Energy Efficient Cooperative Edge Computing with Multi-Source Multi-Relay Devices

A scientific base for optimising energy consumption and performance in 3D printing

Energy intensity is one of the most important energy feature that has a dramatic value in energy system of Iran. Indeed, Iran is one of the most energy intensive countries in the world and its main reason is related to high energy consumption in household section. In this article, we present a non-linear model that considers three scenarios in management of household energy demand reducing. Therefore, a rational percentage of energy consumption reduction in the household sector, which firstly eliminates the imbalance between energy production and consumption, and secondly, derives a rational amount of profit from various reduction scenarios, will be presented. The mentioned rational profits in this article are obtained from three scenarios. In the first scenario, it is assumed that the percentage reduction in household energy consumption will be allocated to reducing energy demand in the industrial sector, resulting in profits from value-added creation in this sector. In the second scenario, all benefits from reducing energy consumption in the household sector will be devoted to energy exports, yielding profits from this source. Finally, in the third scenario, the reduction in energy consumption will lead to a decrease in energy supply and consequently a reduction in energy supply costs. To conduct a comprehensive study, a combination of the mentioned scenarios has also been modeled and investigated. The model results indicate that with a 25% reduction in household energy consumption in the 2024-2034 timeframe, the energy imbalance will be eliminated, and allocating 5% of this reduction entirely to the industrial sector will result in profits equivalent to $164.18 billion. However, it should be noted that in the considered combined scenarios, the greater the share of the first scenario, the higher the resulting profit, and the optimal point is achieved in the first scenario.

Future wireless systems are being designed for extremely high data rates and are directly contributing to the global energy consumption. This trend is undesirable not only due to the environmental concerns, but to cost as well since energy costs are becoming a significant part of the operating expenditures for the telecom operators. Recently, energy efficient wireless systems have become a new research paradigm. Cooperative communication has shown good potential in improving coverage, providing robust radio links, reducing infrastructure cost, and has the possibility to reduce the total system energy consumption. This paper looks at possible deployment strategies for wireless networks that can reduce the energy consumption. We look at the tradeoff between the number of relay nodes and the number of base stations and their implications on the total energy consumption of wireless networks. The obtained results show that adaptive resource allocation between the base station and the relay node is an efficient way of reducing the energy consumption of the system. Furthermore, this reduction in energy consumption increases with increasing the system target spectral efficiency.

The total energy consumption in the U.S. has been rising steadily for decades, and it currently amounts to ~98,000 TBtu/yr, with approximately 30% of this total attributable to the industrial sector. Reasonable estimates indicate that 45–55% of total industry energy consumption derives from chemical separations, and for example, over 120 TBtu/yr alone is used in carrying out olefin/paraffin separations via energy-intensive cryogenic distillation. Therefore, the pursuit of new, even radically different approaches to some of the most energy-intensive industrial separations processes is an imperative scientific pursuit for reducing energy consumption toward a more sustainable future. Adsorbent and membrane-based separations can require a fraction of the energy needed for distillation methods, and as such are considered promising solutions for balancing increasing energy demand in the U.S. with the need for a massive reduction in energy consumption. Although considerable research effort has been devoted to the design of materials capable of carrying out various gas separations, usually operating through size-selective, chemisorptive, or physisorptive mechanisms, it remains a great challenge to design materials that function adequately for real-world applications. Indeed, the chemical and physical differences between molecules in gas mixtures of interest are often small, and therefore it is necessary, through the use of nanoscience and synthetic chemistry, to engineer unprecedented molecular-level control in adsorbate–adsorbent interactions. The overarching mission of the Center for Gas Separations (CGS) was to discover fundamental innovations that have the potential to dramatically reduce the energy associated with critical gas separations. In particular, the CGS developed novel synthetic routes, guided by molecular chemistry principles, as well as advanced characterization and computational methods, that have enabled the discovery of new materials and membranes tailor-made to exhibit exceptional performance for a range of gas separations processes, as required in the clean use of fossil fuels and in reducing CO2 emissions from industry. A challenge of this magnitude required the collaboration and synergy of a large team of researchers with expertise in materials synthesis, characterization, and computations. During the 11-year project period, the CGS created a range of new materials within the family of highly-tunable, porous solids known as metal–organic frameworks (MOFs). These new frameworks demonstrate novel mechanisms for key industrial gas separations, including revolutionary new cooperative adsorption processes that enable low-energy CO2 and CO capture, and are capable of efficiently separating olefins from paraffins, O2 from air, and the shape-selective separation of alkane isomers. In addition, the CGS developed new strategies for incorporating these materials into composite membranes toward highly efficient and selective membrane-based separations. As a testament to the success of the CGS, two start-up companies, Mosaic Materials,4 Inc. and Flux Technology, Inc., grew out of these research efforts, and these companies are seeking to commercialize MOF and composite membranes materials for key separations in industry, including large-scale CO2 capture and hydrocarbon separations, respectively. Another company, framergy, Inc., licensed IP resulting from CGS research toward the commercialization of adsorbents for various energy-relevant applications.

