Global Energy Efficiency Research Articles

Switchgear Digitalization-Research Path, Status, and Future Work.

To keep pace with global energy efficiency trends and, in particular, emission reduction targets in the maritime sector, both onshore and maritime power distribution systems need to be adapted to the relevant new technologies and concepts. As an important link in the distribution chain, medium-voltage switchgear (MV) is expected to be stable and reliable while operating as efficiently as possible. Failures of MV equipment, while rare because the equipment must be safe to handle and use, have far-reaching consequences. The consequences of such failures, whether to the shore or marine power system, present risks to the entire power plant, so an accurate assessment of equipment condition is required to identify potential failures early. The solution is an emerging concept of digital switchgear, where the implementation of sensor technology and communication protocols enables effective condition monitoring, and the creation of a database that, when combined with machine learning algorithms, enables predictive maintenance and/or fault detection. This paper presents, step by step, the previous challenges, the current research (divided into predictive maintenance, condition monitoring, and fault detection categories), and the future directions in this field. The use of artificial intelligence is discussed to eliminate the disadvantage of manually interpreting operational data, and recommendations for future work are formulated, such as the need to standardize test procedures and data sets to train and compare different algorithms before they are used in practice.

(General Student Poster Award Winner - 2nd Place) Analyzing the Soret Coefficient Using Time Resolved Fourier Transform Infrared Spectroscopy (FTIR)

This study varies salt concentrations of lithium bis-trifluoromethanesulfonylimide (LiTFSI) in the block copolymer polystyrene-poly(ethylene oxide) (SEO), at different temperature gradients, analyzing the effect these parameters have on the magnitude of the Soret coefficient. The Soret Coefficient is the phenomena resulting in a concentration gradient via a temperature gradient. It is quantified by the ratio of thermal diffusion to mutual diffusion. The Soret effect can be utilized in thermogalvanic cells, by re-harvesting wasted heat from most power generating systems. Thermogalvanic cells, are a way to utilize a heat sink to create electrical energy, having the temperature gradient drive the system. Specifically, solid state polymer electrolytes are of interest in these cells as they offer benefits such as low flammability, high safety, and improved energy density, compared to common liquid electrolytes. Thus, application of the Soret effect in thermogalvanic cells is a clear way to greatly increase global energy efficiency, saving money for companies, and decreasing the global environmental footprint. To gain insight as to direction of diffusion in solid-state polymer electrolytes, and make this cell a reality, time resolved Fourier Transform Infrared spectroscopy (FTIR-ATR) measurements provide an unprecedented data accuracy for modeling. The FTIR makes the correlation between the infrared absorbance of the presented salt and its boundary concentration in the polymer through the Beer Lambert law, which has been proven true for salts in previous work. This technique overcomes many previous limitations to experimental techniques measuring the Soret Coefficient, making the FTIR-ATR an effective and repeatable instrument to use for collecting diffusion coefficients.

Weighted Energy-Efficiency Maximization for a UAV-Assisted Multiplatoon Mobile-Edge Computing System

With the rapid development of mobile computing, mobile-edge computing (MEC) has increasingly become an essential means to meet the computing power requirements of intelligent networked vehicles. However, users with high mobility and coupled dynamics are rarely considered in the edge computing paradigms. In this article, we studied a UAV-assisted MEC system with multiplatoon vehicles. Our article aims to maximize the system’s weighted global energy efficiency, which can flexibly adjust each vehicle’s energy consumption according to user preferences and system needs. In particular, we design a controller for platooning vehicles based on a 2-D path-following model and Frenet frames, and model the coupled characteristics of air-to-ground communications and onboard computation. Furthermore, due to the nonconvexity of the objective function and constraints of the optimization problem, we propose an optimization algorithm based on the sequential quadratic programming (SQP) method. The simulation results show that the proposed method significantly surpasses conventional schemes.

