Showing papers on "Efficient energy use published in 2020"

Current and future role of Haber–Bosch ammonia in a carbon-free energy landscape

Abstract: The future of a carbon-free society relies on the alignment of the intermittent production of renewable energy with our continuous and increasing energy demands. Long-term energy storage in molecules with high energy content and density such as ammonia can act as a buffer versus short-term storage (e.g. batteries). In this paper, we demonstrate that the Haber–Bosch ammonia synthesis loop can indeed enable a second ammonia revolution as energy vector by replacing the CO2 intensive methane-fed process with hydrogen produced by water splitting using renewable electricity. These modifications demand a redefinition of the conventional Haber–Bosch process with a new optimisation beyond the current one which was driven by cheap and abundant natural gas and relaxed environmental concerns during the last century. Indeed, the switch to electrical energy as fuel and feedstock to replace fossil fuels (e.g. methane) will lead to dramatic energy efficiency improvements through the use of high efficiency electrical motors and complete elimination of direct CO2 emissions. Despite the technical feasibility of the electrically-driven Haber–Bosch ammonia, the question still remains whether such revolution will take place. We reveal that its success relies on two factors: increased energy efficiency and the development of small-scale, distributed and agile processes that can align to the geographically isolated and intermittent renewable energy sources. The former requires not only higher electrolyser efficiencies for hydrogen production but also a holistic approach to the ammonia synthesis loop with the replacement of the condensation separation step by alternative technologies such as absorption and catalysis development. Such innovations will open the door to moderate pressure systems, the development and deployment of novel ammonia synthesis catalysts, and even more importantly, the opportunity for integration of reaction and separation steps to overcome equilibrium limitations. When realised, green ammonia will reshape the current energy landscape by directly replacing fossil fuels in transportation, heating, electricity, etc., and as done in the last century, food.

A Clear, Strong, and Thermally Insulated Transparent Wood for Energy Efficient Windows

Abstract: The energy used for regulating building temperatures accounts for 14% of the primary energy consumed in the U.S. One-quarter of this energy is leaked through ineffcient glass windows in cold weather. The development of transparent composites could potentially provide affordable window materials with enhanced energy effciency. Transparent wood as a promising material has presented desirable performances in thermal and light management. In this work, the performance of transparent wood is optimized toward an energy effcient window material that possesses the following attributes: 1) high optical transmittance (≈91%), comparable to that of glass; 2) high clarity with low haze (≈15%); 3) high toughness (3.03 MJ m−3) that is 3 orders of magnitude higher than standard glass (0.003 MJ m−3); 4) low thermal conductivity (0.19 W m−1 K−1) that is more than 5 times lower than that of glass. Additionally, the transparent wood is a sustainable material, with low carbon emissions and scaling capabilities due to its compatibility with industryadopted rotary cutting methods. The scalable, high clarity, transparent wood demonstrated in current work can potentially be employed as energy effcient and sustainable windows for signifcant environmental and economic benefts.

Internet of Things (IoT) and the Energy Sector

Abstract: Integration of renewable energy and optimization of energy use are key enablers of sustainable energy transitions and mitigating climate change. Modern technologies such the Internet of Things (IoT) offer a wide number of applications in the energy sector, i.e, in energy supply, transmission and distribution, and demand. IoT can be employed for improving energy efficiency, increasing the share of renewable energy, and reducing environmental impacts of the energy use. This paper reviews the existing literature on the application of IoT in in energy systems, in general, and in the context of smart grids particularly. Furthermore, we discuss enabling technologies of IoT, including cloud computing and different platforms for data analysis. Furthermore, we review challenges of deploying IoT in the energy sector, including privacy and security, with some solutions to these challenges such as blockchain technology. This survey provides energy policy-makers, energy economists, and managers with an overview of the role of IoT in optimization of energy systems.

Neuro-inspired computing chips

Abstract: The rapid development of artificial intelligence (AI) demands the rapid development of domain-specific hardware specifically designed for AI applications. Neuro-inspired computing chips integrate a range of features inspired by neurobiological systems and could provide an energy-efficient approach to AI computing workloads. Here, we review the development of neuro-inspired computing chips, including artificial neural network chips and spiking neural network chips. We propose four key metrics for benchmarking neuro-inspired computing chips — computing density, energy efficiency, computing accuracy, and on-chip learning capability — and discuss co-design principles, from the device to the algorithm level, for neuro-inspired computing chips based on non-volatile memory. We also provide a future electronic design automation tool chain and propose a roadmap for the development of large-scale neuro-inspired computing chips. This Review Article examines the development of neuro-inspired computing chips and their key benchmarking metrics, providing a co-design tool chain and proposing a roadmap for future large-scale chips.

