Incorporating Energy Consumption Research Articles

Three-dimensional cooperative guidance with impact angle constraints via value-policy decomposed multi-agent reinforcement learning

For a three-dimensional impact angle-constrained time-coordination guidance problem, a value-policy decomposed multi-agent twin delayed deep deterministic (VPD-MATD3) policy gradient algorithm is proposed in this paper, and a temporal-spatial cooperative guidance law is designed consequently. The derived cooperative engagement kinematics is formulated as a value-policy decomposed partially observable Markov decision process. Value decomposition and policy decomposition are correspondingly proposed to address the two bottlenecks of reward coupling and policy coupling in 3D guidance, thus improving the training process. The training environment with multiple uncertainties, such as observation noise, maneuverability perturbation, autopilot time lag, switching communication topology, and random initialization, is subsequently constructed. Time-energy type reward functions are designed, incorporating energy consumption as a direct optimization indicator via a penalty term. A long short-term memory network layer is inserted inside the policy networks to suppress the negative effect of observation noise. The policy training curves verify the significant effect of the proposed algorithm in improving training convergence. The policy testing results illustrate the robustness and generalization of the VPD-MATD3 cooperative guidance law and its ability to cope with randomly switched communication topologies. Moreover, the comparative testing further reveals its time-energy suboptimal property.

Dynamics between energy intensity and carbon emissions: What does the clustering effect of labor and capital play?

Energy depletion and environmental degradation have emerged as pressing concerns in the era of China's high-quality green economic development. We present an integrated framework that incorporate energy consumption intensity, industrial clustering, and carbon emissions into the STIRPAT framework. Our study examines 30 Chinese provinces from 2006 to 2017. Unlike previous analyses, our study measures industrial clustering from two dimensions: labor and capital clustering. Specifically, the benchmark results confirm that a 1% increase in energy consumption intensity will, on average, raise regional carbon emission intensity by 0.03389%. Moderating analysis shows that labor and capital clustering significantly lower carbon emission intensity by reducing energy consumption. These findings have been consolidated after performing a panel vector autoregressive model. Interestingly, the promoting effect of energy consumption intensity on carbon emission intensity tends to be tightened when labor clustering surpasses a threshold. In contrast, the corresponding effects substantially weaken after capital clustering exceeds a threshold. In all, these findings provide new evidence to understand the energy-emission nexus from an industrial clustering perspective.

Revisiting the nexus between economic growth and disaggregated greenhouse gases in India: Evidence from necessary and sufficient conditions

For India, achieving environmental sustainability in the midst of faster growth remains a challenging objective. Therefore, we are revisiting the nature of the Environmental Kuznets Curve (EKC) at different stages of economic growth through both necessary and sufficient conditions under the cubic model specifications in India. Further, we extend the EKC framework by incorporating energy consumption, agricultural value added, trade, the world uncertainty index, and geopolitical risk as additional variables into the three preferred disaggregated greenhouse gas emission (GHG) models. This study employs an auto-regressive distributed lag model (ARDL). The empirical findings show evidence of the valid N-shaped EKC through both necessary and sufficient conditions for carbon dioxide (CO2) and nitrous oxide (N2O) and exhibit the presence of two real and distinct income turning points for both valid EKCs. In contrast, only the necessary condition supports the N-shaped EKC for methane (CH4) emissions in India. Further, the results highlight that trade has a positive impact, whereas the world uncertainty index has a negative impact on disaggregated GHGs. Similarly, geopolitical risk has a negative effect on CH4 and N2O, whereas energy consumption has a positive impact on CO2 emissions. Moreover, agricultural value added has a negative effect on CH4 and a positive effect on N2O emissions. Our empirical findings suggest policy recommendations that can collectively work as a roadmap for India to become emission-neutral by 2070.

The validity of Okun's Law in the case of Central Asia: The role of weather and energy factors.

This study investigates a modified version of the Okun's Law that incorporates energy consumption and temperature for five Central Asian countries, while it employs the ARDL methodology, spanning the period 1995-2018. While the original Okun's Law is not supported, the analysis does find support for the modified Law, suggesting the importance of regional specific factors. The results document the presence of a negative association between unemployment and real GNP both in the short and in the long run. The impact of energy consumption on unemployment is positive in the long run, while the temperature impacts unemployment both in the short and in the long run.

