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The growing adoption of smart meters enables the measurement of households' energy consumption, influenced not solely by building characteristics such as thermal insulation but also by residents' behavioural patterns, such as heating and ventilation practices. To motivate residents to adopt more sustainable behaviours, user interfaces on smartphones and laptops are increasingly using consumption data from households' smart meters to enable effective goal‐setting. In contrast to previous research largely focusing on goal‐setting in isolation, this study examines the role of specific social comparison‐related design features that future research and practitioners can consider along with goal‐setting to stimulate sustainable behaviours. Specifically, we look into the influence of residents' perception of their relative performance (i.e., whether their behaviour was better or worse than a reference group) on their ambition to act (i.e., targeted improvement goal) and their actual energy consumption behaviour. Moreover, we investigate the influence of a goal's evaluative standard (i.e., whether the goal refers to one's own or other's performance) on the relationship between relative performance, ambition to act, and energy consumption behaviour. Drawing on social comparison theory, we conducted a framed field experiment with 152 households. We find that a goal's evaluative standard influences residents' awareness of their relative performance, affecting their ambition to act and, ultimately, their energy consumption behaviour. More specifically, we find that whereas other‐ (vs. self‐) referencing goals encourage residents from worse‐than‐average performing households more strongly to improve their energy consumption behaviour, they discourage better‐than‐average ones. Overall, our study provides novel insights into the interplay between relative performance and evaluative standards as a means of fostering social comparison in smart meter‐facilitated goal‐setting, highlighting their crucial role in effectively supporting sustainable behaviours. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The charging load of electric vehicles (EV) is one of the most rapidly increasing loads in current power distribution systems. It may cause distribution transformer/feeder overload without proper coordination or control, especially in residential area where household load and EV charging load are sharing transformer capacity. Existing smart meter-based orderly charging control (OCC) approaches commonly require costly but unreliable communication schemes to control EV charging behavior. In this work, a smart meter-based distributed controller is designed to establish a meter-to-EV communication interface with low cost and enhanced reliability, based on the state-of-the-art charging standard. An event-driven OCC algorithm is developed, and then, deployed in the data hub (concentrator) of the AMI with an easy-to-implement optimization formulation. The effectiveness of the proposed approach is validated using a numerical case study and a practical field test in Hangzhou, China. Both results indicate promising advantages of the proposed OCC approach in reducing the peak load of emerging EV charging demand by more than 30 % . [ABSTRACT FROM AUTHOR]

Smart meters monitor hourly water consumption patterns while reducing the cost and inconvenience of traditional meters. This study comprehensively analyzes 1871 households that previously used traditional meters from the distribution point to the distribution area. All the households were equipped with smart meters and the data collected were used for analysis. On the basis of the total estimated water consumption, 227 households were classified as single households. These households were further classified into single-worker and -nonworker households. This study analyzed smart meter data to evaluate the timings and amounts of water consumption peaks. The results indicate that worker households peaked at 8:00, with 29 L/h of consumption on weekdays, and peaked again on evenings at 20:00–21:00, averaging 32 L/h. For nonworker households, the peak occurred at 9:00, with 20 L/h, with no major changes in the afternoon, and a second peak occurred at 19:00–20:00 in evening, with an average of 19 L/h. Moreover, worker households peaked at 8:00 and 20:00 on weekdays, and at 9:00 and 19:00 on weekends. It was revealed that worker households consume 10% more on weekends than on weekdays, and 262% more from 13:00 to 16:00. These findings may assist in water supply planning by supporting distribution schedules on the basis of peak household consumption, leading to more helpful resource management. [ABSTRACT FROM AUTHOR]

Considering the widespread presence of switching devices on the power grid (including renewable energy system inverters), network distortion is more prominent. To maximize network efficiency, our goal is to minimize these distortions. Measuring the voltage and current total harmonic distortion (THD) using power meters and other specific equipment, and assessing power factor and peak currents, represents a crucial step in creating an efficient and stable smart grid. In this paper, we propose a power meter capable for measuring both standard electrical parameters and power quality parameters such as the voltage and current total harmonic distortion factors. The resulting device is compact and DIN-rail-mountable, occupying only three modules in an electrical cabinet. It integrates both wired and wireless communication interfaces and multiple communication protocols, such as Modbus RTU/TCP and MQTT. A microSD card can be used to store the device configuration parameters and to record the measured values in case of network fault events, the device's continuous operation being ensured by the integrated backup battery in this situations. The device was calibrated and tested against three industrial power meters: Siemens SENTRON PAC4200, Janitza UMG-96RM, and Phoenix Contact EEM-MA400, obtaining an overall average measurement error of only 1.22%. [ABSTRACT FROM AUTHOR]

A smart meter is an automation technology that sends real-time power consumption of electric appliances to the outsourced cloud through the aggregator node. An outsourced cloud is used by the Utility providers to release computation and storage overhead. The real-time smart meter data helps in the management of demand and supply in the smart grid. However, the real-time smart meter data exposes the privacy of smart meter customers and inefficient aggregated smart meter data results in unbalanced power management decisions in the smart grid. Therefore, a smart meter data storage (SMDS) model is proposed that aggregates the encrypted smart meter data at the fog node with the property of homomorphic encryption and stores it on the outsourced cloud. Two clouds are used to process the smart meter data and only the utility provider is able to retrieve the actual power consumption of the smart meter. Additionally, a secure query processing model is designed to retrieve the smart meter data on the outsourced cloud. Experimental results show the effectiveness of the proposed work and the feature comparison demonstrates the superiority of the proposed over the existing works. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive and comparative study of solar energy forecasting in Morocco, utilizing four machine learning algorithms: Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), recurrent neural networks (RNNs), and artificial neural networks (ANNs). The study is conducted using a smart metering device designed for a photovoltaic system at an industrial site in Benguerir, Morocco. The smart metering device collects energy usage data from a submeter and transmits it to the cloud via an ESP-32 card, enhancing monitoring, efficiency, and energy utilization. Our methodology includes an analysis of solar resources, considering factors such as location, temperature, and irradiance levels, with PVSYST simulation software version 7.2, employed to evaluate system performance under varying conditions. Additionally, a data logger is developed to monitor solar panel energy production, securely storing data in the cloud while accurately measuring key parameters and transmitting them using reliable communication protocols. An intuitive web interface is also created for data visualization and analysis. The research demonstrates a holistic approach to smart metering devices for photovoltaic systems, contributing to sustainable energy utilization, smart grid development, and environmental conservation in Morocco. The performance analysis indicates that ANNs are the most effective predictive model for solar energy forecasting in similar scenarios, demonstrating the lowest RMSE and MAE values, along with the highest R2 value. [ABSTRACT FROM AUTHOR]

