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Decarbonization in the aeronautical industry is a daunting challenge due to the lack of sustainable aviation fuel. Hydrogen offers a potential option for this problem, given that it is an energy-dense fuel capable of reducing climate impact. Along with it, the liquid hydrogen preheating and thermal protection bring inevitable safety issues. Currently, how to construct the optimal thermal management system (TMS) architecture and further clarify its principles is still unclear. Thus, this study compares different TMS architectures through optimized genetic algorithms (GA), and firstly points out the excellent architecture as well as its operating parameters. Strikingly, our results reveal the optimized algorithm can significantly improve efficiency and precision of the optimizing processes. Benefiting from adaptive population strategy and elite preservation strategy, optimized GA saves half the time compared to conventional GA, and the variance is 60 % smaller than that of conventional GA. Using optimized GA, the series-parallel scheme is determined as the best solution and it exhibits better advantages on lightweight. In addition, results also show the leading factor is air/helium heat exchanger area. In conclusion, we hope this work can provide guidance for the optimal GA, contribute to decarbonization, and provide more ideas for the optimization of hydrogen TMS in the future. • A systematic optimization model of TMS is developed by considering multi-factors. • Dynamic self-adaptation of population strategy is proposed in improved GA. • The optimal framework and controlling parameters of TMS is determined. • The mechanism of how the variety of factors synergetic evolve is found out. [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]

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]

Using the unique micro-data of industrial enterprises' Nitrogen oxides (NOx) and Sulfur oxides (SOx) emissions covering 58,725 enterprise-year observations, this paper investigates how digitalization affect energy efficiency. The empirical results show that regional digitalization can significantly promote the improvement of energy efficiency of industrial enterprises. Specifically, the result of PSM-DID method indicates that the impact of digitalization on energy efficiency is significantly enhanced by the "National innovative pilot cities" policy, while the dynamic result validates the continuity of industrial enterprises' energy efficiency. Moreover, regional digitalization has superior impact on enterprises within eastern region enterprises, the manufacturing sector, and with elevated liquidity and profitability levels. Finally, moderating effect suggests that technological innovation expand the positive impact of digitalization on enterprises' energy efficiency. This study confirms regional digitalization can promote the energy efficiency of Chinese industrial enterprises, providing the policy suggestions to the energy transition in China. • Measure the regional digitalization level of Prefecture-level city and the energy efficiency at Industrial enterprise-level. • Findings demonstrated that digitalization increases energy efficiency. • The impact of digitalization on energy efficiency can be enhanced by the "National innovative pilot cities" policy. • Technological innovation expands the positive impact of digitalization on enterprises' energy efficiency. [ABSTRACT FROM AUTHOR]

To address the drawbacks of low energy utilization and high cost in traditional photovoltaic (PV) vehicle energy management systems, a hybrid energy management system for PV vehicles is proposed, which can automatically manage energy under complex conditions. An improved PV model and the solar irradiation S – temperature T transfer function are used to construct Simulink simulation models. The simulation compares the charging effects of a PV cell and a charging station, as well as the performance of four energy management schemes. Finally, validation experiments are conducted for the improved PV model and the PV charging model. The simulation results show that: (1) The charging speed of the PV cell can reach 3.37 times that of the charging station, highlighting the practicality of PV power supply research. (2) In complex environments, Energy Management Scheme 2 has a more effective energy management capability compared to other schemes. The experimental results show that: (1) Compared to the original PV model, the improved PV model provides simulated data that are closer to the experimental data of the PV cell. (2) The accuracy of simulating the charging voltage and current in the PV charging model has significantly improved, reaching over 94.2% and 89.5%, respectively. • An improved PV model and irradiance – temperature transfer function are proposed. • The implementation of using photovoltaics for powering electric vehicles. • A hybrid energy management system for photovoltaic (PV) vehicles is proposed. • The system ensures normal operation of PV vehicles under complex conditions. • The system can improve the energy efficiency of photovoltaic vehicles. [ABSTRACT FROM AUTHOR]

In recent years, there has been a burgeoning emphasis on advancing the paradigm of energy transformation and expediting the maturation of novel energy systems in order to enhance the structural integrity of China's energy industry. Employing the 2007 "Opinions on Implementing Environmental Protection Policies and Regulations to Prevent Credit Risk" as a quasi-experimental setting, this study utilizes longitudinal panel data spanning from 2000 to 2014, encompassing Chinese industrial enterprises. Through the application of a difference-in-differences analytical framework, this research rigorously evaluates the ramifications of the Green Credit Policy (GCP) on firm-level energy-utilization efficiency (FLEE). The empirical findings proffer compelling evidence of a substantial amelioration in FLEE attributable to the enactment of the GCP. Furthermore, a nuanced investigation of heterogeneity reveals that the GCP exerts a conspicuously more pronounced influence in elevating FLEE levels within the eastern region, state-owned enterprises, entities characterized by low technological sophistication, and those operating within highly pollution-intensive sectors. Mechanism analysis underscores the pivotal role of resource allocation and the amelioration of financing constraints as indirect pathways through which the GCP bolsters FLEE. • Green Credit and Energy Efficiency of Enterprises. • Empirical study matching the data of industrial enterprises above designated size in China from 2000 to 2014. • The promotion of green credit policy on enterprise energy utilization efficiency was discussed. • Green credit has promoted the green transformation of polluting enterprises, but there is a phenomenon of the intensity gap of environmental regulations. [ABSTRACT FROM AUTHOR]

The growing penetration of non-programmable renewable energy sources and the consequent fluctuations in energy prices and availability lead to the need to enhance energy system flexibility and synergies between different energy vectors. This can be reached through sector integration. Among the most relevant technologies used for this purpose, Power-to-Gas systems allow excess renewable electricity to be converted directly into fuels that can be then stored or used. A smart energy system, however, which includes these innovative solutions, requires intelligent management methods to optimize its operation. This work investigates the operational strategy of energy systems integrated with Power-to-Gas solutions for seasonal storage, by developing an optimization model for the system, formulated as Mixed-Integer Linear Programming problem. The algorithm tackles the uncertain nature of future disturbances, such as energy needs, generation and price using two-stage stochastic programming. The algorithm is tested on grid-connected and 100% renewable energy supply case studies. The novel stochastic algorithm allows a more robust optimization compared to a deterministic optimization, and system management is ensured under several future disturbances realization. Furthermore, the integration of Power-to-Gas solutions warrants the energy security of the energy systems and acts as a buffer to forestall unpredictable behavior of the disturbances. • Multi-energy system management optimization with a linear programming algorithm. • Uncertainties on disturbances tackled using two-stage stochastic programming. • Tests on case studies with different climate, energy demand and network. integration. • Power-to-Gas integration enables greater flexibility against uncertainties. • Hydrogen seasonal storage increases energy system robustness. [ABSTRACT FROM AUTHOR]

This paper aims to employ multi-agent-based energy management and optimization to design a set of interconnected micro-grids with the ability to exchange electricity with the main grid. Initially, the micro-grid components, their governing mathematical model, and the pricing mechanism are introduced. In the next step, the energy management framework is constructed based on three objective functions: minimizing electricity costs, maximizing renewable energy penetration, and providing maximum possible satisfaction. A multi-agent system composed of several operational agents is presented. To evaluate the applicability of the proposed approach case study was conducted. The proposed multi-agent-based optimization is employed to define the most promising solution for the energy system. The solution contains PV power installation, battery storage capacity, home appliances operational schedule, and the energy flow between micro-grids and the main grid. Simulation results indicated that the average annual earnings of the households from interacting with the main grid could be enhanced by 32.9% after performing the optimization procedure. Finally, variations in the distribution classes and investment costs were conducted to observe the effect of changing presumed parameters. Varying the investment costs revealed that for PV-specific investment costs greater than 1500 $/kW, employing solar power generation is not favorable from an economic point of view. However, it should be noted that the required PV installation capacity and hence the required investment strongly depends on the available solar energy. As a result, for regions with smaller annual irradiation, the configuration of the energy system may be quite different. To sum up, the study's overall results show that the proposed multi-agent optimization framework provides a powerful tool to design and operate energy systems composed of several energy sources and versatile users. • The configuration and operation of interconnected micro-grids is optimized. • Multi-agent framework surpasses the rule-based optimization. • Energy import from the main grid increases when more users prioritize the comfort. • When the initial cost of PV exceeds 1500 $/kW, using solar power is not efficient. • The minimum grid dependency must be satisfied subsequent to the other objectives. [ABSTRACT FROM AUTHOR]