Decentralized optimization for integrated electricity–heat systems with data center based energy hub considering communication packet loss

Recently, waste heat recovery has enabled data centers to serve as energy prosumers with flexible features that play essential roles in the coordination operation of integrated electricity–heat systems (IEHSs). The energy hub, as a critical component of IEHSs, provides a promising opportunity to improve energy efficiency. In this paper, the decentralized coordination optimization for IEHSs with data centers is explored to protect the confidential information of different entities. First, a novel data center based energy hub (DCEH) model is developed, where energy conversion, consumption and storage present high flexibility. Especially, in addition to heat recovery, several other controllable operational characteristics of a single data center are considered, involving servers, workloads and indoor temperature. Then, the coordination optimization model of IEHSs with DCEH (IEHSs-DCEH) is constructed by incorporating energy consumption cost and carbon emission, where the energy networks of IEHSs are considered. Moreover, a learning-aided relaxed alternating direction method of multipliers (LR-ADMM) algorithm is proposed to solve the dispatching model of IEHSs-DCEH considering communication packet loss. The proposed LR-ADMM algorithm embeds a momentum extrapolation based prediction technique, which can obtain the predicted value of missing boundary information without adding computational burden, even in continuous packet losses. Simulation results demonstrate that the developed coordination dispatching model can achieve higher economic and environmental benefits than the reference one that ignores data centers’ flexibility. Additionally, the proposed LR-ADMM algorithm with proper prediction factors exhibits a faster convergence rate and robustness hedging against packet losses compared to the ADMM and relaxed ADMM approaches.

Digitalized world and carbon footprints: does digitalization really matter for sustainable environment?

There is a growing demand for energy to support economic and social development. There will be many shifts in the energy sector as a result of digitization. Hence, we aim analyzing the linkage between digitalization and environment sustainability by incorporating energy consumption as a moderating factor using data of UK from 1990 to 2020. Different dimensions of digitalization are used as explanatory variables, ecological and carbon footprints are used as outcomes and energy consumption is used as moderator. The findings of autoregressive distributed lag model show that internet users and technological advancement (fixed telephone subscription and mobile cellular) are negatively (positively) linked with ecological and carbon footprints. Energy consumption causes to enhance ecological and carbon footprints and plays an antagonistic role in the nexus of internet users, technological advancement, and ecological and carbon footprints. The effects of mobile cellular and fixed telephone subscription have increased in the presence of energy consumption as moderator which exhibits that energy consumption plays an enhancing role in the links between mobile cellular, fixed telephone subscription and ecological and carbon footprints. The results underscore the importance of taking a holistic approach to addressing the environmental impact of digital technologies. By promoting sustainable communication practices and investing in the development of more energy-efficient technologies, practitioners, managers, and society as a whole can work together to reduce the carbon and ecological footprints of digital technologies and create a more sustainable future for all.

Spatial spillover and determinants of tourism efficiency: A low carbon emission perspective

This paper measures tourism carbon efficiency (TCE) in China by incorporating energy consumption and carbon dioxide (CO2) emissions into an efficiency assessment framework, and to further investigate the determinants of TCE by considering the spatial spillover effects. To do this, a bootstrap slacks-based measure (SBM) model was applied to assess the TCE in 30 provincial-level administrative regions of China from 2008 to 2019. Next, the Moran’s index and spatial Durbin model (SDM) were adopted to explore the spatial distribution and determinants of TCE. The results indicate that regional differences affect the level of China’s TCE, as do spatial spillover effects. In addition, technology innovation, urbanization rate, and government support positively affect TCE. In contrast, economic growth negatively affects TCE. Educational attainment, green infrastructure, and government support have a negative spatial spillover effect on TCE. Transportation infrastructure has a negative total effect on TCE.

Simulation models for public transportation: a state-of-the-art review

This article presents a state-of-the-art review regarding transport simulation models currently developed. To identify the variables and types of models in transport simulation and how the operational characteristics and its associated energy consumption and emissions generation merge in a simulation that aids the decision-making process regarding energy consumption. Several simulation models are addressed and studied to propose a new simulation incorporating energy consumption and emission. In specific for public passenger transport, since it is the mean of movement in urban areas and improvements to its energy consumption and emission needs to be addressed to meet the sustainability goals by minimizing the externalities it produces.