An empirical analysis of the non-linear impacts of ICT-trade openness on renewable energy transition, energy efficiency, clean cooking fuel access and environmental sustainability in South Asia

Abstract: Energy security and environmental sustainability have become an integral policy agenda worldwide whereby the global economic growth policies are being restructured to ensure the reliability of energy supply and safeguard environmental well-being as well However, technological inefficiency is one of the major hindrances in attaining these over-arching goals Hence, this paper probed into the non-linear impacts of ICT trade on the prospects of undergoing renewable energy transition, improving energy use efficiencies, enhancing access to cleaner cooking fuels, and mitigating carbon dioxide emissions across selected South Asian economies: Bangladesh, India, Pakistan, Sri Lanka, Nepal, and Maldives The results from the econometric analyses reveal that ICT trade directly increases renewable energy consumption, enhances renewable energy shares, reduces intensity of energy use, facilitates adoption of cleaner cooking fuels, and reduces carbon-dioxide emissions Moreover, ICT trade also indirectly mitigates carbon-dioxide emissions through boosting renewable energy consumption levels, improving energy efficiencies, and enhancing cleaner cooking fuel access Hence, these results, in a nutshell, portray the significance of reducing the barriers to ICT trade with respect to ensuring energy security and environmental sustainability across South Asia Therefore, it is ideal for the government to gradually lessen the trade barriers to boost the volumes of cross-border flows of green ICT commodities Besides, it is also recommended to attract foreign direct investments for the potential development of the respective ICT sectors of the South Asian economies

Massive MIMO Systems for 5G and Beyond Networks-Overview, Recent Trends, Challenges, and Future Research Direction.

Abstract: The global bandwidth shortage in the wireless communication sector has motivated the study and exploration of wireless access technology known as massive Multiple-Input Multiple-Output (MIMO). Massive MIMO is one of the key enabling technology for next-generation networks, which groups together antennas at both transmitter and the receiver to provide high spectral and energy efficiency using relatively simple processing. Obtaining a better understating of the massive MIMO system to overcome the fundamental issues of this technology is vital for the successful deployment of 5G—and beyond—networks to realize various applications of the intelligent sensing system. In this paper, we present a comprehensive overview of the key enabling technologies required for 5G and 6G networks, highlighting the massive MIMO systems. We discuss all the fundamental challenges related to pilot contamination, channel estimation, precoding, user scheduling, energy efficiency, and signal detection in a massive MIMO system and discuss some state-of-the-art mitigation techniques. We outline recent trends such as terahertz communication, ultra massive MIMO (UM-MIMO), visible light communication (VLC), machine learning, and deep learning for massive MIMO systems. Additionally, we discuss crucial open research issues that direct future research in massive MIMO systems for 5G and beyond networks.

Latent thermal energy storage technologies and applications: A review

Abstract: The achievement of European climate energy objectives which are contained in the European Union's (EU) “20–20–20″ targets and in the European Commission's (EC) Energy Roadmap 2050 is possible, among other things, through the use of energy storage technologies. The use of thermal energy storage (TES) in the energy system allows to conserving energy, increase the overall efficiency of the systems by eliminating differences between supply and demand for energy. The article presents different methods of thermal energy storage including sensible heat storage, latent heat storage and thermochemical energy storage, focusing mainly on phase change materials (PCMs) as a form of suitable solution for energy utilisation to fill the gap between demand and supply to improve the energy efficiency of a system. PCMs allow the storage of latent thermal energy during phase change at almost stable temperature. The article presents a classification of PCMs according to their chemical nature as organic, inorganic and eutectic and by the phase transition with their advantages and disadvantages. In addition, different methods of improving the effectiveness of the PCM materials such as employing cascaded latent heat thermal energy storage system, encapsulation of PCMs and shape-stabilisation are presented in the paper. Furthermore, the use of PCM materials in buildings, power generation, food industry and automotive applications are presented and the modelling tools for analysing the functionality of PCMs materials are compared and classified.

