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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]

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]

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]

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]

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]

Digital infrastructure serves as a crucial foundation for development in the era of the digital economy. In light of this, this paper takes digital infrastructure construction as its focal point. Utilizing Python technology and the Meta-frontier non-radial directional distance function to calculate the level of digital infrastructure construction and industrial energy efficiency. It then employs a dynamic panel data model to analyze the impact effects and mechanisms of digital infrastructure construction on industrial energy efficiency. Additionally, threshold models and modified gravity models are employed to explore the nonlinear effects and spatial impacts of digital infrastructure construction on industrial energy efficiency. The research findings reveal that the enhancement of digital infrastructure significantly improves industrial energy efficiency. Moreover, indicate a shift in the impact of digital infrastructure construction on industrial energy efficiency from inhibitory to promotional as the level of digital infrastructure construction increases. The mechanism of this effect suggests that digital infrastructure construction can promote the improvement of industrial energy efficiency through green technological innovation and the advancement of industrial structure. Further analysis shows that the spatial network characteristics of digital infrastructure construction notably elevate the level of industrial energy utilization efficiency. This study provides practical policy implications for sustainable development. • Digital infrastructure construction markedly enhances industrial energy efficiency. • There is a non-linear picture of the impact of digital infrastructure construction on industrial energy efficiency. • The effects of green technology innovation and advanced industrial structure are crucial channels. [ABSTRACT FROM AUTHOR]

Floating offshore wind turbines, as large equipment for deep-sea wind energy utilization, must be carefully studied for their aerodynamic performances under platform motions. In experiments, the model wind turbine designed by the traditional thrust coefficient similarity is usually insufficient for unsteady aerodynamic reproductions of the prototype wind turbine. In this paper, a dynamic aerodynamic similarity criterion based on the mapping of the optimal tip speed ratio is introduced for wind tunnel model tests, and a model wind turbine is designed using the NREL 5-MW prototype wind turbine. In this case, to avoid the over-height model tower aroused in pitch motions, a method of equivalent surge motions instead of pitch motions is proposed and validated for feasibility. Then dynamic aerodynamic similarities under different pitch amplitudes and frequencies are validated by the Unsteady Blade Element Momentum theory. Results indicate that temporal and spatial characteristics and hysteresis effects of thrust and power coefficients all maintain better similarity with those of the prototype wind turbine than those of the model wind turbine designed by the traditional criterion. This paper provides a criterion with equivalent pitch motions for unsteady aerodynamic studies in wind tunnel model tests, especially the high-fidelity Hardware/Software-In-the-Loop test. • A new similarity criterion is proposed for FOWT wind tunnel model tests. • The feasibility of equivalent surge motions instead of pitch motions is verified. • The problem of over-height model tower is avoided by equivalent pitch motions. • Spatiotemporal and hysteresis characteristics of unsteady aerodynamics are analyzed. • Unsteady aerodynamic performances of the prototype can be reflected more accurately. [ABSTRACT FROM AUTHOR]

To achieve an efficient tidal current turbine (TCT), the impact of three biomimetic trailing edge designs (i.e., slabbing, slab-toothed, and serrated), a blade configuration design (i.e., sweep design) and their various combinations on TCT's power coefficient are numerically and experimentally studied in this paper. The novelty of this paper lies in its pioneering exploration of the synergistic effects of multiple biomimetic designs on the TCT's power generation and start-up performance, as well as the interplays between the sweep design and biomimetic trailing edge designs. Both numerical simulations and experimental studies provide compelling evidence that all three biomimetic trailing edge designs enhance the blade's energy capture efficiency. For example, at a 7° angle of attack, the lift-to-drag ratio for the slabbing trailing edge blade increases by 12.124% and the slab-toothed trailing edge blade increases by 11.770% compared to the standard blade. The incorporation of sweep design and biomimetic trailing edge design produces a further enhancement in both the TCT's power generation and startup performance. In particular, the simultaneous implementation of slabbed trailing edge design and sweep design makes the power generation efficiency of TCT increase by up to 54.72% when compared to TCTs using standard straight blades. • Paper explores synergic effects of multi-biomimetic designs on TCT's efficiency. • Slabbing and slab-tooted tailing edge designs surpass serrated trailing edge design. • Combined use of sweep and slabbing trailing edge design results in best performance. • All biomimetic designs considered in the study boost the TCT's startup performance. [ABSTRACT FROM AUTHOR]

