End-user Equipment Research Articles

Techno-economic and life cycle analysis of synthetic natural gas production from low-carbon H2 and point-source or atmospheric CO2 in the United States

Synthetic natural gas (SNG) is of great interest in reducing fossil energy consumption while maintaining compatibility with existing NG infrastructure and end-use applications equipment. SNG can be produced using clean H2 generated from renewable or nuclear energy and CO2 captured from stationary sources or the atmosphere. In this study, we develop an engineering process model of SNG production using Aspen Plus® and production scales reported by the industry. We examine the levelized cost and life cycle greenhouse gas (GHG) emissions of SNG production under various CO2 supply scenarios. Considering the higher cost of H2 transportation compared with CO2 transportation, we assume that CO2 feedstock is transported via pipeline to the H2 production location, which is collocated with the SNG plant. We also evaluate the cost of CO2 captured from the atmosphere, assuming the direct air capture process can occur near the SNG facility. Depending on the CO2 supply chain, the levelized cost of SNG is estimated to be in the range of $45–76 per million British thermal units (MMBtu) on a higher heating value (HHV) basis. The SNG production cost may be reduced to $27–57/MMBtu-HHV by applying a tax credit available in the United States for low-carbon H2 production (45 V). With a lower electricity price of 3ȼ/kWh for water electrolysis and accounting for a 45 V tax credit, the SNG cost reaches parity with the cost of fossil NG. Depending on the CO2 supply chain, SNG can reduce life cycle GHG emissions by 52–88 % compared with fossil NG.

Harmonics Cancelation in 3-Phase 4-Wire System using Modified Reference Frame

In this paper, A modified synchronous reference frame (SRF) or (d-q theory) with Self-Tuning Filter (STF) for the three-phase four wires system is presented to control of 4-legs shunt active power filter (SAPF) under balanced supply voltages. The main goal of the paper is to apply this control method using unit vector generation with STF instead of PLL circuit. In utility power system, it has been observed that harmonics build a major role in reducing the power quality. These harmonics caused by nonlinear loads. Many consumers appliances demand quality of power continuously for their operation. The performance of the end user equipment is heavily dependent on the quality of power supplied to it. This power is affected by various external and internal factors. They are like voltage and frequency variations, faults, outages etc. The solution to overcome these problems is to filter out these harmonics. For this purpose, there are many filters' topologies and control methods are proposed and studied in the literature to eliminate harmonics currents. In our study, 4-legs shunt active power filter is proposed and controlled by d-q method with modified unit vector generation. The SAPF is proposed to solve problems of line harmonic currents and neutral line currents of three phase 4-wire power distribution system. The use of STF in the modified control method can optimize the performance of this topology regarding to classical one (high pass and low pass filters). The proposed control strategy is simulated in MATLAB Simulink and the results are presented.

About some Effects of Voltage Distortion on End User’s Equipment

The main objective of the paper is to assess the influence of supply voltage distortions, especially voltage harmonics on end user’s equipment. Generally, harmonics are probably the most important issue of power quality, greatly influencing power flow. In the introductory section, the main definitions of power quality and the main methods of harmonic analysis, namely Fourier series, Fourier transform and Wavelet transform. The second section, devoted to experimental results, presents a series of measurements carried out for a setup composed of a programmable source supplying an induction motor, as end user equipment. The motor is supplied successively with three types of voltages, a clean sinusoidal voltage and two distorted ones, namely a 50% clipped voltage and a square voltage, synthesized according their harmonic content, and downloaded to a programmable supply source. The latest two waveforms are not arbitrarily chosen, being often encountered in practice, especially when the equipment is supplied through UPS’s or power converters. The load torque of the motor was adjusted by a controlled electromagnetic powder brake. Concluding, it can be said that distorted supply voltages, having an important harmonics content, highly affect the equipment operation, especially in terms of efficiency.