The Smart Grid represents a modernized electrical infrastructure integrating information and communication technology for bidirectional data exchange between power providers and consumers. This advancement enables seamless digital connectivity among intelligent devices such as Smart Meters, Demand Response Control Units, and Service Providers, facilitating remote data management for optimized energy distribution. However, the reliance on unsecured wireless communication channels poses significant security vulnerabilities, including replay, impersonation, man-in-the-middle, and physical capture attacks. To address these challenges, this study introduces a pioneering approach called Deep-Learning and Blockchain-enabled Secure Data Sharing. Specifically, Deep-Learning techniques are leveraged to develop an effective Intrusion Detection System. The proposed RENS (intRusion detEction aNd clasSification) combines Variational AutoEncoder with Attention-based Bidirectional Long Short-Term Memory for feature extraction and attack detection. Moreover, normal instances identified by RENS are utilized in a blockchain-based access control mechanism, ensuring secure and immutable data exchange among Smart Grid entities. In this framework, participating Service Providers form a peer-to-peer network responsible for generating blocks associated with individual SMs. These blocks undergo validation and are appended to a private blockchain ledger using a smart contract-based Proof-of-Authentication consensus mechanism. Experimental results and security analysis demonstrate the superiority of the DBSDS framework over conventional BiLSTM techniques, confirming its effectiveness in safeguarding Smart Grid operations. [ABSTRACT FROM AUTHOR]

Copyright of HMD: Praxis der Wirtschaftsinformatik is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The transactive energy market (TEM) is a recent development in energy management that enables prosumers to trade directly, promising many environmental and economic benefits. Prosumer trading necessitates sharing information to facilitate transactions. Additionally, many TEMs propose using blockchains to manage auctions and store transactions. These facts introduce privacy concerns: consumption data, trading history, and other identifying information pose risks to users if leaked. Anonymity by trading under a pseudonym is commonly presented as a solution; however, this creates risks for market participants: scammed users will not have recourse, and users with innocent malfunctions may be banned from trading. We propose the Individually Linkable Pseudonymous Trading Scheme (ILPTS), which enables users to trade under a pseudonym, protecting their identity, while a smart contract monitors reputations and can temporarily deanonymize a user, ensuring market integrity. ILPTS was developed in stages. Examination of existing TEM literature was performed to identify desirable features. Analysis of cryptography literature was performed to identify techniques that may confer certain features. It was found through formal analysis that ILPTS adheres to identified design goals, improves upon existing solutions, and resists common attacks against TEMs. Future work includes software simulation and on-device implementation to further verify security and feasibility. [ABSTRACT FROM AUTHOR]

The major component of the smart grid (SG) is the advanced metering infrastructure (AMI), which monitors and controls the existing power system and provides interactive services for invoicing and electricity usage management with the utility. Including a cyber-layer in the metering system allows two-way communication but creates a new opportunity for energy theft, resulting in significant monetary loss. This article proposes an approach to detecting abnormal consumption patterns using energy metering data based on the ensemble technique AdaBoost, a boosting algorithm. Different statistical and descriptive features are retrieved from metering data samples, which account for extreme conditions. The model is trained for malicious and non-malicious data for five different attack scenarios, which are analyzed on the Irish Social Science Data Archive (ISSDA) smart meter dataset. In contrast to prior supervised techniques, it works well even with unbalanced data. The efficacy of the proposed theft detection method has been evaluated by comparing the accuracy, precision, recall, and F1 score with the other well-known approaches in the literature. [ABSTRACT FROM AUTHOR]

The transition to smart grids has served to transform traditional power systems into data-driven power systems. The purpose of this transition is to enable effective energy management and system reliability through an analysis that is centered on energy information. However, energy theft caused by vulnerabilities in the data collected from smart meters is emerging as a primary threat to the stability and profitability of power systems. Therefore, various methodologies have been proposed for energy theft detection (ETD), but many of them are challenging to use effectively due to the limitations of energy theft datasets. This paper provides a comprehensive review of ETD methods, highlighting the limitations of current datasets and technical approaches to improve training datasets and the ETD in smart grids. Furthermore, future research directions and open issues from the perspective of generative AI-based ETD are discussed, and the potential of generative AI in addressing dataset limitations and enhancing ETD robustness is emphasized. [ABSTRACT FROM AUTHOR]

The major component of the smart grid (SG) is the advanced metering infrastructure (AMI), which monitors and controls the existing power system and provides interactive services for invoicing and electricity usage management with the utility. Including a cyber-layer in the metering system allows two-way communication but creates a new opportunity for energy theft, resulting in significant monetary loss. This article proposes an approach to detecting abnormal consumption patterns using energy metering data based on the ensemble technique AdaBoost, a boosting algorithm. Different statistical and descriptive features are retrieved from metering data samples, which account for extreme conditions. The model is trained for malicious and non-malicious data for five different attack scenarios, which are analyzed on the Irish Social Science Data Archive (ISSDA) smart meter dataset. In contrast to prior supervised techniques, it works well even with unbalanced data. The efficacy of the proposed theft detection method has been evaluated by comparing the accuracy, precision, recall, and F1 score with the other well-known approaches in the literature. [ABSTRACT FROM AUTHOR]

Over the years, a rapid evolution of smart grids has been witnessed across the world due to their intelligent operations and control, smart characteristics, and benefits, which can overcome several difficulties of traditional electric grids. However, due to multifaceted technological advancements, the development of smart grids is evolving day by day. Thus, smart grid researchers need to understand and adapt to new concepts and research trends. Understanding these new trends in smart grids is essential for several reasons, as the energy sector undergoes a major transformation towards becoming energy efficient and resilient. Moreover, it is imperative to realize the complete potential of modernizing the energy infrastructure. In this regard, this paper presents a comprehensive bibliometric analysis of smart grid concepts and research trends. In the initial search, the bibliometric data extracted from the Scopus and Web of Science databases totaled 11,600 and 2846 records, respectively. After thorough scrutiny, 2529 unique records were considered for the bibliometric analysis. Bibliometric analysis is a systematic method used to analyze and evaluate the scholarly literature on a particular topic and provides valuable insights to researchers. The proposed analysis provides key information on emerging research areas, high-impact sources, authors and their collaboration, affiliations, annual production of various countries and their collaboration in smart grids, and topic-wise title count. The information extracted from this bibliometric analysis will help researchers and other stakeholders to thoroughly understand the above-mentioned aspects related to smart grids. This analysis was carried out on smart grid literature by using the bibliometric package in R. [ABSTRACT FROM AUTHOR]