Energy-Efficient Data Collection and Device Positioning in UAV-Assisted IoT

Abstract: The Internet of Things (IoT) will significantly change both industrial manufacturing and our daily lives. Data collection and 3-D positioning of IoT devices are two indispensable services of such networks. However, in conventional networks, only terrestrial base stations (BSs) are used to provide these two services. On the one hand, this leads to high energy consumption for devices transmitting at cell edges. On the other hand, terrestrial BSs are relatively close in height, resulting in poor performance of device positioning in elevation. Due to their high maneuverability and flexible deployment, unmanned aerial vehicles (UAVs) could be a promising technology to overcome the above shortcomings. In this article, we propose a novel UAV-assisted IoT network, in which a low-altitude UAV platform is employed as both a mobile data collector and an aerial anchor node to assist terrestrial BSs in data collection and device positioning. We aim to minimize the maximum energy consumption of all devices by jointly optimizing the UAV trajectory and devices’ transmission schedule over time, while ensuring the reliability of data collection and required 3-D positioning performance. This formulation is a mixed-integer nonconvex optimization problem, and an efficient differential evolution (DE)-based method is proposed for solving it. Numerical results demonstrate that the proposed network and the optimization method achieve significant performance gains in both energy-efficient data collection and 3-D device positioning, as compared with a conventional terrestrial IoT network.

An Energy-Efficient Train Operation Approach by Integrating the Metro Timetabling and Eco-Driving

Abstract: Energy-efficient train operation is regarded as an effective way to reduce the operational cost and carbon emissions in metro systems. Reduction of the traction energy and increasing of the regenerative energy are two important ways for saving energy, which is closely related to the train timetable and driving strategy. To minimize the systematic net energy consumption, i.e., the difference between the traction energy consumption and the reused regenerative energy, this paper proposes an integrated train operation approach by jointly optimizing the train timetable and driving strategy. A precise train driving strategy is presented and the timetable model considers the headway between successive trains, the distribution of the trip time, and passenger demand in this paper. In addition, a distributed regenerative braking energy model is proposed, based on which the integrated optimization model is formulated. Then, a two-level approach is proposed to solve the problem. At the driving strategy level, the train control problem is transferred into a multi-step decision problem and the Dynamic Programming method is introduced to calculate the energy-efficient driving strategy with the given trip time. As for the timetable level, the trip times and headway of trains are optimized by using the Simulated Annealing algorithm based on the results of dynamic programming method. The timetable optimization level balances the mechanical traction energy of multi-interstations and the amount of the reused regenerative energy such that the net mechanical energy consumption of the metro system is minimized. Furthermore, two numerical examples are conducted for train operations in the peak and off-peak hours separately based on the real-world data of a metro line. The simulation results illustrate that the proposed approach can produce a good performance on energy-saving.

Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

Abstract: Accurate reporting of energy and carbon usage is essential for understanding the potential climate impacts of machine learning research. We introduce a framework that makes this easier by providing a simple interface for tracking realtime energy consumption and carbon emissions, as well as generating standardized online appendices. Utilizing this framework, we create a leaderboard for energy efficient reinforcement learning algorithms to incentivize responsible research in this area as an example for other areas of machine learning. Finally, based on case studies using our framework, we propose strategies for mitigation of carbon emissions and reduction of energy consumption. By making accounting easier, we hope to further the sustainable development of machine learning experiments and spur more research into energy efficient algorithms.

Energy-Efficient Radio Resource Allocation for Federated Edge Learning

Abstract: Edge machine learning involves the development of learning algorithms at the network edge to leverage massive distributed data and computation resources. Among others, the framework of federated edge learning (FEEL) is particularly promising for its data-privacy preservation. FEEL coordinates global model training at a server and local model training at edge devices over wireless links. In this work, we explore the new direction of energy-efficient radio resource management (RRM) for FEEL. To reduce devices' energy consumption, we propose energy-efficient strategies for bandwidth allocation and scheduling. They adapt to devices' channel states and computation capacities so as to reduce their sum energy consumption while warranting learning performance. In contrast with the traditional rate-maximization designs, the derived optimal policies allocate more bandwidth to those scheduled devices with weaker channels or poorer computation capacities, which are the bottlenecks of synchronized model updates in FEEL. On the other hand, the scheduling priority function derived in closed form gives preferences to devices with better channels and computation capacities. Substantial energy reduction contributed by the proposed strategies is demonstrated in learning experiments.

Phase change materials and energy efficiency of buildings: A review of knowledge

Abstract: Nowadays, the energy efficiency of buildings is one of the biggest preoccupations, due to the high negative impacts in the environment, economy and society. The utilization of phase change materials (PCM) in construction industry was been developed by several authors around the world. In this study, the connections between the PCM, energy efficiency and energy poverty are presented. The main PCM characteristics and an exhaustive description of the PCM application in buildings, more specifically in walls, floors, ceilings and glazed areas, are also presented.

