisis of recent years has made it impossible to operate existing steam power plants in some areas, during summer season. This paper considers water availability and cost in generation expansion planning (GEP) of Iran. There are different types of water resources such as rivers, dams, treated wastewater, seawater, seawater transmission line, saline groundwater, and groundwater. Considering available capacity of water resources in each area and sorting their extraction cost in ascending order, some water consumption cost curves are derived. For this purpose, data of about 200 water resources located in different regions of Iran are analyzed. Provided GEP model includes a 15-years planning horizon and analyzes several scenarios of water availability. Although, in low water price scenarios, air temperature and height above sea level have more impact on placement of TPPs, it is different for high water price scenarios. Also, the effect of emission-related policies on GEP decisions is analyzed by considering various fuel types of TPPs, penalties for emissions, and global restrictions on greenhouse gas production. • Proposes an optimization model for generation expansion planning (GEP) that considers environmental constraints. • Analyzes the availability and price of different water resources in Iran and their effects on GEP decisions. • Examines the impact of temperature and height above sea level, on the siting of thermal power plants in Iran. • Considers decarbonization policies in the expansion planning of power plants in Iran. [ABSTRACT FROM AUTHOR]

Users' energy use behavior has great impact on the economic and efficient operation of integrated energy systems (IES). This paper proposes energy usage strategies based on integrated demand response (IDR) by developing a Stackelberg game based simulation model, in which the user-side flexible resources (FRs) and comfort cost are considered. In the paper, a framework of community integrated energy system (CIES) and an IDR model considering the consumer psychology were established first, in which the community users were viewed as a user aggregator (UA). A Stackelberg game simulation model was then developed for studying the energy interactions between community integrated energy service provider (CIESP) as the leader and UA as the follower based on the proposed IDR model. The benefit gain and energy use behavior of the UA were analyzed, by using the model developed, in six scenarios. The study found the strategy developed with the consideration of FRs and consumer psychology could make the UA's cost of purchasing energy reduced by 17.14 %. Meanwhile, the consumer surplus could increase by 11.58 %. The results also showed that by introducing user-side gas boilers and distributed energy storage more benefit gain for users and improved load regulation capabilities for CIES could be achieved. • A Stackelberg game based simulation model is developed for a CIES. • An IDR model considering the consumer psychology is established. • The optimal energy use strategy is obtained considering the user-side flexible resources and comfort cost. • This strategy can reduce the cost of energy use and increase the consumer surplus. [ABSTRACT FROM AUTHOR]

Accurate prediction of building energy consumption is crucial to the rational scheme of building energy. Combining the entropy-weighted K-means (EWKM) with the random forest (RF) method, a feature selection (EWKM-RF) method is proposed in this paper. Based on the proposed EWKM-RF method, the classification and feature selection of the energy consumption influencing factors can be achieved exclusively. Meanwhile, based on the EWKM-RF method and the bi-directional long-short-term memory neural network (BiLSTM) optimized by the sparrow search algorithm (SSA), an RF-SSA-BiLSTM prediction model for building energy consumption is established in this paper. As the weight, learning rate, and hidden layer node parameters of the BiLSTM neural network are optimized with the SSA, the constraints for manually adjusting parameters are avoided in the proposed prediction model. To examine the accuracy of the proposed model, energy consumption data of a civil public building in Dalian city are collected and tested. Results show the prediction error of RF-SSA-BiLSTM after feature selection is reduced by 24.55 % in high and low energy consumption months. Compared with RF-BiLSTM, RF-PSO-BiLSTM, and RF-CNN-BiLSTM, the RF-SSA-BiLSTM has strong robustness. The average MAE, RMSE and MAPE values of energy consumption prediction in the four seasons are 1.30, 1.63 and 0.02. [Display omitted] • A new prediction method is developed for building energy consumption. • The integration of SSA and BiLSTM neural network is analyzed. • Combinations of EWKM with RF algorithm to reduce influencing factors. • Deep learning is used to extract features of building energy consumption data. • Comparison of the proposed prediction models with different optimization models. [ABSTRACT FROM AUTHOR]

With recent energy and environmental crises, the energy consumption prediction of air conditioning (AC) is essential. Notably, existing general prediction systems use the air temperature, real-time AC usage, time, occupancy, and AC historical energy consumption as inputs and employ feedforward neural networks. However, such systems face certain challenges. First, the input system may be difficult to reflect the real-time interaction among the building environment, occupants, and energy system; Second, the algorithms need to be upgraded to further improve accuracy, speed up convergence, and avoid overfitting. his paper introduces a novel parameter (group occupant behavior probability, GOBP) in the input system; and uses swarm intelligence algorithms with chaotic mapping and adaptive weight adjustment as optimization with LSTM and BP as the basic algorithms. The verification results obtained from five different buildings indicate the following: (1) The novel prediction system improved prediction performance with 20.08%–78.37 % of MAPE and 3.99%–53.28 % of CV-RMSE, however, it will increase 148.70s–1065.11s of calculation time. (2) For accuracy, using GOBP will improve the 0.30%–51.87 % and 0.18%–64.77 % by using swarm intelligence optimization algorithms. (3) This paper discusses the priority of selecting suitable inputs, algorithms, and optimization to combine a whole prediction system. • The definition of a simplified model using the group occupant behavior probability is proposed. • The chaotic mapping and adaptive weight adjustment are used for optimizing the swarm intelligent algorithms. • A reconstituted AC energy consumption prediction system is proposed with novel inputs and improved algorithms. • The reconstructed prediction system demonstrates improved prediction accuracy and data acquisition difficulty\. • The best combination of inputs and algorithms is discussed based on 60 groups of comparative experiments. [ABSTRACT FROM AUTHOR]

Obtaining the aerodynamic performance of the turbine blade by Computational Fluid Dynamics (CFD) methods is accurate. However, it consumes time and computational resources. This paper proposes an evaluation method based on Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) to obtain the aerodynamic performance of the turbine blade accurately and quickly. Compared with the existing data-driven modeling methods, this method innovatively introduces the Residual Network (ResNet), employs a transfer learning strategy for network design, and realizes the automatic extraction of blade profile features and non-parametric input. In processing boundary conditions, the ANN is utilized to fuse the blade profile features with the boundary conditions to realize the mapping between blade profile and aerodynamic performance under different conditions. In addition, to minimize the prediction deviation caused by the severely uneven distribution of the data set, we combined ensemble learning with transfer learning and proposed a two-step prediction strategy. The numerical simulations results show that the ResNet-ANN model established in this paper has a prediction relative error of 5 % on turbine blade aerodynamic parameters under various working conditions. The error is reduced by more than 90 % under −40°-10° incidence angle of incoming flow compared with the empirical model. • The method for predicting turbine blade profile loss is non-parametric. • The scheme is effective on both design and off-design conditions. • A two-step strategy is proposed for precise prediction on unevenly distributed data. • The TSP strategy is based on transfer learning and ensemble learning. • The approach is of high accuracy with considerable generalization. [ABSTRACT FROM AUTHOR]

Urban building layout has an important influence on the formation of shadows on building façades, and photovoltaic (PV) power generation performance is greatly affected by shadows. However, there is limited research on detailed modeling of the power generation of photovoltaic systems on facades with different building layouts. In this paper, according to different urban building layouts, an annual dynamic shadow model of the building and a mathematical model of PV array power generation were established and validated. Then the model was used to calculate the annual power generation hour by hour, optimize the PV array topology, and analyze the shadow shading ratio and PV power generation performance of different building layouts. The results show that 95 % of the best topologies of all partially shaded façades in the four layouts are Total-Cross-Tied (Vertical and Horizontal); the highest annual shading ratio is layout 4 with a value of 60.161 %, the largest annual power generation is layout 3, but layout 4 has the highest broad generation efficiency, with layouts 1, 2, and 3 lower than it by 4.29 %, 0.20 %, and 0.11 %, respectively. This paper provides new ideas for the early design of building layouts and the performance research of PV systems. • Yearly dynamic shadow and detailed PV models developed for urban layouts. • Optimized PV topologies under year-round dynamic shading. • 95 % optimal topologies are Total-Cross-Tied (Vertical and Horizontal). • Compared annual PV output and broad efficiency across building layouts. • Layout 4 excels in broad generation efficiency, leading by 4.29 %. [ABSTRACT FROM AUTHOR]