This paper proposes a dual-objective eco-routing strategy aimed at optimizing driving routes by minimizing both energy consumption and travel time. First, the vehicle and powertrain dynamics are modelled. The energy consumption of three powertrain types, including hybrid electric vehicle (HEV), battery electric vehicle (BEV) and conventional vehicle with internal combustion engine (CV), are calculated based on a white-box model. Second, this paper models the traffic network by considering the intensity of each road. The energy consumption associated with different intensity levels is also modelled using a typical driving cycle. The dual-objective routing problem, considering both energy consumption and travel time, is solved by an Iterative Dijkstra's algorithm. Finally, several simulation results demonstrate that the proposed dual-objective eco-routing strategy achieves a favorable balance between energy consumption and travel time. Specifically, for HEV, the dual-objective eco-routing strategy resulted in a 5.3% reduction in travel time with a marginal increase of only 0.4% in fuel consumption compared to the single-objective eco-route. In addition, the eco-routing strategy exhibits distinct patterns across different vehicle powertrains. HEV and BEV tend to utilize heavy traffic conditions more frequently compared to the CV. • Build the energy consumption models of hybrid electric vehicle (HEV), battery electric vehicle (BEV) and conventional vehicle with internal combustion engine (CV). • Energy consumption associated with different intensity levels is modelled. • Formulate the dual-objective routing problem, considering both energy consumption and travel time. • Eco-routing strategy exhibits distinct patterns across different vehicle powertrains and traffic conditions. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

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

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]

Energy communities (ECs) offer a promising solution for achieving sustainable and decentralized energy systems. However, the successful establishment and operation of ECs requires overcoming barriers that can hinder stakeholder participation. Existing research has primarily focused on incentives and motivations to join ECs, thus neglecting a comprehensive understanding of the key barriers affecting EC stakeholders in European Union (EU) countries. This paper aims to fill this research gap by identifying and ranking the barriers to joining ECs in the EU context with focus on Spain and Italy. To accomplish this, a framework of barriers was developed based on 20 in-depth interviews with diverse stakeholders of established ECs and a survey (n = 56). The barriers identified were categorized into four types: (a) financial, (b) regulatory and bureaucratic, (c) technical and practical, and (d) social and cultural. The analytical hierarchical process (AHP) methodology was employed to estimate and rank these barriers. The findings highlight that the most significant barrier categories are regulatory and bureaucratic, and financial. Specifically, regulatory complexity and legal limitations emerge as the top-ranked barriers among the obstacles identified to joining ECs by research participants. • Energy communities are a promising solution for sustainable decentralized energy systems. • Joining an energy community faces financial, regulatory, technical, social barriers. • The article ranks these barriers using the analytical hierarchical process. • It provides insight of the barriers affecting all energy communities' stakeholders. • It discusses the main policy recommendations derived from the ranking of barriers. [ABSTRACT FROM AUTHOR]

Liquid air energy storage (LAES) presents a promising solution to effectively manage intermittent renewable energy and optimize power grid peaking. This paper introduces a LAES system integrating LNG cold energy to flexibly manage power peaking, including intermediate energy storage, power generation using organic Rankine cycle, multi-stage direct expansion, and solar energy for heating. Aiming to maximize the electrical round-trip efficiency, a genetic algorithm is employed to optimize the operational parameters of the proposed LAES system. The results indicate that the combined design of cold energy utilization, multi-stage direct expansion and solar heating for proposed LAES system significantly improve the power performance, achieving an energy capacity of 0.125 kWh/kg LNG and an electrical round-trip efficiency of 376.7 %. The exergy losses of heat exchange equipment are far larger than mechanical equipment in the LAES system, so the optimization design of heat exchange equipment is preferred to improve the exergy performance. The proposed system achieves both high efficiency and flexibility and hence contributes to the development of liquid air energy storage systems. • LNG cold energy is used for air cooling and liquefaction. • Solar energy is used to increase the maximum temperature of direct expansions. • An outstanding electrical round-trip efficiency of 376.7 % is achieved. • Energy capacity reaches 0.125 kWh/kg LNG. • Heat exchanger equipment optimization is priority to improve exergy performance. [ABSTRACT FROM AUTHOR]

China's rural solar photovoltaic projects (SPVPs), as an innovative initiative to improve livelihoods through clean energy, have played a positive role in poverty reduction. However, whether this measure can enhance energy services for rural families remains unconfirmed. This paper employs a quasi-experimental method from the perspectives of energy service accessibility and affordability to quantify the net contribution of China's rural SPVPs to enhancing family energy services. The results indicate: (1) SPVPs can significantly improve family energy services, with a net effect of 14.7 % on the energy service accessibility and a net effect of 44.5 % on the energy service affordability. (2) SPVPs can increase the energy consumption capacity, energy supply capacity and energy equipment utilization rate by 2.6 %, 7.1 % and 5 %, respectively. Furthermore, these initiatives can effectively slash the energy costs of rural families by 4.3 %, bolster the energy access affordability by 3.9 %, and catapult energy investments by an impressive 44.9 %. (3) These projects also perform well in the family energy consumption cleanliness. (4) The effect of SPVPs in providing energy services presents a regional difference, with the best performance observed in 'Type III' solar resource areas. Meanwhile, the implementation of China's rural SPVPs does not involve "elite capture." • We analyze the changes of energy accessibility and affordability in rural China. • We quantify the energy services effect of solar PV projects (SPVPs). • SPVPs perform well in improving family energy consumption cleanliness. • The energy services effect of solar PV projects has spatial heterogeneity. • The implementation of China's rural SPVPs does not involve "elite capture.". [ABSTRACT FROM AUTHOR]