With the development of the technologies for Vehicle-to-Everything (V2X), driving information sharing leads to a potential solution for intelligent vehicles to enhance their controlling effect, particularly for energy management of the vehicles equipped with the complex powertrain. Transplanting the optimal and adaptive knowledge of the vehicle driving data into a target vehicle can enhance both the adaptability and economic optimization of energy management. In this paper, a V2X-based knowledge-enhanced energy management strategy (KE-EMS) is proposed to transplant offline optimal-oriented prognostic knowledge and adaptive-oriented heuristic knowledge into online driving in practice. Firstly, in the offline processing, based on micro trip segments collected from volunteered sharing vehicles by V2X, optimal-oriented prognostic knowledge and adaptive-oriented heuristic knowledge are generated by dynamic programming (DP) and improved particle swarm optimization algorithm (iPSO) respectively. In the online implementation, the KE-EMS invokes the generated knowledge accordingly by a driving segment matching method (DSM) estimating the similarity between the current driving condition and micro driving segments. Finally, an improved equivalent consumption minimization strategy (ECMS) embedded into KE-EMS further adjusts the electric charging tendency to accomplish the energy-saving potential release. A simulation evaluation and hardware-in-the-loop (HIL) test manifest that the proposed KE-EMS can improve economic performance and guarantee real-time performance for the studied plug-in hybrid electric vehicles (PHEVs). • A knowledge-enhanced EMS releases the energy-saving potential of the studied PHEV. • A parameter optimization method enhances the economic robustness of the EMS. • A driving segment matching method represents the ideal energy consumption tendency. • An improved ECMS enhances the adaptiveness of EMS under various driving conditions. [ABSTRACT FROM AUTHOR]

Ethylene production is an energy-intensive process, hence energy management and optimization play a crucial role in saving energy and increasing economic benefits. In industrial-scale ethylene production, the energy efficiency level is greatly influenced by different working conditions and multiple energy arrangements in different sub-processes. Energy efficiency optimization is a more direct and scientific way to improve efficiency and reduce consumption. However, conventional energy optimization schemes are implemented without due consideration of the above two factors adequately, and energy efficiency indicators are not considered a key objective of optimization. Aiming at the energy efficiency optimization problem of ethylene plant under multiple working conditions, an energy efficiency integration optimization scheme is proposed, combining multi-level production process and multi-condition technology. The traditional single optimization model cannot achieve the energy efficiency improvement of the ethylene production process characterized by the multi-condition and hierarchical architecture. To this end, by establishing dynamic models of the system level, process level and equipment level, and considering the associations at different levels, energy efficiency optimization models of ethylene production for different working conditions are established to realize an energy optimization management that maximizes the overall energy utilization efficiency of production. For the solution of model, a multi-objective particle swarm optimization algorithm based on historical working condition knowledge base is proposed to improve the performance of the optimization algorithm by guiding the oriented local area search. The effectiveness of the proposed scheme is verified through the application in a Chinese ethylene plant. The optimization results show that the overall energy efficiency of ethylene production has been significantly improved despite frequent changes in working conditions. • A cascaded energy efficiency optimization scheme is developed. • Conditions classification is considered in energy efficiency optimization. • A novel historical working condition knowledge base is adopted in the scheme. • Energy efficiency is improved significantly in different working conditions. [ABSTRACT FROM AUTHOR]

The subject of this study is to analyze the heat transfer inside the gas flow channel (GFC) of the proton exchange membrane fuel cell (PEMFC) numerically. Increasing the fluid-solid contact area, as well as, changing the cooling fluid velocity and temperature profiles can improve the heat transfer. Consequently, trapezoid porous ribs with three different geometrical parameters are placed in the GFC to meet this goal. These geometrical parameters are the base and the tip widths of the ribs in addition to their distance from each other. Numerical simulations arose the respective Nu numbers and friction factors of the system to evaluate the influence of these ribs. Afterward, these simulations data are used to train an artificial neural network (ANN) to model the system and produce data to perform the sensitivity analysis and sketch the corresponding three-dimensional diagrams. Finally, the optimum values of the mentioned geometrical parameters are calculated to maximize the heat transfer rate with minimum friction losses. Results indicate that although the lower distance between porous ribs has led to the higher Nu, this also causes the higher friction factor. Therefore, it is better to utilize higher distances to meet the higher performance evaluation criterion. • The effects of trapezoid porous ribs on the heat transfer have been investigated. • Effects of three different geometrical parameters of these ribs analyzed. • By the application of artificial neural network, sensitivity analysis is performed. • Lower width of the trapezoid ribs has the dominant impact on the heat transfer. • Using performance evaluation criterion, the optimum values calculated. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) has been widely used in the energy efficiency analysis of industrial production processes. However, the traditional DEA model is not high in the division of the efficiency value of decision making units (DMUs), and produces a large number of DMUs with an efficiency value equal to 1, making it difficult to identify their merits and demerits. Therefore, a novel DEA model based on the affinity propagation (AP) clustering algorithm (AP-DEA) is proposed. Through the AP clustering algorithm, high influence input data of the energy efficiency can be obtained. The merits and demerits of DMUs can then be identified with a high degree of discrimination to obtain better efficiency groups. Finally, the proposed model is applied to evaluate the energy efficiency and optimize the energy configuration of the ethylene and pure terephthalic acid (PTA) production processes in complex petrochemical industries. The experimental results show that this proposed model can improve the efficiency value discrimination of efficiency values by effective DMUs better than the traditional DEA. Moreover, the energy saving potentials of ethylene and PTA production systems are approximately 0.49% and 24.74%, respectively, and the carbon emission reduction of the ethylene production system is approximately 10.04%. • A novel AP-DEA method is proposed. • Energy efficiency evaluation and energy saving model of petrochemical industries is obtained. • This proposed model is efficient in energy saving and carbon emission reduction. • The energy saving potential of ethylene production systems and PTA production systems is 0.49% and 24.74%. • The carbon emission reduction of ethylene production systems is 10.04%. [ABSTRACT FROM AUTHOR]

The energy conversion efficiency of coal utilization is a focal point in modern energy management practices and in academic literature. This paper explores life cycle energy efficiency of coal (LEEC), which encompasses six distinct stages from the design of coal mines to the comprehensive reuse of resources. First, the study analyzes improvements to the evaluation of efficiencies in energy systems. Secondly, it proposes a multi-stage energy efficiency analysis and constructs an innovative LEEC model for coal exploitation and utilization. Thirdly, the energy efficiency of coal exploitation and utilization was evaluated, and found that total efficiency in China increased from 33.11% in 2010 to 37.69% in 2014, saving 126 Mtce of energy. Finally, this study found that mine design is the most important stage in improving energy efficiency, followed by the coal processing stage. Moreover, we found that coal processing has an indirect contribution to other stages in the life cycle utilization of coal, such as reducing transportation capacity. The primary research contribution is the development of a comprehensive LEEC model for the entire life cycle of coal, rather than just single-stage evaluation. This lays the foundation for further studies on energy efficiency of industrial chains and across entire systems. • Exploration of energy efficiency across the entire coal life cycle. • The energy efficiency of coal exploitation and utilization increased annually. • Analysis shows the implicit contribution of coal processing to life cycle. • Mine design is a crucial stage for overall life cycle energy efficiency. [ABSTRACT FROM AUTHOR]