This paper makes the first attempt to look at the response of the energy market to the shock from the AI industry and the role of AI in the energy market risk spillover network. The innovation of the paper lies in adopting a quantile-based and time-varying framework in the empirical analysis. This enables us to reveal the cross-quantile and lead-lag correlations between AI and the energy market. We find that an extreme negative shock from AI tends to correspond to an extreme downward movement in crude oil, gasoline, gas oil, and clean energy markets and the shock response dissipates after 66 days. Meanwhile, the cross-quantile correlations are subject to structural changes once the extreme events arrive. As for the total spillover effect of the energy market, it increases by 5.34 % after AI is considered as a source of uncertainty in the risk spillover network dominated by the crude oil market. Thus, the shock from AI challenges the stability of the energy market. A close relationship between the AI industry and the clean energy market is also identified. We contribute to studying the relationship between the AI industry and the energy market from the perspective of the risk connectedness between the two fields. [Display omitted] • The extreme negative shock from AI corresponds to the extreme downward performance in the energy market. • The cross-quantile correlations are subject to structural changes once the extreme events arrive. • The total spillover index of the energy market increases by 5.34 % after AI is considered. • A close relationship between the AI industry and the clean energy market is identified. [ABSTRACT FROM AUTHOR]

Solar radiation is an essential meteorological parameter for building energy efficiency analysis, and the quality of the data directly affects the analysis results. This paper investigates the estimation of hourly solar radiation based on the generation of the typical meteorological year(TMY) using various real meteorological parameters and limited solar radiation data. The focus of this paper is to use two types of neural network algorithms to improve the estimation accuracy and applicability, and to solve the problem of hourly solar radiation data acquisition in non-radiation areas. First, select two city station data and use three methods to generate TMY. Then, two neural network models, BP Neural Network (BP)，Convolutional Neural Network (CNN) are used to estimate the hourly solar radiation data and verify the results. Finally, by constructing a photovoltaic-integrated office building model, the accuracy of the hourly solar radiation estimation model is verified using energy consumption simulation and photovoltaic (PV) power generation simulation. The results show that this paper can well solve the problem of limited radiation data, which provides a new idea for the study of building energy efficiency in areas where radiation data is missing. • Multiple neural network is used to estimate the generation of solar radiation data using other meteorological parameters. • An advanced neural network is used to provide an effective TMY generation method in missing hourly solar radiation data. • The accuracy of the experimental results is verified by energy consumption simulation and photovoltaic power generation. [ABSTRACT FROM AUTHOR]

The proper integration of wind power-driven grids relies heavily on a reliable balance between electricity production and demand. Therefore, accurate prediction is essential for planning and efficient operation of wind power systems to ensure their continuous supply. However, increasingly severe power quality disturbance (PQD) constantly disturbs this equilibrium, which affects the accuracy of wind power prediction to a large extent. For this purpose, this paper developed a novel optimization methodology to improve wind power prediction accuracy considering micro PQD dimension reduction and elimination for wind-storage integrated systems. A novel idea has been presented in this optimization methodology to eliminate the barrier of PQD and wind power prediction. In the micro aspect, a PQD dimension reduction and elimination strategy based on dynamic mode decomposition (DMD) and Wiener Filter (WF) is proposed to eliminate the autonomy of PQD. This elimination of autonomy allows the WF to get a higher signal-to-noise ratio (SNR). In the macro aspect, this paper takes PQD of different complexity into full consideration, and compares their effects on improving wind power prediction accuracy based on deep learning-based approaches. Through the experimental verification, it is confirmed that the proposed DMD-WF optimization method has demonstrated an effective dimension reduction and elimination of PQD. Moreover, it is found that the proposed PQD optimization method contributes to improve the deep learning-based prediction accuracy when PQD is more complex. The proposed methodology creates a novel perspective to improve the short-term wind power prediction accuracy, which provides a theoretical and methodological guidance for future development of large-scale integrated wind-storage systems. • Development of a novel methodology of PQD optimization and wind power prediction. • A Wiener Filter and dimension reduction-based PQD optimization method is proposed. • WOA-Attention-LSTM is used to analyze forecast accuracy based on PQD optimization. [ABSTRACT FROM AUTHOR]

The Pelton turbine is a type of hydraulic turbine suitable for medium, high and ultra-high head hydropower exploitation. As energy conversion units, the Pelton turbine has numerous design parameters affecting its energy conversion efficiency. In this paper, a three-dimensional unsteady simulation was conducted to analyze the influence of bucket number on the hydraulic performance and internal flow behavior of a model Pelton turbine. Five different bucket numbers in a total runner were selected for numerical examination, including the optimal number of 21 determined by the empirical formula. As the bucket number increased with the same operating conditions, the predicted hydraulic efficiency of the Pelton turbine rose to the maximum under optimal bucket number and then decline. Compared with the optimal bucket number of 21, the hydraulic efficiency of the Pelton turbine with 15, 17 buckets decreased by 2.8 % and 1 % respectively. By analyzing the variation of torque coefficient and water sheet flow under different bucket numbers, the interacting mechanism between bucket number and performance of the Pelton turbine was discussed. Numerical result indicated that the phase difference of the single bucket torque coefficient affected by the flow pattern exerted an influence on mean of the runner torque coefficient and hydraulic efficiency. This paper provides an in-depth understanding of the bucket number to the flow mechanism in the bucket for exploring the energy conversion efficiency of the Pelton turbine. • Disclosed the interacting mechanism of bucket number on Pelton turbine performance. • Studied the water sheet flow behavior in a Pelton turbine with 5 bucket numbers. • Disclosed the effect of each bucket on runner torque with 5 bucket numbers. • Bucket cutout leakage flow caused the efficiency drop at low bucket numbers. [ABSTRACT FROM AUTHOR]

With the large-scale EV connected into the wind power grid, the intermittent, fluctuating and stability bring rigorous challenges for power quality and dispatch, thus wind curtailment becomes more serious. This paper proposes an advanced multi-objective collaborative scheduling strategy to improve the accuracy of the predictable wind grid and ensure the stable operation of the grid by optimizing wind power and EV synergistically. Firstly, a hybrid power prediction method is proposed based on density-based spatial clustering applied to noise (DBSCAN) and partial least squares regression (PLSR) combined with long and short-term memory neural network (LSTM). It establishes a linear component prediction mode to predict the nonlinear wind power efficiently. Secondly, a multi-objective optimization scheduling model is established according to the predicted wind power, considering the vehicle-to-grid (V2G) characteristics of EV, grid load fluctuations, wind curtailment capacity, EV charging costs, and grid losses. The global control aims to achieve overall energy balance and optimize the charging and discharging of EV in a time dimension under constrained conditions. Additionally, based on the total number of EV charging and discharging determined by the global level control, a spatial scheduling optimization is conducted for EV and wind power by the distribution network control, with the goal of minimizing network losses in the distribution network system. Finally, through simulation and comparison of four scenarios, the results demonstrate that the proposed method achieves synergistic optimization of wind power and EV. This paper provides an efficient solution for the optimized scheduling of large-scale EV connected to the predictable wind power grid. • It can filter the noise data and reduce the disturbance of abnormal values. • The power prediction accuracy is improved obviously based on local actual wind power data. • It can optimize the charging and discharging of EV under constrained conditions. • The charging costs of EV users is decreased significantly. [ABSTRACT FROM AUTHOR]