Home area network (HAN) devices send the electricity consumption and other important data to the smart meter that must remain confidential from other devices. This paper proposes a novel one-dimensional hybrid chaotic map. The proposed map shows excellent chaotic properties when analyzed by bifurcation diagram, Lyapunov exponent & Shannon entropy. We further design an encryption strategy for data transfers between the smart meter and HAN devices. The proposed encryption scheme uses the existing lightweight key management in advanced metering infrastructure (LKM-AMI) architecture for data transfers, in which the encrypted data is transferred through an insecure channel and private keys are provided by trusted third party (TTP) through secure channels. The 2-way communication between HAN devices and the smart meter sends messages that are encrypted by using the proposed novel hybrid one-dimensional chaotic map. The encryption strategy mainly consists of three steps. In the first step, the seed and the control parameters are initialized. The second phase generates two intermediate keys using the proposed hybrid chaotic map. In the last phase, we encrypt the message by applying permutation followed by diffusion using intermediate keys. The proposed encryption strategy is resistant to various attacks. [ABSTRACT FROM AUTHOR]

The incorporation of renewable energy sources necessitates the upgrade of the electrical grid to a smart grid, which involves the implementation of smart meters. Although smart meters provide benefits to users, many smart meter implementation projects have failed to be accepted by users. This article assesses the factors influencing the acceptance of household smart meters in Joinville, a city in the south of Brazil. Based on the Unified and Extended Theory of Acceptance and Use of Technology (UTAUT2), a structural equation model was estimated using data from a sample of 136 respondents in the city of Joinville. The results indicate that Performance Expectancy, Hedonic Motivation, and Social Influence constructs have a more substantial effect on the Intention to Use smart meters. The results provide evidence for planning the upgrade of the electrical grid by implementing smart meters in southern Brazil. [ABSTRACT FROM AUTHOR]

Static flow sensors (e.g., thermal gas micro electro-mechanical sensors—MEMS—and ultrasonic time of flight) are becoming the prevailing technology for domestic gas metering and billing since they show advantages in respect to the traditional volumetric ones. However, they are expected to be influenced in-service by changes in gas composition, which in the future could be more frequent due to the spread of hydrogen admixtures in gas networks. In this paper, the authors present the results of an experimental campaign aimed at analyzing the in-service reliability of both static and volumetric gas meters with different hydrogen admixtures. The results show that the accuracy of volumetric and ultrasonic meters is always within the admitted limits for subsequent verification and even within those narrower of the initial verification. On the other hand, the accuracy of the first generation of thermal mass gas flow sensors is within the limits of the verification only when the hydrogen admixture is below 2%vol. At higher hydrogen content, in fact, the absolute weighted mean error ranges between 3.5% (with 5%vol of hydrogen) and 15.8% (with 10%vol of hydrogen). [ABSTRACT FROM AUTHOR]

Due to recent technical breakthroughs in wireless communication and the Internet of Things (IoT), the smart grid (SG) has been recognized as a next-generation network for intelligent and efficient electric power transmission. Electric vehicle charging is becoming one of the most popular SG application. However, in SG environment the communication between a vehicle user and smart meter is mostly performed using insecure channel for managing demand response during peak hours. This raises serious security and privacy issues. Motivated from the aforementioned challenges, this paper presents a secure and privacy-preserving framework for IoT-enabled SG environment. The proposed framework first uses a secure mutual authentication scheme to register and exchange session key among SG participants. Second, a deep learning method that uses a stacked sparse denoising autoencoder to convert data into a new encoded format is suggested. The attention-based truncated long short-term memory uses this modified data to identify intrusions. The proposed blockchain architecture uses the proof of authentication consensus mechanism to propagate regular transactions in order to validate data integrity and prevent data poisoning attacks. The suggested framework outperforms several current state-of-the-art solutions in terms of security and numerical discoveries. [ABSTRACT FROM AUTHOR]

The Internet of Things is an ecosystem that connects billions of smart devices, meters, and sensors. These devices and sensors collect and share data for use and evaluation by organizations in different industry sectors. Humans may use the IoT to live and work more intelligently and gain total control over their lives. Consequently, IoT can be used to connect devices and integrate them with new digital technologies for customers. On the other hand, smart utility companies in the electric, gas, and water sectors need to deliver services more efficiently and analyze their operations in a way that can help optimize performance, detect growing problems in real time, and initiate fixes to avoid unplanned service interruptions. Building actual smart metering networks is costly and time-consuming. Therefore, in this paper, a new Smart Utilities Traffic Scheduling Algorithm (SUTSA) is proposed. To minimize the system complexity, the model is based on narrowband power line communication, in which a wired hidden network sends data across power lines. A simulation is performed using OPNET Modeler 14.5 to evaluate the proposed model. The results proved that the proposed model is highly scalable and achieves full network-bandwidth utilization in different situations based on different application requirements. [ABSTRACT FROM AUTHOR]

Urbanization increases electricity demand due to population growth and economic activity. To meet consumer’s demands at all times, it is necessary to predict the future building energy consumption. Power Engineers could exploit the enormous amount of energy-related data from smart meters to plan power sector expansion. Researchers have made many experiments to address the supply and demand imbalance by accurately predicting the energy consumption. This paper presents a comprehensive literature review of forecasting methodologies used by researchers for energy consumption in smart buildings to meet future energy requirements. Different forecasting methods are being explored in both residential and non-residential buildings. The literature is further analyzed based on the dataset, types of load, prediction accuracy, and the evaluation metrics used. This work also focuses on the main challenges in energy forecasting due to load fluctuation, variability in weather, occupant behavior, and grid planning. The identified research gaps and the suitable methodology for prediction addressing the current issues are presented with reference to the available literature. The multivariate analysis in the suggested hybrid model ensures the learning of repeating patterns and features in the data to enhance the prediction accuracy. [ABSTRACT FROM AUTHOR]