Urban building energy performance prediction and retrofit analysis using data-driven machine learning approach

Stakeholders such as urban planners and energy policymakers use building energy performance modeling and analysis to develop strategic sustainable energy plans with the aim of reducing energy consumption and emissions from the built environment. However, inconsistent energy data and the lack of scalable building models create a gap between building energy modeling and traditional planning practices. An alternative approach is to conduct a large-scale energy usage survey, which is time-consuming. Similarly, existing studies rely on traditional machine learning or statistical approaches for calculating large-scale energy performance. This paper proposes a solution that employs a data-driven machine learning approach to predict the energy performance of urban residential buildings, using both ensemble-based machine learning and end-use demand segregation methods. The proposed methodology consists of five steps: data collection, archetype development, physics-based parametric modeling, machine learning modeling, and urban building energy performance analysis. The devised methodology is tested on the Irish residential building stock and generates a synthetic building dataset of one million buildings through the parametric modeling of 19 identified vital variables for four residential building archetypes. As a part of the machine learning modeling process, the study implemented an end-use demand segregation method, including heating, lighting, equipment, photovoltaic, and hot water, to predict the energy performance of buildings at an urban scale. Furthermore, the model's performance is enhanced by employing an ensemble-based machine learning approach, achieving 91% accuracy compared to the traditional approach's 76%. Accurate prediction of building energy performance enables stakeholders, including energy policymakers and urban planners, to make informed decisions when planning large-scale retrofit measures.

Energy Efficiency and Feasibility Analysis of Solar Power Generation Using Hybrid System of an Educational Institution in Malaysia

Energy is one of the basic inputs and driving forces for economic and social development modernization. Sustainable energy supply is one of the major challenges in the modern world. Sustainable energy supply can be obtained through alternative energy sources, and efficient energy use is a high priority to optimize the environmental impact. This paper investigates the potential for energy savings involving five end-load equipment in academic institutions. The saving potential is obtained through energy efficiency analysis and feasibility analysis of the solar power generation systems. An energy audit is conducted on end-use consuming equipment (i.e., fans, lights, air conditioning, ICT equipment, etc.) in these five end-load sectors to find out the major energy-consuming equipment, energy consumption, and potential energy savings. The analysis of energy consumption per equipment helps determine the appropriate type of equipment to be upgraded and replaced for energy saving. The feasibility of rooftop solar power generation has been also analysed to integrate into the energy end load. The research findings confirm that laboratory equipment, lighting, and air conditioning are vital to energy consumption in academic buildings. The energy savings, bill savings, and carbon emissions reductions have been estimated based on integrating rooftop solar power generation. The feasibility analysis of onsite energy generation using a hybrid solar system found that the total energy-saving potential and bill savings within five years are 311,131 kWh and RM 113,563, respectively. This value refers to comprehensive energy-saving and bill for all academic buildings involved in this research.

Enhance Power Quality through Series Compensation in Power System

Abstract: The power quality requirement is one of the most important issues especially with the introduction of sophisticated device whose performance is very sensitive to the quality of power supply. Power quality problem is an occurrence manifested as a non standard voltage, current or frequency that results in failure of end user equipment. One of the major problem deal with sag and harmonics .To solve this problem series compensation is used to improve power quality of an isolated power system is considered. The role of compensator not only mitigates voltage sag but also reduces harmonics due to presence of nonlinear load in the network. In this paper series compensation is proposed and principle of harmonics mitigation is explain and the compensation strategies and control philosophy used in series compensation is explain. The isolated power system model is simulated in matlab Simu link software and simulation result shows the reduce harmonics and thus improve power quality of the isolated power system

Thermal-Electric Modeling: A New Approach for Evaluating the Impact of Conservation Voltage Reduction on Cooling Equipment

It has been suggested in the literature that by reducing the incoming voltage at a distribution feeder head or at the supply side of buildings, significant electricity savings can be achieved. This technique is called Conservation Voltage Reduction (CVR). Data-based analysis with and without CVR is primarily used to support such assertions, which does not explain the physics behind reduction in energy consumption with CVR. This paper presents a new approach for evaluating the impact of CVR on cooling equipment. In this approach, a thermal-electric model of the cooling process is developed in MATLAB’s SIMSCAPE toolbox that can be used to explain the physics behind energy reduction with CVR. This model includes an accurate model of a compressor coupled to an induction motor whose supply voltage can be varied to simulate CVR. Simulations performed using this model show that the Coefficient of Performance (COP) of cooling equipment improves with a reduction in supply voltage. However, the energy lost in the motor windings may nullify the impact of the improvement in the COP and render the CVR programs ineffective if the range of speed change is small over the allowable voltage change. The simulation results show an increase in energy consumption of 4% at 90% rated voltage compared to the energy consumed at the rated voltage. However, if variable frequency drives-based cooling equipment is appropriately controlled, it is possible to reduce their net energy consumption using CVR. Simulations performed keeping the ratio of the supply voltage and the frequency constant showed a reduction in energy consumption of 2.5% at 90% rated voltage compared to the energy consumed at the rated voltage. Thermal-electric modeling of building cooling equipment is, therefore, vital to accurately evaluating the benefits of CVR as smart, power electronics-based end-use equipment is globally adopted.

