Efficient Energy Usage Research Articles

ESTIMATED PARAMETERS IN THE SYSTEMS OF A SINGLE-AXIS ORIENTACIS OF A PHOTOELECTRIC MODULE

The development of "green" energy in Ukraine involves an increasingof the amount of electricity generated by photovoltaic modules (PMs). However, PMs have disadvantages associated with the inconsistency of electric generation in time due to the diurnal and annual motion of the Sun and to the shading of the modules. In order to maintain steady generation during the day and the year, there are two ways to minimize the impact of these factors.First one is to install an excess capacity which means higher cost of the solar plant and using an additional area.Second one isto apply a system of orientation PMs perpendicular to the sun's rays. 
 In the article three cases of solar panels installation are considered: usinga two-axistracking system, using aheight tracking system and anazimuth tracking system. There wasgiven the method and algorithms of calculation and analysis the parameters of theuniaxialand biaxial tracking system when installed in Kyiv.The algorithm involves calculation the coordinates of Sun in any moment of time for the considered location, pass-through and absorptive capacityof the protective glass, estimation the amount of the direct and diffuse solar radiation and their impact on the total radiation on the surface of the photoconverter. There was made an estimation of energy usage efficiency levels for the considered systemsusing the proposed algorithm.

Assessing the low-carbon city pilot policy on carbon emission from consumption and production in China: how underlying mechanism and spatial spillover effect?

The low-carbon city pilot (LCCP) policy is an important initiative for China to fulfill its international commitment to carbon emission reduction and achieve low-carbon transformation. In this context, this study investigated whether the LCCP policy of China has achieved carbon emission reduction from the production and consumption perspectives and how its underlying mechanism and spatial spillover effect. Using the panel dataset of 285 Chinese prefecture-level cities from 2003 to 2019, this study applied the staggered DID model to examine the effects and its underlying mechanism of the LCCP policy on carbon intensity (CI) and carbon emission per capita (CP). We also conducted heterogeneity and spatial spillover effect analyses using the textual quantification method and spatial DID. Our results show that the LCCP policy effectively reduced CI and CP, but these effects did not appear until the third year of implementation. The above conclusions passed a series of robustness and endogeneity tests. Reducing industrial emissions, improving technological innovation, and optimizing the efficiency of energy usage were three important mechanisms to reduce CI and CP, validating the effectiveness of the LCCP policy. Command-mandatory and voluntary LCCP policy tools achieved better results, and the LCCP policy exerted a significant emission reduction effect on second-tier pilot cities as compared to others. The carbon emission abatement of the LCCP policy has also demonstrated a spatial spillover impact on neighboring cities. This study focused on analyzing the mechanism paths and spatial spillover effects of the LCCP policy impact and provided an important decision-making reference in promoting the LCCP policy for not only China but also other developing countries. Specifically, low-carbon pilot experiences and typical cases should be refined, ways for accelerating the greening and cleaning of energy usage must be explored, and regional joint control and collaborative governance should be established to achieve China's low-carbon transformation.

Cooperative Recharge Scheme Based on a Hamiltonian Path in Mobile Wireless Rechargeable Sensor Networks

The energy problem and limited capacity of batteries have been fundamental constraints in many wireless sensor network (WSN) applications. For WSN, the wireless energy transmission technology based on magnetic resonance coupling is a promising energy transmission technology. To reduce the cost and energy consumption during charging in mobile wireless rechargeable sensor networks (MWRSNs), a cooperative mobile charging mechanism based on the Hamiltonian path is proposed in this paper. To improve the charging task interval, we study the use of a mobile charger (MC) as a mobile sink node to collect the data in this paper. Then, we used the sink and the charging sensors selected by the MC to construct the undirected complete graph. Finally, the Euclidean distance between nodes is used as the edge weight and a Hamiltonian loop is found by using the improved Clark–Wright (C-W) saving algorithm to solve the problem of charging a rechargeable sensor network. In addition to the energy usage efficiency (EUE) and the network lifetime, the average energy loss per unit time is considered as the evaluation index according to the impact of the MC on the energy consumption during charging. The simulation results show that the proposed scheme increases the average network lifetime, decreases the average energy loss per unit time, and improves the EUE.