Optimizing Operation Strategy in a Simulated High-Proportion Wind Power Wind–Coal Combined Base Load Power Generation System under Multiple Scenes

In order to accommodate more intermittent renewable energy in coal-dominated power systems, conventional thermal power plants need to improve their operational flexibility to balance the energy system at all times. However, flexible operation of coal-fired power plants could reduce energy efficiency and increase CO2 and pollutant emission, so it is important to consider environmental implications and optimize the dispatch of wind and coal power units in the system. In this paper, based on the output profile of wind power, a wind power peak (T, H) simulation model based on Gaussian distribution was established. Using a high-proportion wind power wind–coal combined base load power generation system as an example, the economical and environmentally friendly unit operation based on different wind power penetration was studied by simulation, and the decision strategy was established. Wind energy curtailment boundary was determined with power generation cost, energy consumption, CO2 and pollutant emissions as decision targets, respectively. Weekly scale results indicate that incorporating energy consumption and pollutant emissions into consideration will lead to different decision-making strategies compared with only targeting minimizing wind curtailment. This paper established a decision-making model of wind–coal system operation strategy based on economy and environmental criteria. This work directly contributes to real system operation and is of great significance for future scheduling/dispatch studies of actual power systems.

The Spatial Pattern and Spillover Effect of the Eco-Efficiency of Regional Tourism from the Perspective of Green Development: An Empirical Study in China

Scientifically analyzing the spatial pattern and spillover effect of the eco-efficiency of regional tourism embodies the green development theory. In addition, it is also of important significance for realizing the sustainable development of regional tourism and promoting regional ecological civilization. This study incorporates energy consumption and CO2 emissions of tourism into the efficiency evaluation index system. On this basis, the slacks-based measure–data envelopment analysis (SBM-DEA) with undesirable output, the spatial autocorrelation (SAC) model and the spatial Durbin model (SDM) are used to study the spatial pattern and spillover effect of the eco-efficiency of provincial tourism in China from 2008 to 2017. Results show that the following: (1) The average eco-efficiency of national tourism is 0.534, which is at the medium development level as a whole. Among the decomposed efficiencies of eco-efficiency, the scale efficiency drives the optimal development of eco-efficiency in tourism. (2) The eco-efficiency of tourism shows a spatial differentiation pattern on the regional scale as follows: it is the highest in the central region, moderate in the western region, and lowest in the eastern region. (3) The degree of clustering of the eco-efficiency of tourism first increases and then decreases. The SAC-based cluster pattern is dominated by a low-low (LL) cluster, followed successively by a high-high (HH) cluster and a low-high (LH) outlier, while a high-low (HL) outlier is the least significant (4). Among the influencing factors, the technical level shows spatial spillover effects on both the eco-efficiency and pure technical efficiency of tourism; the economic development level and traffic accessibility mainly have spatial spillover effects on the pure technical efficiency and scale efficiency of tourism; the industrial structure and environmental regulation separately have a spatial spillover effect only on the pure technical efficiency and the scale efficiency of tourism.

Investigation of Pop-in Formation Mechanism in Fracture Toughness Test of Welded Joint and Improvement of Acceptance Criteria

A pop-in is a small, arrested brittle crack that occurs during a fracture toughness test. This is one of the important factors of the CTOD assessment of a welded joint because the fracture toughness of material differs by the significance of the pop-in. The current standard of the pop-in is said to be too conservative because of the reasons below; the absence of the test methodology which ensures the generation of pop-ins, and the difference of the loading mode between the CTOD test and the real structures. The objective of this study is to investigate the mechanism of the pop-in formation and to improve the pop-in acceptance criteria. This study is aimed to come up with a new standard of pop-in significance determination with the idea of the local critical stress criterion and newly proposed crack propagation model incorporating energy consumption theory. That is, the CZM considering that the critical stress works as the local crack propagation criterion, and the fracture surface energy expressing plastic dissipation beneath the running crack wake is defined in each propagation, has been proposed. When the stress of the crack tip approaches the critical fracture stress, the cohesive strength is lost, and the fracture surface is created. When the cohesive strength is lost, it takes away the fracture surface energy. In the physical aspect, the fracture surface energy is increased as the fracture surface roughness increases. The Charpy test with the three-sided specimen is conducted to find the relationship between the fracture surface roughness and the fracture surface energy. The appropriate value or function of the fracture surface energy is determined by the roughness of the fracture surface and trial and error of the FEM analysis. The result of the FEM analysis is well validated with the experimental data. Finally, the authors calculate many extrapolated conditions of toughness distribution and LBZ width and conclude the current pop-in judgment criterion is too severe and propose a new rational one.

