Showing papers on "Efficient energy use published in 2019"

Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication

Abstract: The adoption of a reconfigurable intelligent surface (RIS) for downlink multi-user communication from a multi-antenna base station is investigated in this paper. We develop energy-efficient designs for both the transmit power allocation and the phase shifts of the surface reflecting elements subject to individual link budget guarantees for the mobile users. This leads to non-convex design optimization problems for which to tackle we propose two computationally affordable approaches, capitalizing on alternating maximization, gradient descent search, and sequential fractional programming. Specifically, one algorithm employs gradient descent for obtaining the RIS phase coefficients, and fractional programming for optimal transmit power allocation. Instead, the second algorithm employs sequential fractional programming for the optimization of the RIS phase shifts. In addition, a realistic power consumption model for RIS-based systems is presented, and the performance of the proposed methods is analyzed in a realistic outdoor environment. In particular, our results show that the proposed RIS-based resource allocation methods are able to provide up to 300% higher energy efficiency in comparison with the use of regular multi-antenna amplify-and-forward relaying.

Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network

Abstract: Although the fifth-generation (5G) technologies will significantly improve the spectrum and energy efficiency of today's wireless communication networks, their high complexity and hardware cost as well as increasingly more energy consumption are still crucial issues to be solved. Furthermore, despite that such technologies are generally capable of adapting to the space and time varying wireless environment, the signal propagation over it is essentially random and largely uncontrollable. Recently, intelligent reflecting surface (IRS) has been proposed as a revolutionizing solution to address this open issue, by smartly reconfiguring the wireless propagation environment with the use of massive low-cost, passive, reflective elements integrated on a planar surface. Specifically, different elements of an IRS can independently reflect the incident signal by controlling its amplitude and/or phase and thereby collaboratively achieve fine-grained three-dimensional (3D) passive beamforming for signal enhancement or cancellation. In this article, we provide an overview of the IRS technology, including its main applications in wireless communication, competitive advantages over existing technologies, hardware architecture as well as the corresponding new signal model. We focus on the key challenges in designing and implementing the new IRS-aided hybrid (with both active and passive components) wireless network, as compared to the traditional network comprising active components only. Furthermore, numerical results are provided to show the potential for significant performance enhancement with the use of IRS in typical wireless network scenarios.

Electroactive microorganisms in bioelectrochemical systems

Abstract: The authors acknowledge funding by the US Department of Energy (DOE) Energy Efficiency and Renewable Energy (EERE) Fuel Cell Technologies Office through a contract from the National Renewable Energy Laboratory (NREL), Project #21263, and by the Environmental Security Technology Certification Program via cooperative research agreement W9132T-16-2-0014 through the US Army Engineer Research and Development Center.

Intelligent Edge Computing for IoT-Based Energy Management in Smart Cities

Abstract: In recent years, green energy management systems (smart grid, smart buildings, and so on) have received huge research and industrial attention with the explosive development of smart cities. By introducing Internet of Things (IoT) technology, smart cities are able to achieve exquisite energy management by ubiquitous monitoring and reliable communications. However, long-term energy efficiency has become an important issue when using an IoT-based network structure. In this article, we focus on designing an IoT-based energy management system based on edge computing infrastructure with deep reinforcement learning. First, an overview of IoT-based energy management in smart cities is described. Then the framework and software model of an IoT-based system with edge computing are proposed. After that, we present an efficient energy scheduling scheme with deep reinforcement learning for the proposed framework. Finally, we illustrate the effectiveness of the proposed scheme.