Challenges for real-world applications of nonintrusive load monitoring and opportunities for machine learning approaches

The rapid increase of power consumption calls for efficient and effective energy usage and conservation strategies in buildings. One of the requirements of achieving such a goal is load monitoring of residential appliances. Among the available load monitoring frameworks, nonintrusive load monitoring (NILM) which is used to estimate the appliance-level power usage from the aggregated signals from smart meters, has the potential to be widely deployed. This paper presents an up-to-date review of NILM methods and the challenges existing in each step of NILM. Then this paper reviews two state-of-the-art machine learning based NILM methods including Hidden Markov Model and Deep Learning techniques. Finally, this paper discusses areas for future research and development of NILM in real-world applications, where machine learning approaches can play a more significant and even decisive role.

IoT-Orchestration based nanogrid energy management system and optimal time-aware scheduling for efficient energy usage in nanogrid

Internet of Things (IoT) has brought an immense revolution in diverse fields related to mission-critical systems including healthcare and navigation systems. We argue that the potential of IoT has not been fully exploited in the energy sector. There is a dire need to shift the traditional paradigm of mission-critical electric power architectures to IoT-enabled fully orchestrated architectures to enhance the overall performance. In this study, we present a novel IoT task orchestration architecture for efficient energy management of a nanogrid system that focuses on minimizing the use of non-renewable energy resources and maximizing the use of renewable energy resources. Since network orchestration deals with automating the interaction between multiple components involved to execute a particular service, therefore, scheduling the relevant processes within strict deadlines becomes the core pillar of the system's performance. The mission-critical systems with urgent task execution often suffer from missing task deadlines issues. To overcome this issue, we present a task scheduling algorithm that incorporates the optimized surplus time, and efficiently executes the energy management-related tasks contemplating to their types. The study utilizes sensors to obtain data from physical IoT devices, including photovoltaic (PV), Energy Storage System (ESS), and diesel generator (DG). The performance of the proposed model is evaluated using data set of nanogrid houses. The outcomes revealed that IoT-task orchestration has played a pivotal role in efficient energy management for nanogrid missioncritical systems. Furthermore, the comparison showed that the task starvation rate is reduced to 16% and 12% when compared with state-of-the-art scheduling algorithms.

Multi-objective optimal scheduling of microgrid with electric vehicles

With the increasing global attention to environmental protection, microgrids with efficient usage of renewable energy have been widely developed. Currently, the intermittent nature of renewable energy and the uncertainty of its demand affect the stable operation of a microgrid. Additionally, electric vehicles (EVs), as an impact load, could severely affect the safe dispatch of the microgrid. To solve these problems, a multi-objective optimization model was established based on the economy and the environmental protection of a microgrid including EVs. The linear weighting method based on two-person zero-sum game was used to coordinate the full consumption of renewable energy with the full bearing of load, and balance the two objectives better. Moreover, the adaptive simulated annealing particle swarm optimization algorithm (ASAPSO) was used to solve the multi-objective optimization model, and obtain the optimal solution in the unit. The simulation results showed that the multi-objective weight method could diminish the influence of uncertainty factors, promoting the full absorption of renewable energy and full load-bearing. Additionally, the orderly charging and discharging mode of EVs could reduce the operation cost and environmental protection cost of the microgrid. Therefore, the improved optimization algorithm was capable of improving the economy and environmental protection of the microgrid.

NRG4-U: a novel home energy management system for a unique loadprofile

ABSTRACT Home Energy Management Systems (HEMSs) are developed to facilitate residential energy management toward a more efficient way while enhancing the residents’ energy awareness. Within this paper, a novel non-intrusive, advisory and low-cost home eNeRGy management system FOR a Unique load profile (NRG4-U) is proposed to generate a target personalized load profile for each user to manage energy while preserving comfortable living conditions. The user is able to monitor all systems applications through a visualization dashboard. NRG4-U individual modules gradually generate a user-based profile including preferences, routines, and energy base load. Thermal and visual comfort are estimated through a fusion algorithm for better results. Occupancy and activity inference are estimated by auto-generating an energy base-load exploiting hidden Markov models without using any manual pre-processing task or ground truth. Personalized recommendations are sent to alert the users for energy saving actions while motivating them to adapt to more efficient energy usage. The main source of generating energy savings is the real time recommendations that inform the user about changing the operational states of appliances that may not be necessary at the given time, or may be functioning for excessive time. The suggested HEMS tool (NRG4-U) has been deployed in residential environments, while the experimental results demonstrate that it is a compelling tool for personalized load profile and energy awareness. Results demonstrate that using the NRG4-U HEMS can result in average monthly energy savings of 7%.