Design and Implementation of Multi-Source and Multi-Consumer Energy Sharing System in Collaborative Smart Microgrid Installation

Many published studies debated electrical energy management. They mainly investigate the multi-source installation to develop energy efficiency during its different phases: production, distribution, and consumption. Although it is rarely discussed, energy sharing is a critical part of the energy management system. In this contribution, a demand-side management algorithm is developed, that incorporates energy consumption scheduler capacity. It provides optimal energy sharing, counting on suitable energy cost parameters and adequate multi-source installation. Using this proposal, the electrical bill decreases thanks to the optimal daily attribution of schedules to households formed by a multi-consumer microgrid. This application guarantees a maximal reduction of electrical cost for the set of energy partners as one prosumer used to consume and produce power. In addition, it maintains energy efficiency as it aids in avoiding breakdowns, and depressing the peak-to-average ratio. It admits that the utility company is, as usual, always reachable non-renewable source. At the same time, renewable energy was engendered by photovoltaic panels concomitant with wind turbines stations. The application is based on the JNET protocol stack. The proposed energy sharing algorithm is implemented by using Arduino board and JN5148 nodes as a star Wireless Sensors Network topology. It is installed as a prototype in the Digital Research Center of Sfax in Tunisia. This proposed incentive-based algorithm managed to reduce the smart microgrid annual cost by almost 55% without harming the public utility. It can even ensure a more significant diminution by selling the surplus of renewable power at the end of each day.

Oil price shocks and energy consumption in GCC countries: a system-GMM approach

A close association prevails among energy consumption, energy prices, and income levels. This association has implications for the growth of an economy. Hence, incorporating energy consumption properties in an economy is crucial for policy design purposes especially in the case of energy-exporting countries of the Gulf Cooperation Council (GCC). This paper studies the correlates of energy consumption with an emphasis on shocks in crude oil prices, changes in GDP per capita, carbon dioxide emissions, trade, population growth, and other key indicators using a panel of GCC countries over 30 years of data period (1985–2014). The study provides direct evidence of the oil price shocks and energy consumption using a static panel (POLS and FE) and dynamic panel (system-GMM) estimation technique. The finding shows that oil price shocks affect energy consumption negatively. The greatest positive effect on energy consumption is observed with an increase in GDP per capita, as a percent increase in it increases energy consumption by 0.65 percent. It is followed by trade openness which increases energy consumption by 0.14 percent. While the greatest negative effect on energy consumption is observed by a rise in oil prices, as a percent increase in oil prices reduces energy consumption by approximately 0.22 percent. The study also finds that higher energy consumption increases carbon dioxide emissions as energy consumption is growing with population growth. Thus, it is important to upscale the adoption of renewable energy sources, adopt energy-efficient technologies, reduce energy subsidies, and practice demand-side management.

The impacts of globalization, financial development, government expenditures, and institutional quality on CO2 emissions in the presence of environmental Kuznets curve.

The main objective of this study is to examine the impacts of globalization, financial development, government expenditures, and institutional quality on CO2 emissions, incorporating energy consumption, and GDP per capita in the Environmental Kuznets Curve (EKC) model for 47 Emerging Market and Developing Economies (EMDEs) between 1990 and 2014. Owing to the presence of cross-sectional dependence and slope heterogeneity in the panel data, CADF and CIPS unit root tests are employed to validate the stationarity of the variables. Westerlund (Oxf Bull Econ Stat 69:709-748, 2007) and Banerjee and Carrion-i-Silvestre (J Time Ser Anal 38:610-636, 2017) cointegration tests denote the occurrence of cointegration among the variables. We employed CCEMG, AMG, and DCCE estimators to estimate heterogeneous parameters. The findings demonstrate that globalization, financial development, and energy consumption increase CO2 emissions. Besides, the EKC hypothesis is affirmed in EMDEs. The accrual of governments' financial and governance activities also boosts carbon dioxide emissions. Moreover, the analysis of Dumitrescu and Hurlin causality provides evidences for the feedbacks among the variables and CO2 emissions. From the aforementioned results, there exists the trade-off effect between economic growth and environmental quality in EMDE countries. Finally, the empirical findings of this study indicate profound implications for policy makers, which recommend governments to consider the role of finance and governance in order to ensure that energy consumption, financial development, and sustainable economic growth are in harmony with the environment in the globalization era.