Accurate early prediction of Li-ion battery aging facilitates new product optimization and application management. Here, a joint modeling scheme is proposed. It is dedicated to decoupling cell-to-cell variability and cycle-by-cycle nonlinear aging in Li-ion batteries, enabling accurate cycle life and capacity trajectory predictions. An aging end-point early predictor is established based on the multiple output Gaussian process (MOGP). It enables better performance by sharing knowledge across outputs, including cycle life and life capacity. For degradation trajectory prediction, an aging process trajectory predictor is constructed based on the 'prompt-learning' paradigm neural network, and the life capacity from MOGP is used as the prompt input. Moreover, a Bayesian search strategy is proposed to obtain optimal feature combinations, and feature alignment is used to narrow the gap between source and target domains. The proposed scheme is developed on the source domain with 41 cells and tested on three special target domains with 128 cells. As a result, the predicted error of cycle life can be within 7.87%, and that of the capacity trajectory can be within 6.53%. The prediction results prove its high adaptability and effectiveness. The achievements will promote high-dimensional parameter design and optimization, accelerating battery development and reducing testing costs. [Display omitted] • Accurate prediction is achieved for both LIB cycle life and aging trajectory. • An aging end-point early predictor is built using the multi-output Gaussian process. • A prompt-learning paradigm joint model is used for degradation trajectory prediction. • Optimization strategies are proposed for feature combination and alignment. • Effectiveness is demonstrated on 169 commercial cells for 80 protocols. [ABSTRACT FROM AUTHOR]

The agricultural and industrial indicators that contribute to the efficiency of sugarcane mills were investigated in this study. A two-stage Data Envelopment Analysiswith output approach was applied to 271 mills for the data from the 2006/2007 season to the 2015/2016 season. The first stage consisted of the elaboration of two efficiency models using the total milling yield of the crop (t) and the amount of total recoverable sugars (t) as the input variables and the productions of sugar (t) and ethanol (m3) as the output. The second stage identified the production process variables significantly influencing the efficiency through multiple linear regression. In addition, with the help of sugarcane experts, ten variables related to the efficiencies of the agricultural and industrial processes of sugar and ethanol production, which contribute to the overall productivity of the sugar-energy units, and 40 best practices were developed to observe, improve, and control these indicators in order to increase the global efficiency of the agroindustrial production units. • Best practices improve competitiveness of a production unit. • Improvements in agricultural sector affects directly the global efficiency. • Ten variables represent the most important control points in agroindustry plants. • Field, structural and operational factors set the agroindustrial best practices. • Adopt the best agroindustrial practices increase technical efficiency. [ABSTRACT FROM AUTHOR]

Abstract Waste heat is an abundant resource that if recovered with a heat pump would increase energy efficiency in industrial processes. This will provide improvements in heat utilization and reduce the environmental impact of greenhouse gas emissions from the combustion of fossil fuel. A hydrocarbon high temperature heat pump has been developed to demonstrate the potential to deliver heat at a temperature of 115 °C. The heat pump provides heat for applications such as drying, pasteurization and other processes. Using hydrocarbons, the heat pump aims for a clean energy system. This paper reports on a 20 kW capacity cascade heat pump with propane in the low temperature cycle and butane in the high temperature cycle. Based on a theoretical model, an experimental setup is built with standard components that are commercially available. A prototype compressor is investigated for its performance at high temperature conditions. The heat pump can recover waste heat at 30 °C and deliver heat up to 115 °C. With an average heating coefficient of performance (COP) of 3.1 for a temperature lift of 58–72 K, the heat pump is a more cost efficient and environmentally friendly system compared to existing solutions of a steam boiler. Highlights • Experimental investigation of a high temperature heat pump up to 115 °C. • Experimental investigation of a modified compressor for high temperature heat pump. • Coefficient of performance (COP) of 2.1 and 3.1 for waste heat at 30 °C and 60 °C. • Heat pump COP at 60 °C waste heat break-evens with gas boilers in less than 3 years. • Carbon emissions reduction of over 95% in Norway and 30% in Europe. [ABSTRACT FROM AUTHOR]

Abstract Energy efficiency, which is one of the pillars of the EU's Energy Union strategy, has been proposed as a solution, namely as a highly effective pathway to improve economic competitiveness and sustainability of the European economy, lower emissions, reduce energy dependency and increase security of supply, and job creation. The paper reviews the EU strategies and policies on energy efficiency and argues that further focus should be placed on industrial energy efficiency. Despite a decline in energy consumption in recent years in industry, this sector is one of the largest users of energy in the EU. Therefore, the paper reviews the extent to which the European and national policies in the selected jurisdictions, such as Italy and the UK address energy efficiency in industry and whether there are any measures in place to promote it. Highlights • Legal certainty is required in industrial energy efficiency policies. • White Certificates play a major role in industrial energy efficiency in Italy. • Yet, constant changes in the WC regulations can act as an investment barrier. • The UK's Energy Efficiency policy is underpinned with decarbonisation of the industry. [ABSTRACT FROM AUTHOR]

Abstract This study investigates the mechanism behind the effects of urban agglomeration economies on industrial energy efficiency in theory. On this basis, we match firm level data and panel data of 283 prefecture level and above cities from 2003 to 2010, and we use the dynamic spatial Durbin model to estimate the effects of urban agglomeration economies on industrial energy efficiency. The results reveal that in both the short and long term, the specialization and diversification agglomerations of industries do not have a significant effect on the city itself, but they do significantly reduce the energy efficiencies of the neighboring cities. Therefore, the free rider and "Race to the bottom" effects of industrial agglomeration on industrial energy efficiency exceed the demonstration and synergistic effects. Urban agglomeration has a significant negative spillover effect, and the long-term effect is greater than the short-term effect. Furthermore, we investigate the heterogeneity of the effects based on the industry subdivision. The homogeneity of the industrial structure between cities means that specialization and diversification do not produce the expected promotion effect on industrial energy efficiency in local and surrounding cities in the short and long term, but the degree to which this effect is inhibited differs. Highlights • Study the effects of urban agglomeration economies on industrial energy efficiency. • We match firm level data and panel data of 283 prefecture cities from 2003 to 2010. • We estimate the spatial spillover effect by dynamic spatial Durbin model. • Urban agglomerations reduce energy efficiencies of neighboring cities. • We investigate the heterogeneity of the effects based on the industry subdivision. [ABSTRACT FROM AUTHOR]

Memory effect could significantly fasten hydrate nucleation rate and increase the efficiency of hydrate-based industrial applications. To examine the strength and understand the mechanism of memory effect under different conditions, methane hydrate reformation experiments were carried out. The experiments were designed with four different initial pressures and three different gas-water ratios. The calculated parameters including induction time and hydrate formation rate were analyzed to evaluate the memory effect and understand its mechanism. The results showed that when initial pressure was higher than 8.0 MPa the induction time values were similar in repeated experimental runs, demonstrating that the memory effect was not strengthened with increase of repeated times. Meanwhile the induction time values did vary with different initial pressures and gas-water ratios, revealing that the two factors of pressure or gas-water ratio played as a dominant actor differently in systems with different initial pressures. Besides, a mechanism of memory effect was proposed. We argue that residual water lattice or super-saturated gas nano-bubbles performed as a dominant role in systems with relatively high or low initial pressure, respectively. [Display omitted] • The memory effect was analyzed in methane hydrate system. • The memory effect was not strengthened with increased repeated times of experiment. • Pressure or gas-water ratio played as a dominant influence factor in different systems. • A mechanism of memory effect was proposed combing with experimental results. [ABSTRACT FROM AUTHOR]