Secure Multi-Dimensional and Multi-Angle Electricity Data Aggregation Scheme for Fog Computing-Based Smart Metering System

Smart Meters (SMs) can regularly report their detailed real time electricity consumption data to utility company, which can improve the balance of electrical load and the efficiency of energy usage. However, the fine-grained electricity data reported by smart meters is easy to disclose the privacy of users. Therefore, data aggregation technology is often used to protect the privacy of users. Currently the existing data aggregation schemes cannot meet the requirement of further fine-grained analysis. In this paper, we propose a multi-dimensional and multi-angle electricity data aggregation scheme for fog computing-based smart metering system. In our proposed scheme, the smart meters use the super-incremental sequence matrix to structure and encrypt their obtained electricity consumption data; the fog nodes regularly collect and aggregate these ciphertexts of electricity consumption data by using the homomorphic Paillier cryptosystem; the control center decrypts the aggregated ciphertext to obtain multi-dimensional and multi-angle electricity data, which is accurate to the types of household appliances and their corresponding electricity angles (energy efficiency indexes). According to the security requirements, we analyze the security of our proposed scheme, which can achieve identity anonymity. Additionally, the experimental results show our scheme is efficient.

Appraisal of CO2 emission in Tunisia's industrial sector: a dynamic vector autoregression method.

The world is confronted with a slew of environmental issues, one of which is attenuating the detrimental impacts of carbon dioxide (CO2) emission-induced climate change. The ever-increasing use of energy is eroding natural resources to the point that our economic future may be jeopardized. The Tunisian economic growth indicates the excellent performance in the industrial sector as the minimum required input for these developments which necessitate additional energy consumption, resulting in increased CO2 emissions and environmental degradation. This study explores the role of energy efficiency, urbanization, economic growth, and natural gas energy usage in the industrial sector on the CO2 emissions of Tunisia. The research mainly employs the vector autoregressive model (VAR) to examine the factors driving the evolution of CO2 emissions through the industrial sector from 2000 to 2018. The findings assess that natural gas as an energy source and efficiency is crucial for reducing CO2 emissions. The study has shown the existence of the environmental Kuznets curve (EKC), which demonstrates that economic development in Tunisia has an inverted U-shape connection with CO2 emissions. The findings show that energy consumption and GDP have a considerable impact on CO2 emissions due to large-scale population changes and industrial structure alteration. In contrast, energy efficiency is a key factor in lowering CO2 emissions. Based on the study's results, the article will enable economic decision-makers and relevant authorities to develop an appropriate energy strategy for the industrial sector to safeguard environmental deterioration in the long term by lowering carbon emissions.

Global Prospects, Advance Technologies and Policies of Energy-Saving and Sustainable Building Systems: A Review

Automation, modernization, economic development and global progress depends on efficient extraction and utilization of energy. Power generation by burning fossil fuels makes various adverse impacts on the environment. Additionally, the worldwide fossil fuel reserve is limited and depleting very fast. Hence, efficient energy usage and savings are crucial to address the environmental issues to ensure sustainable development. Buildings, both commercial and residential, represent a major energy consumption sector. Approximately 40% of the total energy is reportedly consumed in the building sector. Worldwide building energy consumption, performance measuring systems and best practices, energy-saving techniques and policies are reviewed and summarized in this article. Underfloor air distribution, double-glazed windows, use of highly efficient electric motors and variable speed drives may play a great role in reducing building energy consumption. In the UK, the application of double-glazed windows in commercial buildings can save 39–53% energy. The proper maintenance of a building’s central heating system can save up to 11% energy. The automatic HVAC control system can reduce up to 20% of the building’s total heating load. Proper utilization of a VSD system in motor and building performance optimization by an ANOVA tool also proved instrumental in saving energy. Apart from this, the building codes of different countries also help to improve building performance by reducing energy consumption. This study will help building researchers and policymakers to make a framework for sustainable, green building.