Impact of Trace Ammonia Impurities on the Utilization of Renewable Natural Gas

Renewable natural gas (RNG), produced from biogas upgrading, is an important alternative to fossil fuels for power generation. Unfortunately, RNG contains several trace contaminants that may be harmful to the existing infrastructure for its transport and use. One such contaminant is ammonia (NH3), of particular concern for RNG produced from farming operations. The presence of NH3 in RNG, particularly if it is injected into the natural gas (NG) pipeline network, can have serious consequences. These can range from damage to components of the NG infrastructure to corrosion of analytical and end-user equipment and added pollutant formation during combustion. This research project addresses all these impacts. Its objectives were (i) to investigate the potential corrosion effects of NH3 on typical pipeline materials during their exposure to RNG derived from biogas and processed to meet pipeline NG quality specifications, (ii) to study the environmental impacts and other concerns during combustion of NH3-containing RNG for power and energy production, and (iii) to analyze the potential impact of NH3-containing RNG on the performance of analytical equipment commonly used during NG distribution and end-use. This paper describes the work performed and the key findings in each of these areas.

Why and how edge cloud computing can address performance and economic sustainability issues for telco domestic networks

This paper analyzes some of the telecommunication companies' (telcos) domestic networks' main objectives and issues related to the application services Quality of Experience (QoE), i.e., "technical QoE," and the economic sustainability of the Very High Capacity (VHC) networks. Telco domestic networks and Over The Top (OTT) networks are the Internet segments connecting the end-user equipment to the servers/clouds that provide application services. With the advent of VHC networks, telcos afford difficulties in managing effectively and efficiently the application services and their business sustainability. This paper affords some issues related to traditional telcos' domestic network architectures and the approach to managing the application services' performance improvement. Some telcos started changing their architectures, although a massive transformation had not yet started in the industry. Traditionally, telcos focus on network services, i.e., the transport of IP packets. Performance improvement is obtained by QoS-based traffic management techniques, such as bandwidth reservation and packet prioritization. To manage application performance improvement, the use of layer 4 techniques and Edge Cloud Computing (ECC) is effective, as demonstrated by the multiyear experience of OTTs which already use these technologies. In telco domestic networks, layer 2 tunnels increase the complexity of deploying ECC. However, some vendors provide non-standard solutions to make the IP layer 3 user plane visible and to deploy ECC. ECC is the key factor for the transformation. It is a mini/micro-data center that distributes some applications and content closer to the end users. The distribution can provide a paradigm shift in a telco's business. In the paper, we first highlight the need for regulators to appreciate the need to encourage this industry transformation fully.

Power quality improvement using Distribution Static Synchronous Compensator

A Power quality problem is an occurrence manifested as a nonstandard voltage, current or frequency that results in a failure or a mis-operation of end user equipments. Hence, the optimal advancement of power quality is concentrated by this proposed model with Deep Belief Nets (DBN) and Convolutional Neural Networks (CNN) utilizing Distribution Static Synchronous Compensator (DSTATCOM). Here, the major objective is that for the mitigation of current distortion, voltage enhancement, along with minimization of harmonics, the reactive power control in DSTATCOM is focused more by utilizing DBN with CNN. Utilizing the proposed Simulink design model of DSTATCOM, which is employed for enhancing the power quality in a distributed system, the aforementioned objective could be obtained. The simulations were performed using MATLAB SIMULINK. From the simulation result it is observed that the proposed framework have successfully accomplished harmonic substance and voltage profile enhancement.