Under the guidance of the "Dual Carbon" goal, integrated energy supply stations have gradually become an essential facility for the energy transition. Promoting user repurchase has become a vital marketing strategy for integrated energy supply station enterprises. This paper proposes a prediction method based on the user profiling method to predict user repurchase behavior accurately. First, using an improved RFM model and the K-means algorithm, this paper constructs user profiles by dividing 10,000 users into three clusters: general-value developmental users, high-value new users, and low-value loyal users. Next, this paper uses the random forest, light gradient boosting machine, and extreme gradient boosting to predict the repurchase behavior of non-clustered users and the three clusters and compares their prediction performance. In addition, this paper adopts the stacking method for model fusion to improve the prediction performance further. The results show that the accuracies of the best prediction models for the three clusters are 93.28 %, 93.68 %, and 92.84 %, respectively. Finally, this paper provides each cluster with the corresponding prediction model of user repurchase behavior and marketing strategy. For the application scenario of integrated energy supply stations, this study accurately predicts the repurchase behavior of each cluster with unique consumption characteristics. It helps to provide personalized services for new energy vehicle consumers, optimize their consumption experience, and facilitate sustainable consumption. • A method for predicting user repurchase behavior for integrated energy supply stations based on the user profiling method. • This paper clusters users based on the unique consumption characteristics and constructs user profiles for each cluster and individual. • Three mainstream ensemble learning algorithms to predict the repurchase behavior of each cluster. • Stacking algorithm for model fusion to improve the prediction performance further. [ABSTRACT FROM AUTHOR]

Spontaneous combustion often occurs when carbonaceous materials are stored for a long time. Up to now, domestic and foreign scholars have done a lot of research on the spontaneous combustion mechanism of coal and biomass fuel, monitoring methods and prevention measures, and achieved fruitful results. It is worth noting that coal and biomass are not the same type of energy, but the internal mechanism and external performance of spontaneous combustion of the two are very similar, and both involve complex physical and chemical reactions. At present, very fruitful results have been achieved in coal spontaneous combustion monitoring and fire prevention in mine, but the spontaneous combustion research around coal storage silos and biomass silos is insufficient, and the spontaneous combustion monitoring and fire prevention technology for them is relatively backward. Therefore, by summarizing and comparing the relevant work in recent years, this paper aims to sort out the similarities and differences of the occurrence mechanism, monitoring methods and prevention measures of the two, explore the technologies and theories that can learn from each other, promote the progress of the two in the study of spontaneous combustion mechanism, and promote the development of more advanced spontaneous combustion monitoring and fire prevention technology. And the application in the mine-out area, coal storage silo, biomass silo and other scenarios, to provide theoretical and technical references for the future safe storage of coal and biomass. • This paper first draws out the similarities and differences between coal and biomass in terms of spontaneous combustion mechanism, monitoring and prevention through case analysis. In order to promote their integration and reference in theory and technology. • Through the comparison of biomass and coal spontaneous combustion studies, this paper puts forward some feasible new ideas in spontaneous combustion monitoring and fire prevention. • This is of great innovative significance for spontaneous combustion monitoring and prevention of coal and biomass under silo conditions. [ABSTRACT FROM AUTHOR]

The drive for improved energy efficiency and flexibility has birthed an innovative solution: an energy hub. This hub operates in sync with natural gas and electricity grids, integrating batteries and thermal storage to optimize renewable energy use, reduce costs, and bolster network stability. In addressing these objectives, a mathematical optimization model for energy hub operation is developed in this paper. This model captures the intricate interplays between electricity and natural gas networks, accounting for the intricacies posed by uncertainties and variations in renewable energy sources and demand profiles. Also, this paper uniquely delves into the integration of load response programs, an approach geared towards amplifying the energy hub system's flexibility and reliability. Through these programs, subscribers gain the agency to calibrate their energy consumption based on real-time pricing signals and incentives dispensed by the energy hub. The simulation findings serve as a compelling testament to the efficacy of the proposed system. Operating costs witness a substantial reduction, the adoption of renewable energy sources is markedly heightened, and the stability and reliability of the upstream energy networks are tangibly ameliorated. The integration of load response programs emerges as a particularly effective strategy in mitigating peak demand and augmenting the system's overall energy efficiency. Underpinning the entirety of this innovative solution is the transformation of the challenges into an optimization problem. This catalyzes the employment of a newly developed particle community algorithm, custom-tailored to surmount the challenges of local optima. This algorithm inherently bolsters the system's search capabilities, minimizing the risk of entrapment within localized solutions. Ultimately, the proposed energy hub system encapsulates a promising trajectory toward the realization of a sustainable and adaptable energy landscape. By seamlessly integrating diverse energy sources and storage systems, augmenting energy flexibility and efficiency, and aligning with broader energy transition and decarbonization goals, this solution presents a formidable contender. The simulation outcomes underscore its potential by showcasing a substantial 4.57 % reduction in the cost of procuring energy from the upstream electrical network. • Holistic energy hub engages in upstream electricity and heat markets, optimizing profit. • Empowered energy hub actively influences markets with optimized strategies. • Robust optimization ensures stable operations in uncertain renewable sources. • Advanced load management optimizes profiles, aligning consumption with pricing. • Particle swarm optimization algorithm enhances search for optimal solutions. [ABSTRACT FROM AUTHOR]

Industrial chain structure optimization and digital transformation are crucial factors for achieving carbon emission reduction goals. Based on China's non-competitive input-output tables, this paper constructed industrial chain panel data using the structural path analysis method. We explored the influence of industrial chain division (ICD) on CO 2 emission intensity through panel fixed effects model, and examined the moderating effect of digitization. The research found that: (1) The ICD can significantly reduce CO 2 emission intensity, the results are robust after some endogeneity tests. (2) The emission reduction effect brought by ICD will be more obvious in high-stage, high-energy consumption, and high-intelligence industrial chains. There is still room to improve the emission reduction brought by the division network effect. (3) Digitization plays a positive moderating role on this effect. Furthermore, digital manufacturing and digital services exhibit heterogeneity in various types of industrial chains. (4) The ICD can reduce carbon emission intensity through pathways such as intelligent transformation, technological innovation, and energy efficiency, and digitization has positively moderated these impact pathways. Based on research results, targeted policy suggestions are provided for optimizing industrial chain structure and promoting digital transformation. • This paper constructed industrial chain carbon emission intensity panel data. • This paper assessed the industrial chain digitization by digital intermediate input. • This paper constructed industrial chain synergistic division (ICSD) index. • This paper explored the influence of ICSD on CO 2 emission intensity. • Digitization plays a positive moderating role on carbon emission reduction effect. [ABSTRACT FROM AUTHOR]

When it comes to energy sources used in electricity production, the future forecasting of electricity production from renewable energy sources is highly important for both the success of technological advancements in the renewable energy field and energy security. To forecast electricity production from renewable energy sources reliably, it is necessary to accurately model the components of the relevant series. The central argument of this paper is that the various components derived from electricity production data, particularly the residual component, retain valuable predictive information despite their intricate and nonlinear nature. While linear modelling may be highly accurate initially, repeating residuals within linear structures is a discrepancy in terms of data type and methodology. In this paper, different types of hybrid models that combine a decomposition method and both machine learning and statistical approaches are suggested for forecasting electricity production from different energy sources. • Every individual component of a time series retains inherent predictive information. • To improve forecast accuracy, a two-tier approach is presented. • The multivariate models use inputs that can offer crucial information for accurate forecasts. • The use of a hybrid model leads to a significant improvement in forecasting accuracy. [ABSTRACT FROM AUTHOR]

Accurate fuel consumption prediction is essential for optimizing the operation of wing-diesel hybrid vessels and improving energy efficiency. This paper proposes a grey box model (GBM) for wing-diesel hybrid vessel fuel consumption prediction based on feature construction. Both parallel and series grey box modelling methods, as well as six machine learning (ML) algorithms are adopted to establish twelve combinations of prediction models. Then, a feature construction method based on the aerodynamic performance of the wing and the energy relationship of the hybrid system is proposed. Three types of wing features, namely wing thrust, wing thrust power, and wing fuel consumption savings are constructed and introduced into each combination respectively. Finally, based on noon report data of a wing-diesel hybrid vessel, the combinations are trained and validated. The best combination is obtained by considering the root mean square error (RMSE), which is parallel modeling method, random forest (RF) algorithm, and wing fuel consumption savings feature. Its RMSE decreased by 41.7 % compared to the white box model (WBM). Therefore, the GBM proposed in this paper can predict the daily fuel consumption of wing-diesel hybrid vessels with high accuracy, facilitating operational optimization and contributing to the greenization and decarbonization of the shipping industry. • A GBM for predicting fuel consumption of wing-diesel hybrid ships is proposed. • Method proposed for constructing wing features to improve GBM prediction performance. • The data integration method is adopted to improve the quality of meteorological data. • The GBM prediction error (RMSE) is reduced by 41.7 % compared to the original WBM. • The GBM can predict the fuel consumption of hybrid system under various conditions. [ABSTRACT FROM AUTHOR]