A review on the role, cost and value of hydrogen energy systems for deep decarbonisation

Abstract: Hydrogen technologies can play an important role in decarbonising our energy system in a variety of ways across the energy value chain. It is therefore critical to identify the strategic roles as well as the conditions under which hydrogen energy systems become attractive for the energy transition. In this paper, the authors present a techno-economic review of hydrogen energy systems including power-to-power, power-to-gas, hydrogen refuelling and stationary fuel cells. We focus on their optimal operation as flexible assets and we identify three actions that can foster their uptake beyond technological progress. First, we recommend optimal electricity supply with dedicated control strategies considering that electricity dominates the levelised cost of hydrogen production via electrolysis. Secondly, hydrogen can enable the further integration of traditionally independent sectors, namely electricity, heat and transport while contributing to decarbonise all. This position can also be advantageous for investors who sell heat and fuels as energy efficient products. Lastly, we examine a whole range of revenues from different products and applications which can be combined (i.e. benefit stacking) to match capital and operational expenditures. We discuss these roles in depth and we conclude that policy makers together with technology developers should elaborate smart strategies to reduce cost by scaling production, stimulate standardisation (e.g., similar to the PV industry) as well as develop new market structures and regulatory frameworks which allow hydrogen technologies to deliver multiple low carbon applications and products.

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

Abstract: Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability.

Pathways and challenges for efficient solar-thermal desalination

Abstract: Solar-thermal desalination (STD) is a potentially low-cost, sustainable approach for providing high-quality fresh water in the absence of water and energy infrastructures. Despite recent efforts to advance STD by improving heat-absorbing materials and system designs, the best strategies for maximizing STD performance remain uncertain. To address this problem, we identify three major steps in distillation-based STD: (i) light-to-heat energy conversion, (ii) thermal vapor generation, and (iii) conversion of vapor to water via condensation. Using specific water productivity as a quantitative metric for energy efficiency, we show that efficient recovery of the latent heat of condensation is critical for STD performance enhancement, because solar vapor generation has already been pushed toward its performance limit. We also demonstrate that STD cannot compete with photovoltaic reverse osmosis desalination in energy efficiency. We conclude by emphasizing the importance of factors other than energy efficiency, including cost, ease of maintenance, and applicability to hypersaline waters.

Hydrogen Fuel Cell Vehicles; Current Status and Future Prospect

Abstract: The hazardous effects of pollutants from conventional fuel vehicles have caused the scientific world to move towards environmentally friendly energy sources. Though we have various renewable energy sources, the perfect one to use as an energy source for vehicles is hydrogen. Like electricity, hydrogen is an energy carrier that has the ability to deliver incredible amounts of energy. Onboard hydrogen storage in vehicles is an important factor that should be considered when designing fuel cell vehicles. In this study, a recent development in hydrogen fuel cell engines is reviewed to scrutinize the feasibility of using hydrogen as a major fuel in transportation systems. A fuel cell is an electrochemical device that can produce electricity by allowing chemical gases and oxidants as reactants. With anodes and electrolytes, the fuel cell splits the cation and the anion in the reactant to produce electricity. Fuel cells use reactants, which are not harmful to the environment and produce water as a product of the chemical reaction. As hydrogen is one of the most efficient energy carriers, the fuel cell can produce direct current (DC) power to run the electric car. By integrating a hydrogen fuel cell with batteries and the control system with strategies, one can produce a sustainable hybrid car.

Energy and economic growth

Abstract: Energy use and economic output are positively correlated, though energy intensity has declined over time and is usually lower in richer countries than in poorer countries Numerous factors affect the energy intensity of economies, and energy efficiency is obviously one of the most important However, the rebound effect might limit the possibilities for energy efficiency improvements to reduce energy intensity Natural science suggests that energy is crucial to economic production, and ecological economists and some economic historians argue that increasing energy supply has been a principal driver of growth On the other hand, most mainstream economic growth theories ignore the role of energy These views may diverge because energy scarcity historically imposed constraints on growth, but the increased availability of modern energy sources has reduced energy’s importance as a driver of growth Empirical research on whether energy causes growth or vice versa is inconclusive, but a meta-analysis finds that the role of energy prices is central to understanding the relationship

Energy-Efficient Dynamic Computation Offloading and Cooperative Task Scheduling in Mobile Cloud Computing