A smart grid is an electricity network that uses advanced technologies to facilitate the exchange of information and electricity between utility companies and customers. Although most of the technologies involved in such grids have reached maturity, smart meters—as connected devices—introduce new security challenges. To overcome this significant obstacle to grid modernization, safeguarding privacy has emerged as a paramount concern. In this paper, we begin by evaluating the security levels of recently proposed authentication methods for smart meters. Subsequently, we introduce an enhanced protocol named PPSG, designed for smart grids, which incorporates physical unclonable functions (PUF) and an elliptic curve cryptography (ECC) module to address the vulnerabilities identified in previous approaches. Our security analysis, utilizing a real-or-random (RoR) model, demonstrates that PPSG effectively mitigates the weaknesses found in prior methods. To assess the practicality of PPSG, we conduct simulations using an Arduino UNO board, measuring computation, communication, and energy costs. Our results, including a processing time of 153 ms, a communication cost of 1376 bits, and an energy consumption of 13.468 mJ, align with the requirements of resource-constrained devices within smart grids. [ABSTRACT FROM AUTHOR]

Smart grid technology's boundary of impact and security risk ranges from utility services, energy management services, manufacturers, law enforcement agencies, and consumers. As smart technology is deployed at a faster pace than regulation can keep up with as well as identifying vulnerabilities within smart technology; it becomes necessary to better understand what smart technology is currently available. To assist everyday consumers in better understanding how their data is collected, used, analyzed, and the potential implications stemming from the use of their data, we discuss multiple state and international privacy laws and examples of misuse cases. [ABSTRACT FROM AUTHOR]

This paper proposes three methods of the optimal smart meter selection for acting as a data concentrator in the automatic meter reading last mile network. The study explains the reasons why the selected smart meter should also act as a data concentrator, in addition to its basic role. To select the smart meter, either the reliability of communication or the speed of the automatic meter reading process was considered. Graph theory is employed to analyse the last mile network, described as sets of nodes and unreliable links. The frame error ratio was used to assess the unreliability whilst the number of hops was used to describe the speed of the reading process. The input data for the analysis are qualitative parameters determined based on observations in the real, operated last mile networks as well as their typical topological arrangements. The results of the research can be useful in the last mile network migration process, which uses concentrators to the networks without them, or during the process of newer last mile network implementation, where data concentrators are no longer applicable. The efficiency of the proposed methods is assessed measurably. [ABSTRACT FROM AUTHOR]

Water utilities in Japan face a number of challenges, including declining water demand due to a shrinking population, shrinking workforce, and aging water supply facilities. Widespread use of smart water meters is crucial for solving these problems. The widespread use of smart water meters is expected to bring many benefits such as reduced labor by automating meter reading, early identification of leaks, and visualization of pipeline data to strengthen the infrastructure of water services, business continuity, and customer service, as detailed data can be obtained using wireless communication. Demonstration tests are actively conducted in Japan; however, many problems have been reported with cast iron meter boxes blocking radio waves. To address the issue, a low-cost slit structure for cast iron meter boxes is investigated in this study. The results confirm that the L-shaped tapered slit array structure with a cavity, which can be fabricated in a cast iron integral structure, satisfies the design loads required for road installation. The proposed slit structure achieved gain characteristics from −3.32 to more than 9.54 dBi in the 800 to 920 MHz band. The gain characteristics of conventional cast iron meter boxes range from −15 to −20 dBi, and the gain has been significantly improved. Antennas with a gain of −2.0 to +1.5 dB (0.8 to 2.5 GHz) were used for the transmitter antenna, which was found to have a higher gain than the transmit antenna in the 800 to 880 MHz frequency band. In the 1.5 to 2.0 GHz band, a high peak gain of 4.25 dBi was achieved at 1660 MHz, with no null and the lowest gain confirmed that this is an improvement of more than 10 dBi over conventional products. [ABSTRACT FROM AUTHOR]

This article summarizes and organizes recent research findings in information and communication technology security developments integrated with smart grids. A vital component of a smart grid is a smart meter. It is relevant because it can collect, process, and transport customer's data over the Internet. Whereas developments in smart grid and smart meter technologies have given new productivity gains, they have also presented new security concerns. Security is essential in defending both the smart grid and smart meter from cyber-attack. Guaranteeing safety is one of the most challenging aspects of designing and deploying a smart metering infrastructure. This study presents a thorough investigation of the integrity of smart metering technologies from multiple different viewpoints. It focuses on threats, countermeasures, and estimations. This article makes four contributions: First, all potential vulnerabilities in smart metering components are described and examined. Second, it assesses the impact of attacks that use these weaknesses to boost the performance of each part and the whole smart meter structure. Thirdly, potential countermeasures to defend smart meters are discussed. lastly, it discusses the unresolved issues surrounding smart meter security and future research areas. This evaluation is distinctive due to its exhaustive treatment of security weaknesses and attacks on smart meter components. In conclusion, the future vision is described. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to analyzing and managing electricity consumption using a clustering algorithm and a high-accuracy classifier for smart meter data. The proposed method utilizes a multilayer perceptron neural network classifier optimized by an Imperialist Competitive Algorithm (ICA) called ICA-optimized MLP, and a CD Index based on Fuzzy c-means to optimally determine representative load curves. A case study involving a real dataset of residential smart meters is conducted to validate the effectiveness of the proposed method, and the results demonstrate that the ICA-optimized MLP method achieves an accuracy of 98.62%, outperforming other classification methods. This approach has the potential to improve energy efficiency and reduce costs in the power system, making it a promising solution for analyzing and managing electricity consumption. [ABSTRACT FROM AUTHOR]