The transportation sector is deemed one of the primary sources of energy consumption and greenhouse gases throughout the world. To realise and design sustainable transport, it is imperative to comprehend relationships and evaluate interactions among a set of variables, which may influence transport energy consumption and CO 2 emissions. Unlike recent published papers, this study strives to achieve a balance between machine learning (ML) model accuracy and model interpretability using the Shapley additive explanation (SHAP) method for forecasting the energy consumption and CO 2 emissions in the UK's transportation sector. To this end, this paper proposes an interpretable multi-stage forecasting framework to simultaneously maximise the ML model accuracy and determine the relationship between the predictions and the influential variables by revealing the contribution of each variable to the predictions. For the UK's transportation sector, the experimental results indicate that road carbon intensity is found to be the most contributing variable to both energy consumption and CO 2 emissions predictions. Unlike other studies, population and GDP per capita are found to be uninfluential variables. The proposed multi-stage forecasting framework may assist policymakers in making more informed energy decisions and establishing more accurate investment. • An interpretable multi-stage forecasting framework is proposed. • The drivers of energy consumption and CO2 emissions in UK's transport sector are identified. • A comprehensive comparison between different machine learning models is carried out. • The impacts of feature selection methods on machine learning models are investigated. [ABSTRACT FROM AUTHOR]

This paper presents a new framework for optimal planning of electrical, heating, and cooling distributed energy resources and networks considering smart buildings' contribution scenarios in normal and external shock conditions. The main contribution of this paper is that the impacts of smart buildings' commitment scenarios on the planning of electrical, heating, and cooling systems are explored. The proposed iterative four-stage optimization framework is another contribution of this paper, which utilizes a self-healing performance index to assess the level of resiliency of the multi-carrier energy system. In the first stage, the optimal decision variables of planning are determined. Then, in the second stage, the smart buildings and parking lots contribution scenarios are explored. In the third stage, the optimal hourly scheduling of the energy system for the normal condition is performed considering the self-healing performance index. Finally, in the fourth stage, the optimization process determines the optimal scheduling of system resources and the switching status of electrical switches, heating, and cooling pipelines' control valves. The proposed method was successfully assessed for the 123-bus IEEE test system. The proposed framework reduced the expected values of aggregated system costs and energy not supplied costs by about 49.92% and 93.64%, respectively, concerning the custom planning exercise. • This paper presents a new framework for optimal planning of distributed energy resources. • The impacts of smart buildings' commitment scenarios on the optimal planning are explored. • The proposed iterative four-stage optimization framework is another contribution of this paper. • The proposed framework reduced the expected values of aggregated system and ENS costs. [ABSTRACT FROM AUTHOR]

This paper develops a two-stage model to site and size a battery energy storage system in a distribution network. The purpose of the battery energy storage system is to provide local flexibility services for the distribution system operator and frequency containment reserve for normal operation (FCR-N) for the transmission system operator. In the first stage, the priority is to fulfil the flexibility needs of the distribution system operator by managing congestions or interruptions of supply in the local network. Thus, the first stage allocates the battery to ensure reliable electricity supply in the local distribution network. The minimum required size of the battery is also determined in the first stage. The second stage optimally sizes the battery energy storage system to boost the profit by providing frequency containment reserve for normal operation. The first and second stages both solve stochastic optimization problems to design the battery energy storage system. However, the first stage considers worst-case scenarios while the second stage utilizes the most probable scenarios derived from the historical data. To validate the proposed model, real-world data from the years 2021 and 2022 in Finland are employed. The battery placement is conducted for both the IEEE 33-bus system and a Finnish case study. The profitability of the model is compared across different cases for the Finnish case study. Finally, the paper assesses the impacts of cycle aging on the battery's total profit. • A two-stage model is developed to site and size distribution-network-connected Battery Energy Storage System (BESS). • The first stage determines BESS location and minimum size for local network security. • In the second stage, the BESS profit is maximized by providing frequency containment reserve for normal operation (FCR-N). • The effects of cycle aging and the capacity fade on model profitability are analyzed. [ABSTRACT FROM AUTHOR]

It is increasing crucial to improve the sustainable use rate of energy and contribute to carbon neutrality and environmental footprints reduction. This work features cutting-edge selected extended papers and developments that were presented at the 25th Conference on Process Integration, Modelling and Optimization for Energy Saving and Pollution Reduction, held on 5–8 September 2022 in Bol, Croatia. More than 400 papers were accepted and presented at this conference. Out of these papers, the 45 contributions in this Virtual Special Issue represent a selection of the most cutting-edge developments in the topic of the Sustainable Use of Energy Contributes to Carbon Emissions Neutrality and Environmental footprint reduction. This paper provides a review of these contributions and above, and groups them into four research themes: (1) Energy Conversion, Storage, Recovery and Efficiency Improvement, (2) Energy Management, Planning and Optimization, (3) Energy, Environment and Economy, and (4) Heat Transfer, Heat Pumps and Heat Convection. [ABSTRACT FROM AUTHOR]

Despite their many advantages, industrial compressed air systems are characterized by high energy consumption and very low energy efficiency. This is primarily due to the oversizing of pneumatic actuators, leading to excessive compressed air consumption in the utilization stage. Various methods have been proposed in the literature to address this issue, which can be classified into three groups: supply pressure reduction, back pressure increase, and intermittent power supply. However, no exergy analysis compares these methods in the utilization stage of a pneumatic system was performed. In this study, we conducted an exergy analysis to compare the exergy consumption and exergy savings of different pneumatic system configurations: the classic configuration, supply pressure reduction, back pressure increase, and intermittent power supply. The paper shows the most key places in CAS in terms of exergy losses, which result mainly from dead volumes, outlet loss and oversizing of components such as directional control valve, distributed pneumatic line and actuator. Losses in those components can amount to over 80% of input exergy and can be reduced even 3–9 times. Exergy evaluation of oversizing configurations showed that the highest exergy savings were obtained for a back pressure configuration (77%) and intermittent air supply (75%). The exergy savings for the pressure reduction configurations amounted to almost 50%. The paper presents mathematical models for several individual pneumatic components, including the actuator, directional control valve, muffler, pneumatic line and pressure reducer. The demonstrated mathematical model and exergy analysis can play a instrumental role in evaluating the exergy efficiency and optimization of a pneumatic system. [Display omitted] • Original exergy analysis tool to assess the exergy efficiency of pneumatic systems. • The overconsumption of exergy are caused by oversizing and dead volumes. • The exergy saving over 70% is obtained for back pressure and intermittent air supply. • Configurations reduce exergy losses in dead volumes and output losses. [ABSTRACT FROM AUTHOR]

In this paper, 41 prefecture level cities in the Yangtze River Delta (YRD) are selected as the research objects. Based on the semantic mining model, the policy data information from three dimensions namely national, provincial and municipal levels is integrated to build data-driven multiple scenarios. Then the long short-term memory neural network optimized by particle swarm optimization (PSO-LSTM) is further used to predict the peaking time of the YRD urban agglomeration under multiple scenarios. The results show that the YRD urban agglomeration as a whole can achieve carbon peaking on time under all three simulation paths, but the peaking situation of specific cities within the region will be significantly different due to differentiated policy orientations. Under the business as usual scenario, some cities in the north of the region cannot achieve carbon peaking by 2030. Under the stable growth scenario, more cities cannot achieve carbon peaking on time. However, under the low carbon scenario, all cities within the region can achieve carbon peaking by 2030. Therefore, when exploring effective carbon peaking paths, adopting more stringent policies and measures to limit carbon emissions can help achieve the regional carbon peaking goal earlier, ensure that all cities within the region can achieve carbon peaking on time, and can also narrow the gap between cities within the region in terms of peaking time. This paper explores a new scenario analysis parameter setting and carbon emission prediction method, which overcomes the subjectivity of current scenario analysis research in parameter setting to a certain extent, and also provides a scientific reference for predicting the diversified development trend of carbon emission. • The semantic mining method is used to determine the scenario parameters. • PSO-LSTM is used to improve the accuracy of carbon emission prediction. • Multiple scenarios of carbon emission development trends. • Differentiated emission reduction paths for cities. [ABSTRACT FROM AUTHOR]