Compared with extensive research on the power generation of thermoelectric generators (TEGs) under medium-high temperatures, research on power generation for cold energy utilization is still limited. To study the performance and feasibility of low-temperature power generation, this paper combines simulation and experiments for research. Firstly, a simulation model based on actual material parameters is established. Then, a thermoelectric low-temperature power generation experimental platform is established to conduct experimental research on TEG in different temperature zones and temperature differences and compared with simulation results. Finally, optimization research is conducted by changing thermoelectric materials and structures. The research results indicate that the maximum errors between the dates of simulation and experiment in terms of output voltage, conversion efficiency, and exergy efficiency are 11.3 %, 10.57 %, and 10.57 %, respectively. Compared with Bi 2 Te 3 , the output voltage, conversion efficiency, and exergy efficiency of CsBi 4 Te 6 are improved by 21.95 %, 34.64 %, and 34.64 %, respectively. The maximum output power and conversion efficiency are linearly related to the output current. The performance is optimal when the thermoelectric unit height is 1.0 mm and the side length is 1.6 mm. This proves the feasibility of TEG application at low temperatures and lays the foundation for low-temperature power generation. • Recycle the remaining cold energy for power generation. • The output performances of TEG in low temperatures are studied. • Compared with Bi 2 Te 3 , the conversion efficiency of CsBi 4 Te 6 increased by 34.64 %. • The principal stress and shear stress of thermoelectric units are analyzed. • The performance is optimal when the height is 1.0 mm and the side length is 1.6 mm. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

Excessive fuel consumption of heavy duty trucks aggravates environmental pollution and leads to reduced economic benefits of road transportation enterprises. In order to study the influencing factors of fuel consumption of heavy duty trucks, based on real-time vehicle operating parameter data, this paper establishes a segment fuel consumption prediction model based on LightGBM algorithm. The fitting ability of the model is improved by feature selection and Optuna hyperparameter search. The performance is compared with other models. The results show that the prediction effect of LightGBM model is optimal. Finally, based on the SHAP framework, the prediction results of the optimal fragmented fuel consumption model are visually explained. The conclusion that average speed and average throttle pedal value have a significant impact on segmented fuel consumption. Ensuring higher and more stable speeds, reducing the frequency of acceleration and deceleration can reduce the fuel consumption appropriately. • Establishing a LightGBM model to predict fuel consumption of heavy duty trucks. • Improving the model prediction performance through feature selection and parameter optimization. • The LightGBM model shows good predictive performance with R2 of 0.8725. • Visual interpretation of prediction results based on the SHAP framework. [ABSTRACT FROM AUTHOR]

Energy consumption on freeway significantly contributes to environmental pollution. Rainy weather, as a common adverse condition, will exert a negative impact on car-following behavior of vehicles on freeway and further affect their energy consumption. The emergence of connected automated vehicles (CAVs) has created an opportunity to mitigate these impacts. This paper aims to propose an optimization strategy for CAVs that can reduce energy consumption during car-following behavior on freeway under different rainy weather conditions. To begin with, a calibrated car-following model for regular vehicles (RVs) on freeway in rainy weather was used to derive an optimization strategy for CAVs that have vehicle-to-vehicle communication capability to stabilize traffic flow with smoothed speed fluctuations. The proposed optimization strategy for CAVs was then subjected to simulation experiments to validate its effectiveness. Results indicate that energy consumption on freeway in rainy weather is closely linked to speed fluctuations. Frequent speed fluctuations during car-following behavior could cause more energy consumption. The proposed optimization strategy for CAVs is capable of reducing energy consumption in rainy weather by smoothing speed fluctuations. CAVs equipped with this optimization strategy shows an energy-saving of 34.69%–61.11% compared to RVs under various rainy weather conditions. • Propose an optimization strategy for connected automated vehicles to reduce energy consumption on freeway in rainy weather. • The proposed optimization strategy provides an insight into the stabilizing of traffic flow by smoothing speed fluctuations. • Conduct simulation experiments to validate the effectiveness of the proposed optimization strategy. • The proposed optimization strategy has an energy-saving of 34.69%–61.11% under different rain conditions. [ABSTRACT FROM AUTHOR]