REDUCING CLIMATE IMPACTS ON WATER RESOURCES AS THE LEGAL AND ECONOMIC BASIS FOR ENVIRONMENTAL SECURITY IN THE EU CANDIDATE COUNTRIES: THE CASE OF UKRAINE

As climate change is one of the greatest challenges of our time, the legal and economic issues of global environmental security deserve high praise. In the area of industrial competitiveness, where the negative effects of global climate change include floods and droughts, forest fires, and rising sea levels, climate change is highly problematic. Climate impacts affect public and private agricultural infrastructure (including the coastal zone), resulting in lost productivity and increased costs for agriculture. The article applies climate change on a global scale in the form of greenhouse gas (GHG) emissions to determine how the mixtures and emissions of any one entity affect other areas (e.g., individual, community, company or country emissions). Exploring the theoretical and practical premises of climate change as a complex phenomenon, the novelty of this article is that it examines the current framework of the environmental-legal concept, not just the political implications of the legal framework. The research aim of the article lies in two dimensions: the European Union's current climate change policy framework (the climate and energy package, a set of climate change strategies and related policies targeting EU candidate countries); recent environmental operations in Ukraine as an EU candidate country under extraordinary conditions. This article examines recent changes in climate legislation and climate policy in EU member and candidate countries, as well as other highly developed countries, such as the United Kingdom, the United States, and China. Focusing on the impact of the EU Climate and Energy Package (2020 and 2030), this article examines the main implications of EU climate legislation regulating the EU Emissions Trading Scheme and promoting the role of renewable energy in global energy consumption and energy efficiency in general. As a result of this study, this analysis offers multifaceted conclusions based on the interaction of a number of current administrative acts on climate change and environmental policy on a global scale.

Microsecond Discharge Produced in Aqueous Solution for Pollutant Cr(VI) Reduction

This paper presents a detailed analysis of underwater electrical discharge parameters in the treatment of chromium (VI) used as a model pollutant to analyze the reduction process by plasma liquid interaction (PLI). Pin-to-pin microsecond discharges were performed in an aqueous Cr(VI) solution and the processes were characterized using electrical measurements, optical imaging and UV-Vis absorption measurements for [Cr(VI)] estimation. For the first time, the total reduction of Cr(VI) was successfully achieved by PLI process and a maximum energy yield of 4.7 × 10−4 g/kJ was obtained. Parametric studies on electrode geometry, applied voltage, electrodes gap and pulse duration are presented in detail. Finally, an analysis of the process is proposed by comparing our results of the energy yield calculation based on the injected energy with those of the literature and by providing an estimation of the global energy efficiency of the process.

Spatially-Correlated Rician-Faded Multi-Relay Multi-Cell Massive MIMO NOMA Systems

We consider the downlink of a relay-aided multi-cell massive multi-input multi-output (mMIMO) system, where in each cell the base station (BS) serves its users via multiple relays by employing non-orthogonal multiple access (NOMA). We model this system by considering spatially-correlated Rician-faded channels and their estimation errors. The users, consequently, perform imperfect successive interference cancellation (SIC). We derive a lower bound on the spectral efficiency (SE) of this system. We then optimize the non-convex global energy efficiency (GEE) metric, which is a fractional function of the optimization variables. We solve this problem by considering a low-complexity alternating minimization maximization approach, which splits a complex joint problem into multiple simpler convex surrogate sub-problems. We propose a novel surrogate function to exploit this framework, and analytically show that it satisfies the desirable properties of a valid surrogate function. We numerically show that 1) reusing the pilots in each cell, when the channel has sufficiently hardened, provides higher SE than using orthogonal pilots in all cells and 2) the proposed GEE algorithm provides similar GEE as that of an existing joint optimization framework, but with much less complexity.