More accurate multivariate load forecasting is conducive to integrated energy system planning optimization and economic operation. In this paper, based on the consideration of exogenous factors such as weather, endogenous factors are added into the prediction, and a short-term prediction model for integrated energy multivariate load based on endo-exogenous uncertainty is established using fine-tuned transfer learning. The LSTM pre-training model is first initially established using genetic algorithm and exogenous factors such as weather. Then the exogenous uncertainty of the pre-training model and the knowledge of endogenous uncertainty in the target model are combined using fine-tuned transfer learning to continuously optimize the basic parameters of the prediction model to obtain the final multivariate load prediction model. The performance of the four models is comprehensively evaluated after the historical load validation of the integrated energy system in Beijing, China. Compared with the traditional LSTM multivariate load forecasting model, the MAPE of the proposed model in this paper is reduced by 21.03 %, 13.00 %, and 24.39 % for electricity, heat, and cooling load forecasting, respectively. • The coupling mechanism between multiple energy sources of the integrated energy system is analyzed. • An endogenous-exogenous uncertainty method is proposed to extract the dynamic coupling relationship between multiple loads. • An endogenous-exogenous uncertainty-based GA-LSTM multivariate load prediction model is developed. • Optimizing model parameters using fine-tuned migration learning to deepen the impact of endo-exogenous uncertainty. [ABSTRACT FROM AUTHOR]

In order for biochar (which is produced via waste wood pyrolysis) to be used in sustainable energy or material production, it is crucial to determine certain properties, such as its mechanical strength. Consequently, in this paper, municipal waste wood (MWW) 1 1 Municipal waste wood. and forestry residue biomass (FRB) 2 2 Forestry residue biomass. – as a point of reference – are examined in an autothermal pyrolysis unit. The mechanical strength of the biochar is analysed via an uniaxial compression experiment. The present paper proposes an advanced method to investigate the strength of pyrolysed biochar in a representative and comprehensive manner, since no standards are available in literature. It has been found that firstly, the mechanical biochar's strength decreases with increasing temperature, and secondly, that biochar prepared from FRB shows a higher breaking strength than biochar produced from MWW. Furthermore, an approximately linear progression between degree of pyrolysis and the mechanical strength is observable. Successful experiments on the gasification of MWW confirm the results obtained in this study. Additional allothermal pyrolysis experiments reveal higher strength values and reflect the more precise temperature adjustment. These results can be used as a point of reference in future research on alternative biogenic energy carriers and biochar products. • Lower mechanical strength with increasing pyrolysis temperature and waste wood usage. • Linear relationship between degree of pyrolysis and mechanical strength. • Pilot-scale experiments on waste wood gasification confirm obtained findings. • Higher mechanical strength with allothermal biochar pyrolysis. • Plot as basis for the switch from fresh wood to waste wood biochar. [ABSTRACT FROM AUTHOR]

To reduce the greenhouse gas emissions of the transport sector, synthetic fuels based on CO 2 and H 2 are one viable option. This paper evaluates the integration of a Fischer Tropsch process into a pulp mill to improve efficiency and to lower CO 2 emissions. Different syngas preparation technologies, plant sizes, and gas loop designs are considered. The processes were simulated with a chemical process simulation software to retrieve mass and energy balances. Based on the simulations, the processes are evaluated regarding economics, CO 2 emissions, and efficiency indicators. The Power-to-Fuel efficiency reaches almost 40 % for closed loop configuration with reverse water gas shift reactor for carbon monoxide production. For the open loop configuration the efficiency is tremendously lower. In closed loop configurations, the carbon efficiency reaches 80 %. The levelized cost of syncrude ranges from 3.28 to 7.69 €/kg. The most important impact on syncrude price comes from electricity price, investment cost, and efficiency of water electrolysis. Integrating the Fischer Tropsch process into the pulp mill shows advantages in terms of Power-to-Fuel efficiency and CO 2 emissions but not necessarily in terms of economics. • Integration in terms of mass and energy exchange between pulp mill and Fischer Tropsch process. • Investigation of different plant sizes, syngas preparation routes and gas loop designs of the Fischer Tropsch process. • Levelized cost of syncrude ranges from 3.28 to 7.96 €/kg. • Beneficial effects of the integration into the pulp mill: CO 2 emissions and Power-to-Fuel efficiency. [ABSTRACT FROM AUTHOR]

In order to solve the security threat brought by the volatility and randomness of large-scale distributed wind power, this paper proposed a wind power prediction model which integrates two-layer decomposition and deep learning, effectively realizing the accurate prediction of wind power series with non-stationary characteristics. Initially, pearson correlation coefficient (PCC) is employed to identify primary meteorological variables as input series. Second, the wind power series are smoothed by implementing complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and then all subseries are decomposed and obtained by utilizing empirical wavelet transform (EWT) for the components with the highest complexity. Subsequently, hidden information related to wind speed, wind direction, and wind power series are extracted through the bidirectional temporal convolutional network (BiTCN), and the obtained information is fed into a bidirectional long short-term memory network (BiLSTM) optimized by attention mechanism for prediction. Finally, the predicted values of all components are summed to derive the final prediction results. In addition, the significant advantages of the prediction model in this paper are verified by five comparison experiments. The mean absolute error (MAE) and root mean square error (RMSE) of the model's one-step prediction in the January dataset are 2.1647 and 2.8456, respectively. • A new wind power prediction method based on signal decomposition and deep learning is proposed • CEEMDAN, EWT is used to decompose the original wind power signal • BiTCN, BiLSTM, Attention are used to predict the decomposed subseries [ABSTRACT FROM AUTHOR]

The current single energy storage approach and market incentives do not fully exploit the potential of diverse energy storage methods and market incentives. Additionally, the traditional operation method often results in a poor alignment between energy supply and demand. Moreover, the conventional solution algorithm struggles to address complex problem-solving involving multiple variables. To address these issues, this paper proposes an improved carbon-neutral energy system (ICNES). Firstly, ICNES enhances residual energy utilization by implementing dual-layer residual power and heat treatment. Secondly, to enhance the effectiveness of market incentives, ICNES incorporates an improved approach that combines a dual-layer carbon capture drive, step carbon penalty, carbon reward, and green certificate trading. Thirdly, to improve the supply–demand matching, ICNES introduces a dynamic hybrid operation approach (DFHL). Finally, the paper proposes a honey badger-pattern search algorithm (HB-PSA) to optimize the configuration of ICNES for the DFHL multivariate scenario. Results indicate that compared to conventional carbon-neutral energy systems, ICNES reduces annual operating costs by 6.686%, CO 2 emissions by 9.538%, primary energy consumption by 12.826%, and energy waste rate by 17.465%. Furthermore, HB-PSA increases the optimization rate from 48.693% to 53.88% of the local optimum. • Dual-layer residual electric and residual heat treatment system is proposed. • Dual-layer carbon treatment for carbon neutrality and carbon profitability. • Carbon markets and green certificate are used in carbon-neutral energy system. • Dynamic hybrid operation to improve the matching of supply and demand. • A honey badger-pattern search algorithm is proposed for solving. [ABSTRACT FROM AUTHOR]