Based on the phenomenon of flow-induced vibrations, harnessing ocean currents for power generation is a relatively novel approach. In comparison to other technologies, it is particularly well-suited for deployment in regions characterized by low flow velocities. However, in the case of deep-sea environments with extremely low flow velocities, there is a need to identify an appropriate cross-sectional for oscillators that can effectively generate power through FIV. To address this problem, This paper proposes a novel Elli-Circ cross-sectional oscillator model, which couples a semi-ellipse with a semi-circle. Numerical simulations are conducted to analyze the FIV characteristics and energy harvesting capabilities of this oscillator under different radius ratios and mass damping ratios. The study reveals that compared to other cross-sectional oscillators, the Elli-Circ cross-sectional oscillator experiences an initial increase and subsequent decrease in vibration amplitude with the increase of reduced velocity. The amplitude peak occurs at lower flow speeds, and the vibration frequency continues to increase. The Elli-Circ oscillator exhibits superior vibration characteristics at extremely low flow velocities (U ≤ 0.4 m/s). for the case of a radius ratio R/r = 1.6, damping ratio ζ = 0.12, and mass ratio m * = 1.001, the Elli-Circ oscillator achieves its maximum energy harvesting power of 0.39 w. Additionally, the highest energy conversion efficiency of 16.77% is obtained for ζ = 0.12 and m * = 1.343. Therefore, the Elli-Circ cross-sectional oscillator coupled with a semi-ellipse and semi-circle exhibits superior energy harvesting characteristics at extremely low flow speeds (U ≤ 0.4 m/s) and high damping ratios. • Propose a novel Elli-Circ oscillator applied in VIVACE converter to enhance power generation. • Analysis of the influence of radius ratio and mass damping ratio on the vortex-induced vibration of an Elli-Circ oscillator. • The proposed oscillator demonstrates enhanced energy harvesting efficiency at extremely low flow velocities. [ABSTRACT FROM AUTHOR]

Government auditing plays a crucial role in enhancing energy efficiency and advancing green and low-carbon economic development. Given that audit oversight's quality and efficiency are influenced by the independence of auditing institutions, this study utilizes the vertical management reform of government auditing institutions as an exogenous shock, and empirically examines its impact on urban energy efficiency (UEE) using a Difference-in-Differences model (DID). We find that the audit reform improved UEE by 0.023 which is both economically and statistically significant. Concurrently, the audit reform and the development of the digital economy play substitutive roles in enhancing UEE. Finally, the results of the mediation analysis using the stepwise regression method indicate that the improvements in audit efficiency, green technology innovation, and energy structure optimization have mediated the promotion of UEE in the audit reform process, accounting for respective mediation effects of 4.35%, 4.35%, and 8.69%. • We investigate the audit independence from the level of audit management systems. • This paper finds a substitution effect of digital economic development. • The study discusses the heterogeneity in audit oversight from a global perspective. [ABSTRACT FROM AUTHOR]

The widespread use of fossil fuels has substantially contributed to global climate change. One of the most crucial ways to address climate change is to minimize carbon emissions in the energy sector. Power-to-Gas (P2G) technology provides a potential solution. In this context, this paper introduces a novel two-stage P2G planning method for the electric-gas coupling system, using carbon emission flow technology. The carbon emission flow model employed facilitates the distribution of carbon emissions across the entire energy network. Central to our planning strategy is the identification of nodes with reduced carbon emissions for the siting of P2G facilities, predicated on the premise that such nodes are indicative of significant renewable energy utilization. Constructing P2G systems at these particular nodes is strategically advantageous for augmenting renewable energy consumption. In the initial phase, a P2G siting planning methodology predicated on a low-carbon criterion is advanced, resulting in a definitive siting scheme. Subsequently, the second phase involves establishing a P2G capacity optimization model that seeks to strike an economic equilibrium between the utilization of surplus wind power and P2G operational investment. An enhanced pigeon optimization algorithm is employed to resolve the proposed model. Our method is verified by simulation. Compared with other conventional models, the accommodation capacity of renewable energy with the proposed method is enhanced, and the carbon emissions and operating costs of IES are also reduced. • This study introduces a new P2G station siting method centered on the carbon flow model. • The research establishes a P2G capacity planning model focused on cost-effectiveness optimization. • The novel P2G planning model addresses the economic balance between investment costs and the level of new energy absorption. • P2G two-stage planning strategy reduces the operating cost of the system and improves the low carbon of the system. [ABSTRACT FROM AUTHOR]