The mileage range of electric vehicles is still restricted incredibly due to the limitation of the onboard battery energy and long charging time; therefore, a comprehensive energy consumption optimization of the thermal management system is necessary to guarantee the normal use of electric vehicles. A test bench of integrated battery thermal management system for pure electrical transportation vehicles is established in this study. The optimal thermal management strategy is developed based on analyzing the energy-saving potential of thermal management components. When the battery system operates under real-vehicle operating conditions at representative ambient temperatures, the thermal management system characteristics applied to the proposed strategy are validated and analyzed. The experimental tests show that the proposed thermal management strategy can effectively reduce the energy consumption of the thermal management system under the premise that the battery temperature is controlled within the appropriate range, compared with the prototype vehicle. • The performances of thermal management system based on a real vehicle prototype test. • Analyzation on the energy-saving potential of the energy consuming components for the battery thermal management system. • Experimental investigation of a collaborative control strategy at the real vehicle application level. • Recommendations for optimizing the energy consumption of battery thermal management systems. [ABSTRACT FROM AUTHOR]

As a new integrated airborne electromechanical system, APTMS extracts air, shaft power and fuel from aircraft engines which will cause complex effects on fuel consumption and stability margin of aircraft engine. The performance conflict between aircraft engine and APTMS caused by multiple forms of energy cross-linking greatly increases the difficulty of energy management and optimization. Considering the contradictory of the optimization objectives of this coupling model. a two-stage Stackelberg game is proposed to solve the energy optimization. By setting aircraft engine as a leader and APTMS as a follower with the constraints, an energy optimization Stackelberg game is established. The existence of Stackelberg equilibrium is proved by multivariate nonlinear fitting and the value of equilibrium is obtained by dynamic iteration. Additionally, in order to jump out of the local optimal solution, adaptive chaos search particle swarm optimization (ACPSO) is introduced to the solution accuracy. Finally, compared with multi-objective optimizations base on Pareto equilibrium under typical conditions, the advantages of this new method are verified. • A multi-objective optimization based on Stackelberg game is proposed. • Revenue models of the aircraft engine and the APTMS are established. • Adaptive chaos search is introduced in PSO to obtain a better solution. • Experiments are designed to prove the effectiveness of this proposed method. [ABSTRACT FROM AUTHOR]

Intelligent coal stockpiles management system is significant for the next-generation cleaner power plants. Prevention of spontaneous combustion is a key issue for such a system, both in economic and environmental terms. As many factors can influence the self heating process of coal such as moisture and ash in coal, temperature distribution and stockpiles' shapes, the remaining ignition time is developed as an aggregative indicator to measure the tendencies of spontaneous coal combustion. Using this value, the grey models have been applied to forecast spontaneous combustion and their performances are good for systems with insufficient information. However, the forecasting accuracy of these models still needs to be improved. Therefore, the ABC-RGM(1,1) model is proposed in this work based on the rolling-GM(1,1) and the Artificial Bee Colony (ABC) optimization algorithm, which has been applied to the management system of a 4 × 600 MW power plant. The computational experiments show that the ABC-RGM(1,1) model achieves better performance than the other popular grey models and accuracy of forecast is greatly improved especially for short-term forecasts. Such an accurate model is highly important and useful for intelligent coal management systems which can improve decision making and reduce risk. [ABSTRACT FROM AUTHOR]

Energy consumption during the post-combustion CO 2 capture process is significant, which decreases energy efficiency of industrial power plant. Many studies have been conducted to increase the CO 2 capture capacity and reduce thermal energy duty by utilizing a new material instead of the amine-based CO 2 absorbent or by optimizing the system. However, vast energy is still consumed in the regeneration process. Herein, sustainable thermal energy harvesting system from post-combustion CO 2 capture (15 vol% CO 2 and 85% N 2 at 20 °C and 1 bar) has been proposed. The functionalized solvent, composed of tetraethylenepentamine-impregnated mesoporous silica (TEPA/SBA-15) and blended chemical aqueous absorbent (MEA + PZ), is developed to maximize thermal energy harvesting density and selective CO 2 capture capacity. The reaction heat of solvents reaches 124.6 kJ/mol CO2 , which is 52.0% higher than that of 30 wt% aqueous monoethanolamine solution. Thermal energy harvesting density is estimated as 202.2 kJ/kg solvent during CO 2 capture process. When the functionalized solvent is applied to the industrial post-combustion CO 2 capture system of thermal power plants, CO 2 emission is reduced to 0.210 t CO2 /MWh e with thermal energy production of 751 kWh/t CO2 , which can contribute to 51.9% reduction in the operational energy. • Sustainable energy harvesting system from point-source CO 2 capture is developed. • CO 2 reaction heat is utilized to harvest thermal energy by functionalized solvent. • CO 2 emission of power plants can be reduced to 0.210 t CO2 /MWh e. • 751 kWh/t CO2 of thermal energy is produced during CO 2 capture process. [ABSTRACT FROM AUTHOR]

A reliable energy optimization strategy incorporating vehicle connectivity is of great importance for the performance enhancement of fuel cell electric vehicles. In this paper, a multihorizon hierarchical model predictive control framework is proposed, which reduces energy consumption while incorporating fuel cell lifetime management though real-time speed preview. Specifically, the trajectories of battery state of charge are explored via convex optimization in the upper layer to provide a suboptimal reference for real-time optimization, and the concept of multihorizon is introduced into convex optimization for the first time. At the lower level, an equivalent consumption minimum strategy-based model predictive control is designed, which improves energy utilization efficiency and prolongs the lifetime of fuel cells. The main contribution of this paper is to use multihorizon optimization to solve the energy optimization and lifetime management of fuel cell electric vehicles over different timescales. Experimental results show that the proposed strategy has great potential in cost saving, which can reduce 10.10% to 16.95% of the total cost in real driving conditions compared with the rule-based strategy. • The multihorizon HMPC framework is applied to energy management of FCEVs. • The sub-optimal reference trajectories of battery SOC are planned. • The fuel cell lifetime is considered in energy management. • The method can realize a sub-optimal result compared with DP algorithm. [ABSTRACT FROM AUTHOR]

The energy management in fuel cell vehicles (FCVs) is crucial to maintain the economical operation of FCVs and the fuzzy logic control (FLC) is mainly used to manage the energy split between the fuel cell and other energy sources. To overcome the limitation of traditional FLC, the dependence on expert knowledge leading to the insufficient energy split, this paper proposes strategy optimization based on FLC with driving cycles recognition achieve near-optimal fuel economy and stable battery charge sustenance. Initially, the whole FCV model is established, which includes electrical system, vehicle dynamic system, energy management system. Additionally, with the objective function which is the minimum equivalent hydrogen consumption of four typical driving cycles, the centers and widths of FLC membership function are optimized by genetic algorithm (GA), respectively. Finally, the driving cycles recognition is achieved based on K-means clustering method, and characteristic parameters are extracted and classified. Compared with GA-optimized fuzzy EMS and the traditional fuzzy EMS, the simulation results demonstrate that the equivalent hydrogen consumption based on proposed strategy is reduced by 16.55% and 40.50%, respectively. Therefore, the proposed strategy can effectively smooth the output of proton exchange membrane fuel cell (PEMFC) and enhance the total fuel economy. • The driving cycles recognition based on unsupervised method is developed. • An optimized fuzzy logic control optimized is investigated. • An entire FCV model is established consisting of multiple subsystems. • Smoother output of fuel cell is achieved while enhancing the total fuel economy. [ABSTRACT FROM AUTHOR]

Regulating photovoltaic (PV) cells temperature using phase change materials (PCMs) is considered a promising thermal management strategy. In this study, a solar PV-PCM collector with rectangular copper fins was proposed, and the effects of the PCM height and fin length, arrangement, and number, on the temperature characteristics, and electrical efficiency of PV cells were numerically investigated. A PCM height of 60 mm could meet the thermal management requirement of a heat flux input of 800 W/m2 during the daytime. Compared with the PV-PCM model without fins, the PV cell performance was improved by 4.62% for the model uniformly arranged three fins with a height of 33.33 mm. The variable length and nonuniform arrangement of fins could further enhance the performance of PV cells; however, the temperature uniformity deteriorated. Compared with fin length and arrangement, the fin number played a more significant role in the thermal management of PV cells. The model with eleven fins performed the best with the average temperature, temperature unevenness, and efficiency of 48.58 °C, 3.20, and 11.19%, respectively. These values were 16.11 °C and 43.06% lower, and 7.70% greater than the model without fins. This study provides valuable insight into the design optimization of PV-PCM systems. • Phase change material was adopted for thermal management of the PV cell. • Temperature uniformity and electrical efficiency of the PV cell were studied. • Suitable PCM amount in PV-PCM collector was evaluated. • Different arrangements of fins in PV-PCM collector were optimized. [ABSTRACT FROM AUTHOR]