Various water distribution leak detection and localization methods have been proposed for supervisory control and data acquisition (SCADA) data collection systems. However, because their available numbers of measurements are limited, the SCADA systems are often insufficient to identify realistic sized leaks. A clear next step is to develop detection and localization methods for smart systems that collect advanced metering infrastructure (AMI) data (i.e., AMI systems); however, only the authors have proposed tools for the AMI data collection systems. To encourage the usage of AMI data, this study tested five types of data collection systems for leak detection and localization that measure (1) only source flows, (2) source flows and a few nodal pressures, (3) source flows and AMI demands, (4) AMI demands and a few nodal pressures, and (5) AMI pressures and AMI demands. An appropriate leak detection and localization tool for each data collection system is applied and tested for two water distribution networks: one located in Austin, TX, and the other in Tucson, AZ. Each system's performance was evaluated using metrics of detection probability, false alarm rate, time to detect, and localization pipe distance. Overall, based on the obtained results, the SCADA systems were poor in detecting realistic-sized leaks, while the AMI systems successfully identified those small failures. Thus, the AMI systems were required to improve detection, and a pressure-supplemented AMI system was necessary to obtain high localization performance, particularly for a large network, such as Austin, Texas. [ABSTRACT FROM AUTHOR]

State estimation for distribution systems faces the challenge of dealing with limited real-time measurements and historical data. This work describes a Bayesian state estimation approach tailored for practical implementation in different data availability scenarios, especially when both real-time and historical data are scarce. The approach leverages statistical correlations of the state variables from a twofold origin: (1) from the physical coupling through the grid and (2) from similar behavioral patterns of customers. We show how these correlations can be parameterized, especially when no historical time series data are available, and that accounting for these correlations yields substantial accuracy gains for state estimation and for the recognition of critical system states, i.e., states with voltage or current limit violations. In a case study, the approach is tested in a realistic European-type, medium-voltage grid. The method accurately recognizes critical system states with an aggregated true positive rate of 98%. Compared to widely used approaches that do not consider these correlations, the number of undetected true critical cases can be reduced by a factor of up to 9. Particularly in the case where no historical smart meter time series data is available, the recognition accuracy of critical system states is nearly as high as with full smart meter coverage. [ABSTRACT FROM AUTHOR]

Smart microgrids are being increasingly deployed within the Department of Defense. The microgrid at Marine Corps Air Station (MCAS) Miramar is one such deployment that has fostered the integration of different technologies, including 5G and Advanced Metering Infrastructure (AMI). The objective of this paper is to develop an anomaly detection framework for the smart microgrid system at MCAS Miramar to enhance its cyber-resilience. We implement predictive analytics using machine learning to deal with cyber-uncertainties and threats within the microgrid environment. An autoencoder neural network is implemented to classify and identify specific cyber-attacks against this infrastructure. Both network traffic in the form of packet captures (PCAP) and time series data (from the AMI sensors) are considered. We train the autoencoder model on three traffic data sets: (1) Modbus TCP/IP PCAP data from the hardwired network apparatus of the smart microgrid, (2) experimentally generated 5G PCAP data that mimic traffic on the smart microgrid and (3) AMI smart meter sensor data provided by the Naval Facilities (NAVFAC) Engineering Systems Command. Distributed denial-of-service (DDoS) and false data injection attacks (FDIA) are synthetically generated. We show the effectiveness of the autoencoder on detecting and classifying these types of attacks in terms of accuracy, precision, recall, and F-scores. [ABSTRACT FROM AUTHOR]

Power utilities worldwide are facing enormous challenges when it comes to the distribution of electricity. With these challenges, electricity theft is regarded as the most common challenge in the electrical distribution system. Electricity theft can be meter tampering done in consumer houses and illegal connections done using hook-ups from the distribution pole grids. These electricity theft challenges have caused power utilities to reconsider customer engagements focusing on feedback, putting loss detection systems in their distribution system networks, using artificial intelligence to schedule maintenance and other asset management activities, etc. The main focus of this paper is to design a real-time power theft monitoring and detection system that is able to detect power theft in distribution systems. This proposed system utilizes smart meters consisting of an Arduino ATMega328P microcontrollers with GSM modules (Global System for Mobile Communication) used for system communication. Cloud storage is created to store the smart meter data. Simulations of the proposed system were done using Proteus Design Suite v.8.10 SP3 software. The proposed system is practically constructed for prototype measurement results. Should power imbalances be measured by the system, the authority office will receive an SMS notification as an alert for power theft detected by a specific smart metering system. The authority office will analyse the power measurements sent to the cloud storage (MATLAB Online, ThingSpeak IoT channels display), and further action will be taken. [ABSTRACT FROM AUTHOR]

Issues such as climate change, water scarcity, population growth, and distribution losses have stimulated the use of new technologies to manage water resources. This is how the concept of smart water management emerged as a subcategory of the concept of smart cities. This article aimed first to identify the applications of smart water-management systems described in academic articles either as applications in development or as applications already implemented or as future trends; and, second, to classify them according to the processes in the value chain of public water supply services. To this end, a systematic review of the literature was carried out, in which 100 mentions of applications were identified in 62 selected articles; then, the mentions were grouped into 10 categories. The most frequent application categories were smart meters, implementation models and architectures, and loss management. Among the processes of the value chain, applications in processes of distribution and water use were highly predominant. The lack of detail about the integration between the different applications for a smart water-management system was pointed out as a limitation and an opportunity for future research development, especially in terms of a technological roadmap study based on the relationship between smart meters and loss management. [ABSTRACT FROM AUTHOR]

The demand response program is an important feature of the smart grid. It attempts to reduce peak demand, improve the smart grid efficiency, and ensure system reliability. Implementing demand-response programs in residential and commercial buildings requires the use of smart meters and smart plugs. In this paper, we propose an architecture for a home-energy-management system based on the fog-computing paradigm, an Internet-of-Things-enabled smart plug, and a smart meter. The smart plug measures in real-time the root mean square (RMS) value of the current, frequency, power factor, active power, and reactive power. These readings are subsequently transmitted to the smart meter through the Zigbee network. Tiny machine learning algorithms are used at the smart meter to identify appliances automatically. The smart meter and smart plug were prototyped by using Raspberry Pi and Arduino, respectively. The smart plug's accuracy was quantified by comparing it to laboratory measurements. To assess the speed and precision of the small machine learning algorithm, a publicly accessible dataset was utilized. The obtained results indicate that the accuracy of both the smart meter and the smart plug exceeds 97% and 99%, respectively. The execution of the trained decision tree and support vector machine algorithms was verified on the Raspberry Pi 3 Model B Rev 1.2, operating at a clock speed of 600 MHz. The measured latency for the decision tree classifier's inference was 1.59 microseconds. In a practical situation, the time-of-use-based demand-response program can reduce the power cost by about 30%. [ABSTRACT FROM AUTHOR]

