Enormous Energy Consumption Research Articles

Resource allocation for UAV-aided energy harvesting-powered D2D communications: A reinforcement learning-based scheme

Unmanned Aerial Vehicle (UAV) has become one of the most significant component in future wireless networks since its on-demand and cost-effective deployment. Meanwhile, Device-to-Device (D2D) communications with the nature of providing proximity-based service to improve network performance has emerged as an important feature in 5th generation cellular networks. However, the explosive growth of smart devices and bandwidth-hungry applications cause enormous energy consumption. Energy Harvesting (EH) as a potential solution of improving energy efficiency has shown great importance. Consequently, in this paper, we investigate the resource allocation problem in UAV-aided EH-powered D2D Cellular Networks (UAV-EH-DCNs). Our objective is to maximize the energy efficiency while guaranteeing the satisfaction of ground users (GUs). Owning to the non-convexity of the problem, we formulate the problem as a Markov decision process. Afterward, Deep Deterministic Policy Gradient (DDPG) is proposed to find the optimal strategy. Additionally, Long Short-Term Memory (LSTM) network is employed to facilitate the convergence speed by extracting the previous information of GUs satisfaction to determine the current resource allocation strategy. Numerical results adduce the validity of the proposed DDPG+LSTM algorithm as compared to the DDPG and deep Q-learning algorithms.

Unified energy-efficient reconfigurable MAC for dynamic Convolutional Neural Network based on Winograd algorithm

There has been a dramatic proliferation of research concerned with Convolutional Neural Networks (CNNs) over the past decade. In the field of smart surveillance, multi-channel frames need to be processed simultaneously as real-time operations, which leads to intensive computation. Deep CNNs have tens of layers which lead to intensive computation. In order to solve tremendous computation pressure, lots of CNN accelerators have been proposed. Moreover, many works focus on the design of a multiply-and-accumulate (MAC) unit since ten billions of MAC computations induce enormous energy consumption for logical operations in CNNs. Although DynCNN (Tsai et al., 2020) improves the structure of conventional CNNs by using the high similarity property of consecutive frames, it generates redundant cells and leads to redundant operations. Therefore, this paper proposed an energy-efficient reconfigurable MAC and a high-performance CNN accelerator based on the Winograd Minimum Filtering Algorithm (WMFA). The proposed design not only dramatically enhances throughput but also acquires better energy efficiency in limited resources. Experimental results showed that the proposed Winograd processing element (PE) gives 8% to 12% power improvements. The DSP efficiency was improved in the range of from 1.48× to 6.82× as well compared with the previous works.

Scheduling Online EV Charging Demand Response via V2V Auctions and Local Generation

Due to the enormous energy consumption and the wide geographic distribution, Electrical Vehicle (EV) charging stations are believed to have great potential in Emergency Demand Response (EDR) participation. However, EDR limits the electricity drawn from the power grid by the charging station, and can pose threats to satisfying EVs’ charging demand. In this paper, in order to complement the charging station’s energy supply to meet the dynamic EV charging demand, we formulate an online EV charging scheduling problem under EDR as a non-linear mixed-integer program, and propose a novel polynomial-time online algorithm and auction mechanism to jointly incentivize EVs with energy to sell their energy and utilize the charging station’s local generator to produce energy. Our approach conducts an auction in each single round based on a primal-dual method and ties these auctions over time to optimize the system’s long-term social cost, while accommodating the local generator’ on/off-state control, each EV bidder’s cumulative energy budget constraint, and the power grid’s EDR energy cap. Our approach achieves the economic properties of truthfulness, individual rationality, and computational efficiency simultaneously for each auction, and a parameterized-constant competitive ratio for the long-term social cost. By rigorous theoretical analysis and trace-driven experimental studies, the results exhibit that our approach outperforms multiple alternative algorithms regarding the social cost, attains the economic properties, and also executes efficiently in practice.

