IEC 62059 Standards' Application in Reliability Prediction and Verification of Smart Meters

This article introduces the current situation of the smart then describes the relationship of meter reliability characteristics meter＇s reliability and the failure mechanisms at first, and combined with its Bathtub Curve. It also introduces both the feasible failure tree model for meter lifecycle prediction based on actual experiences and meter reliability prediction methodology by SN 29500 norms based on this model. This article also brings forward that it is necessary that the ＂Learning Factor＂ shall be adopted in meter reliability prediction for new materials, new process, and customized parts by referring to GJB/Z299C. Thereafter, this article also tries to apply IEC 62059 and JB/T 50070 to introduce the feasible method for the lifecycle prediction result verification by accelerated lifecycle test. Furthermore, the article also explores ways to increase the firmware reliability in smart meter.

Smart Meters and Under-Frequency Load Shedding

In some countries, there exists a risk of power deficit in the EPS （electrical power system）. This is a very serious problem and there are various solutions to deal with it. A power deficit in the EPS leads to frequency decrease in the power system. A dedicated automation to load shedding is used to maintain proper EPS operation. For some time, it has applied a mechanism called demand-side response, which in case of an emergency situation allows for a ＂more civilized＂ rationing of electricity to customers, with their consent. Such programs require that the utilities pay the customers for their agreement. The author proposes a new solution, intermediate between strict ALS （acting relieving automation） and demand-side response programs, where the companies have to send information about the price of energy or control signals to households.

Power ＂Smart Pricing＂ with and without Smart Meters

Over the past several years, the Taiwan Power Company has launched two smart pricing programs to assess the demand response of residential customers： the TOU （time-of-use） rate scheme and the DRI （demand reduction incentive） scheme. This paper discusses these two programs and evaluates their respective performances. We develop an efficient approach based on marginal cost pricing to redesign the TOU rate scheme. In our finding, the TOU price levels could be revised to encourage more customers to participate by enlarging the price gap. Moreover, the DRI scheme can be further improved in order to reach an efficient win-win solution among customers, the utility and society. This can be achieved via a careful design of incentive tariff discounts to take account of the time-of-use or location-specific features of the power supply/demand condition.

SmartMicro Grid Energy System Management Based on Optimum Running Cost for Rural Communities in Rwanda

The governmental electric utility and the private sector are joining hands to meet the target of electrifying all households by 2024.However,the aforementioned goal is challenged by households that are scattered in remote areas.So far,Solar Home Systems(SHS)have mostly been applied to increase electricity access in rural areas.SHSs have continuous constraints to meet electricity demands and cannot run income-generating activities.The current research presents the feasibility study of electrifying Remera village with the smart microgrid as a case study.The renewable energy resources available in Remera are the key sources of electricity in that village.The generation capacity is estimated based on the load profile.The microgrid configurations are simulated with HOMER,and the genetic algorithm is used to analyze the optimum cost.By analyzing the impact of operation and maintenance costs,the results show that the absence of subsidies increases the levelized cost of electricity(COE)five times greater than the electricity price from the public utility.The microgrid made up of PV,diesel generator,and batteries proved to be the most viable solution and ensured continuous power supply to customers.By considering the subsidies,COE reaches 0.186$/kWh,a competitive price with electricity from public utilities in Rwanda.

Intelligent Energy Utilization Analysis Using IUA-SMD Model Based Optimization Technique for Smart Metering Data

Smart metering has gained considerable attention as a research focus due to its reliability and energy-efficient nature compared to traditional electromechanical metering systems. Existing methods primarily focus on data management,rather than emphasizing efficiency. Accurate prediction of electricity consumption is crucial for enabling intelligent grid operations,including resource planning and demandsupply balancing. Smart metering solutions offer users the benefits of effectively interpreting their energy utilization and optimizing costs. Motivated by this,this paper presents an Intelligent Energy Utilization Analysis using Smart Metering Data(IUA-SMD)model to determine energy consumption patterns. The proposed IUA-SMD model comprises three major processes:data Pre-processing,feature extraction,and classification,with parameter optimization. We employ the extreme learning machine(ELM)based classification approach within the IUA-SMD model to derive optimal energy utilization labels. Additionally,we apply the shell game optimization(SGO)algorithm to enhance the classification efficiency of the ELM by optimizing its parameters. The effectiveness of the IUA-SMD model is evaluated using an extensive dataset of smart metering data,and the results are analyzed in terms of accuracy and mean square error(MSE). The proposed model demonstrates superior performance,achieving a maximum accuracy of65.917% and a minimum MSE of0.096. These results highlight the potential of the IUA-SMD model for enabling efficient energy utilization through intelligent analysis of smart metering data.