The decision tree algorithm-based load identification (DTA-LI) system’s fusion data method is crucial in the monitoring of appliance loads for the purposes of improving energy efficiency and management. Common home electrical devices are identified and classified from smart meter data through the analysis of voltage and current variations, allowing for the measurement of energy usage in residential buildings. A load identification system based on a decision tree algorithm may infer information about the residents of a building based on their energy usage habits. Better power savings rates, load shedding management, and overall electrical system performance are the results of the clusters’ ability to capture families’ purchasing patterns and geodemographic segmentation. The DTA-LI system’s fusion data method presents a promising avenue for improving residential buildings’ energy performance and lowering their carbon footprint, especially in light of the widespread use of smart meters in recent years. [ABSTRACT FROM AUTHOR]

Smart meters (SM) arrangement play an essential role in Smart Grid (SG), serving as advanced energy meters crucial for monitoring and managing energy consumption. This paper provides an in-depth exploration of the significance, functions, and technological aspects of smart meters within the context of the SG. Smart meters acquire data from endusers' load devices, offering a comprehensive measurement of consumers' energy consumption. The obtained information is then communicated to utility companies and system operators, facilitating efficient energy management. The integration of control devices and a number of sensors, supported by a dedicated communication infrastructure, enhances the reliability and functionality of smart meters. The paper summarizes hardware model key facets and functions of smart meters, offering a comprehensive view of their capabilities. [ABSTRACT FROM AUTHOR]

The development of dynamic energy distribution grids to optimize energy resources has become very important at the international level in recent years. A very important step in this development is to be able to characterize the population based on their consumption behaviour. However, traditional consumption meters that report information at a monthly rate provide little information for in-depth analysis. In Colombia, this has changed in recent years due to the implementation and integration of advanced metering infrastructure (AMI). This infrastructure allows to record consumption values in small time intervals, and the available data then allows for the execution of many analysis mechanisms. In this paper we present an analysis of the electricity demand profile from a new dataset of energy consumption in Colombia. A characterization of the users demand profiles is presented using a k-means clustering procedure. Whit this customer segmentation technique we show that is possible identify customer consumption patterns and to identify anomalies in the system. In addition, this type of analysis also allows to assess changes in the consumption pattern of users due to social measures such as those resulting from the coronavirus disease (COVID-19) pandemic. [ABSTRACT FROM AUTHOR]

The performance and stability of the electric network are improved through smart grid technologies. The smart grid strongly rely on digital communication technologies creates security issues that must be addressed to distribute power efficiently and safely. For billing, load balancing, and energy management, the customer‐side smart meter regularly transmits the consumption reading to the system operator. However, dishonest customers created cyberattacks by entering fictitious readings of their electrical consumption in order to steal electricity for financial benefit. The distributed monitoring meter‐based network concept is utilized in this paper to closely monitor the losses. A two‐stage clustering‐based technique has also been suggested for the purpose of detecting theft. The approach can be detect the customer ID having abnormality in consumption pattern. To verify the effectiveness of the suggested strategy, numerical experiments on two datasets of smart meters have been conducted and four different types of attack patterns have been constructed. Simulation results show that the proposed scheme is very effective for detecting various types of attack patterns. In addition, the paper helps to predict how much power and financial loss the company will suffer as a result of multiple attacks. [ABSTRACT FROM AUTHOR]

The unabated growth in global energy demand since the advent of the 21st-century call for augmented electrical power grids as a network of smart grids. The key components which shore up the grid's 'Smartness' are the distributed intelligent controller modules. Existing electrical networks are limited by a short lifespan, power theft, and prolonged power outages, by the lack of smart sensors and proper communication equipment. Microgrids are a small network of electricity users with localized source of generation and are usually attached to the main grid but can function independently. The microgrid is a limited subset of the grid that may run either connected to or independently from the main grid, whereas the smart grid is a thorough modernization of the whole power grid infrastructure. Both systems aim to increase grid resilience, integrate renewable energy, increase grid efficiency, and support demand response programs. They work together to create an energy system that is more dependable, effective, and sustainable. There are various components which makes the grid smart and intelligent. This paper presents a review of flexible, intelligent instrumentation, and communication network components that capacitate smart grids with self-governing intelligence. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to propose a machine learning approach-based power theft detection using Garra Rufa Fish (GRF) optimization. Here, the analyzing of power theft is an important part to reduce the financial loss and protect the electricity from fraudulent users. Design/methodology/approach: In this section, a new method is implemented to reduce the power theft in transmission lines and utility grids. The detection of power theft using smart meter with reliable manner can be achieved by the help of GRF algorithm. Findings: The loss of power due to non-technical loss is small by using this proposed algorithm. It provides some benefits like increased predicting capacity, less complexity, high speed and high reliable output. The result is analyzed using MATLAB/Simulink platform. The result is compared with an existing method. According to the comparison result, the proposed method provides the good performance than existing method. Originality/value: The proposed method gives good results of comparison than those of the other techniques and has an ability to overcome the associated problems. [ABSTRACT FROM AUTHOR]

This article presents an umbrella review of blockchain-based smart grid applications. By umbrella review, we mean that our review is based on systematic reviews of this topic. We aim to synthesize the findings from these systematic reviews and gain deeper insights into this discipline. After studying the systematic reviews, we find it imperative to provide a concise and authoritative description of blockchain technology because many technical inaccuracies permeate many of these papers. This umbrella review is guided by five research questions. The first research question concerns the types of blockchain-based smart grid applications. Existing systematic reviews rarely used a systematic method to classify these applications. To address this issue, we propose a taxonomy of these applications, first by differentiating them based on whether the application is focusing on functional or non-functional aspects of smart grid operations, and then by the specific functions or perspectives that the application aims to implement or enhance. The second research question concerns the roles that blockchain technology plays in smart grid applications. We synthesize the findings by identifying the most prominent benefits that blockchain technology could bring to these applications. We also take the opportunity to point out several common technical mistakes that pervade the blockchain literature, such as equating all forms of blockchains to data immutability. The third research question concerns the guidelines for deciding whether a blockchain-based solution would be useful to address the needs of smart grids. We synthesize the findings by proposing benefit-based guidelines. The fourth research question concerns the maturity levels of blockchain-based smart grid applications. We differentiate between academic-led and industry-led projects. We propose a five-level scale to evaluate the maturity levels. The ranking of the industry-led projects is performed through our own investigation. Our investigation shows that more than half of the industry-led projects mentioned in the systematic reviews are no longer active. Furthermore, although there are numerous news reports and a large number of academic papers published on blockchain-based smart grid applications, very few have been successfully embraced by the industry. The fifth research question concerns the open research issues in the development of blockchain-based smart grid applications. We synthesize the findings and provide our own analysis. [ABSTRACT FROM AUTHOR]