Robust Ru-N metal-support interaction to promote self-powered H2 production assisted by hydrazine oxidation

Electrocatalytic overall water splitting is hailed as the most promising approach to sustainably produce hydrogen on big scale, but the sluggish anodic oxygen evolution reaction (OER) with large operating potential results in the enormous energy consumption. To couple the cathodic hydrogen evolution reaction (HER) with low-potential hydrazine oxidation reaction (HzOR) is highly promising to significantly reduce the input cell voltage of water electrolysis, which requires high-performance bifunctional HER and HzOR electrocatalysts. Herein, a new-type Ru nanoparticle/mesoporous N-doped carbon (Ru/MPNC) composite is readily constructed with greatly exposed ultrafine Ru particles fused into N-doped carbon walls, and stabilized by in situ formed robust Ru-N metal-support interaction inherently stemming from the particular precursor, thus exhibiting superior bifunctional HER and HzOR activity, far surpassing commercial Pt/C. Furthermore, the two-electrode electrolyzer only require an ultrasmall cell voltage of 0.149 V to achieve 50 mA cm-2, which can be also easily driven by a direct hydrazine-H2O2 fuel cell to attain a decent H2 production rate of 0.893 mmol h−1. The theoretical calculations uncover the robust integration of Ru with NC can tailor the electronic property of exposed Ru sites, endowing the decreased water dissociation energy barrier, together with optimized adsorption free energies of H* and dehydrogenation intermediates of N2H4.

BESS Aided Renewable Energy Supply Using Deep Reinforcement Learning for 5G and Beyond

The year of 2020 has witnessed the unprecedented development of 5G networks, along with the widespread deployment of 5G base stations (BSs). Nevertheless, the enormous energy consumption of BSs and the incurred huge energy cost have become significant concerns for the mobile operators. As the continuous decline of the renewable energy cost, equipping the power-hungry BSs with renewable energy generators could be a sustainable solution. In this work, we propose an energy storage aided renewable energy supply solution for the BS, which could supply clean energy to the BS and store surplus energy for backup usage. Specifically, to flexibly regulate the battery’s discharging/charging, we propose a deep reinforcement learning based regulating policy, which can adapt to the dynamical renewable energy generations as well as the varying power demands. Our experiments using the real-world data on renewable energy generations and power demands demonstrate that, our power supply solution can achieve an cost saving ratio of 77.9%, compared to the case with traditional power grid supply.

Tech-Economic Assessment of Power Transmission Options for Large-Scale Offshore Wind Farms in China

China is taking initiative in energy transition to cope with the long-term controversy of its enormous energy consumption, aiming to use less carbon. Wind power, especially offshore wind energy, has become a prevailing alternative due to its low carbon emissions, renewability, competitiveness, and operation security. The layout of a transmission channel is a key consideration in marine project implementation. This paper investigates the technical characteristics, application status, and viable advantages of a conventional AC transmission, voltage source converter-based high-voltage direct current (VSC-HVDC) transmission, gas-insulated line (GIL) transmission, and hybrid HVDC transmission. A component-resolved evaluation model was proposed to estimate the costs to be incurred of four electrical transmission options for offshore wind power along the coast of Eastern China, with technical feasibility and economical considerations. Cost comparisons and component sensitivity analyses were developed with different transmission distances and capacities. Results suggest HVAC transmission and VSC-HVDC are the preferable solutions for present offshore wind farm development in Eastern China, and the economic potential of the hybrid HVDC makes it feasible for future deployment. Some conclusions can be applied in disparate regions across the globe.