Technologies Behind the Smart Grid and Internet of Things: A System Survey

Electric smart grids enable a bidirectional flow of electricity and information among power system assets.For proper monitoring and con-trolling of power quality,reliability,scalability and flexibility,there is a need for an environmentally friendly system that is transparent,sustainable,cost-saving,energy-efficient,agile and secure.This paper provides an overview of the emerging technologies behind smart grids and the internet of things.The dependent variables are identified by analyzing the electricity consumption patterns for optimal utilization and planning preventive maintenance of their legacy assets like power distribution transformers with real-time parameters to ensure an uninterrupted and reliable power supply.In addition,the paper sorts out challenges in the traditional or legacy electricity grid,power generation,transmission,distribution,and revenue management challenges such as reduc-ing aggregate technical and commercial loss by reforming the existing manual or semi-automatic techniques to fully smart or automatic systems.This article represents a concise review of research works in creating components of the smart grid like smart metering infrastructure for postpaid as well as in prepaid mode,internal structure comparison of advanced metering methods in present scenarios,and communication systems.

Preserving scheme for user’s confidential information in smart grid based on digital watermark and asymmetric encryption

As an essential part of the industrial Internet of Things(IoT)in power systems,the development of advanced metering infrastructure(AMI)facilitates services such as energy monitoring,load forecasting,and demand response.However,there is a growing risk of privacy disclosure with the wide installation of smart meters,for they transmit readings and sensitive data simultaneously.To guarantee the confidentiality of the sensitive information and authenticity of smart meter readings,we proposed a privacy-preserving scheme based on digital watermarking and elliptic-curve cryptography(ECC)asymmetric encryption.The sensitive data are encrypted using the public key and are hidden in the collected readings using digital watermark.Only the authorized user can extract watermark and can decrypt the confidential data using its private key.The proposed method realizes secure end-to-end confidentiality of the sensitive information.It has faster computing speed and can verify the data source and ensure the authenticity of readings.The example results show that the proposed method has little influence on the original data and unauthorized access cannot be completed within a reasonable time.On embedded hardware,the processing speed of the proposed method is better than the existing methods.

An Overview of the Smart Grid in Great Britain

This paper presents an overview of the current status of the development of the smart grid in Great Britain(GB).The definition,policy and technical drivers,incentive mechanisms,technological focus,and the industry's progress in developing the smart grid are described.In particular,the Low Carbon Networks Fund and Electricity Network Innovation Competition projects,together with the rollout of smart metering,are detailed.A more observable,controllable,automated,and integrated electricity network will be supported by these investments in conjunction with smart meter installation.It is found that the focus has mainly been on distribution networks as well as on real-time flows of information and interaction between suppliers and consumers facilitated by improved information and communications technology,active power flow management,demand management,and energy storage.The learning from the GB smart grid initiatives will provide valuable guidelines for future smart grid development in GB and other countries.

Energy Theft Identification Using Adaboost Ensembler in the Smart Grids

One of the major concerns for the utilities in the Smart Grid(SG)is electricity theft.With the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which makes it possible for advanced data analysis that was not previously possible.For this purpose,we have taken historical data of energy thieves and normal users.To avoid imbalance observation,biased estimates,we applied the interpolation method.Furthermore,the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing.By proposing an improved version of Zeiler and Fergus Net(ZFNet)as a feature extraction approach,we had able to reduce the model’s time complexity.To minimize the overfitting issues,increase the training accuracy and reduce the training loss,we have proposed an enhanced method by merging Adaptive Boosting(AdaBoost)classifier with Coronavirus Herd Immunity Optimizer(CHIO)and Forensic based Investigation Optimizer(FBIO).In terms of low computational complexity,minimized over-fitting problems on a large quantity of data,reduced training time and training loss and increased training accuracy,our model outperforms the benchmark scheme.Our proposed algorithms Ada-CHIO andAda-FBIO,have the low MeanAverage Percentage Error(MAPE)value of error,i.e.,6.8%and 9.5%,respectively.Furthermore,due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93%and 90%.Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms,which also depicts the superiority of our proposed techniques.

Embedded TLS 1.2 Implementation for Smart Metering & Smart Grid Applications

Digital networked communications are the key to all Internet-of-things applications, but especially to smart metering systems and the smart grid. In order to ensure a safe operation of systems and the privacy of users, the transport layer security （TLS） protocol, a mature and well standardized solution for secure communications, may be used. We implemented the TLS protocol in its latest version in a way suitable for embedded and resource-constrained systems. This paper outlines the challenges and opportunities of deploying TLS in smart metering and smart grid applications and presents performance results of our TLS implementation. Our analysis shows that given an appropriate implementation and configuration, deploying TLS in constrained smart metering systems is possible with acceptable overhead.