Industrial steam heating is a significant aspect of energy consumption in China, playing a crucial role in industrial energy efficiency. Historically, demand response research has predominantly focused on the electricity sector, with limited emphasis on steam supply. This study explores a novel Time-of-Use pricing approach for steam heating systems, anchored in the demand response theory of price incentives. It develops a peak-valley segmentation method by considering both the load differences between supply and demand and the thermal storage capacity of the pipeline system. Additionally, the paper delves into user response characteristics to formulate an optimized Time-of-Use pricing model for steam heating systems. Implemented in a Jiangsu-based case study, the model demonstrates significant efficiency improvements: it reduces the peak-valley load difference by up to 68.3% and cuts steam purchasing costs by ¥135,000. Furthermore, the alignment between the users' total steam load curve and the heat source's steam supply is enhanced by 18.8%, illustrating the model's effectiveness in balancing demand and supply. • Cluster analysis to extract steam user load characteristics. • Divide peak/valley times based on network storage and user traits. • Create a multi-objective Time-of-Use model for steam pricing optimization. • Carry out a case validation in an actual industrial park in Jiangsu. [ABSTRACT FROM AUTHOR]

In this paper, TRNSYS is used to simulate the heating systems of air-cooling concentrating photovoltaic/thermal (CPVT) coupled air source heat pump and water-cooling CPVT coupled air source heat pump. Besides, Grasshopper is adopted for multi-objective optimization of air-cooling system. This study not only proposed two hybrid systems and enriched the research of CPVT coupled air source heat pump, but also proposed a multi-objective optimization method, which uses Grasshopper to invoke TRNSYS. Compared with the air source heat pump system, the annual average COP of heat pump in the air-cooling system increases by 22.8%, and that of the water-cooling system decreases by 6%. The primary energy saving rate of the air-cooling system is 1.06 times and 6.67 times that of the water-cooling system and the air source heat pump system. The optimized air-cooling system reduces the annual total cost by 2.83%, and increases the energy utilization coefficient by 4.76%. The proposed air-cooling system has obvious advantages in energy saving and improving COP of heat pump. This study is useful for the cost savings and energy conservation of air-cooling concentrating photovoltaic/thermal (CPVT) coupled air source heat pump systems based on urban public buildings. • Models of different CPVT coupled ASHP systems are established in TRNSYS. • The optimization method integrates the advantages of TRNSYS simulation and the advantages of GH parameterization. • The systems are analyzed and compared from energy utilization coefficient, exergy efficiency, primary energy saving rate, annual total cost and net present value. • The ACPVT-ASHP system performance is best when the ASHP capacity is 9.9 kW and the length of PV cells is 1.819 m. [ABSTRACT FROM AUTHOR]

Modular Gravity Energy Storage (M-GES) systems are emerging as a pivotal solution for large-scale renewable energy storage, essential for advancing green energy initiatives. This study introduces innovative capacity configuration strategies for M-GES plants, namely Equal Capacity Configuration (EC) and Double-Rate Capacity Configuration (DR), tailored to optimize energy storage efficiency and stability. Through comprehensive MATLAB/Simulink simulations, we demonstrate each configuration's distinct power characteristics and operational advantages. Our findings reveal that while the EC configuration offers enhanced power accuracy and stability, it requires a larger number of units. In contrast, the DR configuration, characterized by fewer but larger units, shows a trade-off with reduced power stability. This paper contributes significantly to the field by presenting a first-of-its-kind comparative analysis of these configurations, offering insights into their practical applications and scalability. The proposed strategies and findings lay a foundation for future research and development in gravity energy storage systems, marking a step forward in pursuing sustainable and reliable energy solutions. [Display omitted] • Two typical unit capacity configuration strategies for M-GES power plants are proposed. • The unit scheduling method of the M-GES power plant is proposed. • The coordination control method is provided for each proposed configuration strategy. • Equal capacity configuration has better power accuracy but requires more units. • Double rate configuration needs fewer but larger units and less power stability. [ABSTRACT FROM AUTHOR]

The 2023 revision of the Energy Performance of Building Directive underlines the need to consider the whole life cycle emissions for buildings. The decarbonization targets could not be achieved if reduction of energy demand in the operational phase is accompanied by a high energy resource use in the whole life. This paper proposes a novel investigation of an energy renovation project, involving both building envelope components and space heating system. The adopted method is based on the life cycle assessment, in the variant cradle-to-gate with options, to analyze and compare the design data achieved through an analysis of product (embodied) and use (operational) stages. Different solutions for energy improvement of the building performance are identified, and the scenario with the lowest embodied energy was found to be more energy efficient globally. The most effective scenario, with energy and CO 2 savings of 39% and 40% is characterized by a weight of embodied energy and emissions of 6.5% and 6.9%, respectively. Given the rise of the weight of the embodied energy in the whole life cycle of future buildings, the life cycle assessment will become crucial for sustainable design and in the transition to an energy-efficiency built environment. [Display omitted] • The impact of life cycle analysis in the process of energy efficiency is investigated. • The "cradle-to-gate" with options approach is implemented. • The efficiency of walls and windows involves the greater use of energy resources. • The most energy-efficient scenarios from an operational and non-operational point of view are different. • The scenario identified in the analysis of A1-A4 phases allows a higher total energy saving. [ABSTRACT FROM AUTHOR]