Water-plasma-enhanced and phase-separation-assisted extraction of microalgal lipid for biodiesel production

Traditional methods for lipid extraction from microalgal biomass usually involve harsh reaction conditions or the use of contaminant reagents, which lead to enormous energy consumption and wastage. Hence, a novel strategy was presented, which combined water-plasma and three-phase partitioning (TPP) techniques. Benefiting from its unique advantages such as rapid and low cost, water-plasma strategy can disrupt microalgal cell wall and can thus greatly affect lipid extraction. As a result, assisted with the TPP method, excellent performance lipid recovery (74.34%) was obtained at 200 W in 10 min. The performance was superior to that achieved through cell disruption via water-plasma pretreatment. Importantly, the whole process of lipid extraction prevented the drying of microalgal biomass, contributing to reduced energy consumption in large-scale biodiesel production. Moreover, the high fatty acids content suggested that the extracted lipids are great potential candidate for biodiesel production.

Towards Low Energy and Thermal Comfort of Buildings Through Passive Cooling or Heating Strategies in Hot Arid Climates

Due to strong urban expansion and population growth, the residential sector is the main consumer of electricity and gas, especially the arid region of Algeria. This situation produces enormous energy consumption while the application of thermal regulations was not valid for the entire construction. This article aims to use passive strategies inspired by vernacular architecture, to arrive at passive and ecological houses. Initially, this work consists of knowing the different traditional or current systems used specifically in arid and semi-arid regions. Then, the methodology followed based on a dynamic simulation of the strategies applied in an existing apartment located in the south of Algeria (case study). Afterwards, we will provide a multi-criteria and cost-benefit analysis, to validate an appropriate optimal model, based on energy savings and investment cost. In conclusion, it was observed that using the waterspout wall with night ventilation could reduce energy consumption by 50% and maintain thermal comfort.

Numerical study for energy performance optimization of hollow concrete blocks for roofing in a hot climate of Morocco

• A numerical approach is performed for calculating the thermal inertia parameters. • The traditional roofs had the most significant total thermal load over 24-hours. • Integrate passive techniques into hollow blocks enhances their energy performance. • The inside heat transfer coefficient strongly impacts the roof's thermal inertia. • The outside heat transfer coefficient has no effect on the roof's thermal inertia. The roof is the component of the envelope more subjected to climatic excitations, and it contributes to a significant part of the total building's cooling load in hot climates. Therefore, the thermal optimization of hollow concrete blocks for roofing can considerably reduce the enormous energy consumption used for cooling buildings. For this purpose, the thermal inertia of different configurations of roofs built with traditional concrete blocks used in Morocco with three and six cavities was studied numerically. First, a two-dimensional coupled heat transfer by conduction, convection, and radiation was investigated in the steady-state using the Galerkin finite element method to generate an equivalent homogeneous monolayer block with similar thermal behaviour to the original hollow-block roof. After that, the transient heat conduction equation under real thermal solicitations is solved using a numerical inversion of the Laplace domain solution by utilizing the De Hoog algorithm to evaluate the dynamic thermal characteristics of the roofs. The results report that the emissivity and inside combined convection and radiation heat transfer coefficients significantly impact the roof's thermal inertia. In addition, it is shown that the configuration based on using a low emissivity coating and inserting an insulating material between the outer surface of the block and the slab could reduce the total thermal load over 24-hours by about 93.1% compared to the traditional configuration, which is beneficial to minimize the energy consumption used for cooling buildings.

Polymer and non-polymer admixtures for concrete roofs: Thermal and mechanical properties, energy saving and carbon emission mitigation prospective

Buildings are accountable for the enormous energy consumption in the world. Therefore, energy-efficient building materials are inevitable in modern construction practices to diminish global energy consumption. In this paper, three industrial wastes (fly ash, ground granulated blast-furnace slag, and silica fume), four other admixtures (bentonite, metakaolin, oyster shell, and zeolite), and two polymer admixtures (Styrene-butadiene rubber, styrene-acrylic ester) are considered for the concrete to explore their potential for air conditioning cost-savings, carbon mitigations, and payback periods in buildings of hot-dry (Jodhpur, India) and cold climates (Thessaloniki, Greece). The mechanical and thermophysical properties were measured for admixture-based concretes. The results revealed that among all admixtures studied, styrene-acrylic ester concrete roof exhibited better thermal performance with adequate compressive strength (34 MPa). The best economic benefit of styrene acrylic ester concrete roof in hot-dry/cold climatic conditions leading to the highest total air conditioning cost-savings (1.01 $/m2/1.53 $/m2), highest life cycle saving costs (48.9/31.2 $/m2), highest carbon mitigation (19.26/7.53 kg of CO2/year), and acceptable payback periods (11.6/8.4 years). The results of this research help engineers and architects to select the best admixtures for the concrete in terms of mechanical strength, energy efficiency, and carbon emission mitigation potential.