Green Scheduling of Jobs and Flexible Periods of Maintenance in a Two-Machine Flowshop to Minimize Makespan, a Measure of Service Level and Total Energy Consumption

The success of an industry today depends on its ability to innovate. In terms of energy performance, this innovation is reflected in the ability of manufacturers to implement new solutions or technologies that enable better energy management. In this regard, this paper aims to address this gap by incorporating energy consumption as an explicit criterion in flowshop scheduling of jobs and flexible preventive maintenance. Leveraging the variable speed of machining operations leading to different energy consumption levels, we explore the potential for energy saving in manufacturing. We develop a mixed integer linear multiobjective optimization model for minimizing the makespan and the total energy consumption. In the literature, no papers considering both production scheduling and flexible periods of maintenance with minimizing both objective the total of energy consumption in flowshop and makespan. The performance of the proposed mixed binary integer programming model is evaluated based on the exact method of branch and bound algorithm. A study of the results proved the performance of the model developed.

The impact of globalization and financial development on environmental quality: evidence from selected countries in the Organization for Economic Co-operation and Development (OECD).

This study investigates the impacts of globalization and financial development on environmental quality by incorporating energy consumption in the framework of the Environmental Kuznets Curve (EKC) hypothesis for selected countries in the Organization for Economic Co-operation and Development (OECD) over the 1990-2014 time spans. The cross-sectional dependence is determined by using the cross-sectional dependence and Lagrange Multiplier (LM) methods. This study employs second-generation panel unit root tests to check the unit root properties and the Westerlund panel cointegration test to examine the long-run equilibrium relationship among the variables. The results confirm the presence of cointegration in the long run. The Continuously Updated Fully Modified Ordinary Least Square (CUP-FM) and Continuously Updated Bias-Corrected (CUP-BC) approaches are applied to investigate long-term output elasticities of the variables. The results show the stimulating role of energy consumption on Carbon dioxide (CO2) emissions. This study finds support for the EKC hypothesis as it relates to selected OECD countries. Globalization and financial development increase environmental quality by reducing CO2 emissions. The causal relationship reveals the presence of a bidirectional relationship between energy consumption and CO2 emissions. The feedback causal effect runs between economic growth and CO2 emissions and between globalization and economic growth, while unidirectional causality runs from CO2 emissions to financial development, from economic growth to energy consumption, from energy consumption to financial development, from globalization to energy consumption, and from globalization to financial development. Policies that support green technology transfer among OECD countries, foreign direct investment in the renewable energy sector, financial development to support green infrastructure, and energy generation using renewable energy sources are recommended.

Carbon Productivity: Findings from Industry Case Studies in Beijing

Simultaneously protecting the environment and promoting the economy are two critical dimensions for sustainable development. Carbon productivity is popularly used in assessing the environmental and economic efficiency over time, and is deemed as the appropriate indicator of sustainable development. Given the prominent contribution of energy consumption to sustainable development, this study incorporates energy consumption into the Log Mean Divisia Index (LMDI) decomposition model to explore the main factors influencing carbon productivity change. Based on the data from 19 industries in Beijing from 2013 to 2016, this study then reports the carbon productivities and their changes. Energy productivity change is the main cause of carbon productivity changes, and its correlation with carbon productivity change is significantly positive, whereas there is a weak correlation and no significant difference in energy consumption per unit of carbon emissions. Although the average carbon productivities in all 19 industries increased year over year in Beijing, the average level could be further promoted by improving energy productivity. The carbon productivities of the primary and secondary industries are less than the average, and far below the tertiary industry level. For the primary industry, increasing economic levels is conducive to improving carbon productivity; for secondary industry, reducing energy consumption and enhancing energy efficiency are most effective; and for tertiary industry, maintaining an outstanding performance will guarantee sustainable development in Beijing. This study has defined carbon productivity change from the energy consumption perspective and for the first time, comprehensively measured it for all industries in Beijing. The results are expected to assist these industries to essentially improve productivity performance and thus improve development sustainability.