Reduction of energy consumption is important for reaching a sustainable future. This paper presents a novel method for optimizing the energy consumption of robotic manufacturing systems. The method embeds detailed evaluations of robots' energy consumptions into a scheduling model of the overall system. The energy consumption for each operation is modeled and parameterized as function of the operation execution time, and the energy-optimal schedule is derived by solving a mixed-integer nonlinear programming problem. The objective function for the optimization problem is then the total energy consumption for the overall system. A case study of a sample robotic manufacturing system and an experiment on an industrial robot are presented. They show that there exists a real possibility for a significant reduction of the energy consumption in comparison to state-of-the-art scheduling approaches. Note to Practitioners-The motivation for this work is the great interest of companies on energy consumption optimization, looking for cost reduction and sustainability in manufacturing. Existing optimization methods focus on different levels of details. A high-level model would be able to optimize the overall system. Unfortunately, due to the high computational cost, it can hardly consider the deep level of mechanical and electrical parameters, which determine the real energy consumption. This paper presents a novel method to embed detailed energy consumption models into a scheduling optimization problem. An effective parameterization of the time variables reduces the model complexity, allowing the optimizer to reschedule the complete sequence of operations for minimizing the total energy consumption, while keeping a fixed cycle time. The method has been integrated into a commercial tool for robot programming. The optimization is applicable both on new and existing robotic systems, since the required modifications are limited to the operations rescheduling, and no investments on new hardware are expected.

Currently in China, the energy consumption of buildings is increasing rapidly. In this study, we used a macro-model to forecast the energy consumption of buildings in Shanghai through 2050. Total energy consumption from 2000 to 2050 and the potential energy savings were projected for both the residential and commercial sectors. For urban residential buildings, we developed a forecast model for 2050 to estimate the potential energy savings of residential measures. Compared to the business-as-usual (BaU) scenario, implementation of residential measures achieved a 24% reduction in energy consumption. The reduction rate rose to 65% by combining the implementation of residential and electrical measures. For commercial buildings, we first used official statistical data to determine the energy intensities of air conditioning, lighting, computing, and other thermal uses for the base year 2000. Then, estimates of the labor force, GDP, and floor area were predicted through 2050 according to past growth patterns and the literature. Likewise, estimates for energy intensities through 2050 were projected. Energy-saving scenarios also were integrated into the commercial model. Compared to BaU scenario, implementation of commercial measures achieved an 80% reduction in energy consumption. The reduction rate increased to 99% by combining commercial and electrical measures.

Investigating the effective factors on the reduction of energy consumption in residential buildings with green roofs

ABSTRACTThe increasing processing demands of vehicle applications pose a major challenge for the Internet of Vehicles (IoVs). Multi‐access edge computing (MEC) offloads computation‐intensive activities to edge servers, significantly enhancing computing capacity and extending vehicle battery life. Nevertheless, traditional offloading strategies fail to effectively balance computation tasks among vehicles and edge servers, resulting in suboptimal resource utilization and increased energy consumption. Therefore, an Energy‐Efficient Computation Offloading Scheme for Vehicular Edge Computing utilizing Improved Golden Eagle Optimization Algorithm (EECO‐VEC‐IGEOA) is proposed. An Improved Golden Eagle Optimization Algorithm (IGEOA) is designed to optimize energy‐efficient computation offloading in vehicular edge computing networks (VECN). By dynamically allocating computation tasks between vehicles and edge servers depending on real‐time conditions, the EECO‐VEC‐IGEOA model aims to improve overall network performance and energy efficiency. The EECO‐VEC‐IGEOA method reduces energy consumption by 14.62%, 16.84%, and 19.16%, and task completion time by 15.84%, 18.92%, and 20.69% compared to the existing approaches.