A Fault Diagnosis Method for Smart Meters via Two-layer Stacking Ensemble Optimization and Data Augmentation

The accurate identification of smart meter(SM)fault types is crucial for enhancing the efficiency of operationand maintenance(O&M)and the reliability of power collectionsystems.However,the intelligent classification of SM fault typesfaces significant challenges owing to the complexity of featuresand the imbalance between fault categories.To address these issues,this study presents a fault diagnosis method for SM incorporatingthree distinct modules.The first module employs acombination of standardization,data imputation,and featureextraction to enhance the data quality,thereby facilitating improvedtraining and learning by the classifiers.To enhance theclassification performance,the data imputation method considersfeature correlation measurement and sequential imputation,and the feature extractor utilizes the discriminative enhancedsparse autoencoder.To tackle the interclass imbalance of datawith discrete and continuous features,the second module introducesan assisted classifier generative adversarial network,which includes a discrete feature generation module.Finally,anovel Stacking ensemble classifier for SM fault diagnosis is developed.In contrast to previous studies,we construct a two-layerheuristic optimization framework to address the synchronousdynamic optimization problem of the combinations and hyperparametersof the Stacking ensemble classifier,enabling betterhandling of complex classification tasks using SM data.The proposedfault diagnosis method for SM via two-layer stacking ensembleoptimization and data augmentation is trained and validatedusing SM fault data collected from 2010 to 2018 in Zhejiang Province,China.Experimental results demonstrate the effectivenessof the proposed method in improving the accuracyof SM fault diagnosis,particularly for minority classes.

Smart Grid Cyber Security： An Overview of Threats and Countermeasures

The smart grid is the next generation of power and distribution systems. The integration of advanced network, communications, and computing techniques allows for the enhancement of efficiency and reliability. The smart grid interconnects the flow of information via the power line, intelligent metering, renewable and distributed energy systems, and a monitoring and controlling infrastructure. For all the advantages that these components come with, they remain at risk to a spectrum of physical and digital attacks. This paper will focus on digital vulnerabilities within the smart grid and how they may be exploited to form full fledged attacks on the system. A number of countermeasures and solutions from the literature will also be reported, to give an overview of the options for dealing with such problems. This paper serves as a triggering point for future research into smart grid cyber security.

Exploiting the Cellular Infrastructure for Data Transmission in Smart Metering Systems

To implement the access and backhaul networks for Smart Metering (SM) systems various technologies are combined with the existing communications infrastructure. This paper deals with data transmission in SM systems, focusing on how the existing cellular networks infrastructure is employed to implement SM access communication networks. The analysis aims at analyzing the role of the cellular communications infrastructure taking into account the spatial distribution and installation points of the smart meters, the urban and topological characteristics of the SM deployment areas and the common practice so far followed by the utilities. It is demonstrated that cellular communications, either exclusively or combined with power line communications, enable immediate and scalable deployment of SM access communication networks at low installation cost, thus constituting the basic option for the implementation of smart metering.

New Hybrid IoT LoRaWAN/IRC Sensors:SMARTWater Metering System

The massive development of internet of things(IoT)technologies is gaining momentum across all areas of their possible deployment—spanning from Industry 4.0 to eHealth,smart city,agriculture or waste management.This ongoing development is further pushed forward by the gradual deployment of 5G networks.With 5G capable smart devices,it will be possible to transfer more data with shorter latency thereby resulting in exciting new use cases such as Massive IoT.Massive-IoT(low-power wide area network-LPWAN)enables improved network coverage,long device operational lifetime and a high density of connections.Despite all the advantages of massive-IoT technology,there are certain cases where the original concept cannot be used.Among them are dangerous explosive environments or issues caused by subsurface deployment(operation during winter months or dense greenery).This article presents the concept of a hybrid solution of IoT LoRaWAN(long range wide area network)/IRC-VLC(infrared communication,visible light communication)technology,which combines advantages of both technologies according to the deployment scenario.

Smart Energy Resource Grid in China:What's the Path?

Initiated and approved in 2009,the project of Development Pattern and Implementation Design of Smart Energy Resource Grid in China is now accomplished with the research achievement released to the public and highly valued by authorized organizations such as the Global Smart Grid Federation.In this paper,based on the description of the research achievement,the advantages of the smart energy resource grid in China and the consequential changes are analyzed and discussed,involving the industries of electric power,oil and gas,energy storage,water supply,architecture and transportations etc.