Spatially-Correlated IRS-Aided Multiuser FD mMIMO Systems: Analysis and Optimization

We consider a two-way full-duplex (FD) system where a massive multi-input-multi-output FD base station (BS) communicates with multiple FD users via an intelligent reflecting surface (IRS). We derive a closed-form network spectral efficiency lower bound by considering spatial correlation at the BS and IRS. We demonstrate the importance of modelling spatial correlation by simplifying this lower bound for uncorrelated channels to show that the IRS capability to modify the wireless medium now is significantly impeded. This lower bound, which is a function of only the long-term channel statistics, is also used to maximize the non-concave global energy efficiency metric by optimally allocating the transmit powers, and by designing the IRS phases. We derive closed-form updates for optimizing the transmit powers using Lagrangian dual, Quadratic, and Dinkelbach’s transforms. We then optimize the IRS phases by using the projected gradient ascent algorithm. We numerically show that increasing the number of IRS elements can help a FD mMIMO BS outperform its half-duplex counterpart, which otherwise under-performs due to its limited ability to cancel various FD interferences.

Data augmentation through multivariate scenario forecasting in Data Centers using Generative Adversarial Networks

The Cloud paradigm is at a critical point in which the existing energy-efficiency techniques are reaching a plateau, while the computing resources demand at Data Center facilities continues to increase exponentially. The main challenge in achieving a global energy efficiency strategy based on Artificial Intelligence is that we need massive amounts of data to feed the algorithms. This paper proposes a time-series data augmentation methodology based on synthetic scenario forecasting within the Data Center. For this purpose, we will implement a powerful generative algorithm: Generative Adversarial Networks (GANs). Specifically, our work combines the disciplines of GAN-based data augmentation and scenario forecasting, filling the gap in the generation of synthetic data in DCs. Furthermore, we propose a methodology to increase the variability and heterogeneity of the generated data by introducing on-demand anomalies without additional effort or expert knowledge. We also suggest the use of Kullback-Leibler Divergence and Mean Squared Error as new metrics in the validation of synthetic time series generation, as they provide a better overall comparison of multivariate data distributions. We validate our approach using real data collected in an operating Data Center, successfully generating synthetic data helpful for prediction and optimization models. Our research will help optimize the energy consumed in Data Centers, although the proposed methodology can be employed in any similar time-series-like problem.

UAV-Enabled Hardware-Impaired Spatially Correlated Cell-Free Massive MIMO Systems: Analysis and Energy Efficiency Optimization

We consider a cell-free (CF) massive multi-input multi-output (mMIMO) system, where multi-antenna access points (APs) serve single-antenna unmanned aerial vehicles (UAVs) and ground users (GUEs). We assume, unlike the existing CF mMIMO literature, hardware-impaired UAVs and GUEs, which observe a mixture of spatially-correlated Rician- and Rayleigh-faded channels while communicating with hardware-impaired APs. We derive a closed-form downlink spectral efficiency (SE) expression by using practical models for the channel mixture, and by considering channel estimation errors. We propose a novel block quadratic transformation (block-QT) technique to optimize non-convex network-centric global energy efficiency (GEE) by appropriately modeling circuit, UAV propulsion and fronthaul powers. The novel block-QT approach combines block optimization and quadratic transformation technique to decompose GEE optimization into simpler convex sub-problems. We numerically show that i) it is better to operate a UAV at a larger height when it has severe hardware impairments; and ii) when UAVs operate at a lower height, they do not significantly affect the SE of GUEs.

An innovative edge-based Internet of Energy solution for promoting energy saving in buildings

Due to the ubiquity and maturity of Artificial Intelligence (AI), it became an essential tool in the development real-time Internet of energy (IoE) solutions. Also, since cloud platforms are not being the first implementation choice due to their bandwidth and latency issues that limit data transmission capacity, Edge Computing is becoming a popular alternative in both IoE and IoT applications. To that end, a novel Edge IoE system is proposed, namely M2SP-EdgeIoE, which enables various potential IoE use scenarios for domestic energy efficiency applications. Specifically, it incorporates (i) the sensing unit used as input stage for collecting data; (ii) the energy disaggregation unit, in which non-intrusive load monitoring algorithms are implemented to identify appliances using their disaggregated power traces; (iii) the anomaly detection platform in which energy consumption abnormalities are detected using multimodal data and AI algorithms; and (iv) the recommender system unit that helps in generating tailored recommendations for improving energy consumption behavior. Promising performance has been obtained under a number of scenarios, for example, an accuracy of 98.49% and 95% have been reached for appliance identification and anomaly detection, respectively. In conclusion, the M2SP-EdgeIoE provides a blueprint for the future of Edge IoE solutions for improved global energy efficiency.