In this paper, an energy model is developed customised for the design of low carbon energy systems on business park scale. The model comprises two sequential stages: In the first stage, heat recovery within the system is maximised, while utility system and energy storage are optimally integrated and designed to fulfil remaining energy requirements at minimum total annualised costs. Simultaneously, heat networks can be deployed to transfer heat between separate parts of the system. Intra-annual variations in thermal and electrical energy demand and supply are taken into account, next to a carbon emission cap. In the second stage, the model generates an optimal multi-period heat exchanger network (HEN) enabling all required heat exchanges. The proposed model builds upon a multi-period energy integration model that can deal with restrictions in heat exchange. This model is combined with a generic technology model that can be manipulated to represent the various thermal or electrical energy conversion technology units in the utility system. Moreover, simple models for thermal and electrical energy storage are included that allow for calculation of storage levels subject to energy loss over time, without increasing the number of time steps to be analysed. A method for automated superstructure expansion is incorporated in the solution procedure to enable the optimisation of the number of units per technology in the configuration of the utility system. The HEN is automatically generated using a multi-period stage-wise heat exchanger network model. The energy model is applied to a literature example and to a generic case study demonstrating its holistic and integrated approach towards the synthesis of low carbon energy systems. [ABSTRACT FROM AUTHOR]

Considering the complicated characteristics of traffic flow in city bus route and the nonlinear vehicle dynamics, optimal energy management integrated with clustering and recognition of driving conditions in plug-in hybrid electric bus is still a challenging problem. Motivated by this issue, this paper presents an innovative energy optimization control framework based on the cloud computing for plug-in hybrid electric bus. This framework, which includes offline part and online part, can realize the driving conditions clustering in offline part, and the energy management in online part. In offline part, utilizing the operating data transferred from a bus to the remote monitoring center, K-means algorithm is adopted to cluster the driving conditions, and then Markov probability transfer matrixes are generated to predict the possible operating demand of the bus driver. Next in online part, the current driving condition is real-time identified by a well-trained support vector machine, and Markov chains-based driving behaviors are accordingly selected. With the stochastic inputs, stochastic receding horizon control method is adopted to obtain the optimized energy management of hybrid powertrain. Simulations and hardware-in-loop test are carried out with the real-world city bus route, and the results show that the presented strategy could greatly improve the vehicle fuel economy, and as the traffic flow data feedback increases, the fuel consumption of every plug-in hybrid electric bus running in a specific bus route tends to be a stable minimum. [ABSTRACT FROM AUTHOR]

A Pareto-based multi-objective optimization approach has been proposed to design and obtain Pareto-optimal set of solutions for performance and emission characteristics of a single cylinder, four stroke diesel engine using hydrogen in dual fuel mode. Instead of defining a single optimal objective, the proposed method establishes the multi-objective model by taking two design objectives into account, which are minimizing load (%) and hydrogen flow rate. To address this optimization problem, we develop a two-stage evolutionary computation approach integrating an exclusive non-dominated sorting genetic algorithm (NSGA-II) and technique for order preference by similarity to ideal solution(TOPSIS). NSGA-II has been utilized to search for the candidate solutions in-terms of both objectives. The obtained results have been obtained as Pareto-front. Subsequently, the best compromise solution has been determined by the TOPSIS method from the Pareto-front according to the decision maker's preference. The design results shows that the proposed approach yields a remarkable reduction in the emission spectra with enhancement in performance of the engine under a single podium obtained by multi-objective optimization. [ABSTRACT FROM AUTHOR]

International home energy rating regulations are forcing to use efficient cooking equipment and processes towards energy saving and sustainability. For this reason gas ovens are replaced by the electric ones, to get the highest energy rating. Due to this fact, the study of the technologies related to the energy efficiency in cooking is increasingly developing. Indeed, big industries are working to the energy optimization of their processes since decades, while there is still a lot of room in energy optimization of single household appliances. The achievement of a higher efficiency can have a big impact on the society only if the use of modern equipment gets widespread. The combination of several energy sources (e.g. forced convection, irradiation, microwave, etc.) and their optimization is an emerging target for oven manufacturers towards optimal oven design. In this work, an energy consumption analysis and optimization is applied to the case of bread baking. Each source of energy gets the due importance and the process conditions are compared. A basic quality standard is guaranteed by taking into account some quality markers, which are relevant based on a consumer viewpoint. [ABSTRACT FROM AUTHOR]

Fouling is a crucial problem in refinery HEN (heat exchanger network) that reduces heat transfer and affects regular production. The conventional way to mitigate fouling is cleaning heat exchanger regularly or improving operation conditions, but simultaneous consideration of the two methods is rare. This paper presents a combined approach for mitigating fouling in HEN by optimizing operation condition and cleaning schedule simultaneously. For optimization of operation condition, flow velocity is selected as a key variable since it can correlate fouling, heat transfer and pressure drop. An overall optimization of network performance can be achieved through redistribution of velocity. In a refinery HEN, fouling cannot be completely prevented through optimization of operation conditions, so management of cleaning actions is optimized to deal with the remained fouling. SA (Simulated annealing) algorithm is used in this work to obtain a comprehensive strategy for mitigating fouling. The application of the proposed method is demonstrated using a case study. The results show higher energy saving and economic efficiency compared with existing methods. [ABSTRACT FROM AUTHOR]

Previous literatures have focused on the impact of sectorial urban specialization on energy efficiency. As a future trend of urban specialization, the importance of functional urban specialization (FUS) in improving industrial energy efficiency is ignored. To fill this gap, this study explores the impact of FUS on industrial energy efficiency. Utilizing prefecture-level city data from 2005 to 2017, this paper adopts an extended stochastic frontier analysis (SFA) model to measure industrial energy efficiency. Hierarchical linear model (HLM) is used for the estimation of the nested data with cluster-level and city-level, which reduces the bias by nested estimation. The results show that 1) city-level industrial energy efficiency has increased from 0.4133 in 2005 to 0.4461 in 2017, mainly driven by peripheral cities rather than core cities; 2) FUS has a significantly positive impact on industrial energy efficiency in full sample and peripheral cities; 3) FUS reduces the negative effects of city scale and the positive effects of investment and enhances the positive effects of transportation. In addition, the positive effects of FUS on industrial energy efficiency is significant only at low levels of FUS. Practicable policies to improve industrial energy efficiency in China are suggested and applicable to other emerging economies. • City-level industrial energy efficiency is measured by an extended SFA model. • We demonstrate the mechanism of FUS affecting industrial energy efficiency. • HLM is used to estimate nested data with cluster-level and city-level. • FUS has positive impacts on energy efficiency in full sample and peripheral cities. • Findings are generalizable to other emerging economies. [ABSTRACT FROM AUTHOR]