Investigating the Environmental Kuznets Curve hypothesis in Kenya: A multivariate analysis

Abstract: In the quest towards a cleaner environment via the mitigation of climate change and its impact, this study examined the validity of the environmental Kuznets curve (EKC) hypothesis, energy efficiency and energy consumption indicators in Kenya. The study employed an autoregressive distributed lag technique, statistically inspired modification of partial least squares regression and Utest method to analyze four models with data spanning 1971 to 2013. Both the autoregressive distributed lag model and the Utest estimation confirmed an inverted u-shaped curve, thus, validating the environmental Kuznets curve hypothesis in Kenya. The study revealed that an increase in energy consumption exacerbates carbon dioxide emissions in the long-run. The statistically inspired modification of partial least squares regression revealed that electricity from renewable energy sources plays a critical role in carbon dioxide emission reduction. An increase in GDP per capita and household consumption expenditure increases energy consumption. Energy imports had no long-run effect due to the recent oil discovery, coal, prospects of nuclear energy and the potential for more renewable energy sources in Kenya. The study highlights that using sustainable technologies like, inter alia, carbon capture and storage in the exploitation of oil and coal are essential to reducing pollution. Rural-urban migration increases the burden on electric power consumption, thus, reducing energy efficiency if conservation options are not enforced. As a policy implication, engaging the public on energy conservation and management options will help curb energy challenges like load shedding — which appears troubling in Africa.

A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems

Abstract: Nowadays, with the development of cyber-physical systems (CPS), there are an increasing amount of applications deployed in the CPS to connect cyber space with physical world better and closer than ever. Furthermore, the cloud-based CPS bring massive computing and storage resource for CPS, which enables a wide range of applications. Meanwhile, due to the explosive expansion of applications deployed on the CPS, the energy consumption of the cloud-based CPS has received wide concern. To improve the energy efficiency in the cloud environment, the virtualized technology is employed to manage the resources, and the applications are generally hosted by virtual machines (VMs). However, it remains challenging to meet the Quality-of-Service (QoS) requirements. In view of this challenge, a QoS-aware VM scheduling method for energy conservation, named QVMS, in cloud-based CPS is designed. Technically, our scheduling problem is formalized as a standard multi-objective problem first. Then, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) is adopted to search the optimal VM migration solutions. Besides, SAW (Simple Additive Weighting) and MCDM (Multiple Criteria Decision Making) are employed to select the most optimal scheduling strategy. Finally, simulations and experiments are conducted to verify the effectiveness of our proposed method.

Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization

Abstract: In this paper, we study unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) with the objective to optimize computation offloading with minimum UAV energy consumption. In the considered scenario, a UAV plays the role of an aerial cloudlet to collect and process the computation tasks offloaded by ground users. Given the service requirements of users, we aim to maximize UAV energy efficiency by jointly optimizing the UAV trajectory, the user transmit power, and computation load allocation. The resulting optimization problem corresponds to nonconvex fractional programming, and the Dinkelbach algorithm and the successive convex approximation (SCA) technique are adopted to solve it. Furthermore, we decompose the problem into multiple subproblems for distributed and parallel problem solving. To cope with the case when the knowledge of user mobility is limited, we adopt a spatial distribution estimation technique to predict the location of ground users so that the proposed approach can still be applied. Simulation results demonstrate the effectiveness of the proposed approach for maximizing the energy efficiency of UAV.

Of renewable energy, energy democracy, and sustainable development: A roadmap to accelerate the energy transition in developing countries

Abstract: The world faces two pressing challenges: on the one hand, limiting global warming to 1.5 °C; on the other hand, enabling socio-economic development that is inclusive and equitable. These two challenges should not be seen as conflicting and should be addressed simultaneously. This is particularly true as we look forward to a post-COVID recovery efforts. The solution may partially rest on the transition to sustainable and renewable energy systems. The energy transition comprises presumptions of energy efficiency, affordability, reliability, and energy independence. And in developing countries, in particular, it also entails expectations of economic development, social inclusion, and environmental sustainability. Since most of the remaining renewable energy potential lies in developing countries, these countries will play a crucial role. This paper reviews the status of the energy transition in the Global South, by surveying scientific and grey literature and synthesising the wide scope of alternatives available to accelerate and enhance the transition to renewable energy systems. The alternatives and approaches found are encapsulated in three dimensions: technology, society, and policy. A roadmap presents the potential synergies that could be established across dimensions and sectors to aid the energy transition in developing countries. Concisely, the transition can be achieved by adopting and implementing technologies already commercially-available that improve the efficiency, affordability, and reliability of energy systems, by redefining and reclaiming citizens’ participation in energy planning and policy-making, and by democratically restructuring institutions and monitoring to boost transparency, accountability, and trust.