Zero-energy buildings usually achieve annual net-zero energy balance by generating electricity using on-site renewable energies and interconnecting with the power grid. However, the impact of energy mismatch problem in zero-energy buildings design on the grid is often overlooked, which potentially compromises grid's stability. To address this issue, this paper presents an optimization design method based on "grid-friendly interaction" which optimizes the energy system of zero-energy buildings to ensure better alignment between the building's electricity purchase/sale and grid demand. In this study, an indicator of "grid-friendly interaction", which represents the ratio of the net building friendly interaction power to the total annual interaction power, is firstly established to guide buildings in improving their interaction with the grid. Next, the energy supply, demand, and storage schemes of renewable energy systems in the zero-energy building are iteratively adjusted to achieve the best match with grid's expectations. This optimization design method was applied to a practical case in Hong Kong. Results show that, compared to the benchmark building, the optimal system design scheme increases grid friendliness by 83 %, reduces carbon dioxide emissions by 77 %, but also incurs a total cost increase of 180 %. The optimization design method proposed in this paper can effectively mitigate the impact of zero-energy buildings on the power grid, fostering enhanced interplay between the building structures and the grid. The study significantly contributes to the advancement of exploring the potential for energy flexibility in zero-energy buildings, which is of great significance for the safe and stable operation of future grids. The purpose of this study is to explore the energy flexibility potential of zero-energy buildings, which is of great significance for the safe and stable operation of future grids. • A novel indicator that reflects the degree of friendly interaction is proposed. • An optimization design method based on "grid-friendly interaction" is presented. • Single-objective and multi-objective optimization are applied respectively to ZCB. • It is of great significance for the safe and stable operation of future grids. [ABSTRACT FROM AUTHOR]

The emergence of distributed energy resources (DERs) is expected to result in an increase in the amount of power transmitted through the distribution power grid. If these DERs are not managed properly, the increased power transmission can cause faster degradation of distribution grid assets (pole-top transformers). To prevent this, measures can be taken to manage the DERs connected to a specific pole-top transformer. This paper presents a new coordination framework for home energy management system (HEMS)-integrated peer-to-peer (P2P) trading, with a focus on the impact of such trading on a Distribution Transformer. The proposed framework provides a comprehensive solution to manage the distribution of power within a smart grid environment by enabling HEMS to engage in P2P trading. This paper also examines optimal energy management in a smart neighborhood to minimize the total cost of energy usage. In addition, to prevent power peaks — that could create excessive power transmission and damage the distribution transformer, a cap within the flexibility bound of the household is placed on the total power households can draw/penetrate from/to the power grid. By optimizing P2P energy trading while considering the limitations of the distribution transformer, this approach enables a more efficient, reliable, and sustainable energy system. • Developing a new home energy management system. • Addressing demand-side flexibility with peer-to-peer trading. • Developing a network-aware peer-to-peer trading model. • Analyzing impacts on the distribution transformer. [ABSTRACT FROM AUTHOR]

This virtual special issue of ENERGY features 35 top-notch selected extended papers, which were showcased at the CORFU 2022 9th International Conference on Sustainable Solid Waste Management (https://corfu2022.uest.gr) in Corfu Island, Greece from June 15th to 18th, 2022. This special issue of Energy focuses on the cutting-edge research on waste-to-energy (WtE) in the circular economy, greenhouse gas reduction, and sustainable waste management systems that generate energy. This editorial paper provides a brief overview of the key methodologies, discoveries, and future horizons within the 35 selected papers. The contributions in this special issue are divided into five categories: 1) biofuels and biopower; 2) municipal waste management; 3) emerging resource utilization techniques; 4) current research of waste-to-energy for circular economy; and 5) advancements in waste-to-energy technologies. [ABSTRACT FROM AUTHOR]

Ever since 2002, when the first Conference on Sustainable Development of Energy, Water and Environment Systems (SDEWES) was held in Dubrovnik, the SDEWES conferences series has been providing a forum for world-wide scientists and those interested in learning about the sustainability of development, to present research progress and to discuss the state of the art, the future directions and priorities in the various areas of sustainable development. One of the main issues of the coming decades is to improve efficiencies by integrating various energy systems, using excess from one, as resource in another in the correct moment. Integrating electricity, heating, cooling, transport, water, buildings, waste, wastewater, industry, forestry and agriculture systems will be pivotal towards sustainable development. SDEWES has maintained high publishing standard as 2408 SDEWES papers were published in special issues of leading scientific journals including "Energy - The International Journal". In 2022, the 17th SDEWES Conference was held during November 6–10 in Paphos, Cyprus, the 5th SEE Conference took place in May 22–26, 2022 in Vlorë, Albania, the 3rd Latin American Conference was held through a virtual platform during July 24–28, 2022. The Energy journal has continued its cooperation with SDEWES launching a special issue dedicated to the 2022 Conferences. The special issue includes papers which traditionally cover a range of energy issues - higher renewables penetration and various technologies and fuels assessments at energy supply side, as well as, energy efficiency in various sectors, buildings, district heating, electric vehicles and demand modelling at energy demand side. [ABSTRACT FROM AUTHOR]

Electricity cross-subsidy policies have long been criticized for they involve subsidizing households by charging above-cost prices in industrial sectors. This paper examines the environmental and economic impacts of phasing out cross-subsidy in electricity prices in the context of China's Carbon Peaking and Neutrality goals by exploiting a large panel data from 2012 to 2020. We first adopt a price-gap approach to calculate the scale of negative cross-subsidy in China and different sectors. Next, we estimate the electricity demand price elasticity of high-, medium-, and low-emission industries to evaluate how the cross-subsidy affects electricity consumption, carbon emissions, and the output value. Finally, with the 2020 scenario as our baseline, we simulate three distinct policy change Schemes and evaluate the environmental and economic effects of phasing out electricity cross-subsidy in China. We find that if policy makers do not consider the emission levels of different industries when implementing cross-subsidy removal, great environmental harm will be caused. We also find that the potential burden on households of electricity cross-subsidy removal is likely to be relatively small at all implementation stages. This study provides timely policy implications for the ongoing electricity system reform in China. • This paper examines the environmental and economic impacts of phasing out cross-subsidy in electricity prices in the context of China's Carbon Peaking and Neutrality goals. • We find that if policymakers do not consider the emission levels of different industries in cross-subsidy removal, it will lead to large carbon emissions but small output in average. • If the cross-subsidy is fully borne by high-emission industries, the optimal Scheme is to reduce the cross-subsidy by approximately 50%–70% to obtain more economic output and less carbon emissions. • If high-emission bear twice as much cross-subsidy as the medium-emission industries, the carbon reduction and economic output will be maximised at 10%–30% of cross-subsidy removal. • We also find that the potential burden on households is relatively small at all implementation stages. [ABSTRACT FROM AUTHOR]

Whether renewable energy consumption is beneficial to reducing CO 2 emission intensity and carbon emissions in the context of the goal of "carbon peak and carbon neutrality" in China. To prove this conjecture, considering the spatial spillover effect of carbon dioxide, this paper adopts the spatial Dubin model for empirical analysis based on the panel data of China's 30 provinces over the period 2002–2019. This study explores the CO 2 intensity mitigation effect of China's renewable energy consumption and further investigates the mediation role of technological innovation, industrial structure upgrading and optimization, and energy efficiency in the nexus between renewable energy consumption and carbon intensity. The main findings present that: (1) China's carbon emission intensity is characterized by significant spatial correlation; (2) Renewable energy consumption can significantly reduce carbon intensity and per capita carbon emissions; (3) The mediation effect of renewable energy consumption in reducing carbon emission intensity is to promote technological innovation and industrial structure upgrading, as well as improve energy efficiency. Based on the conclusions, this paper offers several policy implications for governments to reduce CO 2 emission intensity. • The impact of renewable energy consumption on CO 2 emission intensity is explored. • Renewable energy consumption can reduce carbon emission intensity and per capita carbon emissions. • Renewable energy consumption reduces CO 2 intensity through the spatial mediation effect. [ABSTRACT FROM AUTHOR]