Improving the technology of hybrid energy systems is an important development direction for the greening of ships, the configuration optimization and energy management of ship hybrid energy system is vital to enhance the marine electric power system reliability, economic efficiency, and sustainability. Focusing on this problem, this paper has carried out a study on the multi-objective configuration optimization method (COM) and energy management strategy (EMS) for ship hybrid energy system based on quantum computing. First, the mathematical model of distributed power modules of the hybrid energy system is established, and a configuration optimization objective function that aims at low-cost, long equipment life, and high reliability of power supply is constructed. Then, the combination with fuzzy rules and quantum multi-objective artificial bee colony algorithm to solve the objective function, the configuration scheme satisfying multiple constraints is obtained. On this basis, an energy management optimization objective function that meets both low-cost operation and maximum clean energy utilization for ship electric power system is established, the objective function is optimized in multi-objective quantum particle swarm optimization (QPSO) algorithm, and the real-time optimal scheduling for hybrid energy systems of the ship is realized. Experiments on simulative navigation data verify the feasibility of the multi-objective configuration optimization method using the quantum artificial bee colony (QABC) algorithm. Furthermore, energy management experiments with different strategies, experimental results show that the energy management strategy proposed in this paper outperforms other methods- and effectively reduces the operating costs, fuel costs, and pollutant emissions of marine power system, meeting the environmental requirements of the Energy Efficiency Operating Index (EEOI) for ships. • COM and EMS suitable for the hybrid energy system of ships were proposed. • Multi-objective is considered in the COM and EMS. • Combining quantum computing with ABC and PSO to solve the multi-objective function. • The proposed EMS effectively reduces pollutant emissions from ships. [ABSTRACT FROM AUTHOR]

The concept of carbon neutrality makes scholars not only intensify their exploration on the utilization of new energy, but also further optimizing the utilization of traditional energy by improving energy management. Nearly half of energy losing among traditional internal combustion vehicle is due to energy delivering and operation. The CCHMT prototype enabled the vehicle to run smoothly and efficiently under harsh conditions. However, the large number of parameters makes the optimization of energy management system difficult, which adds a lot of troubles to the subsequent product serialization. Thus, after finishing research on CCHMT-3, a comprehensive optimization algorithm based on NSGA-II & SA is constructed to optimize the transmission characteristic parameters of CCHMT-4. By using the algorithm-optimized parameters, the AMEsim model simulation with CCHMT-4 shows better static speed regulation characteristics, efficiency characteristics and load stiffness characteristics. Furthermore, the dynamic characteristics of CCHMT-4 are simulated by DP dynamic programming algorithm, and the result shows that under the operation condition of JN1015, the fuel consumption rate is reduced by 13.9% compared with CCHMT-3. Subsequently, CCHMT-4 prototype processing and test-bed construction were carried out to verify the relevant characteristics of CCHMT-4. The experiment results show that the actual efficiency of CCHMT-4 is 80.6%–86.0%, the experimental data of fuel consumption is 0.701 kg, which shows better operation performance than CCHMT's former research. • NSGA-II&SA is constructed to optimize the transmission characteristic parameters of CCHMT. • It has been presented the simulation and experiment of a heavy-duty vehicle with CCHMT. • The fuel consumption rate is reduced by 13.9% through the parameters' optimization. • The actual efficiency has been reached 80.6%–86.0% with large speed regulation range. [ABSTRACT FROM AUTHOR]

As the physical subject, determining vehicle operating modes is a prerequisite for implementing global optimization energy management. To avoid the case study of different vehicle configurations, a "kinetic/potential energy & onboard energy" conservation framework is proposed to determine vehicle working modes. Firstly, typical topologies and existing work modes for hybrid vehicles with different architectures are summarized. As a numerical method, the state space is meshed, which is restricted by introducing trip information. Then, a "kinetic/potential energy & onboard energy" conservation framework is established to determine the work mode between any reachable state points. By combining external factors, internal factors and additional factors reasonably and feasibly, various trigger conditions are generated to realize the one-to-one mapping between work mode and driving condition, which standardizes the DP optimizing process. Correspondingly, the stage cost and control are determined to achieve the optimal energy distribution. Finally, regarding DP strategy as a benchmark, multiple evaluation indexes are proposed to evaluate the utilization ratio of a control strategy to global trip information. An example is given to evaluate the optimal rule-based strategy. The higher the index is, the higher the similarity with the DP strategy is, and the higher the economic performance of the vehicle is. • An energy conservation framework is established to determine work modes. • An efficient global optimization algorithm is proposed. • Multiple indexes are proposed to evaluate the energy-saving potential. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine the amount of input–output energy used in peach production and to develop an optimal model of production in Chaharmahal va Bakhtiari province, Iran. Data were collected from 100 producers by administering a questionnaire in face-to-face interviews. Farms were selected based on random sampling method. Results revealed that the total energy of production is 47,951.52 MJ/ha and the highest share of energy consumption belongs to chemical fertilizers (35.37%). Consumption of direct energy was 47.4% while indirect energy was 52.6%. Also, Total energy consumption was divided into two groups; renewable and non-renewable (19.2% and 80.8% respectively). Energy use efficiency, Energy productivity, Specific energy and Net energy were calculated as 0.433, 0.228 (kg/MJ), 4.38 (MJ/kg) and −27,161.722 (MJ/ha), respectively. According to the negative sign for Net energy, if special strategy is used, energy dismiss will decrease and negative effect of some parameters could be omitted. In the present case the amount is indicating decimate of production energy. In addition, energy efficiency was not high enough. Some of the input energies were applied to machinery, chemical fertilizer, water irrigation and electricity which had significant effect on increasing production and MPP (marginal physical productivity) was determined for variables. This parameter was positive for energy groups namely; machinery, diesel fuel, chemical fertilizer, water irrigation and electricity while it was negative for other kind of energy such as chemical pesticides and human labor. Finally, there is a need to pursue a new policy to force producers to undertake energy-efficient practices to establish sustainable production systems without disrupting the natural resources. In addition, extension activities are needed to improve the efficiency of energy consumption and to sustain the natural resources. [ABSTRACT FROM AUTHOR]

The bottleneck of China's industrial carbon efficiency improvement is that the contribution of carbon emission technology is less than that of energy technology, and that of neutral technology is less than that of capital-based technology. The key to breaking through this bottleneck is to clarify how heterogeneous technological progress enhances carbon efficiency through industrial structural upgrading. The effects of four levels of technological progress on carbon efficiency under two technical classifications based on technology sources and carbon emission processes from energy consumption are studied by using the DEA method. The suitable choice of the path of technological progress to promoting China's industrial carbon efficiency is provided accordingly. The panel data model is used to deeply investigate the effects of these four levels of technological progress on industrial carbon efficiency in China's 30 provincial industries. The main results are as follows: First, in terms of direct effects, progress in energy technology is more conducive to improving carbon efficiency than progress in carbon emission technology, and progress in neutral technology is more effective in improving carbon efficiency than progress in capital-embodied technology. Second, in terms of indirect effects, progress in capital-embodied technology is effective in upgrading industrial structures and enhancing carbon efficiency; and, through green upgrading of industrial structures, progress in energy technology has a positive and significant impact on carbon efficiency. Third, the level of industrial development and government environmental governance have a positive impact on carbon efficiency, and the energy structure has a negative impact on carbon efficiency. • Different technologies impact carbon efficiency through different paths. • NT and CET promote industrial structure grading and carbon efficiency. • Industrial structure grading promotes ET and CT progress. • Promoted by industrial structure grading promotes carbon efficiency. • ET and CT progress promotes carbon efficiency. [ABSTRACT FROM AUTHOR]

The load forecasting is generally based on historical data extrapolation in most forecasting models, resulting in a poor correlation with the production information and significant application limitations. To improve this, the production information-based backpropagation neural network (BPNN) combined with genetic algorithm (GA) and particle swarm optimization (PSO) hybrid forecasting model was established for a papermaking enterprise with the three-level electric data collected. Based on this, shift electric consumption quotas and air compressor transformation energy-saving predictions were proposed. The results show that when the production management information is included, the average mean absolute percent error (MAPE) of the six forecasting results can be 1.2%, an improvement of 18.3% on average, indicating the high accuracy of our proposed model. The unit energy consumption of paper products can be reduced by 3.26% through management optimization using the proposed shift electric consumption quotas. Under the guidance of energy-saving predictions, the overall power saving rate of the production line after the enterprise reforms the air compressor is 3%. The proposed load-forecasting model and energy optimization methods have high accuracy and practical applicability. • Load forecasting of production line for papermaking enterprises is conducted. • Minute-level data acquisition subdivided into equipment is achieved. • Production management information is introduced to improve forecasting accuracy. • The performance of the forecasting model can be improved by 18.3% on average. • Energy-saving guidance is proposed based on the forecasting model. [ABSTRACT FROM AUTHOR]