Environmental taxes, energy consumption, and environmental quality: Theoretical survey with policy implications

Abstract: Improving energy efficiency and mitigating environmental problems through environmental regulations and taxes are considered as fundamental driving forces of climate change policies. However, the current literature on the theoretical and empirical evidence focusing on the inter-linkages between environmental taxes, energy consumption, and environmental quality is rather meager. This article attempts to provide a detailed survey on the earlier literature for developed, developing, and emerging countries analysis by covering the literature up to 2020. The prime objective of this survey is the coverage of different level of economies, modeling, methodologies, time periods as well as empirical outcomes. The study mainly covers three types of causality direction: (i) environmental taxes, energy consumption, and energy efficiency; (ii) environmental taxes and environmental quality; (iii) energy consumption (renewables, non-renewable, and fossil fuels) and environment deterioration. Most of the empirical studies reported that the energy usage for economic activities significantly affects the pollutant emissions. However, the role of environmental taxes is still ambiguous and demands a more in-depth investigation. Comprehending the literature survey has provided the basis to address the policymaking, designing as well as the implementation of environmental regulations.

Energy Efficient UAV Communication With Energy Harvesting

Abstract: This paper investigates an unmanned aerial vehicle (UAV)-enabled wireless communication system with energy harvesting, where the UAV transfers energy to the users in half duplex or full duplex, and the users harvest energy for data transmission to the UAV. We minimize the total energy consumption of the UAV while accomplishing the minimal data transmission requests of the users. The original optimization problem is decomposed into two subproblems: path planning subproblem and energy minimization subproblem with fixed path planning. For path planning subproblem, the optimal visiting order is obtained by using the dual method and the trajectory is optimized via the successive convex approximation technique. For energy minimization subproblem with fixed path planning, we firstly obtain the optimal portion of data transmission time within the entire procedure and the optimal transmission power of each user. Then, the the energy minimization subproblem is greatly simplified and it is efficiently solved via a one-dimensional search method. Simulation results are illustrated to verify the theoretical findings.

Economics of energy and environmental efficiency: evidence from OECD countries.

Abstract: The purpose of this research is to determine the efficiency of energy usage and its role in carbon dioxide emissions (CI) and economic-environmental efficiency (EEE) for some countries Organization for Economic Co-operation and Development (OECD) economies. For environment quality assessment, data envelopment analysis (DEA) is used to assess the data cover the period from 2013 to 2017. In this study, primary energy consumption (PEC) and population are two basic inputs along with gross domestic product (GDP) and carbon dioxide emissions that are desirable and undesirable outputs, respectively. The practical outcomes illustrate that Brunei, Australia, Singapore, and Hong Kong are the most effective and efficient states for the 5 years periods (2013–2017) in terms of energy efficiency and to reduce emission of carbon dioxide. In addition, other states in the OECD region shows greater economic proficiency than environmental proficiency. Furthermore, the results shows that energy efficiency has strong bonding with carbon emissions; however there is a weaker association between economic-environmental efficiency. Thus, the attainment of optimal level of energy efficiency could be more pivotal than economic efficiency to improve environmental efficiency in countries from the OECD region.

An Energy Efficient and Secure IoT-Based WSN Framework: An Application to Smart Agriculture

Abstract: Wireless sensor networks (WSNs) have demonstrated research and developmental interests in numerous fields, like communication, agriculture, industry, smart health, monitoring, and surveillance. In the area of agriculture production, IoT-based WSN has been used to observe the yields condition and automate agriculture precision using various sensors. These sensors are deployed in the agricultural environment to improve production yields through intelligent farming decisions and obtain information regarding crops, plants, temperature measurement, humidity, and irrigation systems. However, sensors have limited resources concerning processing, energy, transmitting, and memory capabilities that can negatively impact agriculture production. Besides efficiency, the protection and security of these IoT-based agricultural sensors are also important from malicious adversaries. In this article, we proposed an IoT-based WSN framework as an application to smart agriculture comprising different design levels. Firstly, agricultural sensors capture relevant data and determine a set of cluster heads based on multi-criteria decision function. Additionally, the strength of the signals on the transmission links is measured while using signal to noise ratio (SNR) to achieve consistent and efficient data transmissions. Secondly, security is provided for data transmission from agricultural sensors towards base stations (BS) while using the recurrence of the linear congruential generator. The simulated results proved that the proposed framework significantly enhanced the communication performance as an average of 13.5% in the network throughput, 38.5% in the packets drop ratio, 13.5% in the network latency, 16% in the energy consumption, and 26% in the routing overheads for smart agriculture, as compared to other solutions.