Estimating cooling loads of Arizona State University buildings using microclimate data and machine learning

In hot arid urban climates, building cooling equipment consumes the greatest share of energy out of all building end-use equipment. For existing buildings, dynamic system-based building energy simulation tools have provided valuable information on the impact of microclimates on cooling energy use. However, such models require in-depth information on building model parameters and often suffer from modeler bias even when sufficient calibration indices are satisfied. This study presents a data-driven approach for predicting the cooling loads of three university buildings in Arizona using simulated microclimate data. A microclimate model ENVI-met generated the input micro-scale weather data for each building. The ENVI-met simulation was validated using in-situ observations during the summer of 2018. Multiple machine learning algorithms were implemented. A final model was selected and used as baseline to predict cooling loads for each building in the dataset. The model predicts chill water tons per square meter using microclimate variables that include mean air temperature, mean absolute humidity, shading levels, and direct shortwave radiation. The black-box model was explained using an advanced machine learning model interpretation library in Python: SHAP. The baseline model predicted cooling loads with a prediction accuracy score of 0.98 using the tree-based algorithm Random Forest. Sensitivity analyses and scenario results showed that cooler microclimates reduced cooling loads for the modeled buildings. The developed framework will be used in future study extensions to explore the impacts of simulated microclimate scenarios generated by ENVI-met.

A Review of Control Techniques and Energy Storage for Inverter-Based Dynamic Voltage Restorer in Grid-Integrated Renewable Sources

Power quality (PQ) is a key issue, particularly for technologically advanced process equipment, whose performance mainly depends on the quality of supply. Problems with PQ such as voltage swells/sags, interruptions, and harmonics are defined by any voltage, current, or frequency abnormalities causing damage or failure of the end-user equipment. Outages and interruptions lead to malfunctioning of end-user equipment or sensitive loads, such as diagnostic equipment in healthcare facilities, clinics, educational institutions, and detention centers, while further increasing significant economic losses. Custom power devices (CPDs) are recommended for enhancing power quality, and the best and most economical solution is considered to be the dynamic voltage restorer (DVR). Several methods are suggested to improve the PQ by using the dynamic voltage restorer; among them, most encouraging ways are to use a multilevel inverter (MLI) in the dynamic voltage restorer. This article combines the latest work of the literature, as well as a detailed discussion on PQ issues of the grid-integrated renewable energy sources (RESs), DVR principle with its operating procedures, system components, energy storage-based DVR topologies, DVR control unit, and DVR power converter-based topologies. In addition, synthesis of energy storage, control strategies, and multilevel inverters for DVR. This review benefits those interested in investigating DVR as a relevant and comprehensive reference.

An unsupervised learning schema for seeking patterns in rms voltage variations at the sub-10-minute time scale

This paper proposes an unsupervised learning schema for seeking the patterns in rms voltage variations at the time scale between 1 s and 10 min, a rarely considered time scale in studies but could be relevant for incorrect operation of end-user equipment. The proposed framework employs a Kernel Principal Component Analysis (KPCA) followed by a k-means clustering. The schema is applied on 10-min time series with a 1-s time resolution obtained from 44 different periods of a location south of Sweden. Then, ten patterns are obtained by reconstructing the 10-min time series from each cluster center. The results of the proposed schema show a good separation of cluster centers. Moreover, some statistical power-quality indices are applied to the whole dataset, showing voltage variation between (0.5–3) V over a 10-min window. Obtaining the most suitable indices and applying them to the ten obtained cluster centers and their belonging time series shows that the existing statistical indices may not be enough to show a complete picture of the sub-10 min actual variations. This outcome shows the necessity of extracting 10-min patterns through our proposed schema besides the existing statistics to quantify the voltage variations, levels, and patterns together. Findings of this paper are: Not forgetting the sub-10-min time scale; The necessity of employing both statistics and the proposed schema; Extraction of ten typical patterns; The need for the statistics and patterns that are justified as changes in equipment connected to the grid; and compressing a huge amount of data from power-quality monitoring. The proposed schema is applied to a much less understood phenomena/disturbance type so that this work will result in general knowledge beyond the specific case study.