Semisolid Materials Processing: A Sustainability Perspective

Sustainable development is increasing in importance with restrictions on emission and carbon footprint. Similarly, both energy and resources efficiency are required, and at the same time, cost-efficiency is required. The current paper is focusing on carbon footprint, energy usage and material use efficiency of semisolid metal casting. A detailed analysis is made on the RheoMetal process, which is benchmarked to conventional HPDC casting. The analysis includes the gating system and the importance of the use of primary or secondary material. It furthermore includes a discussion of process yield and benefits based on process capability.

Multi-Agent Real-Time Advanced Metering Infrastructure Based on Fog Computing

This effort to make the power grid more intelligent is tightly coupled with the deployment of advanced metering infrastructure (AMI) as an integral part of the future vision of smart grid. The goal of AMI is to provide necessary information for the consumers and utilities to accurately monitor and manage energy consumption and pricing in real time. Immediate benefits are enhanced transparency and efficiency of energy usage and the improvement of customer services. Although the road map toward successful AMI deployment is clearly defined, many challenges and issues are to be solved regarding the design of AMI. In this paper, a multi-agent AMI based on the fog-computing approach is presented. Architecture follows structural decomposition of AMI functionalities encapsulated in a form of local and area-specific service components that reside at the different tiers of hierarchically organized AMI deployment. Fog computing concepts provide the framework to effectively solve the problems of creating refined and scalable solutions capable of meeting the requirements of the AMI as a part of future smart grid. On the other hand, agent-based design enables concurrent execution of AMI operations across the distributed system architecture, in the same time improving performance of its execution and preserving the scalability of the AMI solution. The real-time performance of the proposed AMI solution, related to the periodic and on-demand acquisition of metering data from the connected electricity meters, was successfully verified during one year of pilot project operation. The detailed analysis of the performance of AMI operation regarding data collection, communication and data availability across the deployed pilot AMI, covering several transformer station areas with diverse grid topologies, is also presented.

PV Solar Energy Supply in Smart Cities

The application of solar energy and other renewable energy sources (wind, hydro, etc.) enables the connection of low-power electrical energy generation units to the grid. This new concept called distributed energy generation, provides opportunities to separate parts of existing distribution networks into smaller units, i.e., microgrids, which can be self-sufficient and sustainable for shorter or longer periods. In a smart grid environment, efficient usage of energy and better consumer energy supply is possible, especially in cases of failures in the main grid. This paper presents a method for the assessment of possible rooftop PV system power capacity in a smart city. Special attention will be paid to the generation units coming from renewable energy (solar) and the possibility of supplying existing loads. As a case study, the results of a hypothetical project on the possibility of PV solar energy application for the electricity supply in some cities in Serbia are presented.

An analysis of lighting design in a football stadium

Lighting is an essential element for human life. Based on the decree of the Minister of Health No.1405/Menkes/SK/XI/2002 Date: 19 November 2002 concerning the requirements and procedures for the implementation of healthy working environment. Indonesian National Standard 16-7062-2004 states that the quality of the inadequate lighting gives bad effects for visual function, the environment around the workplace, and psychological aspects that can be felt as fatigue, feeling less comfortable, less alertness until the effects of the most serious such as accident. The purpose of the study was to determine the intensity of the lighting, the spread of light, lighting design, type of lamp, light bulb conditions, efficiency of energy usage, and maintenance of the football stadium lights. This study employed a qualitative descriptive approach using the type of evaluation research. The subject of this research is Harapan Bangsa Football Stadium in Banda Aceh. Data analysis techniques are: data reduction, data display, and drawing conclusion. The findings of the research showed that the lighting intensity of Harapan Bangsa Football Stadium was 341 lux. Meaning that it was deficit of 458.8 lux of the standard set by PT Liga Indonesia (800 lux), deficit of 959 lux of the standard set by AFC (1.200 Lux), and deficit of 1659 of the standard set by FIFA 2000 Lux. Lighting in the Harapan Bangsa Football Stadium spread unevenly. The lighting intensity design of the stadium was 594 lux. The kind of light bulb used was mercury. The electric power needed to support all of lamps was 377. 200 Watt. The type of light bulb used was Philips trademark. Most of the light bulbs were broken and expired. The use of mercury lamps has not been efficient in energy use. The maintenance of the lamps has not maximized yet.