Revisiting global energy efficiency and CO2 emission nexus: fresh evidence from the panel quantile regression model.

Given the importance of energy efficiency in environmental degradation, the effects of energy efficiency and renewable and nonrenewable energy consumption on global environmental pollution were investigated. For this purpose, panel data from 107 countries from 1996 to 2014 were examined. In addition, the present study also tested the well-known environmental Kuznets curve (EKC). The long-run relations were estimated by applying a panel quantile regression (PQR) approach, which is useful for finding heterogeneous effects at lower- and upper-level quantiles of CO2 emissions. The empirical results indicated that energy efficiency had a significantly negative impact on CO2 emissions with low intensity at higher-level quantiles.Furthermore, the impact of renewable and nonrenewable energy consumption on environmental degradation was significantly negative and positive across all quantiles, respectively. The empirical results provide evidence supporting an inverted U-shaped nexus between GDP and CO2, whereby the EKC is found valid. Hence, energy efficiency improvement and renewable energy consumption policies must align with strategies to curb environmental degradation.

Energy Efficient Power Allocation for Cell-Free mmWave Massive MIMO With Hybrid Precoder

This letter investigates the downlink of a cell-free millimeter wave (mmWave) massive multiple-input multiple-output (mMIMO) system, where many access points (APs) cooperatively serve a user. Although the intensive deployment of APs can dramatically improve the system capacity, it also increases the network energy consumption substantially. To track the non-concave global energy-efficiency (GEE) optimization problem, we decompose it into hybrid precoder design and power allocation design. A novel dynamic subarray with quantized phase shifters (DS-QPS) hybrid precoder is introduced, where each radio frequency (RF) chain only connects to a disjointed subset of antennas. The optimization problem of the number of RF chains is formulated as an eigenvalue maximization problem considering a realistic power consumption model. For power allocation, a new centralized framework is exploited to solve a sequence of simpler power allocation subproblems while still aiming at the GEE maximization by merging with fractional programming, non-cooperative game theory, and gradient-assisted binary search (GABS) algorithm. Simulations show that the joint design is more energy-efficient than the baselines.

GREEN: A Global Energy Efficiency Maximization Strategy for Multi-UAV Enabled Communication Systems

In the scenario of limited energy supply, Unmanned Aerial Vehicles (UAVs) enabled communication systems must make efficient use of energy in order to provide long-term service. In this paper, we propose a global energy efficiency maximization (GREEN) strategy for multi-UAV enabled communication systems. In such systems, a group of UAVs communicates with their associated ground terminals (GTs) by using a UAV-enabled interference channel (UAV-IC). In particular, we optimize the UAVs' trajectory control by jointly considering both the communication throughput and the total energy consumption of the whole system. We aim to maximize the global energy efficiency (GEE) of a task for multi-UAV communications, in which the problem is challenging to optimally solve due to its non-convex nature and strongly coupled variables. To tackle this problem, first, we investigate and propose a global energy-efficient optimization problem based on the fly-hover-communicate protocol. Second, we extend our proposed solution from the single UAV-enabled system to multiple UAV-GT pairs cases. In addition, we consider the general scenario in which the UAVs also communicate while flying. Based on the successive convex approximation technique and the path discretization method, the GREEN strategy is designed for optimizing UAV trajectories in this scenario. The simulation results show that the proposed strategy can achieve significantly higher GEE than the benchmark schemes for multi-UAV enabled communications.