This paper proposes a hierarchical optimization energy management strategy (EMS) considering speed profile to explore energy-saving potential and achieve a reasonable power distribution for the hybrid propulsion system of a tramway composed of the catenary, a battery pack and an ultracapacitor pack. For the higher layer, a speed profile is optimized in the space domain through a particle swarm optimization algorithm, which balances energy consumption, running time and passenger comfort. For the lower layer, based on the optimized speed profile, an EMS is designed, using dynamic programming. The boundary range calculation of state variables for the dynamic programming is carried out in advance, based on the working mode analysis to reduce the computation load. In addition, a supervisory controller is developed to achieve reliable real-time control. Finally, to verify the proposed method, real rail line data in China are used as a case study, and the results show that the proposed method offers the better energy and tramway efficiency. • An optimal speed profile is constructed based on real line data. • An improved DP method reduces the computation load. • A supervisory controller improves the reliability of the real-time EMS. • The proposed method shows better energy and tramway efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a hierarchical optimization energy management strategy (EMS) considering speed profile to explore energy-saving potential and achieve a reasonable power distribution for the hybrid propulsion system of a tramway composed of the catenary, a battery pack and an ultracapacitor pack. For the higher layer, a speed profile is optimized in the space domain through a particle swarm optimization algorithm, which balances energy consumption, running time and passenger comfort. For the lower layer, based on the optimized speed profile, an EMS is designed, using dynamic programming. The boundary range calculation of state variables for the dynamic programming is carried out in advance, based on the working mode analysis to reduce the computation load. In addition, a supervisory controller is developed to achieve reliable real-time control. Finally, to verify the proposed method, real rail line data in China are used as a case study, and the results show that the proposed method offers the better energy and tramway efficiency. • An optimal speed profile is constructed based on real line data. • An improved DP method reduces the computation load. • A supervisory controller improves the reliability of the real-time EMS. • The proposed method shows better energy and tramway efficiency. [ABSTRACT FROM AUTHOR]

Limited empirical evidence exists on the role of electricity intensity of residential income and disposable income in China's electricity consumption. Furthermore, little attention has been paid to the variation in the growth rate of electricity consumption. We develop a Logarithmic Mean Divisia Index model to examine factors that affect electricity consumption and whether there are structural changes impacting electricity consumption. The drivers of slowing electricity consumption in China between 2007 and 2019 are quantified. We find that China's electricity consumption is closely associated with economic growth during the current New Normal period 2013–2019. Decelerated electricity consumption rate is linked to adjustments in industrial structure and a decline in energy intensity. Decreasing the electricity intensity of residential income also contributed to the decelerated rate. In 2014 there was a clear structural break in the growth rate of China's electricity consumption. We conclude that the decline in the electricity consumption growth rate is structural, and the low average annual growth rate will be sustained if nascent energy efficiency improvements and industry transition continue. Therefore, both electricity consumption and its influencing factors should be monitored continuously. Policies should be aimed at promoting energy efficiency and optimizing industrial structure. • Measuring the impacts of socioeconomic factors on China's electricity consumption. • Quantifying the structural change in the growth rate of electricity consumption. • Industrial structure and energy intensity changes decreased electricity consumption. • Electricity intensity of residential income contributed to the decelerated rate. • Analyzing the future trend in the growth rate of electricity consumption. [ABSTRACT FROM AUTHOR]

Existing studies of risk spillovers primarily examine returns and volatility but ignore higher-order moment risks and asymmetrical effects, thereby impeding portfolio optimization and risk management. This study addresses those scholarly deficiencies by investigating the realized higher-order moments (realized skewness and kurtosis) risk spillovers, and asymmetrical spillovers in volatility between WTI and Brent oil and China's commodities spanning 2006–2020. For robustness, we explore the spillovers in signed jump variations, realized hyper-skewness, hyper-kurtosis, and realized jump. Based on the DCC framework and the proposed MVHAR-RS/RK models, we propose a model to quantify the dynamic links in volatility and higher-order moments. Empirical results demonstrate that risk-connectedness varies under different moments. Alongside spillovers in realized volatility, analysis reveals significant spillovers in higher-order moment risks and captures the effects of asymmetries in spillovers of good and bad volatility. WTI and Brent oil act as the net transmitters of risk spillovers in all realized moments considered. The dynamic links in realized kurtosis are relatively more volatile and higher than volatility and skewness links. The dynamic risk spillovers under different realized moments and conditional correlations are time-varying and sensitive to major crises. These findings benefit investors and regulators concerned with cross-commodity risks, portfolio optimization, and policymaking. • Higher-order moment risk spillovers between oil and China's commodity futures are quantified. • Asymmetric effects are captured in total and directional spillovers of the realized volatilities. • The novel DCC-MVHAR-RS/RK model is proposed to depict the higher-order moment links. • Intraday high-frequency trading data are applied to construct realized moment measures. • Empirical findings offer considerable implications for cross-commodity investors and regulators. [ABSTRACT FROM AUTHOR]

Under the complicated driving conditions, the sharp acceleration and deceleration actions would cause the high-rate charge and discharge current of electric driving system in hybrid electric vehicle (HEV), which brings about a serious impact on the battery lifetime. The hybrid energy storage system (HESS) combined with battery and ultracapacitor (UC), would be a possible solution to this problem. For HEV with HESS, in addition to improving fuel economy, realizing the protection of battery is also an important objective. However, improving one aspect performance may sacrifice another aspect performance. The tradeoff between multiple optimization objectives remains a challenge for energy management design. Aiming at this problem, a multi-objective optimization energy management strategy based on velocity prediction for a dual-mode power split HEV with HESS is proposed in this paper. Firstly, to get the precise predictive input sequence, generalized regression neural network (GRNN) is used to predict future velocity. Secondly, the power distribution of dual-mode power spilt HEV with HESS is described as a rolling optimization problem in the prediction horizon of model predictive control (MPC). A new cost function considering the fuel consumption and the protection of the battery is brought forward, and the optimization problem is solved using Pontryagin's minimum principle (PMP). Moreover, the Powell-Modified algorithm is introduced to execute the solving process of PMP. Finally, the proposed strategy is verified by comparing it with four other strategies under four different driving cycles. Compared to the rule-based strategy, the proposed strategy reduces root mean square (RMS) of battery current and fuel consumption by up to 18.5 % and 18.9 %, respectively. • Multi-objective optimization strategy is proposed for a dual-mode power split HEV. • The predictive control model is described with Pontryagin's Minimum Principle. • Power distribution over prediction horizon is solved using the Powell-Modified algorithm. • The performance is verified through the comparison among different methods. [ABSTRACT FROM AUTHOR]

Abstract: This paper aims at finding the optimal design of MW-size organic Rankine cycles by employing the multi-objective optimization with the genetic algorithm as the optimizer. We consider three objective functions: thermal efficiency, total volume of the system and net present value. The optimization variables are the working fluid, the turbine inlet pressure and temperature, the condensing temperature, the pinch points and the fluid velocities in the heat exchangers. The optimization process also includes the complete design of the shell and tube heat exchangers utilized in the organic Rankine cycle. The methodology is applied to recover the waste heat from the SGT-500 gas turbine installed on the Draugen off-shore oil and gas platform in the North Sea. Results suggest two optimal working fluids, i.e. acetone and cyclopentane. Thermal efficiency and net present value are higher for cyclopentane than for acetone. Other promising working fluids are cyclohexane, hexane and isohexane. The present methodology can be utilized in waste heat recovery applications where a compromise between performance, compactness and economic revenue is required. [Copyright &y& Elsevier]