The SBE21 Heritage Round Table: a discussion about the role of historic buildings in new European policies

In the framework of the international conference SBE21 Sustainable Built Heritage, a round table was organised to discuss the role of historic buildings in the light of the new European policies recently launched. The European Commission highlighted in a communication related to the European Green Deal that the current renovation rate will need to at least double in order to achieve the EU’s energy efficiency and climate objectives. In order to address the enormous energy and resource consumption in the building sector, the Renovation Wave was also launched. When Ursula van der Leyen explained the idea of a New European Bauhaus (NEB), she clearly pointed out the need for a climate-neutral building sector not only as an environmental or economic project, but as a new cultural project for Europe. It is therefore all the more surprising that in all these initiatives the built cultural heritage has not been given any special attention. Reason enough to discuss possible strategies for anchoring the architectural heritage in the EU climate initiatives with the invited panellists of the SBE21 Heritage Round Table. All of them know the current policy making in the framework of the Green Deal from a different perspective – from EU-Level to the local implementation, from research to practice. The invitation to the round table was accepted by:

Sustainable consumption and production: Modelling product carbon footprint of beverage merchandise using a supply chain input‐process‐output approach

AbstractThe continuous increase in beverage consumption worldwide results in enormous energy consumption and carbon emission along the life cycle of beverage products. A novel input‐process‐output product carbon footprint (PCF) model is developed as a generic framework for products and services with complex operations along the supply chain. A PCF of a carbonated drink product is evaluated at each of the major stages in the product's life cycle with results compared to using GHG Protocol methodology. Results from cradle‐to‐grave analysis indicate retail and distribution has the largest emissions impact along the product life cycle. Retail distribution routing optimisation model is developed and retail vending equipment are analysed to explore opportunities for reducing energy consumption and carbon footprint. Future sustainable development studies on the PCF of same beverage merchandises produced in various countries and mitigation measures using dynamic routing distribution could be carried out.

Miniaturisation of Electronic Components and the Problem of Device Overheating

With the ever-increasing rate of data generation and communication, as well as the constant push to reduce the size and costs of industrial converter systems, the power density of electronics has risen. Consequently, cooling, with its enormous energy and water consumption, has an increasingly large environmental impact, and new technologies are needed to extract the heat in a more sustainable way-that is, requiring less water and energy. Embedding liquid cooling directly inside the chip is a promising approach for more efficient thermal management. However, the electronics and cooling are treated separately, leaving the full energy-saving potential of embedded cooling untapped. By removing the need for large external heat sinks, this approach should enable the realization of very compact power converters integrated on a single chip.

TEAP: Traffic Engineering and ALR policy based Power-aware solutions for green routing and planning problems in backbone networks

Enormous and ever-increasing energy consumption in the Internet and the burgeoning global GreenHouse Gas (GHG) emission that come with it have been crucial issues for the past few years due to an exponential traffic growth and a rapid expansion of communication infrastructures worldwide. In this paper, we target Routing and Planning problems of Green Networking with bundled links referred to as RPGN by leveraging Traffic Engineering (TE) and Adaptive Link Rate (ALR) policy jointly to investigate the power-saving potentialities and effective applicability in the backbone networks. We formulate RPGN as a non-linear multi-commodity flow model and develop green heuristics-TE and ALR policy based Power-aware heuristics (TEAP) to solve it. We have investigated and compared different characterizations of the solutions to RPGN by evaluating network power saving ratio, rate level duration distribution, mean rate switching times and mean running time, under different real backbone network topology scenarios. Our results indicate the different power-saving potential of these solutions once applied in the backbone network.