Seeking patterns in rms voltage variations at the sub-10-minute scale from multiple locations via unsupervised learning and patterns' post-processing

This paper addresses the issue of seeking sub-10-min patterns in fast rms voltage variations from time-limited measurement data at multiple locations worldwide. This is a rarely considered time scale in studies that could be important for the incorrect operation of end-user equipment. Moreover, measurements from multiple locations could be significant from the view of seeking pattern methods. To learn more about this time scale, we propose an unsupervised learning method that employs a Kernel Principal Component Analysis (KPCA) with a Cosine kernel to extract principal features from 10-min time series of voltage variations with a 1-s resolution followed by a k-means clustering to group the features. The scheme is applied to measurements from 57 low-voltage locations in 19 countries from 2009 to 2018. Fifteen initial clusters/patterns are then extracted and converted to ten new (general) patterns using a clusters' merging strategy with highly similar patterns employed in a new post-processing approach useful for multiple locations. Utilizing data from multiple locations in multiple countries ensures a level of generality of the patterns. It also allows comparing the locations. Next to the ten general patterns, some typical patterns are extracted separately for every location. A statistical indices analysis confirms that a complete picture of sub-10-min oscillations needs both statistical indices (quantifying level and variations) and the proposed framework (quantifying patterns). The extracted patterns could be used as a reference for testing/putting requirements on the grid-connected equipment and quantifying the grid's hosting capacity for different types of new distributed generations connected to the grid. The framework is scalable and computationally cheap, making it appropriate for seeking typical patterns in the big data domain. Applying the framework to the much less understood phenomenon will result in providing general knowledge in the field of power quality.

When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm

Motivated by the increasingly powerful computing capabilities of end-user equipment, and by the growing privacy concerns over sharing sensitive raw data, a distributed machine learning paradigm known as federated learning (FL) has emerged. By training models locally at each client and aggregating learning models at a central server, FL has the capability to avoid sharing data directly, thereby reducing privacy leakage. However, the conventional FL framework relies heavily on a single central server, and it may fail if such a server behaves maliciously. To address this single point of failure, in this work, a blockchain-assisted decentralized FL framework is investigated, which can prevent malicious clients from poisoning the learning process, and thus provides a self-motivated and reliable learning environment for clients. In this framework, the model aggregation process is fully decentralized and the tasks of training for FL and mining for blockchain are integrated into each participant. Privacy and resource-allocation issues are further investigated in the proposed framework, and a critical and unique issue inherent in the proposed framework is disclosed. In particular, a lazy client can simply duplicate models shared by other clients to reap benefits without contributing its resources to FL. To address these issues, analytical and experimental results are provided to shed light on possible solutions, i.e., adding noise to achieve local differential privacy and using pseudo-noise (PN) sequences as watermarks to detect lazy clients.

Optimal gas-electric energy system decarbonization planning

As energy utilities implement climate change mitigation policies, system planners require strategies for achieving affordable emissions reductions. Coordinated planning of electric power and natural gas systems will allow synergistic investments to address cross-sector operational constraints, competing uses for net-zero emissions fuels, and shifts in energy demands across energy carriers. In this study, we develop a novel optimization program that finds the cost-minimizing mix of infrastructure expansion or reduction across gas and electric systems to satisfy sector-specific emissions constraints. Alongside energy supply resources, our framework allows for central-planning of end-use equipment stocks to allow switching between gas and electric appliances upon failure or premature replacement. The proposed model is used to simulate case study scenarios for a benchmark 24-pipe gas network coupled to a 24-node power system test network. We find that electrification of greater than 80% of core gas demands is a component of the least-cost solution for modeled energy systems. Despite this substitution, the gas system is maintained to service difficult-to-electrify customers and to deliver net-zero emissions gas to electricity generators in times of peak electricity demand. Restricting electrification of gas appliances increases reliance on power-to-gas technologies and increases annual costs by 15% in 2040. Neglecting constraints on pipeline blending of hydrogen can produce a misleading result that relies on hydrogen blend fractions of greater than 50%. In all cases, we find the average costs of delivered gas increase nearly 5-fold across the decarbonization transition, highlighting the importance of future work investigating cost-allocation strategies for ensuring an equitable energy transition.