Study of energy strategy by evaluating energy–environmental​ efficiency

Energy efficiency is closely related to energy supply and demand, economic development, and environmental pollution, but the key to achieving energy and environmental efficiency remains the energy strategy. However, the method by which economic development and environmental protection are balanced is also important. The resampling data envelopment analysis approach is rarely used because it affects desirable and undesirable output. The current study uses the approach of resampling super-slacks-based measurement data envelopment analysis, and then, regression analysis and principal component analysis are used in combination to evaluate global energy and environmental efficiency. Our findings show that gross domestic product and CO2 emissions have a high slope value with other variables and share a significant relationship, and they also confirm that energy use is indeed closely related to economic development and environmental pollution. The results show that the input and output variables are highly correlated and in line with statistical norms. The energy and environmental efficiency of the Americas and the Middle East are better than those of Europe, Africa, and Asia, indicating that regional influences exist. Some countries must reduce energy consumption, exhibit innovation management of energy production technology, implement green energy strategies, and reduce CO2 emissions in order to gain the opportunity to improve their efficiency.

Air Quality Evaluation and Improvement of China’s Three Major Urban Agglomerations Based on the Modified MetaFrontier Dynamic Slack-Based Measures Model

Urban agglomeration has become a unique form of cities during the rapid development of emerging economies. With the increasing attention on global energy and environmental efficiency, air quality evaluation and pollution control have become important standards to measure the health and orderly development of such agglomerations. Based on panel data of 60 cities in the three major urban agglomerations of Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD), this study uses the Modified MetaFrontier Dynamic SBM model to evaluate their air quality over the 5-year period of 2013–2017. The results present that the development level of air pollution prevention and control in China’s three major urban agglomerations is relatively low, and YRD as the most developed area has the worst effect of air pollution prevention and control. The MetaFrontier and Group Frontier Efficiency analysis confirms the conclusion of the cluster analysis that a significant two-level differentiation exists in China’s three urban agglomerations. Moreover, China’s three major urban agglomerations are still in the stage of high energy consumption and high development. Lastly, we point out different recommendations for industrial structure and governance foci of the three major urban agglomerations. Dust prevention technology should be improved to reduce PM2.5 in BTH, desulfurization technology should be enhanced to cut industrial SO2 emissions in YRD, and better emission reduction targets and other targeted measures should be formulated in PRD.

Game theory-based energy efficiency optimization model for the Internet of Things

This paper conducts a game-theoretic based optimization study on the energy efficiency of the Internet of Things (IoT). For the problem that wireless sensor node information in the IoT requires the selection of a suitable backbone access point for energy efficiency optimization, this paper first establishes a mathematical model for the system-level energy optimization of the sensor node free-choice access point problem and then proposes a game model based on the concept of cooperation and the corresponding utility function, and after theoretical analysis. Among the new connections in the future, more than 30% are suitable for carrying by the cellular network, so the network challenges brought by mobile operators are huge. The paper then studied the current mainstream Internet of Things technology in the development of mobile networks—the​ application principles and key technologies of Narrow Band Internet of Things (NB-IoT), combined with the current wireless network optimization and maintenance work, and studied How does the Internet of Things become the focus of the industry and actively lay out and promote industrial development for operators, and how does NB-IoT move from a concept to small-scale commercial use, become the operator’s leading role in promoting the standard industry, and hope to become an industry leader It is proved that the best access point allocation scheme is the optimal equilibrium point of the proposed game. Then a non-correlated parallel learning algorithm is proposed, according to which the system can converge to the optimal equilibrium point with a very low probability after learning, which is the optimal solution of the proposed system energy efficiency optimization problem, and achieve the global optimal system energy efficiency. Compared with other models, our model has improved efficiency by about 12% and accuracy by about 8%, and it can be applied in practice.

Mapping the impact of COVID-19 crisis on the progress of sustainable Development Goals (SDGs) - a focus on global environment and energy efficiencies