With the accelerated implementation of Energy Efficiency Design Index (EEDI) phase 3 by the International Maritime Organization, shipping carbon reduction is urgently required. This paper explores the possibility to develop the onboard carbon capture and storage (OCCS) system unstraints between EEDI phases 3 and 4. The OCCS is particularly established with the utilization of exhaust gas waste heat and LNG cold energy. The proposed OCCS system is modelled in Aspen HYSYS, laying the foundation for energy, exder the EEDI framework. The OCCS system design principle for an LNG-fueled bulk carrier is determined with the conergy, economic and environment analyses. Furthermore, the multi-objective optimization is based on the energy and exergy efficiency of OCCS system (η en,OCCS × η en,OCCS), payback period (PBP) and metric of carbon capture efficiency degree (MCCED) by non-dominated sorting genetic algorithm-Ⅲ. The Pareto frontier indicates that the system is in optimal operation with the exhaust gas mass flow rate of 29,990 kg/h and the liquid-to-gas ratio of 1.04. The carbon capture is 1048.0 kg/h, which is 70.74% over EEDI phase 3 requirements. The optimized η en,OCCS × η en,OCCS is 2.92%, the PBP is 13.03 years and the MCCED is 23.00%. This study provides an important basis for the maritime post-combustion carbon reduction compliance. • Design principle of the onboard CCS system is established under the EEDI framework. • Balance between waste heat and LNG cold energy is achieved with the OCCS system. • The CO 2 gap for the reference ship to satisfy EEDI phase 3–4 is 613.8–1114.3 kg/h. • The optimized overall system energy and exergy efficiencies are 29.20% and 10.01%. • The PBP for the OCCS system is 13.03 years under the optimal working conditions. [ABSTRACT FROM AUTHOR]

As the national digitization process accelerates, the number and energy consumption of data centers are increasing year by year. Want to reduce the PUE of the data center. It is correct to start from the perspective of optimizing the energy consumption of air conditioning system operation. In this paper, a predictive optimization control method that is different from previous real-time optimization control is proposed. Predict, optimize, control and adjust the operating parameters of the air conditioning water system in the data center. At the same time, it solves the energy saving problem of the air conditioning system and the lag problem of previous adjustments. Predictive optimization control has broad prospects in research on operation optimization of data center air conditioning water systems. • Reducing air conditioning water system energy consumption in data centers can lower PUE. • Predictive optimization can solve the lag problem of water systems. • After global prediction optimization, energy consumption was saved by 12.9%. [ABSTRACT FROM AUTHOR]

With the escalating global energy demands, nations face greater urgency in diversifying their electricity market portfolio with various energy sources. The efficient utilization of these fuel resources necessitates the integrated optimization of the entire energy portfolio. However, the inherent uncertainty of variable renewable energy generators renders traditional deterministic planning models ineffective. Conversely, expansive stochastic models that capture all the complexities of the power generation problem are often computationally intractable. This paper introduces a scalable energy planning framework tailored for policymakers, which is adaptable to diverse geopolitical contexts, including individual countries, regions, or coalitions formed through energy trade agreements. A two-stage stochastic programming approach, including a scenario-based Benders decomposition method, is employed to achieve computational efficiency. Scenarios for representing parameter uncertainty are developed using a k -means clustering algorithm. An illustrative case study is presented to show the practical application of the proposed framework and its ability to inform policymaking by identifying actionable cost-reduction strategies. The findings underscore the importance of promoting resource coordination and demonstrate the impact of increasing interchange and renewable energy capacity on overall gains. Notably, the study shows the superiority of stochastic optimization over deterministic models in energy planning. • A comprehensive nationwide energy planning framework is presented. • A novel mathematical model is developed to make inform energy planning decisions. • Uncertainty with renewable energy is modeled using stochastic programming methods. • Large models are kept computationally tractable through decomposition techniques. [ABSTRACT FROM AUTHOR]