Non-intrusive Load Monitoring Based on Self-attention

Non-intrusive load monitoring is an important means to monitor the power consumption of end-user equipment in real time and timely. At present, there are still some problems in non-invasive load identification, such as the analysis of feature quantity is not detailed enough, the subjectivity of feature selection is strong, and the effect of identification algorithm is not good because it does not fully consider the information expression difference of feature quantity. In order to mine local features and the advantage of the latter in capturing the global relationship between sequence elements, a deep self-attention network method based on self-attention calculation is proposed. The research shows that this method has higher accuracy in solving the problem of load decomposition, and the training speed is faster than the traditional deep learning method. It performs well in the power decomposition of continuous variable state equipment and multi state equipment.

Classification of Power Quality Disturbances in Emerging Power System Using Discrete Wavelet Transform and K-Nearest Neighbor

This paper presents a wavelet and machine learning-based approach for the classification of power quality disturbances (PQDs) in emerging power systems. Renewable energy resources-based distributed generation (DG) is rapidly being used in the emerging power system to address the ever-increasing energy demand. PQDs are thought to be caused by the power electronic converters used in DG systems, DG operating conditions, and other common factors such as faults, switching activities, and non-linear loads. These PQDs must be detected and classified since they can create a variety of difficulties in end-user equipment. The proposed algorithm comprises the simulation of the emerging power system with a solar PV system, creating PQDs cases such as voltage sag, voltage swell, and voltage interruptions, capturing voltage signals which will be further processed using a discrete wavelet transform for feature extraction. The features extracted from DWT analysis are further used to develop the machine learning-based classifier for classification of PQDs. The proposed algorithm has been tested on a variety of PQDs. The simulation result shows that the proposed algorithm is efficient and it outperforms in the classification of PQDs.

Classification of Stockwell Transform Based Power Quality Disturbance with Support Vector Machine and Artificial Neural Networks

The detection and classification of power quality events that disturb the voltage and/or current waveforms in the electrical power distribution networks is very important to generate electrical energy and to deliver this energy to the end-user equipment at an acceptable voltage. Various property extraction methods are used to determine the type of disturbances in the electrical signal. In this study, seven power distortions including voltage sag, voltage swell, voltage harmonics, voltage sag with harmonics, voltage swell with harmonics, flicker, transient signals and pure sine as a reference signal is used. Synthetic data are produced in MATLAB using parametric equations based on TS EN 50160 standard. Four kinds of feature extraction as frequency-amplitude, time-amplitude, geometric mean and standard deviation is made with Stockwell Transform (ST), which is one of the methods used for the feature extraction of the determined GKB. Detection of voltage distortions is interpreted through these properties. 640 simulation data is entered into the classifier by using Support Vector Machines (SVM) and Artificial Neural Networks (ANN) and their classification performance is compared.

Feeding H2-admixtures to domestic condensing boilers: Numerical simulations of combustion and pollutant formation in multi-hole burners

Hydrogen can be produced through electrolysis from excess wind and solar power. Then, its injection into the natural gas network allows mitigating the challenges posed by the variability and intermittency of renewables, by exploiting the existing infrastructure for storage and distribution. However, the addition of hydrogen to natural gas affects gas properties; hence we need to ensure the safe and efficient operation of existing end-user equipment, such as domestic burners and boilers. This work intends to investigate the effect of H2 addition on the combustion process and pollutant emissions in domestic condensing boilers. To this purpose, 3-dimensional numerical simulations of multi-hole geometries mimicking perforated burners, typically encountered in such appliances, are performed by using detailed kinetics and taking into account different hole-to-hole distances. Indeed, since the burner holes are located very close to each other, the neighbour premixed flames can interact, thus differing from single flame behaviour. The impact of H2 on the operating conditions, i.e. equivalence ratio and thermal load, is preliminarily evaluated to ensure that the numerical model closely emulates the practical situation we expect from the fuel composition change. Anticipation of the reaction zone occurs with H2-admixtures, while the downstream temperature decreases because the boiler naturally shifts towards leaner conditions and smaller thermal loads when adding H2 to natural gas. This behaviour has a positive impact on pollutant emissions as the NO thermal formation route is suppressed in a larger measure than the increase of the NNH-intermediate contribution.