For energy conservation, emission reduction and sustainable development, China launched the New Energy Demonstration City Program (NEDC) to try a more decentralized energy governance structure. Based on the theoretical framework between NEDC and carbon emissions, this paper tests its policy impact and mechanism. The results show that NEDC can significantly reduce carbon emissions by about 15.34%. The policy effects of NEDC are characterized by time lag and increasing intensification. NEDC has a stronger effect on reducing carbon emissions in western cities and non-resource-based cities. NEDC can reduce carbon emissions through industrial structure effect, environmental decentralization and information technology effect. NEDC has a synergistic emission reduction effect, which can reduce about 20.39% of nitrogen oxides (NOx) and 18.70% of sulfur dioxide (SO 2). NEDC has policy spillover effect, which can not only reduce the carbon emissions of pilot cities, but also reduce the carbon emissions of neighbouring cities. The space emission reduction effect of NEDC is better within 400 km. Finally, this paper puts forward policy recommendations to strengthen decentralized energy governance to reduce carbon emissions. • NEDC can reduce carbon emissions, which has the characteristics of hysteresis. • Central cities and non-resource-based cities have greater policy effects. • Industry, environmental decentralization, and technology are policy paths. • NEDC has synergistic emission reduction effect, which can reduce NOx and SO 2. • NEDC has spillover effect when the distance is not more than 400 km. [ABSTRACT FROM AUTHOR]

The proposed targets to achieve peak carbon before 2030 and carbon neutrality before 2060 would require China to accelerate the development of natural gas infrastructure. The forecasted future of natural gas consumption in China will not only help learn about the future usage of natural gas but also guide government departments to conduct more scientific planning and management of natural gas production and pipeline channels. Therefore, this paper fully considers the characteristics of natural gas consumption affected by seasonal factors and uses the 2018–2021 quarterly natural gas consumption time-series data to analyze whether natural gas consumption contains trends and seasonality. It is noticeable that seasons impact China's natural gas consumption, so the moving average method is applied to calculate the seasonal index. Based on this, China's natural gas consumption from 2022 to 2035 is forecasted. As results show, China's gas consumption will continue to grow over the next 14 years but at a slower rate. By 2035, it will double from the 2021 level, reaching 765.4 billion cubic meters, but the growth rate will drop to below 4%, entering a period of low growth. Finally, the paper illustrates current situation of natural gas development combined with rationalized suggestions for future natural gas production and energy security in China. • Natural gas consumption is influenced by seasonal factors. • A seasonal forecasting method is applied. • China's gas consumption will continue to grow by 2035. [ABSTRACT FROM AUTHOR]

Trombe Wall (TW) as effective passive solar design technology can improve the building's energy efficiency and thermal comfort. TW may not be very effective alone. Adding Phase Change Materials (PCMs) to TW improves the efficiency to compensate for this shortcoming. In this paper, for better efficiency, a new double-layer Phase Change Material Trombe Wall (PCMTW) containing two layers of different PCMs with different phase-changing points on the exterior walls of the building was proposed. This paper aimed to compare the thermal performance of different models of double-layer PCMTW in different air gap thicknesses and different PCM combinations with a base state. The results show that classical TW with an air gap thickness of 20 cm reduces energy consumption by 34% in winter. The best air gap thickness for the PCMTW was 20 cm too, and the energy consumption was reduced by 39% in this air gap thickness. Also, there was little difference in the energy consumption of the three models in the summer, and the overheating phenomenon was solved by considering external shading. for solving this effect in summer, the best length of external shading was 50 cm. • A double-layer PCM Trombe wall was modeled and analyzed. • Three different models of PCM combinations for double-layer PCM Trombe walls in different scenarios were compared. • Energy consumption, thermal, and exergy performance of the designed model were investigated for a year. • An improved PCM Trombe wall model was obtained for the cold winter. [ABSTRACT FROM AUTHOR]

Demand response is becoming increasingly important due to the high penetration of intermittent and variable renewable energy and electric vehicles in power systems. Real-time Incentive-Based Demand Response (IBDR) is helpful for power balancing in normal and emergency scenarios. The paper focuses on making an efficient IBDR scheme and increasing the participation of residential customers. A feature in the Home Energy Management System (HEMS) that provides the quantity of flexible load demand available with the residential customers for real-time IBDR in a decentralized scheme is explored. This paper analyzes the scheduling of flexible residential appliances in a dispersed manner over time by HEMS to increase the participation of residential customers in real-time IBDR. The error accumulation in the IBDR in a decentralized scheme due to the discrete character of residential appliances is examined and addressed. A financial ratio is proposed to give a fair opportunity to all the participants in IBDR when the cumulative flexible load demand available with the participants is greater than the required quantity of demand response. This paper provides algorithms that improve demand response systems by increasing the flexibility available to participants, reducing the error accumulation in IBDR, and ensuring the fairness of the IBDR opportunity to participants. The simulation analysis is done with 400 residential customers with a different number of flexible appliances and different energy requirements. The paper highlights the potential benefits of using a decentralized scheme to enable real-time IBDR in power systems. • Real-time Incentive-Based Demand Response (IBDR) is beneficial for balancing power. • The dispersed scheduling increases consumer participation in real-time IBDR. • The error in the IBDR due to the discrete nature of residential appliances is solved. • A financial ratio is proposed to provide equal opportunities for all IBDR participants. [ABSTRACT FROM AUTHOR]

Pumped storage power plants, which is known as only large-scale energy management equipment, plays a vital important role in energy field. Hump characteristic and S characteristic are two classical unsteady hydraulic characteristics when pump-turbine operating at turbine mode and pump mode, respectively. It has been found that these two characteristics are both strongly related to the complex vortex evolution process in vaneless region while only few papers focus on the elimination mechanism of them. In present paper, a scaled runner with optimized high-pressure side (HPS) is designed and manufactured based on multi-objective optimization process arming at eliminating unsteady characteristic, i.e. hump characteristic and S characteristic, while maintaining efficiency characteristic unchanged. Thereafter, hydraulic experiments are conducted to investigate the impact of HPS geometry on hydraulic performance (efficiency, hump margin, S margin and pressure fluctuation) of object pump turbine. The experimental results show that compared with the original runner, the weighted average efficiency for pump mode increases by 0.1% while the weighted average efficiency for turbine mode decreases by 0.1%. Moreover, the S margin increases from 76.7 m to 87.2 m and the S 2 unsteady region is largely increased. The hump margin decreases a little while it can recover to the original level through increasing 0.6% of the original ratio scale. Moreover, the runner with optimized HPS can effectively reduce the pressure fluctuation amplitude in vaneless region up to 33.3% and 21.4% for turbine mode and pump mode, respectively. • High-pressure of a pump-turbine runner is systematically optimized and tested for the first time. • The high-efficiency characteristic is inherited by the optimized runner. • The optimized runner increases S margin from 77 m to 87 m. • The optimized runner shows better performance in transient process at turbine mode. • Pressure fluctuation amplitude is reduced up to 33.3% and 21.4% at turbine mode and pump mode, respectively. [ABSTRACT FROM AUTHOR]

Shale reservoirs are rich in organic and inorganic matter, causing a large amount of gas to exist on the surface of microscale cracks in an adsorbed state. Revealing the surface diffusion characteristics of adsorbed gas is of great significance for the microscale numerical simulation of shale gas reservoir. In this paper, the unconventional gas reservoir is taken as the research object, and the characteristics of gas surface diffusion on surface wall of microscale fractures is systematically studied. There are two innovations in this paper: (a) the characteristics of gas surface diffusion inside the planar microscale fractures are analyzed; (b) The influence of mixed gas effect on gas surface diffusion is considered. Research results show that: (a) when the fracture opening is 2 nm or 5 nm, the influence of the opening of microscale fractures on the thickness of adsorption layer can be ignored. (b) The interaction between components refers to the frictional force generated by the relative motion of molecules. (c). The magnitude of frictional force is related to the Maxwell Stefan diffusion term and is proportional to the velocity difference. Due to the interaction between components, fast-moving molecules will accelerate the movement of slow-moving molecules. • The characteristics of gas surface diffusion in microscale fracture is systematically studied. • The characteristics of gas surface diffusion inside the planar microscale cracks are analyzed. • The influence of mixed gas effect on gas surface diffusion is considered. [ABSTRACT FROM AUTHOR]