Smart Meter Deployment Threat and Vulnerability Analysis and Response

Advanced intelligent or ＂smart＂ meters are being deployed in Asia. A result of deployment of smart meters, with associated equipment, is the electric power industry faced with new and changing threats, vulnerabilities and re-evaluate traditional approaches to cyber security. Protection against emerging cyber-security threats targeting smart meter infrastructures will increase risk to both the utility and customer if not addressed within initial rollouts. This paper will discuss the issues in SMI （smart meter infrastructures） deployments that pertain to cyber security. It will cover topics such as the threats to operations, infrastructure, network and people and organization and their associated risks. SMI deployments include not only the smart meter, but also the interfaces for home energy management systems as well as communication interfaces back to the utility. Utilities must recognize and anticipate the new threat landscape that can attack and compromise the meter and the associated field network collectors. They must also include threats to the WAN （wide-area-network） backhaul networks, smart meter headends, MDMS （meter data management systems） and their interfaces to CIS （customer information systems） and billing and OMS （outage management systems）. Lessons learned from SMI implementations from North America, Europe and recently, Japan, will be discussed. How white-box and black-box testing techniques are applied to determine the threat impact to the SMI. Finally, organizational change risk will be discussed and how utilities have responded to re-organizing and developing a security governance structure for the SMI and other smart grid applications.

Data Compression for Next Generation Phasor Data Concentrators (PDCs) in a Smart Grid

The storage space and cost for Smart Grid datasets has been growing exponentially due to its high data-rate of various sensor readings from Automated Metering Infrastructure (AMI), and Phasor Measurement Units (PMUs). The paper focuses on Phasor Data Concentrators (PDCs) that aggregate data from PMUs. PMUs measure real-time voltage, current and frequency parameters across the electrical grid. A typical PDC can process data from anywhere ten to forty PMUs. The paper exploits the need for appropriate security and data compression challenges simultaneously. As a result, an optimal compression method ER1c is investigated for efficient storage of IREG and C37.118 timestamped PDC data sets. We expect that our approach can greatly reduce the storage cost requirements of commercial available PDCs (SEL 3373, GE Multilin P30) by 80%. For example, 2 years of PDC data storage space can be easily replaced with only 10 days of storage space. In addition, our approach in combination with AES 256 encryption can protect PDC data to larger degree as per National Institute of Standards and Technology (NIST) standards.

Some Hotspots Concerned with Smart Grid

The smart grid has been such a hot topic recently.In this paper the hot topics in this field,such as the definition and features of smart grid,key technical problems to be addressed such as new system components,new types of transducers and measurement technologies,advanced interfaces,event-driven fast-simulated decision-making and coordination,and adaptive control,etc.,and diff iculties are analyzed and discussed.

Deep learning analysis of smart meter data from a small sample of room air conditioners facilitates routine assessment of their operational efficiency

Room air conditioners (RACs) are crucial household appliances that consume substantial amounts of electricity. Their efficiency tends to deteriorate over time, resulting in unnecessary energy wastage. Smart meters have become popular to monitor electricity use of home appliances, offering underexplored opportunities to evaluate RAC operational efficiency. Traditional supervised data-driven analysis methods necessitate a large sample size of RACs and their efficiency, which can be challenging to acquire. Additionally, the prevalence of zero values when RACs are off can skew training. To overcome these challenges, we assembled a dataset comprising a limited number of window-type RACs with measured operational efficiencies from 2021. We devised an intuitive zero filter and resampling protocol to process smart meter data and increase training samples. A deep learning-based encoder–decoder model was developed to evaluate RAC efficiency. Our findings suggest that our protocol and model accurately classify and regress RAC operational efficiency. We verified the usefulness of our approach by evaluating the RACs replaced in 2022 using 2022 smart meter data. Our case study demonstrates that repairing or replacing an inefficient RAC can save electricity by up to 17 %. Overall, our study offers a potential energy conservation solution by leveraging smart meters for regularly assessing RAC operational efficiency and facilitating smart preventive maintenance.

Consistency analysis of accelerated degradation mechanism based on gray theory

A fundamental premise of an accelerated testing is that the failure mechanism under elevated and normal stress levels should remain the same. Thus, verification of the consistency of failure mechanisms is essential during an accelerated testing. A new consistency analysis method based on the gray theory is pro- posed for complex products. First of all, existing consistency ana- lysis methods are reviewed with a focus on the comparison of the differences among them. Then, the proposed consistency ana- lysis method is introduced. Two effective gray prediction models, gray dynamic model and new information and equal dimensional （NIED） model, are adapted in the proposed method. The process to determine the dimension of NIED model is also discussed, and a decision rule is expanded. Based on that, the procedure of ap- plying the new consistent analysis method is developed. Finally, a case study of the consistency analysis of a reliability enhancement testing is conducted to demonstrate and validate the proposed method.