The novel coronavirus disease has spread uncontrollably all over the world within a short span of time and has affected the development of the world in many ways. All the nations have directed their technical, financial and political resources towards controlling COVID-19 pandemic across the world. It is predicted that the impact of this crisis will remain for longer period of time, affecting the livelihood of people and all those activities that were contributing to the development of the nations across the globe. In fact, the most promising and significant seventeen Sustainable Development Goals (SDGs) that were adopted in the year 2015 by the United Nations (UN) Member States addressing various global issues, now seem difficult to be achieved by 2030 due to coronavirus pandemic. However, even though the COVID-19 pandemic might have turmoil effect on the existing growth of the world, still few positive developments may be observed in the long run due to this pandemic. Therefore, this study focuses on identifying and quantifying the impact of COVID-19 pandemic in achieving the UN SDGs. This study brings out both negative as well as positive influences of the pandemic on the environment and energy related SDGs. The study uses Analytic Hierarchy Process (AHP) to calculate the weights of the identified positive and negative influences for each environment and energy related goals. Further, 4–1 ratings are used to identify the severity of the influences on the SDGs. Finally, a score is calculated using weights and ratings that indicates the overall impact of the pandemic on environment and energy related SDGs. The result obtained in the study shows that the pandemic offers an opportunity to develop action plans that can build more environmentally sustainable future.

On the Energy Efficiency of OFDMA Cellular Networks

In this article, we study the energy efficiency (EE) of orthogonal frequency-division multiple access (OFDMA) cellular networks under the 5 G requirement of EE enhancement. We aim to present a power allocation scheme maximizing the EE of downlink cellular communications while avoiding numerical methods such as fractional programming. We focus on two EE forms; global EE and weighted sum EE. Therefore, we propose a novel explicit expression of the optimal power allocation related to each subcarrier. We also present the power control with limited power budget or/and minimal transmission rate constraint in both base station and subcarrier perspectives. As a result, we notice the occurrence of some transmission outage events depending on the constraints’ parameters. From another side, we extend our study to analyze the effect of the channel state information (CSI) unavailability on our proposed power scheme. In the numerical results, we show that our proposed power control improves the EE, especially at high power budget regime and low minimal rate regime. We also show that having more subcarriers enhances the OFDMA EE. Finally, we show that EE degradation due to CSI unavailability is very small showing the robustness of the proposed scheme against CSI imperfectness. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>

Assessment of Short- and Long-Run Energy Efficiency: Evidence from 141 Countries

Several studies have estimated the energy efficiency performance of firms, countries and regions. Recently, the efficiency literature highlighted two types of efficiencies- persistent and transient efficiency. However, several studies have failed to distinguish between these two types of efficiencies. In the light that there are no studies addressing this issue from a global perspective, this paper adopts the stochastic frontier approach and the Kumbhakar, Lien and Hardaker (KLH) model in the estimation of persistent, transient and total energy efficiency of 141 countries during the period 1980-2016. Our findings show that global energy efficiency issue is structural in nature. Increasing both transient and persistent energy efficiency will save 13% and 31% of total energy consumed, respectively. Therefore, environmental and energy security issues will profit significantly from long-term policies rather than the short-term policies. Thus, countries should focus on addressing environmental and energy issues using long-term policies.

Reconfigurable Intelligent Surfaces-Assisted Multiuser MIMO Uplink Transmission With Partial CSI

This paper considers the application of reconfigurable intelligent surfaces (RISs) (a.k.a. intelligent reflecting surfaces (IRSs)) to assist multiuser multiple-input multiple-output (MIMO) uplink transmission from several multi-antenna user terminals (UTs) to a multi-antenna base station (BS). For reducing the signaling overhead, only partial channel state information (CSI), including the instantaneous CSI between the RIS and the BS as well as the slowly varying statistical CSI between the UTs and the RIS, is exploited in our investigation. In particular, an optimization framework is proposed for jointly designing the transmit covariance matrices of the UTs and the RIS phase shift matrix to maximize the system global energy efficiency (GEE) with partial CSI. We first obtain closed-form solutions for the eigenvectors of the optimal transmit covariance matrices of the UTs. Then, to facilitate the design of the transmit power allocation matrices and the RIS phase shifts, we derive an asymptotically deterministic equivalent of the objective function with the aid of random matrix theory. We further propose a suboptimal algorithm to tackle the GEE maximization problem with guaranteed convergence, capitalizing on the approaches of alternating optimization, fractional programming, and sequential optimization. Numerical results substantiate the effectiveness of the proposed approach as well as the considerable GEE gains provided by the RIS-assisted transmission scheme over the traditional baselines.