Electric-powered unmanned guidance vehicles provide surface taxiing guidance for arriving and departing aircraft within the airport movement area, enabling sustained safety under complex operational conditions and improving overall operational efficiency, particularly under low-visibility scenarios. In this context, how to design scientifically rigorous operational trajectories for the three phases of unmanned guidance vehicle operations-dispatch, guidance, and recovery-remains an open and important research problem. This study proposes a three-stage trajectory-planning method for unmanned guidance vehicles, including initial trajectory planning, conflict prediction, and conflict resolution. First, the Guidance Unit-composed of the unmanned guidance vehicle and the guided aircraft-is defined, and a standard speed-profile design model is established for this unit. Then, considering airport operational-safety constraints, a conflict prediction algorithm for the guidance process is developed, which identifies potential conflicts in guidance trajectory planning based on time-window overlap analysis. Subsequently, under operational safety constraints, an optimization model aiming to minimize the maximum guidance time is formulated, and a trajectory planning algorithm for unmanned guidance vehicles based on the improved A* algorithm is designed to generate conflict-free operational trajectories. Finally, a simulation study is conducted using a major airport in Southwest China as a case study. The results show that (1) the speed-profile design and airport operational-rule constraints affect the operational trajectories of unmanned guidance vehicles; (2) the proposed algorithm enables coordinated planning of both speed control and path selection, thereby improving overall operational efficiency by 43.65% compared with conventional operations, while ensuring conflict-free airport surface taxiing, due to the adoption of an improved A* trajectory-planning algorithm for unmanned guidance vehicles; (3) under the electric-powered guidance-vehicle scheme proposed in this study, the method achieves a 34.52% reduction in total energy consumption during the guidance phase compared with traditional Follow-Me guidance, enabling the simultaneous optimization of operational efficiency and energy consumption.

Energy retrofit analysis of cross-laminated timber residential buildings in Seoul, Korea: Insights from a case study of packages

The rapid development of urbanization has led to a rapid increase in total energy consumption. The proportion of domestic energy consumption to total energy consumption has gradually increased and has become the major driving force for energy consumption. With the pressure from urbanization and domestic energy consumption, it is necessary to study the impact of urbanization on domestic energy consumption of the regional level and to explore the function paths of these two factors. The findings are helpful to realize sustainable development based on the actual situation analysis, horizontal survey data and statistical yearbook panel data. The current situation and changing trends in domestic energy consumption of Shandong Province are systematically examined through field investigation and survey questionnaire. The time-series econometric model is applied to analyze the relationship between urbanization rate and total domestic energy consumption. The research results show that the total domestic energy consumption and urbanization rate of Shandong are generally increasing. The urbanization rate development and domestic energy consumption have a long-term stable and balanced relationship. An increase in urbanization rate can cause an increase in domestic energy consumption. There is a slight difference between the average total energy consumption levels of urban and rural residents, but their energy consumption structures differ a lot. These findings provide basic data support and reference for local governments to formulate energy- conservation and emission-reduction policies.

Federated edge cloud (FEC) is an edge cloud environment where multiple edge servers in a single administrative domain collaborate together to provide real-time services. This environment reduces the possibility of violating the quality of service (QoS) requirements of target services by locating delay-sensitive services at nearby edge servers instead of deploying them on the cloud. However, as the number of edge servers increases, the amount of energy consumed by servers and network switches also increases. This creates another challenge for how to schedule delay-sensitive services over FEC, while minimizing the total energy consumption and reducing the QoS violation of a service at the same time. This paper proposes an energy-efficient service scheduling algorithm in FEC. The proposed algorithm is based on an observation that as the number of edge servers along the service path is reduced, the total energy consumption can be minimized. Traditional approaches place services using their maximum traffic requirements to ensure QoS without considering the actual traffic change. In contrast, the proposed algorithm schedules them with actual traffic requirements to increase the number of services co-located in a single server. This maximizes the consolidation of services in a single server and thus minimizes the energy consumption. Moreover, when edge servers are overloaded, the proposed algorithm reconfigures the service path such that service migration overhead and energy consumption are minimized while guaranteeing the QoS requirements of services. The simulation results show that the proposed algorithm improves energy efficiency by up to 21% and lowers the service violation rate by up to 80% against existing approaches.