Multi-Criteria Decision-Making for Heterogeneous Multiprocessor Scheduling

This paper proposes a new tri-objective scheduling algorithm called Heterogeneous Reliability-Driven Energy-Efficient Duplication-based (HRDEED) algorithm for heterogeneous multiprocessors. The goal of the algorithm is to minimize the makespan (schedule length) and energy consumption, while maximizing the reliability of the generated schedule. Duplication has been employed in order to minimize the makespan. There is a strong interest among researchers to obtain high-performance schedules that consume less energy. To address this issue, the proposed algorithm incorporates energy consumption as an objective. Moreover, in order to deal with processor and link failures, a system reliability model is proposed. The three objectives, i.e., minimizing the makespan and energy, while maximizing the reliability, have been met by employing a method called Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). TOPSIS is a popular Multi-Criteria Decision-Making (MCDM) technique that has been employed to rank the generated Pareto optimal schedules. Simulation results demonstrate the capability of the proposed algorithm in generating short, energy-efficient and reliable schedules. Based on simulation results, we observe that HRDEED algorithm demonstrates an improvement in both the energy consumption and reliability, with a reduced makespan. Specifically, it has been shown that the energy consumption can be reduced by 5–47%, and reliability can be improved by 1–5% with a 1–3% increase in makespan.

An Energy-Saving Optimization Method of Dynamic Scheduling for Disassembly Line

Concerns have been increasing regarding the environmental sustainability of disassembly activities that take place in various recovery operations at end of life stage of products, and subsequently, disassembly activities have been gaining increased exposure. The disassembly line is a good choice for automated disassembly of end-of-life products. Although interest in addressing energy saving in manufacturing is rising, the study of incorporating energy consumption reduction and disassembly efficiency improvement into disassembly line balancing is still limited. The analysis, design, and balanced decision-making for disassembly lines are urgently needed in order to make the disassembly line as energy-saving as possible. In this paper, an energy-saving optimization method, which was used in scheduling workstation selection and the disassembly sequence for the disassembly line, is proposed. Energy consumption of each stage of the disassembling process was analyzed and modeled. Then, a mathematical model of the disassembly line balancing problem with energy saving considerations was formulated and the objective of energy consumption was integrated into the objectives of cost and working load in order to balance a disassembly line. Optimal solutions for the disassembly line balancing problem with an energy saving consideration were obtained by using an artificial bee colony algorithm, which was a feasible way of minimizing workstations, and ensuring similar working load, as well as minimizing energy consumption. Finally, a case study of disassembly line balancing for a typical driving system is presented to illustrate the proposed method.

New insights into an old issue – examining the influence of tourism on economic growth: evidence from selected small island developing states

This paper uses relatively new heterogeneous panel autoregressive distributed lag cointegration methods to re-examine the long-run equilibrium and Granger causality relationship between tourism and economic growth for the small island developing states (SIDSs). In addition, the study incorporates energy consumption and foreign direct investment (FDI) as alternative growth determinants, during the period 1995–2014. After allowing for the heterogeneous country effect, a positive and statistically significant long-run equilibrium relationship between tourism, energy consumption, FDI, and gross domestic product, with a moderate convergence rate towards the long-run path is confirmed. The panel Granger causality test as proposed by Dumitrescu and Hurlin [(2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450–1460.] shows bidirectional causality running from tourism to economic growth, from tourism to energy consumption and from energy consumption to economic growth, and unidirectional causality between FDI and tourism, between economic growth and FDI, and between FDI and energy consumption. Our empirical findings provide support for tourism-induced growth, tourism-induced energy consumption, tourism-induced investment, and the energy consumption-economic growth relationship in the case of SIDSs. Our empirical results resonate with the existing findings with major policy implications for the SIDSs.