Abstract: In the paper a multi-objective optimization model for distributed energy supply systems optimization is presented. The superstructure of the system comprehends a district heating network that connects the users to each other, small-scale CHP systems, large centralized solar plant and a thermal storage. The optimization has to determine the optimal structure of the system, the size of each component inside the optimal solution and the optimal operation strategy. The multi-objective optimization is based on a MILP (Mixed Integer Linear Programming) model and takes into account as objective function a linear combination of the annual cost for owning, maintaining and operating the whole system and the CO2 emissions associated to the system operation. The model allows to obtain different optimal solutions by varying the relative weight of the economic and the environmental objectives. In this way the Pareto Front is identified and the possible improvements in both economic and environmental terms can be highlighted. The model has been applied to a specific case study and it has been optimized for different superstructure configurations and for two different values of the electricity carbon intensity. The obtained results show that the solar plant, coupled with the optimal thermal storage, allows reaching both environmental and economic goals. [Copyright &y& Elsevier]

Dynamic programming (DP), as a typical global optimization method, requires the prior knowledge of the future driving conditions. To standardize the DP optimizing process, a hierarchical optimization framework of "information layer - physical layer - energy layer - dynamic programming" (IPE-DP) is proposed. The trip information, as the prerequisite for implementing global optimization energy management, is acquired in the information layer. Firstly, full-factor trip information, including the vehicle speed, slope and slip rate, is acquired from three scenarios: deterministic information, information with constraints and information supported by historical data. If only the relevant constraints are available, a "drivers-vehicles-roads" full-factor constraint model is proposed to limit the trip information. Then, information entropy is introduced to measure the uncertainty of the trip information. Particularly, for information with constraints, the independence of various constraints ensures the additivity of the entropy as quantified by the drivers, vehicles and roads. Based on the above, the amount of information to be transmitted is analyzed at the end. To a certain extent, the proposed constraint model can lower the limit on data transfer rate. Furthermore, information entropy provides a theoretical basis for determining the amount of information required to optimize vehicle fuel economy and regional energy consumption. • According to the uncertainty of available information, the full-factor trip information is acquired from three scenarios. • "Drivers-vehicles-roads" full-factor constraint model is constructed to determine the limits of trip information. • Information entropy is introduced to measure the amount of information required. [ABSTRACT FROM AUTHOR]

This paper describes the energy management of energy hubs connected to electricity, gas, and heating networks in which the hub is incorporated as a coordination framework between distributed generations and energy storage systems. The deterministic model of the proposed scheme minimizes the total operating cost of these energy networks in the presence of energy hubs constrained to the optimal power flow equations of different networks and the formulation of hubs with sources and storages. The problem is subject to uncertainties of load, energy prices, renewable sources, and consumption energy of mobile storages. Additionally, the scheme inherently is a non-convex mixed-integer nonlinear programming framework. Adaptive robust optimization is used to model these uncertainties, which is based on a hybrid metaheuristic algorithm due to the nonlinear and non-convex nature of the proposed problem. Hence, a combination of Ant-lion Optimizer and Krill herd Optimization algorithm has been employed, which provides a robust optimal solution with approximate unique response conditions in the worst-case scenario. Eventually, the numerical results obtained by implementing the proposed scheme on a sample test system confirm the capability of the mentioned scheme in improving the operation condition of different energy networks in the worst-case scenario. Consequently, the total energy loss in all mentioned networks and maximum voltage and temperature drop decrease by roughly 8%, 44%, and 74% with respect to power flow analysis in this scenario. • Using flexibility sources alongside renewable sources and combined heat and power systems. • Robust modeling of the operation problem of energy networks with energy hubs. • Utilizing the hybrid heuristic algorithm to achieve the optimal solution. [ABSTRACT FROM AUTHOR]

Understanding the interactions between energy efficiency and industrial structure is conducive to sustainable urban development. The coupling mechanism in between remains to be unraveled in depth for forming a positive cycle. With the Yangtze River Delta (YRD) urban agglomeration as the empirical target, first the total-factor energy efficiency (TFEE) of the 26 cities of the YRD urban agglomeration during 2010–2017 is estimated by slacks-based measure model. The redefined Theil index, industrial advancement index and Lorentz curve statistics are then adopted to quantify industrial reasonability, industrial advancement and industrial concentration of cities. Finally, a coupling coordination degree model is employed to measure the comprehensive coupling level between energy efficiency and industrial structure. The results indicate that the TFEE of cities has great differences. Observed overall, the industrial reasonability and industrial advancement of cities reach a high level, while the industrial concentration is at a middle-lower level. In 2017, the comprehensive coupling of all cities is at high coordination running-in level and above. The results are expected to offer decision-making support on energy efficiency improvement and industrial restructuring for the YRD region, as well as other urban agglomerations in similar conditions. • Total factor energy efficiency of 26 cities in YRD urban agglomeration is measured. • Industrial reasonability, industrial advancement and industrial concentration are quantified. • Comprehensive coupling level between energy efficiency and industrial structure is evaluated. • A higher coupling degree is the premise for a higher coordination degree. • Comprehensive coupling of all cities is at high coordination running-in level and above. [ABSTRACT FROM AUTHOR]

This study investigates the effects of market-incentive environmental regulations on corporate energy efficiency. Using the SO 2 emissions trading system in 2007 in China as a quasi-natural experiment, we employ the difference-in-difference-in-differences approach based on Chinese firm-level data from 2003 to 2010, and empirically examine whether the emissions trading system in China has achieved a win-win situation between environmental effects and energy efficiency. The results show that the implementation of the emissions trading system does improve the energy efficiency of firms. A plausible mechanism appears to be the strengthening of front-end prevention of firms, through which firms reduce their usage of primary energy to transform energy structure, thus improving their energy efficiency. The heterogeneity analysis shows that the results are more pronounced for private and export-oriented firms, firms with larger sizes, firms in the lifecycle of maturity or decline and firms that are capital-intensive. This study provides clear implications by shedding light on the effects of market-incentive environmental regulations on energy efficiency. • The impact of emission trading system (ETS) on energy efficiency is examined. • The Porter effect of a market-incentive environmental regulations is confirmed. • Difference-in-difference-in-differences approach are used for causal estimations. • Mechanism checks suggest that ETS can improve energy efficiency by a decline in coal consumption. [ABSTRACT FROM AUTHOR]

Industrial energy efficiency is acknowledged as a cost-effective mean contributing to sustainable development and industrial competitiveness. Implementing energy management practices becomes even more imperative for developing countries, considering their energy usage trends and economic development forecasts. Based on the circumstances, an empirical investigation is conducted on energy efficiency and management practices, as well as barriers and drivers to energy efficiency in the energy-intensive industries of Bangladesh. The study finds that majority of the companies barely implement the energy management practices. Energy audits represent the mostly implemented energy management practice at the industries, though a comprehensive approach on a detailed level is still lacking. In addition, this study finds that the number of dedicated and specialised energy professionals employed in the industries is yet negligible. The cumulated results show that energy efficiency is mostly disrupted due to inadequate support from preeminent administration and bureaucratic intricacy. Energy blueprint cost-saving due to less use of energy and rules and regulations were distinctively signified as most imperative drivers for energy efficiency. On the other hand, lack of information is found to be the most significant barrier to consult energy service companies. Analysis of the country's energy usage and supply-demand relationship points towards insufficient energy efficiency measures and energy management practices in the country. The study also finds that energy efficiency could be improved by 8%–10% through the practice of energy management. Our findings, besides pointing out specific issues to be tackled in the specific context of investigation, pave the way for further research over industrial energy efficiency in developing countries. • Industrial energy efficiency in developing countries has received little attention. • Barriers, drivers and energy management practices have been discussed in Bangladesh. • 8–10% improvement by implementing energy management in Bangladesh is recognised. • Bangladeshi energy intensive industries are lacking specialised energy professionals. • Energy blueprint can boost energy efficiency in Bangladeshi energy intensive firms. [ABSTRACT FROM AUTHOR]