This paper explores the influence of different turbocharging modes and multi-parameter coordinated control on the performance of compression-ignition (CI) aviation piston engine (APE). Firstly, based on a constructed one-dimensional thermodynamic model of a CI APE, the study investigates the effects of different turbocharging modes and combinations of high and low-pressure stage variable geometry turbines (VGT) on the operational performance of the engine. Subsequently, a novel stepwise approximate multi-objective optimization algorithm is proposed, combining backpropagation neural networks and the non-dominated sorting genetic algorithm II. This algorithm evaluates the influence of multiple control parameters on a two-stage turbocharged engine's performance, achieving a balance between fuel economy and reliability. The research shows that equipping CI APEs with twin VGT for supercharging can notably enhance engine performance, enabling the achievement of favorable power recovery objectives at an altitude of 8000 m. The proposed optimization algorithm exhibits strong predictability and reliability, substantially accelerating the computation speed and reducing the data volume by approximately 95%. At an engine speed of 3887 rpm, compared to the unoptimized conditions, the brake specific fuel consumption of the best scenario at altitudes of 2000 m, 4000 m, 6000 m, and 8000 m is reduced by 7.1%, 7.2%, 8.6%, and 6.9%, respectively. • Influences of different turbocharging modes on the performance of a CI APE are investigated. • A novel stepwise approximate multi-objective optimization method based on a hybrid model is proposed. • Explored the altitude-varying control strategy under coordinated multi-parameter optimization. • Increased the altitude ceiling for the CI APE, reaching 82% power recovery target at 8000 m. [ABSTRACT FROM AUTHOR]

Wind prediction is of great significance for wind energy utilization due to the stochastic nature of wind. To effectively facilitate various downstream decision-making tasks such as wind turbine control, predictive wind Scenario Generation (SG), which is capable of providing a set of deterministic instantiated wind prediction results, plays a critical role. In this paper, a novel Graph neural networks-based Adaptive Predictive Generative Adversarial Network (GAPGAN) model is proposed for accurate prediction of short-term future scenarios of a wind field. In GAPGAN, the original multivariate time series data are first reconstructed into the form of a graph, and spatiotemporal features are then extracted using Graph Convolutional Networks (GCNs). Next, a predictive generative adversarial network (PGAN) framework is proposed, which could generate different outputs corresponding to given historical observations as conditions. Finally, an adaptive PGAN training mechanism is introduced to stabilize the training process, and the best SG model is selected based on the proposed comprehensive evaluation system. Based on wind speed data collected from 11 wind turbines, computational experiments validate that the GAPGAN outperforms five benchmarking models in terms of point prediction accuracy, shape similarity, uncertainty prediction quality, and prediction scenario diversity. • Propose a GCN-based adaptive predictive generative adversarial network (GAPGAN). • Employ a novel evaluation framework to assess the generated wind speed scenarios. • Incorporate both temporal and spatial features in the wind speed scenario generation. • Balance both the accuracy and the diversity of the generated wind speed scenarios. • Validate the proposed GAPGAN using real wind farm data. [ABSTRACT FROM AUTHOR]

In hot coastal regions where fresh water is scarce, absorption chillers driven by industrial waste heat and cooled by seawater are suitable to achieve cooling demands, and it contributes to improving energy efficiency and reducing CO 2 emissions. However, seawater cooling strategy is a trade-off between performance and cost for practical applications, and there are few direct seawater-cooled absorption chillers. In addition, the integration of absorption chillers into a waste heat utilization network is often not well planned, and thus the waste heat is usually not used efficiently. This paper presents the direct seawater cooling strategy for the absorption chiller in terms of high performance and low cost by comparing different seawater cooling strategies numerically and experientially. To realize efficient energy utilization, an existing waste heat utilization network is analyzed graphically based on pinch method, and the available heat for absorption chillers is obtained by the position of the pinch point. According to the available heat, the direct seawater-cooled absorption chiller with a designed cooling capacity of 4200 kW is developed by using titanium tubes, anticorrosive paints and rubber covers to construct the corrosion-resistant condenser and absorber. The site testing results show an average cooling capacity of 3043 kW and an average COP of 0.77 of the absorption chiller when the temperatures of the hot water, seawater and chilled water varies from 98.7 °C to 80.7 °C, from 30.2 °C to 34.2 °C, and from 14.4 °C to 8.6 °C, respectively. Due to the integration, the energy and exergy efficiencies of the waste heat utilization network is improved by 55.9% and 86.1%, respectively. The application demonstrates that the direct seawater-cooled absorption chiller holds the promise for waste heat utilization in coastal regions. • Different seawater cooling strategies for an absorption chiller are compared. • Direct seawater cooled absorption chiller of 4200 kW is developed. • Titanium tubes, anticorrosive paints and rubber covers are employed for anti-corrosion. • The direct seawater cooled absorption chiller is integrated into a waste heat utilization network. • The energy and exergy efficiencies of the network is improved by 55.9% and 86.1%. [ABSTRACT FROM AUTHOR]