Review of thermal characterization techniques for salt-based phase change materials

Phase change materials (PCM)-based energy storage system is a quite promising technology for the efficient usage of the excess solar energy produced and utilize it at the hour of high demand. The major challenge here is the selection of PCMs for energy storage applications. Inorganic PCMs possess higher thermal conductivity and energy storage capacity when compared to organic PCMs. Thus, inorganic PCMs have a great potential to be used in energy storage systems majorly in medium to high-temperature applications where organic PCMs cannot be used. An accurate and reliable data on the thermophysical properties of the PCMs is essential before its selection and installation of a energy storage system. In this study, various characterization methods based on calorimetry, temperature difference, cooling rate, and cooling curve used to date are described. Methods such as conventionally used differential scanning calorimetry (DSC), T-history method, and computer-aided cooling curve analysis (CACCA) are reviewed and discussed in this study. The two modes of CACCA, Newtonian, and Fourier techniques are explained. The advantages and limitations associated with all these methods are outlined. Inverse heat conduction problem (IHCP)-energy balance method based on CACCA which is devoid of the limitations associated with the conventional characterization methods is discussed. Thermal conductivity is the main characterization parameter of the PCMs and therefore methods to measure thermal conductivity are critically reviewed in this study. Thermal cycling stability is discussed in the context of the review.

Demand-Side Energy Management in Smart Buildings: A Case Study

Electrical energy is indispensable in our daily life with the developing technology. The most important feature is reliable and sustainable transmission of electrical energy to consumers is to provide the supply-demand balance in real-time. The ever-increasing demand for electrical energy and gradual depletion of traditional resources used to meet demand and increasing dependence on foreign sources resulted in diverse electricity generation plants. As a result of this diversity, with the increase in importance of electricity storage systems and awareness of energy-saving, Demand Side Management (DSM) gains great importance in ensuring supply-demand balance. DSM reduces costs by scheduling consumption instead of increasing generation to balance supply and demand. Residences constitute a large part of energy consumption worldwide so DSM applications in buildings increase efficient usage of energy. Various management strategies can be used to save energy depending on the building type. In this article, firstly, an overview of Energy Management System (EMS) strategies to increase energy efficiency is presented. Then a case study is carried out in a residential model in Matlab/Simulink environment. The electrical devices were controlled with a Fuzzy Logic Controller (FLC) taking into account comfort, cost, and Demand Response (DR). In addition, Renewable Energy Resources (RES) to demonstrate their contribution were modeled and integrated into the system. Case studies were conducted and a comparative analysis of obtained results was carried out.

Effects of agricultural activities on energy-carbon-water nexus of the Qinghai-Tibet Plateau

Energy, carbon, water and their interactions determine agricultural production efficiency and sustainability. On the Qinghai-Tibet Plateau (QTP), agricultural activities threaten the ecological security of the region, and agriculture itself is susceptible to climate change. In this study, we applied an energy-carbon-water nexus framework to assess the carbon emissions, water utilization, and energy flow, and their links with agricultural production on the QTP, using emergy analysis, footprint analysis (carbon footprint and water footprint) and a coupling model. The results showed that agricultural activities on the QTP released 2.17 × 1010 kgCO2-eq and absorbed 6.00 × 1010 kgCO2-eq, using 3.24 × 1022 sej of emergy and 8.19 × 109 m3 of water in 2018. Meanwhile, the energy-carbon-water nexus of agricultural production on the QTP was imbalanced, and the environmental footprint revealed large spatial heterogeneity at county scale. The coupling interaction among the energy-carbon-water nexus in agricultural production was investigated based on the coupling coordination model. Five categories of coupling interaction were identified by the degree of coordination. The largest emitter was agricultural input, especially electricity and nitrogen fertilizer, accounting for 24% and 7% of the total on average, respectively. With regard to emergy flows, renewable organic resources, accounted for 47%, making them the predominant in emergy inputs. The green water footprint (water from natural rainfall) at 51% was higher than the blue water footprint (water from irrigation) at 49%. More measurements to reduce carbon emission and improve the efficiency of energy usage and water usage are needed, such as optimizing planting patterns and structures, promoting cleaner energy, improving the efficiency of agrochemical use and proposing reasonable spatial planning for regional ecological development. Our findings provide a systematic reference for energy-carbon-water nexus analysis and sustainable agricultural development policy for decision-makers on the QTP.