Ensuring the efficiency of the use of nutrients of grain, protein, vitamin and mineral feeds in the composition of complete feed mixtures in animal husbandry and crop production, as well as the corresponding reduction in the cost of manufacturing of these products, is the relevance of the presented research and prospects for their development. Therefore, the purpose of the study is to substantiate the effective regime parameters for the preparation of premixes of feed mixtures based on the assessment of the energy parameters of the vibration mixing process. This assessment was carried out by comparing the results of applying vibration, blade, and vibroblade technological operations. Under the conditions of low-frequency vibrations, a decrease in the working resistance of the loosened layer of the mixture is observed, which provided the effect of reducing energy consumption in this process. Therefore, in comparison with conventional vibrating mixers have a higher specific productivity of 5-6 times, provide a reduction in the mixing time by 2-3 times, metal consumption – by 17%, energy consumption – by 30%, capital costs for manufacturing – by 18%, and drive power – by 30-35%, which leads to a reduction in total energy consumption by 3-4 times. As a result, the zone of minimal energy consumption was substantiated, in which the reduction of the resistance of the technological medium allowed radically reducing the friction forces during mixing, while ensuring uniform distribution with the concentration of the components of the mixture required by technological requirements. An increase in the oscillation amplitude is accompanied by a quadratic increase in power consumption, which is conditioned by an increase in energy dissipation in the mass of the mixture in the form of heat. The implementation of reasonable operating mixing modes allowed reducing the energy consumption of the proposed vibro-blade mixing by 2.0-2.5 times compared to conventional blade processing. The practical value of these developments includes the use of vibro-blade working bodies and the vibration-free operation of the drive shaft, which, together with the minimisation of energy consumption for the process, reduces dynamic loads on the support units of the developed mixer

China’s economy adopts an extensive growth method. This economic growth method relies on resource input, which consumes huge energy and causes excessive pressure on resources and the environment. The economic growth rate of Fujian Province ranks among the highest in the country in recent years, and its energy consumption is still on the high side. Industrial enterprises, especially the manufacturing industry, account for the majority of energy consumption. In order to ensure the sustainable development of enterprises and society, the government needs to make plans for energy conservation and consumption reduction, policy support and enterprise energy conservation and consumption reduction measures to promote energy conservation and consumption reduction in the whole society, and strive to achieve coordinated development of economic growth and environmental protection.

The high quality of life in the modern sense is directly related to energy consumption and associated with the provision of «clean» food products and environment, comfortable housing, public and private transport. Increasing the availability of energy for the developing countries allows their residents to live longer and more comfortably. At the same time, in the developed European countries with high quality of life, energy consumption is decreasing due to the implementation of energy efficiency and energy saving policies. The Human Development Index, the world’s best-known and most widely used integrated assessment of quality of life, does not include energy consumption indicators. The aim of our research is to study the relationship between energy consumption and quality of life, and prove the need to consider energy consumption indicators in order to improve the methodology framework for assessing quality of life. Using the method of cluster analysis, 77 countries of the world are grouped according to a set of indicators that characterize income, energy consumption, use of renewable energy sources, and CO2 emissions. As a result, the relationship between the level of human development, which is a universal characteristic of quality of life, and these indicators was identified and evaluated. It has been proved that the most prosperous countries in terms of quality of life and energy use are those in which relatively low indicators of primary energy consumption and CO2 emission are combined with high incomes and human development level. The progressive structure of energy consumption ensures the achievement of a higher quality of living, while high energy consumption is not a sufficient condition for this. Against the background of low levels of total primary energy consumption and GDP per capita, CO2 emissions, as well as a low share of renewable energy in total energy consumption, high quality of life is present mainly in the «new» EU member states, which provide it through rational energy consumption. The average level of human development is inherent in a group of countries of the former USSR (which includes Ukraine), as well as some countries in Latin America, the Middle East and Southeast Asia. At the same time, countries such as China and South Korea are reducing energy consumption owing to technological progress and have a positive dynamics of human development indicators. Instead, low levels of energy consumption in the former Soviet Union and Latin America are due to insufficient sustainability of economic and social development, human development in particular. The obtained results substantiate the need to improve the methodology for assessing the quality of life taking into consideration energy consumption indicators.