Abstract: Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and save energy for smart mobile devices (SMDs) by offloading computation-intensive tasks from resource-constrained SMDs onto resource-rich cloud. However, how to achieve energy-efficient computation offloading under hard constraint for application completion time remains a challenge. To address such a challenge, in this paper, we provide an energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time. We first formulate the eDors problem into an energy-efficiency cost (EEC) minimization problem while satisfying task-dependency requirement and completion time deadline constraint. We then propose a distributed eDors algorithm consisting of three subalgorithms of computation offloading selection, clock frequency control, and transmission power allocation. Next, we show that computation offloading selection depends on not only the computing workload of a task, but also the maximum completion time of its immediate predecessors and the clock frequency and transmission power of the mobile device. Finally, we provide experimental results in a real testbed and demonstrate that the eDors algorithm can effectively reduce EEC by optimally adjusting CPU clock frequency of SMDs in local computing, and adapting the transmission power for wireless channel conditions in cloud computing.

A review of performance of zero energy buildings and energy efficiency solutions

Abstract: The enhancement of energy performance of buildings has become a pillar of energy policies. The main target is the cut of energy consumption to reduce buildings footprint. This aim is pursued by introducing constrains on building requirements in terms of properties of basic materials and components and exploitation of renewable energy sources . That results in the definition of the zero-energy building (ZEB) concept. The new paradigm introduced new challenges and, at the same time, involved all the different stakeholders in facing the barriers to the diffusion of the novel solutions proposed by the research development. This paper summarizes the actual state-of-art of whole performance of ZEBs and the related technical solutions, analysing their increasing potential in energy consumption. A collection of the different case studies reported in literature involving ZEBs is presented, compiling an analysis of the performance of the common solutions actually applied. The technologies involved are described discussing their impact in meeting the ZEB requirements. A debate is proposed, pointing out the main aspects deserving further investigations and outlining the critical elements in making the zero-energy target the new standard for the buildings.

A review of energy efficiency in ports: Operational strategies, technologies and energy management systems

Abstract: Many ports and terminals endeavor to enhance energy efficiency as energy prices have increased through years and climate change mitigation is a key target for the port industry. Stricter environmental regulations are adopted by authorities to limit pollutants and GHG emissions arising from energy consumption. Increasingly, port operational strategies and energy usage patterns are under scrutiny. To ingrain sustainability and environmental protection of ports, the use of innovative technology appears as a critical conduit in achieving a transition from a carbon-intensive port industry (dependent on fossil fuels) to a low-carbon port model by harnessing renewable energy, alternative fuels (e.g. LNG, hydrogen, biofuel), smarter power distribution systems, energy consumption measurement systems. In this context, this paper conducts a systematic literature review to analyze operational strategies (e.g. peak shaving, operations optimization), technology usage (e.g. electrification of equipment, cold-ironing, energy storage systems), renewable energy, alternative fuels and energy management systems (e.g. smart grid with renewable energy) for improving the energy efficiency and environmental performance of ports and terminals. Research gaps and future research directions are identified. Analysis shows that there is a great potential for ports to achieve further energy efficiency and researchers have many impactful research opportunities.

A Survey on Energy Efficient Narrowband Internet of Things (NBIoT): Architecture, Application and Challenges