Abstract: A bottom-up methodological framework was developed and applied for the period 1985–2002, to selected manufacturing sub-sectors in Greece namely, food, beverages and tobacco, iron and steel, non-ferrous metals, non-metallic minerals and paper. Disaggregate physical data were aggregated according to their specific energy consumption (SEC) values and physical energy efficiency indicators were estimated. The Logarithmic Mean Divisia index method was also used and the effects of the production, structure and energy efficiency to changes in sub-sectoral manufacturing energy use were further assessed. Primary physical energy efficiency improved by 28% for the iron and steel and by 9% for the non-metallic minerals industries, compared to the base year 1990. For the food, beverages and tobacco and the paper sub-sectors, primary efficiency deteriorated by 20% and by 15%, respectively; finally electricity efficiency deteriorated by 7% for the non-ferrous metals. Sub-sectoral energy use is mainly driven by production output and energy efficiency changes. Sensitivity analysis showed that alternative SEC values do not influence the results whereas the selected base year is more critical for this analysis. Significant efficiency improvements refer to “heavy” industry; “light” industry needs further attention by energy policy to modernize its production plants and improve its efficiency. [Copyright &y& Elsevier]

Accurately and efficiently predicting the LHV of MSW is vital for designing and operating a waste-to-energy plant. However, previous prediction models possess limited geographical applicability. In this paper, we employ multiple linear regression and artificial neural network (ANN) techniques to predict LHV. These data-driven models utilize 151 globally distributed datasets identified during a systematic literature review, describing the wet physical composition of MSW and measured LHV. The results show that models built via both methods exhibited acceptable and compatible levels of performance in predicting LHV, based on the multiple statistical indicators. However, the ANN model proved to be more robust in handling of datasets of diverse quality. Models developed from both methods demonstrate clearly that the wet proportion of food waste has a negative impact on LHV. Supported by the strong and significant correlation between food waste and moisture content, we concluded that the negative impact of high moisture content in food waste on LHV outweighed its calorific value. Separating food waste or any other waste with high moisture content from the MSW for incineration can significantly improve energy recovery efficiency. Contrary to expectation, the models also reveal a higher contribution of paper waste to the LHV of MSW than plastic waste. • Models estimating LHV based on worldwide MSW composition were generated. • Models using multiple linear regression analysis and ANN techniques were compared.. • Models were found to yield acceptable accuracy and were broadly compatible. • ANN models prove to be more robust than regression models.. • Results indicate that high moisture content waste should be separated at source. [ABSTRACT FROM AUTHOR]

Demand side management is a promising alternative to offer flexibility to power systems with high shares of variable renewable energy sources. Numerous industries possess large demand side management potentials but accounting for them in energy system analysis and modelling is restricted by the availability of their demand data, which are usually confidential. In this study, a methodology to synthetize anonymized hourly electricity consumption profiles for industries and to calculate their flexibility potential is proposed. This combines different partitioning and hierarchical clustering analysis techniques with regression analysis. The methodology is applied to three case studies in Chile: two pulp and paper industry plants and one food industry plant. A significant hourly, daily and annual flexibility potential is found for the three cases (15%–75%). Moreover, the resulting demand profiles share the same statistical characteristics as the measured profiles but can be used in modelling exercises without confidentiality issues. • The electricity demand of paper and food companies in Chile is analysed. • The analysis is based on two years of data from three companies. • 22 clustering methods are tested to generate synthetic electricity load profiles. • The demand response potential for a typical hour is approximately ± 15%. • On a daily basis up to 70% of the demand can be mobilized. [ABSTRACT FROM AUTHOR]

As a key exploitative parameter, permeability affects the extraction efficiency and program for natural gas hydrate (NGH). However, few studies have considered the effect of dynamic stress (continuously changing stress) on permeability, let alone permeability evolution during reservoir instability. Given the dynamic stress-seepage coupling process of actual NGH exploitation, this paper upgrades the triaxial apparatus to new equipment that can realize the unified tests of dynamic stress and water permeability. Subsequently, the seabed clay recovered from the South China Sea and quartz sand were used to remold fine-grained hydrate-bearing clayey-silty sediments (HBCSS). Finally, a series of stress-seepage coupling tests were carried out to reveal the permeability characteristics during sediment destabilization and to explain the coupling mechanism. The results show that seepage can reduce the strength and stiffness, and enhance the softening or weaken the hardening of the stress-strain curve. The dynamic stress-influenced permeability exhibits a non-monotonic evolution, which is essentially an expansion or contraction of seepage channels. Stress-strain relationship, volume deformation, and permeability are coupled and have good correspondence. The shear-seepage coupling process can be divided into four stages: initial compression, plastic strain development, plastic expansion and fracture development, plastic failure, and rapid expansion. This paper enriches the existing research from three aspects: permeability properties affected by dynamic stress, mechanical behavior affected by seepage, and stress-seepage coupling mechanism. It has played a significant reference for the safe and efficient exploitation of fine-grained NGH. • The effect of dynamic stress on the permeability of HBS is illustrated. • Particle-scale dynamic stress-seepage coupling mechanism is revealed. • Full permeability-stress-strain-volume curves are obtained. • A typical dynamic stress-seepage coupling process can be divided into four stages. [ABSTRACT FROM AUTHOR]

The solar-assisted heat pump (SAHP) systems are increasingly gaining attention for their potential feasibility in various fields due to their energy saving benefits. However, the available research shows that SAHP still has potential for improvement in drying filed, especially in terms of combining vapor compression heat pumps with solar energy. Consequently, this paper aims to summarize the development of SAHP technology and provide guidance for the application of SAHP technology in the drying field. The development of the SAHP systems is reviewed in this paper, including four ways to enhance the performance of SAHP systems are discussed: photovoltaic/thermal systems, collector optimization, energy storage and ejector enhancement. The technology of energy storage and ejector enhancement will inspire the development of SAHP technology. Additionally, research on optimizing the performance of hybrid heat source heat pump systems is also discussed, and a review of alternative refrigerants that could be used in SAHP systems. It could be found that R134a is the most widely used in SAHP systems currently, but it needs to be replaced soon for its high GWP. HFOs refrigerants are a good alternative. Furthermore, the application prospect of SAHP systems in drying field is also analyzed. Finally, based on the current research status of SAHP technology, the future development trend and research directions of solar-assisted heat pump drying (SAHPD) are proposed: developing suitable refrigerants for heat pump systems; researching high-efficiency heat storage technology; developing multi-heat source assisted heat pump systems; and optimizing the operation strategy of SAHPD using artificial intelligence. • Four ways to enhance SAHP systems are reviewed. • Study on optimization of hybrid heat source heat pump systems is summarized. • The alternative refrigerants in SAHP systems is investigated. • The application prospects of SAHP systems for drying field are analyzed. • The future development trend of SAHPD systems is proposed. [ABSTRACT FROM AUTHOR]

As the main pollutant of paper mills, black liquor has brought great pressure to the wastewater treatment process of paper mills. Based on the coal-water slurry gasification (GS-CWS) technology, this paper designed the gasification system of black liquid coal-water slurry (GS-BLCWS) by utilizing weak black liquor of pulp mills, investigated and modeled the energy requirements of the GS-BLCWS. Feasibility assessment of the GS-BLCWS and the traditional alkali recovery systems (TARS) were carried out, including energy efficiency, economic feasibility and environmental impacts. The comparison results showed that the steam demands of two technologies can be self-sufficient. But the total energy efficiency of GS-BLCWS (69.78%) is higher than that of the TARS integrating GS-CWS (63.07%), and the energy output ratio of black liquor increased from 9.9% to 69.78%. From an economic point of view, GS-BLCWS can generate syngas, which can be used for drying in the papermaking workshop and shows better economic efficiency. Meanwhile, GS-BLCWS has lower environmental impacts that there is no emission of sulfur oxides and particulate matter. Considering the short payback period (5.81 years) of GS-BLCWS, the small environmental impact, and the self-sufficiency of steam and electricity, it has broad application prospects. [Display omitted] • A gasification system for preparing coal-water slurry syngas from weak black liquor. • The feasibility of GS-BLCWS in energy, economy and environment is evaluated. • GS-BLCWS improves the energy utilization efficiency of weak black liquor. • Sensitivity analysis shows that the price of syngas has the greatest impact on PBP. [ABSTRACT FROM AUTHOR]