Opportunities for the State-of-the-Art Production of LIB Electrodes—A Review

A sustainable shift from internal combustion engine (ICE) vehicles to electric vehicles (EVs) is essential to achieve a considerable reduction in emissions. The production of Li-ion batteries (LIBs) used in EVs is an energy-intensive and costly process. It can also lead to significant embedded emissions depending on the source of energy used. In fact, about 39% of the energy consumption in LIB production is associated with drying processes, where the electrode drying step accounts for about a half. Despite the enormous energy consumption and costs originating from drying processes, they are seldomly researched in the battery industry. Establishing knowledge within the LIB industry regarding state-of-the-art drying techniques and solvent evaporation mechanisms is vital for optimising process conditions, detecting alternative solvent systems, and discovering novel techniques. This review aims to give a summary of the state-of-the-art LIB processing techniques. An in-depth understanding of the influential factors for each manufacturing step of LIBs is then established, emphasising the electrode structure and electrochemical performance. Special attention is dedicated to the convection drying step in conventional water and N-Methyl-2-pyrrolidone (NMP)-based electrode manufacturing. Solvent omission in dry electrode processing substantially lowers the energy demand and allows for a thick, mechanically stable electrode coating. Small changes in the electrode manufacturing route may have an immense impact on the final battery performance. Electrodes used for research and development often have a different production route and techniques compared to those processed in industry. The scalability issues related to the comparison across scales are discussed and further emphasised when the industry moves towards the next-generation techniques. Finally, the critical aspects of the innovations and industrial modifications that aim to overcome the main challenges are presented.

An approach towards development of new linear regression prediction model for reduced energy consumption and SLA violation in the domain of green cloud computing

With the increase of mega-cities, the demand for Smart Cities is overgrowing. The mega-cities can be smarter through the Cloud of Things (CoT). Efficient energy consumption in Smart Cities has a massive impact on the environment. But, computational power is increasing rapidly in the cloud computing environment. Enormous energy consumption (EC) and Service Level Agreement violation (SLAV) becomes a key concern. The Virtual Machine (VM) consolidation approach can significantly reduce EC, SLA violation (SLAV), and increase resource utilization. However, dynamic VM consolidation may produce performance degradation of Physical Machines (PMs) and SLAV. Therefore, it becomes essential to find a trade-off between EC and SLAV. Herein, a novel New Linear Regression(NLR) prediction model, host overload/underload, and VM placement policy have been proposed to reduce EC and SLAV. The NLR model’s primary intention is to take that the model goes through a straight line and a mean point. Future CPU utilization is predicted based on the proposed NLR model. Evaluation of proposed algorithms has been accomplished by extending CloudSim Simulator. The experiment shows that proposed algorithms reduced EC and SLAV in cloud data centers and can be used to construct a smart and sustainable environment for Smart Cities.

Cross Service Providers Workload Balancing for Data Centers in Deregulated Electricity Markets

The emerging Internet of things and 5G applications boost a continuously increasing demand for data processing, which results in an enormous energy consumption of data centers (DCs). Considering that existing distributed geographical load balancing is approaching the limit in reducing the energy cost of DCs, cloud service providers (SPs) are motivated to pursue a higher level of cooperation. In this context, cross-SP workload balancing among the DCs operated by different SPs represents a future trend of the DC industry. This article investigates the optimal cross-SP workload balancing when it couples with the electricity markets. First, we assume that there is a central operator (CO) coordinating the DCs owned by various SPs. A noncooperative game is formulated to model the interaction between utilities and CO, which serves as a price maker. Under the centralized coordination of CO, an optimal solution is obtained with an iterative algorithm. Taking into account the computation and privacy issues, a decentralized algorithm is then proposed by utilizing techniques in a state-based potential game. Numerical results corroborate the effectiveness of the proposed algorithm. Simulations using Google workload trace show that the workload balancing among cross-SP DCs results in a lower DC operation cost than the existing price taker approach.