Abstract: The advancement of technologies over years has poised Internet of Things (IoT) to scoop out untapped information and communication technology opportunities. It is anticipated that IoT will handle the gigantic network of billions of devices to deliver plenty of smart services to the users. Undoubtedly, this will make our life more resourceful but at the cost of high energy consumption and carbon footprint. Consequently, there is a high demand for green communication to reduce energy consumption, which requires optimal resource availability and controlled power levels. In contrast to this, IoT devices are constrained in terms of resources—memory, power, and computation. Low power wide area (LPWA) technology is a response to the need for efficient utilization of power resource, as it evinces characteristics such as the capability to proffer low power connectivity to a huge number of devices spread over wide geographical areas at low cost. Various LPWA technologies, such as LoRa and SigFox, exist in the market, offering a proficient solution to the users. However, in order to abstain the need of new infrastructure (like base station) that is required for proprietary technologies, a new cellular-based licensed technology, narrowband IoT (NBIoT), is introduced by 3GPP in Rel-13. This technology presents a good candidature to handle LPWA market because of its characteristics like enhanced indoor coverage, low power consumption, latency insensitivity, and massive connection support towards NBIoT. This survey presents a profound view of IoT and NBIoT, subsuming their technical features, resource allocation, and energy-efficiency techniques and applications. The challenges that hinder the NBIoT path to success are also identified and discussed. In this paper, two novel energy-efficient techniques “zonal thermal pattern analysis” and energy-efficient adaptive health monitoring system have been proposed towards green IoT.

Artificial Intelligence-Driven Mechanism for Edge Computing-Based Industrial Applications

Abstract: Due to various challenging issues such as, computational complexity and more delay in cloud computing, edge computing has overtaken the conventional process by efficiently and fairly allocating the resources i.e., power and battery lifetime in Internet of things (IoT)-based industrial applications. In the meantime, intelligent and accurate resource management by artificial intelligence (AI) has become the center of attention especially in industrial applications. With the coordination of AI at the edge will remarkably enhance the range and computational speed of IoT-based devices in industries. But the challenging issue in these power hungry, short battery lifetime, and delay-intolerant portable devices is inappropriate and inefficient classical trends of fair resource allotment. Also, it is interpreted through extensive industrial datasets that dynamic wireless channel could not be supported by the typical power saving and battery lifetime techniques, for example, predictive transmission power control (TPC) and baseline. Thus, this paper proposes 1) a forward central dynamic and available approach (FCDAA) by adapting the running time of sensing and transmission processes in IoT-based portable devices; 2) a system-level battery model by evaluating the energy dissipation in IoT devices; and 3) a data reliability model for edge AI-based IoT devices over hybrid TPC and duty-cycle network. Two important cases, for instance, static (i.e., product processing) and dynamic (i.e., vibration and fault diagnosis) are introduced for proper monitoring of industrial platform. Experimental testbed reveals that the proposed FCDAA enhances energy efficiency and battery lifetime at acceptable reliability (∼0.95) by appropriately tuning duty cycle and TPC unlike conventional methods.

Mobile Edge Computing-Enabled Internet of Vehicles: Toward Energy-Efficient Scheduling

Abstract: Although modern transportation systems facilitate the daily life of citizens, the ever-increasing energy consumption and air pollution challenge the establishment of green cities. Current studies on green IoV generally concentrate on energy management of either battery-enabled RSUs or electric vehicles. However, computing tasks and load balancing among RSUs have not been fully investigated. In order to satisfy heterogeneous requirements of communication, computation and storage in IoVs, this article constructs an energy-efficient scheduling framework for MEC-enabled IoVs to minimize the energy consumption of RSUs under task latency constraints. Specifically, a heuristic algorithm is put forward by jointly considering task scheduling among MEC servers and downlink energy consumption of RSUs. To the best of our knowledge, this is a prior work to focus on the energy consumption control issues of MEC-enabled RSUs. Performance evaluations demonstrate the effectiveness of our framework in terms of energy consumption, latency and task blocking possibility. Finally, this article elaborates some major challenges and open issues toward energy-efficient scheduling in IoVs.

Development and applications of photovoltaic–thermal systems: A review

Abstract: The commercial solar cells are currently less efficient in converting solar radiation into electricity. During electric power convention, most of the absorbed energy is dissipated to the surroundings. In order to improve energy efficiency, many efforts have been made to investigate and develop hybrid photovoltaic and thermal collector systems. A photovoltaic–thermal (PV/T) system does both the generation of electric power and collection of thermal energy at the same time. Thus, the overall efficiency of the photovoltaic–thermal (PV/T) system can increase accordingly. In this work, we attempt to summarize various research works on technologies like flat–plate PV/T systems and concentrator type PV/T systems, using different kinds of working fluids under a variety of environmental conditions. The purpose of this review is to define the appropriate environmental conditions and applications for different kinds of PV/T systems. Besides, it is also presented that the applications and developments of the PV/T systems. In order to develop novel PV/T systems, more effort is needed in accurate modeling, exploration of novel materials, enhancement of PV/T system stability and the design of a supporting energy storage system.