Skin-conformable photoplethysmogram sensors for energy-efficient always-on cardiovascular monitoring systems

Skin-conformable photoplethysmogram (PPG) sensors enable continuous and accurate monitoring of physiological states to efficiently prevent cardiovascular-related diseases. Herein, novel PPG sensors consisting of polymer/oxide hybrid phototransistors, mini-light-emitting diodes, and a framework conformable to epidermis are developed. The key element, a heterojunction phototransistor for efficient energy usage, is composed of an indium gallium zinc oxide (IGZO)-based active layer for low-power consumption and a specific diketopyrrolopyrrole (DPP) polymer layer affording high near-infrared (NIR) light absorbability and hydrophobicity. Therefore, the phototransistors with NIR detectivity of 1.00 × 1013 Jones, rapid photoresponse within the human heart rate range, high reliability against perspiration and mechanical stress, and low operating voltages (< 5 V) are achieved. Using the developed PPG sensors, the heart rate and oxygen saturation of human subjects are successfully detected, which is comparable to the commercial PPG sensors. Furthermore, controlling potential barrier energy at the interface between heterojunction layers, PPG sensors that operate separately at low and high heart rates are implemented for continuous monitoring. Consequently, a distinguished configuration of skin-conformable PPG sensors and a novel concept of an always-on cardiovascular monitoring system while consuming less power are suggested. The study contributes to the development of PPG sensors and may become a potential solution for Healthcare 4.0 applications.

Influence of the Hydrophilic Surface of Nanofiber Support on the Performance of Hybrid Supercapacitors

Concerns associated with global warming and the depleting reserves of fossil fuels have highlighted the importance of high−performance energy storage systems (ESSs) for efficient energy usage. ESSs such as supercapacitors can contribute to improved power quality of an energy generation system, which is characterized by a slow load response. Composite materials are primarily used as supercapacitor electrodes because they can compensate for the disadvantages of carbon or metal oxide electrode materials. In this study, a composite of oxide nanoparticles loaded on a carbon nanofiber support was used as an electrode material for a hybrid supercapacitor. The addition of a small amount of hydrophilic FeN@GnP (Fe− and N−doped graphene nanoplates) modified the surface properties of carbon nanofibers prepared by electrospinning. Accordingly, the effects of the hydrophobic/hydrophilic surface properties of the nanofiber support on the morphology of Co3O4 nanoparticles loaded on the nanofiber, as well as the performance of the supercapacitor, were systematically investigated.

Potential of infrared temperature measurements for the online estimation of the state-of-charge of a Li-polymer battery

The knowledge of the battery status in terms of state-of-health (SoH), state-of-charge (SoC), and state-of-power (SoP) is of great significance for the efficient usage of the available stored energy and to ensure the safety of electric vehicles. The SoC is the equivalent of the fuel tank gage as it indicates the amount of energy that can still be extracted from the battery before a recharge. Many techniques have been proposed in the literature for the estimation of the SoC based on more or less complex mathematical approaches. In general, the electric parameters of the battery were used. This work aims to trace a new path for the estimation of the SoC of a lithium polymer battery using thermal parameters instead of electric ones. Temperature measurements are already used in battery management systems with a different function than SoC evaluation. The approach presented here aims to give a more prominent role to the temperature data collected during the battery operation. This can serve as a main or secondary (cross-check) tool for the online estimation of the battery Soc.The battery temperature is measured during the discharge phase at a constant current and the following resting phase. Thermal imaging in the infrared band is applied to assess the presence of 2d inhomogeneity on the battery surface. The experimental data are coupled to an extensive theoretical analysis based on the lumped parameter model applying Newton's law of cooling in transient conditions. The first objective is the estimation of the open-circuit voltage over time, which is unknown during the operation. It is put in correlation to the SoC calculated with the Coulomb counting method. The open-circuit voltage versus SoC curve is essential for the online SoC determination. Theoretical results have been validated with the data of a multi-step test cycle performed on purpose. Finally, a new method for the online SoC estimation based on temperature measurements has been set up and validated.