Forecasting Irregular Seasonal Power Consumption. An Application to a Hot-Dip Galvanizing Process

Distribution companies use time series to predict electricity consumption. Forecasting techniques based on statistical models or artificial intelligence are used. Reliable forecasts are required for efficient grid management in terms of both supply and capacity. One common underlying feature of most demand–related time series is a strong seasonality component. However, in some cases, the electricity demanded by a process presents an irregular seasonal component, which prevents any type of forecast. In this article, we evaluated forecasting methods based on the use of multiple seasonal models: ARIMA, Holt-Winters models with discrete interval moving seasonality, and neural networks. The models are explained and applied to a real situation, for a node that feeds a galvanizing factory. The zinc hot-dip galvanizing process is widely used in the automotive sector for the protection of steel against corrosion. It requires enormous energy consumption, and this has a direct impact on companies’ income statements. In addition, it significantly affects energy distribution companies, as these companies must provide for instant consumption in their supply lines to ensure sufficient energy is distributed both for the process and for all the other consumers. The results show a substantial increase in the accuracy of predictions, which contributes to a better management of the electrical distribution.

Multi-objective Energy Aware Scheduling of Deadline Constrained Workflows in Clouds using Hybrid Approach

Cloud computing is undoubtedly one of the most significant advances in the domain of information technology. It facilitates elastic and on-demand provisioning of high performance computing capabilities employing pay-per-use model that has snowballed its adoption by scientists and engineers over the past few years. They often exploit workflows to represent their massive applications. Workflow scheduling in cloud has been devoted considerable investigation by researchers owing to its NP-complete nature of problem. Most of the previous studies targeted optimization of schedule length and execution cost within given deadlines/budget restrictions, or both. However, enormous energy consumption in the cloud data centers is not only negatively impacting the environment but also resulting in increased operational costs and thus cannot be ignored. Efficient scheduling strategies can significantly lessen the energy usage while complying with the user’s Quality of Service limitations. This research study proposes a Hybrid Approach for Energy aware scheduling of Deadline constrained workflows (HAED) using Intelligent Water Drops algorithm and Genetic Algorithm, which provides non-dominated solutions to the user. In particular, it focuses on multiple objectives i.e. reduction of schedule length, execution cost and energy usage within deadlines specified by the user. Its performance has been assessed on four scientific workflows from diverse domains using hypervolume and set coverage. The results achieved with the simulations demonstrate that the solutions produced by HAED are of better quality in terms of accuracy and diversity than non-dominated sorting genetic algorithm and hybrid particle swarm optimization.

An Ancillary Services Model for Data Centers and Power Systems

Enormous energy consumption of data centers has a major impact on power systems by significantly increasing the electrical load. Due to the increase in electrical load, power systems are facing demand and supply miss-management problems. Therefore, power systems require efficient and intelligent ancillary services to maintain robustness, reliability, and stability. Data centers can provide the computational capabilities to manage power systems; however, data centers consume a tremendous amount of energy, and energy price accounts for a significant portion of their operational cost. Power system jobs will make this situation even more critical for data centers. In our work, we seek an Ancillary Services Model (ASM) to service data centers and power systems. In ASM, we find an optimal job scheduling technique for executing power systems’ jobs on data centers in terms of low power consumption, reduced makespan, and fewer preempted jobs. The power systems’ jobs include Optimal Power Flow (OPF) calculation, transmission line importance index, and bus importance index. Moreover, a Service Level Agreement (SLA) between data centers and power systems is shown to provide mutual benefits.