Accurate energy consumption prediction is pivotal in supporting efficient grid scheduling and enhancing renewable energy penetration. This paper proposes a novel hybrid prediction model called CEEMDAN-SE-EC-BiLSTM. The proposed model utilizes Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose the original energy consumption data into Intrinsic Mode Functions (IMFs). Afterwards, Sample Entropy (SE) is employed to evaluate the complexity of the IMFs. The complexed IMFs are then transformed into simpler IMFs using a second decomposition. All IMFs are predicted using Bidirectional Long Short-Term Memory (BiLSTM) to attain the initial predicted sequence. To further refine the prediction, an Error Correction (EC) technique is introduced. This involves subtracting the initial predicted sequence from the original sequence to obtain the error sequence, which is then decomposed using CEEMDAN. Subsequently, the decomposed error sequence is predicted using BiLSTM to generate the error prediction sequence. Finally, the predicted results and the error prediction are combined to obtain the final energy forecast sequence. To verify the superior performance of the proposed method, the model is tested on five selected buildings and benchmarked against seven prediction models. The experimental results indicate that the proposed hybrid prediction model achieves the highest accuracy in building energy prediction. • A novel hybrid approach for building energy prediction is proposed. • The SE is used to evaluate the necessity for secondary decomposition. • The EC technique is employed to improve the prediction accuracy. • The performance of the proposed model is benchmarked against other models. [ABSTRACT FROM AUTHOR]

This paper investigates the challenges isolated islands face in transitioning from fossil fuel-based electricity generation to renewable energy sources. The Canary Islands serve as a case study, where photovoltaic and wind power are the primary renewables, but their variability requires a deep techno-economic analysis. The island's energy demand is predicted to rise by 100% due to economic growth, electrification and electric vehicles. However, implementing renewable systems encounters obstacles, such as limited suitable sites and protected areas. The study uses Wilks' methodology and Monte Carlo sampling to explore 59 combinations of randomly selected inputs of the uncertain variables, aiming for a 95/95% coverage and confidence level in the results. In most cases, they experience energy shortages, failing to meet electric demand. Even though a new generation mix appears to cover demand under all circumstances, the uncertainty unveils a different reality, leading to an approximate 25% increase in system costs. Surpluses in energy generation, while seemingly positive, can pose challenges. The new system's Levelized Cost of Energy increases from around 14 to 17c€/kWh. These cost increases are contingent upon future performance and the variability of uncertain parameters, leading to excesses ranging from slightly below 25% to over 40%. • Demand coverage scenario designed to meet electric demand through renewable sources. • Uncertainty analysis of a scenario for the complete electrification of the economy. • Best Estimate Plus Uncertainty analysis implementing the Wilks' methodology. • Resizing the system to cover the demand with 95% coverage and confidence levels. • Differences in using deterministic and stochastic methods for electric demand coverage. [ABSTRACT FROM AUTHOR]

To promote energy saving, high efficiency and quality production in the mechanical manufacturing industry, a large number of studies have been conducted on modelling and evaluation. However, most of the approaches have focused on single objective modelling, evaluation and conventional machining method like unidirectional turning (UDT). This paper presents a multi-objective modelling and evaluation method for energy, efficiency, economy and quality using a new process of forward-and-reverse multidirectional turning (MDT). Firstly, the multi-objective influential factors of cutting parameters for energy consumption, cutting time, production cost and surface roughness of MDT are analyzed to establish the corresponding models, respectively. Then taking the energy consumption, cutting time, production cost and surface roughness as the objectives, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied for the multi-objective evaluation through different processes. Finally, the case study shows the practicability of the MDT method and evaluation method for energy optimization and energy conservation. • Proposing a new method of energy saving and high efficiency production oriented multidirectional turning (MDT). • Illustrating the characteristics of MDT. • Establishing entropy-weight and fuzzy evaluation models. • Analyzing the practicability of the multi-objective evaluation and models. [ABSTRACT FROM AUTHOR]

In recent years, permanent outages and lack of access to electricity have been significant problems in most cities of Iran. The increasing growth of fossil fuel consumption made it necessary to change the design of buildings. Designing a net-zero energy building (with natural ventilation and renewable energy systems) is a solution for reducing fossil fuels. In this paper, dynamic energy modeling of the building for the smart operation of the trapezoidal Trombe wall and underground air ducts is performed. In the first step, the Trombe Zones are considered for heating. Then the combination of the Trombe zones with underground air ducts for cooling is simulated. Then the system combination with a solar hot water system to supply hot water energy is evaluated. Finally, the economic analysis of the optimal hybrid system (with a photovoltaic system, wind turbine, battery, and inverter) for supplying the electricity of the building equipped with both passive energy strategies was evaluated by Homer software. The results show that by integrating the Trombe wall, the heating load is reduced by 44 %, and by integrating a coupled geothermal cooling system with the air duct and the Trombe wall, the cooling load is reduced by 39%. Also, the net present cost over the 25-year life of the optimized renewable system is reduced by about 12.5% due to both passive strategies. The average daily electricity energy demand of the base building is equal to 86.37 kWh and for the passive building, it is equal to 67.49 kWh. [ABSTRACT FROM AUTHOR]