Electricity is an indispensable element of our daily life. When electricity is used inefficiently, technical and non-technical losses occur at the stages from power plants where energy is produced to the point of consumption. Reduction of these losses is defined as the main goal for more efficient use of existing energy systems. Since the increase in electricity tariffs due to inflation is happening all over the world, it creates conditions for the illegal consumption (theft) of electricity without accounting. For this reason, reducing the illegal consumption of electricity and compensating the financial losses of the distribution companies remains the main problem. [1] According to research, the countries of the world lose 89.3 billion annually from the theft of electricity. The US dollar is losing. The largest losses were in India (16. 2 billion US dollars), followed by Brazil (10.5 billion US dollars), Russia (5.1 billion US dollars) [2]. About 50% of the total cost of non-technical (commercial) losses of electricity in the Russian Federation (according to preliminary estimates, 30 billion kWh) is accounted for by unaccounted (unmetered, non-contractual, etc.) consumption of electricity. [2] The rate of loss and theft in the Republic of Turkey is still higher than the rate of loss and theft in developed countries. For instance, according to the reports of Vangölü Elektrik Dagitim A.§. (VEDAS), connected to the Van, Bitlis and Hakkari provinces, as of the end of 2017, the loss and theft rates were 72% in Hakkari, 53% in Van, 48.5% in Mus and Bitlis. It is shown to be 35%. [7] These issues still remain a problem in our republic. So, despite the serious struggle against the illegal use of electricity in "Bakielektrikshebekea" OJSC, and later in "Azerishiq" OJSC, starting from 2003, according to the indicators of 2020, the number of acts pointed in this field (52,202), lost energy recovery (166.9 million kWh) continues to increase. Losses, including commercial losses, to the extent that they can be released is the method of carrying out and applying balance sheets. It is for this reason that it is very important to conduct research, to determine the methods of accurate measurement of received and consumed energy, to study the methods of carrying out balance reports for all stages, starting from 0.4 kV networks, and to give recommendations, in order to reduce non-technical losses. [ABSTRACT FROM AUTHOR]

Developing a low-cost wireless energy meter with power quality measurements for smart grid applications represents a significant advance in efficient and accurate electric energy monitoring. In increasingly complex and interconnected electric systems, this device will be essential for a wide range of applications, such as smart grids, by introducing a real-time energy monitoring system. In light of this, smart meters can offer greater opportunities for sustainable and efficient energy use and improve the utilization of energy sources, especially those that are nonrenewable. According to the 2020 International Energy Agency (IEA) report, nonrenewable energy sources represent 65% of the global supply chain. The smart meter developed in this work is based on the ESP32 microcontroller and easily accessible components since it includes a user-friendly development platform that offers a cost-effective solution while ensuring reliable performance. The main objective of developing the smart meters was to enhance the software and simplify the hardware. Unlike traditional meters that calculate electrical parameters by means of complex circuits in hardware, this project performed the calculations directly on the microcontroller. This procedure reduced the complexity of the hardware by simplifying the meter design. Owing to the high-performance processing capability of the microcontroller, efficient and accurate calculations of electrical parameters could be achieved without the need for additional circuits. This software-driven approach with simplified hardware led to benefits, such as reduced production costs, lower energy consumption, and a meter with improved accuracy, as well as updates on flexibility. Furthermore, the integrated wireless connectivity in the microcontroller enables the collected data to be transmitted to remote monitoring systems for later analysis. The innovative feature of this smart meter lies in the fact that it has readily available components, along with the ESP32 chip, which results in a low-cost smart meter with performance that is comparable to other meters available on the market. Moreover, it is has the capacity to incorporate IoT and artificial intelligence applications. The developed smart meter is cost effective and energy efficient, and offers benefits with regard to flexibility, and thus represents an innovative, efficient, and versatile solution for smart grid applications. [ABSTRACT FROM AUTHOR]

Advanced metering infrastructure (AMI) is one of the core aspects of the smart grid, and offers numerous possible benefits, such as load control and demand response. AMI enables two-way communication, but it is vulnerable to electricity theft. Due to the tempering of the smart meter, the abnormal pattern of fraud becomes difficult to detect, which introduces the increment of false data consumption. The majority of existing methods depend on factors such as predefined limits, extra information requirements, and the desire for labelled datasets. These factors are difficult to realize or have a poor degree of identification. This paper integrates two novel techniques to detect the False Data Injection attack. One is Principal Component Analysis, which is based on feature correlation, second is an unsupervised learning based technique Density-Based Spatial Clustering of Applications with Noise which helps to identify data patterns to detect outliers from a huge number of load profiles. The combination makes the proposed technique an appropriate tool for detecting an arbitrary pattern attack in high-dimensional data. Four different attack scenarios are analyzed on the Irish Science Data Archive smart meter data set. The efficacy of proposed theft detection method is evaluated by comparing the AUC, mAP, and time with those of other clustering approaches. The detection rate of the proposed scheme has been compared with the other approaches in the literature. The results demonstrate that the detection rate is substantially higher than that of other theft detection methods. Finally, the influence of varied abnormality ratios has been investigated. [ABSTRACT FROM AUTHOR]