An Improved Routing Schema with Special Clustering Using PSO Algorithm for Heterogeneous Wireless Sensor Network

Abstract: Energy efficiency and energy balancing are crucial research issues as per routing protocol designing for self-organized wireless sensor networks (WSNs). Many literatures used the clustering algorithm to achieve energy efficiency and energy balancing, however, there are usually energy holes near the cluster heads (CHs) because of the heavy burden of forwarding. As the clustering problem in lossy WSNs is proved to be a NP-hard problem, many metaheuristic algorithms are utilized to solve the problem. In this paper, a special clustering method called Energy Centers Searching using Particle Swarm Optimization (EC-PSO) is presented to avoid these energy holes and search energy centers for CHs selection. During the first period, the CHs are elected using geometric method. After the energy of the network is heterogeneous, EC-PSO is adopted for clustering. Energy centers are searched using an improved PSO algorithm and nodes close to the energy center are elected as CHs. Additionally, a protection mechanism is also used to prevent low energy nodes from being the forwarder and a mobile data collector is introduced to gather the data. We conduct numerous simulations to illustrate that our presented EC-PSO outperforms than some similar works in terms of network lifetime enhancement and energy utilization ratio.

Cluster head selection for energy efficient and delay-less routing in wireless sensor network

Abstract: Wireless sensor network (WSN) is comprised of tiny, cheap and power-efficient sensor nodes which effectively transmit data to the base station. The main challenge of WSN is the distance, energy and time delay. The power resource of the sensor node is a non-rechargeable battery. Here the greater the distance between the nodes, higher the energy consumption. For having the effective transmission of data with less energy, the cluster-head approach is used. It is well known that the time delay is directly proportional to the distance between the nodes and the base station. The cluster head is selected in such a way that it is spatially closer enough to the base station as well as the sensor nodes. So, the time delay can be substantially reduced. This, in turn, the transmission speed of the data packets can be increased. Firefly algorithm is developed for maximizing the energy efficiency of network and lifetime of nodes by selecting the cluster head optimally. In this paper firefly with cyclic randomization is proposed for selecting the best cluster head. The network performance is increased in this method when compared to the other conventional algorithms.

Hydrogen Fuel Cell Technology for the Sustainable Future of Stationary Applications

Abstract: The climate changes that are becoming visible today are a challenge for the global research community. The stationary applications sector is one of the most important energy consumers. Harnessing the potential of renewable energy worldwide is currently being considered to find alternatives for obtaining energy by using technologies that offer maximum efficiency and minimum pollution. In this context, new energy generation technologies are needed to both generate low carbon emissions, as well as identifying, planning and implementing the directions for harnessing the potential of renewable energy sources. Hydrogen fuel cell technology represents one of the alternative solutions for future clean energy systems. This article reviews the specific characteristics of hydrogen energy, which recommends it as a clean energy to power stationary applications. The aim of review was to provide an overview of the sustainability elements and the potential of using hydrogen as an alternative energy source for stationary applications, and for identifying the possibilities of increasing the share of hydrogen energy in stationary applications, respectively. As a study method was applied a SWOT analysis, following which a series of strategies that could be adopted in order to increase the degree of use of hydrogen energy as an alternative to the classical energy for stationary applications were recommended. The SWOT analysis conducted in the present study highlights that the implementation of the hydrogen economy depends decisively on the following main factors: legislative framework, energy decision makers, information and interest from the end beneficiaries, potential investors, and existence of specialists in this field.