Background and Objectives: The water sector has a national and strategic plan focused on restoring and improving the resilience of groundwater. Due to a decline in water levels and reservoirs in aquifers, over 405 out of 609 study areas in the country have been prohibited. In response, the Ministry of Energy established a resilience plan in 2004 which included artificial feeding and flood spreading programs aimed at improving groundwater resources. Later, the Supreme Water Council approved 15 projects for the restoration and resilience of the country's groundwater resources. Recent studies conducted in different regions of the country demonstrate that the resilience plan has had varying effects on groundwater resources. However, the effects of the resilience plan on study areas in South Khorasan have not been extensively studied. Therefore, this research focuses on the effects of the resilience project in the study area of Boshroyeh, with an emphasis on the installation of smart meters. Materials and Methods: To investigate this issue, 16 observation wells and rainfall data from the Boshroyeh Plain were analyzed for the time period of 1996-2021. Groundwater fluctuations were examined using data from the observation wells for the years before (2009-2015) and after (2015-2021) the installation of the smart meter. The hydrograph of the aquifer wells was drawn and analyzed for the period of 2007-2022. Furthermore, groundwater level and confluence maps were created and analyzed using Arc GIS software and kriging interpolation techniques for the years before and after the installation of the smart meter. Results: Before the installation of the smart meter, the average drop of the groundwater level between 2009-2015 was 3.21 meters. However, after the smart meter was installed, the average drop of the groundwater level between 2015-2021 reduced to 2 meters. This means that the water level has decreased less after the installation of the smart meter. The volume of the aquifer decreased by 67.6% in the six years after the installation of the smart meter compared to the decrease in the volume of the aquifer in the six years before the installation of the smart meter. This indicates a 32.4% reduction in the decline process of the aquifer volume after the installation of the smart meter when compared to the same period before the installation. Conclusion: The results of the study indicated that the severity of the water level drop varied during the statistical period of 2009-2021. After the completion of the installation of smart meters in 2015, the drop in the groundwater level had a lower slope compared to the period before their installation. Additionally, the decline in the volume of the aquifer during the period after the installation of smart meters was lower compared to the same period before the installation. The installation of smart meters has also led to significant economic value due to the amount of saved water. [ABSTRACT FROM AUTHOR]

Smart meters are rapidly installing by utility providers to improve the reliability and performance of Smart Grid. Utility providers analyze real-time smart meter data to monitor, predict, generate and distribute power. The customer's real-time activity and power usage can be revealed by analyzing the smart meter data. Therefore, the security and privacy of the data is a crucial issue for the smart grid. This paper proposes a secure and privacy-preserving data aggregation and classification (SP-DAC) model based on fog and cloud architecture. Data is aggregated at the fog node in the SP-DAC model, and classification is performed at the outsourced cloud with three machine learning classifiers. Simulation results analyze the cryptographic costs and classification performance. Real-world smart meter dataset "UMass Smart" is taken for experiments and classification accuracy, precision, recall, and F1 score achieved upto 88%, 87%, 90%, and 88%, respectively. The comparison with existing models shows the superiority of the SP-DAC model in terms of features and parameters. [ABSTRACT FROM AUTHOR]

The rapid popularization of advanced metering infrastructure (AMI) smart meters produces customer high-frequency energy consumption data. These data provide diverse options for energy economics and policy research. In this review, we examine studies applying high frequency smart meter data to explore the overall impact of household new technology adoption and COVID-19 on energy consumption patterns. We find that high frequency smart meter data boosts the accuracy of forecasting models with various data-driven algorithms. In addition, there is a lack of precise assessment and inclusive understanding of energy poverty in advanced economics. Smart meter data help expand and deepen the energy poverty research. Research on how vulnerable groups exhibit energy poverty can improve society’s understanding of energy poverty and help implement related policy assistance programs. [ABSTRACT FROM AUTHOR]

Electric smart grids enable a bidirectional flow of electricity and information among power system assets. For proper monitoring and controlling of power quality, reliability, scalability and flexibility, there is a need for an environmentally friendly system that is transparent, sustainable, costsaving, 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 reducing 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. [ABSTRACT FROM AUTHOR]

For ten years Polish distribution companies have implemented several AMR and AMI pilot projects. In Poland, the Distribution System Operator (DSO) equips households with smart energy meters (e.g. in the United Kingdom - the energy supplier, which can be changed by the customer). Over these years the operators collected a lot of experience with these pilot projects. The author of this publication describes his personal experience in the research projects in which he was involved, directly or indirectly, as well ones of which he has received information about from the DSOs. This article does not present the experiences from all major AMI system projects in Poland, but only from few projects in which the author of this article participated. The author participated in projects in which the smart meters utilize a variety of different technologies: PLC, GPRS, HSCSD, RS-232, etc. In this article, however, he focuses only on his experience in working with PLC technology. This paper presents the requirements, considerations and experience gained in the development and pilot implementation of AMI systems in Poland. [ABSTRACT FROM AUTHOR]

Human activity event recognition (HAER) within a residence is a topic of significant interest in the field of ambient assisted living (AAL). Commonly, various sensors are installed within a residence to enable the monitoring of people. This work presents a new approach for HAER within a residence by (re-)using measurements from commercial smart water meters. Our approach is based on the assumption that changes in water flow within a residence, specifically the transition from no flow to flow above a certain threshold, indicate human activity. Using a separate, labeled evaluation data set from three households that was collected under controlled/laboratory-like conditions, we assess the performance of our HAER method. Our results showed that the approach has a high precision (0.86) and recall (1.00). Within this work, we further recorded a new open data set of water consumption data in 17 German households with a median sample rate of 0.08 3 ¯ Hz to demonstrate that water flow data are sufficient to detect activity events within a regular daily routine. Overall, this article demonstrates that smart water meter data can be effectively used for HAER within a residence. [ABSTRACT FROM AUTHOR]

The privacy of electricity consumers has become one of the most critical subjects in designing smart meters and their proliferation. In this work, a multilayer architecture has been proposed for anonymous data collection from smart meters, which provides: (1) The anonymity of information for third‐party data consumers; (2) Secure communication to utility provider network for billing purposes; (3) Online control of data sharing for end‐users; (4) Low communication costs based on available Internet of things (IoT) communication protocols. The core elements of this architecture are, first, the digital twin equivalent of the cyber‐physical system and, second, the Tangle distributed ledger network with IOTA cryptocurrency. In this architecture, digital twin models are updated in real‐time by information received from trusted nodes of the Tangle distributed network anonymously. A small‐scale laboratory prototype based on this architecture has been developed using the dSPACE SCALEXIO real‐time simulator and open‐source software tools to prove the feasibility of the proposed solution. The numerical results confirm that after a few seconds of anomaly detection, the microgrid was fully stabilized around its operating point with less than 5% deviation during the transition time. [ABSTRACT FROM AUTHOR]